pdf-to-markdown/examples/Achieving-The-Paris-Climate-Agreement.md

Achieving the

Paris Climate

Agreement Goals

Sven Teske Editor

Global and Regional 100% Renewable

Energy Scenarios with Non-energy GHG

Pathways for +1.5°C and +2°C

Achieving the Paris Climate Agreement Goals

Sven Teske

Editor

Achieving the Paris Climate

Agreement Goals

Global and Regional 100% Renewable

Energy Scenarios with Non-energy GHG

Pathways for +1.5°C and +2°C

ISBN 978-3-030-05842-5 ISBN 978-3-030-05843-2 (eBook) https://doi.org/10.1007/978-3-030-05843-

Library of Congress Control Number: 2018966518

© The Editor(s) (if applicable) and The Author(s) 2019. Open Access This book is licensed under the terms of the Creative Commons Attribution 4. International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Editor Sven Teske Institute for Sustainable Futures University of Technology Sydney Sydney, NSW, Australia

This book is an open access publication

Additional material to this book can be downloaded from http://extras.springer.com.

For the next generation.

For my son, Travis.

vii

In October of 2018, the Intergovernmental Panel on Climate Change issued its

starkest warning yet: we have around 12  years to avoid the worst effects of

anthropogenic climate change. The consumption of fossil fuels, the reckless

destruction of forests and other natural ecosystems, and the release of powerful

greenhouse gases have already caused around 1.0  °C of warming above pre-

industrial levels.

Continuing at the current rate, we are likely to reach 1.5 °C by 2030 – and all the

evidence suggests that a world beyond 1.5 °C is not one we want to live in.

While making the 2016 documentary film, Before the Flood , I witnessed first-

hand the impacts of an already-changing climate: the rapid melting of ice in the

Arctic Circle, massive bleaching of coral reefs in the Bahamas, and rampant defor-

estation in Indonesia and the Amazon. Better than ever, we understand the heart-

breaking impact of human activity on our natural world. It is estimated, for example,

that 60% of animals have been wiped out since 1970.

Higher temperatures and extreme weather events will cause ever more severe

harm to biodiversity and ecosystems and even greater species loss and extinction.

And when we lose biodiversity, we lose resilience. Currently, natural ecosystems

absorb about half of human-caused carbon dioxide emissions. If we continue to

degrade the natural world, we could lose completely the Earth’s ability to adapt to

climate change.

A passion for nature conservation and animal protection has driven much of my

foundation’s work over the past 20 years. Ultimately, however, the climate crisis is

a humanitarian one. If business-as-usual continues, the impact on human beings

will be immeasurable. Water supplies will become more insecure. Sea level rise will

profoundly impact islands, low-lying coastal areas, and river deltas. Small island

communities like those I visited in the South Pacific are already preparing for

migration to safer lands. Fatal floods, droughts, hurricanes, and wildfires are the

Climate Model: Foreword

viii

new normal, and happening closer to home. An estimated 41 million Americans live

within a 100-year flood zone. Texas saw its third 500-year flood 3 years in a row.

Poor air quality is a public health emergency across the world and now the

fourth-highest cause of death  – contributing to strokes, heart attacks, and lung

cancer – causing public unrest in countries like China and India, where the poorest

find themselves at the mercy of pollution from industrial facilities and the burning

of biomass. In states like Texas, Colorado, and North Dakota, communities are

fighting back against gas drilling operations near playgrounds or soccer fields,

where children breathe in poisonous gases.

These health impacts are only part of the story. Climate change, as the US

Pentagon notes, is a national security threat. In a 2017 report by the Environmental

Justice Foundation, senior US military experts pointed to the likelihood of tens of

millions of climate refugees displaced by extreme weather  – in a world already

struggling with a refugee crisis. We already know that many conflicts are driven by

environmental factors and access to natural resources. The truth is that, where

ecosystems collapse, societies collapse too.

Politically, there has been a monumental failure to grasp the scale of this problem.

Climate scientists still face disinformation campaigns and a press corps that often

draws a false equivalence between those who support the scientific consensus for

human-caused climate change and those who do not. Surveys suggest that most

Americans do not know a scientific consensus exists, and scientists like Michael

Mann, who spoke to me for Before the Flood , face abuse for exposing the truth. As

a result, scientific research programs, critical to better understanding and addressing

climate change, are often attacked or defunded.

Nevertheless, in the face of these challenges, some progress is being made. With

the growth of the environmental movement, public awareness of the climate crisis

has increased significantly. Governments and the private sector are beginning to

ramp up their efforts. Renewable energy is booming. And the UN Sustainable

Development Goals, ratified by 193 countries, now call for a halt to deforestation

and land degradation by 2030. After decades of climate negotiations, the Paris

Agreement now calls upon the world’s governments to keep warming “well below

2°C” while striving for 1.5°C.

While we are beginning to move in the right direction, the reality is that these

efforts are simply not ambitious enough to address the climate crisis at scale. The

IPCC warns that to avoid the worst consequences of climate change, we must stay

below the 1.5 °C limit. But what does that mean in practical terms?

Determined to find solutions, my foundation supported a 2-year research program

led by a team of international climate and energy experts to develop a roadmap for

how we can actually stay below this critical climate threshold. The findings, outlined

in this book, give cause for optimism. With a transition to 100% renewable energy

by mid-century and a major land conservation and restoration effort, it is possible to

stay below the 1.5 °C limit with technologies that are available right now. It will be

Climate Model: Foreword

ix

a lot of work, but the costs will be far less than the $5 trillion per year governments

currently spend subsidizing the fossil fuel industries responsible for climate change.

The climate model and energy transition pathways compiled in this book offer an

exciting, positive, and achievable vision of a better world in which we are no longer

dependent on fossil fuels and where the conservation and restoration of nature is

treated as indispensable to our survival. This is not fantasy. This is science.

Science is showing us the way forward, but you do not need to be a scientist to

understand that climate change is the defining issue of our time. If our world warms

past 1.5 °C, our way of life will profoundly change for the worse. Why not manage

the transition in a way that is orderly and equitable? Human beings caused this

problem, but with our vast knowledge and ingenuity, we can also fix it.

We are resilient. We can adapt. We can change.

Chairman of the Leonardo DiCaprio Foundation Leonardo DiCaprio

Climate Model: Foreword

xi

Contact Information

Lead Author: Dr Sven Teske

University of Technology Sydney – Institute for Sustainable Futures (UTS-ISF)

Address: Building 10, 235 Jones Street, Sydney, NSW, Australia 2007/Telephone:

+61 2 9514 4786

https://www.uts.edu.au/research-and-teaching/our-research/institute-sustainable-

futures

Author: Dr. Sven Teske

E-mail: sven.teske@uts.edu.au

Chapters: 1, 2.2, 3.1, 3.2, 3.5, 3.6, 7, 8
(Power Sector analysis), 9, 10, 13

Author: Prof. Dr. Damien Giurco Chapters: 11, 13

E-mail: damien.giurco@uts.edu.au

Author: Tom Morris Chapters: 3.2, 3.5, 3.6, 7

E-mail: tom.morris@uts.edu.au

Author: Kriti Nagrath Chapters: 3.2, 7

E-mail: kriti.nagrath@uts.edu.au

Author: Franziska Mey Chapter: 10

E-mail: franziska.mey@uts.edu.au

Author: Dr Chris Briggs Chapter: 10

E-mail: chris.briggs@uts.edu.au

Author: Elsa Dominish Chapter: 10, 11

E-mail: elsa.dominish@uts.edu.au

Author: Dr Nick Florin Chapter 11

E-mail: nick.florin@uts.edu.au

Graduate School of Energy Science, Kyoto University – for Chapter 11

Author: Takuma Watari,

Author: Benjamin Mclellan

xii

German Aerospace Center (DLR), Institute for Engineering Thermodynamics

(TT),

Department of Energy Systems Analysis

Address: Pfaffenwaldring 38-40, Germany D-70569/Telephone: +49-711 6862 355

http://www.dlr.de/tt/en/desktopdefault.aspx/tabid-2904/4394_read-6500/

Author: Dr. Thomas Pregger

E-mail: thomas.pregger@dlr.de

Chapters: 3.1, 3.4, 5, 8
(Long-term energy model), 13

Author: Dr. Tobias Naegler

E-mail: tobias.naegler@dlr.de

Chapters: 3.1, 3.4, 5, 8
(Long-term energy model), 13

Author: Dr. Sonja Simon

E-mail: sonja.simon@dlr.de

Chapters: 3.1, 3.4, 5, 8
(Long-term energy model), 13

German Aerospace Center (DLR), Institute of Vehicle Concepts (FK),

Department of Vehicle Systems and Technology Assessment

Address: Pfaffenwaldring 38-40, Germany D-70569/Telephone: +49-711 6862 533

https://www.dlr.de/fk/en/desktopdefault.aspx/

Author: Johannes Pagenkopf, Chapters: 3.3, 6, 13

E-mail: johannes.pagenkopf@dlr.de

Author: Bent van den Adel Chapters: 3.3, 6, 13

E-mail: Bent.vandenAdel@dlr.de

Author: Özcan Deniz Chapters: 3.3, 6, 13

E-mail: oezcan.deniz@dlr.de

Author: Dr. Stephan Schmid Chapters: 3.3, 6, 13

E-mail: stephan.schmid@dlr.de

University of Melbourne

Address: Australian-German Climate and Energy College, Level 1, 187 Grattan

Street, University of Melbourne, Parkville, Victoria, Australia 3010

http://www.energy-transition-hub.org

Author: A/Prof. Dr. Malte Meinshausen Chapters: 2.1, 3.8, 4, 12, 13

Affiliation: University of Melbourne

E-mail: malte.meinshausen@unimelb.edu.au/

Telephone: +61 3 90356760

Author: Dr. Kate Dooley Chapters: 3.8, 4.1, 7

Affiliation: University of Melbourne

E-mail: kate.dooley@unimelb.edu.au/

Telephone: +61 3 90356760

Editor: Janine Miller

Contact Information

xiii

Executive Summary

Abstract An overview of the motivations behind the writing of this book, the sci-

entific background and context of the research. Brief outline of all methodologies

used, followed by assumptions and the storyline of each scenario. Presentation of

main results of the renewable energy resources assessment, transport scenario, long-

term energy pathway, the power sector analysis, employment analysis and an assess-

ment for required metals for renewable energy and storage technologies. Key results

of non-energy greenhouse mitigation scenarios which are developed in support of

the energy scenario in order to achieve the 1.5 °C target. Concluding remarks and

policy recommendations including graphs and tables.

Introduction The Paris Climate Agreement aims to hold global warming to well

below 2 degrees Celsius (°C) and to “pursue efforts” to limit it to 1.5 °C. To accom-

plish this, countries have submitted Intended Nationally Determined Contributions

(INDCs) outlining their post-2020 climate actions (Rogelj 2016). This research

aimed to develop practical pathways to achieve the Paris climate goals based on a

detailed bottom-up examination of the potential of the energy sector, in order to

avoid reliance on net negative emissions later on.

The study described in this book focuses on the ways in which humans produce

energy, because energy-related carbon dioxide (CO 2 ) emissions are the main drivers

of climate change. The analysis also considers the development pathways for non-

energy- related emissions and mitigation measures for them because it is essential to

address their contributions if we are to achieve the Paris climate change targets.

State of Research—Climate Beyond reasonable doubt, climate change over the

last 250  years has been driven by anthropogenic activities. In fact, the human-

induced release of greenhouse gas emissions into the atmosphere warms the planet

even more than is currently observed as climate change, but some of that greenhouse-

gas- induced warming is masked by the effect of aerosol emissions.

xiv

Carbon dioxide emissions are so large that they are the dominant driver of

human-induced climate change. A single kilogram of CO 2 emitted will increase the

atmospheric CO 2 concentration over hundreds or even thousands of years. Since the

Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the

finding that cumulative CO 2 emissions are roughly linearly related to temperature

has shaped scientific and political debate. The remaining permissible CO 2 emissions

that are consistent with a target temperature increase of 2  °C or 1.5  °C and their

comparison with remaining fossil fuel resources are of key interest.

The IPCC Fifth Assessment Report concluded that beyond 2011, cumulative CO 2

emissions of roughly 1000 GtCO 2 are permissible for a “likely below 2.0 °C” target

change, and approximately 400 GtCO 2 are permissible for a 1.5  °C target change.

However, the recently published IPCC Special Report on the 1.5 °C target suggests

substantially higher carbon emissions of 1600 GtCO 2 will achieve a 2.0 °C change

and 860 GtCO2 will achieve a 1.5 °C change, which must be reduced by a further 100

GtCO 2 to account for additional Earth system feedback over the twenty- first century.

One of the key reasons behind this difference is definitional: how far do we consider

that we are away from 1.5 °C warming? While that question seems simple, it is sur-

prisingly complex when the observational data on coverage, the internal variability

and the pre-industrial to early-industrial temperature differences are considered.

This study does not resolve the differences in opinions about carbon budgets, but

it does provide emission pathways that are consistent with the 1.5 °C target increase

in the 1.5 °C Scenario, or with the “well below 2.0 °C” target increase in the 2.0 °C

Scenario consistent with other scenarios in the literature and classified as such by

the IPCC Special Report on 1.5 °C.

Global Trends in the Energy Sector In 2017, the ongoing trends continued: solar

photovoltaics (PV) and wind power dominated the global market for new power

plants; the price of renewable energy technologies continued to decline; and fossil

fuel prices remained low. A new benchmark was reached, in that the new renewable

capacity began to compete favourably with existing fossil fuel power plants in some

markets. Electrification of the transport and heating sectors is gaining attention, and

although the amount of electrification is currently small, the use of renewable

technologies is expected to increase significantly.

The growth of solar PV has been remarkable and is nearly double that of the

second-ranking wind power. The capacity of new solar PV in 2017 was greater than

the combined increases in the coal, gas and nuclear capacities. Renewable energy

technologies achieved a global average generation share of 23% in the year 2015,

compared with 18% in the year 2005. Storage is increasingly used in combination

with variable renewables as battery costs decline, and solar PV plus storage has

started to compete with gas peaking plants. However, bioenergy (including

traditional biomass) remains the leading renewable energy source in the heating

(buildings and industry) and transport sectors.

Since 2013, global energy-related carbon dioxide (CO 2 ) emissions from fossil

fuels have remained relatively flat. Early estimates based on preliminary data

suggest that this changed in 2017, with global CO 2 emissions increasing by around

1.4% (REN21-GSR 2018). These increased emissions were primarily attributable to

Executive Summary

xv

increased coal consumption in China, which grew by 3.7% in 2017 after a 3-year

decline. The increased Chinese consumption, as well as a steady growth of around

4% in India, is expected to lead to an upturn in global coal use, reversing the annual

global decline from 2013 to 2016.

In 2017, as in previous years, renewables saw the greatest increases in capacity

in the power sector, whereas the growth of renewables in the heating, cooling and

transport sectors was comparatively slow. Sector coupling—the interconnection of

power, heating and transport and particularly the electrification of heating and

transport—is gaining increasing attention as a means of increasing the uptake of

renewables in the transport and thermal sectors. Sector coupling also allows the

integration of large proportions of variable renewable energy, although this is still at

an early stage. For example, China is specifically encouraging the electrification of

heating, manufacturing and transport in high-renewable areas, including promoting

the use of renewable electricity for heating to reduce the curtailment of wind, solar

PV and hydropower. Several US states are examining options for electrification,

specifically to increase the overall renewable energy share.

Methodology for Developing Emission Pathways The complete decarbonisation

of the global energy supply requires entirely new technical, economic and policy

frameworks for the electricity, heating and cooling sectors as well as for the trans-

port system. To develop a global plan, the authors combined various established

computer models:

• Generalized Equal Quantile Walk (GQW) : This statistical method is used to

complement the CO 2 pathways with non-CO 2 regional emissions for relevant
greenhouse gases (GHGs) and aerosols, based on a statistical analysis of the
large number (~700) of multi-gas emission pathways underlying the recent IPCC
Fifth Assessment Report and the recently published IPCC Special Report on
1.5  °C.  The GQW method calculates the median non-CO 2 gas emission levels
every 5 years—conditional on the energy-related CO 2 emission level percentile
of the “source” pathway. This method is a further development under this
project—building on an earlier Equal Quantile Walk method—and is now better
able to capture the emission dynamics of low-mitigation pathways.

• Land-based sequestration design : A Monte Carlo analysis across temperate,

boreal, subtropical and tropical regions has been performed based on various
literature-based estimates of sequestration rates, sequestration periods and areas
available for a number of sequestration options. This approach can be seen as a
quantified literature-based synthesis of the potential for land-based CO 2
sequestration, which is not reliant on biomass plus sequestration and storage
(bioenergy with carbon capture and storage, BECCS).

• Carbon cycle and climate modelling (Model for the Assessment of Greenhouse

Gas-Induced Climate Change, MAGICC) : This study uses the MAGICC climate
model, which also underlies the classification used by both the IPCC Fifth
Assessment Report and the IPCC Special Report on 1.5  °C in terms of the
abilities of various scenarios to maintain the temperature change below 2 °C or
1.5 °C. MAGICC is constantly evolving, but its core goes back to the 1980s, and

Executive Summary

xvi

it represents one of the most established reduced-complexity climate models in
the international community.

• Renewable Resource Assessment [R]E-SPACE : RE-SPACE is based on a

Geographic Information Systems (GIS) approach and provides maps of the solar
and wind potentials in space-constrained environments. GIS attempts to emulate
processes in the real world at a single point in time or over an extended period
(Goodchild 2005). The primary purpose of GIS mapping is to ascertain the
renewable energy resources (primarily solar and wind) available in each region.
It also provides an overview of the existing electricity infrastructures for fossil
fuel and renewable sources.

• Transport model (TRAEM) : The transport scenario model allows the

representation of long-term transport developments in a consistent and transparent
way. The model disaggregates transport into a set of different modes and
calculates the final energy demand by multiplying each transport mode’s specific
transport demand with powertrain-specific energy demands, using a passenger
km (pkm) and tonne km (tkm) activity-based bottom-up approach.

• Energy system model (EM) : The energy system model (a long-term energy

scenario model) is used as a mathematical accounting system for the energy
sector. It helps to model the development of energy demands and supply
according to the development of drivers and energy intensities, energy potentials,
future costs, emission targets, specific fuel consumption and the physical flow
between processes. The data available significantly influence the model
architecture and approach. The energy system model is used in this study to
develop long-term scenarios for the energy system across all sectors (power,
heat, transport and industry), without applying cost-optimization based on
uncertain cost assumptions. However, an ex-post analysis of costs and investments
shows the main economic effects of the pathways.

• Power system models [R]E 24/7 : Power system models simulate electricity

systems on an hourly basis with geographic resolution to assess the requirements
for infrastructure, such as the grid connections between different regions and
electricity storage, depending on the demand profiles and power-generation
characteristics (Teske 2015). High-penetration or renewable energy-only
scenarios will contain significant proportions of variable solar PV and wind
power because they are inexpensive. Therefore, power system models are
required to assess the demand and supply patterns, the efficiency of power
generation and the resulting infrastructural needs. Meteorological data, typically
in 1 h steps, are required for the power-generation model, and historical solar and
wind data were used to calculate the possible renewable power generation. In
terms of demand, either historical demand curves were used, or if unavailable,
demand curves were calculated based on assumptions of consumer behaviour in
the use of electrical equipment and common electrical appliances. Figure  1
provides an overview of the interaction between the energy- and GIS-based
models. The climate model is not directly linked with it but provided the carbon
budgets for the 2.0 °C and the 1.5 °C Scenarios.

Executive Summary

xvii

Output

Resource model ([R]E SPACE)

GIS based renewable energy potentials based on weather &

land use data

Transport model (TRAEM) freight & passenger transport

demand by mode full energy balances: final energy
demand power, heat & transport,supply structure, primary energy
demand by fuel, emission, investment

balanced RE power

system, storage

demand,
curtailment

total climate change effects

energy demand by

transport mode

RE generation

curves

budget

energy-relatedCO

2

emissions

annual
energy-relatedCO

emissions 2

annual power
& supply demand

Modellingcluster

Power system model

([R]E 24/7)
hourly balancing of

electricity supply & demand

in spatial resolution
for sub regional clusters

Generalized Equal

Quantile Walk
Complementing non-CO

2

gases based on the
IPCC scenario database

characteristics

biofuel constraints

Energy system model (EM) bottom-up simulation of future energy balances based on GDP,

population, technology,

& energy intensity development in all sectors and for 10 world regions

Simplified land-based sequestration model

complementing reforestation,

restoration,
sustainable use and agroforestry options.

Reduced

complexity carbon cycle and climate model (MAGICC)
to calculate the climatic effects

of multi-gas pathways

Fig. 1

Interactions between the models used in this study

Executive Summary

xviii

Besides the climate and energy models, employment effects and the metal

resource requirements for selected materials have been calculated. Now that the

methodology has been outlined, the next sections present the results and assumptions

for the nonenergy GHG mitigation scenarios, followed by the energy sector

scenarios

Nonenergy-GHG Mitigation Scenarios The most important sequestration

measure could be large-scale reforestation, particularly in the subtropics and tropics

(see yellow pathways in Fig. 2). The second most important pathway in terms of the

amount of CO 2 sequestered is the sustainable use of existing forests, which basically

means reduced logging within those forests. In subtropical, temperate and boreal

regions, this could provide substantial additional carbon uptake over time. The time

horizon for this sequestration option is assumed to be slightly longer in temperate

and boreal regions, consistent with the longer time it takes for these forest ecosystems

to reach equilibrium. The “forest ecosystem restoration” pathway is also important,

which basically assumes a reduction in logging rates to zero in a fraction of forests.

Overall, the median assumed sequestration pathways, shown in Fig. 2, would

result in the sequestration of 151.9  GtC.  This is approximately equivalent to all

historical land-use-related CO 2 emissions and indicates the substantial challenges

that accompany these sequestration pathways.

Given the competing forms of land use throughout the world today, the challenge of

reversing overall terrestrial carbon stocks back to pre-industrial levels cannot be

underestimated. There would be significant benefits, but also risks, if this

2000 2050 2100 2150

0

500

1,

1,

Annual sequestration (MtC/yr)

Global aggregate of sequestration pathways

Agroforestry
(subtropics and tropics)
(temperate and boreal)

Forest
ecosystem
restoration
(subtropics
and tropics)
(temperate
and boreal)

Reforestation (subtropics and tropics)

Reforestation (temperate and boreal)

(temperate
and boreal)

Sustainable use
of forests
(subtropics
and tropics)

Fig. 2 Sequestration pathways—annual sequestration over time

Executive Summary

xix

sequestration option were to be used instead of mitigation. However, the benefits

are clearly manifold, ranging from biodiversity protection, reduced erosion,

improved local climates, protection from wind and potentially reduced air pollution.

Assumptions for Scenarios Scenario studies cannot predict the future. Instead,

scenarios describe what is required for a pathway that will limit warming to a certain

level and that is feasible in terms of technology implementation and investment.

Scenarios also allow us to explore the possible effects of transition processes, such

as supply costs and emissions. The energy demand and supply scenarios described

in this study have been constructed based on information about current energy

structures and today’s knowledge of energy resources and the costs involved in

deploying them. As far as possible, the study also takes into account potential

regional constraints and preferences.

The energy modelling used primarily aims to generate transparent and coherent

scenarios, ambitious but still plausible storylines, out of several possible techno-

economic pathways. Knowledge integration is the core of this approach because we

must consider different technical, economic, environmental and societal factors.

Scenario modelling follows a hybrid bottom-up/top-down approach, with no

objective cost-optimization functions. The analysis considers key technologies for

successful energy transition and focuses on the role and potential utility of efficiency

measures and renewable energies. Wind and solar energies have the highest

economic potential and dominate the pathways on the supply side. However, the

variable renewable power from wind and PV remains limited to a maximum of

65%, because sufficient secured capacity must always be maintained in the

electricity system. Therefore, we also consider concentrating solar power (CSP)

with high-temperature heat storage as a solar option that promises large-scale

dispatchable and secured power generation.

The 5.0 °C Scenario (Reference Scenario): The reference scenario only takes into

account existing international energy and environmental policies and is based on the

International Energy Agency (IEA) World Energy Outlook (IEA 2017). Its

assumptions include, for example, continuing progress in electricity and gas market

reforms, the liberalization of cross-border energy trade and recent policies designed

to combat environmental pollution. The scenario does not include additional policies

to reduce GHG emissions. Because the IEA’s projections only extend to 2040, we

extrapolate their key macroeconomic and energy indicators forward to 2050. This

provides a baseline for comparison with the 2.0 °C and 1.5 °C Scenarios.

The 2.0 °C Scenario: The first alternative scenario aims for an ambitious reduction

in GHG emissions to zero by 2050 and a global energy-related CO 2 emission budget

of around 590 Gt between 2015 and 2050. This scenario is close to the assumptions

and results of the Advanced E[R] scenario published in 2015 by Greenpeace (Teske

et  al. 2015). However, it includes an updated base year, more coherent regional

developments in energy intensity, and reconsidered trajectories and shares of the

deployment of renewable energy systems. Compared with the 1.5 °C Scenario, the

Executive Summary

xx

2.0 °C Scenario allows for some delays due to political, economic and societal pro-

cesses and stakeholders.

The 1.5  °C Scenario: The second alternative scenario aims to achieve a global

energy-related CO 2 emission budget of around 450 Gt, accumulated between 2015

and 2050. The 1.5  °C Scenario requires immediate action to realize all available

options. It is a technical pathway, not a political prognosis. It refers to technically

possible measures and options without taking into account societal barriers.

Efficiency and renewable potentials need to be deployed even more quickly than in

the 2.0  °C Scenario, and avoiding inefficient technologies and behaviours is an

essential strategy for developing regions in this scenario.

Global Transport Transport emissions have increased at a rapid rate in recent

decades and accounted for 21% of total anthropogenic CO 2 emissions in 2015. The

reason for this steady increase in emissions is that passenger and freight transport

activities are increasing in all world regions, and there is currently no sign that these

increases will slow in the near future. The increasing demand for energy for transport

has so far been predominantly met by GHG-emitting fossil fuels. Although (battery)

electric mobility has recently surged considerably, it has done so from a very low

base, which is why in terms of total numbers, electricity remains an energy carrier

with a relatively minor role in the transport sector.

The key results of our transport modelling demonstrate that meeting the 2.0 °C

Scenario, and especially the 1.5  °C Scenario, will require profound measures in

terms of rapid powertrain electrification and the use of biofuels and synthetically

produced fuels to shift transport performance to more efficient modes. This must be

accompanied by a general limitation of further pkm and tkm growth in the OECD

countries.

The 5.0  °C Scenario follows the IEA World Energy Outlook (WEO) scenario

until 2040, with extrapolation to 2050. Only a minor increase in electrification over

all transport modes is assumed, with passenger cars and buses increasing their

electric vehicle (EV) shares. For example, this study projects a share of 30% for

battery electric vehicles (BEVs) in China by 2050  in response to the foreseeable

legislation and technological advancement in that country, whereas for the world car

fleet, the share of BEVs is projected to increase to only around 10%. Growth in the

shares of electric powertrains and two- and three-wheel vehicles in the commercial

road vehicle fleet will be small, as will the rise in further rail electrification. Aviation

and navigation (shipping) are assumed to remain fully dependent on conventional

kerosene and diesel, respectively.

In the 2.0 °C Scenario minimal progress in electrification until 2020 will occur,

whereas a significant increase in electrification of the transport sector between 2020

and 2030 is projected. This will occur first in OECD regions, followed by emerging

economies and finally in developing countries. Battery-driven electric passenger

cars are projected to achieve shares of between 21% and 30%, whereas heavy

commercial electric vehicles and buses could achieve even higher shares of between

28% and 52% by 2030. This uptake will require a massive build-up of battery

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production capacity in coming years. Two- and three-wheel vehicles—mainly used

in Asia and Africa—will be nearly completely electrified (batteries and fuel cells)

by 2030. Looking ahead to 2050, 60–70% of buses and heavy trucks will become

(battery-driven) electric, and fuel-cell electric vehicles will increase their market

share to around 37%. In the 2.0  °C Scenario, developing countries in Africa and

countries in the oil-producing countries of the Middle East will remain predominantly

dependent on internal combustion engines, using bio- or synthetic-based fuels.

In the 1.5 °C Scenario , an earlier and more rapid increase in electric powertrain

penetration is required, with the OECD regions at the forefront. The emerging eco-

nomic regions must also electrify more rapidly than in the 2.0 °C Scenario. On a

global level, internal combustion engines will be almost entirely phased out by 2050

in both the 2.0 °C and 1.5 °C Scenarios. In OECD regions, cars with internal com-

bustion engines (using oil-based fuels) will be phased out by 2040, whereas in Latin

America or Africa, for example, a small share of internal combustion engine inter-

nal combustion engine (ICE)-powered cars, fuelled with biofuels or synthetic fuels,

will still be on the road but will be constantly replaced by electric drivetrains

(Fig. 3).

Efficiency improvements are modelled across all transport modes until 2050,

resulting in improved energy intensity over time. We project an increase in annual

efficiency of 0.5–1% in terms of MJ/tonnes km or MJ/passenger km, depending on

the transport mode and region. Regardless of the types of powertrains and fuels,

increasing the efficiency at the MJ/pkm or MJ/tkm level will result from the follow-

ing measures:

• Reductions in powertrain losses through more efficient motors, gears, power

electronics, etc.

• Reductions in aerodynamic drag
• Reductions in vehicle mass through lightweighting
• The use of smaller vehicles
• Operational improvements (e.g. through automatic train operation, load factor

improvements)

Transport performance will increase in all scenarios on a global scale but with dif-

ferent speeds and intensities across modes and world regions. Current trends in

0%

10%

20%

30%

40%

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60%

70%

80%

90%

100% 2.0C 1.5C

Powertrain split of world passenger car fleet^20152020202520302035204020452050
Internal Combuson Engine Plug-In Hybrid Electric Hydrogen

0%

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60%

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2015 2020 2025 2030 2035 2040 2045 2050

Fig. 3 Powertrain split of the world passenger car fleet in the 2  °C Scenario (left) and 1.5  °C Scenario (right)

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transport performance until 2050 are extrapolated for the 5.0 °C Scenario. In rela-

tive terms, all transport carriers will increase their performance from the current

levels, and in particular, energy-intensive aviation, passenger car transport and com-

mercial road transport are projected to grow strongly. In the 2.0  °C Scenario and

1.5 °C Scenario, we project a strong increase in rail traffic (starting from a relatively

low base) and slower growth or even a decline in the use of the other modes in all

world regions (Fig. 4).

The modal shifts from domestic aviation to rail and from road to rail are mod-

elled. In the 2.0 °C and 1.5 °C Scenarios, passenger car pkm must decrease in the

OECD countries (but increase in the developing world regions) after 2020 in order

to maintain the carbon budget. The passenger car pkm decline will be partly com-

pensated by an increase in the performances of other transport modes, specifically

public transport rail and bus systems.

2015 2020 2025 2030 2035 2040 2045 2050

2015 2020 2025 2030 2035 2040 2045 2050

5.0°C 2.0°C

1.5°C

0%

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Transport-Evolution

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2015 2020 2025 2030 2035 2040 2045 2050

Freight Train Truck

Passenger Train Aviation (Domestic) Passenger Car Bus 2- & 3-Wheeler

Fig. 4 Relative growth in world transport demand (2015, 100% pkm/tkm) in the 5.0 °C, 2.0 °C and 1.5 °C Scenarios

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Global Renewable Energy Potential To develop the 2.0 °C and 1.5 °C Scenarios,

the economic renewable energy potential in a space-constrained environment was

analysed. Land is a scarce resource. The use of land for nature conservation, agri-

cultural production, residential areas and industry, as well as for infrastructure such

as roads and all aspects of human settlement, limits the amount of land available for

utility-scale solar and wind projects. Furthermore, solar and wind generation

requires favourable climatic conditions, so not all available areas are suitable for

renewable power generation. To assess the renewable energy potential based on the

area available, all scenario-relevant regions and subregions were analysed with the

[R]E-SPACE methodology to quantify the available land area in square kilometres

with a defined set of constraints:

• Residential and urban settlements
• Infrastructure for transport (e.g. rail, roads)
• Industrial areas
• Intensive agricultural production land
• Nature conservation areas and national parks
• Wetlands and swamps
• Closed grasslands (as the land-use type)

In addition to this spatial analysis, the remaining available land areas were cor-

related with the available solar and wind resources. For CSP, a minimum solar radi-

ation of 2000 kilowatt hours per square meter and year (kWh/m^2 year) is assumed

to be the minimum deployment criterion, whereas the onshore wind potential under

an average annual wind speed of 5 m/s has been omitted.

The 2.0 °C Scenario utilizes only a fraction of the available economic potential

of the assumed suitable land for utility-scale solar PV and CSP plants. This estimate

does not include solar PV rooftop systems, which have significant additional

potential. India has the highest solar utilization rate of 8.5%, followed by Europe

and the Middle East, each of which utilizes around 5%. Onshore wind potential has

been utilized to a larger extent than solar potential. In the 2.0 °C Scenario, space-

constrained India will utilize about half of all the onshore wind energy utilized,

followed by Europe, which will utilize one fifth. This wind potential excludes

offshore wind, which has significant potential, but mapping the offshore wind

potential was beyond the scope of this analysis.

The 1.5  °C Scenario is based on the accelerated deployment of all renewables

and the more ambitions implementation of efficiency measures. Thus, the total

installed capacity of solar and wind power plants by 2050 is not necessarily larger

than it is in the 2.0  °C Scenario, and the utilization rate is in the same order of

magnitude. The increased deployment of renewable capacity in the OECD Pacific

(Australia), the Middle East and Africa will be due to the production of synthetic

bunker fuels based on hydrogen or synthetic fuels ( synfuels ) to supply the global

transport energy for international shipping and aviation.

Key results of the global long-term energy scenarios show that the efficiency

and uptake of renewable energy are two sides of the same coin. All sectors, including

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transport, industry and all commercial and residential buildings, must use energy

efficiently and from a huge range of renewable energy technologies. Compared with

the 5.0  °C Scenario, which was defined using assumptions from the IEA, the

alternative scenarios require more stringent efficiency levels. The 1.5 °C Scenario

involves the even faster implementation of efficiency measures than in the 2.0 °C

Scenario and the decelerated growth of energy services in all regions, in order to

avoid a further strong increase in fossil fuel use after 2020.

Global energy intensity will decline from 2.4 MJ/US$GDP in 2015 to 1.25 MJ/

US$GDP in 2050 in the 5.0 °C Scenario compared with 0.65 MJ/US$GDP in the

2.0 °C Scenario and 0.59 MJ/US$GDP in the 1.5 °C Scenario. This is a result of the

estimated power, heat and fuel demands for all sectors, with more stringent efficiency

levels in the alternative scenarios than in the 5.0  °C case. It reflects a further

decoupling of the energy demand and gross domestic product (GDP) growth as a

prerequisite for the rapid decarbonisation of the global energy system.

Total final energy demand is estimated based on assumptions about the demand

drivers, specific energy consumption and the development of energy services in

each region. In the 5.0 °C Scenario, the global energy demand will increase by 57%

from 342 EJ/year in 2015 to 537 EJ/year in 2050. In the 2.0 °C Scenario, the final

energy will be 19% lower than the current consumption and will reach 278 EJ/year

by 2050. The final energy demand in the 1.5 °C Scenario will be 253 EJ, 26% below

the 2015 demand, and, in 2050, will be 9% lower than in the 2.0 °C Scenario.

Global electricity demand will significantly increase in the alternative scenarios

due to the electrification of the transport and heating sectors, which will replace

fuels, but will also be due to a moderate increase in the electricity demand of

“classical” electrical devices on a global level. In the 2.0 °C Scenario, the electricity

demand for heating will be about 12,600 TWh/year from electric heaters and heat

pumps, and, in the transport sector, there will be an increase of about 23,400 TWh/

year due to electric mobility. The generation of hydrogen (for transport and

high- temperature process heat) and the manufacture of synthetic fuels for transport

will add an additional power demand of 18,800 TWh/year. The gross power demand

will thus rise from 24,300 TWh/year in 2015 to 65,900 TWh/year in 2050  in the

2.0 °C Scenario, 34% higher than in the 5.0 °C Scenario. In the 1.5 °C Scenario, the

gross electricity demand will increase to a maximum of 65,300 TWh/year in 2050.

Global electricity generation from renewable energy sources will reach 100% by

2050  in the alternative scenarios. “New” renewables—mainly wind, solar and

geothermal energy—will contribute 83% of the total electricity generated. The

contribution of renewable electricity to total production will be 62% by 2030 and

88% by 2040. The installed capacity of renewables will reach about 9500 GW by

2030 and 25,600  GW by 2050. The proportion of electricity generated from

renewables in 2030  in the 1.5  °C Scenario is assumed to be 73%. The 1.5  °C

Scenario will have a generation capacity of renewable energy of about 25,700 GW

in 2050.

From 2020 onwards, the continuing growth of wind and PV to 7850  GW and

12,300 GW, respectively, will be complemented by the generation of up to 2060 GW

of solar thermal energy as well as limited biomass-derived (770 GW), geothermal

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(560 GW) and ocean-derived energy (around 500 GW) in the 2.0 °C Scenario. Both

the 2.0 °C and 1.5 °C Scenarios will lead to the generation of high proportions (38%

and 46%, respectively) of energy from variable power sources (PV, wind and ocean)

by 2030, which will increase to 64% and 65%, respectively, by 2050. This will

require a significant change in how the power system is operated. The main findings

of the power sector analysis are summarized in the section below.

Calculated average electricity-generation costs in 2015 (referring to full costs)

were around 6 ct/kWh. In the 5.0 °C Scenario, these generation costs will increase,

assuming rising CO 2 emission costs in the future, until 2050, when they reach

10.6 ct/kWh. The generation costs will increase in the 2.0 °C and 1.5 °C Scenarios

until 2030, when they will reach 9 ct/kWh, and then drop to 7 ct/kWh by 2050. In

both alternative scenarios, the generation costs will be around 3.5  ct/kWh lower

than in the 5.0 °C Scenario by 2050. Note that these estimates of generation costs

do not take into account integration costs such as power grid expansion, storage and

other load-balancing measures.

Total electricity supply costs in the 5.0 °C Scenario will increase from today’s

$1560 billion/year to more than $5 500 billion/year in 2050, due to the growth in

demand and increasing fossil fuel prices. In both alternative scenarios, the total

supply costs will be $5050 billion/year in 2050, about 8% lower than in the 5.0 °C

Scenario.

Global investment in power generation between 2015 and 2050  in the 2.0  °C

Scenario will be around $49,000 billion, which will include additional power plants

to produce hydrogen and synthetic fuels and the plant replacement costs at the end

of their economic lifetimes. This value is equivalent to approximately $1360 billion

per year on average, which is $28,600  billion more than in the 5.0  °C Scenario

($20,400  billion). An investment of around $51,000  billion for power generation

will be required between 2015 and 2050 in the 1.5 °C Scenario ($1420 billion per

year on average). In both alternative scenarios, the world will shift almost 95% of

its total energy investment to renewables and cogeneration.

Fuel Cost Savings Because renewable energy has no fuel costs other than biomass,

the cumulative savings in fuel cost in the 2.0  °C Scenario will reach a total of

$26,300 billion in 2050, equivalent to $730 billion per year. Therefore, the total fuel

costs in the 2.0 °C Scenario will be equivalent to 90% of the energy investments in

the 5.0  °C Scenario. The fuel cost savings in the 1.5  °C Scenario will sum to

$28,800 billion or $800 billion per year.

Final energy demand for heating will increase by 59% in the 5.0  °C Scenario

from 151 EJ/year in 2015 to around 240 EJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 36% in 2050  in the 2.0  °C

Scenario, relative to that in the 5.0 °C case, and by 40% in the 1.5 °C Scenario.

Global Heat Supply In 2015, renewables supplied around 20% of the final global

energy demand for heating, mainly from biomass. Renewable energy will provide

42% of the world’s total heat demand in 2030 in the 2.0 °C Scenario and 56% in the

1.5 °C Scenario. In both scenarios, renewables will provide 100% of the total heat

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xxvi

demand in 2050. This will include the direct use of electricity for heating, which

will increase by a factor of 4.2–4.5 between 2015 and 2050 and will constitute a

final share of 26% in 2050 in the 2.0 °C Scenario and 30% in the 1.5 °C Scenario.

Estimated investments in renewable heating technologies to 2050 will amount to

more than $13,200 billion in the 2.0 °C Scenario (including investments for plant

replacement after their economic lifetimes)—approximately $368 billion per year.

The largest share of investment is assumed to be for heat pumps (around

$5700  billion), followed by solar collectors and geothermal heat use. The 1.5  °C

Scenario assumes an even faster expansion of renewable technologies. However, the

lower heat demand (compared with the 2.0  °C Scenario) will result in a lower

average annual investment of around $344 billion per year.

Energy demand in the transport sector will increase in the 5.0 °C Scenario from

around 97 EJ/year in 2015 by 50% to 146 EJ/year in 2050. In the 2.0 °C Scenario,

assumed changes in technical, structural and behavioural factors will reduce this by

66% (96  EJ/year) by 2050 compared with the 5.0  °C Scenario. Additional modal

shifts, technological changes and a reduction in the transport demand will lead to

even higher energy savings in the 1.5 °C Scenario of 74% (or 108 EJ/year) in 2050

compared with the 5.0 °C case.

Transport Energy Supply By 2030, electricity will provide 12% (2700 TWh/year)

of the transport sector’s total energy demand in the 2.0 °C Scenario, and, in 2050,

this share will be 47% (6500 TWh/year). In 2050, around 8430 PJ/year of hydrogen

will be used as a complementary renewable option in the transport sector. In the

1.5  °C Scenario, the annual electricity demand will be about 5200  TWh in 2050.

The 1.5  °C Scenario also assumes a hydrogen demand of 6850  PJ/year by 2050.

Biofuel use will be limited to a maximum of around 12,000 PJ/year in the 2.0 °C

Scenario. Therefore, around 2030, synthetic fuels based on power-to-liquid will be

introduced, with a maximum amount of 5820 PJ/year in 2050. Because of the lower

overall energy demand in transport, biofuel use will decrease in the 1.5 °C Scenario

to a maximum of 10,000 PJ/year. The maximum synthetic fuel demand will amount

to 6300 PJ/year.

Global primary energy demand in the 2.0  °C Scenario will decrease by 21%

from around 556 EJ/year in 2015 to 439 EJ/year. Compared with the 5.0 °C Scenario,

the overall primary energy demand will decrease by 48% by 2050  in the 2.0  °C

Scenario (5.0  °C, 837 EJ in 2050). In the 1.5  °C Scenario, the primary energy

demand will be even lower (412 EJ) in 2050 because the final energy demand and

conversion losses will be lower.

Global Primary Energy Supply Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly

phase out coal and oil, after which renewable energy will have a primary energy

share of 35% in 2030 and 92% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario,

renewables will have a primary share of more than 92% in 2050 (this will include

nonenergy consumption, which will still include fossil fuels). Nuclear energy is

phased out in both the 2.0 °C and 1.5 °C Scenarios. The cumulative primary energy

consumption of natural gas in the 5.0  °C Scenario will sum to 5580  EJ, the

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xxvii

cumulative coal consumption will be about 6360 EJ, and the crude oil consumption

to 6380  EJ.  In the 2.0  °C Scenario, the cumulative gas demand is 3140  EJ, the

cumulative coal demand 2340  EJ and the cumulative oil demand 2960  EJ.  Even

lower fossil fuel use will be achieved under the 1.5 °C Scenario: 2710 EJ for natural

gas, 1570 EJ for coal and 2230 EJ for oil. In both alternative scenarios, the primary

energy supply in 2050 will be based on 100% renewable energy (Fig. 5).

Bunker Fuels In 2015, the annual bunker fuel consumption was in the order of

16,000 PJ, of which 7400 PJ was for aviation and 8600 PJ for navigation. Annual

CO 2 emissions from bunker fuels accounted for 1.3 Gt in 2015, approximately 4%

of the global energy-related CO 2 emissions. In the 5.0  °C case, we assume the

development of the final energy demand for bunkers according to the IEA World

Energy Outlook 2017, Current Policies scenario. This will lead to a further increase

in the demand for bunker fuels by 120% until 2050 compared with the base year

2015. Because no substitution with “green” fuels is assumed, CO 2 emissions will

rise by the same order of magnitude. Although the use of hydrogen and electricity

in aviation is technically feasible (at least for regional transport) and synthetic gas

use in navigation is an additional option under discussion, this analysis adopts a

conservative approach and assumes that bunker fuels are only replaced by biofuels

and synthetic liquid fuels. In the 2.0 °C and 1.5 °C Scenarios, we assume the limited

use of sustainable biomass potentials and the complementary central production of

power-to-liquid synfuels.

In the 2.0 °C Scenario, this production is assumed to take place in three world

regions: Africa, the Middle East and OECD Pacific (especially Australia), where

synfuel generation for export is expected to be most economic. The 1.5 °C Scenario

requires even faster decarbonisation, so it follows a more ambitious low-energy

pathway. The production of synthetic fuels will cause significant additional

electricity demand and a corresponding expansion of renewable power-generation

capacities. In the case of liquid bunker fuels, these additional renewable

0

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REF2.0°C1.5°CREF2.0°C1.5°C REF2.0°C1.5°CREF2.0°C1.5°C REF2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/y

r

Efficiency
Ocean energy
Geothermal
Solar
Biomass
Wind
Hydro
Natural gas
Crude oil
Coal
Nuclear

Fig. 5 Global projections of total primary energy demand (PED) by energy carrier in the various scenarios

Executive Summary

xxviii

power- generation capacities could amount to 1100 GW in the 2.0 °C Scenario and

more than 1200 GW in the 1.5 °C Scenario if the flexible utilization of 4000 full-

load hours per year can be achieved. However, such a scenario requires high elec-

trolyser capacities and high-volume hydrogen storage to ensure not only flexibility

in the power system but also high utilization rates by downstream synthesis pro-

cesses (e.g. via Fischer-Tropsch plants).

Annual global energy-related CO 2 emissions will increase by 40% in the 5.0 °C

Scenario, from 31,180 Mt in 2015 to more than 43,500 Mt in 2050. The stringent

mitigation measures in both alternative scenarios will cause annual emissions to fall

to 7070 Mt in 2040 in the 2.0 °C Scenario and to 2650 Mt in the 1.5 °C Scenario,

with further reductions to almost zero by 2050. In the 5.0 °C Scenario, the cumulative

CO 2 emissions from 2015 until 2050 will sum to 1388 Gt. In contrast, in the 2.0 °C

and 1.5 °C Scenarios, the cumulative emissions for the period from 2015 until 2050

will be 587 Gt and 450 Gt, respectively. Therefore, the cumulative CO 2 emissions

will decrease by 58% in the 2.0  °C Scenario and by 68% in the 1.5  °C Scenario

compared with the 5.0  °C case. Thus, a rapid reduction in annual emissions will

occur in both alternative scenarios.

Global Power Sector Analysis

Global and regional long-term energy results were used to conduct a detailed power

sector analysis with the methodology described in Sect. 1.7 of Chap. 3. Both the

2.0  °C and 1.5  °C Scenarios rely on high proportions of variable solar and wind

generation. The aim of the power sector analysis was to gain insight into the stability

of the power system in each region—subdivided into up to eight subregions—and

to gauge the extent to which power grid interconnections, dispatch generation

services and storage technologies are required. The results presented in this chapter

are projections calculated based on publicly available data. Detailed load curves for

some subregions and countries were not available, or, in some cases, the relevant

information is classified. Therefore, the outcomes of the [R]E 24/7 model are

estimates and require further research with more detailed localized data, especially

regarding the available power grid infrastructures. The power sector projections for

developing countries, especially in Africa and Asia, assume unilateral access to

energy services by the residential sector by 2050 and require transmission and

distribution grids in regions where there are none at the time of writing. Further

research, in cooperation with local utilities and government representatives, is

required to develop a more detailed understanding of the power infrastructure needs.

Development of Global Power Plant Capacities The size of the global market for

renewable power plants will increase significantly under the 2.0 °C Scenario. The

annual market for solar PV power must increase by a factor of 4.5, from close to

100 GW in 2017 to an average of 454 GW by 2030. The annual onshore wind mar-

ket must expand to 172 GW by 2025, about three times higher than in 2017. The

offshore wind market will continue to increase in importance within the renewable

power sector. By 2050, offshore wind installations will increase to 32  GW annu-

ally—11 times higher than in 2017. Concentrated solar power (CSP) plants will

play an important role in the generation of dispatchable solar electricity to supply

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xxix

bulk power, especially for industry, and to provide secured capacities to power sys-

tems. By 2030, the annual CSP market must increase to 78  GW, compared with

3 GW in 2020 and only 0.1 GW in 2017.

In the 1.5 °C Scenario, the phase-out of coal and lignite power plants is acceler-

ated, and a total capacity of 618 GW—equivalent to approximately 515 power sta-

tions (1.2 GW on average)—must end operation by 2025. This will mean a phase-out

of two coal power plants per week from 2020 onwards, on average. The replacement

power will come from a variety of renewable power generators, both variable and

dispatchable. The annual market for solar PV energy must be around 30% higher

than it was in 2025, as under the 2.0 °C Scenario. The onshore wind market also has

an accelerated trajectory under the 1.5 °C Scenario, whereas the offshore wind mar-

ket is assumed to be almost identical to that in the 2.0 °C Scenario, because of long

lead times for these projects. The same is assumed for CSP plants, which are utility-

scale projects, and significantly higher deployment seems unlikely in the time

remaining until 2025.

Utilization of Power Plant Capacities On a global scale, in the 2.0 °C and 1.5 °C

Scenarios, the shares of variable renewable power generation will increase from 4%

in 2015 to 38% and 46%, respectively, by 2030, and will increase to 64% and 65%,

respectively, by 2050. The reason for the variations in the two cases is the different

assumptions made regarding efficiency measures, which may lead to lower overall

demand in the 1.5 °C Scenario than in the 2.0 °C Scenario. During the same period,

dispatchable renewables—CSP plants, bioenergy generation, geothermal energy

and hydropower—will remain around 32% until 2030 on a global average and then

decrease slightly to 29% under the 2.0 °C Scenario (and to 27% under the 1.5 °C

Scenario) by 2050. The system share of dispatchable conventional generation

capacities—mainly coal, oil, gas and nuclear energy—will decrease from a global

average of 60% in 2015 to only 14% in 2040. By 2050, the remaining dispatchable

conventional gas power plants will be converted to operate on hydrogen as a

synthetic fuel, to avoid stranded investments and to achieve higher dispatch power

capacities. Increased variable shares—mainly in the USA, the Middle East region

and Australia—will produce hydrogen for local and the export markets, as fuel for

both renewable power plants and the transport sector.

Development of Maximum and Residual Loads for the Ten World Regions The

maximum load will increase in all regions and within similar ranges under both the

2.0 °C and 1.5 °C Scenarios. The load in OECD countries will rise most strongly in

response to increased electrification, mainly in the transport sector, whereas the load

in developing countries will increase as the overall electricity demand increases in

all sectors.

The most significant increase will be in Africa, where the maximum load will

surge by 534% over the entire modelling period due to favourable economic

development and increased access to energy services by households. In OECD

Pacific (South Korea, Japan, Australia and New Zealand), efficiency measures will

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xxx

reduce the maximum load to 87% by 2030 relative to that in the base year, and it

will increase to 116% by 2050 with the expansion of electric mobility and the

increased electrification of the process heat supply in the industry sector. The 1.5 °C

Scenario calculates slightly higher loads in 2030 due to the accelerated electrifica-

tion of the industry, heating and business sectors, except in three regions (the Middle

East, India and Non-OECD Asia Other Asia), where the early application of effi-

ciency measures will lead to an overall lower demand at the end of the modelling

period, for the same GDP and population growth rates.

In this analysis, the residual load is the load remaining after the variable renew-

able power generation. Negative values indicate that the energy generated from

solar and wind exceeds the actual load and must be exported to other regions, stored

or curtailed. In each region, the average generation should be consistent with the

average load. However, maximum loads and maximum generations do not usually

occur at the same time, so surplus electricity can be produced and must be exported

or stored as far as possible. In rare individual cases, solar- or wind-based generation

plants can also temporarily reduce their output to a lower load, or some plants can

be shut down. Any reduction in energy generation from solar and wind sources in

response to low demands is defined as “curtailment”. In this analysis, curtailment

rates of up to 5% by 2030 and 10% by 2050 are assumed to have no substantial

negative economic impact on the operation of power plants and therefore will not

trigger an increase in storage capacities. Figure 6 illustrates the development of

maximum loads across all ten world regions under the 2.0 °C and 1.5 °C Scenarios.

Global Storage and Dispatch Capacities The world market for storage and dis-

patch technologies and services will increase significantly in the 2.0 °C Scenario.

The annual market for new hydro-pumped storage plants will grow on average by

6 GW per year to a total capacity of 244 GW in 2030. During the same period, the

total installed capacity of batteries will increase to 12 GW, requiring an annual mar-

ket of 1  GW.  Between 2030 and 2050, the energy service sector for storage and

storage technologies must accelerate further. The battery market must grow by an

annual installation rate of 22 GW and, as a result, will overtake the global cumula-

tive capacity of pumped hydro between 2040 and 2050. The conversion of gas infra-

structure from natural gas to hydrogen and synthetic fuels will start slowly between

2020 and 2030, with the conversion of power plants with annual capacities of

around 2 GW. However, after 2030, the transformation of the global gas industry to

hydrogen will accelerate significantly, with the conversion of a total of 197 GW gas

power plants and gas cogeneration facilities each year. In parallel, the average

capacity of gas and hydrogen plants will decrease from 29% (2578 h/year) in 2030

to 11% (975 h/year) by 2050, converting the gas sector from a supply-driven to a

service-driven industry.

At around 2030, the 1.5 °C Scenario will require more storage throughput than

the 2.0 °C Scenario, but the storage demands for the two scenarios will be equal at

the end of the modelling period. It is assumed that the higher throughput can be

managed with equally higher installed capacities, leading to full-load hours of up to

200 h per year for batteries and hydro-pumped storage.

Executive Summary

xxxi

Trajectories for a Just Transition of the Fossil Fuel Industry The implementation of

the 2.0 °C and 1.5 °C Scenarios will have a significant impact on the global fossil

fuel industry. While this may appear to be stating the obvious, current climate

debates have not yet led to an open debate about the orderly withdrawal from the

coal, oil and gas extraction industries. Instead, the political debate about coal, oil

and gas is focused on the security of supply and price security. However, mitigating

climate change is only possible when fossil fuels are phased out.

Coal: Under the 5.0  °C Scenario, the required production of thermal coal—

excluding coal for nonenergy uses, such as steel production—will remain at 2015

levels, with an annual increase of around 1% per year until 2050. Under the 2.0 °C

Scenario, coal production will decline sharply between 2020 and 2030 at a rate of

around 6% per year. By 2030, global coal production will be equal to China’s annual

production in 2017, at 3.7 billion tonnes, whereas that volume will be reached in

2025 under the 1.5 °C Scenario.

0%

100%

200%

300%

400%

500%

600%

202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050
OECD North
America

Lan
America

OECD Europe Middle East Africa Eurasia Other Asia India China OECD Pacific

Load Development by Region

Max Load Development (Base year 2020) [%] Max Load Development (Base year 2020) [%]

Fig. 6 Development of maximum loads in ten world regions in 2020, 2030 and 2050 under the 2.0 °C and 1.5 °C Scenarios

Executive Summary

xxxii

Oil: Oil production in the 5.0  °C Scenario will grow steadily by 1% annually

until the end of the modelling period in 2050. Under the 2.0 °C Scenario, oil produc-

tion will decline by 3% annually until 2025 and then by 5% per year until 2030.

After 2030, oil production will decline by around 7% per year on average, until the

oil produced for energy use is phased out entirely by 2050. The oil production

capacity of the USA, Saudi Arabia and Russia in 2017 would be sufficient to supply

the global demand in 2035 calculated under the 2.0 °C Scenario. The 1.5 °C Scenario

reduces the required production volume by half by 2030, reducing it further to the

equivalent of the 2017 production volume of just one of the three largest oil produc-

ers (USA, Saudi Arabia or Russia) by 2040.

Gas: In the 5.0 °C Scenario, gas production will increase steadily by 2% a year

for the next two decades, leading to an overall production increase of about 50% by

2050. Compared with coal and oil, the gas phase-out will be significantly slower in

the 2.0  °C and 1.5  °C Scenarios. These scenarios also assume that the gas

infrastructure, such as gas pipelines and power plants, will be used afterwards for

the hydrogen and/or renewable methane produced with electricity from renewable

sources. Under the 2.0 °C Scenario, gas production will only decrease by 0.2% per

year until 2025, by 1% until 2030 and, on average, by 4% annually until 2040. This

represents a rather slow phase-out and will allow the gas industry to gradually

transfer to hydrogen. The phase-out in the 1.5 °C Scenario is equally slow, and a

4%/year reduction will occur after 2025.

The trajectories predicted by the 2.0 °C and 1.5 °C Scenarios for global coal, oil

and gas production are consistent with the Paris Agreement targets and can be used

to calculate possible employment effects, in terms of job losses in the fossil fuel

industry, job gains in the renewable energy industry and options for transitioning the

gas industry into an industry based on renewably produced hydrogen.

Employment The transition to a 100% renewable energy system is not just a tech-

nical task, it is also a socially and economically challenging process. It is imperative

that this transition is managed in a fair and equitable way. One of the key concerns

is the employment of workers in the affected industries. However, it should be noted

that the “just transition” concept is concerned not only with workers’ rights but also

with the broader community. This includes considering, for example, community

participation in decision-making processes, public dialogue and policy mechanisms

that create an enabling environment for new industries to ensure local economic

development. Although it is acknowledged that a just transition is important, there

are limited data on the effects that this transition will have on employment. There is

even less information on the types of occupations that will be affected by the transi-

tion, either by project growth or declines in employment. This study provides pro-

jections for jobs in construction, manufacturing, operations and maintenance and

fuel and heat supply across 12 technologies and 10 world regions, based on the

5.0 °C, 2.0 °C and 1.5 °C Scenarios. Projected employment is calculated regionally,

but the results are presented at the global level.

Executive Summary

xxxiii

Employment—Quantitative Results The 2.0 °C and 1.5 °C Scenarios will generate

more energy-sector jobs in the world as a whole at every stage of the projection. The

1.5  °C Scenario will increase renewable energy capacities faster than the 2.0  °C

Scenario, and, therefore, employment will increase faster. By 2050, both scenarios

will create around 47 million jobs, so employment will be within similar ranges.

• In 2025, there will be 30.9 million energy-sector jobs under the 5.0 °C Scenario,

45.5 million under the 2.0 °C Scenario and 52.3 million under the 1.5 °C.

• In 2030, there will be 31.7 million energy-sector jobs under the 5.0 °C Scenario,

52.9  million under the 2.0  °C Scenario and 58.5  million under the 1.5  °C
Scenario.

• In 2050, there will be 29.9 million energy-sector jobs under the 5.0 °C Scenario,

48.7  million under the 2.0  °C Scenario and 46.3  million under the 1.5  °C
Scenario.

Under the 5.0 °C Scenario, job will drop to 4% below the 2015 levels by 2020 and

then remain quite stable until 2030. Strong growth in renewable energy will lead to

an increase of 44% in total energy-sector jobs by 2025 under the 2.0 °C Scenario and

66% under the 1.5 °C Scenario. In the 2.0 °C (1.5 °C) Scenario, renewable energy

jobs will account for 81% (86%) in 2025 and 87% (89%) in 2030, with PV having

the greatest share of 24% (26%), followed by biomass, wind and solar heating.

Employment—Occupational Calculations Jobs will increase across all occupations

between 2015 and 2025, except in metal trades, which display a minor decline of

2%, as shown in Fig. 7. However, these results are not uniform across regions. For

example, China and India will both experience a reduction in the number of jobs for

managers and clerical and administrative workers between 2015 and 2025.

Mineral and Metal Requirements Under the 2.0 °C and 1.5 °C Scenarios Within

the context of the increasing requirements for metal resources by renewable energy

and storage technologies, the rapid increases in demands for both cobalt and lithium

are of greatest concern. The demands for both metals will exceed the current

production rates by 2023 and 2022, respectively. The demands for these metals will

increase more rapidly than will that for silver, partly because solar PV is a more

established technology and silver use has become very efficient, whereas the

electrification of the transport system and the rapid expansion in lithium battery use

have only begun to accelerate in the last few years. The potential to offset primary

demand is different depending on the technology. Offsetting demand through

secondary sources of cobalt and lithium has the most potential to reduce total

primary demand, as these technologies have a shorter lifetime of approximately 10

years. The cumulative demands for both metals will exceed current reserves, but

with high recycling rates, they can remain below the resource levels. However, there

is a delay in the period during which recycling can offset demand, because there

must be sufficient batteries in use and they must exhaust their current purpose before

they can be collected and recycled. This delay could be further extended by strategies

that reuse vehicular batteries as stationary storage, which might reduce costs in the

Executive Summary

xxxiv

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Executive Summary

xxxv

short term and increase the uptake of PV. The efficiency of cobalt in batteries also

significantly reduces its demand, and this reduction is already happening as manu-

facturers move towards lower cobalt chemistries.

Increasing the efficiency of the material used is potentially the most successful

strategy to offset the demand for PV metals, and recycling will have a smaller

impact on demand because the lifespan of solar PV panels is long and their potential

for recycling is low. Although the increased demand for silver by 2050 will not be

as extreme as that for cobalt or lithium, it will still be considerable. This is important,

especially when considering that solar PV currently consumes approximately 9% of

end-use silver. It is possible to create silver-less solar panels, but these panels are

not expected to be on the market in the near future.

Examination of the Climate Implications of Our Scenarios

One of the Paris Agreement’s most outstanding achievements has been the consensus

by 195 countries to limit climate change to well below 2 °C and to pursue their best

efforts to limit it to 1.5 °C. Together with the goal to reduce emissions to net zero

levels, the international agreement clearly sets a framework in which regional and

national emission trajectories can be designed and evaluated. The strong focus on a

<2.0 °C temperature increase is partly driven by the knowledge that 2 °C warming

does not equate to a safe climate: not for small islands that are threatened by rising

seas, not for farmers dependent on rainfall in drought-stricken areas and not for

communities that are threatened by extreme rainfall events or more intense cyclones.

Here, we use probabilistic methods to examine the scenarios that have been

developed to evaluate their implications for long-term temperature and sea-level

rises, using models and settings that are also used in the recent IPCC Special Report

on 1.5 °C warming. Our lowest scenario has—by design—an approximate 50% or

higher chance of a 2100 temperature level that is below 1.5 °C—after a slight over-

shoot. In contrast to the SSP1_19 scenario, which is the main 1.5  °C-compliant

scenario in the next IPCC Assessment Report, our 1.5 °C Scenario does not rely on

massive net negative emissions. Even the most stringent mitigation scenarios devel-

oped in this study are unable to halt sea-level rise. In fact, a 30 cm rise in sea level

by 2100, which will continue thereafter, seems to be the unavoidable legacy of our

past use of fossil fuels, unless we remove this CO 2 from the atmosphere in much

larger amounts than even the complete reforestation of the planet would permit.

Faced with the grim challenge of ongoing climate risks on the one side and the

many positive effects and economic benefits of switching from fossil fuels to

renewables on the other, the path is clear. A rapid shift towards a new era of smart,

renewable and sector-coupled energy supply, combined with clever demand-side

measures and adaptations to the impacts of climate change, will allow us and our

children to address the legacy of our past reliance on fossil fuels.

Executive Summary

xxxvii

Acknowledgement

The authors would like to thank the Leonardo DiCaprio Foundation (https://www.

leonardodicaprio.org) which funded the research for Chaps 1, 2, 3, 4, 5, 6, 7, 8, and

9 and Chap. 12. Their ongoing support and dedication to this project was key and

kept all researchers highly motivated.

Furthermore, we thank the German Greenpeace Foundation “Umweltstiftung

Greenpeace” (https://umweltstiftung-greenpeace.de/die-stiftung) for funding the

employment calculation research documented in Chap. 10. Last but not least, our

thanks also to Earthworks (earthworks.org) for funding the research about metal

requirements presented in Chap. 11.

This project has been supported by numerous people between July 2017 and

November 2018, and our thanks go to each of them. A special thanks to Karl Burkart

(Leonardo DiCaprio Foundation); Melanie Stoehr and Claudia Voigt (Umweltstiftung

Greenpeace); Payal Sampat (Earthworks); Anna Leidreiter, Anna Skowron and Rob

van Riet from the World Future Council (https://www.worldfuturecouncil.org/); Dr.

Joachim Fuenfgelt from Bread for the World (https://www.brot-fuer-die-welt.de/en/

bread-for-the-world); and Stefan Schurig from F20—Foundations 20 (http://www.

foundations-20.org/) who provided initial support to make this project possible.

Finally, we would like to thank Greenpeace International and Greenpeace Germany

for their ongoing support of the Energy [R]evolution energy scenario research series

between 2004 and 2015 which resulted in the development of the long-term energy

scenario model, the basis for the long-term energy pathways.

xxxix

Contents

1 Introduction ............................................................................................. 1

Sven Teske and Thomas Pregger

2 State of Research ..................................................................................... 5

Sven Teske, Malte Meinshausen, and Kate Dooley

3 Methodology ............................................................................................ 25

Sven Teske, Thomas Pregger, Sonja Simon, Tobias Naegler,
Johannes Pagenkopf, Bent van den Adel, Malte Meinshausen,
Kate Dooley, C. Briggs, E. Dominish, D. Giurco, Nick Florin,
Tom Morris, and Kriti Nagrath

4 Mitigation Scenarios for Non-energy GHG .......................................... 79

Malte Meinshausen and Kate Dooley

5 Main Assumptions for Energy Pathways .............................................. 93

Thomas Pregger, Sonja Simon, Tobias Naegler, and Sven Teske

6 Transport Transition Concepts .............................................................. 131

Johannes Pagenkopf, Bent van den Adel, Özcan Deniz,
and Stephan Schmid

7 Renewable Energy Resource Assessment ............................................. 161

Sven Teske, Kriti Nagrath, Tom Morris, and Kate Dooley

8 Energy Scenario Results ......................................................................... 175

Sven Teske, Thomas Pregger, Tobias Naegler, Sonja Simon,
Johannes Pagenkopf, Bent van den Adel, and Özcan Deniz

9 Trajectories for a Just Transition of the Fossil Fuel Industry ............. 403

Sven Teske

xl

10 Just Transition: Employment Projections for the 2.0 °C

and 1.5 °C Scenarios ............................................................................... 413
Elsa Dominish, Chris Briggs, Sven Teske, and Franziska Mey

11 Requirements for Minerals and Metals for 100%

Renewable Scenarios .............................................................................. 437
Damien Giurco, Elsa Dominish, Nick Florin, Takuma Watari,
and Benjamin McLellan

12 Implications of the Developed Scenarios

for Climate Change ................................................................................. 459
Malte Meinshausen

13 Discussion, Conclusions and Recommendations .................................. 471

Sven Teske, Thomas Pregger, Johannes Pagenkopf,
Bent van den Adel, Özcan Deniz, Malte Meinshausen,
and Damien Giurco

Annex ............................................................................................................... 489

Contents

xli

Fig. 3.1 Interaction of models in this study ............................................... 30

Fig. 3.2 OECD North America broken down into eight sub-regions ......... 33

Fig. 3.3 Current electricity infrastructure in China .................................... 33

Fig. 3.4 Potential sites for onshore wind generation in Africa .................. 34

Fig. 3.5 Existing and potential solar power sites in Central

and South America ....................................................................... 35

Fig. 3.6 Overview of the energy system model (EM)

as implemented in Mesap/PlaNet ................................................. 40

Fig. 3.7 Schematic representation of the [R] E24/7 model structure ......... 44

Fig. 3.8 Spatial concept of the [R]E 24/7 model ....................................... 47

Fig. 3.9 Dispatch order module of the [R]E 24/7 model ........................... 50

Fig. 3.10 Quantitative employment calculation: methodological

overview ....................................................................................... 56

Fig. 3.11 Distribution of human resources required to manufacture

the main components of a 50 MW solar photovoltaic
power plant. (IRENA 2017).......................................................... 58

Fig. 3.12 Differences between the raw LDF emission scenario data ........... 62

Fig. 3.13 The 2.0 °C and 1.5 °C scenarios and their absolute fossil

and industry CO 2 emissions until 2050. The energy-related CO 2
emissions pathways from the other chapters are used until
2050, and then extended beyond 2050 by either keeping
the CO 2 emissions constant (in the case of the 1.5 °C
and 2.0 °C Scenarios, i.e., red and purple dashed lines
beyond 2050 in the upper panel) or by keeping the percentile
level within the literature-reported scenarios constant (in the
case of the reference scenario, i.e., green solid line in the
upper panel). The percentile rank within the other
literature-reported scenarios is shown in the lower panel.
The constant absolute emission level after 2050 in the case

List of Figures

xlii

of the 1.5 °C and 2.0 °C Scenarios can be seen to result
in an increasing percentile rank among all the
literature-reported scenarios (increasing purple–red line
in the lower panel). ....................................................................... 64

Fig. 3.14 Example distributions of emissions scenario characteristics ........ 66

Fig. 4.1 Land-use sequestration pathways showing annual

sequestration rates over time ........................................................ 80

Fig. 4.2 Land-use-related CO 2 emission and sequestration rates ............... 82

Fig. 4.3 Global and regional methane emissions from fossil,

industrial, and land-use-related sources ....................................... 84

Fig. 4.4 Global and regional methane emissions from fossil,

industrial, and land-use-related sources ....................................... 85

Fig. 4.5 Global SF 6 emission levels from literature-reported scenarios

and the LDF pathways derived in this study ................................ 86

Fig. 4.6 Global tetrafluoromethane (CF 4 ) emissions from

the collection of assessed literature- reported scenarios
and the LDF pathways derived in this study ................................ 86

Fig. 4.7 Global and regional sulfate dioxide (SOX) emissions

in the literature-reported scenarios considered
and the LDF pathways derived in this study ................................ 87

Fig. 4.8 Global and regional nitrate aerosol (NOX) emissions

in the literature-reported scenarios considered
and the LDF pathways derived in this study ................................ 88

Fig. 4.9 Global and regional black carbon BC emissions

in the literature-reported scenarios considered
and the LDF pathways derived in this study ................................ 89

Fig. 4.10 Global and regional organic carbon OC emissions

in the literature-reported scenarios considered
and the LDF pathways derived in this study ................................ 90

Fig. 5.1 Historic development and projections of oil prices

(bottom lines) and historical world oil production
and projections (top lines) by the IEA according
to Wachtmeister et al. (2018) ........................................................ 104

Fig. 5.2 Global supply curve for primary biomass in 2030

(IRENA 2014) .............................................................................. 111

Fig. 5.3 Development of the specific final energy use (per $GDP)

in all stationary sectors (i.e., without transport) per world
region under the 2.0 °C Scenario (left)
and 1.5 °C Scenario (right) ........................................................... 119

Fig. 5.4 Development of the average global RES shares

in total power generation in the 2.0 °C Scenario .......................... 122

Fig. 5.5 Development of the average global RES shares of future

heat generation options in ‘Industry’ in the 2.0 °C scenario ........ 124

List of Figures

xliii

Fig. 5.6 Development of the average global shares of future

heat-generation options in the ‘Residential and other’
sector under the 2.0 °C scenario ................................................... 125

Fig. 6.1 World final energy use by transport mode in 2015 ....................... 133

Fig. 6.2 Transport mode performances of road, rail, and aviation ............. 133

Fig. 6.3 Powertrain split for all transport modes in 2015

by transport performance (pkm or tkm) ....................................... 134

Fig. 6.4 Final energy use by world transport in 2015

according to region ....................................................................... 134

Fig. 6.5 Powertrain split for all transport modes in 2050

under the 5.0 °C Scenario in terms of transport performance ...... 136

Fig. 6.6 Powertrain split (fleet) of passenger cars in selected

regions in 2030 ( left ) and 2050 ( right ) under
the 2.0 °C Scenario ....................................................................... 137

Fig. 6.7 Battery and trolley electric bus share of total bus pkm

in the 2.0 °C Scenario ( left ) and fuel-cell electric bus share
of total bus pkm in the 2.0 °C Scenario ( right ) ............................ 138

Fig. 6.8 Electrification of passenger rail ( left ) and freight rail ( right )

under the 2.0 °C Scenario (in PJ of final energy demand) ........... 138

Fig. 6.9 Electricity-performed pkm in domestic aviation under

the 2.0 °C Scenario ....................................................................... 139

Fig. 6.10 Powertrain split of the world passenger car fleet in the 2.0 °C

Scenario ( left ) and 1.5 °C Scenario ( right ) ................................... 140

Fig. 6.11 Final energy demand in urban and inter-urban passenger

transport modes in 2015 (world averages) .................................... 140

Fig. 6.12 Final energy demand in freight transport modes in 2015

(world averages) ........................................................................... 141

Fig. 6.13 World average energy consumption development for

passenger cars per powertrain in 2015 ( left ) and 2050 ( right ) ..... 142

Fig. 6.14 Average global energy intensities of truck drivetrain

technologies in 2015 and 2050 ..................................................... 143

Fig. 6.15 Average global energy intensities of bus drivetrain

technologies in 2015 ( left ) and 2050 ( right ) ................................. 144

Fig. 6.16 Average global energy intensities of two-wheel vehicles

( left ) and three-wheel vehicles ( right ) by drivetrain technology
in 2015 ( left bar ) and 2050 ( right bar ) ......................................... 145

Fig. 6.17 MJ/tkm of freight rail trains ( left ) and MJ/pkm of passenger

rail trains ( right ) for 2015 ( left ) and 2050 ( right ) ......................... 146

Fig. 6.18 Region-specific MJ/tkm and MJ/pkm in 2015 and 2050 for

freight rail trains ( left ) and passenger rail trains ( right ) ............... 146

Fig. 6.19 Shares of bio- and synfuels in all world regions

under all scenarios ........................................................................ 147

Fig. 6.20 Relative growth in world transport demand

(2015 = 100% pkm/tkm) in the 5.0 °C scenario ........................... 150

List of Figures

xliv

Fig. 6.21 Relative growth in world transport demand

(2015 = 100% pkm/tkm) in the 2.0 °C Scenario ( left )
and 1.5 °C Scenario ( right ) ........................................................... 150

Fig. 6.22 Regional pkm development .......................................................... 151

Fig. 6.23 World pkm development in all scenarios ...................................... 152

Fig. 6.24 World pkm development in the 2.0 °C Scenario .......................... 152

Fig. 6.25 Pkm development in OECD Europe ( left ) Africa (middle),

and China ( right ) in the 2.0 °C Scenario ...................................... 153

Fig. 6.26 World tkm development in all scenarios....................................... 154

Fig. 6.27 Regional tkm development ........................................................... 154

Fig. 6.28 World tkm development in the 5.0 °C, 2.0 °C,

and 1.5 °C Scenarios ..................................................................... 156

Fig. 6.29 Road tkm in the 2.0 °C Scenario .................................................. 156

Fig. 6.30 Rail tkm in the 2.0 °C Scenario .................................................... 157

Fig. 6.31 Share of rail tkm in total rail + road tkm

in the 2.0 °C Scenario ................................................................... 157

Fig. 7.1 Electricity infrastructure in Africa—power plants

(over 1 MW) and high-voltage transmission lines ....................... 166

Fig. 7.2 Solar potential in Africa ................................................................ 166

Fig. 7.3 Europe’s potential for utility-scale solar power plants ................. 167

Fig. 7.4 OECD North America: existing and potential

wind power sites ........................................................................... 168

Fig. 7.5 Latin America: potential and existing wind power sites ............... 169

Fig. 8.1 Global: projection of final energy (per $ GDP) intensity

by scenario .................................................................................... 176

Fig. 8.2 Global: projection of total final energy demand by sector

in the scenarios (without non- energy use or heat from
combined heat and power [CHP] autoproducers) ......................... 177

Fig. 8.3 Global: development of gross electricity demand by sector

in the scenarios ............................................................................. 178

Fig. 8.4 Global: development of final energy demand for transport

by mode in the scenarios .............................................................. 179

Fig. 8.5 Global: development of heat demand by sector

in the scenarios ............................................................................. 179

Fig. 8.6 Global: development of the final energy demand by sector

in the scenarios ............................................................................. 180

Fig. 8.7 Global: development of electricity-generation structure

in the scenarios ............................................................................. 181

Fig. 8.8 Global: development of total electricity supply costs

and specific electricity generation costs in the scenarios ............. 182

Fig. 8.9 Global: investment shares for power generation

in the scenarios ............................................................................. 183

Fig. 8.10 Global: development of heat supply by energy carrier

in the scenarios ............................................................................. 184

List of Figures

xlv

Fig. 8.11 Global: development of investment in renewable

heat-generation technologies in the scenarios .............................. 186

Fig. 8.12 Global: final energy consumption by transport

in the scenarios ............................................................................. 187

Fig. 8.13 Global: development of CO 2 emissions by sector

and cumulative CO 2 emissions (since 2015) in the
scenarios (‘Savings’ = lower than in the 5.0 °C Scenario) ........... 188

Fig. 8.14 Global: projection of total primary energy demand

(PED) by energy carrier in the scenarios ...................................... 189

Fig. 8.15 Global: scenario of bunker fuel demand for aviation

and navigation and the resulting cumulative CO 2 emissions ........ 190

Fig. 8.16 Development of maximum load in 10 world regions

in 2020, 2030, and 2050 in the 2.0 °C and 1.5 °C scenarios ........ 202

Fig. 8.17 OECD North America: development of final energy

demand by sector in the scenarios ................................................ 210

Fig. 8.18 OECD North America: development of electricity-generation

structure in the scenarios .............................................................. 212

Fig. 8.19 OECD North America: development of total electricity

supply costs and specific electricity- generation costs
in the scenarios ............................................................................. 213

Fig. 8.20 OECD North America: investment shares for power

generation in the scenarios ........................................................... 214

Fig. 8.21 OECD North America: development of heat supply

by energy carrier in the scenarios ................................................. 215

Fig. 8.22 OECD North America: development of investments

in renewable heat generation technologies in the scenarios ......... 216

Fig. 8.23 OECD North America: final energy consumption

by transport in the scenarios ......................................................... 218

Fig. 8.24 OECD North America: development of CO 2 emissions

by sector and cumulative CO 2 emissions (after 2015)
in the scenarios (‘Savings’ = reduction compared with
the 5.0 °C Scenario) ...................................................................... 219

Fig. 8.25 OECD North America: projection of total primary

energy demand (PED) by energy carrier in the scenarios
(including electricity import balance) .......................................... 220

Fig. 8.26 Latin America: development of final energy demand

by sector in the scenarios .............................................................. 231

Fig. 8.27 Latin America: development of electricity-generation

structure in the scenarios .............................................................. 232

Fig. 8.28 Latin America: development of total electricity supply

costs and specific electricity- generation costs
in the scenarios ............................................................................. 233

Fig. 8.29 Latin America: investment shares for power generation

in the scenarios ............................................................................. 235

List of Figures

xlvi

Fig. 8.30 Latin America: development of heat supply by energy

carrier in the scenarios .................................................................. 236

Fig. 8.31 Latin America: development of investments for renewable

heat generation technologies in the scenarios .............................. 237

Fig. 8.32 Latin America: final energy consumption by transport

in the scenarios ............................................................................. 239

Fig. 8.33 Latin America: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015) in the scenarios
(‘Savings’ = reduction compared with the 5.0 °C Scenario) ........ 240

Fig. 8.34 Latin America: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 241

Fig. 8.35 OECD Europe: development in the scenarios .............................. 251

Fig. 8.36 OECD Europe: development of electricity-generation

structure in the scenarios .............................................................. 253

Fig. 8.37 OECD Europe: development of total electricity supply

costs and specific electricity- generation costs
in the scenarios ............................................................................. 253

Fig. 8.38 OECD Europe: investment shares for power generation

in the scenarios ............................................................................. 255

Fig. 8.39 OECD Europe: development of heat supply by energy

carrier in the scenarios .................................................................. 256

Fig. 8.40 OECD Europe: development of investments for renewable

heat-generation technologies in the scenarios .............................. 257

Fig. 8.41 OECD Europe: final energy consumption by transport

in the scenarios ............................................................................. 259

Fig. 8.42 OECD Europe: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015) in the scenarios
(‘Savings’ = reduction compared with the 5.0 °C Scenario) ........ 260

Fig. 8.43 OECD Europe: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 261

Fig. 8.44 Africa: development of final energy demand by sector

in the scenarios ............................................................................. 269

Fig. 8.45 Africa: development of electricity-generation structure

in the scenarios ............................................................................. 271

Fig. 8.46 Africa: development of total electricity supply costs

and specific electricity-generation costs in the scenarios ............. 272

Fig. 8.47 Africa: investment shares for power generation

in the scenarios ............................................................................. 274

Fig. 8.48 Africa: development of heat supply by energy carrier

in the scenarios ............................................................................. 274

Fig. 8.49 Africa: development of investments for renewable

heat-generation technologies in the scenarios .............................. 276

List of Figures

xlvii

Fig. 8.50 Africa: final energy consumption by transport

in the scenarios ............................................................................. 278

Fig. 8.51 Africa: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015) in the scenarios
(‘Savings’ = reduction compared with the 5.0 °C Scenario) ........ 279

Fig. 8.52 Africa: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 279

Fig. 8.53 Middle East: development of the final energy demand

by sector in the scenarios .............................................................. 287

Fig. 8.54 Middle East: development of electricity-generation

structure in the scenarios .............................................................. 289

Fig. 8.55 Middle East: development of total electricity supply

costs and specific electricity- generation costs
in the scenarios ............................................................................. 290

Fig. 8.56 Middle East: investment shares for power generation

in the scenarios ............................................................................. 292

Fig. 8.57 Middle East: development of heat supply by energy

carrier in the scenarios .................................................................. 292

Fig. 8.58 Middle East: development of investments for renewable

heat-generation technologies in the scenarios .............................. 294

Fig. 8.59 Middle East: final energy consumption by transport

in the scenarios ............................................................................. 296

Fig. 8.60 Middle East: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015) in the scenarios
(‘Savings’ = reduction compared with the 5.0 °C Scenario) ........ 297

Fig. 8.61 Middle East: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 297

Fig. 8.62 Eastern Europe/Eurasia: development of the final

energy demand by sector in the scenarios .................................... 306

Fig. 8.63 Eastern Europe/Eurasia: development of

electricity-generation structure in the scenarios ........................... 309

Fig. 8.64 Eastern Europe/Eurasia: development of total electricity

supply costs and specific electricity- generation costs
in the scenarios ............................................................................. 309

Fig. 8.65 Eastern Europe/Eurasia: investment shares for power

generation in the scenarios ........................................................... 311

Fig. 8.66 Eastern Europe/Eurasia: development of heat supply

by energy carrier in the scenarios ................................................. 312

Fig. 8.67 Eastern Europe/Eurasia: development of investments

for renewable heat-generation technologies in the scenarios ....... 314

Fig. 8.68 Eastern Europe/Eurasia: final energy consumption by

transport in the scenarios .............................................................. 315

List of Figures

xlviii

Fig. 8.69 Eastern Europe/Eurasia: development of CO 2 emissions

by sector and cumulative CO 2 emissions (after 2015)
in the scenarios (‘Savings’ = reduction compared
with the 5.0 °C Scenario) .............................................................. 316

Fig. 8.70 Eastern Europe/Eurasia: projection of total primary

energy demand (PED) by energy carrier in the scenarios
(including electricity import balance) .......................................... 317

Fig. 8.71 Non-OECD Asia: development of the final energy

demand by sector in the scenarios ................................................ 325

Fig. 8.72 Non-OECD Asia: development of electricity-generation

structure in the scenarios .............................................................. 327

Fig. 8.73 Non-OECD Asia: development of total electricity supply

costs and specific electricity generation costs
in the scenarios ............................................................................. 328

Fig. 8.74 Non-OECD Asia: investment shares for power

generation in the scenarios ........................................................... 329

Fig. 8.75 Non-OECD Asia: development of heat supply

by energy carrier in the scenarios ................................................. 330

Fig. 8.76 Non-OECD Asia: development of investments

for renewable heat-generation technologies in the scenarios ....... 332

Fig. 8.77 Non-OECD Asia: final energy consumption

by transport in the scenarios ......................................................... 333

Fig. 8.78 Non-OECD Asia: development of CO 2 emissions

by sector and cumulative CO 2 emissions (after 2015)
in the scenarios (‘Savings’ = reduction compared
with the 5.0 °C Scenario) .............................................................. 334

Fig. 8.79 Non-OECD Asia: projection of total primary energy

demand (PED) by energy carrier in the scenarios
(including electricity import balance) .......................................... 335

Fig. 8.80 India: development of final energy demand by sector

in the scenarios ............................................................................. 345

Fig. 8.81 India: development of electricity-generation structure

in the scenarios ............................................................................. 347

Fig. 8.82 India: development of total electricity supply costs

and specific electricity generation costs in the scenarios ............. 348

Fig. 8.83 India: investment shares for power generation

in the scenarios ............................................................................. 349

Fig. 8.84 India: development of heat supply by energy carrier

in the scenarios ............................................................................. 350

Fig. 8.85 India: development of investments for renewable

heat-generation technologies in the scenarios .............................. 352

Fig. 8.86 India: final energy consumption by transport

in the scenarios ............................................................................. 353

Fig. 8.87 India: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015) in the scenarios
(‘Savings’ = reduction compared with the 5.0 °C Scenario) ........ 354

List of Figures

xlix

Fig. 8.88 India: projection of total primary energy demand (PED)

by energy carrier in the scenarios (including electricity
import balance) ............................................................................. 355

Fig. 8.89 China: development of final energy demand by sector

in the scenarios ............................................................................. 361

Fig. 8.90 China: development of electricity-generation structure

in the scenarios ............................................................................. 363

Fig. 8.91 China: development of total electricity supply costs

and specific electricity-generation costs in the scenarios ............. 364

Fig. 8.92 China: investment shares for power generation

in the scenarios ............................................................................. 366

Fig. 8.93 China: development of heat supply by energy carrier

in the scenarios ............................................................................. 366

Fig. 8.94 China: development of investments for renewable

heat-generation technologies in the scenarios .............................. 369

Fig. 8.95 China: final energy consumption by transport

in the scenarios ............................................................................. 370

Fig. 8.96 China: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015)
in the scenarios (‘Savings’ = reduction compared
with the 5.0 °C Scenario) .............................................................. 371

Fig. 8.97 China: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 371

Fig. 8.98 OECD Pacific: development of final energy demand

by sector in the scenarios .............................................................. 381

Fig. 8.99 OECD Pacific: development of electricity-generation

structure in the scenarios .............................................................. 383

Fig. 8.100 OECD Pacific: development of total electricity

supply costs and specific electricity- generation costs
in the scenarios ............................................................................. 384

Fig. 8.101 OECD Pacific: investment shares for power generation

in the scenarios ............................................................................. 385

Fig. 8.102 OECD Pacific: development of heat supply by energy

carrier in the scenarios .................................................................. 386

Fig. 8.103 OECD Pacific: development of investments for renewable

heat-generation technologies in the scenarios .............................. 388

Fig. 8.104 OECD Pacific: final energy consumption by transport

in the scenarios ............................................................................. 389

Fig. 8.105 OECD Pacific: development of CO 2 emissions by sector

and cumulative CO 2 emissions (after 2015)
in the scenarios (‘Savings’ = reduction compared
with the 5.0 °C Scenario) .............................................................. 390

Fig. 8.106 OECD Pacific: projection of total primary energy demand

(PED) by energy carrier in the scenarios (including
electricity import balance) ............................................................ 391

List of Figures

l

Fig. 9.1 Global coal production in 1981–2017

(BP 2018—Statistical Review) ..................................................... 405

Fig. 9.2 Global coal production until 2050 under

the three scenarios ........................................................................ 405

Fig. 9.3 Global oil production in 1965–2017

(BP 2018—Statistical Review) ..................................................... 406

Fig. 9.4 Global oil production until 2050 under

the three scenarios ........................................................................ 407

Fig. 9.5 Global gas production in 1970–2017

(BP 2018—Statistical Review) ..................................................... 408

Fig. 9.6 Global gas production until 2050 under

the three scenarios ........................................................................ 408

Fig. 10.1 World employment in the energy sector under

the 5.0 °C and 2.0 °C Scenarios ( left ) and the 5.0 °C
and 1.5 °C Scenarios ( right ) ......................................................... 418

Fig. 10.2 Distribution of human resources required to manufacture

the main components of a 50 MW solar photovoltaic
power plant. (IRENA 2017a) ........................................................ 420

Fig. 10.3 Division of occupations between fossil fuels and renewable

energy in 2015 and 2025 under the 1.5 °C Scenario .................... 427

Fig. 10.4 Division of occupations between fossil fuels and

renewable energy in 2015 and 2025 under
the 2.0 °C Scenario ....................................................................... 428

Fig. 10.5 Employment changes between 2015 and 2025

by occupational breakdown under the 2.0 °C Scenario ................ 433

Fig. 10.6 Employment changes between 2015 and 2025

by occupational breakdown under the 1.5 °C Scenario ................ 434

Fig. 11.1 Overview of key metal requirements and supply

chain for solar PV ......................................................................... 439

Fig. 11.2 Overview of key metal requirements and supply

chain for wind power .................................................................... 440

Fig. 11.3 Overview of key metal requirements and supply

chain for LIB and EV ................................................................... 441

Fig. 11.4 Cumulative demand from renewable energy

and transport technologies to 2050 compared with reserves ........ 446

Fig. 11.5 Annual demand from renewable energy and storage

technologies in 2050 compared with current production
rates (note that scale varies across the metals) ............................. 446

Fig. 11.6 Annual primary demand for cobalt from EVs and storage ........... 447

Fig. 11.7 Cumulative primary demand for cobalt from EVs

and storage by 2050 ...................................................................... 447

Fig. 11.8 Annual primary demand for lithium from EVs

and storage .................................................................................... 448

Fig. 11.9 Cumulative primary demand for lithium from EVs

and storage by 2050 ...................................................................... 449

List of Figures

li

Fig. 11.10 Annual primary demand for silver from solar PV (c-Si) .............. 449

Fig. 11.11 Cumulative primary demand for silver from solar PV

(c-Si) by 2050 ............................................................................... 450

Fig. 11.12 Top five oil-producing countries (left) versus

lithium-producing countries (right) .............................................. 453

Fig. 12.1 Global CO 2 , CH 4 and N 2 O concentrations under

various scenarios. The so-called SSP scenarios are going
to inform the Sixth Assessment Report by the IPCC,
the RCP scenarios are the previous generation of scenarios
and the LDF scenarios are those developed in this study ............. 462

Fig. 12.2 CO 2 equivalence concentrations and radiative forcing

of main IPCC scenarios for the forthcoming Sixth
Assessment (so-called SSP scenarios), the RCP scenarios
underlying the Fifth IPCC Assessment Report and
the LDF scenarios developed in this study ................................... 463

Fig. 12.3 Global cumulative CO2 emissions – 2.0 °C and

1.5 °C scenarios ............................................................................ 464

Fig. 12.4 Global-mean surface air temperature projections ......................... 467

Fig. 12.5 Global-mean sea level rise projections under the three

scenarios developed in this study ................................................. 468

List of Figures

liii

Table 3.1 Overview of regions and sub-regions used in the analysis ........ 31

Table 3.2 Input parameters for the dispatch model .................................... 48

Table 3.3 Output parameters for the dispatch model ................................. 49

Table 3.4 Technology groups for dispatch order selection ........................ 51

Table 3.5 Technology options—variable renewable energy ...................... 51

Table 3.6 Technology options—dispatch generation ................................. 51

Table 3.7 Technology options—storage technologies ............................... 52

Table 3.8 Key assumptions ........................................................................ 59

Table 3.9 Regional definitions according to the Integrated

Assessment Modelling community ............................................ 61

Table 3.10 Narrative for each sequestration pathway

per climatic biome ..................................................................... 68

Table 3.11 Assumptions regarding the four land-use sequestration

pathways for two climate domain categories ............................. 71

Table 5.1 World regions used in the scenarios ........................................... 96

Table 5.2 Population growth projections (in millions)............................... 102

Table 5.3 GDP development projections based on average annual

growth rates for 2015–2040 from IEA (WEO 2016a, b)
and on our own extrapolations ................................................... 103

Table 5.4 Investment cost assumptions for power generation

plants (in $2015/kW) in the scenarios until 2050 ...................... 107

Table 5.5 Specific investment cost assumptions (in $2015)

for heating technologies in the scenarios until 2050 ................. 108

Table 5.6 Development projections for fossil fuel prices

in $2015 (IEA 2017) .................................................................. 110

Table 5.7 Biomass price projections for 2030 at 108 EJ

of the biomass demand (IRENA 2014) ...................................... 112

Table 5.8 CO 2 cost assumptions in the scenarios ....................................... 113

List of Tables

liv

Table 5.9 Assumed average development of specific (per $GDP)

electricity use for electrical appliances
in the ‘Industry’ sector ............................................................... 115

Table 5.10 Assumed average development in final energy use

for heating in the industry sector (including power-to-heat)
(per $GDP) ................................................................................. 116

Table 5.11 Assumed average developments of per capita electricity

use in the ‘Residential and other’ sector for electrical
appliances (without power-to-heat) ........................................... 117

Table 5.12 Assumed average development of specific final energy

use for heating in the ‘Residential and other’ sector
(including power-to-heat) .......................................................... 118

Table 5.13 Development of power from co-generation

per $GDP ................................................................................... 126

Table 5.14 Development of heat from co-generation per $GDP ................. 126

Table 6.1 Pkm “per km” shift from domestic aviation

to trains (in %) ........................................................................... 151

Table 6.2 Global tkm shifts from truck to train in the 2.0 °C

and 1.5 °C Scenarios (in %) ....................................................... 155

Table 7.1 Theoretical and technical renewable energy potentials

versus utilization in 2015 ........................................................... 163

Table 7.2 [R]E-SPACE: key results part 1 ................................................. 169

Table 7.3 [R]E-SPACE: key results part 2 ................................................. 171

Table 8.1 Global: development of renewable electricity-generation

capacity in the scenarios ............................................................ 180

Table 8.2 Global: development of renewable heat supply

in the scenarios (excluding the direct use of electricity) ........... 184

Table 8.3 Global: installed capacities for renewable heat generation

in the scenarios .......................................................................... 185

Table 8.4 Global: projection of transport energy demand

by mode in the scenarios ............................................................ 187

Table 8.5 Global: projection of bunker fuel demands for aviation

and navigation by fuel in the scenarios ...................................... 191

Table 8.6 Economic potential within a space-constrained scenario

and utilization rates for the 2.0 °C and 1.5 °C scenarios ........... 194

Table 8.7 World: average annual change in the installed power

plant capacity ............................................................................. 196

Table 8.8 Global: power system shares by technology group .................... 197

Table 8.9 Global: capacity factors for variable and dispatchable

power generation ........................................................................ 198

Table 8.10 Global: load, generation, and residual load development .......... 200

Table 8.11 Global: storage and dispatch ...................................................... 205

Table 8.12 Required increases in storage capacities until 2050 ................... 208

List of Tables

lv

Table 8.13 Estimated average global investment costs for batty

and hydro pump storage ............................................................. 209

Table 8.14 OECD North America: development of renewable

electricity generation capacity in the scenarios ......................... 211

Table 8.15 OECD North America: development of renewable heat

supply in the scenarios (excluding the direct
use of electricity) ....................................................................... 215

Table 8.16 OECD North America: installed capacities

for renewable heat generation in the scenarios .......................... 216

Table 8.17 OECD North America: projection of the transport

energy demand by mode in the scenarios .................................. 217

Table 8.18 OECD North America: average annual change

in installed power plant capacity ............................................... 222

Table 8.19 OECD North America and sub-regions: power system

shares by technology group ....................................................... 223

Table 8.20 OECD North America: capacity factors

by generation type ...................................................................... 225

Table 8.21 OECD North America: load, generation, and residual

load development ....................................................................... 226

Table 8.22 OECD North America: storage and dispatch

service requirements .................................................................. 228

Table 8.23 Latin America: development of renewable

electricity-generation capacity in the scenarios ......................... 232

Table 8.24 Latin America: development of renewable heat

supply in the scenarios (excluding the direct
use of electricity) ....................................................................... 236

Table 8.25 Latin America: installed capacities for renewable

heat generation in the scenarios ................................................. 238

Table 8.26 Latin America: projection of transport energy demand

by mode in the scenarios ............................................................ 239

Table 8.27 Latin America: average annual change in installed

power plant capacity .................................................................. 242

Table 8.28 Latin America: power system shares by technology group........ 243

Table 8.29 Latin America: capacity factors by generation type ................... 245

Table 8.30 Latin America: load, generation, and residual

load development ....................................................................... 246

Table 8.31 Latin America: storage and dispatch service

requirements in the 2.0 °C and 1.5 °C Scenarios ....................... 249

Table 8.32 OECD Europe: development of renewable

electricity-generation capacity in the scenarios ......................... 252

Table 8.33 OECD Europe: development of renewable heat

supply in the scenarios (excluding the direct
use of electricity) ....................................................................... 256

Table 8.34 OECD Europe: installed capacities for renewable heat

generation in the scenarios ......................................................... 258

List of Tables

lvi

Table 8.35 OECD Europe: projection of the transport energy

demand by mode in the scenarios .............................................. 259

Table 8.36 OECD Europe: average annual change in installed

power plant capacity .................................................................. 262

Table 8.37 OECD Europe: power system shares by technology group ....... 263

Table 8.38 OECD Europe: capacity factors by generation type .................. 265

Table 8.39 OECD Europe: load, generation, and residual

load development ....................................................................... 266

Table 8.40 OECD Europe: storage and dispatch service requirements ....... 267

Table 8.41 Africa: development of renewable electricity-generation

capacity in the scenarios ............................................................ 271

Table 8.42 Africa: development of renewable heat supply

in the scenarios (excluding the direct use of electricity) ........... 275

Table 8.43 Africa: installed capacities for renewable heat

generation in the scenarios ......................................................... 276

Table 8.44 Africa: projection of transport energy demand

by mode in the scenarios ............................................................ 277

Table 8.45 Africa: average annual change in installed power

plant capacity ............................................................................. 281

Table 8.46 Africa: power system shares by technology group .................... 282

Table 8.47 Africa: capacity factors by generation type ............................... 283

Table 8.48 Africa: load, generation, and residual load development ........... 284

Table 8.49 Africa: storage and dispatch service requirements ..................... 285

Table 8.50 Middle East: development of renewable

electricity-generation capacity in the scenarios ......................... 289

Table 8.51 Middle East: development of renewable heat supply

in the scenarios (excluding the direct use of electricity) ........... 293

Table 8.52 Middle East: installed capacities for renewable heat

generation in the scenarios ......................................................... 294

Table 8.53 Middle East: projection of transport energy demand

by mode in the scenarios ............................................................ 295

Table 8.54 Middle East: average annual change in installed

power plant capacity .................................................................. 299

Table 8.55 Middle East: power system shares by technology group ........... 300

Table 8.56 Middle East: capacity factors by generation type ...................... 302

Table 8.57 Middle East: load, generation, and residual

load development ....................................................................... 303

Table 8.58 Middle East: storage and dispatch service requirements ........... 304

Table 8.59 Eastern Europe/Eurasia: development of renewable

electricity-generation capacity in the scenarios ......................... 308

Table 8.60 Eastern Europe/Eurasia: development of renewable

heat supply in the scenarios (excluding the direct
use of electricity) ....................................................................... 312

Table 8.61 Eastern Europe/Eurasia: installed capacities for renewable

heat generation in the scenarios ................................................. 313

List of Tables

lvii

Table 8.62 Eastern Europe/Eurasia: projection of transport energy

demand by mode in the scenarios .............................................. 315

Table 8.63 Eurasia: average annual change in installed power

plant capacity ............................................................................. 318

Table 8.64 Eurasia: power system shares by technology group................... 319

Table 8.65 Eurasia: capacity factors by generation type .............................. 321

Table 8.66 Eurasia: load, generation, and residual load development ......... 322

Table 8.67 Eurasia: storage and dispatch service requirements ................... 323

Table 8.68 Non-OECD Asia: development of renewable

electricity-generation capacity in the scenarios ......................... 327

Table 8.69 Non-OECD Asia: development of renewable heat

supply in the scenarios (excluding the direct
use of electricity) ....................................................................... 331

Table 8.70 Non-OECD Asia: installed capacities for renewable

heat generation in the scenarios ................................................. 332

Table 8.71 Non-OECD Asia: projection of transport energy demand

by mode in the scenarios ............................................................ 333

Table 8.72 Non-OECD Asia: average annual change in installed

power plant capacity .................................................................. 337

Table 8.73 Non-OECD Asia: power system shares

by technology group .................................................................. 338

Table 8.74 Non-OECD Asia: capacity factors

by generation type ...................................................................... 340

Table 8.75 Non-OECD Asia: load, generation, and residual

load development—2.0 °C Scenario .......................................... 341

Table 8.76 Non-OECD Asia: storage and dispatch

service requirements .................................................................. 343

Table 8.77 India: development of renewable electricity-generation

capacity in the scenarios ............................................................ 347

Table 8.78 India: development of renewable heat supply

in the scenarios (excluding the direct use of electricity) ........... 350

Table 8.79 India: installed capacities for renewable heat

generation in the scenarios ......................................................... 351

Table 8.80 India: projection of transport energy demand

by mode in the scenarios ............................................................ 353

Table 8.81 India: average annual change in installed power

plant capacity ............................................................................. 356

Table 8.82 India: power system shares by technology group ...................... 358

Table 8.83 India: capacity factors by generation type ................................. 358

Table 8.84 India: load, generation, and residual load development ............. 359

Table 8.85 India: storage and dispatch service requirements....................... 360

Table 8.86 China: development of renewable electricity-generation

capacity in the scenarios ............................................................ 363

Table 8.87 China: development of renewable heat supply

in the scenarios (excluding the direct use of electricity) ........... 367

List of Tables

lviii

Table 8.88 China: installed capacities for renewable heat

generation in the scenarios ......................................................... 368

Table 8.89 China: projection of transport energy demand

by mode in the scenarios ............................................................ 370

Table 8.90 China: average annual change in installed power

plant capacity ............................................................................. 373

Table 8.91 China: power system shares by technology group ..................... 374

Table 8.92 China: capacity factors by generation type ................................ 376

Table 8.93 China: load, generation, and residual load development ........... 377

Table 8.94 China: storage and dispatch service requirements ..................... 379

Table 8.95 OECD Pacific: development of renewable

electricity-generation capacity in the scenarios ......................... 383

Table 8.96 OECD Pacific: development of renewable heat

supply in the scenarios (excluding the direct use
of electricity) .............................................................................. 387

Table 8.97 OECD Pacific: installed capacities for renewable

heat generation in the scenarios ................................................. 387

Table 8.98 OECD Pacific: projection of transport energy demand

by mode in the scenarios ............................................................ 389

Table 8.99 OECD Pacific: average annual change in installed

power plant capacity .................................................................. 393

Table 8.100 OECD Pacific: power system shares by technology group ........ 394

Table 8.101 OECD Pacific: capacity factors by generation type ................... 396

Table 8.102 OECD Pacific: load, generation, and residual load

development ............................................................................... 397

Table 8.103 OECD Pacific: storage and dispatch service requirements ........ 398

Table 9.1 Fossil reserves, resources, and additional occurrences .............. 404

Table 9.2 Summary—coal, oil, and gas trajectories for a just

transition under the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios ......... 410

Table 10.1 Summary of employment factors used in a global

analysis in 2012 ......................................................................... 415

Table 10.2 Employment factors used for coal fuel supply

(mining and associated jobs) ..................................................... 415

Table 10.3 Regional multipliers used for the quantitative calculation

of employment ........................................................................... 416

Table 10.4 Wind and solar PV manufacturing–study methodology ............ 421

Table 10.5 Occupational hierarchy, solar PV construction .......................... 422

Table 10.6 Occupational compositions for renewable and fossil

fuel technologies ........................................................................ 425

Table 10.7 Jobs created and lost between 2015 and 2025 under

the 1.5 °C Scenario .................................................................... 429

Table 10.8 Jobs created or lost between 2015 and 2025 by region

under the 1.5 °C Scenario .......................................................... 430

List of Tables

lix

Table 10.9 Jobs created and lost between 2015 and 2025 under

the 2.0 °C Scenario .................................................................... 431

Table 10.10 Jobs created or lost between 2015 and 2025 by region

under the 2.0 °C Scenario .......................................................... 432

Table 11.1 Summary of metal scenarios ...................................................... 443

Table 11.2 Material intensity and recycling rates ........................................ 444

Table 11.3 Market share ............................................................................... 444

Table 11.4 Metal assumptions ...................................................................... 445

Table 11.5 Comparison of results with other studies ................................... 452

List of Tables

© The Author(s) 2019 1 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_1

Chapter 1

Introduction

Sven Teske and Thomas Pregger

Abstract Brief introduction to the UNFCCC Paris Agreement and its main goals,

followed by the project background, motivation and objectives. Presentation of the

specific research questions for the energy and climate scenario development. Short

overview of published 100% renewable energy scenarios and the main differences

between those scenarios and the newly developed 1.5 °C and 2.0 °C scenarios pre-

sented in the book. Overview about the basic assumptions in regard to technology

preferences in future energy pathways. Discussion of the advantages and limitations

of scenarios in the energy and climate debate.

UNFCCC Paris Agreement, Article 2:

1. This Agreement, in enhancing the implementation of the Convention, including its objective, aims to strengthen the global response to the threat of climate change,in the context of sustainable development and efforts to eradicate poverty, including by:

(a) Holding the increase in the global average temperature to well below
2.0 °C above pre-industrial levels and pursuing efforts to limit the tem-
perature increase to 1.5 °C above pre-industrial levels, recognizing that
this would significantly reduce the risks and impacts of climate change;
(b) Increasing the ability to adapt to the adverse impacts of climate
change and foster climate resilience and low greenhouse gas emission
development, in a manner that does not threaten food production; and

(continued)

S. Teske (*) Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au

T. Pregger Department of Energy Systems Analysis, German Aerospace Center (DLR), Institute for Engineering Thermodynamics (TT), Pfaffenwaldring, Germany e-mail: thomas.pregger@dlr.de

2

The Paris Climate Agreement aims to hold global warming to well below 2.0 °C and

to “pursue efforts” to limit it to 1.5 °C. To accomplish this, countries have submitted

Intended Nationally Determined Contributions (INDCs) outlining their post-2020

climate actions (Rogelj et al. 2016 ). The aim of this research is to develop practical

pathways to achieve the Paris climate goals in an economically feasible and sustain-

able matter.

The study described in this book focuses on changing the ways in which humans

produce energy, because energy-related CO 2 emissions are the main driver of cli-

mate change. The analysis also considers the developmental pathways of non-

energy- related emissions and mitigation measures because it is essential to address

their contributions if we are to achieve the Paris climate change targets. The analysis

considers options or ‘scenarios’ for the transition to net zero emissions across all

sectors that allow unnecessary techno-economic, societal, and environmental risks

to be avoided.

Scenario studies are an important way of linking expected or assumed anthropo-

genic activities and their resulting emissions with environmental effects, such as

global warming. They also provide important insights into these techno-economic,

societal, and political options and their various effects. Therefore, they are widely

used to analyse possible carbon emission pathways, to guide decision-makers, and

to motivate or justify interventions and developments. However, comprehensive,

transparent, and robust results and conclusions are required as the bases for such

decision-making. Ideally, this information will come from scenario studies that

investigate a broad range of possible conditions and available options. Such studies

must adopt a holistic approach and integrate comprehensive state-of-the-art back-

ground knowledge, including about the impacts of sectoral and technological

changes, the influence of market developments, and the effects of certain pathways.

Existing global scenario studies do not provide a comprehensive view of the pos-

sible development pathways and technological options required to achieve these

ambitious climate targets. Each study usually provides a few selected pathways,

representing a narrow range of possible energy futures. One reason for this is that

most scenario models are based on objective cost-optimizing functions, which over-

emphasize the cost efficiency based on uncertain cost assumptions. Another reason

is that disruptive developments are not usually considered in scenario narratives.

The history of scenario-based systems analysis is littered with many examples of

misleading and fallacious ‘optimized’ scenario pathways and derived policy recom-

mendations (see e.g., Mai et al. 2013 ; Mohn 2016 ).

(c) Making finance flows consistent with a pathway towards low green-
house gas emissions and climate-resilient development.

2. This Agreement will be implemented to reflect equity and the principle of common but differentiated responsibilities and respective capabilities, in the light of different national circumstances.

S. Teske and T. Pregger

3

Furthermore, in most existing 2.0  °C and 1.5  °C scenarios, achievement of the

climate targets is based on technologies that have significant, and to some extent

unknown, disadvantages. These technologies include nuclear power generation, car-

bon capture and sequestration, and geoengineering (see e.g., Rogelj et  al. 2018 ;

Kriegler et al. 2015 ). Such scenarios involve considerable risk. Moreover, the reader

is usually given only limited access to the model assumptions and results, and

therefore has limited information about the transparency and traceability of the fac-

tors that influence these model-based analyses and the conclusions drawn from them.

The primary objective of this report is to provide a holistic picture of what will

be involved in the transition to 100% renewable energy. This report examines power,

heat, and fuel supplies on a global scale. Its main focus is on the role of efficiency

and renewable energies. We aim to contribute a different and complementary view

of the global transition to renewable energy. We provide two exemplary develop-

ment pathways for each of the 10 regions of the world. We consider both pathways

to be achievable, based on the current state of knowledge, and both are consistent

with the “well below 2.0 °C” climate target.

In addition to scenario building, we assess the major economic and infrastruc-

tural implications of the two pathways in comparison with a 5.0 °C ‘reference’ sce-

nario based on the International Energy Agency (IEA)‘s Current Policies scenario

published in the Word Energy Outlook 2017 (IEA-WEO 2017 ). We do not claim that

our scenarios are optimal with regard to the economy or society. We want to provide

a transparent basis for the further concretization and development of energy system

transformation, and to demonstrate the enormous challenges we face and the need

for action. In contrast to most other studies, we have excluded options with large

uncertainties about the economic, societal, and environmental risks associated with

technologies such as nuclear power, unsustainable biomass use, CCS, and

geoengineering.

Another important objective is to combine bottom-up energy scenarios with non-

energy greenhouse gas (GHG)-mitigation scenarios to construct a complete picture

of possible climate mitigation pathways and the contributions of the illustrated strat-

egies to achieving the Paris targets. Land-use changes and emissions of other GHGs

and aerosols are the focus of this analysis. Finally, GHG concentrations, radiative

forcing, and the implications of global mean temperature and sea-level rises are

modelled by applying a feasible model with reduced complexity, which is fre-

quently used for integrated assessment models, as the climate model.

Any scenario building on a global scale must severely simplify the complex tran-

sition processes and their interrelations. The introduction of different new technolo-

gies occurs under very different conditions and at different scales, and an in-depth

analysis is required in each case to identify the optimal or feasible solutions. Global

governance will also be required for the fast and deep decarbonisation of the world’s

energy systems, especially in relation to carbon pricing and efficiency standards.

However, all perspectives need a common understanding of what is required to

meet the ambitious Paris climate targets. We believe that the results of this study

will contribute to such a common understanding and will demonstrate how urgent is

the need to act.

1 Introduction

4

The 2.0  °C Scenario represents a far more likely pathway than the 1.5  °C

Scenario. Whereas the 2.0 °C Scenario takes into account unavoidable delays due to

political, economic, and societal processes and stakeholders, the 1.5  °C Scenario

requires immediate action. Under the 1.5  °C Scenario, efficiency measures and

renewable energy options must be deployed, and the further development of energy

services must be limited and constrained. Furthermore, for the 1.5 °C Scenario to be

achievable, it will be essential for developing countries to avoid inefficient tech-

nologies and behaviours.

References

IEA-WEO (2017), International Energy Agency, World Energy Outlook 2017, OECD Publishing, Paris/International Energy Agency, Paris, https://doi.org/10.1787/weo-2017-en. Mai, T.; Logan, J.; Blair, N.; Sullivan, P.; Bazilian, M. (2013): RE-ASSUME- A Decision Maker’s Guide to Evaluating Energy Scenarios, Modeling, and Assumptions. National Renewable Energy Laboratory, Golden, CO (USA). URL: http://iea-retd.org/wp-content/uploads/2013/07/ RE-ASSUME_IEA-RETD_2013.pdf (accessed on July 27th, 2015). Mohn, K. (2016) Undressing the emperor: A critical review of IEA’s WEO. University of Stavanger Business School, Norwegian School of Economics. Available at: http://www1.uis.no/ansatt/ odegaard/uis_wps_econ_fin/uis_wps_2016_06_mohn.pdf Rogelj J, den Elzen M, Höhne M, Franzen T, Fekete H, Winkler H, Schaeffer R, Sha F, et al (2016) Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534: 631–639. DOI:https://doi.org/10.1038/nature18307 Rogelj J, Popp A, Calvin KV, Luderer G, Emmerling J, Gernaat D, Fujimori S, Strefler J, Hasegawa T, Marangoni G, Krey V, Kriegler E, Riahi K, van Vuuren DP, Doelman J, Drouet L, Edmonds J, Fricko O, Harmsen M, Havlik P, Humpenöder F, Stehfest E, Tavoni M (2018) Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8 (4): 325–332. DOI:https://doi.org/10.1038/s41558-018-0091-3 Kriegler E, Riahi K, Bauer N, Schwanitz VJ, Petermann N, Bosetti V, et  al (2015) Making or breaking climate targets: The AMPERE study on staged accession scenarios for climate policy. Technological Forecasting & Social Change 90 (2015) 24–44

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

S. Teske and T. Pregger

© The Author(s) 2019 5 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_2

Chapter 2

State of Research

Sven Teske, Malte Meinshausen, and Kate Dooley

Abstract This chapter sets the context for the climate and energy scenario develop-

ment. The first part summarizes the scientific status quo of climate change research

and explains how the global climate has changed over recent decades and the likely

outcomes if we continue with business as usual and fail to drastically reduce GHG

emissions.

The second part reviews the development of the global energy markets during the

past decade. Trends in the power-, transport- and heating sector in regard to technolo-

gies and investments are provided for the year of writing (2018). The developments

put the energy scenarios presented in the following chapters into a global context.

2.1 Scientific Status Quo of Climate Change Research

A summary of the latest scientific publications explains how the global climate has

changed in recent decades and the likely outcomes if we continue with ‘business as

usual’ and fail to drastically reduce greenhouse gas emissions.

2.1.1 Basics of Climate Change and Radiative Forcing

The Earth’s current climate is the result of a delicate balance between incoming short-

wave solar radiation and outgoing long-wave radiation that moves back to space.

Roughly half (165 W/m^2 ) the incoming short-wave radiation (340 W/m^2 ) reaches the

S. Teske (*) Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au

M. Meinshausen · K. Dooley Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: malte.meinshausen@unimelb.edu.au; kate.dooley@unimelb.edu.au

6

surface of the Earth. The rest is reflected back to space by clouds, aerosol particles, or

scattering gases, such as N 2 and O 2 (100  W/m^2 ) or is absorbed by the troposphere

(75 W/m^2 ) (Stephens et al. 2012 ). With the exception of clouds and aerosols, this win-

dow to incoming solar radiation onto Earth’s surface is relatively transparent, so that

most of the Sun’s energy that comes towards Earth is absorbed either in the atmosphere

or on the Earth’s surface. This window is our basic heating engine, and the incoming

radiation that is our energy source is somewhat dimmed by aerosol emissions and by

changes in the amount of sunlight reflected back to space by changes in land use.

However, these are not humanity’s greatest influences on the Earth’s climate.

There is a second window for radiation, through which outgoing long-wave radi-

ation passes, and we are much more effectively closing this window than the one for

incoming solar radiation. We can imagine that this second atmospheric window

already has a thick curtain across it, largely formed by water vapour. That curtain

prevents surface long-wave radiation from going directly to space. In other words,

that curtain acts like a thick blanket. The long-wave radiation that the Earth loses

back into space originates not from its surface but from much higher atmospheric

layers, which tend to be colder. Without absorbers of long-wave radiation in the

atmosphere, the Earth’s surface would be much colder and inhospitable to humans.

To the parts of the outgoing radiation window that are not yet covered by water

vapour, humanity is now adding more layers of absorbers of other long-wave radia-

tion in the form of GHGs. By adding an assortment of GHGs, we are thickening the

existing curtain and closing the curtain further across the long-wave radiation win-

dow. Compared with the overall incoming solar radiation at 340 W/m^2 , the ‘curtain’

generated by human-induced increases in the concentrations of long-lived green-

house gases (CO 2 , CH 4 , halocarbons, N 2 O and fluorinated gases) appears to be of

little importance, as it “only” amounts to 2.83 W/m^2. The addition and subtraction

of many other smaller human influences results in a slightly reduced net current

(year 2011) forcing of 2.29 W/m^2.

2.1.1.1 Anthropogenic Contribution

Beyond reasonable doubt, climate change over the last 250 years has been driven by

anthropogenic activities. In fact, the human-induced release of GHGs into the atmo-

sphere has the potential to cause more than 100% of the currently observed climate

change. The reason that climate change is not even greater than it is that some

human-induced changes mask some of the warming attributable to elevated GHG

concentrations. This masking effect arises from the emission of cooling aerosols

and changes in land use that increase the reflectivity of the Earth’s surface.

2.1.1.2 Carbon Budget and Future Warming

Although the anthropogenic contribution to climate change occurs via a large set of

GHG emissions (the current emission scenarios that feed into CMIP6 include 43

GHG emission species), and multiple aerosols and land-use changes, there is one

dominant influence: carbon dioxide (CO 2 ) emissions.

S. Teske et al.

7

It is not only the magnitude of the anthropogenic emissions of CO 2 that makes it

such a significant driver of human-induced climate change. There is also an inherent

difference between CO 2 and almost all other GHGs and aerosols. Over the time

scales of interest here, CO 2 does not have a finite lifetime in the atmosphere. All

other gases react chemically, become photo-dissociated in the stratosphere, or are,

for example, consumed by the bacteria in soils. However, once CO 2 is released from

the near-permanent carbon pools of fossil fuel reservoirs, it only travels between a

set of ‘active’ carbon pools. These active pools are the land biosphere, the ocean,

and the atmosphere. Therefore, if CO 2 is added to one, the level in all three pools

will rise and over time, a new, higher equilibrium concentration is reached. For

example, whereas CO 2 is consumed by plants during the photosynthesis process and

then built into plant tissue as carbon, this same carbon is released again as CO 2

when forests burn, when organic matter in the soil decomposes, and when humans

and other animals oxidize the food they eat. Therefore, a kilogram of CO 2 emissions

will increase the atmospheric CO 2 concentration for hundreds or even thousands of

years. Initially, the average CO 2 concentration will shoot up by that kilogram, and

then drop relatively quickly again before a new equilibrium is slowly re-established

by the redistribution of carbon into the land biosphere and the ocean.

The IPCC Fifth Assessment Report highlighted this key difference between CO 2

and other GHGs. The airborne fraction of CO 2 emissions diminishes over time, as

for other GHGs. However, the airborne CO 2 fraction does not decline to zero over

100 years, 1000 years, or even longer periods. Furthermore, carbon-cycle feedback

mechanisms mean that higher CO 2 concentrations cause more carbon to remain in

the atmosphere. Acting in the other direction, any extra amount of CO 2 in the atmo-

sphere will have less and less effect on radiative forcing, i.e., how much each CO 2

molecule contributes to the warming of the planet. These factors act in concert with

another feature of the climate system: the Earth’s inertia to warming. When the

thermostat of your kitchen oven is set to 220 °C, the oven will take a while to heat

to that level. The situation is much the same with the Earth’s climate. The IPCC

Fifth Assessment Report notes that three effects (the carbon cycle and its feedbacks,

the saturation effect of forcing, and the delayed response of the atmosphere to

warming) combine to create what is almost a stepwise function in the warming

caused by CO 2 emissions. In other words, every extra kilogram of CO 2 produces a

slightly greater increase in temperature than the preceding kilogram, and the warm-

ing effect is much the same 10 years after the emission of that kilogram as it is after

100 or 500 years. Over time, less of the CO 2 will remain in the atmosphere, but the

Earth’s inertia will still cause the temperature to reflect the extra warmth arising

from the initial emission.

This feature of the Earth’s warming and the carbon cycle can be exploited to

derive a very simple linear relationship between cumulative carbon emissions and

warming. In fact, the resultant warming is a simple function of the sum of all the

CO 2 that has ever been emitted, largely independent of when a certain amount of

CO 2 was emitted in the past. Based on this understanding, we can compute the car-

bon budgets for specific levels of warming. As a complication, of course, an

unknown amount of warming arises in response to other GHG emissions and aero-

2 State of Research

8

sols. When deriving carbon budgets, this extra level of warming is normally derived

from a range of future emission scenarios. Therefore, the ultimate level of warming

is the sum of the linear CO 2 -induced warming level (often described as the ‘tran-

sient climate response to cumulative emissions of carbon’) and a smaller and some-

what uncertain contribution that depends on the other GHGs and aerosols.

2.1.2 Carbon Budgets for 1.5 °C and 2.0 °C Warming

The IPCC Fifth Assessment Report used the results of earth system models to derive

its carbon budgets. Earth system models are the most complex computer models we

have of how the Earth, its atmosphere, oceans, and vegetation are interacting with

each other. The IPCC investigated the amounts of cumulative carbon emissions (in

a multi-gas world) that would be consistent with, for example, temperatures main-

tained below either 1.5 °C or 2.0 °C higher than to pre-industrial levels. The recently

published IPCC Special Report on the 1.5  °C degree target cites different carbon

budget numbers, depending on whether a low estimate of historical temperatures is

assumed or surface air temperatures are consistently applied. Therefore, there is

some complexity and uncertainty around the carbon budget, which is related to the

fact that different interpretations can be made about how far we are still away from

the 1.5 °C target (for example). If we assume that we are still 0.63 °C away from

1.5 °C warming (a very optimistic estimate, which is unfortunately based on a too

optimistic account of historical emissions), from January 2018 onwards, we can still

emit 1320 GtCO 2 before reaching 2.0  °C warming (66% chance) and 770 GtCO 2

before reaching 1.5 °C warming (50% chance). These figures must be reduced by a

further 100 GtCO 2 to account for the additional Earth system feedback that has

occurred over the twenty-first century. However, when a more realistic measure of

historical temperature evolution is used (i.e., calculated by consistently using prox-

ies for surface air temperatures over the land and ocean, rather than by mixing ocean

surface water temperatures with air temperatures over land), the carbon budgets are

no longer very high. Specifically, the carbon budget required to maintain the Earth’s

temperature below 2.0  °C, with 66% probability, then decreases to 1170 GtCO2

from 1 January 2018 onwards and to 560 GtCO2 for a 50% chance of staying below

1.5  °C (before the extra 100 GtCO2 that must be subtracted for additional Earth

system feedbacks is taken into account; see Table 2.2 in the IPCC Special Report on

1.5 °C warming).

The substantial difference between these two sets of figures (and also their dif-

ferences from earlier IPCC estimates of the carbon budget) is the focus of a current

intense scientific debate, which is unlikely to be settled in the next few months (see

e.g., Schurer et al. 2017 , 2018; Hawkins et al. 2017 ). As already mentioned, one of

the key factors in deriving a carbon budget is the estimated current level of warm-

ing. Among other reasons, the recent warming ‘hiatus’ in temperature may explain

why the recently derived carbon budgets are more relaxed than expected. When the

‘hiatus’ period substantially influences the level of warming that is taken as a start-

S. Teske et al.

9

ing point, the ‘distance’ from 1.5 °C and 2.0 °C might seem larger than it is. The

recent upswing in the global mean temperatures gives us an idea of how much natu-

ral variability is superimposed on the long-term warming trend. Other points for

discussion in the determination of carbon budgets include the pre-industrial warm-

ing level and the already-mentioned amount of warming induced by non-CO 2 gases.

This study does not aim to resolve the differences in opinions about carbon bud-

gets, but it does provide emissions pathways that can be considered to be consistent

with both a target level of 1.5 °C warming in the case of the 1.5 °C scenario, and

with a “well-below 2.0 °C” target level in the case of the 2.0 °C scenario.

Whatever the precise carbon budget, recent effects of climate change provide

another set of stark reminders that it is more urgent than ever to replace fossil fuels.

If we wait for the wild-fire seasons that will occur at global warming levels of

1.5 °C or 2.0 °C, with intensified droughts or ever-more intense hurricane, it might

be much too late to avoid their widespread catastrophic impacts. Even at 1.5  °C

warming, there is a risk that the continuous melting of the Greenland ice sheet will

cause sea levels to rise by meters over the coming centuries. Fossil fuels have

undoubtedly allowed great growth in prosperity across the globe, but their replace-

ment with the cleaner, cheaper and emission-free technologies that are available

today is overdue.

2.2 Development of Energy Markets—Past and Present

Renewable energy technologies have been developing rapidly since the beginning

of the century, and they have emerged from niche markets to become mainstream.

This section provides an overview of the development of renewable energy in the

power, heating, and transport sectors up to the year of writing (2018). These devel-

opments will put the energy scenarios presented in the following chapters into a

global context. The research and data in Sect. 2.2.1 are based on the REN21

Renewables 2018—Global Status Report Renewables.

2.2.1 Global Trends in Renewable Energy in 2018

In 2017, ongoing trends continued: solar photovoltaics (PV) and wind power domi-

nated the global market for new power plants, the price of renewable energy tech-

nologies continued to decline, and fossil fuel prices remained low. A new benchmark

was reached, in that the new renewable capacity began to compete favourably with

existing fossil fuel power plants in some markets (Malik 2017 ). Electrification of

the transport and heating sectors is gaining attention, and although the amount of

electrification is currently small, the use of renewable technologies is expected to

increase significantly.

2 State of Research

10

The growth in solar PV has been remarkable, nearly double that of second-

ranking wind power, and the capacity of new solar PV is greater than the combined

increases in the coal, gas, and nuclear capacities (FS-UNEP 2018 ). Storage is

increasingly used in combination with variable renewables as battery costs decline,

and solar PV plus storage has started to compete with gas peaking plants (Carroll

2018 ). However, bioenergy (including traditional biomass) remains the leading

renewable energy source in the heating (buildings and industry) and transport

sectors.

Renewable energy’s share of the total final energy consumption has increased

only modestly in recent years, despite tremendous growth in the modern renewable

energy sector. There are two main reasons for this. One is that the growth in the

overall energy demand (except for the drop in 2009 after the global economic reces-

sion) has counteracted the strong forward momentum of modern renewable energy

technologies. The other is the declining share of traditional biomass, as people

switch to other forms of energy. Traditional biomass makes up nearly half of all

renewable energy used, and its use has increased at a rate lower than the growth in

total energy consumption.

Since 2013, the global energy-related CO 2 emissions from fossil fuels have

remained relatively flat. Early estimates based on preliminary data suggest that this

changed in 2017, with global CO 2 emissions growing by around 1.4% (REN21-

GSR 2018 ). These increased emissions were primarily due to increased coal con-

sumption in China, which grew by 3.7% in 2017 after a 3-year decline (ENERDATA

2018 ). This increased Chinese consumption, as well as steady growth of around 4%

in India, is expected to lead to an upturn in global coal use, reversing the annual

global decline observed from 2013 to 2016 (ENERDATA 2018 ).

In contrast to the upturn in global coal use, in 2017, 26 countries joined the

Powering Past Coal Alliance, which is committed to phasing-out coal power by

2030, with new pledges from Angola, Denmark, Italy, Mexico, New Zealand, and

the United Kingdom (Carrington 2017 ). An increasing number of companies who

owned, developed or operated coal power plants have moved away from the coal

business (Shearer 2017 ). Also in 2017, 26 of 28 European Union member states

signed an agreement to build no more coal-fired power plants from 2020 onwards,

and the Port of Amsterdam, which currently handles 16 million tonnes of coal per

year, announced plans to become coal-free by 2030 (Campbell 2017 ).

The global oil price averaged USD 52.5 per barrel in 2017, equivalent to about

half the record high prices that occurred between 2011 and 2014, although it was

still almost double the prices from 1996 to 2005 (Statista 2018 ). Natural gas prices

fell from 2013 to 2016, and early indicators suggest that prices remained low or

decreased further in 2017 (BP 2017 ). Low fossil fuel prices have challenged renew-

able energy markets, especially in the heating and transport sectors (IEA-RE 2016 ).

The value of direct global fossil fuel consumption subsidies in 2016 was esti-

mated to be about USD 360 billion, a 15% reduction since 2015—but still more

than 20% higher than the total renewable industry turnover in 2017 (IEA-WEB

S. Teske et al.

11

2018 ). The value of fossil fuel subsidies also increases by an order of magnitude if

externalities are considered (IMF 2015 ). Although the Group of Twenty (G20) reaf-

firmed their 2009 commitment to phasing-out inefficient fossil fuel subsidies in

2017, progress has been slow and there are calls from large investors, insurers, and

civil society to both increase transparency and accelerate the process (G20- 2017 ).

The main problems identified include that the G20 has not defined ‘inefficient sub-

sidies’; there is no mandatory reporting; and there are no timelines for phase-out

commitments (Asmelash 2017 ).

At the global policy level, international climate negotiations have continued to

influence energy markets. Following the 2015 Paris Agreement of the United

Nations Framework Convention on Climate Change (UNFCCC), a technical meet-

ing on its implementation took place in Bonn, Germany, in November 2017 at the

23rd Conference of the Parties (COP23) (UNFCCC 2017 ). Although renewable

energy figured prominently in a large proportion of the Nationally Determined

Contributions (NDCs) that countries submitted in the lead-up to COP22 in 2016, the

climate negotiations in 2017 were unable to resolve the question of how NDCs

should be organized, delivered, and updated, leaving uncertainty about how national

renewable energy commitments could be ramped up (Timberley 2017 ).

Despite these uncertainties, an increasing number of communities, cities, and

regions have introduced 100% renewable energy targets. The number of cities pow-

ered by at least 70% renewable electricity has more than doubled in 2 years, from

42 in 2015 to 101 in 2017. These cities now include Auckland, Brasilia, Nairobi,

and Oslo (CDP-WEB 2018 ).

Carbon pricing policies, which include carbon taxes and emission trading

schemes, were in place in 64 jurisdictions around the world in 2017, up from 61 in

2016_._ In December 2017 (REN21-GSR 2018 ), China launched the first phase of its

long-awaited nationwide carbon emissions trading scheme, which will focus on the

power sector. Carbon trading will be based in Shanghai and will include about 1700

power companies emitting more than 3 billion tonnes of CO 2 annually (Xu and

Mason 2017 ). For comparison, the emissions trading scheme of the European Union

included around 1.7 billion tonnes of CO 2 in 2016 (EC 2017 ). Reforms to the

European Union scheme were agreed upon at the end of 2017, which will reduce the

number of emission certificates issued and accelerate the cancellation of surplus

certificates (Agora 2018 ).

The global investment in renewable energy in 2017 (excluding hydropower

plants larger than 50 megawatts [MW]) was USD 280 billion (REN21-GSR 2018 ),

up by 2% from 2016, but 13% below the all-time high, which occurred in 2015. It

is noteworthy that each dollar represents more capacity on the ground as prices per

GW decrease. Nearly all the investment was in either solar PV (58%) or wind power

(38%). Developing countries accounted for the largest share of investment for the

third consecutive year, at 63% of the total investment. China alone accounted for

45% of global investment, with a 30% increase since 2016. The United States was

next, with 14%, followed by Japan (5%) and India (4%). Investment remained

steady or trended upwards in Latin America and the USA, but has been falling in

Europe since about 2010, with a drop of 30% from 2016 to 2017 (UNEP-FS 2018 ).

2 State of Research

12

Pressure to diversify and stable growth in the renewables sector over the past

decade has increased the interest of the fossil fuel industry in renewables. Large oil

corporations more than doubled their acquisitions, project investments, and venture

capital stakes in renewable energy in 2016 relative to those in 2015. This increased

the investment in clean energy companies to USD 6.2 billion over the past 15 years,

with more than 70% of deals involving solar PV or wind, and 16% involving biofu-

els (Bloomberg 2017 ). However, this is dwarfed by the spending of these companies

on fossil fuels. One estimate is that renewables account for about 3% of the total

annual spending (around USD 100 billion) by the world’s five biggest oil companies

(Schneyer and Bousso 2018 ). Bank finance for fossil fuels increased in 2017 by

11% relative to that in 2016, after a significant decline in 2016 (RAN 2018 ).

In 2017, as in previous years, renewables saw the greatest increases in capacity

in the power sector, whereas the growth of renewables in the heating, cooling, and

transport sectors was comparatively slow. Sector coupling—the interconnection of

power, heating, and transport, and particularly the electrification of heating and

transport—is gaining increasing attention as a way to increase the uptake of renew-

ables by the transport and thermal sectors. Sector coupling also allows the integra-

tion of large shares of variable renewable energy, although this is still at an early

stage. For example, China is specifically encouraging the electrification of heating,

manufacturing, and transport in high-renewable areas, including promoting the use

of renewable electricity for heating to reduce the curtailment of wind, solar PV, and

hydropower. Several USA states are examining options for electrification, specifi-

cally to increase the overall renewable energy share (NEEP 2017 ).

2.2.1.1 Trends in the Renewable Power Sector

The capacity for generating renewable power saw its largest annual increase ever in

2017, with an estimated 178 GW of capacity added. The total global renewable

power capacity increased by almost 9% relative to that in 2016. Solar PV additions

reached a record high and represented about 55% of newly installed renewable

power capacity in 2017. The increase in the solar PV capacity was greater than the

combined net additions to the fossil fuel and nuclear capacities. For the first time,

the installed solar PV capacity surpassed the global operating capacity of nuclear

power. Wind and hydropower installations were in second and third positions, con-

tributing about 29% and 11% of the increase in renewable generation capacity,

respectively (REN21-GSR 2018 ).

In 2017, renewables accounted for an estimated 70% of net additions to the

global power-generating capacity, up from 63% in 2016 (REN21-GSR 2018 ). The

cost-competitiveness of renewable power generation continued to improve. Wind

power and solar PV are now competitive with the generation of new fossil fuel

energy in many markets, and even with existing fossil fuel generation in some mar-

kets. The costs of bio-electricity, hydropower, and geothermal power projects com-

missioned in 2017 were mostly within the range of the cost of fossil-fuel-fired

electricity generation. Offshore wind prices also fell significantly in 2017, as com-

S. Teske et al.

13

petitive tenders in Germany, the UK, and the Netherlands resulted in bids that were

competitive with new conventional power generation.

By the end of 2017, the countries with the greatest total installed renewable elec-

tric capacities were China, the USA, Brazil, Germany, and Canada. When only solar

and wind capacities are considered, the top countries were China, the USA, and

Germany, followed by Japan, India, and Italy, and then by Spain and the UK, which

had about equal amounts of capacity by the year’s end.

Seventeen countries have more than 90% renewable electricity, with the majority

supplied almost completely by hydropower. However, three of these, Uruguay,

Costa Rica, and Ethiopia, also have significant contributions from wind power

(32%, 10%, and 7%, respectively) (REN21-GSR 2018 ). Increasing proportions of

variable renewable electricity (VRE) must be integrated into electricity systems,

and VRE penetration reached locally significant levels in 2017. The countries lead-

ing the way with wind and solar penetration are Denmark (52%), Uruguay (32%),

and Cape Verde (31%), with another three countries at or above the 25% VRE pen-

etration mark. Several countries and regions integrated much higher shares of VRE

into their energy systems as instantaneous shares of the total demand for short peri-

ods during 2017, e.g., South Australia (more than 100% of load from wind power

alone, and 44% of load from solar PV alone on another occasion), Germany (66%

of load from wind and solar combined), Texas (54% of load from wind alone), and

Ireland (60% of load from wind alone) (Parkinson 2017 ).

Curtailment—the forced reduction of wind and solar generation—is an indicator

of grid integration challenges. In six of the jurisdictions with the highest VRE pen-

etration, the curtailment rates were low (0–6%) in 2016 (Wynn 2018 ). Although

curtailment initially tends to increase as the VRE share increases, some jurisdictions

have successfully introduced measures, such as transmission upgrades, that have

significantly reduced curtailment (Wynn 2018 ). Integration challenges have led to

high curtailment rates in China, the world’s largest wind and solar PV market (ECNS.

CN 2018 ). These were reduced in 2017, with the average curtailment of wind power

for the year at around 12%, down from 17% in 2016, and the average curtailment of

solar PV was 6–7%, down 4.3% relative to that in 2016 (Haugwitz 2018 ).

The ongoing increase in the growth and geographic expansion of renewable

energy was driven by the continued decline in the prices for renewable energy

technologies (in particular, for solar PV and wind power), caused by the increasing

power demand in some countries and by targeted renewable energy support mecha-

nisms.^44 Solar PV and onshore wind power are now competitive with new fossil fuel

generation in an increasing number of locations, due in part to declines in system

component prices and to improvements in generation efficiency. The bid prices for

offshore wind power also dropped significantly in Europe during 2016 (FT

12.9. 2017 ).

Such declines in cost are particularly important in developing and emerging

economies, and in isolated electric systems (such as on islands or in isolated rural

communities) where electricity prices tend to be high (if they are not heavily subsi-

dized), where there is a shortage of generation, and where renewable energy

resources are particularly plentiful, making renewable electricity more competitive

2 State of Research

14

relative to other options. Many developing countries are racing to bring new power-

generating capacities online to meet rapidly increasing electricity demands, often

turning to renewable technologies (which may be grid-connected or off-grid)

through policies such as tendering or feed-in tariffs, in order to achieve the desired

growth quickly.

Approximately 1.06 billion people, most in sub-Saharan Africa, lived without

electricity in 2016, 223 million fewer than in 2012 (IEA-WEO 2016 ; IEA-EAO

2017 ). Distributed renewables for energy access (DREA) systems were serving an

estimated 300 million people at the end of 2016, and they comprised about 6% of

new electricity connections worldwide between 2012 and 2016 (IRENA-P 2017 ). In

places where the electricity grid does not reach or is unreliable, DREA technologies

provide a cost-effective option to improve energy access. For example, about 13%

of the population of Bangladesh gained access to electricity through solar home

systems (SHS), and more than 50% of the off-grid population in Kenya is served by

DREA systems (Dahlberg 2018 ). Off-grid solar devices, such as solar lanterns and

SHS, displayed annual growth rates of 60% between 2013 and 2017 (Dahlberg

2018 ).

2.2.1.2 Heating and Cooling

Energy use for heating and cooling is estimated to have accounted for just over half

of the total world final energy consumption in 2017, with about half of that used for

industrial process heat (IEA-RE 2017 ). Around 27% of this was supplied by renew-

ables. The largest share of renewable heating was from traditional biomass, which

continued to supply about 16.4% of the global heat demand, predominantly for

cooking in the developing world (IEA-RE 2017 ). Renewable energy—excluding

traditional biomass—supplied approximately 9% of the total global heat production

in 2017, up from about 6% in 2008 (REN21-GSR 2018 ).

Renewable heating and cooling receives much less attention than renewable

power generation, and has been identified as the ‘sleeping giant of energy policy’

for the past decade (IEA-Collier 2018 ). The use of modern renewable heat has

increased at an average rate close to 3% per year since 2008, gradually increasing

its share of heat supply, but it lags well behind the average annual increase of 17%

in modern renewables for electricity (IEA-RE 2017 ). Renewable energy technolo-

gies for heating and cooling include a variety of solar thermal collectors for differ-

ent temperature levels; geothermal and air-sourced heat pumps; bioenergy used in

traditional combustion applications or converted to gaseous, liquid, or solid fuels

and subsequently used for heat; and any type of renewable electricity used for heat-

ing. Heat may be supplied by on-site equipment or by a district heating network.

A wide range of temperature requirements exist, from temperatures of around

40–70  °C for space and water heating in buildings, to steam at several hundred

degrees Celsius for some industrial processes (Averfalk et  al. 2017 ; USA-EPA

2017 ). The variety in renewable heating systems and applications is much greater

than in the renewable power sector, which makes standardization to reduce costs by

S. Teske et al.

15

economies of scale more challenging, and makes it difficult for policy makers to

find effective mechanisms to increase the renewable share. Trends in the use of

modern renewable energy for heating vary according to the technology, although

the relative shares of the main renewable heat technologies have remained stable for

the past few years. In 2017, bioenergy (excluding traditional biomass) accounted for

the greatest share, providing an estimated 68% of renewable heat, followed by

renewable electricity at 18%, solar thermal at around 7%, district heating at 4%

(which was nearly all bioenergy), and geothermal at 2% (REN21-GSR 2018 ).

Although additional bio-heat and solar thermal capacities were added in 2017, the

growth in both markets continued to slow. The trends in direct geothermal heating

are unclear.

Bioenergy systems provide individual heating in residential and medium-sized

office buildings, either as stand-alone systems or in addition to an existing central

heating system, and bioenergy also accounts for 95% of district heating (IEA-RE

2017 ). District heating systems are suitable for use in densely populated regions

with an annual heating demand during ≥4 months of the year, such as in the north-

ern part of China, Denmark, Germany, Japan, Poland, Russia, Sweden, the UK, and

the northern United States (IRENA-RE-H 2017 ). However, district heating supplies

a very small proportion of global heating needs. The majority of district heating

systems are fuelled by either coal or gas, with the share of renewables ranging from

0% to 42% (IRENA-RE-H 2017 ). Switching existing districting heating systems

from fossil fuels to renewables has considerable potential (IRENA-RE-H 2017 ).

Since the 1980s, Sweden has progressively switched from an almost entirely fossil-

fuelled heating supply to systems supplied by 90% renewables and recycled heat

(Brown 2018 ). District heating can combine different sources of heat, and can play

a positive role in the integration of VRE through the use of electric heat pumps.

Solar thermal collector installations continued to decline globally in 2017, with

a reduction of 3% (REN21-GSR 2018 ) compared with 2016, but the markets in

China and India remained strong. In Europe, hybrid systems, in which solar thermal

systems are used in combination with gas-fired central heating or bioenergy, are

becoming more common, with specialized companies offering standardized

technology.

Electricity accounts for an estimated 6% of the total heating and cooling con-

sumption in buildings and industry, with about half of that electricity estimated to

be renewable (IEA-RE 2017 ). The further electrification of heating and cooling

drew increasing attention in 2017, particularly in the United States and China.

Residential solar PV systems are also increasingly connected to electricity-using

heat pumps in buildings rather than feeding the energy into the public electricity

grid, especially when feed-in tariffs for solar electricity are reduced or have been

entirely phased out.

Space cooling accounts for about 2% (REN21-GSR 2018 ) of the total world final

energy consumption, and is supplied almost entirely by electricity (IEA-RE 2017 ).

Solar-based space-cooling systems are still in the minority compared with conven-

tional air-conditioning systems.

2 State of Research

16

2.2.1.3 Transport

The global energy demand for transport increased by an average of 2.1% between

2000 and 2016, and is responsible for approximately 29% of the final global energy

use and 24% of GHG emissions (IEA-WEO 2017 ). The vast majority (92%) of

global transport energy needs are met by oil, with small proportions met by biofuels

(2.9%) and electricity (1.4%) (IEA-WEO 2017 ).

There are three main entry points for renewable energy in the transport sector:

the use of 100% liquid biofuels or biofuels blended with conventional fuels; natural

gas vehicles and infrastructure (these can run on upgraded biogas); and the electri-

fication of transport (if the electricity is itself renewable), which can be via batteries

or hydrogen in fuel cells.

Biofuels (bioethanol and biodiesel) make by far the greatest contribution to

renewable transport. The overall renewable share of road transport energy use was

estimated to be 4.2% in 2016, with nearly all of that from biofuels (IEA-RE 2017 ).

In 2017, global bioethanol production increased by 2.5% relative to that in 2016,

with a slight decline in Brazil offset by increases in the USA, Europe, and China

(IEA OIL 2018 ). Biodiesel production remained relatively stable in 2017, following

a 9% increase in 2016 relative to 2015 (IEA OIL 2018 ).

The technology for producing, purifying, and upgrading biogas for use in trans-

port is relatively mature, and the numbers of natural gas vehicles (NGVs) and the

associated infrastructure are increasing slowly but steadily internationally. Many

countries have relatively well-developed NGV infrastructures, and NGVs provide a

good entry point for biogas in the transport sector (IRENA-RV- 2017 ). The largest

producers of biogas for vehicle fuel in 2016 were Germany, Sweden, Switzerland,

the UK, and the USA (IRENA-RV- 2017 ). The main barriers to the further expansion

of biogas for transport are economic, with supply costs of USD 0.22–1.55 per cubic

metre (m^3 ), compared with natural gas prices, which are as low as USD 0.13 per m^3.

However, the lack of consistent regulation and access to gas grids are also signifi-

cant barriers (IRENA-RV-2017).

Historically, the electrification of the transport sector has been limited to trains,

light rail, and some buses. In 2017, there were signs that the entire sector would

open to electrification. Fully electric passenger cars, scooters, and bicycles are rap-

idly becoming common-place, and prototypes for heavy-duty trucks, planes, and

ships were released in 2017 (Hawkins 2017 ).

The number of electric vehicles (EVs) on the road passed the three million mark

in 2017 (Guardian 25.12. 2017 ). Annual sales are still only a very small proportion

of the global total (1%), but this is set to change. In 2017, partly influenced by the

scandal over diesel emissions cheating, five countries announced their intention to

ban sales of new diesel and petrol cars from 2030 (India, the Netherlands, and

Slovenia) or 2040 (France and the UK) (Bloomberg 11. 2017 ). The announcement

of electric product lines by car manufacturers in 2017 was another breakthrough.

However, the number of EVs on the road is dwarfed by the number of electric bikes.

S. Teske et al.

17

The global fleet was estimated to be around 200 million at the end of 2016, most of

them in China, and 30% of bicycles sold in the Netherlands were e-bikes in 2017

(Wang 2017 ). Electric two- and three-wheel vehicles account for less than 0.5% of

all transport energy use, but they account for most of the remaining renewable share

after biofuels (IEA-RE 2017 ).

Further electrification of the transport sector will potentially create a new market

for renewable energy and ease the integration of variable renewable energy, if mar-

ket and policy settings ensure that the charging patterns are effectively harmonized

with the requirements of electricity systems. There are already examples of coun-

tries and cities supplying electricity for both heavy and light rail from renewable

electricity, including the Netherlands (BI 2017 ), Delhi (Times of India 2017 ), and

Santiago de Chile (CT 2017 ).

Road transport accounts for 67% of global transport energy use, and two-thirds

of that is used for passenger transport.

Aviation accounts for around 11% of the total energy used in transport

(US-EIA- 2017 ). In October 2016, the International Civil Aviation Organization

(ICAO), a UN agency, announced a landmark agreement to mitigate GHG emis-

sions in the aviation sector. By the end of 2017, 106 states representing 90.8% of

global air traffic had settled on a global emissions reduction scheme (Guardian

6.10. 2016 ). Together with technical and operational improvements, this agreement

will support the production and use of sustainable aviation fuels, specifically drop-

in fuels produced from biomass and different types of waste (ICAO 2018 ). In 2017,

Norway announced a target of 100% electric short-haul flights by 2040 (Guardian

18.1. 2018 ).

Shipping consumes around 12% of the global energy used in transport

(US-EIA- 2017 ) and is responsible for approximately 2.0% of global CO 2 emis-

sions. There are multiple entry points for renewable energy: ships can incorporate

wind (sails) and solar energy directly, and can use biofuels, synthetic fuels, or

hydrogen produced with renewable electricity for propulsion. China saw the launch

of the world’s first all-electric cargo ship in 2017, and in Sweden, two large ferries

were converted from diesel to electricity in 2017 (China Daily 14.11. 2017 ). In 2017,

the International Maritime Organization’s (IMO’s) Marine Environment Protection

Committee (MEPC) approved a roadmap (2017–2023) to develop a strategy for

reducing GHG emissions from ships. The roadmap includes plans for an initial

GHG strategy to be adopted in 2018 (IMO 2017 ).

Rail accounts for around 1.9% of the total energy used in transport and is the

most highly electrified transport sector. The share of rail transport powered by elec-

tricity was estimated to be 39% in 2015, up from 29% in 2005 (IEA-UIC 2017 ). Just

over a third of the electricity (9% of rail energy) is estimated to be derived from

renewable sources (IEA-UIC 2017 ). Some jurisdictions are opting to ensure that the

proportion of energy from renewable sources in their transport sectors is well above

the share of renewable energy in their power sectors. For example, the Dutch rail-

2 State of Research

18

way company NS announced that its target to power all electric trains with 100%

renewable electricity was achieved ahead of schedule in 2017 (Caughill 2017 ), and

the New South Wales Government in Australia announced a renewable tender for

the Sydney’s light rail system.

Following the historic Paris Climate Agreement in December 2015, the interna-

tional community has focused increasing attention on the decarbonisation of the

transport sector. At the climate conference in November 2017 in Bonn, Germany, a

multi-stakeholder alliance launched the Transport Decarbonisation Alliance (UN-P

2018 ). France, the Netherlands, Portugal, Costa Rica, and the Paris Process on

Mobility and Climate (PPMC) are members of the Alliance, which includes coun-

tries, cities, regions, and private-sector companies committed to ambitious action on

transport and climate change (UN-P 2018 ).

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2 State of Research

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Schurer, A. P., Mann, M. E., Hawkins, E., Tett, S. F., & Hegerl, G. C. (2017). Importance of the pre-industrial baseline for likelihood of exceeding Paris goals. Nature climate change, 7(8), 563 Statista (2018), Statista: “Average annual Brent crude oil price from 1976 to 2018 (in U.S. dol- lars per barrel)”,https://www.statista.com/statistics/262860/uk-brent-crude-oil-price-changes- since-1976/ , updated 2018, Viewed 21 March 2018.; “Average annual OPEC crude oil price from 1960 to 2018 (in U.S. dollars per barrel)”, https://www.statista.com/statistics/262858/ change-in-opec-crude-oil-prices-since-1960/. viewed September 2018. Schneyer, Bousso (2018), Ernest Scheyder, Ron Bousso, “Peak Oil? Majors Aren’t Buying Into The Threat From Renewables”, Reuters, 8 November 2018. https://uk.reuters.com/article/us-oil- majors-strategy-insight/peak-oil-majors-arent-buying-into-the-threat-from-renewables-idUKK- BN1D80GA Stephens G.L., Wild M., Stackhouse Jr P.W., L’Ecuyer T., Kato S., Henderson D.S. (2012) The Global Character of the Flux of Downward Longwave Radiation, American Meteorological Society, 25, pp:2329–2340. DOI: https://doi.org/10.1175/JCLI-D-11-00262.1 Timberley (2017), Jocelyn Timperley, COP23: Key outcomes agreed at the UN cli- mate talks in Bonn, CarbonBrief, 19 November 2017, https://www.carbonbrief.org/ cop23-key-outcomes-agreed-un-climate-talks-bonn Times of India (2017) “Solar Energy To Power Delhi Metro’s Phase Iii”. The Times of India, Saurabh Mahapatra.. 24 April 2017. https://timesofindia.indiatimes.com/city/delhi/solar- energy-to-power-metro-ph-iii/articleshow/58332740.cms; UNEP-FS (2018), Frankfurt School  - UNEP Collaborating Centre for Climate & Sustainable Energy Finance (FS-UNEP) in co-operation with Bloomberg New Energy Finance. Global Trends in Renewable Energy Investment 2018 (Frankfurt, Germany: 2018). Pages 14, 15 & 26. UNFCCC (2017), United Nation—Climate Change, UNFCCC—The Paris Agreement, website viewed 12th March 2018, http://unfccc.int/paris_agreement/items/9485.php; Jocelyn Timperley, COP23: Key outcomes agreed at the UN climate talks in Bonn, CarbonBrief, 19 November 2017, https://www.carbonbrief.org/cop23-key-outcomes-agreed-un-climate-talks-bonn UN-P (2018), United Nations, Press release, New Transport Decarbonisation Alliance for Faster Climate Action, 11th November 2018, https://cop23.unfccc.int/news/ new-transport-decarbonisation-alliance-for-faster-climate-action USA-EPA (2017), United States of America, Environmental Protection Agency (EPA), Renewable Heating and Cooling, Renewable Industrial Process Heat, https://www.epa.gov/rhc/renewable- industrial-process-heat, updated 26 October 2017, US-EIA (2017) U.S.  Energy Information Administration. International Energy Outlook 2017. Transportation sector passenger transport and energy consumption by region and mode: https:// http://www.eia.gov/outlooks/aeo/data/browser/#/?id=50-IEO2017&region=0-0&cases=Reference& start=2010&end=2020&f=A&linechart=Reference-d082317.2-50-IEO2017&sourcekey=0 Wynn (2018), Wynn, G.  Power-Industry Transition, Here and Now: Wind and Solar Won’t Break the Grid: Nine Case Studies. Institute for Energy Economics and Financial Analysis. (Cleveland, US, 2018) p.15. http://ieefa.org/wp-content/uploads/2018/02/Power-Industry- Transition-Here-and-Now_February-2018.pdf

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Wang (2017) Brian Wang. “Electric bikes could grow from 200 million today to 2 billion in 2050”, Next Big Future, 27t April 2017. https://www.nextbigfuture.com/2017/04/electric-bikes-could- grow-from-200-million-today-to-2-billion-in-2050.html; Sales and Trends. “E-Bike Puts Dutch Market Back on Growth Track”. Bike Europe. 6 March 2018. http://www.bike-eu.com/ sales-trends/nieuws/2018/3/e-bike-puts-dutch-market-back-on-growth-track-10133083 XU, Mason (2017), Muyu Xu, Josephine Mason, “China aims for emission trading scheme in big step vs. global warming”, Reuters, 19 December 2017, https://www.reuters.com/article/ us-china-carbon/china-aims-for-emission-trading-scheme-in-big-step-vs-global-warming- idUSKBN1ED0R6

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

2 State of Research

© The Author(s) 2019 25 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_3

Chapter 3

Methodology

Sven Teske, Thomas Pregger, Sonja Simon, Tobias Naegler,

Johannes Pagenkopf, Bent van den Adel, Malte Meinshausen, Kate Dooley,

C. Briggs, E. Dominish, D. Giurco, Nick Florin, Tom Morris, and Kriti Nagrath

Abstract A detailed overview of the methodologies used to develop the 2.0 °C and

1.5 °C scenario presented in this book. Starting with the overall modelling approach,

the interaction of seven different models is explained which are used to calculate

and developed detailed scenarios for greenhouse gas emission and energy pathways

to stay within a 2.0 °C and 1.5 °C global warming limit. The following models are

presented:

• For the non-energy GHG emission pathways, the Generalized Equal Quantile

Walk (GQW) method, the land-based sequestration design method and the
Carbon cycle and climate (MAGICC) model.

• For the energy pathways, a renewable energy resources assessment for space

constrained environments ([R]E-SPACE, the transport scenario model (TRAEM),
the Energy System Model (EM) and the power system model [R]E 24/7.

The methodologies of an employment analysis model, and a metal resource

assessment tool are outlined. These models have been used to examine the analysis

of the energy scenario results.

S. Teske (*) · C. Briggs · E. Dominish · D. Giurco · N. Florin · T. Morris · K. Nagrath Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au; chris.briggs@uts.edu.au; elsa.dominish@uts.edu.au; damien.giurco@uts.edu.au; nick.florin@uts.edu.au; tom.morris@uts.edu.au; kriti.nagrath@uts.edu.au

T. Pregger · S. Simon · T. Naegler Department of Energy Systems Analysis, German Aerospace Center (DLR), Institute for Engineering Thermodynamics (TT), Pfaffenwaldring, Germany e-mail: thomas.pregger@dlr.de; sonja.simon@dlr.de; tobias.naegler@dlr.de

J. Pagenkopf · B. van den Adel Department of Vehicle Systems and Technology Assessment, German Aerospace Center (DLR), Institute of Vehicle Concepts (FK), Pfaffenwaldring, Germany e-mail: johannes.pagenkopf@dlr.de; Bent.vandenAdel@dlr.de

M. Meinshausen · K. Dooley Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: malte.meinshausen@unimelb.edu.au; kate.dooley@unimelb.edu.au

26

Achieving the goals of the Paris Climate Agreement (UNFCCC 2015 ) will require

the total decarbonisation of the energy system by 2050, with a global emissions

peak no later than 2020 (Hare and Roming 2016 ) and a drastic reduction in non-

energy- related greenhouse gases (GHGs), including land-use-related emissions

(Rogelj and den Elzen 2016 ). Over the past decades, numerous computer models

have been developed to analyse different emissions pathways and to investigate the

effects of changes in policy and technology and adjustments in global and regional

economies. A wide range of climate models is used to calculate non-energy-related

GHG emissions pathways and their impacts on the global climate. The

Intergovernmental Panel on Climate Change (IPCC) states that “Climate models

have continued to be developed and improved since the AR4 [published in

2007-author], and many models have been extended into Earth System models by

including the representation of biogeochemical cycles important to climate change”

(Flato and Marotzke 2013 ). Whereas climate models analyse the effects of a variety

of GHG emissions, energy scenarios only cover energy-related CO 2. Their purpose

is to investigate future energy systems to identify feasible technological and/or eco-

nomic pathways. Like climate models, energy models are diverse and vary signifi-

cantly in their methodologies. The IPCC’s Special Report on Renewable Energy

Sources and Climate Change Mitigation states that there is “enormous variation in

the detail and structure of the models used to construct the scenarios” (Fischedick

and Schaeffer 2011 ). Energy scenarios with high penetrations of variable renewable

power generation—solar photovoltaic (PV) and wind power—require a higher

degree of time resolution to assess the security of 24/7 electricity supplies than

those with mainly dispatchable power generation.

Modelling the energy system involves a variety of methodological requirements,

which pose specific challenges when addressed on the global level: the quantitative

projection of developments in (future) technologies and potential markets; a consis-

tent database of renewable energy potentials and their temporal and spatial distribu-

tions; reliable data on the current situations in all regions; an assessment of energy

flows and emissions across all energy subsectors, such as industry, transport, resi-

dential, etc.; and a comprehensive assessment of all CO 2 emissions, in order to assess

the impact of the energy system on climate change. Finally, analysing and assessing

the energy transition require a long-term perspective on future developments.

Changes to energy markets require long-term decisions to be made because

infrastructural changes are potentially required, and are therefore independent of

short-term market developments. The power market cannot function optimally

without long-term infrastructure planning. Grid modifications and the roll-out of

smart metering infrastructure, for example, require several years to implement.

These technologies form the basis of the energy market and allow energy trading.

Therefore, the time required for infrastructure planning and other substantial trans-

formation processes must be considered in the scenario-building approach.

Although numerous energy scenarios that provide 100% renewable energy at the

community, state, and national levels have been published in the past decade

(Elliston and MacGill 2014 ; Teske and Dominish 2016 ; Klaus et al. 2010 ; Teske and

Brown 2012 ), only a handful of analyses have been performed on a global level. The

main research projects on 100% renewable energy supplies published between 2015

and 2018 were:

S. Teske et al.

27

• A Road Map to 100 Percent Renewable Energy in 139 Countries by 2050, Mark

Jacobson, Charles Q.  Choi, Stanford Engineering, Stanford University, USA,
2017 (Jacobson and Choi 2017 );

• Internet of Energy, A 100% Renewable Electricity System, Christian Breyer,

Neo Carbon Energy, Lappeenranta University of Technology, Finland, 2016
(Breyer 2016 ; Breyer and Bogdanov 2018 );

• Energy [R]evolution—A sustainable World Energy Outlook 2015, Greenpeace

International with the German Aerospace Centre (DLR), Institute of Engineering
Thermodynamics, System Analysis and Technology Assessment, Stuttgart,
Germany (Teske and Pregger 2015 ).

All the studies listed above share the same modelling horizon until 2050 and focus

clearly on the fast and massive deployment of renewable energy resources (RES).

Options with large uncertainties in terms of techno-economic, societal, and environ-

mental risks, such as large hydro power, nuclear power, or unsustainable biomass use,

carbon capture and storage (CCS), and geoengineering are excluded. However, each

of these studies has a specific strength. On the one hand, the analyses from Stanford

University and the University of Technology Lappeenranta include an hourly simula-

tion of power demand and supply, in addition to the pathway modelling. On the other

hand, the Energy [R]evolution study covers the complete energy sector, with detailed

insights into the heat and transport sectors. However, all these studies cover only CO 2

emissions from the energy system, without further investigation of other GHG sources.

Therefore, our project combines these strengths into a single approach by com-

bining a set of models. The approach is based on the scenario modelling used for the

Energy [R]evolution scenario series developed by the authors between 2004 and

2015. It models scenarios of comprehensive pathways for power, heat, and fuel sup-

ply in 5-year steps, and includes specific insights from a transport model. The sce-

nario building is also complemented by a simulation with hourly resolution to

calculate the electricity storage demand and to increase the spatial resolution from

10 to 72 regions. Another significant improvement over existing studies is its com-

bination with a climate model. The interaction between non-energy GHG pathways

and a high-resolution integrated energy assessment model (IAM) provides addi-

tional information on how to achieve the goals of the Paris Agreement.

3.1 100% Renewable Energy—Modelling Approach

The complete decarbonisation of the global energy supply requires entirely new

technical, economic, and policy frameworks for the electricity, heating, and cooling

sectors, and the transport system. Such new framework conditions and the political

and regulative interventions necessary for their implementation are widely discussed

in the literature. However, assessing their feasibility and effectiveness requires an

in-depth analysis of specific regional and national conditions and mechanisms.

Therefore, societal frameworks, measures, and policy interventions are not explicitly

discussed in this scenario analysis, but they are implicit elements in the definition of

the narratives and assumptions as core step of scenario development (see Chap. 5).

3 Methodology

28

Modelling Approach

To develop a global plan, the authors combined various established computer

models:

• Global GHG Model: The non-energy GHG emissions scenarios are calculated

with the following models:

• Generalized Equal Quantile Walk (GQW): This statistical method is used to complement the CO 2 pathways with the non-CO 2 regional emissions for the relevant GHGs and aerosols, based on a statistical analysis of the large num- ber (~700) of multi-gas emission pathways underlying the recent IPCC Fifth Assessment Report and the recently published IPCC Special Report on 1.5 °C. The GQW method calculates the median non-CO 2 gas emission levels every 5 years, conditional on the energy-related CO 2 emission level percentile of the ‘source’ pathway. This method is further developed in this project— building on an earlier ‘Equal Quantile Walk’ method—and is now better able to capture the emission dynamics of low-mitigation pathways.
• Land-based sequestration design: A Monte Carlo analysis across temperate, boreal, subtropical, and tropical regions has been performed based on various literature-based estimates of sequestration rates, sequestration periods, and the areas available for a number of sequestration options. This approach can be seen as a quantified literature synthesis of the potential for land-based CO 2 sequestra- tion, which is not reliant on bioenergy with sequestration and storage (BECCS)
• Carbon cycle and climate modelling (MAGICC): This study used the MAGICC climate model, which also underlies the classification of both the IPCC Fifth Assessment Report and the IPCC Special Report on 1.5  °C in terms of the ability of various scenarios to limit the temperature increase to below 2.0  °C or 1.5  °C.  MAGICC is constantly evolving, but its core goes back to the 1980s, and it represents one of the most established reduced- complexity climate models in the international community.
• Renewable Resource Assessment [R]E-SPACE: This is based on a Geographic

Information Systems (GIS) approach and provides maps of the solar and wind poten-
tials in space-constrained environments. GIS attempts to emulate processes in the real
world, at a single point in time or over an extended period (Goodchild 2005 ). The
primary purpose of GIS mapping is to ascertain whether renewable energy resources
(primarily solar and wind) are sufficiently available in each region. It also provides an
overview of the existing electricity infrastructures for fossil fuel and renewable sources.

• Transport model (TRAEM): The transport scenario model allows the representa-

tion of long-term transport developments in a consistent and transparent way.
The model disaggregates transport into a set of different modes and calculates
the final energy demand by multiplying the specific transport demand of each
transport mode with the powertrain-specific energy demands, using passenger–
km and tonne–km activity-based bottom-up approaches. The model applied is an
accounting system, without system or ownership cost-optimization.

• Energy system model (EM): The scenario model is a mathematical accounting

system for the energy sector that applies different methodologies. It aims to

S. Teske et al.

29

model the development of energy demand and supply according to the energy
potentials, future costs, emissions, specific fuel consumptions, and physical
flows between processes. The data available and the objectives of the analysis
significantly influence the model architecture and approach. It is very important
to differentiate between an energy model and a scenario. An energy model is the
technical basis for a scenario. Scenarios are the results of the energy model,
which have been calculated with different input data and assumptions. The
energy model is used in this study to develop long-term scenarios for the energy
systems across all sectors (power, heat, transport, and industry) without the
application of cost-optimization based on uncertain cost assumptions. However,
an ex-post analysis of costs and investments shows the main economic effects of
the pathways.

• Power system model [R]E 24/7: This simulates the electricity system on an

hourly basis and at geographic resolution to assess the requirements for infra-
structure, such as grid connections, between different regions and electricity
storages, depending on the demand profiles and power-generation characteristics
(Teske 2015 ). High-penetration or renewable-energy-only scenarios will contain
significant proportions of variable solar photovoltaic (PV) and wind power
because they are inexpensive. Therefore, a power system model is required to
assess the demand and supply patterns, the efficiency of power generation, and
the resulting infrastructural needs. On the generation side, meteorological data,
typically in 1 h steps, are required and historical solar and wind data are used to
calculate the possible renewable power generation. On the demand side, either
historical demand curves are used, or—if unavailable—demand curves are cal-
culated based on assumptions of consumer behaviour, the electrical equipment
and common electrical appliances.

Figure 3.1 provides an overview of the interactions between the energy- and

GIS-based models. The climate model is not directly connected but provided the

probabilistic temperatures for the 2.0  °C and 1.5  °C Scenarios. The land-use and

non-CO 2 emissions modules provide information on additional gases based on the

energy-related CO 2 emissions (output of the energy model). Besides the climate and

energy models, the effects on employment and the requirements for selected metal

resources have been calculated (see Sects. 3.6 and 3.7).

3.2 Global Mapping—Renewable Energy Potential in Space-

Constrained Environments: [R]E-SPACE

The primary purpose of GIS mapping is to ascertain the renewable energy resources

(primarily solar and wind) available in each region. It also provides an overview of

the existing electricity infrastructures for fossil fuel and renewable sources.

In this project, mapping was undertaken with the computer software QGIS. QGIS

is a free, cross-platform, open-source desktop GIS application that supports the

viewing, editing, and analysis of geo-spatial data. It analyses and edits spatial infor-

mation and composes and exports graphical maps, and was used to allocate solar

3 Methodology

30

tuptuO

Resource model ([R]E SPACE)

GIS based renewable energy
potentials based on weather &

land use data

Transport model (TRAEM

)

freight & passenger transport

demand by mode
full energy balances: final energy
demand power, heat & transport,supply structure, primary energy
demand by fuel, emission, investmen

t

balanced RE power

system, storage

demand,
curtailment

total climate
change effects

energy demand

by

transport

mode

RE

generationcurves

budget
energy

-related

CO

2
emissions

annual
energ

y-

related

CO

2
emission

s

annual

power

demand

& supply

Modellingcluster

Power system model

([R]E 24/

7
)

hourly balancing of

electricity supply & demand

in spatial resolutio

n

for sub regional clusters

Generalized Equal
Quantile Walk
Complementing no

n-
CO

2

gases based on the
IPCC scenario database

characteristics

biofuel

constraint

s

Energy system model (E

M

)

botto

m-

up simulation of future

energy balances based on GDP,

population, technology,

& energy intensity development in
all sectors and for 10 world regions

Simplified land

-based

sequestration mode

l

complementing reforestation,
restoration,
sustainable use and agroforestry options.

Reduced

complexity carbon cycle and climate model (MAGICC)

to calculate the climatic effects

of mult

i-
gas

pathways

Fig. 3.1

Interaction of models in this study

S. Teske et al.

31

and wind resources and for demand projection for each region analysed. Open-

source data and maps from various sources were used to visualize each country and

its regions and districts. The regions and districts are divided into clusters. The

regions are divided along geographic boundaries, using the IEA regions as a guide.

Some of the larger countries, such as China and India, have been extracted to create

individual scenarios. The clusters are also divided on geographic and political bases.

A list of regions and their respective clusters is given in Table 3.1.

Table 3.1 Overview of regions and sub-regions used in the analysis

Regions Cluster/Sub-regions Regions Cluster / Sub-regions
North
America

USA-Alaska
West Canada
East Canada
North-West USA
North-East USA
South-West USA
South East USA
Mexico
Mexico

Eurasia Central Asia
Eastern Europe
East Caspian
West Caspian
Kazakhstan
Mongolia
Russia

Latin
America

Argentina
Brazil
Caribbean
Central America
Central—South
America
Chile
North Latin America
Uruguay

Non-OECD
Asia

Asia West: Pakistan, Afghanistan, Nepal,
Bhutan
Sri Lanka
Asia Central North: Viet Nam, Laos and
Cambodia
Asia North West: Bangladesh, Myanmar,
Thailand
Asia South-West: Malaysia, Brunei
Pacific Island States
Indonesia
Philippines
Europe Balkans & Greece
Baltic
Central Europe
Nordic
Iberian Peninsula
Turkey
UK & Ireland

India East India
North India
Northeast India
South India including Islands
West India

Africa Central Africa
East Africa
North Africa
South Africa
Southern Africa
West Africa

China Central China
East China
North China
Northeast China
Northwest China
South China
Taiwan
Tibet
Middle East East ME
North ME
Iran
Iraq
Israel
Saud Arabia
UAE

OECD Pacific South Korea
North Japan
South Japan
North New Zealand
South New Zealand
Australia—NEM
Australia—SWIS

3 Methodology

32

Wind speed data at different levels, in metres per second (m/s), were obtained

from Vaisala 2017. For this analysis, wind speed at a height of 80 m was used to

determine the electricity-generation potential. Wind speeds are categorized and

mapped within the range of 5–12 m/s to gain an understanding of the potential gen-

eration across the regions. Speeds under 5 m/s are ignored when plotting optimal

sites. Land-cover types were constrained to bare soil and grasslands. The model

only accounts for the onshore wind-generation potential.

Land-cover data were obtained from the Global Land Cover 2000 project (Global

Land Cover 2015 ), hosted by the European Commission’s Joint Research Centre.

The classification was based on the FAO Land Cover Classification System.

Similarly, solar resource data were obtained from the Global Solar Atlas (Global

Solar Atlas 2016 ), owned by the World Bank Group and provided by SolarGis. Data

categorized by direct normal irradiation were mapped to estimate the potential PVs

in the different regions. To avoid conflict with competing uses of land, only the land-

cover types ‘bare soil’ and ‘grasslands’ were included in the analysis.

The area of land available for potential solar and wind power generation was

calculated at the cluster level using the Geometry tool in the QGIS-processing tool-

box. Intersects (overlapping areas between different layers) were created between

the transmission-level layers and the solar/wind utility vector layers to break down

the total land area available into clusters. A correction was put in place manually for

sites that intersected the cluster boundaries and were part of two clusters.

For some maps (India, China, the Middle East, and OECD Pacific) with large

data files, the analysis was performed using raster files for land use and renewable

potentials. The raster tools ‘clipper’ (used to cut a raster file to the size of the clus-

ter) and ‘merge’ (used to extract common areas between two layers) were used. This

input was fed into the calculations for the [R]E 24/7 Model.

The regional maps illustrate the different clusters that were identified for sce-

nario modelling. The existing infrastructure maps highlight the power plants and

transmission networks in the regions. The wind and solar potential maps indicate

the land available for new power generation given the current land-use patterns.

These maps show utility-scale installations. There are much larger expanses of land

available for small-scale distributed energy generation.

The following types of maps were created for 10 world regions:

Regional breakdown into a maximum of eight clusters:

The example given in Fig. 3.2 shows OECD North America—one of the 10

world regions—broken down into eight sub-regions (clusters). The [R]E 24/7 power

system analysis (see Sect. 3.5) calculates an electricity demand and supply scenario

for each of those eight clusters. The clusters can exchange electricity with each

other (see Sect. 3.8).

S. Teske et al.

33

Fig. 3.2 OECD North America broken down into eight sub-regions

Fig. 3.3 Current electricity infrastructure in China

3 Methodology

34

Current electricity infrastructure

The example given in Fig. 3.3 shows the current electricity infrastructure—

power plants generating > 1 MW—and high-voltage transmission lines in China.

For the development of future electricity scenarios, it is important to know whether

the generation capacity for dispatch and the transmission capacity to transport elec-

tricity from utility-scale wind and/or solar power plants to demand centres are

available.

Potential sites for onshore wind power

Figure 3.4 gives an overview of the potential onshore wind-power-generation

sites in Africa. Only the blue areas are available for new wind development, whereas

the remaining regions are used for nature conservation, agriculture, settlement, or

other forms of land use that do not allow the installation of wind farms. The darker

the blue area, the better the wind potential.

Potential sites for utility-scale solar power plants

Figure 3.5 shows the suitable sites for utility-scale solar power sites in Central

and South America. The scale from yellow to orange to red indicates increasing

available solar radiation. Red areas—in this example, the Atacama Desert in Chile—

indicate the best solar resources and are suitable for both solar PV power plants and

concentrated solar power plants.

Fig. 3.4 Potential sites for onshore wind generation in Africa

S. Teske et al.

35

3.3 Transport Energy Model-TRAEM

3.3.1 Transport Model Structure

The transport scenario model TRAEM (TRAnsport Energy Model) allows the mod-

elling of long-term transport developments for the 10 world regions. It is divided

into several sub-models according to the transport modes, which are discussed

below. All 10 world regions are aggregated in the world model using a passenger–

km (pkm) and tonne–km (tkm) activity-based bottom-up approach. The model cal-

culates the final energy demand by multiplying the specific transport demand of

each transport mode with the powertrain-specific energy demands. This gives the

annual energy demand for electricity, fossil fuels (diesel, petrol), natural gas, bio-

based fuels, synthetically produced fuels (also called ‘synfuels’), and hydrogen for

each of the 10 world regions. The calculation is performed in 5-year steps, from

2015 to 2050.

For all scenarios (5.0 °C, 2.0 °C, and 1.5 °C), the 2015 energy demand by region

was adjusted to the IEA World Energy Balances 2017 and is therefore identical in

all scenarios. The projected total energy demands for the reference scenario (5.0 °C)

from 2020 until 2040 follow the IEA World Energy Outlook 2017 Current Policies

Scenario (IEA 2017b). The total energy demands by region for the years 2045 and

2050 were extrapolated linearly based on the 2035–2040 change rates. The 2.0 °C

Fig. 3.5 Existing and potential solar power sites in Central and South America

3 Methodology

36

Scenario was adjusted from 2020 onwards to 2050 in line with the carbon budget of

the 2.0 °C pathway and the 1.5 °C pathway.

In the transport model, the CO 2 emissions from biofuels are given a GHG emis-

sion factor of zero, because the downstream emissions level out with the upstream

emissions. The CO 2 emissions from synthetic fuels are also given a value of zero,

because the CO 2 used for producing the fuels upstream level out the downstream

emissions. The upstream emissions from electricity and hydrogen production and

all other fuels are factored into the energy system model described in Section 5 with

which the transport model has a data interface. The model distinguishes between

road, rail, aviation, and maritime passenger and freight transport modes.

Road passenger transport modes include:

• Light duty vehicles (cars): automobiles, vans and sports utility vehicles with up to eight seats for private transport, which are further distinguished into small, medium and large cars;
• 2- and 3-wheel vehicles: includes rollers, motorbikes, and rickshaws;
• Busses: urban, suburban, and long-distance buses and minibuses serving pub- lic and private-company transport services.

Rail passenger:

• Urban metro/light rail vehicles;
• Regional/intercity trains;
• High-speed trains.

Aviation (passenger):

• Small and medium aircrafts for domestic flights;
• Medium and large aircrafts for international flights, distinguishing narrow- body, wide-body, and regional jets.

Road freight:

• Light-duty trucks (< 3.5 t gross vehicle weight [GVW]);
• Medium-duty trucks (3.5–15 t GVW);
• Heavy-duty trucks (> 15 t GVW).

Rail freight:

• Ordinary freight, intermodal, and low-density high-value freight trains are distinguished.

Navigation (freight):

• Inland navigation;
• Coastal ships for domestic navigation and maritime shipping are distinguished in the model.

We assume that energy efficiency improves over time. The changes in the pow-

ertrain shares over time are mainly driven by fleet electrification. Energy intensities

S. Teske et al.

37

per pkm and per tkm are region-dependent, based on the occupancy rates of the

passenger transport modes and the loading factors for freight vehicles. The energy

demands of all transport modes (passenger and freight) are summed to the total

energy demand by region.

Backcasting transport scenarios are modelled iteratively by fitting the drivetrain

shares, transport performance (pkm or tkm), and modal shares until the specific

downstream CO 2 budgets of the world regions are met. The emission reductions are

based on a combination of technical, operational, and behavioural measures during

modelling—such as powertrain electrification, the use of biomass-based and syn-

thetically produced fuels, efficiency increases within transport modes, and modal

shifts towards more-efficient modes.

The replacement of internal combustion engines by electric powertrains is priori-

tized in our modelling. However, the rapid electrification of fleets is quantity-

restricted over the immediately subsequent years until the capacities for battery

production, battery recharging, hydrogen production, and refuelling stations have

ramped up ubiquitously. Therefore, a shift towards more energy-efficient and elec-

trified passenger and freight transport modes, such as railways, is required and is

therefore one measure implemented in the model. Such modal shifts are especially

required in the OECD countries, to reduce carbon emissions while maintaining

transport performance at the current levels. Supply constraints on biomass and espe-

cially synfuel production will also limit rapid decarbonisation right from the start,

and motivate modal shifts and general restrictions to overall transport activities by

carbon-intensive transport modes. The 1.5  °C Scenario requires electrification,

modal shifts, and alternative fuel uptake to start earlier than the 2.0 °C Scenario and

particularly the 5.0  °C Scenario, and their more rapid implementation. However,

because electrification will remain quantity-restricted until the 2020s in any case,

widespread modal shifts and changes in mobility behaviour are modelled more

stringently within the 1.5 °C Scenario. The detailed modelling results are discussed

in Chap. 6.

3.3.2 Transport Data

We have derived historical and current data on transport activities (pkm, tkm) and

total energy consumption levels according to transport mode from statistical agen-

cies, governmental and intergovernmental organizations, etc., including:

• IEA Mobility Model;
• OECD statistics;
• World Bank Open Data;
• National and supranational statistical bodies;
• UIC IEA Railway Handbook;
• UIC Railway Synopsis;
• Railway operators data;

3 Methodology

38

• HBEFA (Handbuch Emissionsfaktoren);
• EIA Open Data.

However, statistical data are often unavailable or lack consistency with other

derived data (for example, on vehicle stock or occupancy rates in certain world

regions). In these cases, we applied best guesses based on the scientific and grey

literature. Data for energy intensity per transport mode were derived from the

German Aerospace Centre (DLR) vehicle databases and the state-of-the-art

literature.

3.3.3 Transport Model Output

Based on the TRAEM model, energy consumption and CO 2 emissions can be calcu-

lated for each transport sub-category.

The final energy demand (ED) of the passenger and freight transport modes is

calculated for every world region and all powertrains in 5-year steps from 2015 to

2050 in the following way:

TTEDt TPPtSECP tTPF
wr

WR

m

M

i

I
mi

wr
mi

wr
mi

wr
()= ()× ()+
===

∑∑∑

111

,,, ttSECFtmi

wr
()× , ()

with:

• SECFmit wr ,(): specific freight mode energy consumption of powertrain i and mode m in world region wr at time step t [MJ/tkm]
• SECPmit wr ,(): specific passenger mode energy consumption of powertrain i and mode m in world region wr at time step t [MJ/pkm]
• TPFtmi wr ,(): freight transport performance of powertrain i and mode m in world region wr at time step t [tkm/a]
• TPPtmi wr ,(): passenger transport performance of powertrain i and mode m in world region wr at time step t [pkm/a]
• TTED(t): total transport (final) energy demand at time step t [PJ/year]

The estimated plug-in hybrid electric vehicles, battery electric vehicles, and fuel-

cell-electric vehicles stocks are considered mode by mode, using their respective

battery capacities, vehicle-specific life expectancies, total battery capacity by mode,

world region, and year, to estimate the total transport battery demand (Chap. 11).

S. Teske et al.

39

3.4 Energy System Model (EM)

The focus of this study is the development of normative, long-term scenarios. The

scenarios are target-oriented. Starting from the identified desirable future in 2050,

they use a backcasting process to deliver potential transformation pathways for the

energy system. Technical bottom-up scenarios are developed to meet the climate

targets in terms of cumulative CO 2 emissions and are then compared with a refer-

ence case. The scenarios are based on detailed input data sets that consider defined

targets, renewable and fossil fuel energy potentials, and specific parameters for

power, heat, and fuel generation in the energy systems. The scenarios are repre-

sented in the Energy System model (EM) developed by the DLR, which is imple-

mented in the energy simulation platform Mesap/PlaNet (Seven2one 2012 ;

Schlenzig 1998 ). Mesap/PlaNet is an accounting framework that allows the calcula-

tion of detailed and complete energy system balances, from demand to energy sup-

ply, in 5-year steps up to 2050. The model consists of two independent modules:

• a flow calculation module, which balances energy supply and demand annually,

and

• a cost calculation module for the calculation of the corresponding investment,

generation, and fuel costs.

The strength of the model framework is in its flexible and transparent modelling

of different normative paths. The approach requires exogenously defined expansion

rates and market shares. It explicitly renounces economic optimization because of

the uncertainty of long-term cost assumptions. Therefore, scenario development

using this modelling approach is mainly based on background knowledge and

derived narratives, and the experience and knowledge of the scenario developer is

essential to the success of the scenario-building process. The model acts as a frame-

work for integrating a wide variety of aspects of the transformation of energy sys-

tems, and therefore differs fundamentally from optimization models. The

standardized cost calculation for the power sector is used for the ex-post evaluation

of the scenarios. The modelling framework combines a database with a graphical

programming interface. The database allows the management of both the input

parameters and the simulation output for the different scenarios calculated. The

graphical interface allows the definition of the structure of the modelled system and

the quantitative interdependences between the individual structural elements at dif-

ferent structural depths.

The scope of the scenario model allows the increasing electrification processes

in the heating and transport sectors to be considered, such as electric vehicles, elec-

tric boilers, heat pumps, and hydrogen use. Co-generation in different sectors is also

explicitly represented in the model. The EM is implemented in this framework and

Figure 3.6 gives an overview of its structure.

Details of the structure and relevant model equations were also recently described

by Simon et  al. ( 2018 ). The model calculates the energy flows of a system on an

3 Methodology

40

annual basis. These flows connect a set of technologies in each sector and for all

relevant energy carriers, using linear equations. The equation system is solved

sequentially and the model thus balances demand and supply. This approach is

applied over the scenario period in 5-year steps until 2050. Ultimately, the overall

final energy is calculated as described in the following equations:

FDssfe tUED tMSt t
et

ss ss

et
fe

et

()=∑ ()⋅ ()⋅η ()

FDfe tFDt
ss

ss

fe

()=∑ ()

TFDt FD tFDt UEDtMS
fe

fe
ss fe

ss

fe
ss fe et

ss ss

et

()=∑∑()= ∑ ()=∑∑∑ ()⋅ tttfe

et
()⋅η ()

with:

• FDss,fe(t): demand of (final) energy carrier fe in sub-sector ss^1 at time step t [PJ/

year]

• FDss,fe(t): total demand of (final) energy carrier fe at time step t [PJ/year]
• TFD(t): total final energy demand at time step t [PJ/year]

(^1) The sub-sectors include ‘heat’ and ‘non-heat electrical appliances’ in the sectors ‘Industry’ and ‘Residential and other’, aviation, road transport, navigation, rail transport, non-energy consump- tion, the conversion sector, and storage and transmission losses for power and district heat. Conversion losses are taken into account in the calculation of the primary energy demand. Input Energy System Model Results Sectors: Final energy demand

• transport(seeTM)
• industry
• residential& service etc.

power demand

Heatingtechnologies:

• directheating: fossil burner, biomass burner, heat pump, solar collectors, electric heat
• district heating: CHP & heat plants

Power sector:

• power plants: fossil, nuclear, renewable
• CHP plants: fossil, fuel cell
• biomass, geothermal,

Primary energy
supply:

• fossil: coal, gas, oil
• nuclear
• renewable:hydro, wind, solar, biomass, geothermal, wave

heatdemand

CO 2 -emissions

Drivers:
GDP,
population

Technology database:
efficiency, emission
factors, allocation factors,
costs (power sector)

LCOE

Fuel production:

• H 2 -production
• biofuels
• refineriesetc.

fuel
demand

emissions

costs

energy

intensities

Fig. 3.6 Overview of the energy system model (EM) as implemented in Mesap/PlaNet

S. Teske et al.

41

• UEDss(t): useful energy demand / transport services in sub-sector ss at time step

t [PJ/year]

• MSsset(t): market share of end-sector technology et in sub-sector ss

[dimensionless]

• ηfeet(t): efficiency of end-sector technology et using energy carrier fe^2 at time step

t [dimensionless]

• t : time step

The indices denote:

ss : sub-sector
fe : (final) energy carrier
et : end-sector technology

The primary energy demand (without exports) is calculated as follows:

PD tFDtMS tt
pe
ct fe

fe
fe

ct
fe

ct

()=∑∑ ()⋅ ()⋅η ()

TPDt PD t
pe

pe

()=∑ ()

with

• PDpe(t): total demand of (primary) energy carrier pe at time step t [PJ/year]
• TPD(t): total primary energy demand at time step t [PJ/year]
• MSfect(t): market share of conversion technology ct in the generation of final

energy carrier fe [dimensionless]

• ηfect(t): efficiency of conversion technology^3 ct using the final energy carrier fe at

time step t [dimensionless]

The indices denote:

• pe : (primary) energy carrier
• ct : conversion sector technology^4

The drivers of energy consumption include forecasts of population growth, gross

domestic product (GDP), and energy intensities. Specific energy intensities are

assumed for:

• electricity and heat consumption per person and per GDP;
• the ratio of industrial heat demand to GDP;

(^2) Note that some technologies (e.g., electric heat pumps) require two energy carriers as inputs (electricity and environmental heat), with a specific efficiency for each energy carrier. (^3) Some conversion technologies produce more than one output, e.g. CHP plants, leading to con- straints on efficiencies or market shares. (^4) Power and district heat generation, biofuel, synfuel, and H 2 generation, and refineries. 3 Methodology

42

• demand for energy services, such as useful heat;
• different transport modes based on the Transport Model (see Sect. 3.4).

The model consists of a broad technology database across the heat, fuel, and

power sectors, including sector coupling via combined heat and power (CHP),

power-to-heat, and power-to-fuels technologies, and electric mobility.

For both heat and electricity production, the model distinguishes between differ-

ent technologies, characterized by their primary energy sources, efficiency, and

costs. Examples include biomass or gas burners, heat pumps, solar thermal and

geothermal technologies, and several power-generation technologies, such as PV,

onshore and offshore wind, biomass, gas, coal, nuclear, and CHP. In the transport

sector, the model is directly linked to the results of the transport model (Sect. 3.3).

For each technology, the market share with respect to total heat or electricity pro-

duction is specified according to a range of assumptions, including targets, potential

costs, and societal, structural, and economic barriers. The model eventually calcu-

lates the annual energy flows for a set of energy carriers.

The main inputs of the Energy System Model are:

• IEA World Energy Balances 2017 (IEA 2016a) for the calibration of the model

for each world region in the years 2005–2015;

• IEA World Energy Outlook 2016/2017 (IEA 2016b, 2017a) for the parameteriza-

tion of the model for the reference case (5.0 °C Scenario);

• various studies and statistics used for the assumption of further specific values,

such as the power-to-heat ratios of co-generation plants, coefficients of perfor-
mance of heat pumps, and the efficiency of hydrogen electrolysers and synthetic
fuel production plants;

• narratives and assumptions regarding the further development of demand and

supply technologies in line with the climate targets and by taking into account
RES potentials and costs, stable market developments, and the constraints
imposed by production capacities and regional implementation. These assump-
tions and narratives are described in detail in Chap. 5, Sect. 4.

The main outputs of the model are:

• the final and primary energy demands, broken down by fuel, technology, and

energy sectors, as defined by the International Energy Agency (IEA)—industry,
power generation, transport, and other (buildings, forestry, and fisheries);

• the results broken down by the three main types of energy demand—electricity,

heating, and mobility (transport); specifically, the energy required, technology
deployment, and financial investment for each of these energy demand types;

• total energy budget, which is the total cost of energy for the whole power

system;

• energy-related CO 2 emissions over the projected period.

S. Teske et al.

43

3.5 [R]E 24/7 (UTS-ISF)

The long-term scenarios calculated with the EM for 2020, 2030, 2040, and 2050

(see Sect. 3.4) are used as the input data for the dispatch modelling described in this

section. The [R]E 24/7 model transforms a long-term scenario for a specific year

into hourly load and generation curves. The annual electricity demand is trans-

formed into an hourly load curve (see Sect. 3.2) and the annual power generation is

transformed into a generation time series for variable power generation from

regional solar and wind data and dispatchable power-generation data via inter-

changeable dispatch orders (see Sect. 3.7). The [R]E24/7 model is an accounting

framework used to calculate the complete power system balance at 1 h resolution,

and consists of two modules:

1. a flow calculation module, which balances the energy supply and demand; and
2. a cost calculation module, which calculates the corresponding generation and

fuel costs.

The [R]E 24/7 model examines the influence of the dispatch order of power-

generation technologies, the storage technologies, and the interconnection of up to

eight regions. It calculates the impact of these variables on the overall system costs.

[R]E 24/7 also calculates load curves for the residential, industry, and transport sec-

tors based on the sector-specific energy intensity factors and applications that are in

use. The factors and applications used depend on the GDP and population (see Sect.

3.2).

3.5.1 [R]E 24/7—Model Structure

Teske ( 2015 ) has developed a three-level grid model called ‘[R]E 24/7’ as a grid

analysis tool that differentiates between four voltage levels. For this analysis, the

model has been simplified to eight interconnected clusters to reduce the data volume

and the calculation time. High resolution, with multiple voltage levels, is impracti-

cal for a global energy scenario, because the required input data would not be avail-

able for all regions and—if the data were available—the calculation time would be

extremely long. Therefore, the simplified [R]E 24/7 model uses eight clusters that

can exchange electricity on an hourly basis with a user-defined interconnection

capacity (see Sect. 3.8). Different voltage levels are not calculated. Figure 3.7 pro-

vides an overview of the different modules of the [R]E 24/7 model. In the first step,

a database provides the main input data for the base year, including socio-economic

parameters, the currently available power generation, and the energy infrastructure.

The data are partly with the GIS tool (see Sect. 3.2) and partly from other informa-

tion resources, such as publicly available databases of populations (UN PD DB

2018 ), GDP (CIA 2018 ; ST 7- 2018 ), and energy efficiency indicators (WEC 2018 ),

3 Methodology

44

and statistical data on renewable power generation from IRENA (REN21-GSR

2018 ) and the World Resources Institute (WRI 2018 ).

3.5.2 Development and Calculation of Load Curves

Energy demand projections and the calculation of load curves are important factors

in calculating supply security and the dispatch and storage capacities required, espe-

cially for energy supply concepts with high proportions of variable renewable power

generation. The [R]E 24/7 model calculates the development of the future power

demand and the resulting possible load curves, because:

(a) Actual demand curves are not available for all countries and/or regions and are

sometimes classified information.

(b) Future load curves with high penetration of storage, electric heating systems

and electric mobility will have a very different shape than current load curves.

(c) For developing countries with low access to energy rates or little access to suf-

ficient data, the curves must be calculated based on a set of assumptions because
actual curves are neither available nor representative of future load curves.

The model generates load curves and the resulting annual power demands for

three different consumer groups/sectors:

1. Calculation of each cluster
2. Identifies over/under supply

Capex, Opex, Fuel costs
LCOE

StandardReport

5. Result Analysis

Base year, 2020, 2030, 2040, 2050

in hourly resolution

4. Records results
5. Connects clusters

Macro

5. Connection to standard report

DemandandLoad RegionDistribution

Calculation Module
Matching Demand + Supply

Generationdata

Distributionof
calculated load curves

DispatchModule

> by region generation capacity

Socioeconomicdata

RegionDistribution
Distributionof

HouseholdDemand

Interco nnection

Storage
Capacity by region

Calculation of load curves

Power Generation
and Infrastructur

3. Graphs and tables
4. Key results costs
5. Hourly results - Demand

storage capacity

ResultingDemand

by addition of 3 load curces

> by technology

to sub-regions (clusters)

Capacity by technology

installed capacities

2. Hourly results - Supply & Storage

by subregions

EnergyConsumerdata TransportDemand

CostCalculation

average annual demand

GDP, total & by sector IndustryDemand

Capacity by region

Calculation Module

Capacity by technology

> selection of dispatch order

Base year

Population, Household-size Calculation module

Generation

RegionalDatabase
CurveProjection

Fig. 3.7 Schematic representation of the [R] E24/7 model structure

S. Teske et al.

45

• households;
• industry and business; and
• transport (public and individual electric mobility).

Although each sector has its specific consumer groups and applications, the same

set of parameters is used to calculate load curves:

• electrical applications in use;
• demand pattern (24 h);
• efficiency progress (base year 2015) for 2020 until 2050, and individual effi-

ciency input for each year.

The calculations involve detailed bottom-up projections of the increased use of

electricity for heating in buildings, for industrial process heat, for electric mobility,

and for the production of synthetic fuels and hydrogen. They also include increased

access to energy in developing countries based on the applications used, the demand

patterns, and the household types. This allows detailed demand projections to be

made.

Infrastructure needs, such as power grids combined with storage facilities,

require in-depth knowledge of local loads and generation capacities. In this project,

the annual electricity demand for each of the 10 world regions was calculated with

the long-term EM. The [R]E 24/7 model breaks each region into up to eight sub-

regions (or clusters) to calculate hourly load and generation curves.

3.5.3 Load Curve Calculation for Households

The model differentiates nine household groups, with various degrees of electrifica-

tion and equipment:

• Rural—phase 1: Minimal electrification stage
• Rural—phase 2: White goods are introduced and increase the overall demand
• Rural—phase 3: Fully equipped western-standard household with electrical

cooking and air conditioning and electric vehicle(s)

• Urban single: Single-person household with minimal equipment
• Urban shared flat: 3–5 persons share one apartment; fully equipped western

household, but without electric vehicles

• Urban—Family 1: 2 adults and 2–3 children; middle income, middle western

standard

• Urban—Family 2: 2 adults and > 3 children and/or higher income, full western

standard

• Suburbia 1: average family, middle income, full equipment, high transport

demand due to extensive commuting

• Suburbia 2: High-income household, fully equipped, extremely high transport

demand due to high-end vehicles and extensive commuting

3 Methodology

46

The following electrical equipment and applications can be selected:

• Lighting: 4 different light bulb types (LED, three efficiency classes of CFLs),
• Cooking: 10 different cooking stoves (2+4 burners, electricity, gas, firewood)
• Entertainment: 3 different efficiency levels of computers, TV, and radio types
• White goods: 2 different efficiency levels each for washing machine, dryer,

fridge, freezer

• Climatization: 2 different efficiency levels each for fan, air-conditioning
• Water heating: A selection of direct electric, heat-pump, and solar heating

systems

3.5.4 Load Curve Calculation for Business and Industry

The industrial sector is clustered into eight groups based on widely used statistical

categories:

• Agriculture
• Manufacturing
• Mining
• Iron and steel
• Cement industry
• Construction industry
• Chemical industry
• Service and trade

Each sector has a definite energy intensity in energy per dollar GDP (MJ/$GDP),

which is been converted to electrical units (kW/$GDP) based on an estimated fuel

efficiency factor, electricity shares, and operational hours per year. The calculated

electricity intensity per dollar GDP conversion can only show the required con-

nected load and the specific consumption of an industrial sector to a first approxima-

tion because there is a variety of uncertainty factors, such as:

(a) significant regional differences;

(b) significant demand differences within one industry sector, such as manufactur-

ing or chemical industry ;

(c) lack of standardized data on industry energy demands, especially for the elec-

tricity sector.

Despite the high degree of uncertainty, we decided to apply this methodology

because after an initial calibration, the current statistically recorded industrial elec-

tricity consumption in some well-documented countries (e.g., USA) and regions

(e.g., Europe) can be recalculated with a tolerance of ± 10%. However, this method-

ology requires further research.

S. Teske et al.

47

3.5.5 Load Distribution by Cluster

The spatial concept of the [R]E 24/7 is shown in Fig. 3.8. The model calculates the

load distribution for one region, which can be broken down further to a maximum

of eight sub-regions (or ‘clusters’). Therefore, the 10 world regions modelled in this

analysis are calculated separately. OECD North America, for example, includes

Canada, USA, and Mexico. These three countries can be subdivided into up to eight

clusters. A cluster can be a country (e.g., Mexico), a province/state of a country

(e.g., Alaska), or a selection of several provinces/states (e.g., West Canada = British

Columbia, Alberta, Yukon Territory, and North-West Territories). A cluster is

defined to capture the existing interconnected power supply areas of a region, a

country, or across several provinces. In Europe, for example, one cluster is the

Iberian Peninsula (Spain and Portugal), a region within Europe that has only very

limited interconnection with the central European grid system (UCT-E). However,

data availability and the model limitations (maximum of eight clusters) force sim-

plifications, and countries or state/provinces must be bundled together in one cluster

even though they may have significant differences. Therefore, further research is

required to obtain more detailed results for selected countries or provinces.

The distribution of the regional load, calculated in Sects. 3.3 and 3.4, is con-

nected to the projected GDP, population, and power plant capacities for each cluster.

C 1 C 5
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC 1 CapacityC 5

C 2 C 6
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC 2 CapacityC 6

C 3 C 7
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC 3 CapacityC 7

C 4 C 8
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC 4 CapacityC 8

Data:USAStatistics Data:MexicoStatistics

INTERCONNECTION OF CLUSTERS
Clusters im/export from all clusters: -Total
interconnecon capacity limited to x% of total
generaon capacity (individuel input)

Power Plant Capacity Power Plant Capacity
Demand Demand

Populaon Populaon
GDPGDP

C 5 C 8
Cluster=XUSStates Cluster=Country

Data:USAStatistics Data:CANADAStatistics

Power Plant Capacity Power Plant Capacity
Demand Demand

Populaon Populaon
GDPGDP

C 1 C 2
Cluster=USState Cluster= 4 CanadianStates

10. Oceania Pacific
11. IndiaC7. South East USA
12. China C8 Mexico
13. Eurasia C5. South West USA
14. Non OECD Asia C6. North East USA
15. Africa C3. East Canada
16. Middle East C4. North West USA
17. Lan America C1. Alaska
18. Europe C2. West Canada

Regions Sub-Regions(Cluster)

1. OECD North America e.g.NorthAmerica

Fig. 3.8 Spatial concept of the [R]E 24/7 model

3 Methodology

48

The cluster-specific data for the base year (2015) are taken from the model’s data-

base interface to calculate the demand and supply for the base year. When data are

not available for each sub-region, the input data from the entire region will be bro-

ken down (in percentages) by cluster, according to the population—as a result of the

GIS analysis. For the global analysis, the spatial distribution of the population,

GDP, and power plant capacities remain unchanged over the modelled years (2020–

2050. for all 10 regions and their respective sub-regions. In the next step, the result-

ing population and GDP values for each cluster are multiplied by the normalized

load curves calculated as described in Sects. 3.3 and 3.4. Each cluster has an hourly

load curve over one entire year (8760  h). Thus, one region (e.g., OCED North

America) has eight different load curves.

3.5.6 The [R]E 24/7 Dispatch Module

Although the dispatch module for the [R]E 24/7 energy access model has been

developed specifically for this study, integral parts have been taken from a model

developed to analyse the generation and storage needs for a micro grid on Kangaroo

Island (Dunstan and Fattal 2016 ), the Australian Storage Requirements (Rutovitz

and James 2017 ), and a 100% Renewable Energy Analysis for Tanzania (Teske and

Morris 2017 ). The key objective of this modelling is to calculate the theoretical

generation and storage requirements for energy adequacy in each cluster and for the

whole survey region (Tables 3.2 and 3.3).

Figure 3.9 provides an overview of the dispatch calculation process for one clus-

ter. The key inputs include the generation capacities by type, the demand projec-

tions and load curves for each cluster, the interconnection with other clusters, and

the meteorological data from which to calculate the solar and wind power genera-

Table 3.2 Input parameters for the dispatch model

Input parameter
L Cluster Load Cluster [MW]
L Interconnection Load Interconnection (Im- or Export) [MW]
L Initial Initial Load (Cluster + Interconnection) [MW]
Cap Var.RE Installed capacity Variable Renewables [MW]
Meteo Norm Meteorological data for solar and wind [MW/MWINST]
L Post_Var.RE Load after Variable Renewable Supply [MW]
Cap Storage Capacity Storage [MW]
CapFact Max_Storage Max capacity factor storage technologies [h/year]
L Post_Storage Load after Storage Supply [MW]
Cap Dispatch Capacity Dispatch Power Plants [MW]
CapFact Max_Dispatch Max capacity factor Dispatch Power Plants [h/year]
L Post_Dispatch Load after Dispatch Power Plant Supply [MW]
Cap Interconnection Capacity Interconnection [MW]

S. Teske et al.

49

tion at hourly resolution. The calculation of one region with eight sub-regions will

require eight calculation intervals. Table 3.4 shows the four different supply tech-

nology groups: variable renewables, dispatch power plants, storage technologies,

and interconnections. The model allows the order in which the technology groups

will be utilized to be changed to satisfy the demand. Storage and interconnection

cannot be selected as the first supply technology. Within each technology group, the

dispatch order can be changed. Tables 3.5, 3.6, and 3.7 provide an overview of all

the available technologies and examples of different dispatch scenarios. While CSP

plants with storage are dispatchable to some extent—depending on the storage size

and the available solar radiation—they are part of the variable renewable group in

the [R]E 24/7 model. Although the model allows the dispatch order to change, the

100% renewable energy analysis always follows the same dispatch logic. The model

identifies excess renewable production, which is defined as potential wind and solar

PV generation greater than the actual hourly demand in MW during a specific hour.

To avoid curtailment, the surplus renewable electricity should be stored with some

form of electric storage technology or exported to a different cluster. Within the

model, the excess renewable production accumulates through the dispatch order. If

storage is present, it will charge the storage within the limits of the input capacity.

If no storage is present, this potential excess renewable production is reported as

‘potential curtailment’ (pre-storage).

Limitations: It is important to note that the calculation of possible interconnection

capacities for transmission grids between sub-regions does not replace technical

grid simulation. Grid services, such as inductive power supply, frequency control,

and stability, should be analysed, although this is beyond the scope of this analysis.

The results of [R]E 24/7 provide a first rough estimate of whether the increased use

of storage or increased interconnection capacities or a mix of both will reduce sys-

tems costs.

Table 3.3 Output parameters for the dispatch model

Output parameter
L Initial Initial Load (Cluster + Interconnection) [MW]
L Post_Var.RE Load after Variable Renewable Supply [MW]
S EXECC_VAR.RE Access supply Renewables [MW]
L Post_Storage Load after Storage Supply [MW]
S Storage Storage Requirement/Curtailment [MW]
CapFact Actual_Storage Utilization Factor Storage [h/year]
L Post_Dispatch Load after Dispatch Power Plant Supply [MW]
S Dispatch Dispatch Requirement [MW]
CapFact Actual_Dispatch Utilization Factor Dispatch Power Plants [h/year]
L Post_Interconnection Load after Interconnection Supply [MW]
S Interconnection Interconnection Requirement [MW]
CapFact Actual_Interconnection: Utilization Factor Interconnection [h/year]

3 Methodology

50

INPUT OUTPUT

Equation 1

Equation 2

Equation 3

Equation 4

Result
Dispatch Order for each Sluppy Category exchangeable

DispatchOrder

L Cluster [MW/h]
L Interconnecon [MW/h]

Load
LInial [MW/h]

LInial [MW/h]
Cap VAR.RE [MW]
Meteo Norm[MW/MWinst]

Variable
Renewables

LPost_VAR.RE [MW/h]
SEXESS_VAR.RE [MW/h]

LPost_VAR.RE [MW/h]
Cap STORAGE [MW]
CapFact Max [h/yr]

Storage

LPost_Storage [MW/h]
SStorage [MW/h]
CapFactActual [h/yr]

LPost_Storage [MW/h]
Cap Dispatch [MW]
CapFact Max_DP[h/yr]

Dispatch
Generation

LPost_Dispatch [MW/h]
SDispatch [MW/h]
CapFactActual_DP[h/yr]

LPost_Dispatch [MW/h]
Cap Intercon [MW]
Inter-
connecon

LPost_IN [MW/h]

CapFactActual_IN[h/yr]

ion 1

ion 2

ion 3

ion 4

LInial [MW/h]

LPost_VAR.RE [MW/h]
SEXESS_VAR.RE [MW/h]

LPost_Storage [MW/h]
SStorage [MW/h]
CapFactActual [h/yr]

LPost_Dispatch [MW/h]
SDispatch [MW/h]
CapFactActual_DP[h/yr]

LPost_IN [MW/h]

Dispatch Sto
Generation

Inter-
Sto connecon

SIntercon [MW/h]

Fig. 3.9 Dispatch order module of the [R]E 24/7 model

S. Teske et al.

51

3.5.7 Meteorological Data

Variable power-generation technologies are dependent on the local solar radiation

and wind regimes. Therefore, all installed capacities of this technology group are

connected to cluster-specific time series. The data were derived from the database

Renewable Ninja (RE-N DB 2018 ), which allows the simulation of the hourly

power output from wind and solar power plants at specific geographic positions

throughout the world. Weather data, such as temperature, precipitation, and snow-

fall, for the year 2014 are also available.

Table 3.4 Technology groups for dispatch order selection

Technology options Input
Variable renewables Variable renewables
Storage Storage
Dispatch generation Dispatch generation
Interconnector Interconnector

Table 3.5 Technology options—variable renewable energy

Variable renewable power
technology options Input
Photovoltaic—roof top - Photovoltaic—roof top
Photovoltaic—utility scale - Photovoltaic—utility scale
Wind—onshore - Wind—onshore
Wind—offshore - Wind—offshore
CSP (Dispatchable) CSP

Table 3.6 Technology options—dispatch generation

Dispatch generation
Technology options Input
Bioenergy Hydropower
Geothermal Bioenergy
Hydropower CoGen Bio
Ocean Geothermal
Oil CoGen Geothermal
Gas Ocean
CoGen bio Gas
CoGen geothermal CoGen Gas
CoGen gas Coal
CoGen coal CoGen Coal
Coal Brown Coal
Brown coal Oil
Nuclear Nuclear

3 Methodology

52

To utilize climatization technologies for buildings (air-conditioning, electric

heating), the demand curves for households and services are connected to the

cluster- specific temperature time series. The demand for lighting is connected to the

solar time series to accommodate the variability in lighting demand across the year,

especially in northern and southern regions, which have significantly longer day-

light periods in summer and very short daylight periods in winter.

3.5.7.1 Solar and Wind Time Series

For every region included in the model, hourly output traces are utilized for onshore

wind, offshore wind, utility solar, CSP, and roof-top solar energies. Given the num-

ber of clusters and the geographic extent of the study, and the uncertainty associated

with the prediction of the spatial distribution of future generation systems, an repre-

sentative site was selected for each of the five generation types. For utility solar and

CSP, the indicative sites were situated in areas of high solar output, close to the

transmission network or regional centre or city, and in areas without competing land

uses (as described in the mapping methodology). A roof-top solar indicative site

was chosen in the demographic centre of the region, usually the capital city.

The onshore wind indicative site selected for each region was situated in an area

of non-competing land use with the highest average wind speed and close to the

transmission network or regional centre or city. The offshore wind indicative site

was an area within 100 km of the shore with the highest average wind speed, and

close to the transmission network or regional centre or city. In some cases, no

acceptable wind area within a region was available, in which case the wind potential

was set to zero.

Once the indicative sites were chosen, the hourly output values for typical solar

arrays and wind farms were selected using the database of Stefan Pfenninger at

ETH Zurich and Iain Stafell (Renewables.ninja; see above). The model methodol-

ogy used by the Renewables.ninja database is described by Pfenninger and Staffell

(2016a, b), and is based on weather data from global reanalysis models and satellite

observations (Rienecker and Suarez 2011 ; Müller and Pfeifroth, 2015 ; SARAH

2018 ). It was assumed that the utility solar sites were optimized, and as such, a tilt

angle was selected within a couple of degrees of the latitude of the indicative site.

For roof-top solar, this was left at the default 35° because it is likely that the panels

matched the roof tilt.

Table 3.7 Technology options—storage technologies

Storage
Technology option Input
Battery Battery
Hydro pump STORAGE Hydro Pump storage
H2 H2

S. Teske et al.

53

The wind outputs for both onshore and offshore wind were calculated at an 80 m

hub height because this reflects the wind data sets used in the mapping exercise.

Although onshore wind and offshore wind are likely be higher than this, 80 m was

considered a reasonable approximation and made our model consistent with the

mapping-based predictions. A turbine model of Vestas V90 2000 was used.

Limitations: The solar and wind resources can differ within one cluster. In some

cases, there are even different climate zones within one large cluster, e.g., in

Australia and Russia. Therefore, the potential generation output can vary within a

cluster and across the model period (2020–2050). Furthermore, some clusters

extend significantly across several time zones, such as Russia. The model can only

take into account the time variations in sunrise and sunset between different clus-

ters, but not within a single cluster. The effect of time differences within clusters

with a large east–west spread requires high-resolution modelling, which is possible

with the [R]E 24/7 model but beyond the scope of this research project.

3.5.8 Interconnection Capacities

The interconnection capacities are set as a function of the total generation capacity

within a cluster and a manually set percentage. Defining the relevant percentage of

a country’s overall (peak) capacity and/or total generation capacity is based on

European energy policy. The European Union (EU) proposed in 2002 that all EU

member states must establish a transmission capacity of at least 10% of the peak

demand (in megawatts) by 2005 (EMP-BARCELONA 2002 ). The EU developed

this regulation further, improved the calculation method, and increased the target to

15% (EU-EG 2017 ), whereas the [R]E 24/7 model implements a simplified approach

by taking a percentage of the overall installed capacity. Clusters that are not con-

nected at all to the real energy market (e.g., South Korea, Japan, Australia, and New

Zealand in the OECD Pacific region) are assigned 0% interconnection capacity.

Responsibly well-connected clusters (such as the south-western USA) are set to

15%, and highly interconnected countries (such as Denmark) are assigned up to

40%.

Several simplifications have been made to the [R]E 24/7 model for ease of com-

putation and to accommodate the paucity of data and uncertainty about the future

when designing the interconnector algorithms:

• Interconnections between the project-defined regions are the only ones consid-

ered, so all intra-regional interconnections or line constraints are excluded (‘ cop-
per plate ’);

• Optimal load flow is neglected because policy and price signals are considered to

be the factors dominating the international and inter-regional load flow;

• Non-adjacent inter-regional interconnections are neglected for computational

reasons, e.g., one region cannot buy power from a region with which it does not
share a border.

3 Methodology

54

The algorithm devised for the function of the interconnectors is based on three

key pieces of information for each region in a cluster:

• Excess generation capacity;
• Unmet load;
• Interconnection capacity with each adjacent region, both in and out.

The excess generation capacity and unmet load were calculated by running the

model without the interconnectors to determine the excess or shortfall in generation

when the load within the region is met. These excesses and shortfalls were calcu-

lated at the point in the dispatch cascade at which the interconnectors provide or

consume power, for example, after the variable renewables and dispatchable gen-

erators and before the storage elements.

The interconnection capacity between adjacent regions was defined based on a

percentage of the maximum regional load. The capacity was defined in a matrix,

both to and from every region to every other region. For non-adjacent regions, the

capacities were set to zero. A priority order for each region to every other region was

given, so that if the region had an unmet load, it would be served sequentially with

the excess generation of the loads in their defined order.

For every hour and for every region in each cluster, the possible interconnection

inflow or outflow for load balancing was calculated. Each region was considered in

turn, and the algorithm attempted to meet the unmet load with excess generation by

adjacent regions, keeping track of the residual excess load and interconnector

capacities. Each region’s internal load was met first, before its generation resources

were considered for other interconnected regions.

Once the total inflow and outflow of the interconnectors were calculated, the

hourly values were fed into the model once more at the position in the cascade to

which they were assigned, and the model was run again to give the total system

behaviour. For regions sending generation capacity to other regions, the intercon-

nector element behaved as an increase in load, whereas for regions accepting power

from neighbouring regions, the interconnector element behaved as an additional

generator, from the model’s perspective.

3.6 Employment Modelling (UTS-ISF)

Two of the key dimensions influencing the social and economic impacts of the tran-

sition from fossil-fuel to clean energy are the quantity and type of jobs that are lost

and created. Currently, there are limited data on the volumes of jobs that will be lost

and created within particular occupations and locations during the transition to

clean energy. National statistical agencies classify and collect data on occupations

within the fossil fuel sectors but not within the renewable energy sectors (ABS

2017 ). ISF has developed a model to estimate the volume of renewable energy jobs

under different 100% global renewable energy scenarios (Rutovitz and Dominish

2015 ), and an increasing body of research is estimating the jobs created by

S. Teske et al.

55

renewable energy. The following section provides an overview of the basic method-

ology. Based on this, UTS/ISF has developed this methodology further, as presented

in Sect. 3.2.

3.6.1 Quantitative Employment Calculation

In 2015, the Institute for Sustainable Futures (ISF) at the University of Technology

Sydney (UTS) developed a quantitative employment model that calculates employ-

ment development in the electricity, heating, and fuel production sectors for the

analysis of future energy pathways (Rutovitz and Dominish 2015 ). Figure 3.10 pro-

vides a simplified overview of how the calculations are performed, based on

Rutovitz (2015b). The main inputs for the quantitative employment calculations

are:

for each calculated scenario, e.g., the 5.0 °C (Sect. 5.1.1) and 2.0 °C Scenarios

(Sect. 5.1.2),

• the electrical and heating capacity that will be installed each year for each

technology;

• the primary energy demand for coal, gas, and biomass fuels in the electricity and

heating sectors;

• the amount of electricity generated per year from nuclear power, oil, and diesel.

for each technology:

• ‘employment factors’, or the number of jobs per unit of capacity, separated into

manufacturing, construction, operation, and maintenance, and per unit of pri-
mary energy for fuel supply;

• for the 2020, 2030, and 2050 calculations, a ‘decline factor’ for each technology,

which reduces the employment factors by a certain percentage per year. This
reflects the fact that employment per unit decreases as technology efficiencies
improve.

for each region:

• the percentage of local manufacturing and domestic fuel production in each

region, to calculate the proportions of jobs in manufacturing and fuel production
that occur in the region;

• the percentage of world trade in coal and gas fuels, and traded renewable compo-

nents that originates in each region.

A ‘regional job multiplier’, which indicates how labour-intensive the economic

activity is in that region compared with the OECD, is used to adjust the OECD

employment factors when local data are not available. The figures for the increase

in electrical capacity and energy use from each scenario are multiplied by the

employment factors for each of the technologies, and then adjusted for regional

3 Methodology

56

labour intensity and the proportion of fuel or manufacturing that occurs locally. The

calculation is summarized in Fig. 3.10.

A range of data sources were used for the model inputs, including the International

Energy Agency, US Energy Information Administration, BP Statistical Review of

World Energy, US National Renewable Energy Laboratory, International Labour

Organization, World Bank, industry associations, national statistics, company

reports, academic literature, and the ISF’s own research.

These calculations only take into account direct employment; for example, the

construction team required to build a new wind farm. They do not include indirect

employment; for example, the extra services provided in a town to accommodate

the construction team. The calculations do not include jobs in energy efficiency

because this is beyond the scope of this project. The large number of assumptions

required to make these calculations means that employment numbers are only esti-

mates, especially for regions where few data exist. However, within the limits of

data availability, the figures presented are representative of employment levels

under the 5.0 °C and 2.0 °C Scenarios.

Manufacturing (for
local use) =

MW installed per
year in region x

Manufacturing
employment factor x

Regional job multiplier
for year x

%local
manufacturing

Manufacturing
(for export) =

MW exported per
year x

Manufacturing
employment factor x

Regional job multiplier
for year

Construction = MW installed peryear^ x Construction employment factor x Regional job for year multiplier

Operation and
Maintenance =Cumulative capacityx

O&M employment
factor x

Regional job multiplier
for year

Fuel supply
(nuclear) =

Electricity
generation xFuel employment factor x

Regional job multiplier
foryear

Fuel supply (coal,
gas and biomass) =

Primary energy
demand plus
exports

x

Fuel employment factor
(always regional for
coal)

x Regional job for year multiplier^ x % of local production

Heat supply = MW installed peryear^ x Emheat ployment factor for x Regional job for year multiplier^

Jobs in region = Manufacturing+Construction+Operation and maintenance (O&M) + Fuel + Heat

Employment factors at 2020,
2030 or 2050 =2015 employment factor x Technology decline factor^

[number of years after 2015]

Fig. 3.10 Quantitative employment calculation: methodological overview

S. Teske et al.

57

3.6.2 Occupational Employment Modelling

The quantitative employment model documented in Sect. 3.6.1 were further devel-

oped to analyse the qualitative occupational composition of employment in the fos-

sil fuel and renewable energy industries. UTS-ISF has developed a framework for

modelling disaggregated occupational change, and this framework is described in

this section.

Quantitative employment studies at the level of technology and project phases

(manufacturing, construction, and O&M) are useful when providing estimates of

aggregate job creation. However, more disaggregated, granular data on the locations

and types of occupations are required to plan a just transition to renewable energy.

For example, it is necessary to know how many electricians are currently employed

in fossil fuel industries and how many will be employed in the renewable energy

sectors. Although our forecasts will almost inevitably be wrong, key trends can be

established. For example, we can direct our focus to areas of the workforce in which

an increase in the supply of labour will probably be required, and to areas where the

effects of dislocation will be greatest.

Using a variety of data sources, ISF has developed a framework for classifying

and measuring job changes at different levels of occupational disaggregation, to

provide a richer picture of the composition of this employment change. The meth-

odology and key figures are detailed below.

Three primary studies that classify and measure the occupational compositions

of renewable energy industries have been conducted by the International Renewable

Energy Agency (IRENA). Using surveys of the participants in around 45 industries

across a range of developed and developing nations, IRENA has estimated the per-

centages of person-days for the various occupations across the solar PV and onshore

and offshore wind farm supply chains (IRENA 2017 ). Figure 3.11 is an example (in

this case, for solar PV manufacturing).

IRENA’s studies are the most detailed estimates available of the occupational

compositions of the solar PV and onshore wind sectors. ISF has extended the

application of IRENA’s work. Chapter 10 provides more details about the methodol-

ogy and the specific factors used in this analysis.

3.7 Material and Metal Resources Analysis (UTS-ISF)

3.7.1 Methodology—Material and Metal Resources Analysis

The future demands for metals have been modelled to better understand the resource

requirements of the shift to renewable energy and transport systems. The future

demands for metals have been modelled for the projection of 100% renewable

energy and the full electrification of the transport system by 2050, as described in in

Chap. 6.

3 Methodology

58

The predicted demand for the metals required to produce clean energy each year

is estimated based on the increase in capacity plus an additional amount required to

replace the capacity or vehicles that reach the ends of their lives in each year (based

on a lifetime distribution curve for the average lifetime). From this, the GW of

capacity or number of vehicles introduced in each year is estimated (also accounting

for the replacement stock for end-of-life technologies).

When assuming that the introduced amount of specific technologies in year t is lt ,

the accumulated stock amount in year t (generation capacity or in-use stock) is St , and

the discarded amount in year t is Ot , can be expressed by:

SStt =+− 1 IOtt −
(3.1)

Where Ot depends on the number of use years of each product. This use year

varies from product to product, and even within the same product group introduced

into a society in the same year. The discarded year is not constant and has a lifetime

distribution. Therefore, if the number of use years of the product is assumed as a ,

lifetime distribution can be defined as g(a). Hence, is given by following:

OIt ga
a

a
= ta ()
=

∑ −

0

max
(3.2)

Health and Safety
4%
Quality control
4%
Regulation and
standardisation experts
4%

Factory workers and
technicians
62%

Engineers
12%

Admin & Management 5%

Marketing and sales
5%

Logistics
4%

Fig. 3.11 Distribution of human resources required to manufacture the main components of a 50 MW solar photovoltaic power plant. (IRENA 2017 )

S. Teske et al.

59

Where amax is the maximum value of the product life. Therefore ,It can be calcu-

lated with equation (3.3).

ISttSIt ga
a

a
=−− + ta ()
=

1 ∑ −

0

max
(3.3)

In this book, the Weibull distribution is used to consider the life characteristics of

the products described above, with the key assumptions shown in Table 3.8.

Based on the annual introduced amount of clean energy technologies given by

equation (3.3), the metal demand for technology p in year t is estimated as:

Demandpt ,,=⋅ IMpt etal intensitypt ,
(3.4)

Where Metal intensity (^) p,t is the amount of required metal in technology p in year t. Because this value can change over time with technological developments, we assume that the various scenarios incorporating the material efficiency improvement. The demand estimated with equation (3.4) indicates the total metal requirements for the introduction of clean energy technologies. This demand arises from primary production (mined from natural deposits) and secondary production (recovered from end-of-life products). Secondary production could play an important role in the future by increasing metal availability and reducing the environmental impact. Therefore, we evaluated the effects of recycling by estimating the potential reduction in primary production entailed. When the recycling of end-of-life products is con- sidered, primary production is given by equation (3.5). Primaryp roductionpt ,,=− Demandpt DiscardRpt ,⋅ ecyclingratep (3.5) Where Discardp,t is end-of-life technology in year , and is estimated from the Weibull distribution, and the Recycling ratep indicates the proportion of metals recovered from end-of-life technology. Since this value can be increased by techno- logical improvements, the metal price, and the amount of end-of-life product avail- able, we assumed both the current recycling rate and an improved recycling rate. This recycling rate is based on the rate of recycling of the metal within the tech- nology (e.g., silver discarded from solar panels can be recycled into new solar pan- els), rather than as an average across the use of the metal, as has been done in previous studies. This has been chosen as the most appropriate recycling rate to use because we assume that by using recycling rates specific to the technology, it is more likely to offset demand for new materials for that technology. Table 3.8 Key assumptions Technology Lifetime (years) Shape parameter Solar PV 30 5.38 Battery 8 3.5 3 Methodology

60

Ultimately, the mineral requirements estimated with equations (3.4) and (3.5)

under the various assumptions were compared with metal reserves and annual pro-

duction (in 2017). A ‘reserve’ is regarded as the amount economically extractable

with the current technologies and at the current metal price, and can change signifi-

cantly over time. However, comparing reserves with estimated future requirements

can provide insight into how the introduction of clean energy technologies will

affect the physical availability of metals in the future. We also compared current

production with the estimated future requirements to estimate the likelihood of a

rapid increase in requirements. The key results are presented in Chap. 11.

3.8 Climate Model

3.8.1 Deriving Non-CO 2 GHG Pathways

This section provides an overview of the methodology that has been used to com-

plement the energy-related CO 2 emission pathways for non-energy-related CO 2

emissions, other GHG emissions, and aerosols.

The energy-related CO 2 emissions were derived using energy-system modelling

frameworks, but two different approaches have been used to derive the land-use CO 2

emissions and other GHG emissions. First, we will describe the approach that was

used to determine other GHG emissions. This approach can be summarized as a

statistical analysis of currently published scenarios. To derive non-CO 2 pathways

that are consistent with the relevant emission mitigation levels, the non-CO 2 emis-

sions were regressed against the fossil fuel and industrial CO 2 emissions. These

regression characteristics were then used to derive the non-CO 2 emissions. This

method has been newly developed in the context of this study and can be regarded

as a further development of the Equal Quantile Walk method introduced by

Meinshausen et al. ( 2006 ).

One challenge in applying the collective knowledge that is enshrined in multi-

gas- emission scenarios in the literature is that regional and sectoral definitions differ

slightly between the various modelling groups. Because most IPCC scenarios work

are based on the emission categories used by the IAM community, their emission

categories and regions have been adopted in this analysis of non-CO 2 emission path-

ways. The steps in the analysis are described in the following sub-sections.

3.8.1.1 Regional Definitions

First, the regional energy-related CO 2 emissions developed in the previous sections

must be transformed to match the five Renewable Communities Program (RCP)

regions used by the IAM community, into the regions OECD90 (OECD countries,

S. Teske et al.

61

membership status as of 1990), ASIA (Asian countries), REF (economies in transi-

tion), LAM (Latin America), and MAF (Middle East and Africa). Table 3.9 (above)

indicates the overlap and differences between the RCP regions with the other

regions described in this report. As an indicator of how different the regional defini-

tions are, we used the fossil fuel and industrial emissions for the year 2015 accord-

ing to the 2017 update of the PRIMAP database (Gütschow et al. 2016 ).

Table 3.9 provides an overview to the regional definitions used in this study. The

top row indicates the regions for the CO 2 fossil and industrial emissions, and the

various rows refer to the five regions used in IAMs. To derive the non-CO 2 emis-

sions, we used the IAM’s five RCP regions. The numbers indicate the fossil fuel and

industrial emissions in the year 2015 in MtCO 2 , aggregated from country-level data.

The colour shading of the cells indicates where most of the 2015 emissions occurred.

Table 3.9 Regional definitions according to the Integrated Assessment Modelling

community (so-called ‘RCP5’ regions) compared with the other regions used in this

study The overlap and differences between the two sets of regional definitions are

shown with the 2015 fossil and industrial CO 2 emissions. For example, the first row

indicates that the largest sub-region in the RCP5_Asia group is China, with 8,826

MtCO 2 of emissions.

The transfer of the energy-related CO 2 emission results to fit the IAM’s regional

categorization (which is consistent with IEA WEO reports) was performed by first

disaggregating all the results to country-level data. A simple proportional scale was

applied to the 2015 energy-related country-level CO 2 emissions from the PRIMAP

database. The disaggregated country-level data were then re-aggregated at the RCP5

regional level.

3.8.1.2 Harmonization: Emission Category Adjustments

Before proceeding with the application of CO 2 versus non-CO 2 statistical relation-

ships, a harmonization step is necessary. Various IAM use slightly different catego-

ries, emission factors, and activity data to estimate emissions. This can result in

Regions
Developing
Asia Africa
Middle
East

Central &
South
Amer Eurasia China India
North
America

OECD
Asia
Oceania Subtotal
RCP5_Asia 1,561 - - - 19 21 8,826 1,985 - 674 13 ,086
RCP5_REF - 720 - - - 2,153 - - - - 2,873
RCP5_MAF - - 1,174 2,021 - - - - - - 3,195
RCP5_OECD 9
0 2 3,031 - - - - - - 5,766 1,577 10 ,376
RCP5_LAM - - - - 1,216 - - - 461 - 1,676
Subtotal 1,563 3,751 1,174 2,021 1,235 2,174 8,826 1,985 6,226 2,251 31 ,206

Europe

Table 3.9 Regional definitions according to the Integrated Assessment Modelling community

3 Methodology

62

some spread in the current emission estimates for the same regions and categories.

To address this issue, the standard practice in the IAM community is to work with

harmonized emissions scenarios, meaning that the original emissions scenarios

have either been scaled or shifted towards a common reference point. A recent his-

torical emission level is normally chosen as this reference point. Here, we chose the

2015 emissions across the five RCP regions.

Harmonization was performed in two steps. First, emissions were added that

were related to the CO 2 fossil and industrial emission categories (such as waste-

related emissions) and that were outside the scope of emissions in the energy-related

CO 2 emission chapters. The scenarios from which these ‘other’ energy-related CO 2

emissions were taken were: the SSP2_Ref_SPA0_V25_upscaled_MESSAGE_

GLOBIOM (for the 5.0 °C reference scenario); SSP1_26_SPA1_V25_IMAGE (for

the 2.0  °C Scenario), and SSP1_19_SPA1_V25_IMAGE scenario (for the 1.5°C

Scenario). In the second harmonization step, the overall sum of the complemented

2.0 °C and 1.5 °C scenario CO 2 emissions were compared with the overall fossil and

industrial sum of CO 2 emissions in the year 2015, which were used for scenario

harmonization under the CMIP6 ScenarioMIP process (Meinshausen et al., in prep-

aration). This comparison revealed that there were still differences between the

complemented energy scenarios (see Chapter 8) and the harmonization emission

levels for the various regions. These differences could again have resulted from dif-

ferent emission factors or activity assumptions, or they could simply reflect genuine

uncertainty in the overall global and regional anthropogenic emissions. Consistent

with the processing steps used in the CMIP6 process, we up- and down-scaled the

raw and regionally disaggregated energy scenario emissions towards the harmoni-

zation emission levels. Figure 3.12 shows the differences between the raw emission

scenario data, the data re-aggregated into the RCP regions, and the CMIP6 emission

harmonization fossil and industrial CO 2 emission levels for 2015 (in GtC). The dif-

ferences were bridged by applying a time-constant scaling factor.

3.57
2.83

0.87 0.78
0.46 0.35

4.16

3.03

0.91
0.63 0.51 0.48

0.00

1.00

2.00

3.00

4.00

5.00

Asia OECD90 MAF REF LAM BUNKERS

GtC Emissions in 201

5

Harmonisation of Fossil & Industry CO 2 Emissions

5.0C (Reference) Harmonised Fossil & Industrial

Fig. 3.12 Differences between the raw LDF emission scenario data

S. Teske et al.

63

3.8.1.3 A New Quantile Regression Method for Non-CO 2 Gases

The completed fossil and industrial CO 2 emission time series can now be compared

with the set of scenarios in the literature. In this study, we used 811 scenarios from

CMIP6 databases or the databases underlying the IPCC SR1.5 report. These

literature- reported studies are either reference scenarios or mitigation scenarios

with a specific forcing target or climate target. Some of the scenarios aim for 1.5 °C

levels of change, others for 450 ppm CO 2 -equivalence concentrations, and yet oth-

ers assume fragmented worlds, with regional rivalries and no consistent policy

approach. In summary, the input assumptions of all these literature-reported sce-

narios vary widely, yet all have some formal energy-system modelling framework

behind them that provides first-level assurance that the envisaged CO 2 , methane,

nitrous oxide, and other gas emission levels are not set below the limits considered

technologically feasible under a certain set of boundary conditions, such as the

requirement to continuing feeding the human population. The technological and

economic feasibilities of emission pathways are fluid concepts, subject to change in

response to technological advances and changes in policy settings.

This study and the approach it uses are not dependent on absolute levels of miti-

gation costs or precise definitions of technological feasibility. Instead, the method

used assumes that that non-CO2 gases are reduced with a similar effort as that

required to reduce CO 2 emissions. Therefore, using the emission characteristics

from a large set of scenarios reported in the literature, we assume similar levels of

technological feasibility, economic mitigation costs, and implementation opportuni-

ties will be required to reduce emissions of CO 2 and various other gases.

More specifically, we derived the non-CO 2 emissions in a particular year by

ranking all the scenarios against the indicator of fossil and industrial CO 2 emissions

in that year (see Fig. 3.11 below). By comparing them to the ‘crowd’ of other

literature- reported scenarios, the LDF pathways could also be ranked. Specifically,

the LDF reference scenario turned out to be around the 75th percentile of the

distribution of the fossil and industrial CO 2 emissions across all 811 scenarios con-

sidered. By contrast, the lower 1.5 °C Scenario and 2.0 °C Scenario were not at the

absolute lower boundary of the 811 scenario distribution, but were close to it. The

1.5 °C Scenario ranked between the zero and first percentile—that is, among the 1%

most stringent scenarios in the literature for the years 2025–2045. In the period until

2050, the 2.0 °C Scenario was situated between the 5th and 10th percentiles of the

scenario distribution (see Fig. 3.13).

Figure 3.13 shows the 1.5  °C and 2.0  °C Scenarios, their absolute fossil and

industry CO 2 emissions until 2050 (upper panel), and their respective locations in

the set of 811 literature-reported scenarios considered (lower panel). The post-2050

scenario extensions were extrapolated differently for fossil and industrial CO 2 and

the non-CO 2 gases. To derive the non-CO 2 gases, the 2050 percentile location was

assumed constant for the remainder of the twenty-first century. For the fossil and

industrial CO 2 emissions in the 2.0 °C and 1.5 °C Scenarios, which do not assume

3 Methodology

64

BECCS to achieve net negative emission levels, a continuation of the constant zero

emission level was assumed (straight constant emission level in the upper panel cor-

responding to the increasing percentile level of the red–blue dashed line in the lower

panel).

3.8.1.4 ‘Pseudo’ Fossil and Industrial CO 2 Extensions Beyond 2050

By the end of the century, almost 40% of all of 811 scenarios will feature net nega-

tive fossil and industrial CO 2 emissions, largely because there is some level of bio-

mass and CCS deployment. Given that the energy scenarios developed for this study

do not assume, by design, any BECCS-related emission uptake, the extended post-

2050 energy scenarios are assumed to be consistent with other scenarios in which

emissions will be around zero by the end of the century. However, those other

-5

0

5

10

15

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

0%

20%

40%

60%

80%

100%

Global F

ossil & Industrial

CO

Emissions ( 2

GtC

)

Pe

rcentile of Global

Fo

ssil

& Indsutrial Emissions (%)

5.0C (Reference)

5.0C (Reference)

LDF 2.0C
LDF 1.5C

LDF 1.5C

LDF 2.0C

Theoretical post-2050 percentile
evolution of zero emissions
within set of 811 scenarios (incl. BECCS)

Thereoretical emission evolution
under constant post-2050 percentile

Assumed constant post-2050 percentile

Assumed constant zero post-2050 emissions

Fig. 3.13 The 2.0 °C and 1.5 °C scenarios and their absolute fossil and industry CO 2 emissions until 2050. The energy-related CO 2 emissions pathways from the other chapters are used until 2050, and then extended beyond 2050 by either keeping the CO 2 emissions constant (in the case of the 1.5 °C and 2.0 °C Scenarios, i.e., red and purple dashed lines beyond 2050 in the upper panel) or by keeping the percentile level within the literature-reported scenarios constant (in the case of the reference scenario, i.e., green solid line in the upper panel). The percentile rank within the other literature-reported scenarios is shown in the lower panel. The constant absolute emission level after 2050 in the case of the 1.5 °C and 2.0 °C Scenarios can be seen to result in an increasing percentile rank among all the literature-reported scenarios (increasing purple–red line in the lower panel)

S. Teske et al.

65

scenarios with zero emissions by the end of the century tend to reflect a much lower

level of mitigation effort. Therefore, to derive non-CO 2 emissions that involve a

level of effort that is comparable to the mitigation effort involved in reducing

energy-related CO 2 emissions, we assumed that the energy scenarios developed for

this study were comparable to other scenarios that share zero emissions around

2050. This percentile ‘stringency’ level was then held constant for the remainder of

the twenty-first century. Therefore, whereas the actual fossil and industrial CO 2

emissions in the LDF scenarios are assumed to remain constant at zero, the non-CO 2

gas emissions are derived from data in the existing literature, as if the scenarios

remained at a stringency level of ~3% in the second half of the twenty-first century

(see the lower panel in Fig. 3.13).

We now have the fossil and industrial CO 2 emission levels throughout the twenty-

first century for each of the three scenarios, and have complemented these with the

‘pseudo’ CO 2 emission levels for the second half of the twenty-first century.

Therefore, we can derive the corresponding non-CO 2 emissions.

In the first step, we derived the total non-CO 2 emissions for a specific year and

for the world as a whole. In the second step, we determined the shares of global fos-

sil and industrial emissions versus the land-use-related emissions—again regressed

against the overall fossil CO 2 emission level as an indicator of the ‘stringency’ of the

scenario. In the third step, we disaggregated these fossil and land-use-specific emis-

sion time series into regional time series. Again, the shares of the regional emissions

were derived with the same quantile regressions shown in Fig. 3.13 above. With

these three quantile regression steps, we inferred either the lower (if lower quantile

ranges are chosen), medium (for a median 50% quantile regression), or higher emis-

sion levels of the other gases. In this study, we do not intend to provide probabilistic

emission scenarios and therefore limited our quantile regression choice to the

median 50% setting for all regions, sectoral divisions, and other global total gases.

The major advantage of this newly developed method compared with the EQW

method developed earlier (Meinshausen et al. 2006 ) is that the negative correlations

between CO 2 and other gases can also be taken into account. By performing all

quantile regressions in the space defined by the global fossil and industrial CO 2

emissions in a particular year, any kind of non-linear, positive or negative relation-

ship with other non-CO 2 gas emission levels, sectoral divisions, or regional divi-

sions are automatically incorporated into the final result—reflecting the overall

characteristics of the chosen set of emission scenarios. Not all the 811 emission

scenarios contained details of all the sectoral and regional divisions, but the step-

wise approach of this method can incorporate the characteristics from all the sce-

narios in whatever detail is available.

Figure 3.14 shows sample distributions of the emission scenario characteristics for

the year 2040 and a subset of 21 GHGs. The x-axis of each plot shows the global fossil

and industrial CO 2 emissions, and the y-axis shows the global emission levels of

another GHG, with one marker (blue dot) for each literature-reported scenario consid-

ered. The five red lines are quantile regressions at the levels of 20%, 33%, 50%

(median), 66%, and 80% of the scenario distribution. It can be clearly seen that some

total gas emissions correlate strongly with the fossil and industrial CO 2 emissions

3 Methodology

66

0

100

200

300

400

500

600

700

800
CH4

0

2

4

6

8

10

12
N2O

0

10

20

30

40

50

60

70

80

90

100
SOX

2

4

6

8

10

12

14

16
BC

10

15

20

25

30

35

40
OC

300

400

500

600

700

800

900

1000

1100

1200
CO

50

100

150

200

250

300
NMVOC

10

20

30

40

50

60

70

80
NOX

10

20

30

40

50

60

70

80

90

100

110
NH3

-2

0

2

4

6

8

10

12

14

16

18
SF6

-0.5

0

0.5

1

1.5

2
C2F6

-0.1

0

0.1

0.2

0.3

0.4

0.5
C6F14

0

5

10

15

20

25
CF4

-5

0

5

10

15

20

25

30

35

40
HFC23

-50

0

50

100

150

200

250
HFC32

-50

0

50

100

150

200

250

300

350

400

450
HFC125

-100-5 0510 15 20 25

0

100

200

300

400

500

600

700

800

900
HFC134A

Global Fossil & Industrial CO 2 Emissions (GtC)

-2-5 0510 15 20 25

0

2

4

6

8

10
HFC227EA

-2-5 0510 15 20 25

0

2

4

6

8

10
HFC227EA

10

20

30

40

50

60
HFC245FA

0

2

4

6

8

10

12
HFC4310

Global Emissions (kt)

Global Emissions (kt)

Global Emissions (kt) Global Emissions (kt)

Global Emissions (kt)

Global Emissions (kt)

Global Emissions (kt)

Global Emissions (kt) Global Emissions (kt)

Global Emissions (kt) Global Emissions (kt) Global Emissions (kt)

Global Emissions (Mt) Global Emissions (MtN)

Global Emissions (Mt) Global Emissions (Mt)

Global Emissions (MtCH4) Global Emissions (MtN2O-N) Global Emissions (MtS)

Global Emissions (MtN)

Global Emissions (MtCO)

Fig. 3.14 Example distributions of emissions scenario characteristics

S. Teske et al.

67

(such as the SOX aerosols in the top-right panel), whereas others correlate less strongly.

This method reflects the level of correlation in the finally derived multi-gas scenarios.

3.8.1.5 Land-Use Assumptions

In principle, the same methodological approach can be used for land-use emissions.

In the IAM scenarios, the emission sequestration with the BECCS technology is

reported as a negative emission in the fossil and industrial CO 2 emission categories.

Therefore, the quantile regression approach can also be applied to the land-use CO 2

sector. However, to more explicitly define the land-use choices that are implied by

various land-use scenarios, we developed a new (probabilistic) scenario in conjunc-

tion with another land-use emission project (Dooley et al. 2018 )

This method is based on various literature-reported studies and we provide an

overall synthesis of four different land-use-based sequestration pathways: ‘forest

ecosystem restoration’, ‘reforestation’, ‘sustainable use of forests’, and ‘agrofor-

estry’. These land-use sequestration pathways are based on the premise that the

better management of terrestrial ecosystems should allow previously degraded car-

bon stocks to be restored, entailing the removal of significant atmospheric CO 2

(DeCicco and Schlesinger 2018 ; Law et  al. 2018 ; Mackey et  al. 2013 ; Mackey

2014 ; Nabuurs et al. 2017 ). We derived the overall pathways separately for the tem-

perate and boreal regions on the one side and the subtropical and tropical regions on

the other. This distinction is largely consistent with the dominant distinction of dif-

ferent climate domain characteristics in the literature (Grace et al. 2014 ; Houghton

and Nassikas 2018 ; Pan et al. 2011 ), although the temperate and boreal biomes are

as different in terms of land-use and forest ecosystem characteristics as the tropical

biomes are from each of them. However, we derived only two climate domains

because several of the RCP regions cross both temperate and boreal biomes. A nar-

rative for each of these pathways is available in Table 3.10 below.

Based on literature studies and Food and Agriculture Organization (FAO) statis-

tics, we then defined the available areas (and their uncertainties) for each of the four

sequestration pathways. Similarly, we sourced average estimates of the maximal

annual sequestration rates for the biomes (and their levels of uncertainty) for those

four sequestration pathways—again distinguished in the large temperate/boreal and

subtropical/tropical climate domains. We assume that after a certain ‘phase-in

period’, this maximal annual sequestration rate can be reached and sustained for a

number of years. We assume that after some decades to centuries, the capacities of

these terrestrial ecosystems as carbon sinks will slowly decline until they reach

equilibrium, termed the ‘saturation’ period. At the equilibrium point, these ecosys-

tems have a net zero effect on atmospheric CO 2 over the time scales of interest here

(Houghton and Nassikas 2018 ). The period over which the maximal sequestration

rate is assumed, is reduced by the half-length of the corresponding phase-in and

phase-out periods to account for the cumulative carbon uptake in those periods. As

the last element in this framework, we assume a cap on the median carbon density

change that is achieved over the full period. The difference between a degraded for-

3 Methodology

68

Table 3.10 Narrative for each sequestration pathway per climatic biome

Pathway

Climatic
domain Narrative
Forest ecosystem
restoration (set aside
areas of degraded
natural forest to restore
to primary forest—25%
of total)

Te,B Assume 25% of degraded natural forest put aside for full
ecosystem and carbon stock recovery. Saturation times in
temperate and boreal forests can be well over 100, or even
200 years (Luyssaert et al. 2008 ; Roxburgh et al. 2006 ).
25% set-aside is slightly higher than assumptions made in
recent literature (Böttcher et al. 2018 ; Nabuurs et al. 2017 ),
but in line with calls from conservation and indigenous
movements.
S,Tr Assume 25% of degraded natural forests across the tropics
set aside for full ecosystem and carbon stock recovery.
Stopping all deforestation, wood harvest and temporary
use, while traditional and customary uses continue. Net
Primary Productivity (NPP) is higher across the tropics
than in temperate and boreal biomes (Anav et al. 2015 ),
hence sequestration rates are higher, but saturation times
are shorter. We assume 60 years to ecosystem maturity
(Arneth et al. 2017 ; Poorter et al. 2016 ). Sequestration rates
across all biomes for forest ecosystem restoration are lower
than post-logging recovery rates, as here we assume mixed
age-class forests which have not been recently logged than
(>20 years ago), which then saturates when forest reaches
maturity.
Reforestation (forest
expansion through
natural regeneration)

Te Forest expansion on recently deforested land via natural
regeneration of forests (passive regeneration) or
reforestation of mixed native species (assisted
regeneration). Extent of forest expansion is assumed to
occur in line with current political targets: 350 Mha of
reforestation by 2030 under the Bonn Challenge. Further,
this 350 Mha is assumed to be reforestation for
conservation purposes, which creates an ongoing sink from
2030 to 2100, with saturation assumed at 100 years. Boreal
areas excluded due to albedo effect (Houghton and
Nassikas 2018 ).
S,Tr Natural forest expansion on recently deforested land as
described above. We assume 80% of forest expansion
occurs in the tropics, given 80% of Bonn Challenge pledges
are in tropical regions (Wheeler et al. in press). All
regeneration is assumed to be with natural forests rather
than plantations, as this delivers the highest mitigation and
biodiversity values (Grace et al., 2014 ; Wheeler et al. in
press). Saturation of tropical regrowth forests is assumed at
60 years, although large trees can take well over a century
to mature (Poorter et al. 2016 ).
(continued)

S. Teske et al.

69

est ecosystem and its natural carbon-carrying capacity is the maximum potential for

additional sequestration. Therefore, we used biome-averaged values for the per

hectare carbon density of undisturbed forest ecosystems (Keith et al. 2009 ), rather

than average global biome values (Liu et al. 2015 ), to define the maximum carbon

density. Although the LDF scenarios only extend to 2050 or to 2100 for all the other

GHGs, we modelled the land-use sequestration pathway assumptions until 2300 to

be able to apply the overall ‘added carbon density’ cap.

Our climate-domain- and sequestration-specific assumptions regarding the

median values, their uncertainty ranges, and confidence intervals are given in

Table 3.10 (continued)

Pathway

Climatic
domain Narrative
Sustainable use of
forests (secondary
forests under continued
(but reduced) forest
harvest )

Te,B Sustainable use of natural production forests (i.e. excluding
plantations) under continued wood harvest. Multiple studies
show the potential to double forest carbon stocks in
production forests through reducing harvest intensity and
extending rotation lengths (Law et al. 2018 ; Nabuurs et al.
2017 ; Pingoud et al. 2018 ). Wood harvest is slightly
reduced, requiring reduced demand for wood products,
more efficient wood use and compensation to land-owners
(Law et al. 2018 ; Pingoud et al. 2018 ). Including harvested
wood products (HWP) in calculations could increase
mitigation values (Houghton and Nassikas 2018 ; Nabuurs
et al. 2017 ), but the life-time of HWP is generally too short
to realise mitigation value compared to residence times in
forest biomass (Law et al. 2018 ; Keith et al. 2015 ).
S,Tr Reduced harvest intensity and management practices such
as reduced impact logging have not been shown to increase
carbon stock in tropical forests (Martin et al. 2015 ). Carbon
stocks are concentrated in commercially-valuable
hardwood trees taking >100 years to reach maturity; hence
selective logging as practiced across the tropics
significantly decreases standing carbon stocks (Lutz et al.
2018 ; Zimmerman and Kormos 2012 ). Our scenario
assumes no commercial logging of tropical forests, and the
extent of shifting cultivation is halved, allowing traditional
practices to continue with lengthened fallow times and/or
improved swidden practices (Mackey et al. 2018 ; Ziegler
et al. 2012 ).
Agroforestry (Trees in
croplands)

T,B We calculate biome-average sequestration rates from the
literature for above-ground carbon uptake due to a broad
range of agroforestry practices (Watson et al. 2000 ;
Nabuurs et al. 2017 ; Ramachandran Nair et al. 2009 ), and
subtract from this the baseline increase observed by Zomer
et al. ( 2016 ). We apply this uptake to 20% of permanent
cropland, and assume the resulting sequestration rate could
be sustained for 50 years (Watson et al. 2000 ).

S,Tr

These domains are defined as temperate (Te), boreal (B), subtropical (S), or tropical (Tr). Note, this narrative overlaps with another land-use-related study (Dooley et al. 2018 )

3 Methodology

70

Table 3.11. Our method of combining these input assumptions is a basic Monte Carlo

ensemble. Given the symmetry or asymmetry and respective confidence intervals of

the factors provided, we then created normal, lognormal, or skewed normal distribu-

tions (the latter is a linear combination of the normal and lognormal distributions to

achieve the desired skewedness). We then made 500 independent draws from all four

factors considered (area, maximum sequestration rate, phase-in time, and phase-out

time). We repeated that process independently for each sequestration pathway and

for each country within each climate domain. The areas for each country within the

climate domains were assumed to be proportionally distributed by the relevant ‘FAO

Scaling Area’ (see third column in Table 3.11), so that the climate domain aggregate

areas matched our input assumptions for the respective sequestration pathway.

After combining all the country-specific and sequestration-pathway-specific

time series for carbon uptake per sequestration pathway, we then checked whether

the resulting cumulative sequestration over time (specifically its median) was at or

below the specified maximum for the median carbon density change per hectare. If

it was not, we scaled all the country-specific results proportionally, so that the

median matched the cap on the carbon density gain.

3.8.2 Model for the Assessment of GHG-Induced Climate

Change

To compute GHG concentrations and the implications for radiative forcing, global

mean temperatures, and global mean sea-level rise, we used the reduced-complexity

‘Model for the Assessment of Greenhouse-gas-induced Climate Change’

(MAGICC), as described by Meinshausen et  al. ( 2011 ). The model has recently

been extended by the addition of a newly designed sea-level rise module, as

described in Nauels et al. ( 2016 ).

The MAGICC model has at its core an upwelling-diffusion ocean with 50 layers,

in both the northern and southern hemispheres. Some simpler model approaches,

with only a diffusive one-box ocean, for example, tend to overestimate the medium-

term warming compared with the longer-term warming, (i.e., they tend to reach

equilibrium too quickly). In the short term, the MAGICC modelling structure pro-

vides faster warming, but a lower approach to equilibrium, due to the effective cool-

ing cycle that mimics the sinking polar ocean waters.

Although simple in its general structure, the MAGICC model uses a broad cover-

age of GHGs and aerosols. This is much broader than for earth system models,

because it would be too computationally expensive to carry around tracers for every

minute GHG concentrations of, say, HFC227EA.  Because of the breadth of the

GHGs that MAGICC can model, its calibrated carbon cycle, and its calibrated cli-

mate system with feedbacks and heat exchange parameterizations, it is frequently

used as a climate model in IAMs. For example, the IMAGE and MESSAGE teams

both have MAGICC inbuilt.

IPCC Assessment Reports also frequently use MAGICC as the modelling frame-

work to determine the exceedance probabilities of various emission pathways. For

S. Teske et al.

Table 3.11

Assumptions regarding the four land-use sequestration pathways for two climate domain categories

Pathway

Climatic domain (Te = Temperature; B = Boreal; S = Subtropical; Tr = Tropical)

FAO scaling area

Assumed available area (Mha)

Related sources

Assumed

median
added carbon
maximum
(MgC/h)

Assumed maximum sequestration rate (MgC/ha/year)

Related sources

Saturation period (Years)

Related sources

Phase in period (Years)

Phase out period (Years)

Forest ecosystem restoration (set aside areas of secondary forest to restore to primary forest—25% of total)

Te,B

Other Natural Forest Area 2015

276 (80%: 248–303)

FAO (^2016

)

185
(Keith
et al. 2009

)

0.5 (90%: 0.25–1)

Pan et al. (

2011

)

100 (80%: 70–130)

Luyssaert et al. (

2008

)

and Roxburgh et al. (

2006

).

20 (90%: 7–20)

30 (90%: 10–100)

S,Tr

335 (80%: 302–369)

FAO (^2016

)

172

1.1 (90%: 0.55–2.2)

Pan et al. (

2011

)

60 (80%: 42–78)

Pan et al. (^2011

), Grace

et al. (

2014

),

and Asner et al. (

2018

)

15 (90%: 7–20)

20 (90%: 10–100)

Forest expansion via reforestation (land-use change from non-forest to forest through natural regeneration)

Te

Change in total forest area from 1990 to 2015

50 (80%: 45–55)

FAO (^2016

)

185

2.62 (80%: 0.56–7.05)

IPCC (

2006

)

100 (80%: 70–130)

Roxburgh et al. (

2006

)

and Luyssaert et al. (

2008

)

25 (90%: 7–20)

30 (90%: 10 to 100)

S,Tr

300 (80%: 270–330)

FAO (^2016

)

172

3.1 (80%: 0.42–8.46)

IPCC (

2006

)

60 (80%: 42–78)

Pan et al. (^2011

), Grace

et al. (

2014

),

and Poorter et al. (

2016

)

20 (90%: 7–20)

20 (90%: 10 to 100)
(continued)

Table 3.11

(continued)

Pathway

Climatic domain (Te = Temperature; B = Boreal; S = Subtropical; Tr = Tropical)

FAO scaling area

Assumed available area (Mha)

Related sources

Assumed

median
added carbon
maximum
(MgC/h)

Assumed maximum sequestration rate (MgC/ha/year)

Related sources

Saturation period (Years)

Related sources

Phase in period (Years)

Phase out period (Years)

Sustainable use of forests (secondary forests under continued but reduced forest harvest )

Te,B

Production Forest 2015

743 (80%: 669–817)

FAO (^2016

)

185 (an
upper end estimate,

possibly too high,
cf. Liu

et al. 2015

)

0.4 (80%: 0.36–0.44)

Nabuurs et al. (^2017

)

100 (80%: 70–130)

Roxburgh et al. (

2006

)

and Luyssaert et al. (

2008

)

20 (90%: 7–20)

30 (90%: 10–100)

S,Tr

419 (80%: 377–461)

FAO (^2016

)

172

1.19 (80%: 1.07–1.31)

Houughton and Nassikas (2018)

60 (80%: 42 to 78)

Pan et al. (^2011

) and

Grace et al. (^2014

)

15 (90%: 7–20)

20 (90%: 10–100)

Agroforestry (trees in croplands)

T,B

Permanent crop area 2015

100 (80%: 90–110)

Zomer et al. (^2016

)

and Watson et al. (^2000

);

10

(Zomer

et al. 2016

)

0.65 (80%: 0.59–0.72)

Nabuurs et al. (^2017

) and

Zomer et al. (^2016

)

50 (80%: 35 to 65)

Watson et al. (^2000

)

20 (90%: 7–20)

20 (90%: 10–100)

S,Tr

300 (66%: 270–330)

30

1.09 (80%: 0.98–1.2)

Ramachdradan Nair et al (2009) and Zomer et al. (^2016

)

50 (80%: 35–65)

Watson et al. (^2000

)

15 (90%: 7–20)

20 (90%: 10–100)

73

example, see Chapter 6 of the Working Group 3 contribution to the Fifth Assessment

Report and Chapter 2 of the IPCC Special Report on 1.5 °C. MAGICC has also been

used in the preparation of the forthcoming IPCC Sixth Assessment Report to design

the GHG concentration scenarios (Meinshausen et al., in preparation).

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it charts a new course in the global climate effort. https://unfccc.int/process-and-meetings/ the-paris-agreement/the-paris-agreement (Vaisala 2017) Free Wind and Solar Resource Maps; Accessed 25 October 2018; ONLINE; https:// http://www.vaisala.com/en/lp/free-wind-and-solar-resource-maps. Watson, B., Noble, L., Bolin, B., et. al., 2000. Summary for policymakers: land use, land-use change, and forestry: a special report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change. WEC (2018) World Energy Council—Energy Efficiency Indicators, Online database, viewed June 2018), https://wec-indicators.enerdata.net/specific-electricity-use.html Wheeler, C., Mitchard, E., Koch, A., Lewis, S.L., in press. The mitigation potential of large-scale tropical forest restoration: assessing the promise of the Bonn Challenge. in review. WRI (2018), World Resources Institute, A Global Database of Power Plants, April 2018, viewed in July 2018, http://www.wri.org/publication/global-power-plant-database Ziegler, A.D., Phelps, J., Yuen, J.Q., Webb, E.L., Lawrence, D., Fox, J.M., Bruun, T.B., Leisz, S.J., Ryan, C.M., Dressler, W., Mertz, O., Pascual, U., Padoch, C., Koh, L.P., 2012. Carbon outcomes of major land-cover transitions in SE Asia: great uncertainties and REDD+ policy implications. Global Change Biology 18, 3087–3099. https://doi.org/10.1111/j.1365-2486.2012.02747.x Zimmerman, B., Kormos, C., 2012. Prospects for Sustainable Logging in Tropical Forests. BioScience 62, 479–487. https://doi.org/10.1525/bio.2012.62.5.9 Zomer, R.J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D., Trabucco, A., van Noordwijk, M., Wang, M., 2016. Global Tree Cover and Biomass Carbon on Agricultural Land: The contribu- tion of agroforestry to global and national carbon budgets. Scientific Reports 6. https://doi. org/10.1038/srep29987

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

S. Teske et al.

© The Author(s) 2019 79 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_4

Chapter 4

Mitigation Scenarios for Non-energy GHG

Malte Meinshausen and Kate Dooley

Abstract Presentation of non-energy emission pathways in line with the new

UNFCCC Shared Socio-Economic Pathways (SSP) scenario characteristics and the

evaluation of the multi-gas pathways against various temperature thresholds and

carbon budgets (1.5  °C and 2.0  °C) over time, and additionally against a 1.5  °C

carbon budget in 2100, followed by a discussion of the results in the context of the

most recent scientific literature in this field. Presentation of the non-energy GHG

mitigation scenarios calculated to complement the energy-related CO 2 emissions

derived in Chap. 8.

In this section, we present the results for the land-use CO 2 and non-CO 2 emissions

pathways that complement the 2.0 °C and 1.5 °C energy-related CO 2 scenarios.

4.1 Land-Use CO 2 emissions

This section presents the aggregate results for the land-use sequestration pathways

designed for this study. Figure 4.1 below shows the annual sequestration in the

sequestration pathways over time, differentiated into climate domains. The path-

ways shown are the results of the Monte Carlo analysis described in Table 3.11 in

Sect. 3.8.1.5 and the text. We focus on the median values (thick lines in Fig. 4.1).

Note that the area under the curve for a given pathway is an indication of the cumu-

lative CO 2 uptake. By far the most important sequestration may result from large-

scale reforestation measures, particularly in the subtropics and tropics (see yellow

pathways in Fig. 4.1 below). The second most important pathway in terms of the

amount of CO 2 sequestered is the sustainable use of existing forests, which basically

means reducing logging within those forests. Although effective mitigation is not

M. Meinshausen (*) · K. Dooley Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: malte.meinshausen@unimelb.edu.au; kate.dooley@unimelb.edu.au

80

achieved in the tropics (Martin et  al. 2015 ), in the temperate and boreal regions,

improved forest management could provide substantial additional carbon uptake

over time. The time horizon for this sequestration option is assumed to be relatively

long in temperate and boreal regions, consistent with the longer time it takes for

these forest ecosystems to reach equilibrium (Roxburgh et al. 2006 ; Luyssaert et al.

2008 ). The ‘forest ecosystem restoration’ pathway is also important, which basi-

cally assumes a reduction to zero in logging rates in a fraction of the forest, allowing

these forests to be restored to full ecosystem function, including their carbon stocks

and resilience due to biodiversity (Mackey 2014 ).

Overall, the median of all the assumed sequestration pathways, shown in Fig. 4.1,

would result in the sequestration of 151.9 GtC by 2150. This is approximately equiv-

alent to all historical land-use-related CO 2 emissions to date (Houghton and Nassikas

2017 ; Mackey et al. 2013 ). The magnitude of these figures indicates the substantial

challenges that go hand in hand with these sequestration pathways. Given the com-

peting forms of land use that exist today, the challenge of converting overall terres-

trial carbon stocks back to pre-industrial levels cannot be underestimated. There

would be significant benefits, but also risks, if this sequestration option were to be

used instead of mitigation. The benefits are clearly manifold, ranging from biodiver-

sity protection, reduced erosion, improved local climates, wind protection, and

potentially a reduction in air pollution (Mackey 2014 ). Despite this, terrestrial car-

bon sequestration is inherently impermanent. However, a future warming climate

with an increased fire risk also brings with it the risk of large reversals in sequestered

carbon. Similarly, prolonged droughts in some areas could reverse the gains in ter-

restrial carbon stocks. Although the increased resilience of natural and biodiverse

ecosystems compared with that of monoculture plantations can guard against this

2000 2050 2100 2150

0

500

1000

1500

Annual sequestration (MtC/yr)

Global aggregate of sequestration pathways

Agroforestry
(subtropics and tropics)
(temperate and boreal)

Forest
ecosystem
restoration
(subtropics
and tropics)
(temperate
and boreal)

Reforestation (subtropics and tropics)

Reforestation (temperate and boreal)

(temperate
and boreal)

Sustainable use
of forests
(subtropics
and tropics)

Fig. 4.1 Land-use sequestration pathways showing annual sequestration rates over time

M. Meinshausen and K. Dooley

81

risk (DellaSala, 2019 ; Lindenmayer and Sato 2018 ), a future mitigation pathway

that relies on sequestration instead of mitigation action is ultimately always more

susceptible to higher long-term climate change, given the risk of ‘non-permanence’.

However, in this study, the land-use CO 2 sequestration pathways complement some

of the most ambitious mitigation pathways, and should therefore be regarded, not as

‘offsetting’ mitigation action, but as complementary measures to help reduce the

CO 2 concentrations that have arisen from the overly high emissions in the past.

The thin lines in Fig. 4.1 indicate individual draws in the Monte Carlo analysis.

The thick lines are the median values from the ensemble of draws for each seques-

tration pathway and domain.

We aggregated the four sequestration pathways from our country-level data to

the five RCP regions (Fig. 4.2). The country-level data were subject to substantial

uncertainties and simplifications because we used climate-domain average uptakes,

carbon density caps, and saturation periods. The re-aggregated sequestration rates

over the five RCP regions can be considered approximate illustrations of the biome-

average sequestration rates if those sequestration pathways were pursued with a

range of institutional and policy measures.

For the 1.5 °C Scenario, we assumed the full extent of sequestration shown in

Fig. 4.1, whereas for the 2.0  °C pathway, we assumed that only a third of that

sequestration will occur. The reference scenario is assumed to follow the SSP2

‘middle of the road’ reference scenario created by the MESSAGE-GLOBIOM

modelling team. As illustrated in Fig. 4.2, the reference scenario does not assume a

complete phasing-out of global land-use-related net emissions over the next 20 or

30 years. Instead, it assumes that they are not phased-out until approximately 2080.

The 2.0  °C pathway (brighter blue in Fig. 4.2) aligns relatively well with the

SSP1 1.9 and SSP1 2.6 scenarios from the forthcoming CMIP6 model inter-

comparison project. The 1.5 °C pathway, with three times the sequestration rates, is

consistent with the lower land-use CO 2 scenarios analysed here—with mitigation

rates of up to −2 GtC per annum from 2040 to 2050.

Figure 4.2 shows the land-use-related CO 2 emission and sequestration rates of

the 2.0°C and 1.5  °C pathways in this study compared with those in the CMIP6

CEDS scenarios (turquoise) and the scenarios from the IPCC SR1.5 database (thin

green lines). The global total pathway is the sum of the five regional pathways

shown in the lower row of the panels.

4.1.1 Other GHG and Aerosol Emissions

This section examines the other main GHGs (methane and N 2 O) and gives examples

of some fluorinated gases. The full results, with the species-by-species time series,

are provided in a data appendix.

Methane (CH 4 ) emissions are the second-largest contributor to anthropogenically

induced climate change. Our approach, described in the Methods section earlier,

derives pathways for the 1.5 °C and 2.0 °C Scenarios that are close to the lower end

of the overall scenario distribution. This is mainly because the methane distribution at

4 Mitigation Scenarios for Non-energy GHG

82

Fig. 4.2

Land-use-related CO

emission and sequestration rates 2

M. Meinshausen and K. Dooley

83

the lower end of the fossil fuel CO 2 emissions is relatively narrow, and there is a

strong correlation between the fossil CO 2 and total CH 4 emissions in the scenarios in

any given year (see top-left methane panel in Fig. 3.14). As with almost all literature-

reported scenarios, a lower plateau of methane emissions is associated with agricul-

tural activities required to feed the world’s population. Our quantile regression method

resulted in long-term methane emission levels that are quite similar to those in the two

lower SSP scenarios, SSP1 1.9 and SSP1 2.6 (Fig. 4.3). The derived CH 4 pathways for

1.5 °C and 2.0 °C track towards the lower of the scenario distributions.

Nitrous oxide (N 2 O) is one of the longer-lived GHGs, although the overall

amounts in the atmosphere are much smaller than those of methane or CO 2. The

relatively high plateau of global emissions, around 5 MtN 2 O-N for N 2 O, are reflected

in the SSP1. 1.9 and SSP1 2.6 scenarios (dark green lines in Fig. 4.4) and in the

quantile regression results for the 2.0  °C and 1.5  °C scenarios in this study. This

plateau of emissions is related to agricultural activities, mainly the use of fertilizers,

and combined with the long lifetime of N 2 O, it means that the N 2 O concentrations

are projected to increase further over the course of the century, even for the lower

1.5 °C and 2.0 °C pathways.

The derived methane pathways for 1.5 °C and 2.0 °C track towards the lower of

the scenario distributions.

Halocarbons and fluorinated gases are another group of important GHGs.

Recently, some of these gases, such as HFCs, were also subjected to control under

the Montreal Protocol, with clear phase-out schedules. Some of the halocarbons and

fluorinated gases (such as tetrafluoromethane, CF 4 ) are only produced and emitted

in relatively small quantities—largely for industrial purposes in the semi-conductor

industry. Some also have applications in the agricultural sector, including methyl

bromide, which is used for soil fumigation. SF 6 is one of the strongest GHGs on a

per mass basis. It is controlled under the Kyoto Protocol and included in the nation-

ally determined contributions (NDCs) by many countries under the Paris Agreement.

Our applied quantile regression method practically phases-out many of the haloge-

nated species over the course of the next 10–20 years, although some small back-

ground emissions remain. The full results for 40 halocarbons, HFCs, PFCs, and SF 6

are provided in a data appendix (Figs. 4.5 and 4.6).

Aerosols have an important temporary masking effect on GHG-induced warm-

ing. The most important anthropogenically emitted aerosol coolant in the climate

system is sulfur dioxide or SOX. With higher fuel standards and concerns about local

air pollution, future SOX emissions are projected to be substantially lower than cur-

rent levels. In fact, most emission inventories assume that SOX emissions peaked in

the 1990s. Therefore, even in the most high-fossil-fuel-emitting reference scenarios,

SOX emissions are projected to decrease. Asia produces by far the most SOX emis-

sions of any continent because of the coal-fuelled power plants in China and India.

In the 2.0 °C Scenario, our quantile regression method sets sulfate aerosol emissions

at levels in between those in the SSP1 2.6 and SSP1 1.9 scenarios, whereas in the

1.5 °C Scenario, the level is even lower.

Similarly, the projected emissions of NOX, which is largely a by-product of fossil

fuel burning, are highest in Asia. In the derived 1.5 °C and 2.0 °C Scenarios, NOX

4 Mitigation Scenarios for Non-energy GHG

84

Fig. 4.3

Global and regional methane emissions from fossil, industrial, and land-use-related sources

M. Meinshausen and K. Dooley

85

Fig. 4.4

Global and regional methane emissions from fossil, industrial, and land-use-related sources

4 Mitigation Scenarios for Non-energy GHG

86

levels are between the levels in the SSP1 2.6 and SSP1 1.9 scenarios for most of the

twenty-first century (Figs. 4.7 and 4.8).

Black and organic carbon emissions are also accruing substantially in the Middle

East and Africa, largely from biomass burning (Figs. 4.9 and 4.10). Similar to other

aerosol emissions, black and organic carbon emissions are projected to decrease.

Although black carbon is a substantial warming agent, organic carbon is a net cool-

ant. Because both species are often co-emitted, the net effect of policies to reduce

black carbon do not have as large a mitigation benefit as might be initially assumed.

This is because a reduction in the processes and activities that produce black carbon

emissions will also lead to lower organic carbon emissions, partially offsetting both

the warming and cooling effects. The emissions projected as part of the IMAGE

model SSP1 2.6 and SSP1 1.9 scenarios are very low compared with those in other

studies. Furthermore, the correlation between fossil CO 2 emissions and black or

Fig. 4.5 Global SF 6 emission levels from literature-reported scenarios and the LDF pathways derived in this study

Fig. 4.6 Global tetrafluoromethane (CF 4 ) emissions from the collection of assessed literature- reported scenarios and the LDF pathways derived in this study

M. Meinshausen and K. Dooley

87

Fig. 4.7

Global and regional sulfate dioxide (SO

) emissions in the literature-reported scenarios considered and the LDF pathways derived in this studyX

4 Mitigation Scenarios for Non-energy GHG

88

Fig. 4.8

Global and regional nitrate aerosol (NO

) emissions in the literature-reported scenarios considered and the LDF pathways derived in this studyX

M. Meinshausen and K. Dooley

89

Fig. 4.9

Global and regional black carbon BC emissions in the literature-reported scenarios considered and the LDF pathways derived in this study

4 Mitigation Scenarios for Non-energy GHG

90

Fig. 4.10

Global and regional organic carbon OC emissions in the literature-reported scenarios considered and the LDF pathways derived in this study

M. Meinshausen and K. Dooley

91

organic carbon is less pronounced than the correlations of fossil CO 2 with other

aerosols, such as NOX and SOX, partly because it results from biomass burning,

which is not related to the burning of fossil fuels. Therefore, with our quantile

regression method, the black carbon and organic carbon emission pathways are not

as low as those found in the lower SSP scenarios (see Figs. 4.9 and 4.10).

For Tabular overview of three scenarios see Annex

References

DellaSala, D.L., 2019. “Real” vs. “Fake” Forests: Why Tree Plantations are Not Forests, in: Encyclopedia of the World’s Biomes. Elservier, UK. Houghton, R.A., Nassikas, A.A., 2017. Global and regional fluxes of carbon from land use and land cover change 1850-2015: Carbon Emissions From Land Use. Global Biogeochemical Cycles 31, 456–472. https://doi.org/10.1002/2016GB005546 Lindenmayer, D.B., Sato, C., 2018. Hidden collapse is driven by fire and logging in a socioeco- logical forest ecosystem. Proceedings of the National Academy of Sciences 115, 5181–5186. https://doi.org/10.1073/pnas.1721738115 Luyssaert, S., Schulze, E.-D., Börner, A., Knohl, A., Hessenmöller, D., Law, B.E., Ciais, P., Grace, J., 2008. Old-growth forests as global carbon sinks. Nature 455, 213–215. https://doi. org/10.1038/nature07276 Mackey, B., 2014. Counting trees, carbon and climate change. The Royal Statistical Society - Significance 19–23. Mackey, B., Prentice, I.C., Steffen, W., House, J.I., Lindenmayer, D., Keith, H., Berry, S., 2013. Untangling the confusion around land carbon science and climate change mitigation policy. Nature Climate Change 3, 552–557. https://doi.org/10.1038/nclimate1804 Martin, P.A., Newton, A.C., Pfeifer, M., Khoo, M., Bullock, J.M., 2015. Impacts of tropical selec- tive logging on carbon storage and tree species richness: A meta-analysis. Forest Ecology and Management 356, 224–233. https://doi.org/10.1016/j.foreco.2015.07.010 Roxburgh, S.H., Wood, S.W., Mackey, B.G., Woldendorp, G., Gibbons, P., 2006. Assessing the carbon sequestration potential of managed forests: a case study from temperate Australia: Carbon sequestration potential. Journal of Applied Ecology 43, 1149–1159. https://doi. org/10.1111/j.1365-2664.2006.01221.x

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

4 Mitigation Scenarios for Non-energy GHG

© The Author(s) 2019 93 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_5

Chapter 5

Main Assumptions for Energy Pathways

Thomas Pregger, Sonja Simon, Tobias Naegler, and Sven Teske

Abstract The aim of this chapter is to make the scenario calculations fully trans-

parent and comprehensible to the scientific community. It provides the scenario

narratives for the reference case (5.0 °C) as well as for the 2.0 °C and 1.5 °C on a

global and regional basis. Cost projections for all fossil fuels and renewable energy

technologies until 2050 are provided. Explanations are given for all relevant base

year data for the modelling and the main input parameters such as GDP, population,

renewable energy potentials and technology parameters.

Scenario studies cannot predict the future, but they can describe what is needed for a

successful pathway in terms of technology implementation and investments. Scenarios

also help us to explore the possible effects of transition processes, such as supply

costs and emissions. The energy demand and supply scenarios in this study are based

on information about current energy structures and today’s knowledge of energy

resources and the costs involved in deploying them. As far as possible, we also take

into account potential constraints and preferences in each world region. However, this

remains difficult due to large sub-regional variations. Our energy modelling primarily

aims to achieve a transparent and consistent scenario, an ambitious but still plausible

storyline from several possible techno-economic pathways. Knowledge integration

is the core of this approach because we must consider different technical,

economic, environmental, and societal factors. The scenario modelling follows a

hybrid bottom- up/top-down approach, with no cost optimising objective functions.

The analysis considers the key technologies required for a successful energy transi-

tion, and focuses on the roles and potential of renewable energies. Wind and solar

energies have the highest economic potential and dominate the pathways on the

T. Pregger (*) · S. Simon · T. Naegler Department of Energy Systems Analysis, German Aerospace Center (DLR), Institute for Engineering Thermodynamics (TT), Pfaffenwaldring, Germany e-mail: thomas.pregger@dlr.de; sonja.simon@dlr.de; tobias.naegler@dlr.de

S. Teske Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au

94

supply side. However, variable renewable power from wind and photovoltaics (PV)

remains limited by the need for sufficient secured capacity in energy systems.

Therefore, we also consider concentrated solar power (CSP) with high-temperature

heat storage as a solar option that promises large-scale dispatchable and secured

power generation.

5.1 Scenario Definition

Scenario modelling was performed for three main scenarios that can be related to dif-

ferent overall carbon budgets between 2015 and 2050 and derived mean global tem-

perature increases. The (around) 5.0 °C Scenario was calculated based on the Current

Policies scenario published by the International Energy Agency (IEA) in World

Energy Outlook 2017 (IEA 2017 ), and the emission budget for this scenario simply

uses and extrapolates from the corresponding narratives. The 2.0  °C and 1.5  °C

Scenarios were calculated in a normative way to achieve defined emission budgets.

5.1.1 The 5.0 °C Scenario (Reference Scenario)

The reference case only takes into account existing international energy and envi-

ronmental policies. Its assumptions include, for example, continuing progress in

electricity and gas market reforms, the liberalization of cross-border energy trade,

and recent policies designed to combat environmental pollution. The scenario does

not include additional policies to reduce greenhouse gas (GHG) emissions. Because

the IEA’s projections only extend to 2040, we have extrapolated their key macroeco-

nomic and energy indicators forward to 2050. This provides a baseline for compari-

son with the 2.0 °C and 1.5 °C Scenarios.

5.1.2 The 2.0 °C Scenario

The first alternative scenario aims to achieve an ambitious emissions reduction to

zero by 2050 and a global energy-related CO 2 emissions budget between 2015 and

2050 of around 590 Gt. The scenario is close to the assumptions and results of the

Advanced E[R] scenario published in 2015 by Greenpeace (Teske et  al. 2015 ).

However, the scenario includes an updated base year, more coherent regional devel-

opments of energy intensities, and reconsidered trajectories and shares of renewable

energy resource (RES) deployment. The 2.0  °C Scenario represents a far more

likely pathway than the 1.5 °C Scenario, because the 2.0 °C case takes into account

unavoidable delays due to political, economic, and societal processes and

stakeholders.

T. Pregger et al.

95

5.1.3 The 1.5 °C Scenario

The second alternative scenario aims to achieve a global energy-related CO 2 emis-

sion budget of around 450 Gt, accumulated between 2015 and 2050. The 1.5  °C

Scenario requires immediate action to realize all available options. It is a technical

pathway, not a political prognosis. It refers to technically possible measures and

options without taking into account societal risks or barriers. Efficiency and renew-

able potentials must be deployed even more quickly than in the 2.0  °C Scenario.

Furthermore, avoiding inefficient technologies and behaviours are essential strate-

gies for developing regions in this time period.

5.2 Scenario World Regions and Clusters

The regional implementation of the long-term energy scenarios is defined according

to the breakdown of the ten world regions of the IEA WEO 2016 (IEA 2016a, b).

This approach has been chosen because the IEA also provides the most comprehen-

sive global energy statistics and, in contrast to the regional breakdown of the IEA

WEO 2017, it is also consistent with the Energy [R]evolution study series. Table 5.1

provides a country breakdown of the ten world regions considered in the scenarios.

Regional conditions play an important role in the layout of the scenario path-

ways. Therefore, scenario building tries to take into account important factors, such

as current demand and supply structures, RES potentials, urbanization rates, and as

far as possible, societal and behavioural factors. The following sections provide

some regional information. Statistical data for the energy systems in the regions can

be found in Sect. 5.3.

5.2.1 OECD North America

The energy system in OECD North America (USA, Canada, and Mexico) is domi-

nated by developments in the USA, where more than 80% of the region’s demand

occurs. In the highly developed countries of the USA and Canada, reducing the

demand for energy by increasing efficiency will play a crucial role in decarbonisa-

tion. However, the high energy intensity (i.e., the high demand per capita or per

gross domestic product [GDP]) requires even more ambitious measures than in

other regions to reduce the energy demand as quickly as possible. In Mexico, in

contrast, increasing living standards and the increasing population will increase the

difficulties associated with reducing the energy demand, despite ambitious increases

in efficiency. Wind and solar power generation will be the backbone of the power

supply in OECD North America. They will be supplemented by hydro power

(mainly in Canada) and also concentrated solar power (CSP). The high potential for

5 Main Assumptions for Energy Pathways

96

CSP in Mexico and the southern parts of the USA will allow the large-scale use of

CSP plants for grid balancing and grid stabilization. This will reduce the need for

power storage, demand-side management, and other balancing strategies. In the

large metropolitan areas in North America, electromobility and hydrogen cars will

enter the market earlier and at a faster rate than in many other world regions. Large

biomass potentials (residues) mean that biofuels could play important roles as

climate- neutral fuels to bridge the gap until new powertrain technologies dominate

Table 5.1 World regions used in the scenarios

World
region Countries
OECD
Europe

Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany,
Greece, Hungary, Iceland, Ireland, Italy, Israel, Luxembourg, the Netherlands,
Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden,
Switzerland, Turkey, United Kingdom
OECD
North
America

Canada, Mexico, United States of America

OECD
Pacific

Australia, Japan, Korea (South), New Zealand

Eastern
Europe/
Eurasia

Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, Croatia,
former Yugoslav Republic of Macedonia, Georgia, Kazakhstan, Kosovo, Kyrgyz
Republic, Latvia, Lithuania, Montenegro, Romania, Russia, Serbia, Tajikistan,
Turkmenistan, Ukraine, Uzbekistan, Cyprus, Gibraltar and Malta
China People’s Republic of China, including Hong Kong
India India
Non-OECD
Asia
(without
China and
India)

Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, Chinese Taipei,
Cook Islands, East Timor, Fiji, French Polynesia, Indonesia, Kiribati, Democratic
People’s Republic of Korea, Laos, Macao, Malaysia, Maldives, Mongolia,
Myanmar, Nepal, New Caledonia, Pakistan, Papua New Guinea, Philippines,
Samoa, Singapore, Solomon Islands, Sri Lanka, Thailand, Tonga, Vanuatu,
Vietnam,
Latin
America

Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bermuda,
Bolivia, Brazil, British Virgin Islands, Cayman Islands, Chile, Colombia, Costa
Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland
Islands, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti,
Honduras, Jamaica, Martinique, Montserrat, Netherlands Antilles, Nicaragua,
Panama, Paraguay, Peru, St. Kitts and Nevis, Saint Lucia, St. Pierre et Miquelon,
St. Vincent and Grenadines, Suriname, Trinidad and Tobago, Turks and Caicos
Islands, Uruguay, Venezuela
Africa Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape
Verde, Central African Republic, Chad, Comoros, Congo, Democratic Republic of
Congo, Cote d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia,
Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya,
Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique,
Namibia, Niger, Nigeria, , Rwanda, Sao Tome and Principe, Senegal, Seychelles,
Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, United
Republic of Tanzania, Togo, Tunisia, Uganda, Western Sahara, Zambia,
Zimbabwe
Middle East Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria,
United Arab Emirates, Yemen

T. Pregger et al.

97

the vehicle market. In the heating sector, particularly for process heat, solid biomass

and biogas will be required as alternative fuels until the (direct or indirect) electrifi-

cation of the heat sector is accomplished.

5.2.2 Latin America

Latin America’s energy system is dominated by Brazil, which accounts for around

half the region’s energy demand. In the reference (5.0 °C) scenario, this region has a

particularly high demand for electrification and a strong increase in CO 2 emissions

per capita. Latin America has the highest urbanization rate of all non-OECD regions.

This provides opportunities for efficiency measures and the large-scale electrification

of the heat and transport sectors based on renewable resources. Latin America has a

high overall potential for the use of renewable energies (Herreras Martínez et  al.

2015 ) and the largest biomass potential of all regions. It already meets more than

60% of its power demand from renewable sources, and higher shares are the focus of

research (Nascimento et al. 2017 ; Barbosa et al. 2017 ; Gils et al. 2017 ). However, in

many studies, heat and transport demands are not integrated into the assessments,

even though the region has a large potential for renewable heat and decarbonised

transport. Given the abundance of biomass, there is potential for generating more

than 12 EJ from residues (Seidenberger et al. 2008 ). Biomass will also play a signifi-

cant role in the industry sector. Because the region has a long experience of biofuels,

they will play a major role in the 2.0 °C and 1.5 °C Scenarios, especially in Brazil,

where bioethanol for transport is already competitive (Lora and Andrade 2009 ; La

Rovere et al. 2011 ; Nass et al. 2007 ). However, the high urbanization rate in Latin

America means there is also an opportunity to develop electromobility early. In the

power sector, the use of biomass from residues will help to balance the increasing

share of variable renewable energy from the excellent solar and wind resources. Grid

extensions will contribute to inter-regional stability (Nascimento et al. 2017 ).

5.2.3 OECD Europe

The OECD Europe region includes countries with quite different energy supply

systems, different potentials for renewable energy sources, and different power and

heat demand patterns. High solar potentials and low heat demand for buildings are

characteristic of the south. The northern and western parts of Europe have high wind

potentials, especially offshore wind. In northern and central Europe, there are high

potentials for hydropower and a high energy demand for space heating (such as in

Eastern Europe). Biomass potentials exist predominantly in the north and east, but

are only limited in the southern regions. The industrial demands for electricity and

process heat are quite different in highly industrialized countries, such as the

Scandinavian countries, Germany, and France compared with some eastern and

southern countries. Most European countries, particularly European Union (EU)

member countries, already have policies and market mechanisms for the

5 Main Assumptions for Energy Pathways

98

implementation of renewable energy. The European Network of Transmission

System Operators (ENTSO-E) can be used as a well-established basis for the further

development of an interconnected European grid, which would be able to imple-

ment the large-scale and long-range transmission of renewable power to demand

centres. This may also lead to important interconnections to the Middle East/North

Africa (MENA) region and Eastern Europe/Eurasia. The possible large-scale impor-

tation of solar thermal electricity from MENA countries via high-voltage direct-

current lines has been described in many studies and still represents a promising

option in the long term, despite the currently difficult political conditions.

5.2.4 Eastern Europe/Eurasia

The Eastern Europe/Eurasia region includes some eastern EU member countries that

are not part of the OECD, some other countries of the former Yugoslav Republic, and

several countries of the former Soviet Union. However, the region is dominated by

the economy and energy system of Russia. The main energy carrier today is natural

gas, followed by oil. The region has large energy resources in biomass and wind

power, but also geothermal energy and PV. Eastern Europe/Eurasia is the only world

region that may face a significant population decline with expected demographic

developments, particularly in Russia. Today, the region has by far the highest final

and primary energy demand per $GDP.  This indicates the existence of energy-

intensive industries, but also large efficiency potentials in all sectors. The high heat

demand, large rural areas, enormous oil and natural gas potentials, and the uneven

distribution of economic wealth are some of the major challenges in this region. So

far, only low expansion rates for renewable energies have been achieved in the region.

5.2.5 The Middle East

The Middle East consists of a series of oil-dependent countries, all of which have

tremendous solar potential. The transport demand in the Middle East is very high,

as is the electrification rate in urban areas, where currently almost 70% of the fast-

growing population lives. Therefore, the electrification of transport systems is a

major target in our scenarios. For many Middle East countries, water scarcity is a

problem, and there are opportunities to combine large CSP plants with water

desalination, to reduce the pressure on water supply systems. Biomass is very

scarce, so its use must be limited to high-temperature process heat, especially in

industry, where other renewable sources cannot be used. This will lead to a high

demand for hydrogen or synthetic fuels. Naturally, this also limits the potential for

combined heat and power generation (CHP), which is primarily seen as a transi-

tion technology to provide the most efficient use of the remaining fossil fuels and

low- value biomass wastes. However, because the Middle East has extraordinary

solar and wind potentials (Nematollahi et al. 2016 ; Hess 2018 ), the solar market is

T. Pregger et al.

99

taking off. Projects with a capacity of 11 GW are planned for 2018 (MESIA 2018 ).

With the extraordinarily high number of full-load hours, there is also the potential

to use high-temperature solar heat. These resources also provide excellent condi-

tions for hydrogen production, which are extensively exploited in the 2.0 °C and

the 1.5 °C Scenarios. Therefore, the Middle East is a model solar and hydrogen

region.

5.2.6 Africa

Africa is a very heterogeneous region, both economically and geographically. One

of the few things African nations have in common is their very fast population

growth. Africa includes the arid regions of North Africa, the undeveloped sub-

Saharan region, and the emerging market of South Africa. North Africa features a

high electrification rate and a strong dependence on oil. The water and biomass

potentials for energy are very low, because water and biomass are prioritized for

nutrition (or at least nutrition competes strongly with energy use). The region has

outstanding solar irradiation, an excellent renewable energy source. Sub-Saharan

Africa is characterized by low urbanization and a lack of access to electricity for

two-thirds of its people (IEA 2014 ). Its energy supply is characterized by a high

share of low-efficiency forms of generation, such as traditional biomass use. There

is a general lack of energy services. Modernizing traditional biomass use could lead

to significant reductions in energy demand, while maintaining or improving energy

services (van der Zwaan et al. 2018 ). A broad variety of renewable energy sources,

including biomass, hydro, geothermal, solar, and wind, have great potential.

However, it will be a major challenge to find the investment required to tap these

power sources under the present economic conditions (van der Zwaan et al. 2018 ).

The picture is somewhat different in South Africa, which has a coal-based energy

system and a comparatively stable and well-connected electricity grid, with access

to electricity for more than 85% of its population (IEA 2014 ). The dependence on

traditional biomass is extensive in the household and commerce sectors. Over 700

million people rely on fuel wood or charcoal for cooking on inefficient cooking

stoves or open fires, with an efficiency of 10–20%. Modern biomass technologies

provide multiple advantages. The introduction of more-efficient technologies, even

those as simple as improved cooking stoves (with an average efficiency of 25%) or

biogas stoves (with an average efficiency of 65%) (IEA 2014 ), will reduce the bio-

mass input and thus the primary energy demand. This will also alleviate the heavy

pressure on the ecosystem from the unsustainable exploitation of natural forests.

The introduction of modern technologies will improve the supply of useful energy,

lower indoor pollution, and improve living standards. Therefore, we assume in our

scenarios that the overall biomass efficiency will improve from 35% in 2015 to 65%

in 2050, while biomass’s share will decrease and be partially replaced by electric

power and solar heat.

5 Main Assumptions for Energy Pathways

100

5.2.7 Non-OECD Asia

The Non-OECD Asia region includes all the developing countries of Asia, except

China and India. This group covers a large spectrum in terms of size, economy,

stability, and developmental status. The region is spread over a large area from the

Arabian Sea to the Pacific. Electricity access varies widely in these countries,

according to WEO 2014. In Southeast Asia, the average access is 77%, with only

30% access in Myanmar and Cambodia, and nearly 100% access in Singapore,

Thailand, and Vietnam. In Indonesia, there is 76% access (92% in urban areas), in

Bangladesh 60% (90%), and in Pakistan 69% (88%). In Southeast Asia, 46% of the

population still relies on traditional biomass, with the highest use in Myanmar

(93%) and Cambodia (89%). In Indonesia, 42% of the population used biomass for

cooking in 2012; in Bangladesh the figure was 89%; and in Pakistan, it was 62%.

The lowest values are in Singapore (0%), Malaysia (0%), and Thailand (24%). The

scenarios thus cover the whole band-width of renewable resources and technologi-

cal development, even though the outlooks for individual countries deviate widely

from the average.

5.2.8 India

India has a fast-growing population of over 1.2 billion people and is the world’s

seventh largest country by area. However, the population density is already 2.7

times higher than that in China. Due to its climate, India has a rather limited CSP

potential but a large potential for PV power generation. Its wind power potential is

expected to be limited by land-use constraints, but the technical potential estimated

from available meteorological data is large. According to the WEO 2014 database,

electricity access is on average 75%, with 94% in urban areas and 67% in rural

areas. In India, about 815 million people still relied on the traditional use of biomass

for cooking in 2012. Due to population and GDP growth, increasing living stan-

dards, and increasing mobility, it is expected that the energy demand in India will

increase significantly, although large potentials for efficiency savings exist.

Electrification is a core strategy for decarbonisation in India, which, combined with

the rising demand for energy services, will lead to strong growth in the per capita

and overall electricity demand. It is also expected that the need for mobility in India

will increase rapidly and more strongly than in other regions of the world.

5.2.9 China

China has great potential renewable energy resources, especially for the generation

of solar thermal power in the west, onshore wind in the north, and offshore wind in

the east and southeast. Photovoltaic power generation could play an important role

T. Pregger et al.

101

throughout all parts of China. The expansion of hydropower generation is currently

also seen as a major strategy, but the potential for small hydro systems is rather low.

China will face further large increases in energy demand in all sectors of the energy

system. Chinese economic prosperity has mainly been underpinned by coal, which

provides over two-thirds of China’s primary energy supply today (IEA WEO 2014 ).

The increase in electricity use due to higher electrification rates will be a major fac-

tor in the successful expansion of renewable energy in the industry, building, and

transport sectors. In China, nearly all households are connected to the electricity

grid. However, according to WEO 2014, about 450 million Chinese still relied on

the traditional use of biomass for cooking in 2012. China has pledged to reduce CO 2

emissions before 2030, and already has some ambitious political targets for renew-

able energy deployment.

5.2.10 OECD Pacific

OECD Pacific consists of Japan, New Zealand, the peninsula of South Korea, and

the continent of Australia. The region is dominated by the high energy demand in

Japan, which has rather limited renewable energy resources. The lack of physical

grid connections prevents power transmission between these countries. Therefore, it

is a huge effort to supply the large Japanese nation with renewable energy and to

stabilize the variable wind power without tapping the large solar potential in other

countries, such as Australia. Here, hydrogen and synfuels will not only be used for

the long-term storage of renewable power, but also as an option for balancing the

renewable energy supply across borders. The early market introduction of fuel-cell

cars in Japan may support such a strategy. Following the accident at Fukushima and

the implementation of feed-in tariffs for renewable electricity, the expansion rates

for renewable energies, in particular PV, have risen sharply.

5.3 Key Assumptions for Scenarios

5.3.1 Population Growth

Population growth is an important driver of energy demand, directly and through its

impact on economic growth and development. The assumptions made in this study

up to 2050 are based on United Nations Development Programme (UNDP) projec-

tions for population growth (UNDP 2017 (medium variant)). Table 5.2 shows that

according to the UNDP, the world’s population is expected to grow by 0.8% per

year on average over the period 2015–2050. The global population will increase

from 7.4 billion people in 2015 to nearly 9.8 billion by 2050. The rate of population

growth will slow over this period, from 1.1% per year during 2015–2020 to 0.6%

per year during 2040–2050. From a regional perspective, Africa’s population growth

5 Main Assumptions for Energy Pathways

102

will continue to be the most rapid (on average 2.2%/year), followed by the Middle

East (1.3%/year). In contrast, in China and OECD Pacific, a population decline of

about 0.1%/year. is expected. The populations in OECD Europe and OECD North

America are expected to increase slowly through to 2050. The proportion of the

population living in today’s non-OECD countries will increase from its current 81%

to 85% in 2050. China’s contribution to the world population will drop from 19%

today to 15% in 2050. Africa will remain the region with the highest population

growth, leading to a share of 26% of world population in 2050. Satisfying the energy

needs of a growing population in the developing regions of the world in an environ-

mentally friendly manner is the fundamental challenge in achieving a sustainable

global energy supply.

5.3.2 GDP Development

Economic growth is a key driver of energy demand. Since 1971, each 1% increase

in the global GDP has been accompanied by a 0.6% increase in primary energy

consumption. Therefore, the decoupling of energy demand and GDP growth is a

prerequisite for the rapid decarbonisation of the global energy industry. In this

study, the economic growth in the model regions is measured in GDP, expressed in

terms of purchasing power parity (PPP) exchange rates. Purchasing power parities

compare the costs in different currencies of fixed baskets of traded and non-traded

goods and services. GDP PPP is a widely used measure of living standards and is

independent of currency exchange rates, which might not reflect a currency’s true

value (purchasing power) within a country. Therefore, GDP PPP is an important

basis of comparison when analysing the main drivers of energy demand or when

comparing the energy intensities of countries.

Table 5.2 Population growth projections (in millions)

Million capita 2015 2020 20252030 2035 20402045 2050

Change
2015–2050
OECD North America 482 503 524 543 560 575 588 599 24%
OECD Pacific 207 208 208 208 206 204 201 198 −4%
OECD Europe 570 582 587 592 596 598 599 598 5%
Eastern Europe/
Eurasia

343 346 347 346 345 343 341 339 −1%

Middle East 234 254 276 295 314 331 348 363 56%
Latin America 506 531 552 571 587 599 609 616 22%
China 1405 1433 14471450 1442 14261403 1374 −2%
Africa 1194 1353 15221704 1897 21002312 2528 112%
India 1309 1383 14521513 1565 16051636 1659 27%
Non-OECD Asia 1132 1203 12691329 1382 14281467 1499 32%
Global 7383 7795 81858551 8893 92109504 9772 32%

Source: UN World Population Prospects—2017 revision, medium variant

T. Pregger et al.

103

Although PPP assessments are still relatively imprecise compared with statistics

based on national incomes, trade, and national price indices, it is argued that they

provide a better basis for global scenario development. Therefore, all the data on

economic development in the IEA World Energy Outlook 2016 (WEO 2016a, b)

refer to purchasing-power-adjusted GDP in international US$ (2015).  However,

because WEO 2016 only covers the time period up to 2040, projections for 2040–

2050 in the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios are based on German Aerospace

Center (DLR) estimates, which are mainly used to extrapolate the GDP trends in the

world regions used in our modelling.

GDP growth in all regions is expected to slow gradually over the coming decades

(Table 5.3). It is assumed that world GDP will grow on average by 3.2% per year

over the period 2015–2050. China, India, and Africa are expected to grow faster

than other regions, followed by the Middle East, Africa, other non-OECD Asia, and

Latin America. The growth of the Chinese economy will slow as it becomes more

mature, but it will nonetheless become the economically strongest region in the

world in PPP terms by 2020. The GDP in OECD Europe and OECD Pacific is

assumed to grow by 1.3–1.5% per year over the projection period, while economic

growth in OECD North America is expected to be slightly higher (2.1%). The

OECD’s share of global PPP- adjusted GDP will decrease from 45% in 2015 to 28%

in 2050.

Table 5.3 GDP development projections based on average annual growth rates for 2015–2040 from IEA (WEO 2016a, b) and on our own extrapolations

Billion $
(2015)
PPP

2015 2020 2025 2030 2035 2040 2045 2050 change
2050/2015

OECD
North
America

22,123 24,787 27,650 30,513 34,038 37,562 41,675 45,788 107%

OECD
Pacific

8284 8880 9644 10,407 11,125 11,842 12,462 13,081 58%

OECD
Europe

21,632 23,883 26,076 28,269 30,538 32,807 34,885 36,963 71%

Eurasia 6397 6757 7919 9081 10,467 11,853 13,439 15,025 135%
Middle
East

5380 6236 7646 9055 10,853 12,650 14,909 17,167 219%

Latin
America

7181 7473 8807 10,141 11,951 13,761 16,218 18,675 160%

China 20,179 28,567 37,997 47,427 56,207 64,986 74,906 84,825 320%
Africa 5851 7118 9247 11,376 14,437 17,498 21,950 26,403 351%
India 8021 11,515 17,084 22,652 30,309 37,966 46,020 54,074 574%
Other
Asia

10,061 11,361 14,577 17,794 21,835 25,876 30,055 34,234 240%

Global 115,108136,578 166,646 196,715231,758266,801306,519346,236 201%

5 Main Assumptions for Energy Pathways

104

5.3.3 Technology Cost Projections

The parameterization of the models requires many assumptions about the develop-

ment of the particular characteristics of technologies, such as specific investment

and fuel costs. Therefore, because long-term projections are highly uncertain, we

must define plausible and transparent assumptions based on background informa-

tion and up-to-date statistical and technical information.

The speed of an energy system transition also depends on overcoming economic

barriers. These largely relate to the relationships between the costs of renewable

technologies and their fossil and nuclear counterparts. For our scenarios, the projec-

tion of these costs is vital in making valid comparisons of energy systems. However,

there have been significant limitations to these projections in the past in relation to

investment and fuel costs.

In addition, efficiency measures also generate costs which are usually difficult to

determine depending on technical, structural and economic boundary conditions. In

the context of this study, we have therefore assumed uniform average costs of 3 ct

per avoided kWh of electricity consumption in our cost accounting.

During the last decade, fossil fuel prices have seen huge fluctuations. Figure 5.1

shows oil prices since 1997. After extremely high oil prices in 2012, we are cur-

rently in a low-price phase. Gas prices saw similar development (IEA 2017 ).

Therefore, fossil fuel price projections have also seen considerable variations (IEA

2013 , 2017) and have had a considerable influence on scenario results ever since.

Although in the past, oil-exporting countries provided the best oil price projec-

tions, institutional price projections have become increasingly accurate, with the

International Energy Agency (IEA) leading the way in 2018 (Roland Berger 2018 ).

0

50

100

150

200

250

0

20

40

60

80

100

120

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040

Production (Mb/d) Price (USD 2013/b)

WEO 2000
WEO 2001
WEO 2002
WEO 2003
WEO 2004
WEO 2005
WEO 2006
WEO 2007
WEO 2008
WEO 2009
WEO 2010
WEO 2011
WEO 2012
WEO 2013
WEO 2014
WEO 2015
WEO 2016
Actual

Fig. 5.1 Historic development and projections of oil prices (bottom lines) and historical world oil production and projections (top lines) by the IEA according to Wachtmeister et al. ( 2018 )

T. Pregger et al.

105

An evaluation of the oil price projections of the IEA since 2000 by Wachtmeister

et  al. ( 2018 ) showed that price projections have varied significantly over time.

Whereas the IEA’s oil production projections seem comparatively accurate, oil price

projections showed errors of 40–60%, even when made for only 10  years ahead.

Between 2007 and 2017, the IEA price projections for 2030 varied from $70 to

$140 per barrel, providing significant uncertainty regarding future costs in the sce-

narios. Despite this limitation, the IEA provides a comprehensive set of price

projections. Therefore, we based our scenario assumptions on these projections, as

described below.

However, because most renewable energy technologies provide energy without

fuel costs, the projections of investment costs become more important than fuel cost

projections, and this limits the impact of errors in the fuel price projections. It is

only for biomass that the cost of feedstock remains a crucial economic factor for

renewables. Today, these costs range from negative costs for waste wood (based on

credit for the waste disposal costs avoided), through inexpensive residual materials,

to comparatively expensive energy crops. Because bioenergy holds significant mar-

ket shares in all sectors in many regions, a detailed assessment of future price pro-

jections is provided below.

Investment cost projections also pose challenges for scenario development.

Available short-term projections of investment costs depend largely on the data

available for existing and planned projects. Learning curves are most commonly

used to assess the future development of investment costs as a function of their

future installations and markets (McDonald and Schrattenholzer 2001 ; Rubin et al.

2015 ). Therefore, the reliability of cost projections largely depends on the uncer-

tainty of future markets and the availability of historical data.

Fossil technologies provide a large cost data set featuring well-established mar-

kets and large annual installations. They are also mature technologies, where many

cost reduction potentials have already been exploited.

For renewable technologies, the picture is more mixed. For example, hydro

power is, like fossil fuels, well established and provides reliable data on investment

costs. Other technologies, such as PV and wind, are currently experiencing tremen-

dous developments in installation and cost reduction. Solar PV and wind are the

focus of cost monitoring, and considerable data are already available on existing

projects. However, their future markets are not easily predicted, as can be seen from

the evolution of IEA market projections over recent years in the World Energy

Outlook series (compare for example IEA 2007 , 2014, 2017). For PV and wind,

small differences in cost assumptions will lead to large deviations in the overall

costs, and cost assumptions must be made with special care. Furthermore, many

technologies feature only comparably small markets, such as geothermal, modern

bioenergy applications, and CSP, for which costs are still high and for which future

markets are insecure. The cost reduction potential is correspondingly high for these

technologies. This is also true for technologies that might become important in a

transformed energy system but are not yet widely available. Hydrogen production,

ocean power, and synthetic fuels might deliver important technology options in the

5 Main Assumptions for Energy Pathways

106

long term after 2040, but their cost reduction potential cannot be assessed with any

certainty today.

Thus, cost assumptions are a crucial factor in evaluating scenarios. Because costs

are an external input into the model and are not internally calculated, we assume the

same progressive cost developments for all scenarios. In the next sections, we pres-

ent a detailed overview of our assumptions for power and renewable heat technolo-

gies, including the investment and fuel costs, and the potential CO 2 costs in the

scenarios.

5.3.3.1 Power and CHP Technologies

The focus of cost calculations in our scenario modelling is the power sector. We

compared the specific investment costs estimated in previous studies (Teske et al.

2012 , 2015), which were based on a variety of studies, including the European

Commission-funded NEEDS project (NEEDS 2009 ), projections from the European

Renewable Energy Council (Zervos et al. 2010 ), investment cost projections by the

IEA ( 2014 ), and current cost assumptions by IRENA and IEA (IEA 2016b). We

found that investment costs generally converged, except for PV. Therefore, for con-

sistency reasons, the investment costs and operation and maintenance costs for the

power sector are based primarily on the investment costs within WEO 2016 (IEA

2016b) up to 2040, including their regional disaggregation. We extended the projec-

tions until 2050 based on the trends in the preceding decade.

For renewable power production, we used investment costs from the 450  ppm

scenario from IEA 2016b. For technologies not distinguished in the IEA report

(such as geothermal CHP), we used cost assumptions based on our own research,

from the Energy [R]evolution Scenario 2015 (Teske et al. 2015 ). As the cost assump-

tions for PV systems by the IEA do not reflect recent cost degressions, we based our

assumptions on a more recent analysis by Steurer et al. ( 2018 ), which projects lower

investment costs for PV in 2050 than does the IEA. The costs for onshore and off-

shore wind in Europe were adapted from the same source, in order to reflect more

recent data. The cost assumptions for hydrogen production come from our own

analysis in the PlanDelyKaD project (Michalski et al. 2017 ). Table 5.4 summarizes

the cost trends for power technologies derived from the assumptions discussed

above for OECD Europe. It is important to note that the cost reductions are, in real-

ity, not a function of time, but of cumulative capacity (production of units), so

dynamic market development is required to achieve a significant reduction in spe-

cific investment costs. Therefore, we might underestimate the costs of renewables in

the reference (5.0  °C) scenario compared with the 2.0  °C and 1.5  °C Scenarios.

However, our approach is conservative when we compare the reference scenario

with the 2.0 °C or 1.5 °C Scenarios. The cost assumptions for the other nine regions

are in the same range, but differ slightly for different renewable energy technolo-

gies. Fossil fuel power plants have a limited potential for cost reductions because

they are at an advanced stage of technology and market development. Gas and oil

T. Pregger et al.

107

plants are relatively cheap, at around $670/kW and $822/kW, respectively. CHP

applications and coal plants are more expensive, ranging between $2000/kW and

$2500/kW. The IEA sees some cost reduction potential for expensive nuclear plants,

tending towards $4500/kW by 2050, whereas gas might even increase in cost.

In contrast, several renewable technologies have seen considerable cost reduc-

tions over the last decade. This is expected to continue if renewables are deployed

extensively. Fuel cells are expected to outpace other CHP technologies, with a cost

reduction potential of more than 75% (from currently high costs). Hydro power and

biomass remain stable in terms of costs. Tremendous cost reductions are still

expected for solar energy and offshore wind, even though they have experienced

significant reductions already. Although CSP might deliver dispatchable power at

half its current cost in 2050, variable PV costs could drop to 35% of today’s costs.

Offshore wind could see cost reductions of over 30%, whereas the cost reduction

potential for onshore wind seems to have been exploited already to a large extent

(Table 5.4).

Table 5.4 Investment cost assumptions for power generation plants (in $2015/kW) in the scenarios until 2050

Investment costs power generation plants in Europe 2015 2020 2030 2040 2050 CHP Coal $/kW 2500 2500 2500 2500 2500 CHP Gas $/kW 1000 1000 1000 1000 1000 CHP Lignite $/kW 2500 2500 2500 2500 2500 CHP Oil $/kW 1310 1290 1240 1180 1130 Coal power plant $/kW 2000 2000 2000 2000 2000 Diesel generator $/kW 900 900 900 900 900 Gas power plant $/kW 670 500 500 500 670 Lignite power plant $/kW 2200 2200 2200 2200 2200 Nuclear power plant $/kW 6600 6000 5100 4500 4500 Oil power plant $/kW 950 930 890 860 820 CHP Biomass $/kW 2550 2500 2450 2350 2250 CHP Fuel cell $/kW 5000 5000 2500 2500 1120 CHP Geothermal $/kW 13,200 11,190 8890 7460 6460 Biomass power plant $/kW 2400 2350 2300 2200 2110 Geothermal power plant $/kW 12,340 2800 2650 2500 2400 Hydro power planta $/kW 2650 2650 2650 2650 2650 Ocean energy power plant $/kW 6950 6650 4400 3100 2110 PV power plant $/kW 1300 980 730 560 470 CSP power plantb $/kW 5700 5000 3700 3050 2740 Wind turbine offshore $/kW 4000 3690 3190 2830 2610 Wind turbine onshore $/kW 1640 1580 1510 1450 1400 Hydrogen production $/kW 1380 1220 920 700 570 aCosts for a system with solar multiple of two and thermal storage for 8 h of turbine operation bValues apply to both run-of-the-river and reservoir hydro power

5 Main Assumptions for Energy Pathways

108

In the 2.0 °C and 1.5 °C Scenarios, hydrogen is introduced as a substitute for natu-

ral gas, with a significant share after 2030. Hydrogen is assumed to be produced by

electrolysis. With electrolysers just emerging on larger scale on the markets, they have

considerable cost reduction potential. Based on the Plan-DelyKaD studies (Michalski

et al. 2017 ), we assume that costs could decrease to $570/kW in the long term.

5.3.3.2 Heating Technologies

Assessing the costs in the heating sector is even more ambitious than in the power

sector. Costs for new installations differ significantly between regions and are inter-

linked with construction costs and industry processes, which are not addressed in

this study. Moreover, no data are available to allow the comprehensive calculation

of the costs for existing heating appliances in all regions. Therefore, we concentrate

on the additional costs resulting from new renewable applications in the heating

sector.

Our cost assumptions are based on a previous survey of renewable heating tech-

nologies in Europe, which focused on solar collectors, geothermal, heat pumps, and

biomass applications. Biomass and simple heating systems in the residential sector

are already mature. However, more-sophisticated technologies, which can provide

higher shares of heat demand from renewable sources, are still under development

and rather expensive. Market barriers will slow the further implementation and cost

reduction of renewable heating systems, especially for heating networks.

Nevertheless, significant learning rates can be expected if renewable heating is

increasingly implemented, as projected the 2.0 °C and 1.5 °C Scenarios.

Table 5.5 presents the investment cost assumptions for heating technologies for

OECD Europe, disaggregated by sector. Geothermal heating displays the same high

costs in all sectors. In Europe, deep geothermal applications are being developed for

Table 5.5 Specific investment cost assumptions (in $2015) for heating technologies in the scenarios until 2050

Investment costs heat generation plants in OECD Europe
2015 2020 2030 2040 2050
Geothermal $/kW 2390 2270 2030 1800 1590
Heat pumps $/kW 1790 1740 1640 1540 1450
Biomass heat plants $/kW 600 580 550 510 480
Residential biomass stoves Industrialized countries $/kW 840 810 760 720 680
Residential biomass stoves Developing countries $/kW 110 110 110 110 110
Solar collectors Industry $/kW 850 820 730 650 550
In heat grids $/kW 970 970 970 970 970
Residential $/kW 1060 1010 910 800 680

T. Pregger et al.

109

heating purposes at investment costs ranging from €500/kWth (shallow) to €3000/

kWth (deep), with the costs strongly dependent on the drilling depth. The cost reduc-

tion potential is assumed to be around 30% by 2050.

Heat pumps typically provide hot water or space heat for heating systems with

relatively low supply temperatures, or they supplement other heating technologies.

Therefore, they are currently mainly used for small-scale residential applications.

Costs currently cover a large band-width and are expected to decrease by only 20%

to $1450/kW by 2050.

For biomass and solar collectors, we assume significant differences between the

sectors. There is a broad portfolio of modern technologies for heat production from

biomass, ranging from small-scale single-room stoves to heating or CHP plants on

an MW scale. Investment costs show similar variations: simple log-wood stoves can

be obtained from $100/kW, but more sophisticated automated heating systems that

cover the whole heat demand of a building are significantly more expensive. Log-

wood or pellet boilers range from $500 to 1300/kW. Large biomass heating systems

are assumed to reach their cheapest costs in 2050 at around $480/kW for industry.

For all sectors, we assume a cost reduction of 20% by 2050. In contrast, solar col-

lectors for households are comparatively simple and will become cheap at $680/kW

by 2050. The costs of simple solar collectors for swimming pools might have been

optimized already, whereas their integration in large systems is neither technologi-

cally nor economically mature. For larger applications, especially in heat grid sys-

tems, the collectors are large and more sophisticated. Because there is not yet a mass

market for such grid-connected solar systems, we assume there will be a cost reduc-

tion potential until 2050.

5.3.4 Fuel Cost Projections

5.3.4.1 Fossil Fuels

Although fossil fuel price projections have seen considerable variations, as described

above, we based our fuel price assumptions up to 2040 on the WEO 2017 (IEA

2017 ). Beyond 2040, we extrapolated from the price developments between 2035

and 2040. Even though these price projections are highly speculative, they provide

a set of prices consistent to our investment assumptions. Fuel prices for nuclear

energy are based on the values in the Energy [R]evolution report 2015 (Teske et al.

2015 ), corrected by the cumulative inflation rate for the Eurozone of 1.82% between

2012 and 2015 (Table 5.6).

5 Main Assumptions for Energy Pathways

110

5.3.4.2 Biomass Prices

Biomass prices depend on the quality of the biomass (residues or energy crops) and

the regional supply and demand. The variability is large. Lamers et al. ( 2015 ) found

a price range of €4–4.8/GJ for forest residues in Europe in 2020, whereas agricul-

tural products might cost €8.5–12/GJ.  Lamers et  al. modelled a range for wood

pellets from €6/GJ in Malaysia to 8.8€/GJ in Brazil. IRENA modelled a cost supply

curve on a global level for 2030 (see Fig. 5.2), ranging from $3/GJ for a potential of

35 EJ/year. up to $8–10/GJ for a potential up to 90–100 EJ/year (IRENA 2014 ) (and

up to $17/GJ for an potential extending to 147 EJ).

Table 5.6 Development projections for fossil fuel prices in $2015 (IEA 2017 )

Development projections for fossil fuel prices
Reference scenario 2015 2020 2030 2040 2050
Oil All $/GJ 8.5 12.3 21.5 24.2 35.1
Gas OECD North America $/GJ 2.5 3.3 5.5 6.2 8.9
OECD Europe $/GJ 6.6 7.2 9.2 10.0 12.9
China $/GJ 9.2 9.5 10.3 10.5 11.4
OECD Pacific $/GJ 9.8 10.0 10.7 10.9 11.8
Others $/GJ 2.5 3.3 5.5 6.2 8.9
Coal OECD North America $/GJ 2.3 2.5 2.9 3.0 5.3
OECD Europe $/GJ 2.6 3.1 4.1 4.3 5.3
China $/GJ 3.2 3.5 4.3 4.5 5.3
OECD Pacific $/GJ 2.6 3.3 4.4 4.5 5.3
Others $/GJ 2.9 3.3 4.2 4.4 5.3
Nuclear All $/GJ 1.1 1.2 1.5 1.8 2.1
2.0 °C and 1.5 °C scenarios
Oil all $/GJ 8.5 10.2 12.6 13.0 14.3
Gas OECD North America $/GJ 2.5 2.8 4.5 5.1 7.6
OECD Europe $/GJ 6.6 6.6 8.5 9.4 13.0
China $/GJ 9.2 8.5 9.2 10.0 12.9
OECD Pacific $/GJ 9.8 9.0 9.6 10.3 13.2
Others $/GJ 2.5 2.8 4.5 5.1 7.6
Coal OECD Europe $/GJ 2.9 2.7 2.5 2.5 2.4
OECD North America $/GJ 2.3 2.6 2.9 2.9 2.7
China $/GJ 2.6 2.9 3.1 3.1 2.9
OECD Pacific $/GJ 3.2 3.4 3.5 3.5 3.4
Others $/GJ 2.9 2.8 3.0 3.0 2.8
Nuclear All $/GJ 1.1 1.2 1.5 1.8 2.1

T. Pregger et al.

111

IRENA projected regional supply costs for liquid and other biomass sources in

2030 based on a global biomass use of around 108 EJ, using current primary bio-

mass prices as a proxy (see Table 5.7). Liquid biofuels demand higher prices because

of their production and transformation processes; ‘other biomass’ includes primary

biomass, such as fuel wood, energy crops, and residues.

The prices cited above hold true for modern biomass applications. Traditional

biomass use is still often based on firewood or other biomass, which is acquired

without a price (and with the labour cost not considered). No price data are yet

available for a considerable range of residues. Therefore, the average primary bio-

mass costs across the complete energy system in many regions are lower than the

available market prices for biomass commodities. Consequently, today’s market

prices represent the upper limit of today’s biomass costs.

Therefore, for our scenarios, we assumed a lower average biomass price in all

regions, starting from the lower end of the cost supply curve at around $7.50/GJ for

OECD regions, with predominantly modern applications. For Africa, Latin America,

and Asia, including Russia, which have abundant biomass residue potential, current

prices were assumed to be $3/GJ. For the remaining regions (the Middle East, and

Eastern Europe), we assumed $5/GJ.

The prices for primary biomass will increase proportionately to the IRENA ref-

erence price for ‘other biomass’ by 2030, following the increasing uptake of modern

Fig. 5.2 Global supply curve for primary biomass in 2030 (IRENA 2014 )

5 Main Assumptions for Energy Pathways

112

biomass technologies and increasing trade, representing a further biomass potential

uptake along the supply curve. For the period until 2050, we consider that biomass

prices will be stable. The prices calculated by IRENA are valid for a demand of

108 EJ/year. The biomass demand considered in this study never exceeds a total of

100 EJ/year. However, the international trade in biomass may heavily influence bio-

mass prices in the future, representing a significant source of uncertainty in our

assumptions.

5.3.5 CO 2 Costs

The WEO 2017 (IEA 2017 ) considers the future price of CO 2 in the power and

industry sectors. There is considerable variation between the current policy sce-

nario, the new policy scenario, and the 450 ppm scenario, not only in value, but also

in regional range. Various studies have indicated a close relationship between decar-

bonisation and the implicit or explicit CO 2 price (regardless of the most efficient

implementation measure). On the one hand, the carbon price is a precondition for a

decarbonisation of the energy sector (Lucena et  al. 2016 ), but on the other hand,

decarbonisation may limit the costs of CO 2 emissions if an efficient pricing measure

is in place (Jacobson et al. 2017 ). Because the scenarios in this study rely heavily on

effective reductions in CO 2 emissions, we used the CO 2 prices of the 450 ppm sce-

nario in the 2.0 °C and 1.5 °C Scenarios. In the reference case, we deviated from the

WEO 2017, which applies rather low CO 2 emission costs. Instead, we applied CO 2

costs equivalent to the cost of the resulting climate damage. Based on existing stud-

ies of fossil-energy-induced damage (Anthoff and Tol 2013 ; Stern et al. 2006 ), we

assumed that $78/t of CO 2 is a plausible cost estimate in the wide range of estimates

of the social costs of CO 2 emissions (Table 5.8).

Table 5.7 Biomass price projections for 2030 at 108 EJ of the biomass demand (IRENA 2014 )

Liquid biofuel reference price Other biomass reference price
US $/GJ US $/GJ
Africa 36 10
Asia 40 7
Europe 58 18
North America 34 15
OECD Pacific 61 15
Latin America 59 12
World 42 11

T. Pregger et al.

113

5.4 Energy Scenario Narratives and Assumptions for World

Regions

The scenario-building process involves many assumptions and explicit, but also

implicit, narratives about how future economies and societies, and ultimately energy

systems, may develop under the overall objective of ‘deep and rapid decarbonisa-

tion’ by 2050. These narratives depend on three main strategic pillars:

• Efficiency improvement and demand reduction: stringent implementation of

technical and structural efficiency improvements in energy demand and supply.
These will lead to a continuous reduction in both final and primary energy con-
sumption. In the 1.5  °C Scenario, these measures must be supplemented with
responsible energy consumption behaviour by the consumer.

• Deployment of renewable energies: massive implementation of new technolo-

gies for the generation of power and heat in all sectors. These will include vari-
able renewable energies from solar and wind, which have experienced
considerable cost reductions in recent years, but also more expensive technolo-
gies, such as large-scale geothermal and ocean energy, small hydro power, and
CSP.

• Sector coupling: stringent direct electrification of heating and transport technolo-

gies in order to integrate renewable energy in the most efficient way. Because
this strategy has its limitations, it will be complemented by the massive use of
hydrogen (generated by electrolysis) or other synthetic energy carriers.

Some alternative or probably complementary future technical options are explic-

itly excluded from the scenarios. In particular, those options with large uncertainties

with respect to technical, economic, societal, and environmental risks, such as large

hydro and nuclear power plants, unsustainable biomass use, carbon capture and

storage (CCS), and geoengineering, are not considered on the supply side as mitiga-

tion measures or—in the case of hydro—not expanded in the future. The sustainable

use of biomass will partly substitute for fossil fuels in all energy sectors. However,

this use will be limited to an annual global energy potential of less than 100 EJ per

year for sustainability reasons, according to the calculations of Seidenberger et al.

(2008), Thrän et al. ( 2011 ), and Schueler et al. ( 2013 ).

The transformations described in the two alternative scenarios are constrained, to

a certain degree, by current short- to medium-term investment planning, as described

in the reference case, because most technical and structural options to change the

demand or supply side require years of planning and construction. This means that

Table 5.8 CO 2 cost assumptions in the scenarios

CO 2 costs
2020 2025 2030 2040 2050
Reference All regions $/t CO 2 0 42 69 78 78
2.0 °C and 1.5 °C OECD Economies $/t CO 2 0 62 87.6 138 189.0
Other regions $/t CO 2 0 42 69.5 124 177.5

5 Main Assumptions for Energy Pathways

114

both alternative scenarios start deviating significantly from the reference case only

after 2025. However, some short-term developments shown in the IEA WEO

Current Policies scenario have been corrected and are not adopted in the alternative

scenarios because there is newer statistical information (that renders the reference

development implausible). This is the case for the IEA estimates of demand devel-

opment in some regions and sectors, and it is partly true for investments in fossil-

fuel- based heat and power generation.

5.4.1 Efficiency and Energy Intensities

It is obvious that a major increase in energy efficiency is the backbone of each ambi-

tious transition scenario, because energy efficiency significantly reduces the need

for energy conversion and infrastructure investment. The development of the future

global energy demand is determined by three key factors:

• Population growth, which affects the number of people consuming energy or

using energy services. Associated with this, increasing access to energy services
in developing countries and emerging economies is an additional influencing
factor, bearing in mind that this could mean power grid access or the implemen-
tation of isolated, usually small-scale, local power systems.

• Economic development, which is commonly measured as GDP. In general, GDP

growth triggers an increase in energy demand, directly via additional industrial
activities and indirectly via an increase in private consumption arising from the
higher incomes associated with a prospering economy.

• Energy intensity, which is a measure of how much final energy is required in the

industrial sector to produce a unit of GDP. Efficiency measures help to reduce
energy intensity and can result in a decoupling of economic growth and final
energy consumption. In the ‘Residential and other’ sector, energy intensity refers
to the per capita demand for final energy (for electrical appliances and heat gen-
eration). Efficiency improvement is also a result of reduced conversion losses, in
particular those achieved by replacing thermal power generation with renewable
technologies, which leads to a further reduction in the primary energy intensity.

The reference scenario and both target scenarios are based on the same projec-

tions of population and economic growth. Therefore, the scenarios represent the

specific, although widely accepted, development of future societies. However, the

future development of energy intensities differs between the reference and alterna-

tive scenarios, taking into account the different efficiency pathways and therefore

the successful implementation of measures to intensify required investments in effi-

cient technologies or to change consumer behaviour.

The assumptions made about the potential to further increase the economic and

technical efficiency in all sectors are based on various external studies. However,

the lower benchmarks for the assumptions on efficiency potentials are derived from

the Current Policies scenario of the IEA WEO 2017 (IEA 2017 ). The upper bench-

T. Pregger et al.

115

marks for efficiency potentials per world region are taken from Graus et al. ( 2011 ),

Kermeli et al. ( 2014 ), and recently published low-energy-demand scenarios devel-

oped by Grubler et al. ( 2018 ).

5.4.1.1 Industrial Electricity Demand

‘Industrial electricity demand’ refers to many appliances of different sizes and pur-

poses. Large potentials for saving electricity have been identified in various studies

in most branches of industry. This particularly applies to electric drives for com-

pressed air, pumps, and fans. The scenario model approach distinguishes between

electric appliances and power-to-heat devices for space and process heating. The

consumption of electricity per GDP varies widely between regions, depending on

their industrial structures and efficiency standards. The trajectories for industrial

electricity demands are constrained by the abovementioned lower and upper bench-

marks and aim for similar electricity uses per $GDP in the industrial sectors in all

regions by 2050. The resulting trajectories for OECD and non-OECD countries are

shown in Table 5.9. The average global electricity demand for electric appliances in

‘industry’ (without power-to-heat) will decrease from 55 kWh/$1000 GDP in 2015

to 36 kWh/$1000 in 2050 in the reference case, but to 24 kWh/$1000 in the 2.0 °C

Scenario and 23 kWh/$1000 in the 1.5 °C Scenario. However, the increased electri-

fication of industrial heat in both alternative scenarios almost cancels out the greater

efficiency increases in those two scenarios when compared with the reference case.

The average power-to-heat share in industry will increase in this period from 6% to

34% in 2050 in the 2.0 °C Scenario and to 37% in the 1.5 °C Scenario. In the 1.5 °C

Scenario, the annual electricity demand for industrial electrical appliances will be

around 5% lower than in the 2.0  °C Scenario between 2020 and 2025, and up to

10% lower between 2025 and 2035. However, between 2035 and 2050, the electric-

ity demand for electric appliances in the industry sector converges under the two

scenarios.

Table 5.9 Assumed average development of specific (per $GDP) electricity use for electrical appliances in the ‘Industry’ sector

kWh/$1000 2015 2020 2025 2030 2035 2040 2045 2050

Change
2050/2015
Reference case
OECD regions 42.8 40.8 38.2 35.8 33.5 31.5 29.7 28.2 −34%
Non-OECD regions 65.8 62.5 55.7 52.9 48.7 45.7 41.9 39.1 −41%
2.0 °C Scenario
OECD regions 42.8 40.4 35.6 32.2 28.7 25.7 23.5 21.7 −49%
Non-OECD regions 65.8 60.9 49.2 42.9 37.2 32.9 28.3 24.8 −62%
1.5 °C Scenario
OECD regions 42.8 40.1 33.4 27.4 24.3 22.4 20.7 20.0 −53%
Non-OECD regions 65.8 58.6 46.4 39.8 34.4 30.8 27.4 24.7 −62%

5 Main Assumptions for Energy Pathways

116

5.4.1.2 Demand for Fuel to Produce Heat in the Industry Sector

Industrial heat is required for different purposes and at different temperatures.

Currently, industrial (process) heat is mainly produced by burning fossil fuels.

Biomass plays a minor role, except in heat use from the combustion of residues and

biogenic waste. Some low- and medium-temperature heat is produced by co-

generation plants with combined heat and power provisions. Power-to-heat makes

up only a small percentage of the industrial energy demand for heat. Regional dif-

ferences in the nature of industry, especially in terms of the presence of energy-

intensive heavy or manufacturing industries, strongly influence the amounts of

low-, medium-, and high-temperature heat that must be produced today and in the

future (because we assume that the regional industry structure will remain the

same). Various technological improvements, process substitutions, and innovations

are technically possible and have been implemented to some extent already. An

important example is highly efficient waste heat recovery. Shorter investment cycles

and incentives to replace old technologies will help to reduce energy consumption

as quickly as possible. It is obvious that incentives are essential to trigger rapid

innovation and the implementation of new technologies. Any political strategy for

introducing such a pathway requires strong support from various industry stake-

holders and regional or even global governance to overcome the economic and tech-

nical obstacles and conflicting interests. Therefore, both alternative scenarios

assume that the conditions exist to allow rapid technological change. Table 5.10

provides the resulting final energy demands for heating per $GDP for OECD and

non-OECD countries. The average global values will decrease from 680 MJ/$1000

GDP in 2015 to 366 MJ/$1000 in 2050 in the reference case, and to 185 MJ/$1000 in

the 2.0 °C Scenario and 172 MJ/$1000 in the 1.5 °C Scenario. Compared with the

2.0 °C Scenario, the 1.5 °C Scenario assumes a significantly more rapid reduction

in the industrial heat demand. Between 2020 and 2025, the annual energy demand

for heat will be up to 8% lower under the 1.5  °C Scenario than under the 2.0  °C

Scenario, and up to 17% lower between 2025 and 2035. After 2035, the difference

will decrease again, and by 2050, it will be around 7%.

Table 5.10 Assumed average development in final energy use for heating in the industry sector (including power-to-heat) (per $GDP)

MJ/$1000 2015 2020 2025 2030 2035 2040 2045 2050 Change 2050/2015
Reference case
OECD regions 406 417 384 352 320 293 270 249 −39%
Non-OECD regions 911 823 687 620 553 506 453 410 −55%
2.0 °C Scenario
OECD regions 406 383 330 284 242 207 177 157 −61%
Non-OECD regions 911 791 608 491 381 302 238 196 −79%
1.5 °C Scenario
OECD regions 406 377 306 239 199 177 158 143 −65%
Non-OECD regions 911 762 558 428 318 259 212 182 −80%

T. Pregger et al.

117

5.4.1.3 Electricity Demand in the ‘Residential and Other’ Sector

The electricity demand in the ‘Residential and other’ sector includes electricity use

in households, for commercial purposes, and in the service and trade sectors, fish-

ery, and agriculture. Besides lighting, information, and communication, a large

amount of electricity is used for cooking, cooling, and hot water. It has been esti-

mated that in 2015, electricity use for heating had a global average share of 5% of

the final energy use for heating. It is assumed that this share will increase signifi-

cantly to 30% in 2050 in the 2.0 °C Scenario and to 37% in the 1.5 °C Scenario.

These increases are attributed to sector coupling, the provision of storage for vari-

able renewable energy in the heat sector, and the provision of high-temperature heat

without fuel combustion. The average global electricity use for appliances in the

‘Residential and other’ sector will decrease in the reference case from 78 kWh/$1000

GDP in 2015 to 60 kWh/$1000 in 2050, whereas it will decrease to 38 kWh/$1000 in

the 2.0  °C Scenario and to 37  kWh/$1000  in the 1.5  °C Scenario, a reduction of

more than 50% relative to today’s energy consumption. The average global electric-

ity use for appliances in the ‘Residential and other’ sector, which is related to per

capita consumption, will increase in the reference scenario from 1350 kWh/capita

in 2015 to 2370 kWh/capita in 2050, whereas it will increase to only 1490 kWh/

capita in the 2.0  °C Scenario and to 1460  kWh/capita in the 1.5  °C Scenario.

Table 5.11 shows the changes in electricity use for appliances in OECD and non-

OECD countries (without electricity for heating). Significant reduction potentials

are assumed for all world regions. Similar to the development in the industry sector,

between 2020 and 2025, the annual power demand for electrical appliances in the

‘Residential and other’ sector will be around 5% lower in the 1.5 °C Scenario than

in the 2.0  °C Scenario, and more than 10% lower between 2025 and 2035. After

2035, the two scenarios will converge again, so that in 2050, the global demand will

be only 2% higher in the 2.0 °C Scenario than in the 1.5 °C Scenario.

Table 5.11 Assumed average developments of per capita electricity use in the ‘Residential and other’ sector for electrical appliances (without power-to-heat)

kWh/capita 2015 2020 2025 2030 2035 2040 2045 2050 Change 2050/2015
Reference case
OECD regions 4457 4585 4753 4972 5189 5419 5626 5837 31%
Non-OECD regions 712 851 977 1191 1366 1532 1661 1788 151%
2.0 °C Scenario
OECD regions 4457 4526 4366 4137 3837 3590 3304 3023 −32%
Non-OECD regions 712 834 894 1004 1083 1143 1193 1238 74%
1.5 °C Scenario
OECD regions 4457 4502 4078 3346 2987 2896 2889 2872 −36%
Non-OECD regions 712 806 842 928 1001 1086 1164 1224 72%

5 Main Assumptions for Energy Pathways

118

5.4.1.4 Fuel Demand for Heat in the ‘Residential and Other’ Sector

The fuel demand for heat in households has quite different characteristics depend-

ing on the consumption structures in each world region and their climatic condi-

tions. In regions with harsh winters, the heat demand is dominated by the building

sector (space heat and hot water in private and commercial buildings), but in regions

with a comparatively warm climate, the demand for space heat is generally low and

heat is predominantly used for cooking and as low-temperature heat for hot water.

In the commercial sector, the energy mix for heat is more diverse. The medium- to

high-temperature process heat demand arises in this sector. Reducing the final

energy use for heating will involve reducing the demand (e.g., by improving the

thermal insulation of building envelopes) and replacing inefficient procedures and

technologies, such as the traditional use of biomass, which is still widely used for

cooking and heating in some regions. In contrast to traditional biomass, the effi-

ciency of electrical appliances can improve significantly, and they produce zero

direct emissions and no air pollution. Table 5.12 shows the assumed average final

energy demand for heating in OECD and non-OECD countries. The average global

consumption will decrease from 560 MJ/$1000 GDP in 2015 to 280 MJ/$1000 in

2050 in the reference scenario, but to 173 MJ/$1000 in the 2.0 °C Scenario and to

160 MJ/$1000 in the 1.5 °C Scenario. The average global per capita energy demand

for fuels in ‘Residential and other sectors’ will decrease from around 12,600 MJ/

capita per year in 2015 to 11,700 MJ/capita in the reference case. This will mainly

be due to a shift in the global population shares towards the developing regions.

Compared with the reference scenario, the energy intensity will decrease to

7300 MJ/capita in the 2.0 °C Scenario and to 6700 MJ/capita in the 1.5 °C Scenario.

Table 5.12 Assumed average development of specific final energy use for heating in the ‘Residential and other’ sector (including power-to-heat)

MJ/capita 2015 2020 2025 2030 2035 2040 2045 2050

Change
2050/2015
Reference case
OECD
regions

24,932 24,421 24,163 23,980 23,794 23,696 23,821 24,121 −3%

Non-OECD
regions

10,047 9933 9786 9749 9678 9628 9623 9666 −4%

2.0 °C Scenario
OECD
regions

24,932 24,282 22,300 20,578 19,064 17,677 16,599 15,800 −37%

Non-OECD
regions

10,047 9650 8961 8209 7498 6807 6217 5868 −42%

1.5 °C Scenario
OECD
regions

24,932 24,047 20,413 16,222 15,143 14,538 14,172 13,901 −44%

Non-OECD
regions

10,047 9549 8593 7515 6737 6234 5808 5510 −45%

T. Pregger et al.

119

The 1.5 °C Scenario assumes a significantly stronger reduction in demand than the

2.0 °C Scenario. In the 1.5 °C Scenario, additional efficiency measures will reduce

the final energy demand until 2025 by around 5%, and by up to 13% between 2025

and 2035 (compared with the 2.0  °C Scenario). Thereafter, the differences will

become smaller, finally reaching around 8% by 2050.

5.4.1.5 Resulting Energy Intensities by Region

Figure 5.3 shows the final energy intensities related to $GDP for each of the ten

world regions between 2015 and 2050 and for both alternative scenarios. The final

energy use per GDP will decrease significantly in all regions, but the decreases will

be larger (and faster) in OECD countries. This will result in smaller regional differ-

ences in the final energy demand compared with the current situation. Compared

with the very ambitious assumptions of Grubler et al. ( 2018 ) for the specific final

energy demands in northern and southern world regions, the assumptions made in

this study are conservative. In Grubler et al. ( 2018 ), the annual global final energy

use, including non-energy consumption, will decrease from 363 EJ in 2015 to 245

EJ by 2050, whereas in our study, the annual global value will decrease to 310 EJ in

the 2.0 °C Scenario and to 284 EJ in the 1.5 °C Scenario compared with 586 EJ in

the reference case (see Chap. 8). Because the 1.5  °C target requires a significant

reduction in emissions before 2030, the 1.5  °C Scenario necessarily reduces the

energy demand more rapidly than the 2.0  °C Scenario, but only a slightly lower

annual consumption is assumed in 2050.

5.4.2 RES Deployment for Electricity Generation

The power demand will increase significantly in all scenarios. In the 2.0  °C and

1.5 °C Scenarios, this will result from the continuous electrification of the heating

and transport sectors, and the increasing production of synthetic fuels for indirect

electrification and sector coupling. The available energy sources for renewable

0

500

1,000

1,500

2,000

2,50 0

3,000

3,500

4,000

4,500

2010 2020 2030 2040 2050

P
DG
$r
ep
yg
re
ne
la
nif

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

4,00 0

4,50 0

2010 2020 2030 2040 2050

final energy per $ GD

P OECD North America
OECD Pacific
OECD Europe
Eastern Europe/Eurasia
Middle East
Latin America
China
Africa
India
Non-OECD Asia
weighted average

Fig. 5.3 Development of the specific final energy use (per $GDP) in all stationary sectors (i.e.,
without transport) per world region under the 2.0 °C Scenario (left) and 1.5 °C Scenario (right)

5 Main Assumptions for Energy Pathways

120

power generation and their costs vary from region to region. Therefore, the scenar-

ios follow regionally different strategies and storylines, taking the different regional

conditions into account on the supply side. The core strategy is the replacement of

conventional thermal power and heat generators with solar, wind, geothermal, and

other renewable options for the highly efficient generation of electricity for final

energy consumption and the generation of synthetic fuels.

Our estimates of the potential for renewable power generation are based on the

results of the REMix EnDat tool developed (Scholz 2012 ; Stetter 2014 ; Pietzcker

et al. 2014 ). The technical potentials for solar and wind power in each world region

were estimated while taking into account the different exclusion criteria and con-

straints documented by Stetter ( 2014 ). The analysis was used to estimate the ‘eco-

nomic’ potential for each world region, which is the upper limit of the technological

expansion under the different scenarios. ‘Economic’ potentials were derived by

assuming the minimum annual full-load hours for each technology. In the case of

PV, the assumed global economic potential was estimated to be in the order of 5.4

million TWh per year. The potential of CSP was even larger, at around 5.6 million

TWh per year. The annual wind power potentials were estimated to be in the order

of 500,000 TWh for onshore wind and around 100,000 TWh for offshore wind.

The harvesting of global solar radiation by PVs has enormous economic poten-

tial worldwide. In the last few years, economies of scale have led to a significant

cost degression for PV modules, and large PV production capacities have been cre-

ated. The PV technology also plays a major role in our scenarios because of its

decentralized characteristics, which make it easy to build cost-efficient renewable

power supplies in rural and isolated areas. However, its restriction to sunny hours

causes high daily and seasonal variability. This results in rather low annual full-load

hours. Therefore, large quantities of storage capacity must also be installed for

short-term storage to support the major expansion of PV. The storage options con-

sidered are pumped hydro storage (e.g., by the enhancement of existing hydro sites)

and a massive expansion of battery storage. This leads to uncertainties in the total

infrastructure costs for the integration of high shares of PV into the power system

and the mineral resources required for this. For this reason, the share of PV globally

remains in the range of about 30% of total power generation, with the highest shares

in the Middle East (40%), followed by OECD North America and Other Asia (35%

each). The lowest shares, in the range of 20–25%, are in Eurasia, OECD Europe,

Latin America, and China.

Wind power on land will achieve an average global generation share of 25% in

2050 in both alternative scenarios (compared with 8% in the reference case). The

highest generation shares for onshore wind are assumed in India and Eastern

Europe/Eurasia, at about 30% each. The lowest shares will be in China, the Middle

East, and Non-OECD Asia, at 18–23%. For offshore wind, the global generation

share will rise to 8% by 2050 under both alternative scenarios, compared with only

1% in the reference scenario. The highest offshore wind shares of 10–15% will be

achieved under the 2.0  °C Scenario in the OECD regions and Eastern Europe/

Eurasia. The lowest shares are predicted in the Middle East (2%), India (6%), and

China (6.5%), where the potential is rather limited. The offshore shares under the

T. Pregger et al.

121

1.5 °C Scenario will tend to be slightly lower because of the stronger focus on PV

and onshore wind as the best options for a very rapid expansion of RES.

Compared with PV and wind power, CSP plants promise highly flexible power

and heat generation, with high capacity factors due to high-temperature heat stor-

age. The use of heat for desalination can also contribute to secure water supplies in

the sunbelt of the world. We assume that its multi-purpose uses and dispatchable

generation capability can lead to a significant role for CSP in the medium- to long-

term future, although levelized costs of CSP are today still much higher than they

are for PV or wind, and investment costs for batteries for short-term electricity stor-

age might also decrease in the future. Therefore, it is assumed that CSP will achieve

an average global electricity generation share of 15% by 2050 in the 2.0 °C Scenario

and 13% in the 1.5 °C Scenario, compared with 0.5% in the reference case. A high

share, close to 30%, will be achieved, especially in the Middle East. This will

include electricity generation for export to OECD Europe of up to 120 TWh per

year by 2050. High CSP potentials are also assumed for Africa (16%), which will

also export up to 280 TWh/year in 2050 from North Africa to OECD Europe—and

for China (18–20%) and Non-OECD Asia (15–17%).

Hydro power generation will increase only moderately under the alternative sce-

narios compared with the reference case. This source has already been tapped and

large hydro plants usually have significant ecological and societal consequences.

Therefore, the average global power generation share will decrease in the alterna-

tive scenarios from today’s 16% to around 8%, whereas the reference scenario

assumes a generation share of 14% in 2050. Nevertheless, a 30% increase in the

global hydro power generation is assumed between 2015 and 2050. The highest

power generation shares in 2050 are assumed to be in Latin America (24%), fol-

lowed by OECD Europe (11%) and China (10%). The generation of hydro power

plays only a minor role in the long term in the Middle East (1%), Africa, and India

(each 4%).

Even smaller contributions are assumed for geothermal energy, ocean energy,

and biomass. All three options have comparably high power-generation costs, but

offer complementary characteristics and availabilities that may stabilize future elec-

tricity supply systems. The global average share of geothermal power generation is

assumed to be about 5% in 2050, ocean energy use will contribute another 2%, and

biomass, including co-generation, will achieve a maximum of 6–7% around 2030

and 5% in 2050. The highest share for geothermal power generation is assumed to

occur in Eastern Europe/Eurasia (11%), for ocean energy in OECD Pacific (4%),

and for biomass use in Latin America, Eastern Europe/Eurasia, and OECD Europe

(8–9%). It is predicted that hydrogen will take an increasing share of the remaining

thermal power generation, as a substitute for natural gas in gas turbines and com-

bined cycle gas turbine plants and with increasing contributions from hydrogen fuel

cells to co-generation. Figure 5.4 shows the basic storyline in global power genera-

tion under the 2.0 °C Scenario.

5 Main Assumptions for Energy Pathways

122

5.4.3 RES Deployment for Heat Generation

Heat generation covers a broad range of processes, including district heat (either

from co-generation or public heating plants), direct heating in buildings, and pro-

cess heat in industry, commerce, and other sectors. Different technologies are con-

sidered for each sector, and strong RES expansion is assumed. The increasing heat

extraction from co-generation will trigger increasing district heat use, which will

stabilizes in the long term or decrease by 2050 with the declining heat demand

attributable to ambitious efficiency measures. However, CHP will only contribute

significantly in regions with a tradition of district heating and/or a high heat demand

in the building sector. No strong expansion of heat grids is assumed in regions with-

out existing heat grids and a low demand for space heat.

Electricity for heating is assumed to play a significant role in future energy sys-

tems. In contrast to today’s technology, flexible use is assumed, adjusted to variable

feed-ins from variable renewable generation. This implies the availability of heat

storages, smart operation controls, and flexible electricity tariffs. Electricity can be

used with relatively low investment for space heating and is therefore easily com-

bined with other heating technologies. For example, simple electric heaters can eas-

ily be integrated into heat storage or heating grids. However, more-efficient electric

heat pumps generally require higher investment in both the heat pump itself and in

the heat distribution within the building. Electricity can also be used to provide

process heat in industry at high-temperature levels. The alternative scenarios glob-

ally assume a significant increase in the average electricity share of the final energy

for heating in ‘Industry’ from 6% in 2015 to around 34% in the 2.0 °C Scenario and

37% in the 1.5 °C Scenario by 2050. All regions achieve shares of between 23% and

43%. Electricity shares in the ‘Residential and other’ sector are assumed to grow

from 5% in 2015 to 30% in the 2.0 °C Scenario and 35% in the 1.5 °C Scenario.

0%

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100%

2015 2020 2025 2030 2035 2040 2045 2050

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op
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Hydrogen
Biomass
Geothermal
Hydro
Ocean
CSP
Photovoltaics
Wind onshore
Wind offshore

Fig. 5.4 Development of the average global RES shares in total power generation in the 2.0 °C Scenario

T. Pregger et al.

123

However, the need for process heat at high temperature levels, problems associ-

ated with process integration, and specific process requirements call for additional

process-specific strategies for replacing fossil fuels in the industry sector. As men-

tioned above, electrification is a comparatively efficient strategy. Other strategies

are the use of biomass and hydrogen or—with some limitations—concentrated solar

energy. While hydrogen is used to provide high temperature process heat in this

study, it could—at least partially—be replaced by other synthetic energy carriers,

such as synthetic methane, which can be generated from hydrogen and a (renew-

able) carbon source. This power-to-gas option has the advantage that it can be fed in

into the gas grid, and act in storage and transport. However, energy losses are around

20% higher (compared with hydrogen). As a consequence, synthetic methane is not

taken into account in the scenarios.

Overall, biomass use for heating in the ‘Residential and other’ sector is decreas-

ing as the traditional and currently inefficient use of biomass is replaced by advanced

efficient technologies, and biomass is used in a more efficient way in the energy

system. Thus, the average global share of biomass as a final energy source for heat-

ing will decrease from today’s 34% to 22% by 2050 in the 2.0 °C Scenario and to

17% in the 1.5 °C Scenario, when direct electrification and indirect electrification

(via synthetic gases and fuels) play a stronger role. In contrast, biomass use in the

‘Industry’ sector will increase continuously as the combustion of biomass can be

used to generate high-temperature heat for many industrial processes for which

renewable low-temperature heat sources are unsuitable. The average global share of

biomass for the final energy for heating in the ‘Industry’ sector will increase from

9% in 2015 to 19% by 2050 in the 2.0 °C Scenario and to 14% in the 1.5 °C Scenario.

The largest shares are assumed for Latin America (47%) and Africa (35%) in the

2.0 °C Scenario, where biomass residues are still rather abundant, with much lower

values for the 1.5 °C Scenario (18–21%). In that scenario, biomass as a transition

technology will be avoided due to the earlier development of alternative renewable

technologies and electrification. The lowest shares are assumed for the Middle East

(4%) and China (7%) in both alternative scenarios because of their limited sustain-

able potentials.

Solar collectors are suitable for hot water preparation and for supporting heating

systems using heat storage. In heat grids, large heat storage systems can also be used

to balance the seasonal heat demand and solar generation, and the integration of

solar heat at low costs in the long term. The contributions of solar collectors to the

heat supply are limited by the temperatures that collectors can provide (below

120 °C for traditional collectors and up to 300 °C for concentrated collectors) and

the seasonal variations in regions with significant space heat demand. In the alterna-

tive scenarios, the global average share of solar thermal final energy for heating will

rise to 19% in the ‘Residential and other’ sector. The largest shares are assumed to

be in the Middle East (30–25%), where the abundance of solar radiation can be

exploited by concentrated solar heat applications. All other regions have shares

between 15% and 22%, considering the limited applicability of concentrated sys-

tems. The global average solar share in the ‘Industry’ sector will increase to 16% by

2050. The largest shares are achieved in the Middle East (25%) and Africa (20%)

5 Main Assumptions for Energy Pathways

124

and the lowest shares are assumed in Eastern Europe/Eurasia (9%), followed by the

OECD regions (10–13%).

Heat pumps allow very efficient heat supply. System-wide CO 2 emissions depend

on the CO 2 emissions in the power mix. Because of their generation of low-

temperature heat, heat pumps play a role in space heating in regions with moderate

or cold climates, but large industrial heat pumps can also generate low-temperature

heat for industrial processes and tap the enormous potential of waste heat. A con-

tinuous improvement in the coefficient of performance of heat pumps, which

describes the ratio of useful energy in the form of heat to the required compressor

energy in the form of electricity, in all existing plants from an average of 3 today to

a value of 4 in 2050 is assumed. The application potential of heat pumps (in terms

of MWth) is assumed to be limited by the low temperature of the heat provided, the

increasing share of the grid-connected heat supply, the assumed increase in exten-

sive building insulation, and the low space heating demand in some world regions.

In addition to heat pumps, an increase in deep geothermal energy use is assumed for

the ‘Industry’ sector, with an increase of up to 11% in the global average share of

final energy by 2050 under both alternative scenarios.

Hydrogen use for direct heating is linked to the increasing substitution of natural

gas with hydrogen in all sectors, except transport. This will lead to a hydrogen share

of 12–14% in final energy for heating in the ‘Industry’ sector under the two alterna-

tive scenarios and 3% in the ‘Residential and other’ sector under the 2.0 °C Scenario

and 4% under the 1.5 °C Scenario. The highest hydrogen shares in ‘Industry’ are

assumed for OECD North America (22–23%), followed by other OECD regions,

Eastern Europe/Eurasia, China, and the Middle East with shares of 13%–18%. The

lowest shares, below 5%, are assumed for Latin America and Africa, where biomass

from residues provides a flexible alternative. The highest hydrogen shares in the

‘Residential and other’ sector will be in the Middle East (7–10%), followed by

OECD Pacific (6–8%) and OECD Europe (up to 9%) (Figs. 5.5 and 5.6).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2015 2020 2025 2030 2035 2040 2045 2050

er
ah
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it
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eh
la
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ar
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District heat
Hydrogen
Geothermal/heat pumps
Biomass
Solar collectors
Electricity

Fig. 5.5 Development of the average global RES shares of future heat generation options in ‘Industry’ in the 2.0 °C scenario

T. Pregger et al.

125

5.4.4 Co-generation of Heat and Power and District Heating

Compared with condensing power plants with high efficiency losses due to waste

heat, the co-generation of heat and power (combined heat and power, CHP) allows

the highly efficient use of fossil and renewable fuels. If this approach is made more

flexible with the help of heat storage, and if it is powered by renewable fuels, such

as biomass or hydrogen from renewable electricity, CHP promises to generate not

only renewable power in a flexible way but also to integrate efficiently large shares

of renewable heat into energy systems via large and small district heating systems.

Therefore, co-generation is a particularly good option in regions with high low- to

medium-temperature heat demands (e.g., industrial consumers and space heating).

Our modelling distinguishes between public generation in large CHP plants and

CHP autoproduction. The latter comprises industrial CHP generators but also

smaller plants in the ‘Residential and other’ sector. Power-to-heat ratios, efficien-

cies, and assumed costs reflect these different structural options. The IEA Energy

Balances provides statistical values with which to calibrate the co-generation for

each world region. The scenarios then assume the similar development of these

parameters according to a defined advanced state of technology, with overall effi-

ciencies of 85–90%.

Although absolute electricity generation from CHP will increase in all regions,

all scenarios assume a decreasing power generation share for CHP in the long term

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2015 2020 2025 2030 2035 2040 2045 2050

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District heat
Hydrogen

Geothermal/heat pumps
Biomass

Solar collectors
Electricity

Fig. 5.6 Development of the average global shares of future heat-generation options in the ‘Residential and other’ sector under the 2.0 °C scenario

5 Main Assumptions for Energy Pathways

126

due to the decommissioning of fossil-fuel generators, the limited availability of bio-

mass, and the assumption that heat losses in CHP technologies will be reduced,

leading to higher overall efficiency. Tables 5.13 and 5.14 show the resulting devel-

opments for power and heat as intensities summed across the ‘Industry’ and

‘Residential and other’ sectors. Whereas the absolute power supply from CHP will

increase in all regions, the power-related intensities will decrease. Under the 2.0 °C

Scenario, higher CHP power production is assumed to be higher than in the refer-

ence case, as a balancing option for variable renewable sources. CHP plants will

play a smaller role in the future, particularly in non-OECD countries.

For heat, the situations are more diverse between the scenarios and regions.

While the intensity of heat use from CHP will increase in the OECD regions, it will

decrease in the non-OECD regions. Even though the intensity converges between

OECD and non-OECD regions under all scenarios, the 2.0 °C and 1.5 °C Scenarios

will achieve higher levels of intensity. However, the early reduction in demand in

the 1.5 °C Scenario will preclude any additional demand for CHP.

Table 5.13 Development of power from co-generation per $GDP

kWh/$1000 2015 2020 2025 2030 2035 2040 2045 2050

Change
2050/2015
Reference case
OECD regions 18.8 17.4 15.9 14.3 13.0 12.1 11.3 10.7 −43%
Non-OECD regions 34.0 28.7 23.1 19.3 16.3 14.1 12.1 10.7 −69%
2.0 °C Scenario
OECD regions 18.8 18.0 18.8 18.8 18.1 16.7 15.1 13.6 −28%
Non-OECD regions 34.0 29.4 25.9 23.5 20.9 18.6 16.3 14.3 −58%
1.5 °C Scenario
OECD regions 18.8 18.0 18.1 17.7 16.6 15.3 13.8 12.4 −34%
Non-OECD regions 34.0 29.4 25.9 23.5 20.8 18.7 16.3 14.2 −58%

Table 5.14 Development of heat from co-generation per $GDP

MJ/$1000 2015 2020 2025 2030 2035 2040 2045 2050

Change
2050/2015
Reference case
OECD regions 39.5 39.7 38.8 39.4 40.2 41.8 43.6 47.2 19%
Non-OECD regions 108.8 94.8 78.3 67.9 60.0 54.8 50.1 47.9 −56%
2.0 °C Scenario
OECD regions 39.5 40.5 49.3 58.1 65.2 70.1 75.2 78.4 98%
Non-OECD regions108.8 101.2 92.6 89.8 86.3 83.4 77.6 70.4 −35%
1.5 °C Scenario
OECD regions 39.5 41.0 51.9 58.4 60.4 61.6 63.8 66.0 67%
Non-OECD regions108.8 101.1 93.8 93.3 89.8 84.5 77.9 69.9 −36%

T. Pregger et al.

127

5.4.5 Other Assumptions for Stationary Processes

Although the energy losses in the production of synthetic fuels are significant, these

fuels are expected to be mandatory in the deep decarbonisation scenarios for sectors

and processes in which the direct use of other renewable sources, including renew-

able power, is not technically feasible. We assume an optimistic increase in the

efficiency^1 of electrolytic hydrogen generation, from 66% today to 77% by 2050

(ratio of energy output [H 2 ] to energy input [electricity]). The generation of syn-

thetic fuels (such as Fischer-Tropsch fuels) from hydrogen, using CO 2 as the carbon

source, is assumed to be a complementary option that will allow the decarbonisation

of long-range transport, particularly aviation and international bunkers, without

exceeding the defined maximum sustainable biomass use. Therefore, the assumed

shares of power-to-liquid synfuels in the aggregated biofuel/synfuel fraction of all

transport modes (according to Sect. 6.3.3.) is a result of the sectoral allocation of the

limited biomass potentials in each world region. The assumed efficiency^2 of synfuel

generation will increase from 35% in 2020 to 42% in 2050. As well as in fuel-cell

vehicles, hydrogen can be used to replace natural gas in stationary processes by

2030  in both scenarios, mostly for use in industry and co-generation plants. The

scenarios do not assume a hydrogen economy in the long term. The development of

hydrogen infrastructure is expected to be inefficient, especially for residential use.

However, because hydrogen is assumed to be fed into the gas grid with a share of up

to 100% by 2050, it will also be partly used in the ‘Residential and other’ sector,

which includes various commercial applications.

The allocation of the limited biomass to the power, heat, and transport sectors

differs significantly between the available scenarios. In this study, the biomass in the

alternative scenarios is mainly for use in transport, co-generation, and industry.

Traditional biomass use is strongly reduced, but biomass remains an important pil-

lar of heat supply in the ‘Residential and other’ sector under the assumption that the

most-efficient technologies are implemented. Biofuel production for transportation

remains limited. An increase in overall efficiency of up to 75% is assumed, implying

the use of residues for heat and power generation.

The efficiency of fossil technologies will also increase, especially for gas power

plants. This goes along with the decreasing utilization rates that result from the vari-

able feed-in from renewable energies. Therefore, the scenarios implicitly assume

that the future innovations in all combustion technologies will focus on maximum

efficiency at lower utilization rates. Gas power plants will be used for backup,

requiring low investment but providing high flexibility to the power system.

Therefore, a part of the energy transition is the rapid replacement of inflexible

medium- to base-load power plant capacities with flexible gas power plants.

(^1) Ratio energy output (H 2 ) to energy input (electricity) (^2) Ratio of energy output (synfuels) to energy input (electricity). 5 Main Assumptions for Energy Pathways

128

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Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

T. Pregger et al.

© The Author(s) 2019 131 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_6

Chapter 6

Transport Transition Concepts

Johannes Pagenkopf, Bent van den Adel, Özcan Deniz, and Stephan Schmid

Abstract Detailed background for all transport scenarios and development path-

ways including all key parameters, and story-lines for the 5.0 °C, 2.0 °C and 1.5 °C

transport scenario pathways. Mode specific efficiency improvement over time for

road-, rail- and aviation transport technologies. Explanations of all vehicle tech-

nologies are included in the scenarios, along with the rationale for their selection.

Description of key technology parameters for all relevant transport modes such as

energy demand per passenger, and per freight tonne. Detailed regional breakdown

for developments in regard to transport energy demand for ten world regions and all

transport modes are provided.

6.1 Introduction

Global transport accounted for 23% of total anthropogenic CO 2 emissions in 2010

and those emissions have increased at a rapid rate in recent decades, reaching 7 Gt

in 2010 according to the IPCC Fifth Assessment Report (Sims et  al. 2014 ). The

reason for this steady increase in emissions is that passenger and freight transport

activities are increasing in all world regions, and there is currently no sign that this

growth will slow down in the near future. The increasing energy demand in the

transport sector has mainly been met by greenhouse gas (GHG)-emitting fossil

fuels. Although (battery) electric mobility has recently surged considerably, it has

done so from a very low base, which is why, in terms of total numbers, electricity

still plays a relatively minor role as an energy carrier in the transport sector.

Apart from their impacts on climate, increasing transport levels, especially of

cars, trucks, and aeroplanes, also have unwanted side-effects, including accidents,

traffic jams, the emission of noise and other pollutants, visual pollution, and the

disruption of landscapes by the large-scale build-up of the transport infrastructure.

J. Pagenkopf (*) · B. van den Adel · Ö. Deniz · S. Schmid Department of Vehicle Systems and Technology Assessment, German Aerospace Center (DLR), Institute of Vehicle Concepts (FK), Pfaffenwaldring, Germany e-mail: johannes.pagenkopf@dlr.de; Bent.vandenAdel@dlr.de; oezcan.deniz@dlr.de; stephan.schmid@dlr.de

132

However, road, rail, sea, and air transport are also an integral part of our globalized

and interconnected world, and guarantee prosperity and inter-cultural exchange.

Therefore, if we are to cater to people’s desire for mobility while keeping the econ-

omy running and meeting the Paris climate goals, fundamental technical, opera-

tional, and behavioural measures are required immediately.

In this transport chapter, we discuss potential transport activity pathways and

technological developments by which the requirement that warming does not exceed

pre-industrial levels by more than 2.0 °C or 1.5 °C can be met—while at the same

time maintaining a reasonable standard of mobility.

For our transport scenario modelling, the global warming limits of 2.0  °C and

1.5  °C were translated into transport CO 2 budgets. We structured our scenario

designs around the following key CO 2 -reducing measures^1 :

• Powertrain electrification;
• Enhancement of energy efficiency through technological development;
• Use of bio-based and synthetically produced fuels;
• Modal shifts (from high- to low-energy intensity modes) and overall reductions

in transport activity in energy-intensive transport modes.

These measures are outlined in more detail in the subsequent chapters.

6.2 Global Transport Picture in 2015

The world final energy demand in the transport sector totalled 94,812 PJ^2 in 2015,

according to the IEA Energy Balances (IEA 2017a, b, c). Based on this estimate, we

used TRAEM (Sect. 3.3 to model the freight and passenger transport performance

in our transport model with statistical data and energy efficiency figures.

The following paragraphs outline the 2015 transport structure modelled in

TRAEM, which is the starting point for the subsequent scenario building until 2050.

As can be seen from Fig. 6.1, road passenger transport had the biggest transport

final energy share of 51% in 2015. Most of this comprised individual road passenger

modes (mostly cars, but also two- and three-wheel vehicles), which accounted for

45% of all end energy in the transport sector. In total, road transport (passenger and

freight) accounted for around 90% of total final energy demand for transport.

The majority of total passenger–km (pkm) in passenger transport (around 85%

of total pkm) is contributed by road transport modes. Freight is much more rail-

oriented, and has a 42% share of total tonne–km (tkm), as shown in Fig. 6.2. The

tkm share is much larger than the energy share arising from the much higher energy

efficiency of railways compared with trucks.

Figure 6.3 shows the powertrain split of all transport modes in 2015 (by pkm or

tkm respectively). With a few exceptions, the majority of modes were still heavily

dependent on conventional internal combustion engines (ICE). A small number of

buses had electric powertrains, which were mainly trolleybuses and increasingly

(^1) See also Teske et al. ( 2015 ). (^2) International aviation and navigation bunkers are not included in this figure. J. Pagenkopf et al.

133

also battery- powered electric buses, predominantly in China. China also has a par-

ticularly large number of electric two- and three-wheel vehicles. Almost all battery

electric scooters worldwide were in China. Passenger rail was electrified to a large

extent (e.g., metro and high-speed trains), whereas freight trains were predomi-

nantly not electrified.

OECD America and OECD Europe together make up nearly half the total energy

demand (Fig. 6.4), and China is almost on the same level as OECD Europe, although

it has about twice as many inhabitants as OECD Europe.

6.3 Measures to Reduce and Decarbonise Transport Energy

Consumption

This section describes the measures required to reduce the final energy demand and

decarbonise the transport sector. A variety of actions will be required so that the

transport sector can conform to the <2.0 °C or 1.5 °C global warming pathways. The

45 %

6%

35%

1%

1%

5% 5% 2%

2015

Road Private Passenger

Road Public Passenger

Road Commercial

Freight Train

Passenger Train

Aviation (Domestic)

Aviation (International)

Navigation (Domestic)

Fig. 6.1 World final energy use by transport mode in 2015

0204060

pkm

tkm

pkm/tkm x10^12
Rail Road Aviation (Domestic & International)

Fig. 6.2 Transport mode performances of road, rail, and aviation

6 Transport Transition Concepts

134

set of actions described can be clustered into technical and operational measures

(e.g., energy efficiency increases, drivetrain electrification); behavioural measures

(e.g., shifts to less-carbon-intensive transport carriers and an overall reduction in

transport activity); and accompanying policy measures (e.g., taxation, regulations,

urban planning, and the promotion of less-harmful transport modes). This study

focuses on the 2.0  °C and 1.5  °C Scenarios and sets out the differences between

these scenarios and the business-as-usual 5.0 °C Scenario.

We found that urgent and profound measures must be taken because the emis-

sions reduction window will soon close. Therefore, temporary reductions in fossil-

fuel- related transport activities (in terms of pkm and tkm of passenger cars, trucks,

0%

10%

20%

30 %

40 %

50%

60%

70 %

80 %

90%

100%

Aviation
(Domestic)

Passenger CarBus 2- & 3-WheelerTruck Passenger
Train

Freight Train

ec
na
mr
of
re
p
tr
op
sn
ar
t
re
pt
il
ps
ni
ar
tr
e
wo
P

Internal Combustion Engine Electric

Fig. 6.3 Powertrain split for all transport modes in 2015 by transport performance (pkm or tkm)

32%

15%
13%

7%

7%

6%

6%

5%

5%4%

2015

OECD North America
OECD Europe

China
Latin America

Non-OECD Asia
OECD Pacific

Middle East

Eurasia
Africa

India

Fig. 6.4 Final energy use by world transport in 2015 according to region

J. Pagenkopf et al.

135

and aviation) in OECD countries seem nearly unavoidable until the electrification

(based on renewable energy production) of the transport sector undergoes a

breakthrough.

6.3.1 Powertrain Electrification

Increasing the market penetration of highly efficient (battery and fuel-cell) electric

vehicles, coupled with clean electricity generation, is a powerful lever and probably

also the most effective means of moving toward a decarbonised transport system.

All electric vehicles have the highest efficiency levels of all the drivetrain options.

Today, only a few countries have significant proportions of electric vehicles in their

fleets. The total numbers of electric vehicles, particularly in road transport, are

insignificant, but because road transport is by far the largest CO 2 emitter in overall

transport, it offers a very powerful lever for decarbonisation. In terms of drivetrain

electrification, we cluster the world regions into three groups, according to the dif-

fusion theory (Rogers 2003 ):

• Innovators : OECD North America (excluding Mexico), OECD Europe, OECD

Pacific, and China

• Moderate : Mexico, Non-OECD Asia, India, Eurasia, and Latin America
• Late adopters : Africa and the Middle East.

Although this clustering is rough, it sufficiently mirrors the basic tendencies we

modelled. The regions differ in the speed with which novel technologies, especially

electric drivetrains, will penetrate the market.

6.3.1.1 The 5.0 °C Scenario

The 5.0 °C Scenario follows the IEA Current Policies Scenario (IEA 2017a) until

2040, with extrapolation to 2050. We model only minor electrification over all

transport modes (see Fig. 6.5), with passenger cars and buses making relevant gains

in electric vehicle (EV) shares. For example, we project a share of 30% for battery

electric vehicles (BEV) in China by 2050 due to foreseeable legislation and techno-

logical advancements in that country (Cui and Xiao 2018 ), whereas for the world

car fleet, the share is projected to increase to only around 10%. The growth in the

share of the commercial road vehicle fleet and of the fleet of two- and three-wheel

vehicles held of electric powertrains will be small, as will be the increase in further

rail electrification. Aviation and navigation (shipping) will remain fully dependent

on conventional kerosene and diesel, respectively.

6 Transport Transition Concepts

136

6.3.1.2 The 2.0 °C Scenario

Based on the low market share of BEV observed today (2018), minimal progress in

electrification until 2020 is assumed in the 2.0 °C Scenario. Moving towards 2030,

the innovator regions will experience strong electrification, encouraged by purchase

incentives, EV credit systems, and tightened CO 2 fleet emission targets. Passenger

cars and light commercial vehicles are projected to achieve shares of BEVs in the

regional stocks between 21% and 30%, whereas heavy commercial vehicles and

buses will attain even higher EV fleet shares of between 28% and 52% by 2030.

This will require a massive build-up of battery production capacity in the coming

years. Electric city buses and some trolley trucks will make a significant contribu-

tion to this development. Two- and three-wheel vehicles will be nearly completely

electrified (batteries and fuel cells) in a couple of regions. OECD Pacific will head

the fuel-cell-electric vehicle (FCEV) market introduction, which will account for up

to 6% of passenger cars and light commercial vehicles by 2030, with Japan and

South Korea the main market drivers. Higher shares of FCEV in innovator regions

are more likely in the bus and heavy truck sector, which reach up to 10% in 2030.

In 2050 in the innovative regions, only a minor proportion of vehicles will have ICE

(up to 9%). Passenger cars and light commercial vehicles will predominantly be

electrified, with a BEV share of around 80%. FCEV will also gain a significant

share of 17% in OECD Pacific and OECD North America.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Aviation
(Domestic)

Passenger CarBus 2- & 3-Wheeler TruckPassenger TrainFreig ht Train

Powertrain split per transport performance

Internal Combustion Engine Plug-In Hybrid Electric

Fig. 6.5 Powertrain split for all transport modes in 2050 under the 5.0 °C Scenario in terms of transport performance

J. Pagenkopf et al.

137

Looking ahead to 2050, 60–70% of buses and heavy trucks will probably be (bat-

tery) electric, whereas FCEV will increase their market share to around 37%. The

proportion of buses and heavy trucks that will be BEV in 2050 will be around

60–70%. Two- and three-wheel vehicles will be nearly fully electrified in all regions

(80–100%).

In the moderate regions, the BEV share of road transport vehicles is set in a

range of 1–15%. Except for Latin America, the moderate regions will have an ICE

share of 83% or less for passenger cars. For example, India is progressing with its

current electrification strategy and up to 14% of its passenger cars will be battery-

powered by 2030. It is likely that ICE will dominate buses and trucks in the moder-

ate regions in 2030. Fuel-cell cars now have small shares of 1–2%. Non-OECD Asia

is positively influenced by its innovator neighbour region, OECD Pacific. In the

2.0 °C Scenario, moderate regions will reach BEV shares of up to 67% for passen-

ger cars and light commercial vehicles and between 54% and 65% for buses and

heavy trucks by 2050. Compared with the innovator regions, the moderate regions

will not experience a significant uptake of FCEVs. Africa and the Middle East will

remain mostly dependent on ICE in 2050, with shares of around 90%. Only slow

electrification will occur in Africa, with small and cheap BEVs (Fig. 6.6).

As an example of how different the electrification speeds will be across the world

regions, Fig. 6.7 shows the uptake of electric and fuel-cell drivetrains in buses and

two- and three-wheel vehicles, region by region, in terms of pkm. In 2015, a sub-

stantial proportion (15%) of China’s buses were already electrified. OECD Pacific

and OECD Europe will follow, with substantial electrification after 2020, as will

India, Non-OECD Asia, and Eurasia after 2030. The remaining regions will elec-

trify their fleets predominantly after 2035. Fuel-cell drivetrains will not begin to

penetrate the market to a significant extent until 2025. We project a fleet that is

40–70% electric (battery and trolley) and 10–30% fuel-cell electric by 2050 in the

2.0 °C Scenario.

Figure 6.8 plots the projected electrification of passenger and freight rail in terms

of final energy demand. Substantial electric passenger rail was present in OECD

Europe, OECD Pacific, and China in 2015, and substantial electric freight transport

in OECD Europe and Eurasia. In most other world regions, freight transport by rail

predominantly relied on diesel locomotives.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

OECD North
America
OECD
Europe
Powertrain split per transport performanceOECD PacificChina India Africa

2030

OECD North
America
OECD
Europe
OECD PacificChina IndiaAfrica

2050

Internal Combustion Engine Plug-In Hybrid Electric Hydrogen

Fig. 6.6 Powertrain split (fleet) of passenger cars in selected regions in 2030 ( left ) and 2050 ( right ) under the 2.0 °C Scenario

6 Transport Transition Concepts

138

In most world regions, nearly all rail traffic is projected to be electric after

2040 in the 2.0 °C Scenario. Total diesel consumption in rail operations is projected

to increase slowly until 2030, mainly because railway vehicles have long lifespans,

and once diesel cars are put into operation, they are not replaced overnight.

Furthermore, line electrification usually requires several years of planning and

construction.

Aviation will probably remain predominantly powered by liquid fossil fuels (ker-

osene and bio- and synfuel derivatives) in the medium to long term because of limi-

tations in electrical energy storage. We project a moderate increase in domestic pkm

flown in electric aircrafts starting in 2030, with larger shares in OECD Europe

because the flight distances are shorter than, for example, in the USA (Fig. 6.9).

Norway has announced plans to perform all short-haul flights electrically by 2040

(Agence France-Presse 2018 ).

However, no real electrification breakthrough in aviation is foreseeable unless

the attainable energy densities of batteries increase to 800–1000  Wh/kg, which

would require fast-charging capable post-lithium battery chemistries.

0%

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100%

2015 2020 2025 2030 2035 2040 2045 2050
Ba

tte
ry a

nd

Tro

lle
y el

ec
tric

sh

are

of

tota

l

bus

pk

m

USA Canada Mexico Latin America

Fuel cell eletric share of total bus pkm 2015 2020 2025 2030 2035 2040 2045 2050
OECD Europe Eurasia
Africa Middle East India Chin a Non-OECD Asia OECD Pacific

Fig. 6.7 Battery and trolley electric bus share of total bus pkm in the 2.0 °C Scenario ( left ) and fuel-cell electric bus share of total bus pkm in the 2.0 °C Scenario ( right )

0

500

1,00 0

1,50 0

2,00 0

2,50 0

passenger rail freight rail

2015 2020 2025 2030 2035 2040 2045 2050

Final energy demand for rail transport in PJ

Total Passenger Rail Diesel Electric Biofuel / Synfuel

2015 2020 2025 2030 2035 2040 2045 2050
Total Freight Rail

Fig. 6.8 Electrification of passenger rail ( left ) and freight rail ( right ) under the 2.0 °C Scenario (in PJ of final energy demand)

J. Pagenkopf et al.

139

6.3.1.3 The 1.5 °C Scenario

In the 1.5  °C Scenario, an earlier and more rapid ramp-up of electric powertrain

penetration is required than in the 2.0 °C Scenario and the innovative regions will

be at the forefront. The moderate regions will also need to electrify more rapidly

than in the 2.0 °C Scenario, but will end up with only a minimally higher share by

2050. In the passenger car sector in particular, plug-in hybrid electric vehicles

(PHEVs) will ensure a sharp reduction in conventional combustion engine vehicles

between 2030 and 2050. In the late adopter regions, there is no difference between

the 2.0  °C and 1.5  °C Scenarios. The phasing out of internal combustion engine

(ICE) vehicles will occur more quickly under the 1.5  °C Scenario than under the

2.0 °C Scenario (Fig. 6.10).

6.3.2 Mode-Specific Efficiency and Improvements Over Time

In passenger transport, trains and buses are much more energy efficient per pkm

than passenger cars or aeroplanes. This situation does not change fundamentally if

only electric drivetrains are compared (Fig. 6.11). It is apparent that railways and

especially ships are clearly more energy efficient than trucks in transporting freight

(Fig. 6.12). The 2015 figures are the starting point for a more detailed discussion,

mode by mode, later in this chapter, and these figures are the basis for the rationale

of our discussion in terms of modal shift (Chap. 6, Sect. 4 ). The efficiency data are

0%

2%

4%

6%

8%

10%

12%

2015 2020 2025 2030 2035 2040 2045 2050

Electric share of domestic aviation pkm

USA
Canada

Mexico

Latin America
OECD Europe

Eurasia

Africa
Middle East

India

China
Non-OECD Asia

OECD Pacific

Fig. 6.9 Electricity-performed pkm in domestic aviation under the 2.0 °C Scenario

6 Transport Transition Concepts

140

based on literature-reported and transport operator information. The efficiency lev-

els in terms of pkm or tkm depend to a large extent on the underlying capacity uti-

lization of the vehicles, which differs between world regions. The numbers are

average values and differences are evaluated at the regional level.

In addition to powertrain electrification, there are other potential improvements

in energy efficiency, and their implementation will steadily improve energy intensity

over time. Regardless of the types of powertrains and fuels used, efficiency improve-

ments on the MJ/pkm or MJ/tkm level will result from (for example):

• Reductions in powertrain losses through more-efficient motors, gears, power

electronics, etc.;

• Reductions in aerodynamic drag;

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

MJ/pkm (2015)

Fig. 6.11 Final energy demand in urban and inter-urban passenger transport modes in 2015 (world averages)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Powertrain split of world passenger car fleet 2015 2020 2025 2030 2035 2040 2045 2050
Internal Combustion Engine Plug-In Hybrid Electric Hydrogen

2015

2.0°C 1.5°C

2020 2025 2030 2035 2040 2045 2050

Fig. 6.10 Powertrain split of the world passenger car fleet in the 2.0 °C Scenario ( left ) and 1.5 °C Scenario ( right )

J. Pagenkopf et al.

141

• Reductions in vehicle mass through light-weighting;
• The use of smaller vehicles;
• Operational improvements (e.g., through automatic train operation, load factor

improvements).

The measures are discussed in the following mode-specific sub-chapters.

6.3.3 Road Transport

6.3.3.1 Passenger Cars

As of 2017, 99% of the passenger cars produced worldwide were estimated to be

equipped with an ICE: the majority of them gasoline or diesel (95%), 3.4% hybrid

electric vehicles (HEV), and 0.7% PHEV (BCG 2017 ). Only about 0.9% of the cars

sold were pure battery electric vehicles (BEV). There are several options for energy

efficiency improvements. The fuel consumption reduction potential of petrol

engines as a result of engine improvements and hybridization is around 25–30%,

and it is around 15–20% for diesel engines (van Basshuysen and Schäfer 2015 ).

Maximum efficiencies of 38–40% can be reached by ICE (Schäfer 2016 ), whereas

electric drivetrains have efficiencies of 80–85% (including [re]charging losses).

Besides the reduction in engine losses, both lightweight construction and reductions

in rolling resistance will result in additional fuel savings.

Hybrid systems increase the complexity of powertrains, resulting in higher

masses and higher costs, but they offer additional fuel-saving potentials. Energy can

thus be recovered by recuperation during braking in hybridized and all-electric

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

MJ/tkm

(2015)

Fig. 6.12 Final energy demand in freight transport modes in 2015 (world averages)

6 Transport Transition Concepts

142

vehicles. In BEV and FCEV, advanced battery technologies can reduce the overall

vehicle mass. However, post-lithium technologies, such as lithium–sulfur and solid-

state batteries with increased energy densities and lower systems masses compared

with today’s Li-ion battery technologies, will probably not enter the transport sector

before 2030 (Schmuch et al. 2018 ).

In total, we project a 1% increase in annual efficiency on a per passenger/km

basis (the same for all drivetrains) for the 5.0  °C and 2.0  °C Scenarios. The effi-

ciency improvements for 2050 in the 2.0 °C Scenario will be achieved in 2040 under

the 1.5 °C Scenario (Fig. 6.13).

6.3.3.2 Light and Heavy Freight Vehicles

Like passenger cars, trucks are currently driven almost exclusively by conventional

ICE. With a market share of 84% of diesel-fuelled vehicles in 2015 (IEA 2017c),

the global fleet of trucks was predominantly operating on diesel (IEA 2017b). In

some regions, such as the Middle East, trucks are also powered by gasoline to a

considerable degree. Electric drivetrains will enter the truck sector gradually in

coming years because of changes in exhaust emissions legislation and because the

higher efficiency of electric drivetrains compared with ICE will offer economic

advantages to road carriers (Fig. 6.14). Compared with diesel-powered trucks, fuel-

cell electric drivetrains in trucks can substantially reduce the energy intensity per

tkm, and allow higher operating ranges than battery-powered trucks. To achieve

rapid improvements in energy efficiency in the truck sector, the hybridization of

diesel powertrains, especially those operating in stop-and-go intensive urban envi-

ronments, is promising (Burke and Hengbing 2017 ; Lischke 2017 ). However, after

2030, the hybrid diesel powertrain will be seen merely as a transitional technology

before the advent of fully electric powertrains (overhead catenary, battery electric,

and fuel-cell electric). Therefore, the development of battery recharging and hydro-

gen refuelling infrastructures will require massive investments in coming years. In

1.7
1.4

1.9

0.5

0.8

1.1
0.9

1.3

0.3

0.5

0.0

0.5

1.0

1.5

2.0

Gasoline Diesel Natural GasElectricHydrogen

MJ/pkm

Fig. 6.13 World average energy consumption development for passenger cars per powertrain in 2015 ( left ) and 2050 ( right )

J. Pagenkopf et al.

143

the commercial sector, it is likely that many electrical charging stations will be set

up on the private grounds of logistics operators. The market growth of BEV in the

truck sector will require considerable public spending on the installation of over-

head catenary lines along major highways for trolley trucks (hybridized with diesel,

fuel cells, or batteries). The first pilot lanes are being developed in Germany,

Sweden, and California (USA) (Siemens 2017 ).

6.3.3.3 Buses

State-of-the-art city buses operate on ICE (diesel or gas) or are trolley buses (all-

electric or hybridized with an additional battery or diesel motor). Diesel hybrids

have entered the city bus market and constitute a large proportion of the bus fleets

in a range of cities today. The short distances between stations, small operating

radii, and moderate daily mileages make the use of batteries in city buses a viable

option, complemented by fuel cells for routes with higher ranges or difficult ter-

rains. Battery electric buses are between the prototype/experimental stage and a

mature technology. In China, battery electric buses are already an integral part of

public bus transport systems, and the city of Shenzen has had a 100% electric bus

fleet since 2016 (over 16,000 battery electric buses) (Sisson 2018 ). Fuel-cell electric

buses still lag behind battery electric buses in terms of numbers, but are increasing.

In 2017, about 80 fuel-cell electric buses were in operation in Europe (Element

Energy Ltd. 2018 ) and 26 in the USA (Eudy and Post 2017 ). Full battery operation

is more difficult to achieve in regional and long-distance buses and coaches, so the

4

3.5

3

4.1

1.6

2.3

3.4

2.8
2.6

3.2

1.3

1.9

0

0.5

1

1.5

2

2.5

3

3.5

4

Gasoline Diesel Hybrid Diesel CNG/LPG Electric Hydrogen

MJ/tkm

Fig. 6.14 Average global energy intensities of truck drivetrain technologies in 2015 and 2050

6 Transport Transition Concepts

14 4

uptake of full battery electric and also fuel-cell powertrains will be slower than in

city buses. Diesel-powered buses will remain in the market longer, complemented

by diesel hybrid powertrains. Ultimately, fuel-cell-powered inter-urban coaches

will probably be more common than fuel-cell-powered city buses. In our model, we

divided the regions into innovators (i.e., China and OECD countries) and all the

other regions lagging behind the innovators. We also identified a clear trend towards

electrification.

We project a 0.5% (diesel, diesel-hybrid and natural gas) to 0.8% (electric and

fuel cell hydrogen electric) increase in efficiency on a per passenger per km basis in

all three scenarios (Fig. 6.15).

6.3.3.4 Two- and Three-Wheel Vehicles

Two-wheel vehicles are probably the most important component of everyday traffic

in large parts of Asia and Africa. The drivetrain efficiencies of e-scooters are

reported to be 1.2–3 kWh/100 vehicle–km. China is by far the biggest market for

electric scooters in the world, with more than 200 million electric scooters on the

road by 2015, that translates to an electric share of about 70%.

Three-wheel vehicles (also country-specifically called ‘rickshaws’ or ‘tuk-tuks’)

have at least two, and usually three or more seats, and are often overloaded in daily

traffic. India alone is reported to have 2.5 million rickshaws on the road, each travel-

ling 70–150  km/day (Abu Mallouh et  al. 2010 ). Most three-wheel vehicles are

fuelled by gasoline and some by liquid petroleum gas (LPG), although some com-

0.51

0.41

0.61

0.26

0.43 0.43

0.34

0.51

0.20

0.33

0.0

0.2

0.4

0.6

Gasoline/Diesel Hybrid
(Gasoline/Diesel)

Natural Gas Electric Hydrogen

MJ/pkm

Fig. 6.15 Average global energy intensities of bus drivetrain technologies in 2015 ( left ) and 2050 ( right )

J. Pagenkopf et al.

145

munities incentivize the conversion of two-stroke engines to battery electric three-

wheel vehicles. Electric tuk-tuks are increasingly emerging in South-East Asia,

together with battery swap stations and solar-powered rickshaws (Moran 2018 ;

Reddy et al. 2017 ). Thailand has announced that it plans to convert all existing two-

stroke- powered tuk-tuks to battery electric powertrains within 5  years (Coconuts

Bangkok 2017 ). In India, too, plans are repeatedly announced to electrify all new

two- and three-wheel vehicles within the next two decades (Ghoshal 2017 ).

The efficiency of battery electric drivetrains is much better than that of conven-

tional two-stroke engines in two- and three-wheel vehicles (Fig. 6.16). We project a

0.5% annual increase in efficiency on a per pkm basis (the same for all drivetrains)

in all three scenarios.

6.3.3.5 Rail Transport

No other transport mode is more suited to operate electrically than railways. Urban

rail systems, for instance, are invariably electric. Electric trains consume about

60–70% less energy than diesel trains when their final energy use is compared (on

catenary and tank levels, respectively). According to the International Railway

Association, about 32% of the worldwide rail network was electrified in 2015 (UIC

2017 ). However, in terms of transport performance (pkm or tkm), the ratio of elec-

tric to diesel is higher because electrified rail lines usually experience more traffic

than non-electrified lines. The electrification of rail lines with overhead catenaries

has been the state-of-the-art technology for decades and new lines are almost exclu-

sively equipped with overhead power lines right from the start.

However, line electrification, especially of existing lines, is often difficult to

achieve due to unsettled and complex right-of-way issues, narrow tunnel diameters,

or simply because line electrification is not economically viable due to low line

utilization. The use of on-board batteries for mixed electrified/non-electrified lines

and shunting operations and the use of fuel-cell hybrid powertrains for longer lines

with little or no electrification whatsoever could be feasible fully electric alterna-

tives to diesel power or full-line electrification. (Fuel-cell electric trains have not

been modelled in this research).

0.82

0.98

0.09

0.28

0.69

0.83

0.07

0.21

0.00

0.25

0.50

0.75

1.00

Gasoline/Diesel NG/LPGElectric Hydrogen

2-Wheeler

0.68

0.82

0.08

0.23

0.57

0.69

0.06

0.18

Gasoline/Diesel NG/LPGElectricHydrogen

MJ/pkm

3-Wheeler

Fig. 6.16 Average global energy intensities of two-wheel vehicles ( left ) and three-wheel vehicles ( right ) by drivetrain technology in 2015 ( left bar ) and 2050 ( right bar )

6 Transport Transition Concepts

146

We project a 0.5% annual increase in efficiency on a per passenger per km basis

and a 0.8% annual increase in efficiency on a per tonne per km basis (the same for

all drivetrains) in all three scenarios (Fig. 6.17).

The actual efficiency depends on the type of train and the world region. These

differences are considered in every transport sub-model and are exemplified in

Fig. 6.18 for freight and passenger trains.

6.3.3.6 Water and Air Transport

Inland navigation will probably remain predominantly powered by ICE in the next

few decades. Therefore, we did not model the electrification of inland navigation

vessels. However, pilot projects using diesel hybrids, batteries, and fuel cells are in

preparation (DNV GL 2015 ). We assumed the same increase in the share of bio- and

synthetic fuels over time as in the road and rail sectors.

In aviation, energy efficiency can be improved by measures such as winglets,

advanced composite-based lightweight structures, powertrain hybridization, and

enhanced air traffic management systems (Madavan 2016 ; Vyas et  al. 2013 ). We

project a 1% annual increase in efficiency on a per pkm basis.

0.31

0.38

0.12

0.18

0.35

0.22

0.26

0.08

0.13

0.24

0.00

0.10

0.20

0.30

0.40

D - FreightNG - FreightE - FreightE - Intermodal
Freight
E - LDHV Freight

MJ/tkm

0.76

1.00

0.24

0.33 0.28

0.64

0.84

0.20

0.28 0.23

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

D - REG/IC NG - REG/IC E - REG/IC E -Metro/LRT E - HST

MJ/pkm

Fig. 6.17 MJ/tkm of freight rail trains ( left ) and MJ/pkm of passenger rail trains ( right ) for 2015 ( left ) and 2050 ( right )

0

0.2

0.4

0.6

0.8

1

1.2

r1.4
otc
af
yc
nei
ciff
eci
fic
eps
noi
ge
R 0

0.25

0.5

0.75

1

1.25

1.5

Region

specific

efficiency

factor

Fig. 6.18 Region-specific MJ/tkm and MJ/pkm in 2015 and 2050 for freight rail trains ( left ) and passenger rail trains ( right )

J. Pagenkopf et al.

147

6.3.4 Replacement of Fossil Fuels by Biofuels and Synfuels

The use of biofuels in the transport sector offers a potential lever to reduce the CO 2

emissions from fossil fuels. Biofuels can be used either as a direct drop-in or as

admixtures to fossil fuels. Biofuels are widely used, especially in Latin America

(e.g., E85  in Brazil, a blend of 85% ethanol and 15% gasoline). Biofuels will be

replaced by synthetic fuels (synfuels) within the next few decades.

In the three scenarios, we use region-specific shares of bio- and synfuels to

replace fossil fuels such as diesel, gasoline, and kerosene. Figure 6.19 shows the

scenario-specific band-widths. In the 5.0 °C Scenario, Latin America will increase

its proportion of bio- and synfuels to around 15%, whereas in the Middle East,

Mexico, and Eurasia will even not reach a 0.5% share by 2050. Bio-fuel and

especially synfuel shares will remain constant in the 5.0 °C Scenario, whereas they

will increase in the 2.0  °C Scenario between 2030 and 2050 and in the 1.5  °C

Scenario from 2020 onwards.

6.3.5 Operational Improvements and Novel Service Concepts

In addition to technical improvements, such as the energy efficiency savings

described in previous chapters, behavioural changes can potentially reduce energy

demand and help decarbonise the transport sector as a whole. These measures

include, among other things, the efficient operation of vehicles, for example by

5.0°C 2.0°C 1.5°C

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2015 2020 2025 2030 2035 2040 2045 2050

Fig. 6.19 Shares of bio- and synfuels in all world regions under all scenarios

6 Transport Transition Concepts

148

increasing the occupancy rates of moving vehicles, reductions in mileages, and

reductions in vehicle stocks.

6.3.5.1 Passenger Transport

The classic fixed boundary between private and public transport will blur in the

future as novel platforms and sharing concepts emerge. This trend will potentially

reduce the number and usage of privately owned vehicles. Private car ownership is

generally becoming less a ‘must’ for many people in the car-oriented OECD regions.

Driven by digitization, app-based cars and ride-sharing concepts are emerging.

They include traditional car sharing (e.g., Zipcar), free-floating car sharing busi-

nesses (e.g., car2go), and ride-hailing mobility concepts (e.g., Uber, lyft). In Europe,

private ride-sharing mobility platforms, such as BlaBlaCar , are seen as alternative

passenger car usership schemes. All shared-use mobility concepts have in common

that they reduce the vehicle stock and increase occupation rates on trips. To own a

car is no longer considered a status symbol, but has become simply a mode of trans-

port in the Western world.

Flexibility and passenger-friendly usability is becoming increasingly important

to users of public transport. Various app-based on-demand mobility services are

currently being tested in pilot projects—for example, in the German city of

Schorndorf (Brost et  al. 2018 ). Under these business models, innovative vehicle

concepts are tested for suitability. Automated battery electric (mini) buses can make

public transit more flexible. Their modularity also allows them to connect to other

modes of transport, such as rail and air transport, and thus opens up fundamentally

new transport chains that are more energy efficient than traditional passenger car

usage. However, decision-makers and transport planners must consider the poten-

tially detrimental rebound effects of highly personalized automated mobility

(Pakusch et al. 2018 ). For example, an increase in total energy consumption may

occur as a result of the higher transport demand arising from transport options that

are seen as more attractive than more-energy-efficient mass transit systems or

cycling. Therefore, novel transport concepts could cancel out the gains in energy

efficiency on a per vehicle level.

6.3.5.2 Freight Transport

The automatization of road freight traffic can reduce fuel consumption. The tech-

nology for partially automated trucks is already available and being tested to make

it widely available. In the Society of Automotive Engineers (SAE) automation level

3 (SAE 2018 ), (multi-brand) truck platooning has become possible, which is a

driver-assistance technology (Janssen et  al. 2015 ). With communication between

the first truck and the following trucks, the control of the following vehicles is

J. Pagenkopf et al.

149

handed over to the leading vehicle. Thus, the following vehicles are driving in the

slipstream and lower air drag is achieved, which can reduce energy consumption by

up to 5% (Daimler 2018 ).

In urban freight transport, last-mile delivery concepts based on light commercial

vehicles can help reduce fuel consumption. By fragmenting the transport routes into

the main leg and urban traffic (last mile), different vehicle types and modes can be

used more efficiently for specialized transport tasks. Like light commercial vehicles

(electric), cargo bikes could shape the urban freight traffic of the future as they do

already today in many countries in Asia.

Further novel logistic concepts, such as the use of drones for delivery and decen-

tralized production using 3D printers, will help to reduce the overall transport

demand and thus energy consumption through more-efficient operational

solutions.

6.4 Transport Performance

We outline achievable 2.0 °C and 1.5 °C Scenario paths that will allow us to achieve

very ambitious emission reduction targets. The levels of pkm and tkm for every

world region in 2015 were calibrated against statistical data, the literature, and our

own estimates when concrete data were not available.

To complement the anticipated technologically driven mode-inherent improve-

ments in efficiency that we took as the input for our modelling in the previous sec-

tions of this chapter, we now describe the scenario assumptions in terms of the

transport mode choices and transport demand. The main idea is to model low-

emission pathways that shift from fossil-dependent, low-energy-efficiency modes

toward more-energy-efficient and electrified modes. We also look at the general

level of performance of transport modes and suggest region-specific development

pathways. We first outline the scenario-specific pkm and tkm pathways to 2050 and

then discuss the rationales for the mode choices that will result in the transport per-

formance projections.

For the 5.0 °C Scenario, we extrapolate current trends in transport performance

until 2050. In relative terms, the transport performance of all transport carriers will

increase from current levels. Aviation, passenger car, and commercial road transport

are particularly projected to grow strongly (Fig. 6.20). These modes consume more

energy than trains, ships, and buses, as discussed in Sect. 6.3.2. Even if the full

efficiency potential of these transport modes is realized, energy intensity per pkm or

tkm will remain higher than that of trains, ships, or buses.

In the 2.0 °C Scenario and 1.5 °C Scenario, we project a strong increase in rail

traffic (starting from a relatively low level) and a slower growth or even a reduction

in the use of the other modes in all world regions (Fig. 6.21). The next two sections

describe the specific changes and developments for each type of passenger and

freight transport.

6 Transport Transition Concepts

150

6.4.1 Passenger Transport Modes

To reduce transport-related CO 2 emissions, a shift towards low-energy-intensity

modes of transport is required for both ambitious climate-protection scenarios.

Travelling by rail is the most energy-efficient form of transport, and therefore we

suggest a strong shift from domestic aviation to trains, especially high-speed trains

and magnetic levitation trains. The assumed shifts from domestic aviation to trains

are shown in Table 6.1. The mode shift potential differs, depending predominantly

on the country-specific distance between origin–destination pairs. The shifts from

international aviation to trains were not analysed because the potential is lower

0%

50 %

100%

150%

200%

250%

300%

350%

2015 2020 2025 2030 2035 2040 2045 2050

Transpor

t-

Evolutio

n

Passenger Train Aviation (Domestic) Passenger Car Bus
2- & 3-Wheeler Freight Train Truck

Fig. 6.20 Relative growth in world transport demand (2015  =  100% pkm/tkm) in the 5.0  °C scenario

0%

50%

100%

150%

200%

250%

300%

350%

2015
Passenger train Aviation (Domestic)

2020 2025 2030 2035 2040 2045
Passenger CarBus 2- & 3-Wheeler Freight Train Truck

2050

Transport

-E

volution

2015 2020 2025 2030 2035 2040 2045 2050

2.0°C1.5°C

Fig. 6.21 Relative growth in world transport demand (2015  =  100% pkm/tkm) in the 2.0  °C Scenario ( left ) and 1.5 °C Scenario ( right )

J. Pagenkopf et al.

151

(although not zero). The maximum global shift from domestic aviation to trains will
be stronger in the 1.5 °C Scenario than in the 2.0 °C Scenario.
In the urban context, investments in public transit systems and limitations on the
use of private cars are cornerstones of a more-energy-efficient transport system. The
increased use and integration of homes and offices, and video conferencing can
reduce traffic. With further urbanization, two- and three-wheel vehicles are suitable
for travelling small distances quickly. A shift to small and medium cars and away
from larger cars and SUVs will also allow lower energy intensity. Whereas occu-
pancy rates remain steady for the passenger car sector in the 2.0 °C Scenario, in the
1.5 °C Scenario, we assume a significant increase in occupancy rates. Ultimately,
pkm will stabilize, whereas vehicle–km (vkm) will decline in response to incentives
such as high-occupancy vehicle lanes and novel ride-sharing services.
Figure 6.22 shows the development of pkm in all scenarios for sample world
regions. In the 2.0  °C Scenario, pkm in the OECD countries will predominantly
remain on the same level in 2050 as in 2015 due to saturation and sufficiency effects.
In all the other world regions, we project a growth in pkm, reflecting economic
catch-up processes in the developing world induced by increases in population and

Table 6.1 Pkm “per km” shift from domestic aviation to trains (in %)

2015 2020 2025 2030 2035 2040 2045 2050
2.0 °C scenario 0 0 0.5–0.75 0.7–1.1 1–1.7 1.4–2.5 1.9–3.8 2.7–5.7
1.5 °C scenario 0 2 4 6 8 10 12 14

0%

50%

100%

150%

200%

250%

300%

350%

2015 2020 2025

USA OECD Europe India China Africa

2030 2035 2040 2045 2050

pkm-Evolution

5.0 ̊C 2.0 ̊C 1.5 ̊C

Fig. 6.22 Regional pkm development

6 Transport Transition Concepts

152

GDP.  As can be seen for the 1.5  °C Scenario, India, which represents a strongly

growing economy, will double its pkm by 2050, whereas OECD areas, such as the

USA and Europe, will experience a decline. Although China is expected to experi-

ence continuous economic growth over the next few decades, pkm will rise slowly

compared with that in the 5.0 °C Scenario.

Pkm in absolute numbers in 2050 will be highest in the 5.0 °C Scenario. It will

also increase in the 2.0 °C and 1.5 °C Scenarios, but at a slower rate (Figs. 6.23 and

6.24).

Looking more closely at the 2.0  °C Scenario, the transport modes will evolve

differently in the world regions, both quantitatively and in relative terms (Fig. 6.25)

due to the diverse mobility patterns. For example, Africa has a high bus share in

total pkm today, whereas OECD Europe has a high passenger car share (LDV), but

pkm must decrease by 2020 and in subsequent years to meet the CO 2 reduction

targets because fleet electrification will not be able to keep up. In parallel, rail pkm

20

40

60

80

100

120

2015 2020 2025 2030 2035 2040 2045 2050

pk

m

x 10

12

LDF ADV REF

Fig. 6.23 World pkm development in all scenarios

0

10

20

30

40

50

60

70

80

90

2015 2020 2025 2030 2035 2040 2045 2050

pk

m

x 10

12

Passenger Train Passenger Car Bus
2- & 3-Wheeler Aviation (Domestic) Aviation (International)

Fig. 6.24 World pkm development in the 2.0 °C Scenario

J. Pagenkopf et al.

153

will increase strongly until 2050 and this will compensate, in part, the decline in

passenger car pkm. Population and GDP are very likely to catch up in Africa and

will result in a sharp rise in mobility demand. We project that most of this rise to be

covered by informal and formal public transport systems, with buses and mini-

buses. In China, the pkm split among modes is more balanced. All modes are pro-

jected to rise in pkm in the future.

6.4.2 Freight Transport Modes

Total freight activity is modelled to increase strongly in the 5.0  °C Scenario and

more slowly in the 2.0 °C Scenario (Fig. 6.26). In the 1.5 °C Scenario, freight trans-

port activity in 2050 is projected to remain at the 2015 level. Freight intensity will

stagnate or decrease in the 1.5 °C Scenario in the OECD countries and increase in

other regions, such as China and India (Figs. 6.26 and 6.27).

0

2.5

5

7.5

10

12.5

15

20152020 2025 20302035

Passenger Train Passenger Car Bus 2- & 3- wheeler Aviaon (Domesc)Aviaon (Internaonal)

2040 2045 2050

pkm

in trillion

20152020 2025 20302035 20402045 2050

20152020 20252030 2035 20402045 2050

OECD Europe Africa

China

Fig. 6.25 Pkm development in OECD Europe ( left ) Africa (middle), and China ( right ) in the 2.0 °C Scenario

6 Transport Transition Concepts

154

0

1

2

3

4

5

6

2015 2020 2025 2030 2035 2040 2045 2050

tk

m

x 10

12

LDF ADV REF

Fig. 6.26 World tkm development in all scenarios

2015

400%

350%

300%

250%

200%

tkm-Evolution
150%

100%

50%

0%
2020 2025 2030 2035 2040 2045 2050

OECD Europe

5.0°C2.0°C 1.5°C
USA India ChinaAfrica

Fig. 6.27 Regional tkm development

J. Pagenkopf et al.

155

We modelled the shift from high-energy-intensity modes to low-energy-intensity

modes, especially from road to rail freight. This will require substantial investments

in additional rail infrastructure. Table 6.2 shows the assumed global tkm shift from

truck to train in the 2.0 °C and 1.5 °C Scenarios. Heavy freight trucks (HFT) are

more likely to operate over long-haul distances, and are therefore more suitable for

the shift to rail freight than light freight trucks (LFT) or medium freight trucks

(MFT). In the 2.0 °C Scenario, we assume an average shift of 18% for HFT, whereas

in the 1.5 °C Scenario, the shift is projected to reach up to 27%. The ramp- up of

shift potential is slow, because the provision of rail and terminal infrastructures and

rolling stock will require considerable time.

We modelled tkm for truck (road) and rail freight transport. In the 5.0  °C

Scenario, transport activity is projected to increase clearly until 2050. In the 2.0 °C

and 1.5 °C Scenarios, this increase will be slower and the tkm on rail will exceed the

road tkm in numbers by 2050 (Fig. 6.28).

In the 2.0 °C Scenario, road tkm will decrease in the OECD countries and stag-

nate or increase slightly in China (Fig. 6.29). Rail tkm is projected to increase in all

other world regions (Fig. 6.29). Rail tkm in China will temporarily decrease slightly

because of an anticipated medium-term decline in coal transport (Fig. 6.30).

In 2015, rail’s share of total tkm differed between regions, but will increase in the

2.0 °C Scenario in all regions except India, where road tkm will increase more than

rail tkm (Fig. 6.31).

Energy Scenario Results

Our scenario modelling involves merging the transport performance for all modes

and powertrains with specific energy demands, and yields the accumulated demands

for electricity, hydrogen, gas, and liquid fuels across all world regions between 2015

and 2050. The transport energy scenario results are outlined in Chap. 8.

Table 6.2 Global tkm shifts from truck to train in the 2.0 °C and 1.5 °C Scenarios (in %)

2015 2020 2025 2030 2035 2040 2045 2050
LFT (2.0) 0 0 0 0 0 0 0 0
LFT (1.5) 0 1 4 6 7–8 9–10 11–12 13–14
MFT (2.0) 0 1 2 3 4 5 6 7
MFT (1.5) 0 2–3 5–10 9–14 13–18 13–19 13–19 13–18
HFT (2.0) 0 3 5 8 10 13 15 18
HFT (1.5) 0 2–3 8–18 12–22 16–27 16–27 16–27 18–27

6 Transport Transition Concepts

156

50
45
40
35
30
25
20
15
10
5
0
2015 2020 2025 2030 2035 2040 2045 2050

2015 2020 2025 2030 2035 2040 2045 2050

2015 2020 2025 2030 2035 2040 2045 2050

5.0°C 2.0°C

1.5°C

tkm

X10

12

Freight train Road Commerical

Fig. 6.28 World tkm development in the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios

0

1

2

3

4

5

6

2015 2020 2025 2030 2035 2040 2045 2050

tkm

x 10

12

OECD North America
OECD Europe
China
Latin America
Non-OECD Asia
OECD Pacific
Middle East
Eurasia
Africa
India

Fig. 6.29 Road tkm in the 2.0 °C Scenario

J. Pagenkopf et al.

157

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0

1

2

3

4

5

6

2015 2020 2025 2030 2035 2040 2045 2050

tk

m

x 10

12

OECD North America
OECD Europe
China
Latin America
Non-OECD Asia
OECD Pacific
Middle East
Eurasia
Africa
India

Fig. 6.30 Rail tkm in the 2.0 °C Scenario

0%

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100%

20152020202520302035204020452050

Share of tkm via

rail

USA
Mexico

Canada

Latin America

OECD Europe
Eurasia

Africa

Middle East

India

China

Fig. 6.31 Share of rail tkm in total rail + road tkm in the 2.0 °C Scenario

6 Transport Transition Concepts

158

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Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

6 Transport Transition Concepts

© The Author(s) 2019 161 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_7

Chapter 7

Renewable Energy Resource Assessment

Sven Teske, Kriti Nagrath, Tom Morris, and Kate Dooley

Abstract Literature overview of published global and regional renewable energy

potential estimates. This section provides definitions for different types of RE

potentials and introduces a new category, the economic renewable energy potential

in space constrained environments. The potential for utility scale solar and onshore

wind in square kilometre and maximum possible installed capacity (in GW) are

provided for 75 different regions. The results set the upper limits for the deployment

of solar- and wind technologies for the development of the 2.0 °C and 1.5 °C energy

pathways.

There is a wide range of estimates of global and regional renewable energy poten-

tials in the literature, and all conclude that the total global technical renewable

energy potential is substantially higher than the current global energy demand

(IPCC/SRREN 2011 ). Furthermore, the IPCC has concluded that the global techni-

cal renewable energy potential will not limit continued renewable energy growth

(IPCC/SRREN 2011 ). However, the technical potential is also much higher than the

sustainable potential, which is limited by factors such as land availability and other

resource constraints.

This chapter provides an overview of various estimates of global renewable

energy (RE) potential. It also provides definitions of different types of RE potential

and presents mapping results for the spatial RE resource analysis (see Sect. 1.3 in

Chap. 3)—[R]E-SPACE. The [R]E-SPACE results provide the upper limit for the

deployment of all solar and wind technologies used in the 2.0  °C and 1.5  °C

Scenarios.

S. Teske (*) · K. Nagrath · T. Morris Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au; kriti.nagrath@uts.edu.au; tom.morris@uts.edu.au

K. Dooley Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: kate.dooley@unimelb.edu.au

162

7.1 Global Renewable Energy Potentials

The International Panel on Climate Change—Special Report on Renewable Energy

Sources and Climate Change Mitigation (IPCC-SRRN 2011 ) defines renewable

energy (RE) as:

“ (...) any form of energy from solar, geophysical or biological sources that is replenished
by natural processes at a rate that equals or exceeds its rate of use. Renewable energy is
obtained from the continuing or repetitive flows of energy occurring in the natural environ-
ment and includes low-carbon technologies such as solar energy, hydropower, wind, tide
and waves and ocean thermal energy, as well as renewable fuels such as biomass.”

For the development of the 2.0 °C and 1.5 °C Scenarios, an additional renewable

energy potential—the economic renewable energy potential in a space-constrained

environment (Sect. 3.2 in Chap. 3)—has been analysed and utilized in this study.

The theoretical and technical potentials of renewable energy are significantly

larger than the current global primary energy demand. The minimum technical

potential of solar energy, shown in Table 7.1 (Turkenburg et al. 2012 ), could supply

the global primary energy of 2015 112 times over.

However, the technical potential is only a first indication of the extent to

which the resource is available. There are many other limitations, which must be

considered. One of the main constraints on deploying renewable energy tech-

Different types of renewable energy potentials have been identified by the
German Advisory Council on Climate Change (WBGU)—World in Transition:
Towards Sustainable Energy Systems, Chap. 3, page 44, published in 2003 :
(WBGU 2003 ) (Quote):

“Theoretical potential: The theoretical potential identifies the physical upper limit of
the energy available from a certain source. For solar energy, for example, this would
be the total solar radiation falling on a particular surface. This potential does there-
fore not take account of any restrictions on utilization, nor is the efficiency of the
conversion technologies considered.
Conversion potential: The conversion potential is defined specifically for
each technology and is derived from the theoretical potential and the annual effi-
ciency of the respective conversion technology. The conversion potential is
therefore not a strictly defined value, since the efficiency of a particular technol-
ogy depends on technological progress.
Technological potential: The technological potential is derived from the conver-
sion potential, taking account of additional restrictions regarding the area that is real-
istically available for energy generation. [...] like the conversion potential, the
technological potential of the different energy sources is therefore not a strictly
defined value but depends on numerous boundary conditions and assumptions.
Economic potential: This potential identifies the proportion of the technological
potential that can be utilized economically, based on economic boundary conditions
at a certain time [...].
Sustainable potential: This potential of an energy source covers all aspects of
sustainability, which usually requires careful consideration and evaluation of differ-
ent ecological and socio-economic aspects [...].” (END Quote)

S. Teske et al.

163

nologies is the available space, especially in densely populated areas where there

are competing claims on land use, such as agriculture and nature conservation, to

name just two.

It is neither necessary nor desirable to exploit the entire technical potential. The

implementation of renewable energy must respect sustainability criteria to achieve

a sound future energy supply. Public acceptance is crucial, especially because the

decentralised character of many renewable energy technologies will move systems

closer to consumers. Without public acceptance, market expansion will be difficult

or even impossible (Teske and Pregger 2015 ). The energy policy framework in a

particular country or region will have a profound impact on the expansion of renew-

ables, in terms of both the economic situation and the social acceptance of renew-

able energy projects.

7.1.1 Bioenergy

The discrepancy between the technical potential for bioenergy and the likely sus-

tainable potential raises some issues that warrant further discussion. Recent analy-

ses put the technical potential for primary bioenergy supply at 100–300 EJ/year.

(GEA 2012 ; Smith et al. 2014 ). However, the dedicated use of land for bioenergy—

whether through energy crops or the harvest of forest biomass—raises concerns

over competition for land and the carbon neutrality of bioenergy (Field and Mach

2017 ; Searchinger et  al. 2017 ). Research that focused on the trade-offs between

bioenergy production, food security, and biodiversity found that less than 100 EJ /

year. could be produced sustainably (Boysen et al. 2017 ; Heck et al. 2018 ), although

such production levels would be dependent on strong global land governance sys-

tems (Creutzig 2017 ).

The carbon neutrality of bioenergy is based on the assumption that the CO 2

released when bioenergy is combusted is then recaptured when the biomass stock

Table 7.1 Theoretical and technical renewable energy potentials versus utilization in 2015

Renewable
energy resource

Theoretical potential
(Annual energy flux) [EJ/
year] IPCC 2011

Technical potential [EJ/
year] Global energy
assessment 2012, Chap. 11,
p. 774

Utilization in 2015
[EJ/year] IEA-WEO
2017
Solar energy 3,900,000 62,000–280,000 1.3
Wind energy 6000 1250–2250 1.9
Bioenergy 1548 160–270 51.5
Geothermal
energy

1400 810–1545 2.4

Hydropower 147 50–60 13.2
Ocean energy 7400 3240–10,500 0.0018
Total 76,000–294,500 (Total primary
energy demand
2015) 555 EJ/year

7 Renewable Energy Resource Assessment

16 4

regrows (EASAC 2017 ). Most land is part of the terrestrial carbon sink or is used

for food production, so that harvesting for bioenergy will either deplete the exist-

ing carbon stock or displace food production (Searchinger et al. 2015 , 2017). The

use of harvested forest products (e.g., wood pellets) for bioenergy is not carbon

neutral in the majority of circumstances because an increased harvesting in forests

leads to a permanent increase in the atmospheric CO 2 concentration (Sterman et al.

2018 ; Smyth et  al. 2014 ; Ter Mikaelian et  al. 2015 ). Leaving carbon stored in

intact forests can represent a better climate mitigation strategy (DeCicco and

Schlesinger 2018 ), because increased atmospheric concentrations of CO 2 from the

burning of bioenergy may worsen the irreversible impacts of climate change before

the forests can grow back to compensate the increase (EASAC 2017 ; Booth 2018 ;

Schlesinger 2018 ).

Bioenergy sourced from wastes and residues rather than harvested from dedi-

cated land can be considered carbon neutral, because of the ‘carbon opportunity

cost’ per hectare of land (i.e., bioenergy production reduces the carbon-carrying

capacity of land) (Searchinger et al. 2017 ). The supply of waste and residues as

a bioenergy source is always inherently limited (Miyake et al. 2012 ). Although

in some cases, burning residues can still release more emissions into the atmo-

sphere in the mid-term (20–40 years) than allowing them to decay (Booth 2018 ),

there is general agreement that specific and limited waste materials from the for-

est industry (for example, black liquor or sawdust) can be used with beneficial

climate effects (EASAC 2017 ). The use of secondary residues (cascade utiliza-

tion) may reduce the logistical costs and trade-offs associated with waste use

(Smith et al. 2014 ).

7.2 Economic Renewable Energy Potential in Space-

Constrained Environments

Land is a scarce resource. The use of land for nature conservation, agricultural pro-

duction, residential areas, and industry, as well as for infrastructure, such as roads

and all aspects of human settlements, limits the amount of land available land for

utility-scale solar and wind projects. Furthermore, solar and wind generation require

favourable climatic conditions, so not all available areas are suitable for renewable

power generation. To assess the renewable energy potential of the available area, all

ten world regions defined in Table 8 in Sect. 1 of Chap. 5 were analysed with the [R]

E-SPACE methodology described in Sect. 3 of Chap. 3.

Given the issues involved in dedicated land-use for bioenergy outlined above, we

assume that bioenergy is sourced primarily from cascading residue use and wastes,

and do not analyse the availability of land for dedicated bioenergy crops.

This analysis quantifies the available land area (in square kilometres) in all

regions and sub-regions with a defined set of constraints.

S. Teske et al.

165

7.2.1 Constrains for Utility-Scale Solar and Wind Power

Plants

The following land-use areas were excluded from the deployment of utility-scale

solar photovoltaic (PV) and concentrated solar power plants:

• Residential and urban settlements;
• Infrastructure for transport (e.g., rail, roads);
• Industrial areas;
• Intensive agricultural production land;
• Nature conservation areas and national parks;
• Wetlands and swamps;
• Closed grasslands (a land-use type) (GLC 2000 ).

7.2.2 Mapping Solar and Wind Potential

After the spatial analysis, the remaining available land areas were analysed for their

solar and wind resources. For concentrated solar power, a minimum solar radiation

of 2000 kilowatt hours per square meter and year (kWh/m^2 year) is assumed as the

minimum deployment criterion, and onshore wind potentials under an average

annual wind speed of 5 m/s have been omitted.

In the next step, the existing electricity infrastructure of power lines and power

plants was mapped for all regions with WRI ( 2018 ) data. Figure 7.1 provides an

example of the electricity infrastructure in Africa. These maps provide important

insights into the current situation in the power sector, especially the availability of

transmission grids. This is of particular interest for developing countries because it

allows a comparison of the available land areas that have favourable solar and wind

conditions with the infrastructure available to transport electricity to the demand

centres. This assessment is less important for OECD regions because the energy

infrastructure is usually already fairly evenly distributed across the country—except

in some parts of Canada, the United States, and Australia. For some countries, cov-

erage is not 100% complete due to a lack of public data sources. This is particularly

true for renewable energy generation assets such as solar, wind, biomass, geother-

mal energy, and hydropower resources.

Figure 7.2 shows the solar potential for utility-scale solar power plants—both

solar PV and concentrated solar power—in Africa. The scale from light yellow to

dark red shows the solar radiation intensity: the darker the area, the better the solar

resource. The green lines show existing transmission lines. All areas that are not

yellow or red are unsuitable for utility-scale solar because there is conflicting land

use and/or there are no suitable solar resources. Africa provides a very extreme

example of very good solar resources far from existing infrastructure. While roof- top

7 Renewable Energy Resource Assessment

166

Fig. 7.2 Solar potential in Africa

Fig. 7.1 Electricity infrastructure in Africa—power plants (over 1 MW) and high-voltage trans- mission lines

S. Teske et al.

167

solar PV can be deployed virtually anywhere and only needs roof space on any sort

of building, bulk power supply via solar—to produce synthetic and hydrogen

fuels—requires a certain minimum of utility-scale solar applications. The vast solar

potential in the north of Africa—as well as in the Middle East—has been earmarked

for the production of synthetic and hydrogen fuels and for the export of renewable

electricity (via sub-sea cable) to Europe in the long-term energy scenarios in the

2.0 °C and 1.5 °C Scenarios.

Europe, in contrast, is densely populated and has fewer favourable utility-scale

solar sites because of both its lower solar radiation and conflicting land-use patterns.

Figure 7.3 shows Europe’s potential for utility-scale solar power plants. Only the

yellow and red dots across Europe, most visible in the south of Spain, south of the

Alps, south-west Italy, and the Asian part of Turkey, mark regions suitable for

utility- scale solar, whereas roof-top solar can be deployed economically across

Europe, including Scandinavia.

However, Africa and Europe are in a good position, from a technical point of

view, to form an economic partnership for solar energy exchange.

The situation for onshore wind power differs from that for solar energy. The best

potential is in areas that are more than 30° north and south of the equator, whereas

the actual equatorial zone is less suitable for wind installation. North America has

significant wind resources and the resource is still largely untapped, even though

there is already a mature wind industry in Canada and the USA. Figure 7.4 shows

the existing and potential wind power sites. While significant wind power

Fig. 7.3 Europe’s potential for utility-scale solar power plants

7 Renewable Energy Resource Assessment

168

installations are already in operation, mainly in the USA, there are still very large

untapped resources across the entire north American continent, in Canada and the

mid-west of the USA.

Unlike the situation in the USA, wind power in Latin America is still in its initial

stages and the industry, which has great potential, is still in its infancy. Figure 7.5

shows the existing wind farm locations—marked with blue dots—and the potential

wind farm sites, especially in coastal regions and the entire southern parts of

Argentina and Chile.

The available solar and wind potentials are distributed differently across all

world regions. Whereas some regions have significantly more resources than others,

all regions have enough potential to supply their demand with local solar and wind

resources—together with other renewable energy resources, such as hydro-, bio-,

and geothermal energies.

Table 7.2 provides an overview of the key results of the [R]E-SPACE analysis.

The available areas (in square kilometres) are based on the space-constrained

assumptions (see Sect. 2.1 in Chap. 7). The installed capacities are calculated based

on the following space requirements (Table 7.2):

• Solar photovoltaic: 1 MW = 0.04 km^2
• Concentrated solar power: 1 MW = 0.04 km^2
• Onshore wind: 1 MW = 0.2 km^2

Note: Mapping Eurasia was not possible because the data files were

incomplete.

Fig. 7.4 OECD North America: existing and potential wind power sites

S. Teske et al.

169

Fig. 7.5 Latin America: potential and existing wind power sites

Table 7.2 [R]E-SPACE: key results part 1

Region Subregion

Solar Onshore wind
Potential availability
for utility-scale
installations

Space
potential

Potential availability
for utility-scale
installations

Space
potential
[km^2 ] [GW] [km^2 ] [GW]
OECD
North
America

Canada East 2,742,668 68,567 2,530,232 12,651
Canada West 2,242,715 56,068 2,180,271 10,901
Mexico 3,365,974 84,149 3,341,940 16,710
USA – South
East

269,650 6741 254,976 1275

USA – North
East

1,043,033 26,076 1,043,026 5215

USA – South
West

1,847,162 46,179 1,840,980 9205

USA – North
West

431,277 10,782 427,709 2139

USA –
Alaska

1,152,288 28,807 1,091,698 5458

(continued)

7 Renewable Energy Resource Assessment

170

Table 7.2 (continued)

Region Subregion

Solar Onshore wind
Potential availability
for utility-scale
installations

Space
potential

Potential availability
for utility-scale
installations

Space
potential
[km^2 ] [GW] [km^2 ] [GW]
Latin
America

Caribbean 34,238 856 34,238 171
Central
America

17,529 438 17,603 88

North Latin
America

869,811 21,745 869,811 4349

Brazil 1,623,625 40,591 1,623,625 8118
Central South
America

1,023,848 25,596 1,024,340 5122

Chile 693,990 17,350 693,990 3470
Argentina 1,651,168 41,279 1,651,168 8256
CSA –
Uruguay

32,360 809 32,360 162

Europe EU – Central 146,797 3670 146,797 734
EU – UK and
Islands

22,406 560 22,406 112

EU – Iberian
Peninsula

15,608 390 15,608 78

EU – Balkans
+ Greece

4825 121 4825 24

EU – Baltic 32,090 802 32,090 160
EU – Nordic 218,496 5462 218,496 1092
Turkey 134,354 3359 134,354 672
Middle
East

East –
Middle East

165,302 4133 5738 29

North –
Middle East

91,970 2299 7123 36

Iraq 119,967 2999 9104 46
Iran 586,595 14,665 57,965 290
United Arab
Emirates

530 13 530 3

Israel 386 10 217 1
Saudi Arabia 13,284 332 13,284 66
Africa North –
Africa

9,726,388 243,160 9,784,694 48,923

East – Africa 6,378,561 159,464 6,980,497 34,902
West – Africa8,336,960 208,424 8,669,628 43,348
Central –
Africa

7,229,129 180,728 7,509,351 37,547

Southern –
Africa

3,269,644 81,741 3,547,591 17,738

Rep. South
Africa

1,626,528 40,663 1,650,471 8252

S. Teske et al.

171

Table 7.3 [R]E-SPACE: key results part 2

Region Subregion

Solar Onshore wind
Potential
availability for
utility-scale
installations

Space
potential

Potential
availability for
utility-scale
installations

Space
potential
[km^2 ] [GW] [km^2 ] [GW]
Non-
OECD
Asia

Asia-West
(Himalaya)

1,315,395 32,885 801,044 4005

South and South
East Asia

9062 227 8184 41

Asia North West 184,503 4613 43,710 219
Asia Central
North

138,861 3472 81,228 406

Philippines 2634 66 941 5
Indonesia 106,581 2665 12,162 61
Pacific Island
States

5510 138 673 3

India North – India 229,314 5733 163,118 816
East – India 32,511 813 5195 26
West – West 224,355 5609 121,441 607
South – Incl.
Islands

129,346 3234 103,177 516

Northeast –
India

77,379 1934 1821 9

China East – China 47,621 1191 39,648 198
North – China 425,350 10,634 825,272 4126
Northeast –
China

193,006 4825 192,110 961

Northwest –
China

5,642,854 141,071 1,603,909 8020

Central – China 256,272 6407 211,229 1056
South – China 500,211 12,505 317,046 1585
Taiwan 5862 147 2791 14
Tibet 5460 137 377,610 1888
OECD
Pacific

North Japan 8697 217 8213 41
South Japan 3567 89 3036 15
North Korea 10,724 268 9854 49
South Korea 2411 60 1892 9
North New
Zealand

22,699 567 22,163 111

South New
Zealand

25,106 628 46,266 231

Australia –
South and East
(NEM)

2,080,117 52,003 2,035,523 10,178

Australia – West
and North (NT)

2,813,791 70,345 2,762,499 13,812

7 Renewable Energy Resource Assessment

172

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Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

7 Renewable Energy Resource Assessment

© The Author(s) 2019 175 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_8

Chapter 8

Energy Scenario Results

Sven Teske, Thomas Pregger, Tobias Naegler, Sonja Simon,

Johannes Pagenkopf, Bent van den Adel, and Özcan Deniz

Abstract Results for the 5.0 °C, 2.0 °C and 1.5 °C scenarios for ten world regions

in regard to energy-related carbon-dioxide emissions, final-, primary-, transport-

and heating demand and the deployment of various supply technologies to meet the

demand. Furthermore, the electricity demand and generation scenarios are pro-

vided. The key results of a power sector analysis which simulates further electricity

supply with high shares of solar- and wind power in one hour steps is provided. The

ten world regions are divided into eight sub-regions and the expected development

of loads, capacity-factors for various power plant types and storage demands are

provided. This chapter contains more than 100 figures and tables.

This chapter provides a condensed description of the energy scenario results on a

global scale, for each of the ten world regions. The descriptions include a presenta-

tion of the calculated energy demands for all sectors (power and heat/fuels for the

following sectors: industry, residential and other, and transport) and of supply strat-

egies for all the technologies considered, from 2015 to 2050. The results of the

model-based analyses of hourly supply curves and required storage capacities are

also discussed based on key indicators. Graphs, tables, and descriptions are pro-

vided in a standardized way to facilitate comparisons between scenarios and

between regions.

S. Teske (*) Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au

T. Pregger · T. Naegler · S. Simon Department of Energy Systems Analysis, German Aerospace Center (DLR), Institute for Engineering Thermodynamics (TT), Pfaffenwaldring, Germany e-mail: thomas.pregger@dlr.de; tobias.naegler@dlr.de; sonja.simon@dlr.de

J. Pagenkopf · B. van den Adel · Ö. Deniz Department of Vehicle Systems and Technology Assessment, German Aerospace Center (DLR), Institute of Vehicle Concepts (FK), Pfaffenwaldring, Germany e-mail: johannes.pagenkopf@dlr.de; Bent.vandenAdel@dlr.de; oezcan.deniz@dlr.de

176

The following global summary of the regional results is presented in the same

structure as that used for individual regions. Consistent with the regional results,

these tables do not include the demand and supply details for the bunker fuels used

in international aviation and navigation. Section 8.2 outlines a global demand and

supply scenario for renewable bunker fuels in the long term, including estimates of

additional CO 2 emissions from fossil bunker fuels between 2015 and 2050.

8.1 Global: Long-Term Energy Pathways

8.1.1 Global: Projection of Overall Energy Intensity

Combining the assumptions for the power, heat, and fuel demands for all sectors

produced the overall final energy intensity (per $ GDP) development shown in

Fig. 8.1. Compared with the 5.0 °C case based on the Current Policies Scenario of

the IEA, the alternative scenarios follow more stringent efficiency levels. The 1.5 °C

Scenario represents an even faster implementation of efficiency measures than the

2.0  °C Scenario. The 1.5  °C Scenario involves the decelerated growth of energy

services in all regions, to avoid any further strong increase in fossil fuel use after

2020. The global average intensity drops from 2.4  MJ/$GDP in 2015 to

1.25  MJ/$GDP in 2050  in the 5.0  °C case compared with 0.65  MJ/$GDP in the

2.0 °C Scenario and 0.59 MJ/$GDP in the 1.5 °C Scenario. The average final energy

consumption decreases from 46.3 GJ/capita in 2015 to 28.4 GJ/capita in 2050 in the

2.0 °C Scenario and to below 26 GJ/capita in the 1.5 °C Scenario. In the 5.0 °C case,

it increases to 55 GJ/capita.

0,0

0,5

1,0

1,5

2,0

2,5

3,0

2015 2020 2025 2030 2035 2040 2045 2050

final energy intensity [MJ/$GDP]

5.0°C 2.0°C 1.5°C

Fig. 8.1 Global: projection of final energy (per $ GDP) intensity by scenario

S. Teske et al.

177

8.1.2 Global: Final Energy Demand by Sector

(Excluding Bunkers)

Combining the assumptions for population growth, GDP growth, and energy inten-

sity produced the future development pathways for the global final energy demand

shown in Fig. 8.2 for the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios. In the 5.0 °C Scenario,

the total final energy demand will increase by 57% from 342 EJ/year in 2015 to 537

EJ/year in 2050. In the 2.0 °C Scenario, the final energy demand will decrease by

19% compared with the current consumption and reach 278  EJ/year by 2050,

whereas the final energy demand in the 1.5  °C Scenario will reach 253 EJ, 26%

below the 2015 demand. In the 1.5 °C Scenario, the final energy demand in 2050 is

9% lower than in the 2.0 °C Scenario. The electricity demand for ‘classical’ electri-

cal devices (without power-to-heat or e-mobility) will increase from around

15,900  TWh/year in 2015 to 23,800 TWh/year (2.0  °C) or to 23,300  TWh/year

(1.5 °C) by 2050. Compared with the 5.0 °C case (37,000 TWh/year in 2050), the

efficiency measures in the 2.0 °C and 1.5 °C Scenarios will save 13,200 TWh/year

and 13,700 TWh/year, respectively, by 2050.

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0  °C Scenario, the electricity demand for heating will be about

0

100,000

200,000

300,000

400,000

500,000

600,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Demand reduction (compared to 5.0°C)
Residential & other sectors

Industry
Transport

Fig. 8.2 Global: projection of total final energy demand by sector in the scenarios (without non- energy use or heat from combined heat and power [CHP] autoproducers)

8 Energy Scenario Results

178

12,600 TWh/year due to electric heaters and heat pumps, and in the transport sector

there will be an increase of about 23,400 TWh/year due to increased electric mobil-

ity. The generation of hydrogen (for transport and high-temperature process heat)

and the manufacture of synthetic fuels (mainly for transport) will add an additional

power demand of 18,800 TWh/year The gross power demand will thus rise from

24,300 TWh/year in 2015 to 65,900 TWh/year in 2050 in the 2.0 °C Scenario, 34%

higher than in the 5.0 °C case. In the 1.5 °C Scenario, the gross electricity demand

will increase to a maximum of 65,300 TWh/year in 2050.

The efficiency gains in the heating sector could be even larger than in the

electricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption

equivalent to about 85.7 EJ/year and 95.4 EJ/year, respectively, is avoided

through efficiency gains by 2050 compared with the 5.0 °C Scenario (Figs. 8.3,

8.4, 8.5, and 8.6).

8.1.3 Global: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power coming from

renewable sources. In the 2.0 °C Scenario, 100% of the electricity produced glob-

ally will come from renewable energy sources by 2050. ‘New’ renewables—mainly

wind, solar, and geothermal energy—will contribute 83% of the total electricity

generation. Renewable electricity’s share of the total production will be 62% by

2030 and 88% by 2040. The installed capacity of renewables will reach about 9500

0

50,000

100,000

150,000

200,000

250,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Own consumption and losses
Generation of H2 and Synfuels

Transport
Residential & other sectors

Industry

Fig. 8.3 Global: development of gross electricity demand by sector in the scenarios

S. Teske et al.

179

GW by 2030 and 25,600 GW by 2050. The share of renewable electricity generation

in 2030 in the 1.5 °C Scenario is assumed to be 73%. The 1.5 °C Scenario indicates

a generation capacity from renewable energy of about 25,700 GW in 2050.

Table 8.1 shows the development of different renewable technologies in the

world over time. Figure 8.7 provides an overview of the global power-generation

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Rail Road (PC & LDV) Road (HDV)
Domestic Navigation Domestic Aviation Efficiency

Fig. 8.4 Global: development of final energy demand for transport by mode in the scenarios

0

50,000

100,000

150,000

200,000

250,000

2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C 2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Industry Residential & other sectors Efficiency

Fig. 8.5 Global: development of heat demand by sector in the scenarios

8 Energy Scenario Results

Table 8.1 Global: development of renewable electricity-generation capacity in the scenarios

in GW (°C) 2015 2025 2030 2040 2050
Hydro 5.0 1202 1420 1558 1757 1951
2.0 1202 1386 1416 1473 1525
1.5 1202 1385 1415 1471 1523
Biomass 5.0 112 165 195 235 290
2.0 112 301 436 617 770
1.5 112 350 498 656 798
Wind 5.0 413 880 1069 1395 1790
2.0 413 1582 2901 5809 7851
1.5 413 1912 3673 6645 7753
Geothermal 5.0 14 20 26 41 62
2.0 14 49 125 348 557
1.5 14 53 147 356 525
PV 5.0 225 785 1031 1422 2017
2.0 225 2194 4158 8343 12,306
1.5 225 2829 5133 10,017 12,684
CSP 5.0 4 13 20 39 64
2.0 4 69 361 1346 2062
1.5 4 92 474 1540 1990
Ocean 5.0 0 1 3 9 22
2.0 0 22 82 307 512
1.5 0 22 80 295 450
Total 5.0 1971 3285 3902 4899 6195
2.0 1971 5604 9478 18,243 25,584
1.5 1971 6644 11,420 20,980 25,723

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0

100,000

200,000

300,000

400,000

500,000

600,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

PJ/yr

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.6 Global: development of the final energy demand by sector in the scenarios

181

structure. From 2020 onwards, the continuing growth of wind and photovoltaic

(PV), up to 7850 GW and 12,300 GW, respectively, will be complemented by up to

2060 GW of solar thermal generation, and limited biomass, geothermal, and ocean

energy in the 2.0 °C Scenario. Both the 2.0 °C Scenario and 1.5 °C Scenario will

lead to a high proportion of variable power generation (PV, wind, and ocean) of

38% and 46%, respectively, by 2030 and 64% and 65%, respectively, by 2050.

8.1.4 Global: Future Costs of Electricity Generation

Figure 8.8 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated average

electricity generation costs in 2015 (referring to full costs) were around 6 ct/kWh.

In the 5.0 °C case, the generation costs will increase until 2050, when they reach

10.6 ct/kWh. The generation costs will also increase in the 2.0  °C and 1.5  °C

Scenarios until 2030, when they will reach 9 ct/kWh, and then drop to 7 ct/kWh by

2050. In both alternative scenarios, the generation costs will be around 3.5 ct/kWh

lower than in the 5.0 °C Scenario by 2050. Note that these estimates of generation

costs do not take into account integration costs such as power grid expansion, stor-

age, or other load-balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

cause the total electricity supply costs to increase from today’s $1560 billion/year to

around $5500 billion/year in 2050. In both alternative scenarios, the total supply

costs will be $5050 billion/year in 2050. Therefore, the long-term costs for electric-

ity supply in both alternative scenarios are about 8% lower than in the 5.0  °C

Scenario as a result of the estimated generation costs and the electrification of

heating and mobility.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.7 Global: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

182

Compared with these results, the generation costs (without including CO 2 emis-

sion costs) will increase in the 5.0 °C case to only 7.9 ct/kWh. The generation costs

will increase in the 2.0 °C Scenario until 2030 to 7.7 ct/kWh and to a maximum of

8.1 ct/kWh in the 1.5 °C Scenario. Between 2030 and 2050, the costs will decrease

to 7 ct/kWh. In the 2.0  °C Scenario, the generation costs will be, at maximum,

0.1 ct/kWh higher than in the 5.0 °C Scenario and this will occur in 2040. In the

1.5 °C Scenario, the generation costs will be, at maximum, 0.5 ct/kWh higher than

in the 5.0 °C Scenario, again by around 2040. In 2050, the generation costs in the

alternative scenarios will be 0.8–0.9 ct/kWh lower than in the 5.0  °C case. If the

CO 2 costs are not considered, the total electricity supply costs in the 5.0 °C case will

rise to about $4150 billion/year in 2050.

8.1.5 Global: Future Investments in the Power Sector

In the 2.0  °C Scenario, around $49,000 billion in investment will be required for

power generation between 2015 and 2050—including for additional power plants to

produce hydrogen and synthetic fuels and for the plant replacement costs at the end

of their economic lifetimes. This value will be equivalent to approximately $1360

billion per year on average, and is $28,600 billion more than in the 5.0  °C case

($20,400 billion). An investment of around $51,000 billion for power generation

will be required between 2015 and 2050 in the 1.5 °C Scenario. On average, this

will be an investment of $1420 billion per year. In the 5.0  °C Scenario, the

0

2

4

6

8

10

12

0

1,000

2,000

3,000

4,000

5,000

6,000

2015 2025 2030 2040 2050

billion $ ct/kWh

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.8 Global: development of total electricity supply costs and specific electricity generation costs in the scenarios

S. Teske et al.

183

investment in conventional power plants will comprises around 40% of total cumu-

lative investments, whereas approximately 60% will be invested in renewable power

generation and co-generation (Fig. 8.9).

However, in the 2.0 °C (1.5 °C) Scenario, the world will shift almost 94% (95%)

of its total energy investment to renewables and co-generation. By 2030, the fossil

fuel share of the power sector investment will predominantly focus on gas power

plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $26,300 billion in 2050,

equivalent to $730 billion per year. Therefore, the total fuel cost savings in the

2.0  °C Scenario will be equivalent to 90% of the additional energy investments

compared to the 5.0 °C Scenario. The fuel cost savings in the 1.5 °C Scenario will

add up to $28,800 billion, or $800 billion per year.

8.1.6 Global: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 59%, from

151 EJ/year in 2015 to around 240 EJ/year in 2050. In the 2.0 °C Scenario, energy

efficiency measures will help to reduce the energy demand for heating by 36% in

2050, relative to that in the 5.0 °C Scenario, and by 40% in the 1.5 °C Scenario. Today,

renewables supply around 20% of the global final energy demand for heating. The

Fossil
30%
Nuclear
10%

CHP
7%

Renewable
53%

5.0°C: 2015-205 0

total 20,40 0
billion $

Fossil (incl.H2)
5%Nuclear
1%
CHP
9%

Renewable
85%

1.5°C: 2015-2050
Fossil (incl. H2)
6%
Nuclear
1%
CHP
9%

Renewable
84%

2.0°C: 2015-2050

total 49,000
billion $

total 51,100
billion $

Fig. 8.9 Global: investment shares for power generation in the scenarios

8 Energy Scenario Results

184

main contribution is from biomass. Renewable energy will provide 42% of the world’s

total heat demand in 2030 in the 2.0 °C Scenario and 56% in the 1.5 °C Scenario. In

both scenarios, renewables will provide 100% of the total heat demand in 2050.

Figure 8.10 shows the development of different technologies for heating world-

wide over time, and Table 8.2 provides the resulting renewable heat supply for all

scenarios. Until 2030, biomass will remain the main contributor. In the long-term,

the growing use of solar, geothermal, and environmental heat will lead to a biomass

share in total heating of 33% in the 2.0 °C Scenario and 30% in the 1.5 °C Scenario.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 15,900 PJ/year in

0

50,000

100,000

150,000

200,000

250,000

300,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 20402050

PJ

/y

r

Efficiency (compared
to 5.0°C)
Hydrogen
Electric heating
Geothermal heat and
heat pumps
Solar heating
Biomass
Fossil

Fig. 8.10 Global: development of heat supply by energy carrier in the scenarios

Table 8.2 Global: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year (°C) 2015 2025 2030 2040 2050
Biomass 5.0 25,470 27,643 28,878 31,568 34,564
2.0 25,470 32,078 35,134 38,187 37,536
1.5 25,470 33,493 36,927 36,385 30,151
Solar heating 5.0 1246 2091 2754 4353 6220
2.0 1246 6485 12,720 23,329 27,312
1.5 1246 7656 14,153 21,665 24,725
Geothermal heat
and heat pumps

5.0 563 804 925 1293 1823
2.0 563 4212 8956 21,115 33,123
1.5 563 4615 10,288 20,031 29,123
Hydrogen 5.0 0 0 0 0 0
2.0 0 193 508 5670 15,877
1.5 0 180 1769 10,461 17,173
Total 5.0 27,278 30,538 32,557 37,214 42,608
2.0 27,278 42,967 57,318 88,301 113,848
1.5 27,278 45,944 63,137 88,542 101,172

S. Teske et al.

185

the 2.0  °C Scenario and 17,200 PJ/year in the 1.5  °C Scenario. The direct use of

electricity for heating will also increase by a factor of 4.2–4.5 between 2015 and

2050 and will have a final share of 26% in 2050 in the 2.0 °C Scenario and 30% in

the 1.5 °C Scenario (Table 8.2).

8.1.7 Global: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $13,230 billion in the 2.0 °C Scenario (including investments

for plant replacement after their economic lifetimes)—approximately $368 billion

per year. The largest share of this investment is assumed to be for heat pumps (around

$5700 billion), followed by solar collectors and geothermal heat use. The 1.5  °C

Scenario assumes an even faster expansion of renewable technologies. However, the

lower heat demand (compared with the 2.0 °C Scenario) will result in a lower aver-

age annual investment of around $344 billion per year (Table 8.3, Fig. 8.11).

8.1.8 Global: Transport

The energy demand in the transport sector will increase in the 5.0 °C Scenario by

50% by 2050, from around 97,200 PJ/year in 2015 to 145,700 PJ/year in 2050. In

the 2.0  °C Scenario, assumed technical, structural, and behavioural changes will

reduce the energy demand by 66% (96,000 PJ/year) by 2050 compared with the

5.0 °C Scenario. Additional modal shifts, technology switches, and a reduction in

Table 8.3 Global: installed capacities for renewable heat generation in the scenarios

in GW (°C) 2015 2025 2030 2040 2050 Biomass 5.0 10,215 10,180 9938 9423 8997 2.0 10,215 10,202 9456 7875 5949 1.5 10,215 10,418 9568 7073 4141 Geothermal 5.0 5 7 7 8 4 2.0 5 85 181 492 656 1.5 5 101 200 433 551 Solar heating 5.0 378 615 781 1175 1652 2.0 378 1685 3198 5722 6575 1.5 378 1993 3555 5286 5964 Heat pumps 5.0 89 126 144 199 270 2.0 89 497 906 1821 2857 1.5 89 514 967 1726 2430 Totala 5.0 10,688 10,928 10,871 10,805 10,923 2.0 10,688 12,469 13,741 15,910 16,036 1.5 10,688 13,026 14,290 14,517 13,086 a Excluding direct electric heating

8 Energy Scenario Results

186

the transport demand will lead to even higher energy savings in the 1.5 °C Scenario

of 74% (or 108,000 PJ/year) in 2050 compared with the 5.0  °C case (Table 8.4,

Fig. 8.12).

By 2030, electricity will provide 12% (2700 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 47%

(6500 TWh/year). In 2050, around 8430 PJ/year of hydrogen will be used in the

transport sector, as a complementary renewable option. In the 1.5 °C Scenario, the

annual electricity demand will be about 5200 TWh in 2050. The 1.5 °C Scenario

also assumes a hydrogen demand of 6850 PJ/year by 2050.

Biofuel use is limited in the 2.0 °C Scenario to a maximum of around 12,000 PJ/

year Therefore, by around 2030, synthetic fuels based on power-to-liquid will be

introduced, with a maximum amount of 5820 PJ/year in 2050. Because of the lower

overall energy demand by transport, biofuel use will be reduced in the 1.5  °C

Scenario to a maximum of 10,000 PJ/year The maximum synthetic fuel demand

will amount to 6300 PJ/year.

biomass
technologies
61%

geothermal
heat use
0%

solar
collectors
22%

heat
pumps
17%

5.0°C: 2015-2050

total 3,025 billion $

biomass
technologies
9%

geothermal
heat use
10%

solar
collectors
38%

heat pumps
43%

2.0°C: 2015-2050

total 13,200 billion $

biomass
technologies
9%

geothermal
heat use
9%

solar
collectors
40%

heat pumps
42%

1.5°C: 2015-2050

total 12, 400 billion $

Fig. 8.11 Global: development of investment in renewable heat-generation technologies in the scenarios

S. Teske et al.

187

8.1.9 Global: Development of CO 2 Emissions

In the 5.0 °C Scenario, the annual global energy-related CO 2 emissions will increase

by 40%, from 31,180 Mt. in 2015 to more than 43,500 Mt. in 2050. The stringent

mitigation measures in both alternative scenarios will cause annual emissions to fall

to 7070 Mt. in 2040 in the 2.0 °C Scenario and to 2650 Mt. in the 1.5 °C Scenario,

with further reductions to almost zero by 2050. In the 5.0 °C Scenario, the cumula-

tive CO 2 emissions from 2015 until 2050 will add up to 1388 Gt. In contrast, in the

2.0  °C and 1.5  °C Scenarios, the cumulative emissions for the period 2015–2050

will be 587 Gt and 450 Gt, respectively.

Table 8.4 Global: projection of transport energy demand by mode in the scenarios

in PJ/year (°C) 2015 2025 2030 2040 2050
Rail 5.0 2705 2708 2814 3024 3199
2.0 2705 2875 3149 3520 3960
1.5 2705 2932 3119 3559 4087
Road 5.0 85,169 94,755 102,556 116,449 127,758
2.0 85,169 79,975 68,660 48,650 40,089
1.5 85,169 67,579 48,949 34,055 28,859
Domestic aviation 5.0 4719 6544 7745 9080 9176
2.0 4719 4732 4239 3291 2640
1.5 4719 4461 3612 2361 1845
Domestic navigation 5.0 2130 2304 2392 2537 2663
2.0 2130 2303 2388 2512 2601
1.5 2130 2301 2383 2506 2601
Total 5.0 94,723 106,310 115,506 131,091 142,796
2.0 94,723 89,886 78,436 57,973 49,290
1.5 94,723 77,274 58,063 42,482 37,392

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ

/y

r

Efficiency (compared to
5.0°C)
Hydrogen
Electricity
Synfuels
Biofuels
Natural Gas
Oil products

Fig. 8.12 Global: final energy consumption by transport in the scenarios

8 Energy Scenario Results

188

Thus, the cumulative CO 2 emissions will decrease by 58% in the 2.0 °C Scenario

and by 68% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid reduction

in annual emissions will occur in both alternative scenarios. In the 2.0 °C Scenario,

the reduction will be greatest in ‘Power generation’, followed by the ‘Residential

and other’ and ‘Transport’ sectors (Fig. 8.13).

8.1.10 Global: Primary Energy Consumption

The levels of primary energy consumption based on the assumptions discussed

above in the three scenarios are shown in Fig. 8.14. In the 2.0 °C Scenario, the pri-

mary energy demand will decrease by 21%, from around 556 EJ/year in 2015 to 439

EJ/year in 2050. Compared with the 5.0  °C Scenario, the overall primary energy

demand will decrease by 48% by 2050 in the 2.0 °C Scenario (5.0 °C: 837 EJ in

2050). In the 1.5 °C Scenario, the primary energy demand will be even lower (412

EJ in 2050) due to the lower final energy demand and lower conversion losses.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 35% in 2030 and

92% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 92% in 2050 (this will includes non-energy con-

sumption, which will still include fossil fuels). Nuclear energy will be phased-out in

both the 2.0 °C and 1.5 °C Scenarios. The cumulative primary energy consumption

of natural gas in the 5.0 °C Scenario will be 5580 EJ, the cumulative coal consump-

0

200

400

600

800

1,000

1,200

1,400

1,600

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

cumulated emissions [Gt]

CO

2
emissions [Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.13 Global: development of CO 2 emissions by sector and cumulative CO 2 emissions (since 2015) in the scenarios (‘Savings’ = lower than in the 5.0 °C Scenario)

S. Teske et al.

189

tion will be about 6360 EJ, and the crude oil consumption will be 6380 EJ. In the

2.0 °C Scenario, the cumulative gas demand will amount to 3140 EJ, the cumulative

coal demand to 2340 EJ, and the cumulative oil demand to 2960 EJ. Even lower fos-

sil fuel use will be achieved in the 1.5 °C Scenario: 2710 EJ for natural gas, 1570 EJ

for coal, and 2230 EJ for oil.

8.2 Global: Bunker Fuels

Bunker fuels for international aviation and navigation are separate categories in the

energy statistics. Their use and related emissions are not usually directly allocated

to the regional energy balances. However, they contribute significantly to global

greenhouse gas (GHG) emissions and pose great challenges regarding their substi-

tution with low-carbon alternatives. In 2015, the annual bunker fuels consumption

was in the order of 16,000 PJ, of which 7400 PJ was for aviation and 8600 PJ for

navigation. Between 2009 and 2015, bunker fuel consumption increased by 13%.

The annual CO 2 emissions from bunker fuels accounted for 1.3 Gt in 2015, approxi-

mately 4% of global energy-related CO 2 emissions. In the 5.0  °C Scenario, the

development of the final energy demand for bunker fuels is assumed to be that of the

IEA World Energy Outlook 2017 Current Policies scenario. This will lead to a fur-

ther increase of 120% in the demand for bunker fuels until 2050 compared with that

in the base year, 2015. Because no substitution with ‘green’ fuels is assumed, CO 2

emissions will rise by the same order of magnitude.

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency
Ocean energy
Geothermal
Solar
Biomass
Wind
Hydro
Natural gas
Crude oil
Coal
Nuclear

Fig. 8.14 Global: projection of total primary energy demand (PED) by energy carrier in the scenarios

8 Energy Scenario Results

190

Although the use of hydrogen and electricity in aviation is technically feasible (at
least for regional transport) and synthetic gas use in navigation is an additional option
under discussion, this analysis uses a conservative approach and assumes that bunker
fuels are only replaced by biofuels or synthetic liquid fuels. Figure 8.15 shows the
5.0 °C and two alternative bunker scenarios, which are defined in consistency to the
scenarios for each world region. For the 2.0 °C and 1.5 °C Scenarios, we assume the
limited use of sustainable biomass potentials and the complementary central produc-
tion of power-to-liquid synfuels. In the 2.0 °C Scenario, this production is assumed
to take place in three world regions: Africa, the Middle East, and OECD Pacific
(especially Australia), where synfuel generation for export is expected to be the most
economic. The 1.5 °C Scenario requires even faster decarbonisation, and therefore
follows a more ambitious low-energy pathway. This will lead to a faster build-up of
the power-to-liquid infrastructure in all regions, which in the long term, will also be
used for limited ‘regional’ bunker fuel production to maintain the utilization of the
existing infrastructure. Therefore, the production of bunker fuels is assumed to occur
in more regions, with lower exports from the supply regions mentioned above, in the
2.0  °C Scenario. Another assumption is that, consistent with the regional 1.5  °C
Scenarios, the biomass consumption for energy supply will decrease in the long term,
whereas power-to-liquid will continue to increase as the main option for international
aviation and navigation. Finally, the expansion of the power- to- liquid infrastructure
for the generation of bunker fuel will be closely associated with the assumed devel-
opment of regional synthetic fuel demand and generation for transportation in each
world region. Figure 8.15 also shows the resulting cumulative CO 2 emissions from
bunker fuel consumption between 2015 and 2050, which amount to around 70 Gt in
the 5.0  °C case, 30 Gt in the 2.0  °C Scenario, and 21 Gt in the 1.5  °C Scenario.
Table 8.5 provides more-detailed data for the three bunker fuel scenarios.

0

10

20

30

40

50

60

70

80

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

cumulated CO2 emissions [Gt]

final energy for bunkers [PJ/yr]

Fossil fuels Biofuels Synfuels (liquid) Savings
5.0°C 2.0°C 1.5°C

Fig. 8.15 Global: scenario of bunker fuel demand for aviation and navigation and the resulting cumulative CO 2 emissions

S. Teske et al.

191

Table 8.5

Global: projection of bunker fuel demands for aviation and navigation by fuel in the scenarios

World bunkers 5.0 °C scenario

Unit

2015

2020

2025

2030

2035

2040

2045

2050

Total final energy consumption

PJ/year

15,985

17,976

20,090

22,593

25,443

28,293

31,462

34,987

thereof aviation

PJ/year

7408

8385

9431

10,674

12,097

13,537

15,148

16,950

thereof navigation

PJ/year

8576

9591

10,658

11,919

13,346

14,756

16,314

18,037

fossil fuels

PJ/year

15,985

17,976

20,090

22,593

25,443

28,293

31,462

34,987

biofuels

PJ/year

0

0

0

0

0

0

0

0

synthetic liquid fuels

PJ/year

0

0

0

0

0

0

0

0

Primary energy demandcrude oil

PJ/year

17,663

19,754

21,956

24,558

27,506

30,423

33,650

37,220

CO

emissions 2

Mt/year

1296

1450

1611

1802

2018

2232

2468

2730

World bunkers 2.0 °C Scenario

unit

2015

2020

2025

2030

2035

2040

2045

2050

Total final energy consumption

PJ/year

15,985

17,538

16,836

15,274

15,053

14,826

14,483

14,014

thereof aviation

PJ/year

7408

8594

8418

7713

7602

7487

7314

7077

thereof navigation

PJ/year

8576

8944

8418

7561

7451

7339

7169

6937

fossil fuels

PJ/year

15,985

17,270

16,180

13,748

10,537

5189

3621

0

biofuels

PJ/year

0

268

657

1526

3146

5417

6381

7430

synthetic liquid fuels

PJ/year

0

0

0

0

1370

4220

4481

6584

Assumed regional structure of synthetic bunker productionAfrica

PJ/year

0

0

0

0

846

2607

2768

4067

Middle East

PJ/year

0

0

0

0

183

564

598

879

OECD Pacific

PJ/year

0

0

0

0

341

1050

1115

1638

Primary energy demandcrude oil

PJ/year

17,663

18,978

17,683

14,943

11,391

5580

3872

0

biomass

PJ/year

0

400

952

2150

4369

7420

8623

9907

RES electricity demand for PtL

TWh/year

0

0

0

0

961

2880

3058

4375 (continued)

8 Energy Scenario Results

192

Table 8.5

(continued)

World bunkers 5.0 °C scenario

Unit

2015

2020

2025

2030

2035

2040

2045

2050

CO

emissions 2

Mt/year

1296

1391

1296

1095

835

409

284

0

World bunkers 1.5 °C Scenario

unit

2015

2020

2025

2030

2035

2040

2045

2050

Total final energy consumption

PJ/year

15,985

17,538

15,995

13,747

12,795

12,602

12,311

11,912

thereof aviation

PJ/year

7408

8594

7997

6942

6462

6364

6217

6016

thereof navigation

PJ/year

8576

8944

7997

6805

6334

6238

6094

5896

fossil fuels

PJ/year

15,985

17,538

15,179

7836

2559

0

0

0

biofuels

PJ/year

0

0

816

4536

6398

6931

5540

4527

synthetic liquid fuels

PJ/year

0

0

0

1375

3839

5671

6771

7385

Assumed regional structure of synthetic bunker productionAfrica

PJ/year

0

0

0

717

2002

2863

3093

2882

Middle East

PJ/year

0

0

0

155

433

619

669

873

OECD Pacific

PJ/year

0

0

0

289

836

1265

1622

1697

OECD North America

PJ/year

0

0

0

213

568

798

924

977

OECD Europe

PJ/year

0

0

0

0

0

126

262

557

Eurasia

PJ/year

0

0

0

0

0

0

200

400

Primary energy demandcrude oil

PJ/year

17,663

19,273

16,589

8517

2766

0

0

0

biomass

PJ/year

0

0

1182

6389

8885

9495

7486

6035

RES electricity demand for PtL

TWh/year

0

0

0

964

2693

3870

4621

4896

CO

emissions 2

Mt/year

1296

1413

1216

624

203

0

0

0

S. Teske et al.

193

The production of synthetic fuels will cause significant additional electricity

demand and a corresponding expansion of the renewable power generation

capacities. In the case of liquid bunker fuels, these additional renewable power

generation capacities will amount to 1100 GW in the 2.0  °C Scenario and more

than 1200 GW in the 1.5 °C Scenario if a flexible utilization rate of 4000 full-load

hours per year can be achieved. However, such a situation will require high amounts

of electrolyser capacity and hydrogen storage to allow not only flexibility in the

power system, but also high utilization rates of the downstream synthesis processes

(e.g., via Fischer- Tropsch plants). Other options for renewable synthetic fuel pro-

duction are solar thermal chemical processes, which directly use high-temperature

solar heat.

8.3 Global: Utilization of Solar and Wind Potential

The economic potential, under space constraints, of utility solar PV, concentrated

solar power (CSP), and onshore wind was analysed with the methodology described

in Sect. 3.3 of Chap. 3.

The 2.0 °C Scenario utilizes only a fraction of the available economic potential

of the assumed suitable land for utility-scale solar PV and concentrated solar power

plants. This estimate does not include solar PV roof-top systems, which have sig-

nificant additional potential. India (2.0  °C) will have the highest solar utilization

rate of 8.5%, followed by Europe (2.0 °C) and the Middle East (2.0 °C), with 5.9%

and 4.6%, respectively.

Onshore wind potential has been utilized to a larger extent than solar potential.

In the 2.0 °C Scenario, space-constrained India will utilize more than half of onshore

wind, followed by Europe with 20%. This wind potential excludes offshore wind,

which has significant potential but the mapping for the offshore wind potential was

beyond the scope of this analysis (Table 8.6).

The 1.5  °C Scenario is based on the accelerated deployment of all renewables

and the more ambitious implementation of efficiency measures. Therefore, the total

installed capacity of solar and wind generators by 2050 is not necessarily larger than

it is in the 2.0 °C Scenario, and the utilization rate is in the same order of magnitude.

The increased deployment of renewable capacity in OECD Pacific (Australia), the

Middle East, and OECD North America (USA) will be due to the production of

synthetic bunker fuels from hydrogen to supply global transport energy for interna-

tional shipping and aviation.

8 Energy Scenario Results

194

Table 8.6

Economic potential within a space-constrained scenario and utilization rates for the 2.0 °C and 1.5 °C scenarios

Economic Potential within available space

SOLAR

Installed capacity by 2050

Utilization rate

WIND

Installed capacity by 2050

Utilization rate

2.0 °C

1.5 °C

2.0 °C

1.5 °C

2.0 °C

1.5 °C

2.0 °C

1.5 °C

Tech. space potential

PV

CSP

PV

CSP

Onshore wind

[GW]

[GW]

[%]

[GW]

[GW]

[%]

OECD North America

445,954

1688

208

1816

236

0.4%

0.5%

86,846

847

833

1.0%

1.0%

Latin America

148,664

317

66

425

79

0.3%

0.3%

29,736

220

237

0.7%

0.8%

OECD Europe

14,364

793

54

918

57

5.9%

6.8%

2873

577

636

20.1%

22.1%

Middle East

24,451

881

252

742

216

4.6%

3.9%

470

455

434

96.8%

92.4%

Africa

914,180

767

247

930

257

0.1%

0.1%

190,711

485

509

0.3%

0.3%

Eurasia

Not available

658

22

657

34

Not available

564

544

Non-

OECD-

Asia

44,064

1065

274

1005

224

3.0%

2.8%

4740

515

506

10.9%

10.7%

India

1323

1257

209

1129

209

8.5%

7.7%

1974

1139

983

57.7%

49.8%

China

176,916

1756

762

1772

614

1.4%

1.3%

17,848

1180

1345

6.6%

7.5%

OECD Pacific

124,178

665

57

745

67

0.6%

0.7%

24,447

244

303

1.0%

1.2%

S. Teske et al.

195

8.4 Global: Power Sector Analysis

The long-term global and regional energy results were used to conduct a detailed

power sector analysis with the methodology described in Sect. 3.5 of Chap. 3. Both

the 2.0 °C and 1.5 °C Scenarios rely on high shares of variable solar and wind genera-

tion. The aim of the power sector analysis was to gain insight into the power system

stability for each region (subdivided into up to eight sub-regions) and to gauge the

extent to which power grid interconnections, dispatch generation services, and storage

technologies are required. The results presented in this chapter are projections calcu-

lated from publicly available data. Detailed load curves for some of the sub-regions

and countries discussed in this chapter were not available and, in some cases, the rel-

evant information is classified. Therefore, the outcomes of the [R]E 24/7 model are

estimates and require further research with more detailed localized data, especially

regarding the available power grid infrastructure. Furthermore, power sector projec-

tions for developing countries, especially in Africa and Asia, assume unilateral access

to energy services for the residential sector by 2050, and they require transmission and

distribution grids in regions where there are none at the time of writing. Further

research—in cooperation with local utilities and government representatives—is

required to develop a more detailed understanding of power infrastructure needs.

8.4.1 Global: Development of Power Plant Capacities

The size of the global market for renewable power plants will increase significantly

under the 2.0  °C Scenario. The annual market for solar PV power must increase

from close to 100 GW in 2017 (REN21-GSR 2018 ) by a factor of 4.5 to an average

of 454 GW by 2030. The onshore wind market must expand to 172 GW by 2025,

about three times higher than in 2017 (REN21-GSR 2018 ). The offshore wind mar-

ket will continue to increase in importance within the renewable power sector. By

2050, offshore wind installations will increase to 32 GW annually—11 times higher

than in 2017 (GWEC 2018 ). Concentrated solar power plants will play an important

role in dispatchable solar electricity generation for the supply of bulk power, espe-

cially for industry, and will provide secured capacity to power systems. By 2030,

the annual CSP market must increase to 78 GW, compared with 3 GW in 2020 and

only 0.1 GW in 2017 (REN21-GSR2018) (Table 8.7).

In the 1.5 °C Scenario, the phase-out of coal and lignite power plants is acceler-

ated and a total capacity of 618 GW—equivalent to approximately 515 power sta-

tions^1 —must end operation by 2025. The replacement power must come from a

variety of renewable power generators, both variable and dispatchable. The annual

market for solar PV must be around 30% higher in 2050 than it was in 2025, as in the

2.0 °C Scenario. While the onshore wind market also has an accelerated trajectory

(^1) Assumption: average size of one coal power plant side contains multiple generation blocks, with a total of 1200 MW on average for each location. 8 Energy Scenario Results

196

under the 1.5 °C Scenario as well, the offshore wind market is assumed to be almost

identical to that in the 2.0  °C pathway because of the longer lead times for these

projects. The same is assumed for CSP plants, which are utility-scale projects and

significantly higher deployment seems unlikely in the time remaining until 2025.

8.4.2 Global: Utilization of Power-Generation Capacities

On a global scale, in the 2.0 °C and 1.5 °C Scenarios, the shares of variable renew-

able power generation will increase from 4% in 2015 to 39% and 47%, respectively,

by 2030, and to 64% and 60%, respectively, by 2050. The reason for the variations

in the two cases is their different assumptions regarding efficiency measures, which

may lead to lower overall demand in the 1.5 °C Scenario than in the 2.0 °C Scenario.

During the same period, dispatchable renewables—CSP plants, biofuel generation,

geothermal energy, and hydropower—will remain around 32% until 2030 on a

global average and decrease slightly to 29% in the 2.0 °C Scenario (and to 27% in

the 1.5 °C Scenario) by 2050. The shares of dispatchable conventional generation—

mainly coal, oil, gas, and nuclear—will decline from a global average of 60% in

2015 to only 14% in 2040. By 2050, the remaining dispatchable conventional gas

power plants will have been converted to operate with hydrogen and synthetic fuels,

to avoid stranded investments and to achieve higher quantities of dispatch power

capacity. Table 8.8 shows the increasing shares of variable renewable power

Table 8.7 World: average annual change in the installed power plant capacity

Global power generation: average
annual change of installed
capacity [GW/a]

2015–2025 2026–2035 2036–2050

2.0 °C 1.5 °C 2.0 °C 1.5 °C 2.0 °C 1.5 °C
Hard coal 2 − 107 − 96 − 119 − 68 − 12
Lignite − 25 − 34 − 16 − 9 − 3 − 1
Gas 41 70 44 72 − 199 − 28
Hydrogen-gas 1 17 12 57 240 246
Oil/diesel − 18 − 32 − 29 − 28 − 6 − 2
Nuclear − 15 − 27 − 23 − 24 − 7 − 10
Biomass 24 40 26 29 25 21
Hydro 19 10 7 7 7 8
Wind (onshore) 121 307 273 333 242 158
Wind (offshore) 16 64 75 91 64 45
PV (roof top) 170 413 368 437 399 324
PV (utility scale) 57 138 123 146 133 108
Geothermal 5 16 22 24 28 23
Solar thermal power plants 9 57 93 109 102 85
Ocean energy 4 10 20 20 28 23
Renewable fuel based
co-generation

13 31 27 31 25 20

S. Teske et al.

197

Table 8.8 Global: power system shares by technology group

Power
generation
structure in 10
world regions 2.0 °C 1.5 °C

World

Variable
renewables

Dispatch
renewables

Dispatch
fossil

Variable
renewables

Dispatch
renewables

Dispatch
fossil
OECD North
America

2015 7% 35% 58% 7% 41% 52%
2030 48% 30% 23% 59% 27% 15%
2050 68% 19% 13% 68% 21% 11%
Latin America 2015 3% 63% 34% 3% 62% 35%
2030 24% 51% 25% 36% 61% 3%
2050 39% 45% 16% 40% 46% 13%
Europe 2015 15% 47% 38% 15% 47% 38%
2030 44% 44% 12% 51% 39% 10%
2050 67% 28% 4% 69% 27% 4%
Middle East 2015 0% 12% 88% 0% 13% 87%
2030 51% 19% 31% 56% 18% 27%
2050 81% 19% 0% 70% 16% 13%
Africa 2015 2% 26% 73% 2% 17% 81%
2030 47% 21% 32% 52% 13% 35%
2050 73% 27% 0% 64% 15% 21%
Eurasia 2015 1% 35% 63% 1% 35% 63%
2030 36% 43% 21% 40% 46% 14%
2050 69% 23% 7% 65% 25% 10%
Non-OECD
Asia

2015 1% 35% 64% 1% 35% 64%
2030 26% 35% 39% 36% 34% 30%
2050 52% 28% 19% 55% 28% 17%
India 2015 4% 32% 64% 4% 32% 64%
2030 45% 26% 29% 60% 21% 19%
2050 72% 27% 1% 58% 26% 16%
China 2015 6% 35% 59% 6% 21% 73%
2030 30% 24% 46% 39% 30% 31%
2050 49% 47% 5% 49% 42% 9%
OECD Pacific 2015 4% 34% 61% 4% 34% 61%
2030 40% 31% 30% 45% 29% 27%
2050 71% 26% 2% 64% 22% 14%
Global
average

2015 4% 35% 60% 4% 34% 62%
2030 39% 32% 29% 47% 32% 21%
2050 64% 29% 7% 60% 27% 13%

Note: Variable renewable generation shares in long term energy pathways and power sector analy- sis differ due to different calculation methods. The power sector analysis results are based on the sum of up to eight sub-regional simulations, while the long term energy pathway is based on the regional average generation excluding variations in solar and wind resources within that region

8 Energy Scenario Results

198

Table 8.9 Global: capacity factors for variable and dispatchable power generation

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
World 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr]49.5% 37% 37% 33% 31% 34% 30% 33% 31%

Limited
dispatchable:
fossil and
nuclear

[%/yr]58.7% 34% 34% 24% 16% 25% 10% 17% 9%

Limited
dispatchable:
renewable

[%/yr]36.9% 45% 45% 42% 36% 58% 31% 39% 34%

Dispatchable:
fossil

[%/yr]42.9% 28% 28% 19% 15% 33% 15% 19% 19%

Dispatchable:
renewable

[%/yr]43.1% 56% 56% 54% 47% 42% 39% 51% 43%

Variable:
renewable

[%/yr]14.6% 14% 14% 28% 26% 28% 26% 29% 27%

generation—solar PV and wind power—under the 2.0 °C and 1.5 °C Scenarios over

the entire modelling period. The main difference between the two scenarios is the

time horizon until variable renewable power generation is implemented, with more

rapid implementation in the 1.5  °C Scenario. Again, increased variable shares—

mainly in the USA, the Middle East region, and Australia—will produce synthetic

fuels for the export market, as fuel for both renewable power plants and the trans-

port sector.

Table 8.9 provides an overview of the capacity factor developments by technol-

ogy group for the 2.0 °C and 1.5 °C Scenarios. The operational hours shown are

the result of [R]E 24/7 modelling under the ‘Dispatch case’, which assumes that

the highest priority is given to the dispatch of power from variable sources, fol-

lowed by dispatchable renewables. Conventional power generation will only pro-

vide power for electricity demand that cannot be met by renewables and storage

technologies. Only imports via interconnections will be assigned a lower priority

than conventional power. The reason that interconnections are placed last in the

supply cascade is the high level of uncertainty about whether these interconnec-

tions can actually be implemented in time. Experience with power grid projects—

especially transmission lines—indicates that planning and construction can take

many years or fail entirely, leaving large-scale utility-based renewable power

projects unbuilt.

S. Teske et al.

199

On the global level, the average capacity factor across all power-generation tech-

nologies is around 45%. For this analysis, we created five different power plant

categories based on their current usual operation times and areas of use:

• Limited dispatchable fossil and nuclear power plants : coal, lignite, and

nuclear power plants with limited ability to respond to changes in demand. These
power plants are historically categorized as ‘baseload power plants’. Power sys-
tems dominated by renewable energy usually contain high proportions of vari-
able generation, and therefore quick reaction times (to ramp up and down) are
required. Limited dispatchable power plants cannot deliver these services and are
therefore being phased-out.

• Limited dispatchable renewable systems are CSP plants with integrated stor-

age and co-generation systems with renewable fuels (including geothermal heat).
They cannot respond quickly enough to adapt to minute-by-minute changes in
demand, but can still be used as dispatch power plants for ‘day ahead’
planning.

• Dispatchable fossil fuel power plants are gas power plants that have very quick

reaction times and therefore provide valid power system services.

• Dispatchable renewable power plants are hydropower plants (although they

are dependent on the climatic conditions in the region where the plant is used),
biogas power plants, and former gas power plants converted to hydrogen and/or
synthetic fuel. This technology group is responsible for most of the required
load-balancing services and is vital for the stability of the power system, as stor-
age systems, interconnections, and, if possible, demand-side management.

• Variable renewables are solar PV plants, onshore and offshore wind farms, and

ocean energy generators. A sub-category of ocean energy plants—tidal energy
plants—is very predictable.

Table 8.9 shows the development of the utilization of limited and fully dispatch-

able power generators—both fossil and renewable fuels—and variable power gen-

eration. In the 2.0  °C Scenario, conventional power generation in the baseload

mode—currently with an annual operation time of around 6000  h per year or

more—will decline sharply after 2030 and the annual operation time will be halved,

whereas medium-load and dispatch power plants will predominate. The system

share of dispatchable renewables will remain around 45%–50% throughout the

entire modelling period.

8.4.3 Global: Development of Load, Generation,

and Residual Load

Table 8.10 shows the development of the maximum and average loads for the 10

world regions, the average and maximum power generation in each region in mega-

watts, and the residual loads under both alternative scenarios. The residual load in

8 Energy Scenario Results

200

Table 8.10

Global: load, generation, and residual load development

Power generation structure in 10 world regions

2.0 °C

1.5 °C

World

Max demand [GW]

Max generation [GW]

Max residual load [GW]

Max load development (Base 2020) [GW]

Max demand [GW]

Max generation [GW]

Max residual load [GW]

Max load development (Base 2020) [GW]

OECD North America

2020

753

723

57

100%

755

989

58

100%

2030

864

1159

145

115%

919

1532

194

122%

2050

1356

2779

469

180%

1362

2900

496

180%

Latin America

2020

218

214

30

100%

218

274

18

100%

2030

343

377

74

157%

312

418

25

143%

2050

533

601

154

244%

550

696

122

252%

OECD Europe

2020

574

584

121

100%

574

583

125

100%

2030

620

718

95

108%

639

936

104

111%

2050

862

1530

417

150%

900

1727

448

157%

Middle East

2020

174

181

−
29

100%

174

180

−

26

100%

2030

229

297

−
20

132%

237

346

−

13

136%

2050

551

1164

−
67

317%

522

1018

−

161

300%

Africa

2020

164

125

47

100%

164

135

37

100%

2030

280

261

101

171%

296

305

105

181%

2050

875

1363

647

534%

915

1562

412

559%

Eurasia

2020

257

163

107

100%

257

171

106

100%

2030

316

332

147

123%

330

416

139

129%

2050

630

1338

271

245%

632

1296

275

246%

S. Teske et al.

201

Non-OECD Asia

2020

248

135

122

100%

248

133

124

100%

2030

415

389

256

167%

423

465

296

171%

2050

935

1459

728

377%

841

1394

656

339%

India

2020

288

266

44

100%

288

249

61

100%

2030

493

624

112

171%

491

861

148

170%

2050

1225

1880

854

425%

1207

1865

558

419%

China

2020

957

935

74

100%

953

946

57

100%

2030

1233

1249

173

129%

1219

1613

179

128%

2050

1967

2724

1415

206%

1990

3203

−

609

209%

OECD Pacific

2020

354

322

47

100%

354

318

47

100%

2030

308

468

21

87%

318

544

36

90%

2050

410

997

196

116%

471

1140

173

133%

8 Energy Scenario Results

202

0%

100%

200%

300%

400%

500%

600%

202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050
OECD North
America

Latin
America

OECD Europe Middle East Africa Eurasia Other Asia India China OECD Pacific

Load Development by Region

2.0C Max Load Development (Base year 2020) [%] 1.5C Max Load Development (Base year 2020) [%]

Fig. 8.16 Development of maximum load in 10 world regions in 2020, 2030, and 2050  in the 2.0 °C and 1.5 °C scenarios

this analysis is the load remaining after variable renewable power generation.

Negative values indicate that the power generation from solar and wind exceeds the

actual load and must be exported to other regions, stored, or curtailed. In each

region, the average generation should be on the same level as the average load. The

maximum loads and maximum generations shown do not usually occur at the same

time, so surplus production of electricity can appear and this should be exported or

stored as much as possible. In rare individual cases, solar or wind generation plants

can also temporarily reduce their output to a lower load, or some plants can be shut

down. Any reduced generation from solar and wind in response to low demand is

defined as curtailment.

Figure 8.16 illustrates the development of the maximum loads across all 10

world regions under the 2.0 °C and 1.5 °C Scenarios. The most significant increase

appears in Africa, where the maximum load surges over the entire modelling period

by 534% in response to favourable economic development and increased access to

S. Teske et al.

203

energy services by households. In OECD Pacific, efficiency measures will lead to a

reduction in the maximum load to 87% of the base year value by 2030 and will

increase to 116% by 2050 with the expansion of electric mobility and the increased

electrification of the process heat supply in the industry sector. The 1.5 °C Scenario

has slightly higher loads in response to the accelerated electrification of the indus-

try, heating, and business sectors, except in three regions (the Middle East, India,

and Non OECD Asia), where early action on efficiency measures will lead to an

overall lower demand at the end of the modelling period, with the same GDP and

population growth rates.

8.4.4 Global System-Relevant Technologies—Storage

and Dispatch

The global results of introducing system-relevant technologies are shown in

Table 8.8. The first part of this section documents the required power plant markets,

the changes and configurations of power-generation systems, and the development

of loads in response to high electrification rates in the industry, heating, and trans-

port sectors. The next step in the analysis documents the storage and dispatch

demands and possible technology utilization. It is important to note that the results

presented here are not cost-optimized. The mixture of battery storage and pumped

hydropower plants with hydrogen- and synthetic-fuel-based dispatch power plants

presented here represents only one option of many.

Significant simplification is required for the computer simulations of large

regions, to reduce the data volumes (and calculation times) or simply because there

is not yet any data, because several regions still have no electricity infrastructure in

place. Detailed modelling requires access to detailed data. Although the modelling

tools used for this analysis could be used to develop significantly more-detailed

regional analyses, this is beyond the scope of this research.

The basic concept for the management of power system generation is based on

four principles:

1. Diversity;
2. Flexibility;
3. Inter-sectorial connectivity;
4. Resource efficiency.

Diversity in the locally deployed renewable power-generation structure. For exam-

ple, a combination of onshore and offshore wind with solar PV and CSP plants will

reduce storage and dispatch demands.

Flexibility involves a significant number of fast-reacting dispatch power plants

operated with fuels produced from renewable electricity (hydrogen and synthetic

fuels). The existing gas infrastructure can be further utilized to avoid stranded

8 Energy Scenario Results

204

investments, and the actual fuel production can also be used—with some technical

limitations—for load management, which again will reduce the need for storage

technologies.

Inter-sectorial connectivity involves the connection of the heating (including pro-

cess heat) and transport sectors. Neither the transport sector nor the heating sector

will undergo complete electrification. To supply industrial process heat, the capacity

of co-generation plants—operated with bio-, geothermal, or hydrogen fuels—will

be increased by a factor of 2.5 in the 1.5 °C Scenario. Co-generation heating sys-

tems with heat storage capacities and heat pumps operated with renewable electric-

ity will lead to more flexibility in the management of both load and demand.

However, an analysis of the full potential for these heating systems was not within

the scope of this project, so they have not been included in the modelling. Further

research with localized data is required.

Resource efficiency In addition to energy and GHG modelling, a resource assess-

ment of selected metals has been undertaken (see Chap. 11). A variety of technolo-

gies—especially storage technologies—can be used to reduce the pressure on

resource requirements, namely for cobalt and lithium for batteries and electric

mobility and the silver required for solar technologies. Therefore, the choice of stor-

age technologies has taken the specific requirements for metals into account.

Table 8.11 shows the storage volumes (in GWh per year) required to avoid the

curtailment of variable renewable power generation and the utilization of storage

capacities for batteries and pumped hydro for charging with variable renewable

electricity in the calculated scenarios. The total storage throughput, including the

hydrogen production and the amount of hydrogen-based dispatch power plants, is

also shown.

Pumped hydropower will remain the backbone of the storage concept until 2030,

when batteries start to overtake pumped hydropower by volume. The model does

not distinguish between different battery or pumped hydro technologies. Hydrogen-

based dispatch will remain the largest contributor to systems services after 2030

until the end of the modelling period.

8.4.5 Global: Required Storage Capacities for the Stationary

Power Sector

The world market for storage and dispatch technologies and services will increase

significantly in the 2.0 °C Scenario. The annual market for new hydro pump storage

plants will grow on average by 6 GW per year to a total capacity of 244 GW in

2030. During the same period, the total installed capacity for batteries will grow to

12 GW, requiring an annual market of 1 GW. Between 2030 and 2050, the energy

service sector for storage and storage technologies must accelerate further. The

S. Teske et al.

205

Table 8.11

Global: storage and dispatch

Storage and dispatch

2.0 °C

1.5 °C

World

Required to avoid curtailment

Utilization battery-charge-

Utilization PSH-charge-

Total (incl. H2)

Dispatch H2

Required to avoid curtailment

Utilization battery-charge-

Utilization PSH-charge-

Total (incl. H2)

DispatchH2

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

OECD North America

2020

0

0

0

0

0

0

0

0

0

0

2030

62,369

341

192

1065

11,181

243,235

243,235

475

2405

11,181

2050

853,401

21,805

868

45,331

238,730

999,704

999,704

924

46,766

238,730

Latin America

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

34

1207

1207

99

318

34

2050

1314

640

34

1347

127,226

30,526

30,526

621

12,875

127,226

OECD Europe

2020

0

0

0

0

0

0

0

0

0

0

2030

6238

315

5265

11,161

60,223

38,504

38,504

20,566

42,827

60,223

2050

212,060

30,546

58,368

177,632

814,585

301,234

301,234

72,812

215,641

814,585

Middle East

2020

0

0

0

0

0

0

0

0

0

0

2030

18,088

2

943

1890

0

44,945

44,945

1469

2943

0

2050

752,882

109

4636

9180

0

554,222

554,222

4371

8618

0

Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

4877

118

2244

4726

0

11,264

11,264

2672

5591

0

2050

367,201

6514

8977

30,974

212,902

585,423

585,423

9282

31,210

212,902

Eurasia

2020

0

0

0

0

0

0

0

0

0

0

2030

736

1

169

341

14,106

6031

6031

644

1295

14,106

2050

296,490

948

8396

18,661

401,044

249,984

249,984

7258

16,303

401,044(continued)

8 Energy Scenario Results

206

Table 8.11

(continued)

Storage and dispatch

2.0 °C

1.5 °C

World

Required to avoid curtailment

Utilization battery-charge-

Utilization PSH-charge-

Total (incl. H2)

Dispatch H2

Required to avoid curtailment

Utilization battery-charge-

Utilization PSH-charge-

Total (incl. H2)

DispatchH2

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Non-OECD Asia

2020

0

0

0

0

0

0

0

0

0

0

2030

137

2

15

34

0

6848

6848

311

646

0

2050

171,973

2478

2261

9465

386,454

228,160

228,160

2943

8789

386,454

India

2020

0

0

0

0

0

0

0

0

0

0

2030

59,399

52

2983

6069

1759

182,561

182,561

8577

17,487

1759

2050

372,809

2125

6715

17,678

28,113

437,884

437,884

6595

17,199

28,113

China

2020

0

0

0

0

0

0

0

0

0

0

2030

1102

19

394

827

2582

45,217

45,217

7266

14,957

2582

2050

102,042

57,483

2966

120,899

623,254

264,729

264,729

20,885

60,022

623,254

OECD Pacific

2020

16

0

0

0

0

16

16

0

0

0

2030

84,079

623

4601

10,403

831

146,440

146,440

6688

14,855

831

2050

654,287

70,404

14,815

170,431

81,215

760,962

760,962

14,865

169,093

81,215

Total global

2020

16

0

0

0

0

16

0

0

0

0

2030

237,026

1474

16,808

36,517

90,716

726,252

2945

48,767

103,323

90,716

2050

3,784,459

193,051

108,037

601,598

2,913,522

4,412,827

153,528

140,555

586,516

2,913,522

S. Teske et al.

207

battery market must grow by an annual installation rate of 22 GW, and as a result, it

will overtake the global capacity of pumped hydro between 2040 and 2050. The

conversion of the gas infrastructure from natural gas to hydrogen and synthetic fuels

will start slowly between 2020 and 2030, with the conversion of power plants with

an annual capacity of around 2 GW. However, after 2030, the transformation of the

global gas industry to hydrogen will accelerate significantly, with a total of 197 GW

of gas power plants and gas co-generation capacity converted each year. In parallel,

the average capacity factor for gas and hydrogen plants will decrease from 29%

(2578 h/year) in 2030 to 11% (975 h/year) by 2050, turning the gas sector from a

supply-driven to a service-driven industry.

At around 2030, the 1.5 °C Scenario requires more storage throughput than does

the 2.0 °C Scenario, but storage demands for the two scenarios will be equal at the

end of the modelling period. It is assumed that this higher throughput can be man-

aged with equally high installed capacities, leading to annual capacity factors for

battery and hydro pump storage of around 5–6% by 2050 (Table 8.12).

Table 8.13 shows the average global investment costs for the battery and hydro

pump storage capacities in the 2.0  °C and 1.5  °C Scenarios. Both pathways have

equal storage capacities and cost projections, especially for batteries, but are highly

uncertain in the years beyond 2025. Therefore, the costs are only estimates and

require research.

8.5 OECD North America

8.5.1 OECD North America: Long-Term Energy Pathways

8.5.1.1 OECD North America: Final Energy Demand by Sector

Combining the assumptions for population growth, GDP growth, and energy inten-

sity will result in the development pathways for OECD North America’s final energy

demand shown in Fig. 8.17 under the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios. Under

the 5.0 °C Scenario, the total final energy demand will increase by 10% from the

current 70,500 PJ/year to 77,800 PJ/year in 2050. In the 2.0 °C Scenario, the final

energy demand will decrease by 47% compared with current consumption and will

reach 37,300 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will

reach 33,700 PJ, 52% below the 2015 demand level. In the 1.5 °C Scenario, the final

energy demand in 2050 will be 10% lower than in the 2.0 °C Scenario. The electric-

ity demand for ‘classical’ electrical devices (without power-to-heat or e-mobility)

will decrease from 4230 TWh/year in 2015 to 3340 TWh/year (2.0  °C) or 2950

TWh/year (1.5  °C) by 2050. Compared with the 5.0  °C case (6050 TWh/year in

2050), the efficiency measures in the 2.0 °C and 1.5 °C Scenarios will save a maxi-

mum of 2710 TWh/year and 3100 TWh/year, respectively.

Electrification will lead to a significant increase in the electricity demand by

2050. The 2.0 °C Scenario will require approximately 1400 TWh/year of electricity

8 Energy Scenario Results

208

Table 8.12

Required increases in storage capacities until 2050

Global storage and H2-dispatch market volume under 2 scenariosBatteries

Storage technology share

Pumped hydro

Storage technology share

Hydrogen-production + dispatch

[Through-
put]

Cumulative capacity

[Through-
put]

Cumulative capacity

[Through-
put]

Cumulative capacity

[GWh/year]

[GW]

[%]

[GWh/year]

[GW]

[%]

[GWh/year]

[GW]

2015

No data

2

1

No data

153

99

No data

2030

2.0 °C

1474

12

8

16,808

244

92

90,716

35

2030

1.5 °C

2945

13

6

48,767

255

94

351,496

137

2050

2.0 °C

193,051

347

64

108,037

267

36

2,913,522

2990

2050

1.5 °C

153,528

340

52

140,555

278

48

2,075,533

3423

S. Teske et al.

209

Table 8.13

Estimated average global investment costs for batty and hydro pump storage

Estimated storage investment costs (In $ billion)

2015– 2020

Average annual

2021–2030

Average annual

2031–2040

Average annual

2041–2050

Average annual

2015–2050

Average annual

StorageBattery

4.8

0.967

44.5

4.4

148.1

14.8

655.8

65.6

853.3

24.4

Hydro pump storage

0

0

38.7

3.9

42.7

4.3

47.2

4.7

128.6

3.7

Total

4.8

0.967

83.2

8.3

190.8

19.1

703.0

70.3

981.9

28.1

8 Energy Scenario Results

210

for electric heaters and heat pumps, and in the transport sector, it will require

approximately 3300 TWh/year for electric mobility. The generation of hydrogen

(for transport and high-temperature process heat) and the manufacture of synthetic

fuels (mainly for transport) will add an additional power demand of 3000 TWh/year.

Therefore, the gross power demand will rise from 5300 TWh/year in 2015 to 9500

TWh/year in 2050 in the 2.0 °C Scenario, 30% higher than in the 5.0 °C case. In the

1.5 °C Scenario, the gross electricity demand will increase to a maximum of 9400

TWh/year in 2050 for similar reasons.

The efficiency gains in the heating sector will be similar in magnitude to those in

the electricity sector. Under the 2.0  °C and 1.5  °C Scenarios, a final energy con-

sumption equivalent to about 7000 PJ/year and 9400 PJ/year, respectively, will be

avoided by 2050 through efficiency gains compared with the 5.0 °C Scenario.

8.5.1.2 OECD North America: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. In the 2.0  °C Scenario, 100% of the electricity produced in OECD

North America will come from renewable energy sources by 2050. ‘New’ renew-

ables—mainly wind, solar, and geothermal energy—will contribute 85% of the total

electricity generated. Renewable electricity’s share of the total production will be

68% by 2030 and 89% by 2040. The installed capacity of renewables will reach

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

PJ

/y

r

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.17 OECD North America: development of final energy demand by sector in the scenarios

S. Teske et al.

211

about 1880 GW by 2030 and 3810 GW by 2050. In the 1.5 °C Scenario, the share

of renewable electricity generation in 2030 is assumed to be 84%. The 1.5  °C

Scenario projects a generation capacity from renewable energy of about 3920 GW

in 2050.

Table 8.14 shows the development of the installed capacities of different renew-

able technologies in OECD North America over time. Figure 8.18 provides an over-

view of the overall power-generation structure in OECD North America. From 2020

onwards, the continuing growth of wind and PV—to 1090 GW and 2130 GW,

respectively—is complemented by up to 210 GW of solar thermal generation, as

well as limited biomass, geothermal, and ocean energy, in the 2.0 °C Scenario. Both

the 2.0  °C and 1.5  °C Scenarios will lead to a high proportion of variable power

generation (PV, wind, and ocean) of 49% and 59%, respectively, by 2030, and 73%

and 74%, respectively, by 2050.

Table 8.14 OECD North America: development of renewable electricity generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 194 202 207 216 217
2.0 °C 194 199 202 206 206
1.5 °C 194 199 202 203 203
Biomass 5.0 °C 22 25 27 30 35
2.0 °C 22 27 32 42 52
1.5 °C 22 35 39 43 45
Wind 5.0 °C 87 157 172 197 253
2.0 °C 87 323 540 812 1092
1.5 °C 87 358 656 924 1059
Geothermal 5.0 °C 5 5 6 9 12
2.0 °C 5 6 9 23 37
1.5 °C 5 5 8 25 37
PV 5.0 °C 29 133 162 220 358
2.0 °C 29 534 991 1419 2129
1.5 °C 29 659 1097 1783 2269
CSP 5.0 °C 2 2 3 4 12
2.0 °C 2 22 87 168 209
1.5 °C 2 39 148 257 236
Ocean 5.0 °C 0 0 1 2 4
2.0 °C 0 3 15 59 85
1.5 °C 0 2 13 52 66
Total 5.0 °C 338 523 577 678 891
2.0 °C 338 1115 1878 2729 3810
1.5 °C 338 1298 2163 3288 3916

8 Energy Scenario Results

212

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.18 OECD North America: development of electricity-generation structure in the scenarios

8.5.1.3 OECD North America: Future Costs of Electricity Generation

Figure 8.19 shows the development of the electricity-generation and supply costs

over time, including CO 2 emission costs, in all scenarios. The calculated electricity-

generation costs in 2015 (referring to full costs) were around 4.9 ct/kWh. In the

5.0 °C case, the generation costs will increase until 2050, when they reach 10.1 ct/

kWh. The generation costs in the 2.0 °C Scenario will increase in a similar way until

2030, when they reach 8.3 ct/kWh, and then drop to 6.8 ct/kWh by 2050. In the

1.5 °C Scenario, they will increase to 8.8 ct/kWh and then drop to 7.1 ct/kWh by

2050. In the 2.0 °C Scenario, the generation costs in 2050 are 3.3 ct/kWh lower than

in the 5.0 °C case. In the 1.5 °C Scenario, the generation costs in 2050 are 3.1 ct/

kWh lower than in the 5.0 °C case. Note that these estimates of generation costs do

not take into account integration costs such as power grid expansion, storage, or

other load-balancing measures.

Under the 5.0  °C case, the growth in demand and increasing fossil fuel prices

will result in an increase in total electricity supply costs from today’s $270 billion/

year to more than $760 billion/year in 2050. In both alternative scenarios, the total

supply costs in 2050 will be around $690 billion/year The long-term costs for elec-

tricity supply in 2050 will be 8%–9% lower than in the 5.0 °C Scenario as a result

of the estimated generation costs and the electrification of heating and mobility.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0  °C case to 7.5 ct/kWh. In the 2.0  °C

Scenario, they will increase until 2030, when they reach 7.3 ct/kWh, and then drop

to 6.8 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 8.4 ct/kWh in

2030, and then drop to 7.1 ct/kWh by 2050. In the 2.0 °C Scenario, the generation

costs will be, at maximum, 1 ct/kWh higher than in the 5.0 °C case, and this will

S. Teske et al.

213

occur in 2030. In the 1.5 °C Scenario, compared with the 5.0 °C Scenario, the maxi-

mum difference in generation costs will be 2 ct/kWh in 2030. If the CO 2 costs are

not considered, the total electricity supply costs in the 5.0 °C case will increase to

$570 billion/year in 2050.

8.5.1.4 OECD North America: Future Investments in the Power Sector

An investment of around $7600 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments in plant replace-

ment after the end of their economic lifetimes. This value is equivalent to approxi-

mately $211 billion per year on average, which is $4400 billion more than in the

5.0 °C case ($3200 billion). In the 1.5 °C Scenario, an investment of around $8180

billion for power generation will be required between 2015 and 2050. On average,

this is an investment of $227 billion per year. In the 5.0 °C Scenario, the investment

in conventional power plants will be around 48% of the total cumulative invest-

ments, whereas approximately 52% will be invested in renewable power generation

and co-generation (Fig. 8.20). However, under the 2.0 °C (1.5 °C) Scenario, OECD

North America will shift almost 93% (93%) of its entire investment to renewables

and co- generation. By 2030, the fossil fuel share of the power sector investment will

mainly focus on gas power plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $3240 billion in 2050,

equivalent to $90 billion per year. Therefore, the total fuel cost savings will be

2.0°C efficiency measures 2.0°C

1.5°C

efficiency measures 1.5°C

5.0°C

Spec. Electricity Generation Costs 5.0°C Spec. Electricity Generation Costs 2.0°C

Spec. Electricity Generation Costs 1.5°C

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

800

900

2015 2025 20302040 2050

ct

/kWh
billio

n

$

Fig. 8.19 OECD North America: development of total electricity supply costs and specific electricity- generation costs in the scenarios

8 Energy Scenario Results

214

equivalent to 70% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $3910 billion,

or $109 billion per year.

8.5.1.5 OECD North America: Energy Supply for Heating

The final energy demand for heating will increases in the 5.0 °C Scenario by 32%,

from 19,700 PJ/year in 2015 to 26,000 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 27% by 2050 in the 2.0 °C

Scenario relative to the 5.0  °C case, and by 36% in the 1.5  °C Scenario. Today,

renewables supply around 11% of OECD North America’s final energy demand for

heating, with the main contribution from biomass. Renewable energy will provide

38% of OECD North America’s total heat demand in 2030 in the 2.0 °C Scenario

and 61% in the 1.5 °C Scenario. In both scenarios, renewables will provide 100% of

the total heat demand in 2050.

Figure 8.21 shows the development of different technologies for heating in

OECD North America over time, and Table 8.15 provides the resulting renewable

heat supply for all scenarios. Until 2030, biomass will remain the main contributor.

The growing use of solar, geothermal, and environmental heat will lead, in the long

term, to a biomass share of 25% under the 2.0  °C Scenario and 19% under the

1.5 °C Scenario. Heat from renewable hydrogen will further reduce the dependence

on fossil fuels under both scenarios. Hydrogen consumption in 2050 will be around

Fossil
Nuclear 34%
14%

CHP
4%

Renewable
48%

5.0°C: 2015-2050

total 3,560
billion $

Fossil
(incl. H2)
7%
Nuclear 2%
CHP 5%

Renewable 86%

2.0°C: 2015-2050

total 8,460
billion $

Fossil
(incl. H2)
7%
Nuclear 1%
CHP 3%

Renewable
89%

1.5°C: 2015-2050

total 9,100
billion $

Fig. 8.20 OECD North America: investment shares for power generation in the scenarios

S. Teske et al.

215

0

5,000

10,000

15,000

20,000

25,000

30,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency (compared
to 5.0°C)
Hydrogen
Electric heating
Geothermal heat and
heat pumps
Solar heating
Biomass
Fossil

Fig. 8.21 OECD North America: development of heat supply by energy carrier in the scenarios

3000 PJ/year in the 2.0 °C Scenario and 2700 PJ/year in the 1.5 °C Scenario. The

direct use of electricity for heating will also increase by a factor of 4.6–4.9 between

2015 and 2050 and will have a final energy share of 21% in 2050  in the 2.0  °C

Scenario and 26% in the 1.5 °C Scenario.

8.5.1.6 OECD North America: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $2580 billion in the 2.0 °C Scenario (including investments

for plant replacement after their economic lifetimes) or approximately $72 billion

Table 8.15 OECD North America: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 1868 2142 2334 2787 3279
2.0 °C 1868 2758 3019 3493 3686
1.5 °C 1868 2707 3149 3191 2378
Solar heating 5.0 °C 107 210 277 451 695
2.0 °C 107 887 1772 2639 2962
1.5 °C 107 1290 2169 2839 3128
Geothermal heat and heat pumps 5.0 °C 17 17 18 18 19
2.0 °C 17 875 1378 3031 5257
1.5 °C 17 1076 2185 3463 4152
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 144 276 1014 3045
1.5 °C 0 22 677 2100 2666
Total 5.0 °C 1991 2369 2629 3256 3994
2.0 °C 1991 4664 6445 10,176 14,949
1.5 °C 1991 5095 8180 11,592 12,324

8 Energy Scenario Results

Table 8.16 OECD North America: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 292 315 330 366 411 2.0 °C 292 381 387 355 272 1.5 °C 292 360 384 334 179 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 17 30 44 52 1.5 °C 0 34 57 82 109 Solar heating 5.0 °C 29 58 76 124 191 2.0 °C 29 232 466 697 780 1.5 °C 29 331 557 728 793 Heat pumps 5.0 °C 3 3 3 3 3 2.0 °C 3 123 188 393 677 1.5 °C 3 143 292 479 568 Totala 5.0 °C 324 375 410 494 605 2.0 °C 324 752 1071 1489 1781 1.5 °C 324 868 1290 1622 1649 a Excluding direct electric heating

biomass
technologies
53%

geothermal
heat use
0%

solar
collectors
45%
heat
pumps
2%

5.0°C: 2015-2050

total 417 billion $

biomass
technologies
7%
geothermal
heat use
4%

solar
collectors
37%

heat pumps 52%

2.0°C: 2015-2050

total 2,580 billion $

biomass
technologies
7%
geothermal
heat use
7%

solar
collectors
38%

heat pumps
48%

1.5°C: 2015-2050

total 2,800 billion $

Fig. 8.22 OECD North America: development of investments in renewable heat generation tech- nologies in the scenarios

217

per year. The largest share of investment in OECD North America is assumed to be

for heat pumps (around $1300 billion), followed by solar collectors and biomass

technologies. The 1.5 °C Scenario assumes an even faster expansion of renewable

technologies, resulting in a lower average annual investment of around $78 billion

per year (Table 8.16, Fig. 8.22).

8.5.1.7 OECD North America: Transport

Energy demand in the transport sector in OECD North America is expected to

decrease by 8% in the 5.0  °C Scenario, from around 31,000 PJ/year in 2015 to

28,600 PJ/year in 2050. In the 2.0 °C Scenario, assumed technical, structural, and

behavioural changes will save 73% (20,970 PJ/year) in 2050 compared with the

5.0 °C case. Additional modal shifts, technology switches, and a reduction in trans-

port demand will lead to even higher energy savings in the 1.5 °C Scenario, of 74%

(or 21,100 PJ/year) in 2050 compared with the 5.0 °C case (Table 8.17, Fig. 8.23).

By 2030, electricity will provide 11% (620 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, and in 2050, the share will be 44% (930

TWh/year). In 2050, up to 2090 PJ/year of hydrogen will be used in the transport

sector as a complementary renewable option. In the 1.5  °C Scenario, the annual

electricity demand will be 1030 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 2020 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 2540 PJ/year

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

duced, with a maximum amount of 270 PJ/year in 2050. Because the reduction in

Table 8.17 OECD North America: projection of the transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 674 628 609 570 529
2.0 °C 674 660 655 523 516
1.5 °C 674 743 730 773 806
Road 5.0 °C 26,686 25,691 24,838 24,222 23,414
2.0 °C 26,686 21,257 15,933 7731 5124
1.5 °C 26,686 18,612 11,973 6717 5251
Domestic aviation 5.0 °C 2421 2978 3274 3398 3186
2.0 °C 2421 2309 2026 1530 1242
1.5 °C 2421 2167 1689 1063 840
Domestic navigation 5.0 °C 461 482 493 514 535
2.0 °C 461 481 489 489 473
1.5 °C 461 479 484 483 473
Total 5.0 °C 30,241 29,779 29,214 28,704 27,664
2.0 °C 30,241 24,707 19,104 10,273 7354
1.5 °C 30,241 22,000 14,875 9036 7370

8 Energy Scenario Results

218

fossil fuel for transport will be faster, biofuel use will increase in the 1.5 °C Scenario

to a maximum of 5900 PJ/year. The demand for synthetic fuels will decrease to zero

by 2050  in the 1.5  °C Scenario because of the lower overall energy demand by

transport.

8.5.1.8 OECD North America: Development of CO 2 Emissions

In the 5.0 °C Scenario, OECD North America’s annual CO 2 emissions will decrease

by 9% from 6170 Mt. in 2015 to 5612 Mt. in 2050. Stringent mitigation measures

in both the alternative scenarios will lead to reductions in annual emissions to 930

Mt. in 2040 in the 2.0 °C Scenario and to 120 Mt. in the 1.5 °C Scenario, with fur-

ther reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emis-

sions from 2015 until 2050 will add up to 216 Gt. In contrast, in the 2.0  °C and

1.5 °C Scenarios, the cumulative emissions for the period from 2015 until 2050 will

be 99 Gt and 72 Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 54% in the 2.0  °C

Scenario and by 67% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

decrease in the annual emissions will occur under both alternative scenarios. In the

2.0 °C Scenario, the reduction will be greatest in ‘Power generation’, followed by

the ‘Transport’ and ‘Residential and other’ sectors (Fig. 8.24).

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C
20152025 2030 2040 2050

PJ/yr

Efficiency
(compared
to 5.0°C)
Hydrogen

Electricity

Synfuels
Biofuels

Natural Gas

Oil products

Fig. 8.23 OECD North America: final energy consumption by transport in the scenarios

S. Teske et al.

219

8.5.1.9 OECD North America: Primary Energy Consumption

Taking into account the assumptions discussed above, the levels of primary energy

consumption under the three scenarios are shown in Fig. 8.25. In the 2.0 °C Scenario,

the primary energy demand will decrease by 46%, from around 111,600 PJ/year in

2015 to 60,600 PJ/year in 2050. Compared with the 5.0  °C Scenario, the overall

primary energy demand will decrease by 50% by 2050  in the 2.0  °C Scenario

(5.0 °C: 121,000 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand

will be even lower (56,600 PJ in 2050) because the final energy demand and conver-

sion losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. As a

result, renewable energy will have a primary energy share of 34% in 2030 and 91%

in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a pri-

0

50

100

150

200

250

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

cumulated emissions [Gt]

CO

2
emissions [Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.24 OECD North America: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

8 Energy Scenario Results

220

mary share of more than 91% in 2050 (including non-energy consumption, which

will still include fossil fuels). Nuclear energy will be phased-out by 2040 under both

the 2.0 °C and the 1.5 °C Scenarios. The cumulative primary energy consumption

of natural gas in the 5.0 °C case will add up to 1290 EJ, the cumulative coal con-

sumption to about 470 EJ, and the crude oil consumption to 1300 EJ. In contrast, in

the 2.0 °C Scenario, the cumulative gas demand will amount to 730 EJ, the cumula-

tive coal demand to 120 EJ, and the cumulative oil demand to 640 EJ. Even lower

cumulative fossil fuel use will be achieved in the 1.5 °C Scenario: 480 EJ for natural

gas, 80 EJ for coal, and 440 EJ for oil.

8.5.2 Regional Results: Power Sector Analysis

The key results for all 10 world regions and their sub-regions are presented in this

section, with standardized tables to make them comparable for the reader. Regional

differences and particularities are summarized. It is important to note that the elec-

tricity loads for the sub-regions—which are in several cases also countries—are

calculated (see Chap. 3) and are not real measured values. When information was

available, the model results are compared with published peak loads and adapted as

far as possible. However, deviations of 10% or more for the base year are in the

range of the probability. The interconnection capacities between sub-regions are

simplified assumptions based on current practices in liberalized power markets, and

include cross-border trade (e.g., between Canada and the USA) (C2ES 2017 ) or

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

net electricity
imports
Efficiency
Ocean energy
Geothermal
Solar
Biomass
Wind
Hydro
Natural gas
Crude oil
Coal
Nuclear

Fig. 8.25 OECD North America: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

S. Teske et al.

221

within the European Union (EU). The EU set a target of 10% interconnection capac-

ity between their member states in 2002 (EU-EG 2017 ). The interconnection capac-

ities for sub-regions that are not geographically connected are set to zero for the

entire modelling period, even when there is current discussion about the implemen-

tation of new interconnections, such as for the ASEAN Power Grid (ASEAN-CE

2018 ).

8.5.3 OECD North America: Power Sector Analysis

The OECD North America region includes Canada, the USA, and Mexico, and

therefore contains more than one large electricity market. Although the power sec-

tor is liberalized in all three countries, the state of implementation and the market

rules in place vary significantly. Even within the USA, each state has different mar-

ket rules and grid regulations. Therefore, the calculated scenarios assume the prior-

ity dispatch of all renewables and priority grid connections for new renewable

power plants, and a streamlined process for required construction permits. The

power sector analysis for all regions is based on technical, not political,

considerations.

8.5.3.1 OECD North America: Development of Power Plant Capacities

The size of the renewable power market in OECD North America will increase

significantly in the 2.0 °C Scenario. The annual market for solar PV must increase

from 22.76 GW in 2020 by a factor of 5 to an average of 95 GW by 2030. The

onshore wind market must expand to 35 GW by 2025, an increase from around 13

GW 5  years earlier. By 2050, offshore wind generation will increase to 9.7 GW

annually, by a factor of 7 compared with the base year (2015). Concentrated solar

power plants will play an important role in dispatchable solar electricity generation

to supply bulk power, especially for industry and industrial process heat. The annual

market in 2030 will increase to 16 GW, compared with 1.7 GW in 2020. The 1.5 °C

Scenario accelerates both the phase-out of fossil-fuel-based power generation and

the deployment of renewables—mainly solar PV and wind in the first decade—

about 5–7 years faster than the 2.0 °C Scenario (Table 8.18).

8.5.3.2 OECD North America: Utilization of Power-Generation

Capacities

Table 8.19 shows the increasing shares of variable renewable power generation

across all North American regions. Whereas Alaska and Canada are dominated by

variable wind power generation, Mexico and the sunny mid-west of the USA have

significant contributions from CSP. Solar PV is distributed evenly across the entire

8 Energy Scenario Results

222

region. Onshore and offshore wind penetration is highest in rural areas, whereas

solar roof-top power generation is highest in suburban regions where roof space and

electricity demand from residential buildings correlate best. The south-west of the

USA will have the highest share of variable renewables—mainly solar PV for resid-

ual homes and office buildings, connected to battery systems. There are no struc-

tural differences between the 2.0  °C and 1.5  °C Scenarios, except faster

implementation in the latter. It is assumed that all regions will have an interconnec-

tion capacity of 20% of the regional average load, with which to exchange renew-

able and dispatch electricity to neighbouring regions.

Capacity factors for the five generation types and the resulting average utiliza-

tion are shown in Table 8.20. Compared with the global average, North America

will start with a capacity factor for limited dispatchable generation of about 10%

over the global average. By 2050, the average capacity factor across all power-

generation types will be 29% for both scenarios. A low average capacity factor

requires flexible power plants and a power market framework that incentivizes

them.

8.5.3.3 OECD North America: Development of Load, Generation,

and Residual Load

Table 8.21 shows the development of the maximum load, generation, and resulting

residual load (the load remaining after variable renewable generation). With

increased shares of variable solar PV and wind power, the minimum residual load

Table 8.18 OECD North America: average annual change in installed power plant capacity

Power generation: average annual change
of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C 2.0 °C 1.5 °C 2.0 °C 1.5 °C
Hard coal − 7 − 16 − 6 − 8 − 4 0
Lignite − 14 − 18 − 7 0 0 0
Gas 6 9 12 1 − 55 − 4
Hydrogen-gas 1 10 4 24 55 39
Oil/diesel − 5 − 7 − 3 − 4 − 1 0
Nuclear − 4 − 9 − 10 − 10 0 − 1
Biomass 1 2 1 1 1 0
Hydro − 5 − 3 0 0 0 2
Wind (onshore) 24 48 36 36 24 19
Wind (offshore) 2 19 11 19 10 3
PV (roof top) 39 94 64 68 61 55
PV (utility scale) 13 31 21 23 20 18
Geothermal 0 0 1 1 2 2
Solar thermal power plants 3 18 15 18 6 4
Ocean energy 1 2 4 4 4 3
Renewable fuel based co-generation 1 2 2 2 2 0

S. Teske et al.

223

Table 8.19

OECD North America and sub-regions: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

OECD North America

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

USA Alaska

2015

4%

35%

61%

10%

2030

29%

31%

40%

15%

36%

30%

34%

15%

2050

42%

23%

35%

20%

42%

26%

32%

20%

Canada West

2015

6%

35%

59%

10%

2030

43%

30%

27%

15%

53%

28%

19%

15%

2050

63%

21%

16%

20%

63%

23%

14%

20%

Canada East

2015

7%

35%

59%

10%

2030

45%

30%

25%

15%

56%

27%

16%

15%

2050

66%

21%

13%

20%

66%

23%

11%

20%

USA North East

2015

7%

35%

58%

10%

2030

47%

31%

22%

15%

58%

28%

14%

15%

2050

69%

20%

11%

20%

69%

22%

9%

20%

USA North West

2015

4%

35%

61%

10%

2030

36%

32%

32%

15%

47%

30%

23%

15%

2050

59%

23%

18%

20%

59%

25%

16%

20%

USA South West

2015

7%

35%

58%

10%

2030

53%

28%

19%

15%

64%

25%

11%

15%

2050

73%

17%

10%

20%

73%

18%

8%

20%

USA South East

2015

8%

35%

58%

10%

2030

53%

28%

19%

15%

63%

25%

12%

15%

2050

71%

18%

11%

20%

71%

20%

9%

20%

(continued)

8 Energy Scenario Results

224

Power generation structure and interconnection

2.0 °C

1.5 °C

OECD North America

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Mexico

2015

5%

35%

61%

10%

2030

37%

30%

32%

15%

46%

28%

26%

15%

2050

56%

23%

22%

20%

55%

25%

19%

20%

OECD North America

2015

7%

35%

58%

2030

48%

30%

23%

59%

27%

15%

2050

68%

19%

13%

68%

21%

11%

Table 8.19

(continued)

S. Teske et al.

225

can become negative. If this happens, the surplus generation can either be exported

to other regions, stored, or curtailed. The export of load to other regions requires

transmission lines. If the theoretical utilization rate of transmission cables (= inter-

connection) exceeds 100%, the transport capacity must be increased. We assume

that the entire load need not be exported, and that surplus generation capacities can

be curtailed because interconnections are costly and require a certain level of utili-

zation to make them economically viable. An analysis of the economic viability of

new interconnections and their optimal transmission capacities is beyond the scope

of this research project.

In Alaska in the 2.0 °C Scenario, for example, generation and demand are bal-

anced in 2020 and 2030, but peak generation is substantially higher than demand in

2050. In the 1.5 °C Scenario, a significant level of overproduction is achieved by
2051. In the two scenarios, the surplus peak generation is equally high. These results

have been calculated under the assumption that surplus generation will be stored in

a cascade of batteries and pumped-storage hydroelectricity (PSH) or used to pro-

duce hydrogen and/or synthetic fuels. Therefore, the maximal interconnection

requirements shown in this chapter represent the maximum surplus generation

capacity. To avoid curtailment, these overcapacities have mainly been used for

hydrogen production. Therefore, Alaska could remain an energy exporter but switch

from oil to wind-generated synthetic gas and/or hydrogen.

Table 8.22 provides an overview of the calculated storage and dispatch power

requirements by sub-region. To store or export the entire electricity output during

Table 8.20 OECD North America: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
OECD North
America 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
Factor – average

[%/yr]53.1% 35% 33% 29% 28% 34% 28% 29% 29%

Limited
dispatchable:
fossil and
nuclear

[%/yr]68.6% 40% 10% 28% 2% 20% 6% 10% 10%

Limited
dispatchable:
renewable

[%/yr]45.9% 46% 57% 37% 39% 59% 36% 36% 35%

Dispatchable:
fossil

[%/yr]39.7% 23% 21% 11% 5% 30% 8% 12% 11%

Dispatchable:
renewable

[%/yr]44.0% 52% 68% 49% 52% 47% 44% 49% 45%

Variable:
renewable

[%/yr]18.9% 12% 12% 25% 26% 34% 27% 28% 28%

8 Energy Scenario Results

226

Table 8.21

OECD North America: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

OEC D North America

Max demand

Max generation

Max Residual Load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

NA – USA Alaska

2020

1.4

1.4

0.0

1.4

18.8

0.1

2030

1.5

1.5

0.1

0

1.6

13.5

0.3

12

2050

2.4

11.8

0.5

9

2.4

11.5

0.5

9

NA – Canada West

2020

21.1

21.1

0.0

21.2

34.0

0.3

2030

23.0

31.2

5.7

3

24.5

39.8

4.6

11

2050

37.2

73.1

15.2

21

37.3

76.4

15.3

24

NA – Canada East

2020

53.0

53.0

0.0

53.1

117.3

0.8

2030

58.0

88.0

14.6

15

61.6

117.5

15.3

40

2050

94.3

213.7

41.2

78

94.6

223.0

41.0

87

NA – USA North East

2020

258.6

243.6

29.9

259.5

273.2

21.8

2030

288.5

355.7

47.7

20

304.2

468.8

63.5

101

2050

433.0

853.7

175.3

246

434.6

891.6

176.7

280

NA – USA North West

2020

25.6

25.6

0.0

25.7

81.1

2.2

2030

28.5

30.6

5.9

0

30.1

40.8

6.0

5

2050

42.5

74.3

16.0

16

42.7

77.7

16.1

19

S. Teske et al.

227

NA – USA South West

2020

109.4

109.1

4.6

109.8

167.5

9.3

2030

121.8

163.0

11.8

29

128.5

208.8

20.0

60

2050

181.8

384.2

38.3

164

182.4

402.3

42.0

178

NA – USA South East

2020

217.7

217.7

0.4

217.4

232.1

15.3

2030

255.8

372.6

38.0

79

270.9

490.7

64.7

155

2050

393.3

890.9

102.6

395

394.5

927.6

122.3

411

Mexico

2020

66.6

51.3

22.3

2030

87.2

116.1

21.3

8

97.6

151.9

19.8

35

2050

171.9

277.1

80.5

25

173.3

289.7

81.9

34

8 Energy Scenario Results

228

Table 8.22

OECD North America: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

OECD North America

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total Storage demand (incl. H2)

Dispatch Hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

USA Alaska

2020

0

0

0

0

0

0

0

0

0

0

2030

11

0

0

0

136

68

1

1

2

136

2050

328

38

1

39

542

407

41

1

42

542

Canada West

2020

0

0

0

0

0

0

0

0

0

0

2030

1011

14

7

21

1957

4078

31

18

49

1957

2050

14,665

1044

34

1078

7776

17,557

1100

38

1137

7776

Canada East

2020

0

0

0

0

0

0

0

0

0

0

2030

3014

38

20

58

4482

13,352

82

53

135

4482

2050

42,780

2545

91

2636

18,129

50,077

2623

97

2720

18,129

USA North East

2020

0

0

0

0

0

0

0

0

0

0

2030

9092

148

73

221

17,290

50,047

404

239

643

17,290

2050

212,448

13,990

509

14,499

60,398

252,243

14,457

546

15,004

60,398

USA North West

2020

0

0

0

0

0

0

0

0

0

0

2030

90

4

1

5

2394

1854

26

13

39

2394

2050

11,806

1013

33

1046

8707

14,933

1085

37

1122

8707

USA South West

2020

0

0

0

0

0

0

0

0

0

0

2030

10,722

121

68

189

6370

47,636

238

172

410

6370

2050

172,771

6661

301

6962

22,741

201,316

6894

316

7210

22,741

S. Teske et al.

229

USA South East

2020

0

0

0

0

0

0

0

0

0

0

2030

35,827

320

195

516

15,281

115,409

579

402

981

15,281

2050

372,747

15,600

690

16,290

53,958

429,227

15,734

725

16,459

53,958

Mexico

2020

0

0

0

0

0

0

0

0

0

0

2030

2604

37

18

55

7792

10,790

95

52

147

7792

2050

25,855

2706

75

2781

32,716

33,945

2985

86

3071

32,716

OECD North America

2020

0

0

0

0

0

0

0

0

0

0

2030

62,369

682

384

1065

55,702

243,235

1456

949

2405

55,702

2050

853,401

43,597

1735

45,331

204,967

999,704

44,919

1846

46,766

204,967

8 Energy Scenario Results

230

each production peak would require significant additional investment. Therefore, it

is assumed that not all surplus solar and wind generation must be stored, and that up

to 5% (in 2030) and 10% (in 2050) of the annual production can be curtailed with-

out significant economic disadvantage. We assume that regions with favourable

wind and solar potentials, and advantages regarding available space, will use their

overcapacities to export electricity via transmission lines and/or to produce syn-

thetic and/or hydrogen fuels.

The southern part of the USA will achieve a significant solar PV share by 2050

and storage demand will be highest in this region. Storage and dispatch demand will

increase in all sub-regions between 2025 and 2035. Before 2025, storage demand

will be zero in all regions.

8.6 Latin America

8.6.1 Latin America: Long-Term Energy Pathways

8.6.1.1 Latin America: Final Energy Demand by Sector

Combining the assumptions on population growth, GDP growth, and energy inten-

sity will produce the future development pathways for Latin America’s final energy

demand shown in Fig. 8.26 for the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios. Under the

5.0 °C Scenario, the total final energy demand will increase by 70% from the cur-

rent 19,200 PJ/year to 32,600 PJ/year in 2050. In the 2.0  °C Scenario, the final

energy demand will decrease by 11% compared with current consumption and will

reach 17,000 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will

fall to 15,800 PJ in 2050, 18% below the 2015 demand. In the 1.5 °C Scenario, the

final energy demand in 2050 will be 7% lower than in the 2.0  °C Scenario. The

electricity demand for ‘classical’ electrical devices (without power-to-heat or

e-mobility) will increase from 740 TWh/year in 2015 to around 1560 TWh/year in

2050 in both alternative scenarios, around 300 TWh/year lower than in the 5.0 °C

Scenario (1860 TWh/year in 2050).

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0 °C Scenario, the electricity demand for heating will be about 600

TWh/year due to electric heaters and heat pumps, and in the transport sector an

increase of approximately 1700 TWh/year will be caused by electric mobility. The

generation of hydrogen (for transport and high-temperature process heat) and the

manufacture of synthetic fuels (mainly for transport) will add an additional power

demand of 600 TWh/year. The gross power demand will thus increase from 1300

TWh/year in 2015 to 3500 TWh/year in 2050 in the 2.0 °C Scenario, 25% higher

than in the 5.0  °C case. In the 1.5  °C Scenario, the gross electricity demand will

increase to a maximum of 3800 TWh/year in 2050.

S. Teske et al.

231

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

4,00 0

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

TW
h/
yr
PJ

/y

r

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.26 Latin America: development of final energy demand by sector in the scenarios

Efficiency gains in the heating sector could be even larger than in the electricity

sector. Under the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 4300 PJ/year will be avoided through efficiency gains in both scenarios

by 2050 compared with the 5.0 °C Scenario.

8.6.1.2 Latin America: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power coming from

renewable sources. By 2050, 100% of the electricity produced in Latin America will

come from renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—

mainly wind, solar, and geothermal energy—will contribute 63% of the total elec-

tricity generation. Renewable electricity’s share of the total production will be 87%

by 2030 and 96% by 2040. The installed capacity of renewables will reach about

530 GW by 2030 and 1030 GW by 2050. The share of renewable electricity genera-

tion in 2030 in the 1.5 °C Scenario will be 91%. In the 1.5 °C Scenario, the genera-

tion capacity from renewable energy will be approximately 1210 GW in 2050.

Table 8.23 shows the development of different renewable technologies in Latin

America over time. Figure 8.27 provides an overview of the overall power-

generation structure in Latin America. From 2020 onwards, the continuing growth

of wind and PV, up to 230 GW and 410 GW, respectively, will be complemented by

up to 60 GW solar thermal generation, as well as limited biomass, geothermal, and

8 Energy Scenario Results

232

Table 8.23 Latin America: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 161 200 222 269 302
2.0 °C 161 180 180 183 184
1.5 °C 161 180 180 183 184
Biomass 5.0 °C 18 23 25 29 34
2.0 °C 18 43 57 75 89
1.5 °C 18 43 61 75 81
Wind 5.0 °C 11 31 38 50 66
2.0 °C 11 56 95 199 234
1.5 °C 11 67 134 272 285
Geothermal 5.0 °C 1 1 2 3 4
2.0 °C 1 3 5 12 18
1.5 °C 1 3 5 12 15
PV 5.0 °C 2 14 19 29 42
2.0 °C 2 108 175 295 409
1.5 °C 2 133 237 529 537
CSP 5.0 °C 0 1 1 2 3
2.0 °C 0 4 20 51 63
1.5 °C 0 4 20 76 78
Ocean 5.0 °C 0 0 0 0 4
2.0 °C 0 1 2 20 37
1.5 °C 0 1 2 20 30
Total 5.0 °C 193 270 306 382 456
2.0 °C 193 395 534 834 1034
1.5 °C 193 432 640 1167 1209

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.27 Latin America: development of electricity-generation structure in the scenarios

S. Teske et al.

233

ocean energy, in the 2.0 °C Scenario. Both the 2.0 °C and 1.5 °C Scenarios will lead

to a high proportion of variable power generation (PV, wind, and ocean) of 31% and

39%, respectively, by 2030, and 52% and 57%, respectively, by 2050.

8.6.1.3 Latin America: Future Costs of Electricity Generation

Figure 8.28 shows the development of the electricity-generation and supply costs

over time, including CO 2 emission costs, under all scenarios. The calculated

electricity- generation costs in 2015 (referring to full costs) were around 4.5 ct/kWh.

In the 5.0 °C case, the generation costs will increase until 2050, when they reach 8.3

ct/kWh. The generation costs in the 2.0 °C Scenario will increase until 2030, when

they reach 7 ct/kWh, and will then drop to 5.9 ct/kWh by 2050. In the 1.5  °C

Scenario, they will increase to 6.7 ct/kWh, and then drop to 5.6 ct/kWh by 2050. In

the 2.0 °C Scenario, the generation costs in 2050 will be 2.4 ct/kWh lower than in

the 5.0 °C case. In the 1.5 °C Scenario, the maximum difference in generation costs

will be 2.6 ct/kWh in 2050. Note that these estimates of generation costs do not take

into account integration costs such as power grid expansion, storage, or other load-

balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

result in an increase in total electricity supply costs from today’s $70 billion/year to

more than $240 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs

will be $230 billion/year and in the 1.5 °C Scenario, they will be $240 billion/year

in 2050. The long-term costs for electricity supply will be more than 5% lower in

0

1

2

3

4

5

6

7

8

9

-50

0

50

100

150

200

250

300

2015 2025 2030 2040 2050

ct

/k

Wh

b

illi

on

$

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.28 Latin America: development of total electricity supply costs and specific electricity- generation costs in the scenarios

8 Energy Scenario Results

234

the 2.0 °C Scenario than in the 5.0 °C Scenario as a result of the estimated genera-

tion costs and the electrification of heating and mobility. Further electrification and

synthetic fuel generation in the 1.5 °C Scenario will result in total power generation

costs that are similar to the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0  °C case to 7.1 ct/kWh. In the 2.0  °C

Scenario, they will increase until 2030, when they will reach 6.6 ct/kWh, and then

drop to 5.9 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 6.5 ct/kWh

and then drop to 5.6 ct/kWh by 2050. In the 2.0 °C Scenario, the generation costs

will be maximum, at 0.25 ct/kWh higher than in the 5.0 °C case, in 2030 (0.1 ct/

kWh in the 1.5 °C Scenario). The generation costs in 2050 will again be lower in the

alternative scenarios than in the 5.0 °C case: 1.2 ct/kWh in the 2.0 °C Scenario and

1.5 ct/kWh in the 1.5 °C Scenario. If CO 2 costs are not considered, the total electric-

ity supply costs in the 5.0 °C case will increase to about $210 billion/year in 2050.

8.6.1.4 Latin America: Future Investments in the Power Sector

An investment of about $1920 billion will be required for power generation between

2015 and 2050  in the 2.0  °C Scenario, including additional power plants for the

production of hydrogen and synthetic fuels and investments in plant replacement

after the ends of their economic lives. This value is equivalent to approximately $53

billion per year, on average, which is $880 billion more than in the 5.0  °C case

($1040 billion). An investment of around $2190 billion for power generation will be

required between 2015 and 2050 in the 1.5 °C Scenario. On average, this will be an

investment of $61 billion per year. Under the 5.0  °C Scenario, the investment in

conventional power plants will be around 25% of the total cumulative investments,

whereas approximately 75% will be invested in renewable power generation and

co-generation (Fig. 8.29).

However, under the 2.0  °C (1.5  °C) Scenario, Latin America will shift almost

94% (95%) of its entire investment to renewables and co-generation. By 2030, the

fossil fuel share of the power sector investment will predominantly focus on gas

power plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $820 billion in 2050,

equivalent to $23 billion per year. Therefore, the total fuel cost savings will be

equivalent to 90% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $900 billion,

or $25 billion per year.

S. Teske et al.

235

8.6.1.5 Latin America: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0  °C Scenario by 72%,

from 7800 PJ/year in 2015 to 13,300 PJ/year in 2050. In the 2.0  °C and 1.5  °C

Scenarios, energy efficiency measures will help to reduce the energy demand for heat-

ing by 32% in 2050, relative to that in the 5.0 °C Scenario. Today, renewables supply

around 42% of Latin America’s final energy demand for heating, with the main con-

tribution from biomass. Renewable energy will provide 68% of Latin America’s total

heat demand in 2030 in the 2.0 °C Scenario and 75% in the 1.5 °C Scenario. In both

scenarios, renewables will provide 100% of the total heat demand in 2050.

Figure 8.30 shows the development of different technologies for heating in Latin

America over time, and Table 8.24 provides the resulting renewable heat supply for

all scenarios. Biomass will remain the main contributor. The growing use of solar,

geothermal, and environmental heat will supplement mainly fossil fuels. This will

lead in the long term to a biomass share of 65% under the 2.0 °C Scenario and 50%

under the 1.5 °C Scenario.

Fossil
22%

Nuclear
3%

CHP
6%

Renewable
69%

5.0°C: 2015-2050

total 1,040
billion $

Fossil (incl. H2)
5%

CHP
8%

Renewable
87%

1.5°C: 2015-2050

total 2,200
billion $

Fossil (incl. H2)
6%

CHP
9%

Renewable
85%

2.0°C: 2015-2050

total 1,920
billion $

Fig. 8.29 Latin America: investment shares for power generation in the scenarios

8 Energy Scenario Results

236

Table 8.24 Latin America: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 2684 2760 2888 3335 3622
2.0 °C 2684 3550 3895 4412 4654
1.5 °C 2684 3632 4007 4023 2767
Solar heating 5.0 °C 32 64 88 146 227
2.0 °C 32 394 712 1217 1418
1.5 °C 32 394 783 1265 1445
Geothermal heat and heat pumps 5.0 °C 0 0 0 0 0
2.0 °C 0 133 206 458 910
1.5 °C 0 133 204 452 930
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 4 169 220
1.5 °C 0 0 88 473 404
Total 5.0 °C 2715 2824 2976 3480 3849
2.0 °C 2715 4077 4817 6255 7202
1.5 °C 2715 4159 5082 6213 5546

0

2,00 0

4,00 0

6,00 0

8,00 0

10,00 0

12,00 0

14,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 20402050

PJ

/y

r

Efficiency (compared
to 5.0°C)
Hydrogen
Electric heating
Geothermal heat and
heat pumps
Solar heating
Biomass
Fossil

Fig. 8.30 Latin America: development of heat supply by energy carrier in the scenarios

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 200 PJ/year in the

2.0 °C Scenario and 400 PJ/year in the 1.5 °C Scenario. The direct use of electricity

for heating will also increase by a factor of 2–4 between 2015 and 2050 and will

attain a final energy share of 20% in 2050 in the 2.0 °C Scenario and 39% in the

1.5 °C Scenario.

S. Teske et al.

237

8.6.1.6 Latin America: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $580 billion in the 2.0 °C Scenario (including investments in

plant replacement after their economic lifetimes), or approximately $16 billion per

year. The largest share of investment in Latin America is assumed to be for solar

collectors (more than $200 billion), followed by biomass technologies and heat

pumps. The 1.5 °C Scenario assumes an even faster expansion of renewable tech-

nologies, but due to the lower heat demand, the average annual investment will

again be around $16 billion per year (Fig. 8.31, Table 8.25).

biomass
technologies
90%

geothermal
heat use
0%

solar
collectors
10%

heat
pumps
0%

5.0°C: 2015-2050

total 115 billion $

biomass
technologies
28%

geothermal
heat use
7%

solar
collectors
37%

heat pumps
28%

2.0°C: 2015-2050

total 581 billion $

biomass
technologies
25%

geothermal
heat use
7%

solar
collectors
39%

heat pumps
29%

1.5°C: 2015-2050

total 573 billion $

Fig. 8.31 Latin America: development of investments for renewable heat generation technologies in the scenarios

8 Energy Scenario Results

238

8.6.1.7 Latin America: Transport

Energy demand in the transport sector in Latin America is expected to increase by

63% under the 5.0 °C Scenario, from around 7100 PJ/year in 2015 to 11,700 PJ/year

in 2050. In the 2.0  °C Scenario, assumed technical, structural, and behavioural

changes will save 69% (8090 PJ/year) by 2050 relative to the 5.0  °C Scenario.

Additional modal shifts, technology switches, and a reduction in transport demand

will lead to even greater energy savings in the 1.5 °C Scenario of 77% (or 9040 PJ/

year) in 2050 compared with the 5.0 °C case (Table 8.26, Fig. 8.32).

By 2030, electricity will provide 6% (110 TWh/year) of the transport sector’s

total energy demand under the 2.0 °C Scenario, whereas in 2050, the share will be

47% (470 TWh/year). In 2050, up to 480 PJ/year of hydrogen will be used in the

transport sector as a complementary renewable option. In the 1.5 °C Scenario, the

annual electricity demand will be 390 TWh in 2050. The 1.5  °C Scenario also

assumes a hydrogen demand of 430 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 1340 PJ/year

Around 2030, synthetic fuels based on power-to-liquid will be introduced, with a

maximum of 190 PJ/year by 2050. Due to the lower overall energy demand in trans-

port, biofuel use will be reduced in the 1.5 °C Scenario to a maximum of 1030 PJ/

year The maximum synthetic fuel demand will amount to 350 PJ/year.

8.6.1.8 Latin America: Development of CO 2 Emissions

In the 5.0 °C Scenario, Latin America’s annual CO 2 emissions will increase by 48%,

from 1220 Mt. in 2015 to 1806 Mt. in 2050. The stringent mitigation measures in

both alternative scenarios will cause the annual emissions to fall to 240 Mt. in

Table 8.25 Latin America: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 549 531 536 552 542 2.0 °C 549 730 742 657 603 1.5 °C 549 770 752 513 179 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 2 4 12 16 1.5 °C 0 2 4 12 17 Solar heating 5.0 °C 7 15 20 34 52 2.0 °C 7 91 164 281 327 1.5 °C 7 91 181 292 333 Heat pumps 5.0 °C 0 0 0 0 0 2.0 °C 0 13 18 36 88 1.5 °C 0 13 18 36 89 Totala 5.0 °C 556 546 556 585 594 2.0 °C 556 835 929 986 1034 1.5 °C 556 876 955 853 619 a Excluding direct electric heating

S. Teske et al.

239

2040 in the 2.0 °C Scenario and to 50 Mt. in the 1.5 °C Scenario, with further reduc-

tions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions from

2015 until 2050 will add up to 56 Gt. In contrast, in the 2.0 °C and 1.5 °C Scenarios,

the cumulative emissions for the period from 2015 until 2050 will be 21 Gt and 17

Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 63% in the 2.0  °C

Scenario and by 70% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario, the reduction will be greatest in ‘Power generation’, followed by the

‘Residential and other’ and ‘Industry’ sectors (Fig. 8.33).

Table 8.26 Latin America: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 90 110 122 145 163
2.0 °C 90 113 133 157 192
1.5 °C 90 130 145 163 224
Road 5.0 °C 6662 7486 8102 9754 10,610
2.0 °C 6662 6424 5799 4107 3112
1.5 °C 6662 5196 3971 2744 2161
Domestic aviation 5.0 °C 211 348 453 593 638
2.0 °C 211 228 213 175 139
1.5 °C 211 218 196 137 104
Domestic navigation 5.0 °C 101 104 107 113 117
2.0 °C 101 104 107 113 117
1.5 °C 101 104 107 113 117
Total 5.0 °C 7064 8047 8783 10,605 11,529
2.0 °C 7064 6868 6251 4551 3559
1.5 °C 7064 5648 4419 3157 2605

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ

/y

r

Efficiency (compared
to 5.0°C)
Hydrogen
Electricity
Synfuels
Biofuels
Natural Gas
Oil products

Fig. 8.32 Latin America: final energy consumption by transport in the scenarios

8 Energy Scenario Results

240

8.6.1.9 Latin America: Primary Energy Consumption

The levels of primary energy consumption under the three scenarios when the

assumptions discussed above are taken into account are shown in Fig. 8.34. In the

2.0  °C Scenario, the primary energy demand will decrease by 2%, from around

28,400 PJ/year in 2015 to 27,900 PJ/year in 2050. Compared with the 5.0  °C

Scenario, the overall primary energy demand will decrease by 38% in 2050 in the

2.0  °C Scenario (5.0  °C: 45000 PJ in 2050). In the 1.5  °C Scenario, the primary

energy demand will be even lower (25,700 PJ in 2050) because the final energy

demand and conversion losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 55% in 2030 and

94% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will also

have a primary energy share of more than 94% in 2050 (including non-energy con-

sumption, which will still include fossil fuels). Nuclear energy will be phased-out

by 2035 under both the 2.0 °C and the 1.5 °C Scenarios. The cumulative primary

energy consumption of natural gas in the 5.0  °C case will add up to 290 EJ, the

cumulative coal consumption to about 60 EJ, and the crude oil consumption to 460

EJ. In contrast, in the 2.0 °C Scenario, the cumulative gas demand will amount to

130 EJ, the cumulative coal demand to 20 EJ, and the cumulative oil demand to 200

0

10

20

30

40

50

60

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 203020402050

cu
mu
la
te
d
emi
ssi
on
s
[G
t]
CO

2
emi

ssi

on

s

[M

t/
yr

]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.33 Latin America: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

2 41

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

net electricity
imports
Efficiency
Ocean energy
Geothermal
Solar
Biomass
Wind
Hydro
Natural gas
Crude oil
Coal
Nuclear

Fig. 8.34 Latin America: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

EJ. Even lower fossil fuel use will be achieved in the 1.5 °C Scenario: 110 EJ for

natural gas, 10 EJ for coal, and 150 EJ for oil.

8.6.2 Latin America: Power Sector Analysis

The Latin American region is extremely diverse. It borders Mexico in the north and

its southern tip is in the South Pacific. It also includes all the Caribbean islands and

Central America. The power-generation situation is equally diverse, and the sub-

regional breakdown tries to reflect this diversity to some extent. In the Caribbean,

which contains 28 island nations and more than 7000 islands, the calculated storage

demand will almost certainly be higher than the region’s average, because a regional

power exchange grid between the islands seems impractical. To calculate the

detailed storage demand, island-specific analyses would be required, as has recently

been done for Barbados (Hohmeyer 2015 ). The mainland of South America has

been subdivided into the large economic centres of Chile, Argentina, and Brazil, and

Central America and the northern part of South America have been clustered into

two parts.

8 Energy Scenario Results

242

8.6.2.1 Latin America: Development of Power Plant Capacities

The most important future renewable technologies for Latin America are solar PV

and onshore wind, followed by CSP (which will be especially suited to the Atacama

Desert in Chile) and offshore wind, mainly in the coastal areas of Brazil and

Argentina. The annual market for solar PV must increase from 6.5 GW in 2020 by

a factor of three to an average of 15.5 GW by 2030 under the 2.0 °C Scenario and

to around 23 GW under the 1.5 °C Scenario. The onshore wind market in the 1.5 °C

Scenario must increase to 15 GW by 2025, compared with the average annual

onshore wind market of around 3 GW between 2014 and 2017 (GWEC 2018 ). By

2050, offshore wind will have increased to a moderate annual new installation

capacity of around 2–3 GW from 2025 to 2050  in both scenarios. Concentrated

solar power plants will be limited to the desert regions of South America, especially

Chile. The market for biofuels for electricity generation will play an important role

in all agricultural areas, including the Caribbean and Central America, where most

geothermal resources are located (Table 8.27).

8.6.2.2 Latin America: Utilization of Power-Generation Capacities

Table 8.28 shows that our modelling assumes that for the entire modelling period,

there will be no interconnection capacity between the Caribbean, Central America,

and South America, whereas the interconnection capacity in the rest of South

America will increase to 15% by 2030 and to 20% by 2050. The shares of variable

Table 8.27 Latin America: average annual change in installed power plant capacity

Latin Power Generation: average annual change
of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal 0 − 1 0 − 1 − 1 0
Lignite 0 0 0 0 0 0
Gas 4 2 1 6 − 9 5
Hydrogen-gas 0 1 1 4 11 14
Oil/diesel − 1 − 4 − 4 − 3 0 0
Nuclear 0 0 0 0 0 0
Biomass 3 5 3 4 4 3
Hydro 2 0 0 0 0 0
Wind (onshore) 5 11 11 17 6 3
Wind (offshore) 0 1 2 2 3 2
PV (roof top) 9 18 14 25 9 8
PV (utility scale) 3 6 5 8 3 3
Geothermal 0 1 1 1 1 1
Solar thermal power plants 0 2 4 5 2 3
Ocean energy 0 0 1 1 2 2
Renewable fuel based co-generation 1 2 2 2 2 1

S. Teske et al.

243

Table 8.28

Latin America: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

Latin America

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Caribbean

2015

3%

63%

34%

0%

2030

25%

62%

12%

0%

25%

62%

12%

0%

2050

44%

53%

3%

0%

44%

53%

3%

0%

Central America

2015

2%

64%

35%

0%

2030

21%

64%

14%

0%

21%

64%

14%

0%

2050

40%

58%

2%

0%

40%

58%

2%

0%

North L. America

2015

2%

64%

34%

10%

2030

20%

41%

39%

15%

20%

41%

39%

15%

2050

30%

40%

30%

20%

30%

40%

30%

20%

Central L. America

2015

1%

64%

36%

10%

2030

16%

52%

32%

15%

16%

52%

32%

15%

2050

29%

49%

22%

20%

29%

49%

22%

20%

Brazil

2015

4%

63%

33%

10%

2030

30%

54%

16%

15%

30%

54%

16%

15%

2050

47%

44%

8%

20%

47%

44%

8%

20%

Uruguay

2015

2%

61%

37%

10%

2030

21%

57%

22%

15%

21%

57%

22%

15%

2050

37%

52%

11%

20%

37%

52%

11%

20%

Argentina

2015

2%

62%

36%

10%

2030

19%

42%

38%

15%

19%

42%

38%

15%

2050

31%

40%

29%

20%

31%

40%

29%

20%

(continued)

8 Energy Scenario Results

244

Power generation structure and interconnection

2.0 °C

1.5 °C

Latin America

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Chile

2015

2%

64%

35%

10%

2030

18%

45%

37%

15%

18%

45%

37%

15%

2050

33%

47%

19%

20%

33%

47%

19%

20%

Latin America

2015

3%

63%

34%

2030

24%

51%

25%

24%

51%

25%

2050

39%

45%

16%

39%

45%

16%

Table 8.28

(continued)

S. Teske et al.

245

renewables are almost identical in the 2.0 °C and 1.5 °C Scenarios. The lowest rates

of variable renewables are in central South America and Central America because

the onshore wind potential is limited by average wind speeds that are lower than

elsewhere. Compared with all the other world regions, Latin America has the high-

est share of dispatchable renewables, mainly attributable to existing hydropower

plants.

Compared with other regions of the world, Latin America currently has a small

fleet of coal and nuclear power plants, but they are operated with a high capacity

factor (Table 8.29). The dispatch order for all world regions in all cases is assumed

to be the same, to make the results comparable. Therefore, the capacity factors of

these dispatch power plants (mainly gas) will increase at the expense of those for

coal and nuclear power plants, which explains the rapid reduction in the capacity

factor in 2020. Therefore, this effect is the result of the assumed dispatch order,

rather than of an increase in variable power generation.

8.6.2.3 Latin America: Development of Load, Generation

and Residual Load

The sub-regions of Latin America are highly diverse in their geographic features

and population densities, so the maximum loads in the different sub-regions vary

widely. Table 8.30 shows that the sub-region with the smallest calculated maximum

load is Uruguay, with only 2.3 GW, which seems realistic because the maximum

Table 8.29 Latin America: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
Latin America 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr]48.9% 31% 25% 36% 21% 41% 18% 34% 24%

Limited
dispatchable:
fossil and
nuclear

[%/yr]73.4% 14% 3% 17% 0% 45% 0% 13% 4%

Limited
dispatchable:
renewable

[%/yr]26.0% 53% 48% 46% 19% 56% 23% 47% 33%

Dispatchable:
fossil

[%/yr]53.2% 24% 11% 31% 2% 37% 6% 31% 11%

Dispatchable:
renewable

[%/yr]45.6% 37% 28% 46% 26% 43% 25% 46% 35%

Variable:
renewable

[%/yr]12.2% 12% 12% 21% 14% 31% 15% 22% 15%

8 Energy Scenario Results

246

Table 8.30

Latin America: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

Latin America

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Caribbean

2020

14.9

4.9

10.4

14.8

7.4

8.1

2030

23.4

23.1

8.9

0

21.0

27.5

1.5

5

2050

36.6

38.6

14.8

0

36.9

48.4

6.9

5

Central America

2020

13.1

13.0

3.3

13.1

20.0

0.5

2030

20.7

23.6

7.8

0

18.8

31.8

1.4

12

2050

33.3

42.6

14.3

0

33.2

47.3

7.2

7

North Latin America

2020

37.5

41.5

1.4

37.4

67.9

1.4

2030

59.1

76.7

7.1

11

53.1

80.9

5.0

23

2050

92.9

117.2

15.8

9

94.7

108.3

24.9

0

Central South America

2020

16.8

9.9

6.9

16.8

14.7

2.1

2030

26.2

29.4

5.7

0

24.1

39.9

2.3

14

2050

42.0

46.3

11.3

0

42.9

59.5

11.5

5

Brazil

2020

99.0

96.4

5.7

98.9

102.3

4.7

2030

153.8

150.1

38.4

0

140.7

145.2

9.9

0

2050

241.0

247.5

74.1

0

250.7

306.1

45.5

10

S. Teske et al.

247

Uruguay

2020

2.3

2.9

0.4

2.3

4.4

0.1

2030

3.4

4.0

1.1

0

3.1

5.3

0.2

2

2050

4.9

6.6

1.7

0

5.1

7.8

1.0

2

Argentina

2020

25.5

26.2

1.0

25.5

35.7

1.0

2030

40.1

176.4

3.1

133

36.6

176.4

3.6

136

2050

56.4

71.8

14.0

2

59.4

82.7

18.2

5

Chile

2020

9.3

19.2

0.4

2030

16.5

21.0

1.7

3

15.0

23.5

1.4

7

2050

26.1

30.7

7.7

0

27.7

35.5

7.2

1

8 Energy Scenario Results

248

load was 1.7 GW in 2012 according to IDB ( 2013 ). Brazil, Uruguay’s direct neigh-

bour, has the largest load of close to 100 GW, which will increase by a factor of 2.5

to around 250 GW by 2050 under both scenarios. Brazil’s maximum generation will

increase accordingly, without significant overproduction peaks. The calculated

maximum increase in interconnection required is only 10 GW. In Argentina, peak

generation matches peak demand because Argentina has one of the best wind

resources in the world in Patagonia. Surplus wind power can either be exported after

a significant increase in transmission capacity or, as assumed in our scenario, it can

be used to produce synthetic and hydrogen fuels.

Table 8.31 provides an overview of the calculated storage and dispatch power

requirements by sub-region. As indicated in the introduction to the Latin America

results, the storage requirements for the Caribbean might be high because the region

cannot exchange solar or wind electricity with other sub-regions. However, all other

sub-regions contain either several countries or larger provinces, so they are more

suited to the integration of variable electricity. Compared with other world regions,

Latin America has one of the lowest storage capacities and one of the lowest needs

for additional dispatch. This is because the region’s installed capacity of hydro-

power is high. However, this research does not include a water resource assessment

for hydropower plants. Droughts may increase the demand for storage and/or hydro-

gen dispatch.

8.7 OECD Europe

8.7.1 OECD Europe: Long-Term Energy Pathways

8.7.1.1 OECD Europe: Final Energy Demand by Sector

Combining the assumptions on population growth, GDP growth, and energy inten-

sity produces the future development pathways for OECD Europe’s final energy

demand shown in Fig. 8.35 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the

5.0 °C Scenario, the total final energy demand will increase by 9%, from the current

46,000 PJ/year to 50,000 PJ/year in 2050. In the 2.0 °C Scenario, the final energy

demand will decrease by 39% compared with current consumption and will reach

28,000 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will reach

25,200 PJ, 45% below the 2015 demand. In the 1.5 °C Scenario, the final energy

demand in 2050 will be 10% lower than in the 2.0  °C Scenario. The electricity

demand for ‘classical’ electrical devices (without power-to-heat or e-mobility) will

decrease from 2300 TWh/year in 2015 to 2040 TWh/year by 2050 in both alterna-

tive scenarios. Compared with the 5.0 °C case (3200 TWh/year in 2050), the effi-

ciency measures implemented in the 2.0  °C and 1.5  °C Scenarios will save 1160

TWh/year in 2050.

S. Teske et al.

249

Table 8.31

Latin America: storage and dispatch service requirements in the 2.0 °C and 1.5 °C Scenarios

Storage and dispatch

2.0 °C

1.5 °C

Latin America

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Caribbean

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

6

81

6

11

17

0

2050

100

46

3

49

15,282

1816

534

59

594

1808

Central America

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

5

57

5

8

13

0

2050

34

47

2

49

15,010

1462

560

59

619

5843

North Latin America

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

3

1

1

1

0

2050

0

0

0

0

7086

1047

633

57

690

0

Central L. America

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

3

82

9

14

23

0

2050

36

41

1

42

16,031

2768

1032

104

1136

40

Brazil

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

19

774

83

138

221

0

2050

475

666

27

693

63,131

18,024

6977

769

7746

1103

Uruguay

2020

77

0

0

0

0

511

0

0

0

0

2030

0

0

0

0

0

20

1

2

3

0

2050

42

20

2

22

1591

279

78

9

86

65 (continued)

8 Energy Scenario Results

250

Table 8.31

(continued)

Storage and dispatch

2.0 °C

1.5 °C

Latin America

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Argentina

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

177

14

23

37

0

2050

617

446

32

478

315

4969

1727

180

1908

0

Chile

2020

2

0

0

0

0

2669

1

0

1

0

2030

0

0

0

0

1

13

1

2

3

0

2050

10

14

1

15

8781

162

91

7

97

58

Latin America

2020

79

0

0

0

0

3180

2

0

2

0

2030

0

0

0

0

34

1207

121

197

318

1

2050

1314

1279

68

1347

127,226

30,526

11,633

1243

12,875

8917

S. Teske et al.

251

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

10,00 0

20,00 0

30,00 0

40,00 0

50,00 0

60,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 202520302040 2050

TW
h/
yr
PJ

/y

r

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.35 OECD Europe: development in three scenarios

Electrification will cause a significant increase in the electricity demand by 2050.

In the 2.0 °C Scenario, the electricity demand for heating will increase to approxi-

mately 1300 TWh/year due to electric heaters and heat pumps, and in the transport

sector, the demand will increase to approximately 2600 TWh/year in response to

increased electric mobility. The generation of hydrogen (for transport and high-

temperature process heat) and the manufacture of synthetic fuels (mainly for trans-

port) will add an additional power demand of 1600 TWh/year The gross power

demand will thus rise from 3600 TWh/year in 2015 to 6000 TWh/year by 2050 in

the 2.0 °C Scenario, 28% higher than in the 5.0 °C case. In the 1.5 °C Scenario, the

gross electricity demand will increase to a maximum of 6400 TWh/year by 2050.

Efficiency gains could be even larger in the heating sector than in the electricity

sector. Under the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 6200 PJ/year and 8200 PJ/year, respectively, are avoided by efficiency

gains by 2050 compared with the 5.0 °C Scenario.

8.7.1.2 OECD Europe: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in OECD Europe will come

from renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—mainly

8 Energy Scenario Results

252

wind, solar, and geothermal energy—will contribute 75% of the total electricity
generation. Renewable electricity’s share of the total production will be 68% by
2030 and 89% by 2040. The installed capacity of renewables will reach about 1200
GW by 2030 and 2270 GW by 2050. The share of renewable electricity generation
in 2030 in the 1.5 °C Scenario is assumed to be 74%. The 1.5 °C Scenario will have
a generation capacity from renewable energy of approximately 2480 GW in 2050.
Table 8.32 shows the development of different renewable technologies in OECD
Europe over time. Figure 8.36 provides an overview of the overall power-generation
structure in OECD Europe. From 2020 onwards, the continuing growth of wind and
PV, up to 790 GW and 1000 GW, respectively, will be complemented by generation
from biomass (ca. 110 GW) CSP and ocean energy (more than 50 GW each), in the
2.0 °C Scenario. Both the 2.0 °C and 1.5 °C Scenarios will lead to high proportions
of variable power generation (PV, wind, and ocean) of 38% and 45%, respectively,
by 2030 and 67% and 68%, respectively, by 2050.

Table 8.32 OECD Europe: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 207 224 231 238 248
2.0 °C 207 218 219 221 225
1.5 °C 207 218 219 221 225
Biomass 5.0 °C 40 51 56 60 65
2.0 °C 40 78 105 115 113
1.5 °C 40 84 111 113 113
Wind 5.0 °C 138 216 254 296 347
2.0 °C 138 279 409 655 787
1.5 °C 138 299 468 778 847
Geothermal 5.0 °C 2 3 3 3 4
2.0 °C 2 6 11 27 39
1.5 °C 2 6 11 27 39
PV 5.0 °C 95 137 157 172 191
2.0 °C 95 264 422 745 996
1.5 °C 95 364 598 1028 1151
CSP 5.0 °C 2 3 4 7 11
2.0 °C 2 7 17 38 54
1.5 °C 2 7 22 48 57
Ocean 5.0 °C 0 1 1 4 8
2.0 °C 0 7 16 42 53
1.5 °C 0 7 16 42 53
Total 5.0 °C 484 635 706 780 873
2.0 °C 484 859 1198 1842 2267
1.5 °C 484 985 1444 2256 2485

S. Teske et al.

253

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TW

h/

yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.36 OECD Europe: development of electricity-generation structure in the scenarios

8.7.1.3 OECD Europe: Future Costs of Electricity Generation

Figure 8.37 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated elec-

tricity generation costs in 2015 (referring to full costs) were around 7 ct/kWh. In the

5.0 °C case, generation costs will increase until 2050, when they will reach 10.4 ct/

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

2015 2025 2030 2040 2050

billion $ ct/kWh

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.37 OECD Europe: development of total electricity supply costs and specific electricity- generation costs in the scenarios

8 Energy Scenario Results

254

kWh. The generation costs in both alternative scenarios will increase until 2030,

when they will reach 10.3 ct/kWh, and they will drop by 2050 to 8.9 ct/kWh and 8.8

ct/kWh, respectively, 1.5–1.6 ct/kWh lower than in the 5.0 °C case. Note that these

estimates of generation costs do not take into account integration costs such as

power grid expansion, storage, or other load-balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

result in an increase in total electricity supply costs from today’s $270 billion/year

to more than $550 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs

will be $560 billion/year and in the 1.5 °C Scenario, they will be $590 billion/year

The long-term costs for electricity supply will be more than 2% higher in the 2.0 °C

Scenario than in the 5.0 °C Scenario as a result of the estimated generation costs and

the electrification of heating and mobility. Further electrification and synthetic fuel

generation in the 1.5 °C Scenario will result in total power generation costs that are

8% higher than in the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0 °C Scenario to 8.8 ct/kWh by 2050. In the

2.0 °C Scenario, they will increase until 2030 when they reach 9.5 ct/kWh, and then

drop to 8.9 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 9.7 ct/kWh,

and then drop to 8.8 ct/kWh by 2050. In the 2.0 °C Scenario, the generation costs

will reach a maximum of 1 ct/kWh higher than in the 5.0 °C case in 2030. In the

1.5  °C Scenario, the maximum difference in generation costs compared with the

5.0 °C Scenario will be 1.2 ct/kWh, which will occur in 2040. If the CO 2 costs are

not considered, the total electricity supply costs in the 5.0 °C case will rise to about

$470 billion/year in 2050.

8.7.1.4 OECD Europe: Future Investments in the Power Sector

An investment of around $4900 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments to replace plants

at the ends of their economic lives. This value is equivalent to approximately $136

billion per year on average, which is $2150 billion more than in the 5.0  °C case

($2750 billion). An investment of around $5340 billion for power generation will be

required between 2015 and 2050 under the 1.5 °C Scenario. On average, this will be

an investment of $148 billion per year. In the 5.0 °C Scenario, investment in conven-

tional power plants will be around 26% of the total cumulative investments, whereas

approximately 74% will be invested in renewable power generation and co-

generation (Fig. 8.38).

However, in the 2.0 °C (1.5 °C) Scenario, OECD Europe will shift almost 96%

(97%) of its entire investments to renewables and co-generation. By 2030, the fossil

fuel share of the power sector investments will predominantly focus on gas power

plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $2340 billion in 2050,

S. Teske et al.

255

equivalent to $65 billion per year. Therefore, the total fuel cost savings will be

equivalent to 110% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $2600 billion,

or $72 billion per year.

8.7.1.5 OECD Europe: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 16%,

from 20,600 PJ/year in 2015 to 24,000 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 26% in 2050  in the 2.0  °C

Scenario relative to that in the 5.0  °C case, and by 34% in the 1.5  °C Scenario.

Today, renewables supply around 19% of OECD Europe’s final energy demand for

heating, with the main contribution from biomass. Renewable energy will provide

44% of OECD Europe’s total heat demand in 2030 under the 2.0 °C Scenario and

53% under the 1.5 °C Scenario. In both scenarios, renewables will provide 100% of

the total heat demand in 2050.

Fossil
18%

Nuclear
CHP 8%
12%

Renewable
62%

5.0°C: 2015-2050

total 2,754
billion $

Fossil
(incl. H2)
3%
Nuclear
1%

CHP
13%

Renewable
83%

1.5°C: 2015-2050

total 5,340
billion $

Fossil
(incl. H2)
4%

CHP
14%

Renewable 81%

2.0°C: 2015-2050

total 4,900
billion $

Fig. 8.38 OECD Europe: investment shares for power generation in the scenarios

8 Energy Scenario Results

256

Figure 8.39 shows the development of different technologies for heating in

OECD Europe over time, and Table 8.33 provides the resulting renewable heat sup-

ply for all scenarios. Up to 2030, biomass will remain the main contributor. The

growing use of solar, geothermal, and environmental heat will lead in the long term

to a biomass share of 27% in the 2.0 °C Scenario and 28% in the 1.5 °C Scenario.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 1900 PJ/year in

the 2.0 °C Scenario and 2200 PJ/year in the 1.5 °C Scenario. The direct use of elec-

tricity for heating will also increase by a factor of 1.5–1.6 between 2015 and 2050,

and will have a final energy share of 22% in 2050 in the 2.0 °C Scenario and 23%

in the 1.5 °C Scenario.

Table 8.33 OECD Europe: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 2681 3115 3343 3713 4153
2.0 °C 2681 3109 3295 3483 3772
1.5 °C 2681 3046 3096 3220 3433
Solar heating 5.0 °C 119 216 251 345 454
2.0 °C 119 1043 1788 2904 3243
1.5 °C 119 1013 1464 2182 2327
Geothermal heat and heat pumps 5.0 °C 203 291 336 479 717
2.0 °C 203 968 1731 3572 5080
1.5 °C 203 878 1430 2933 4147
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 1 788 1895
1.5 °C 0 0 162 1595 2227
Total 5.0 °C 3003 3623 3931 4537 5325
2.0 °C 3003 5121 6815 10,748 13,989
1.5 °C 3003 4937 6152 9930 12,134

0

5,00 0

10,000

15,000

20,000

25,000

30,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electric heating

Geothermal heat
and heat pumps
Solar heating

Biomass

Fossil

Fig. 8.39 OECD Europe: development of heat supply by energy carrier in the scenarios

S. Teske et al.

257

8.7.1.6 OECD Europe: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $2410 billion in the 2.0 °C Scenario (including investments

for plant replacement at the ends of their economic lifetimes), or approximately $67

billion per year. The largest share of investments in OECD Europe is assumed to be

for heat pumps (around $1200 billion), followed by solar collectors ($1080 billion).

The 1.5 °C Scenario assumes an even faster expansion of renewable technologies.

However, the lower heat demand (compared with the 2.0 °C Scenario) will results

in a lower average annual investment of around $51 billion per year (Fig. 8.40,

Table 8.34).

biomass
technologies
52%

geothermal
heat use
2%

solar
collectors
22 %

heat pumps
24%

5.0°C: 2015-2050

total 644 billion $

biomass
technologies
2%
geothermal
heat use
3%

solar
collectors
45 %

heat pumps
50 %

2.0°C: 2015-205 0

total 2,400 billion $

biomass
technologies
2%
geothermal
heat use
1%

solar
collectors
46 %

heat
pumps
51%

1.5°C: 2015-2050

total 1,830 billion $

Fig. 8.40 OECD Europe: development of investments for renewable heat-generation technologies in the scenarios

8 Energy Scenario Results

258

8.7.1.7 OECD Europe: Transport

Energy demand in the transport sector in OECD Europe is expected to decrease by

3% in the 5.0 °C Scenario, from around 14,000 PJ/year in 2015 to 13,600 PJ/year in

2050. In the 2.0 °C Scenario, assumed technical, structural, and behavioural changes

will save 69% (9460 PJ/year) by 2050 compared with the 5.0 °C Scenario. Additional

modal shifts, technology switches, and a reduction in the transport demand will lead

to even higher energy savings in the 1.5 °C Scenario of 76% (or 10,300 PJ/year) in

2050 compared with the 5.0 °C case (Table 8.35, Fig. 8.41).

By 2030, electricity will provide 18% (430 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 64%

(740 TWh/year). In 2050, up to 840 PJ/year of hydrogen will be used in the trans-

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand will be 580 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 730 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 600  PJ/year

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

duced, with a maximum amount of 130 PJ/year in 2050. Biofuel use will be reduced

in the 1.5 °C Scenario to a maximum of 590 PJ/year. The maximum synthetic fuel

demand will reach 170 PJ/year.

8.7.1.8 OECD Europe: Development of CO 2 Emissions

In the 5.0 °C Scenario, OECD Europe’s annual CO 2 emissions will decrease by 15%

from 3400 Mt. in 2015 to 2876 Mt. in 2050. The stringent mitigation measures in

both alternative scenarios will cause the annual emissions to fall to 570 Mt. in

Table 8.34 OECD Europe: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 434 467 486 507 519 2.0 °C 434 407 339 293 289 1.5 °C 434 381 276 256 242 Geothermal 5.0 °C 5 7 7 7 3 2.0 °C 5 15 24 49 48 1.5 °C 5 14 16 21 11 Solar heating 5.0 °C 36 65 76 104 137 2.0 °C 36 298 510 790 885 1.5 °C 36 291 423 624 685 Heat pumps 5.0 °C 29 40 46 62 84 2.0 °C 29 134 228 417 566 1.5 °C 29 121 183 336 444 Totala 5.0 °C 504 579 615 681 744 2.0 °C 504 855 1101 1548 1789 1.5 °C 504 807 897 1237 1383 a Excluding direct electric heating

S. Teske et al.

259

Table 8.35 OECD Europe: projection of the transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 323 334 335 337 344
2.0 °C 323 362 409 509 643
1.5 °C 323 383 458 453 400
Road 5.0 °C 13,087 12,699 12,633 12,529 12,464
2.0 °C 13,087 10,163 7540 4196 3097
1.5 °C 13,087 8197 4404 3215 2556
Domestic aviation 5.0 °C 300 397 448 485 474
2.0 °C 300 294 254 182 142
1.5 °C 300 273 198 105 82
Domestic navigation 5.0 °C 227 236 240 248 259
2.0 °C 227 236 240 247 258
1.5 °C 227 236 240 247 258
Total 5.0 °C 13,938 13,665 13,656 13,598 13,541
2.0 °C 13,938 11,055 8443 5134 4140
1.5 °C 13,938 9090 5300 4020 3296

2040  in the 2.0  °C Scenario and to 270 Mt. in the 1.5  °C Scenario, with further

reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions

from 2015 until 2050 will add up to 116 Gt. In contrast, in the 2.0 °C and 1.5 °C

Scenarios, the cumulative emissions for the period from 2015 until 2050 will be 55

Gt and 44 Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 53% in the 2.0  °C

Scenario and by 62% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in the annual emissions will occur in both alternative scenarios. In the

2.0 °C Scenario, this reduction will be greatest in ‘Power generation’, followed by

the ‘Transport’ and the ‘Residential and other’ sectors (Fig. 8.42).

0

2,000

4,000

6,000

8,000

10,00 0

12,00 0

14,00 0

16,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)

Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.41 OECD Europe: final energy consumption by transport in the scenarios

8 Energy Scenario Results

260

8.7.1.9 OECD Europe: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-
tions discussed above are taken into account are shown in Fig. 8.43. In the 2.0 °C
Scenario, the primary energy demand will decrease by 44%, from around 71,200
PJ/year in 2015 to 40,100 PJ/year in 2050. Compared with the 5.0 °C Scenario, the
overall primary energy demand will decrease by 43% by 2050 in the 2.0 °C Scenario
(5.0 °C: 70,700 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand will
be even lower (39,000 PJ in 2050) because the final energy demand and conversion
losses will be lower.
Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This
will cause renewable energy to have primary energy shares of 39% in 2030 and 92%
in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a pri-
mary energy share of more than 92% in 2050 (including non-energy consumption,
which will still include fossil fuels). Nuclear energy will be phased-out by 2040
under both the 2.0  °C and the 1.5  °C Scenarios. The cumulative primary energy
consumption of natural gas in the 5.0 °C case will add up to 670 EJ, the cumulative
coal consumption to about 300 EJm, and the crude oil consumption to 660 EJ. In
contrast, in the 2.0 °C case, the cumulative gas demand will amount to 420 EJ, the
cumulative coal demand to 100 EJ, and the cumulative oil demand to 320 EJ. Even
lower fossil fuel use will be achieved in the 1.5 °C Scenario: 340 EJ for natural gas,
70 EJ for coal, and 240 EJ for oil.

0

20

40

60

80

100

120

140

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

4,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 20252030 2040 2050

cumulated

emissions

[Gt]
CO

2
emissions

[Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.42 OECD Europe: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

261

0

10,00 0

20,000

30,00 0

40,00 0

50,00 0

60,00 0

70,00 0

80,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025203020402050

net electricity
imports

Efficiency
Ocean energy
Geothermal
Solar
Biomass

Wind

Hydro

Natural gas

Crude oil

Coal

Nuclear

PJ/yr

Fig. 8.43 OECD Europe: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

8.7.2 OECD Europe: Power Sector Analysis

The European power sector is liberalized across the EU and cross-border trade in

electricity has a long tradition and is very well documented. The European Network

of Transmission System Operators for Electricity (ENTSO-E) publishes detailed

data about the annual cross-border trade (ENTSO-E 2018 ) and produces the Ten-

Year- Network Development Plan (TYNDP) , which aims to integrate 60% renewable

electricity by 2040 (TYNDP 2016 ). While the extent to which the power sector is

liberalised and open for competition for generation and supply varies significantly

across the EU, at the time of the writing of this book all 28-member states had

renewable electricity and energy efficiency targets and policies to implement them.

However, the OECD Europe region covers not only the EU but also neighbouring

countries such as Norway, Switzerland and Turkey, which are not members of the

EU, but are connected to the EU grid and are also involved in the cross-border elec-

tricity trade. The region also includes Iceland, Malta, and a significant number of

islands in the coastal waters of the European continent and the Mediterranean Sea.

The storage demand for all the islands and island nations cannot be calculated with

a regional approach, and doing so was beyond the scope of this research. Israel is

also part of OECD Europe in the IEA world regions used for this analysis. However,

because of its geographic position, and to reflect current and possible future inter-

connections with its neighbours, Israel has been taken out of the energy balance of

OECD Europe and integrated into the Middle East region.

8 Energy Scenario Results

262

8.7.2.1 OECD Europe: Development of Power Plant Capacities

The annual market for solar PV must increase from 11 GW in 2020 by a factor of 2

to an average of 40 GW by 2030. The onshore wind market must expand to 18 GW

by 2025 under the 2.0  °C Scenario. This is only a minor increase on the average

European wind market of 10–14 GW between 2009 and 2016 and 16.8 GW in 2017.

However, the 1.5  °C Scenario requires that the size of the onshore wind market

double between 2020 and 2025. The offshore wind market for both scenarios is

similar and must increase from 3 GW (GWEC 2018 ) in 2017 to around 10 GW per

year throughout the entire modelling period until 2050. All European lignite power

plants will have stopped operations by 2035, and the last hard coal power plant will

have gone offline by 2040 under the 2.0 °C Scenario. The 1.5 °C pathway requires

the phase-out 5 years earlier (Table 8.36).

8.7.2.2 OECD Europe: Utilization of Power-Generation Capacities

The UK, Ireland, and the Iberian Peninsula are the least interconnected sub-regions

of OECD Europe, and they already have relatively high shares of variable renew-

ables, as shown in Table 8.37.

Table 8.37 shows that the Nordic countries, especially Norway and Sweden, have

very high shares of hydropower, including pumped hydropower. Therefore, an

increased interconnection capacity with other sub-regions by 2030 will contribute

to the integration of larger shares of wind and solar in other European regions.

Table 8.36 OECD Europe: average annual change in installed power plant capacity

OECD Europe power generation: average
annual change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal − 5 − 9 − 8 − 4 0 0
Lignite − 5 − 6 − 3 − 2 0 0
Gas 2 1 0 − 5 − 22 − 19
Hydrogen-gas 0 1 2 6 14 14
Oil/diesel − 7 − 5 − 1 − 2 0 0
Nuclear − 6 − 9 − 6 − 6 − 2 − 2
Biomass 5 7 4 3 1 1
Hydro 1 0 0 0 0 0
Wind (onshore) 13 28 22 32 13 10
Wind (offshore) 4 9 10 11 8 8
PV (roof top) 16 43 30 42 25 21
PV (utility scale) 5 14 10 14 8 7
Geothermal 0 1 2 2 2 2
Solar thermal power plants 1 2 2 4 2 2
Ocean energy 1 2 3 3 2 2
Renewable fuel based co-generation 3 6 4 4 1 1

S. Teske et al.

263

Table 8.37

OECD Europe: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

OECD Europe

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Central

2015

12%

47%

41%

20%

2030

38%

47%

15%

20%

45%

43%

12%

20%

2050

62%

33%

5%

20%

64%

31%

5%

20%

UK & Islands

2015

25%

47%

28%

10%

2030

63%

31%

6%

20%

71%

25%

5%

20%

2050

84%

15%

2%

20%

85%

13%

2%

20%

Iberian Peninsula

2015

26%

47%

26%

10%

2030

67%

30%

3%

20%

76%

22%

3%

20%

2050

86%

13%

1%

20%

88%

12%

1%

20%

Balkans + Greece

2015

17%

47%

35%

10%

2030

53%

42%

6%

20%

60%

35%

5%

20%

2050

73%

24%

3%

20%

74%

23%

3%

20%

Baltic

2015

15%

47%

38%

10%

2030

44%

45%

12%

20%

50%

40%

10%

20%

2050

67%

29%

4%

20%

68%

28%

4%

20%

Nordic

2015

13%

47%

39%

10%

2030

39%

46%

14%

20%

46%

43%

11%

20%

2050

65%

31%

4%

20%

67%

29%

4%

20%

(continued)

8 Energy Scenario Results

264

Power generation structure and interconnection

2.0 °C

1.5 °C

OECD Europe

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Turkey

2015

10%

47%

42%

5%

2030

35%

48%

17%

5%

40%

44%

16%

5%

2050

59%

35%

6%

5%

60%

34%

6%

5%

OECD Europe Central

2015

15%

47%

38%

2030

44%

44%

12%

51%

39%

10%

2050

67%

28%

4%

69%

27%

4%

Table 8.37

(continued)

S. Teske et al.

265

Across the EU, it is assumed that the average interconnection capacities will increase

to 20% of the regional peak load.

Both alternative scenarios assume that limited dispatchable power generation—

namely coal, lignite, and nuclear—will not have priority dispatch and will be last in

the dispatch queue. Therefore, the average calculated capacity factor will decrease

from 57.5% in 2015 to only 14% in 2020, as shown in Table 8.38.

Table 8.38 shows that by 2020, most of the installed coal and nuclear capacity

will not be required to secure power supply. Instead, dispatchable renewable power

plants will fill the gap and their capacity factors will increase.

8.7.2.3 OECD Europe: Development of Load, Generation,

and Residual Load

The loads of the European sub-regions will not increase until 2030 in the two alter-

native scenarios, as shown in Table 8.39. The only exception is Turkey, which will

have a constantly increasing load. This is attributed to Turkey’s assumed economic

development and increasing per capita electricity demand, which is currently lower

than in most EU countries (WB-DB 2018 ). The calculated load will increase in all

sub-regions between 2030 and 2050 due to the increased deployment of electric

mobility. Central Europe has a very high requirement for increased transmission

interconnection—or storage, see Table 8.40—because of increases in variable gen-

eration, including offshore wind in the North Sea and Baltic Sea. Central Europe,

Table 8.38 OECD Europe: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
World 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr]45.2% 37% 37% 48% 44% 35% 36% 39% 38%

Limited
dispatchable:
fossil and
nuclear

[%/yr]57.5% 14% 14% 3% 2% 19% 1% 20% 9%

Limited
dispatchable:
renewable

[%/yr]54.0% 60% 60% 52% 48% 60% 39% 41% 40%

Dispatchable:
fossil

[%/yr]32.0% 20% 20% 7% 7% 30% 10% 15% 16%

Dispatchable:
renewable

[%/yr]43.7% 67% 67% 67% 61% 39% 49% 52% 50%

Variable:
renewable

[%/yr]22.5% 22% 22% 40% 38% 29% 35% 36% 35%

8 Energy Scenario Results

266

Table 8.39

OECD Europe: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

OECD Europe

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Central

2020

328.9

322.3

73.6

328.9

322.3

77.8

2030

350.7

397.4

44.5

2

360.3

520.4

47.4

113

2050

491.9

842.8

243.1

108

511.2

954.3

259.0

184

UK & Islands

2020

66.1

73.5

33.2

66.1

73.4

33.1

2030

71.6

87.6

21.0

0

73.7

112.9

23.4

16

2050

98.0

187.9

51.5

38

102.2

210.7

55.1

53

Iberian Peninsula

2020

47.0

56.1

10.3

47.0

56.1

10.3

2030

50.8

62.3

7.3

4

52.6

80.8

7.9

20

2050

70.8

133.2

31.7

31

74.3

149.4

34.6

41

Balkans + Greece

2020

37.9

38.2

1.4

37.9

37.9

1.4

2030

39.5

49.3

6.3

4

41.6

63.1

6.8

15

2050

55.6

105.4

24.1

26

59.8

117.8

27.5

30

Baltic

2020

4.6

4.5

0.1

4.6

4.5

0.1

2030

4.9

6.1

0.7

1

5.1

7.9

0.7

2

2050

6.8

13.1

3.2

3

7.2

14.7

3.5

4

Nordic

2020

52.0

50.8

1.3

52.0

50.8

1.3

2030

54.4

65.9

8.7

3

55.2

86.0

10.4

20

2050

71.0

140.3

30.0

39

72.6

158.5

31.0

55

Turkey

2020

37.5

38.5

0.8

37.5

38.2

0.8

2030

48.4

49.1

6.9

0

50.8

64.4

7.5

6

2050

68.2

107.4

33.1

6

73.0

121.5

37.4

11

S. Teske et al.

267

Table 8.40

OECD Europe: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

OECD Europe

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Central

2020

0

0

0

0

0

0

0

0

0

0

2030

425

67

728

796

38,043

6947

515

7996

8511

139,501

2050

59,495

28,998

32,425

61,423

546,511

99,134

35,542

48,679

84,222

549,376

UK & Islands

2020

0

0

0

0

0

0

0

0

0

0

2030

3419

293

5808

6101

4148

12,239

440

13,977

14,417

13,195

2050

57,089

9507

34,158

43,665

41,134

72,011

9738

38,301

48,039

40,932

Iberian Peninsula

2020

0

0

0

0

0

0

0

0

0

0

2030

1688

186

2763

2949

2712

12,555

407

11,672

12,079

8127

2050

52,580

7952

27,526

35,478

22,000

69,483

8273

30,928

39,201

22,448

Balkans + Greece

2020

0

0

0

0

0

0

0

0

0

0

2030

523

62

895

957

3274

3699

172

3996

4168

11,349

2050

19,794

5717

10,649

16,366

39,208

25,680

6267

12,033

18,300

42,798

Baltic

2020

0

0

0

0

0

0

0

0

0

0

2030

27

2

41

42

482

190

7

174

181

1775

2050

1071

360

542

902

6365

1504

413

677

1090

6636

Nordic

2020

0

0

0

0

0

0

0

0

0

0

2030

149

16

274

291

6276

2111

95

2237

2332

23,031

2050

14,144

4425

6905

11,330

80,577

22,171

5219

9360

14,580

78,294
(continued)

8 Energy Scenario Results

268

Storage and dispatch

2.0 °C

1.5 °C

OECD Europe

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Turkey

2020

0

0

0

0

0

0

0

0

0

0

2030

8

4

21

25

5287

762

72

1067

1139

20,038

2050

7887

4120

4348

8467

78,788

11,251

4744

5467

10,211

82,142

OECD Europe

2020

0

0

0

0

0

0

0

0

0

0

2030

6238

630

10,531

11,161

60,223

38,504

1710

41,118

42,827

217,016

2050

212,060

61,078

116,554

177,632

814,585

301,234

70,196

145,445

215,641

822,626

Table 8.40

(continued)

S. Teske et al.

269

the Iberian Peninsula, and the UK have the highest storage demands, as shown in

Table 8.40. This corresponds to the calculated results for increased interconnec-

tions. To avoid curtailment, renewably produced hydrogen will be used to store

surplus generation for dispatch when required. Finding the optimal mix of battery

capacity, pumped hydro capacity, hydrogen production, and expansion of transmis-

sion capacity was beyond the scope of this analysis, and further research is required

on this issue.

8.8 Africa

8.8.1 Africa: Long-Term Energy Pathways

8.8.1.1 Africa: Final Energy Demand by Sector

The development pathways for Africa’s final energy demand when the assumptions

on population growth, GDP growth, and energy intensity are combined are shown in

Fig. 8.44 for the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios. In the 5.0 °C Scenario, the total

final energy demand will increase by 103% from the current 23,200 PJ/year to 47,100

PJ/year in 2050. In the 2.0 °C Scenario, the final energy demand will increase at a

much slower rate, by 39% compared with current consumption, and will reach

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

0

5,000

10,00 0

15,00 0

20,00 0

25,00 0

30,00 0

35,00 0

40,00 0

45,00 0

50,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 202520302040 2050

TWh/yr

PJ/yr

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.44 Africa: development of final energy demand by sector in the scenarios

8 Energy Scenario Results

270

32,300 PJ/year by 2050. The final energy demand under the 1.5  °C Scenario will

reach 30,100 PJ, 30% above the 2015 demand level. In the 1.5 °C Scenario, the final

energy demand in 2050 will be 7% lower than in the 2.0 °C Scenario. The electricity

demand for ‘classical’ electrical devices (without power-to-heat or e-mobility) will

increase from 540 TWh/year in 2015 to around 2590 TWh/year in 2050 in both alter-

native scenarios, which will be 590 TWh/year higher than in the 5.0 °C case. Although

efficiency measures will reduce the specific energy consumption by appliances, the

scenarios consider higher consumption to achieve higher living standards.

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0  °C Scenario, the electricity demand for heating will increase to

approximately 1200 TWh/year due to electric heaters and heat pumps, and in the

transport sector, the demand will increase to approximately 1300 TWh/year in

response to increased electric mobility. The generation of hydrogen (for transport

and high-temperature process heat) and the manufacture of synthetic fuels (mainly

for transport) will add an additional power demand of 1100 TWh/year The gross

power demand will thus increase from 800 TWh/year in 2015 to 5700 TWh/year in

2050  in the 2.0  °C Scenario, 119% higher than in the 5.0  °C case. In the 1.5  °C

Scenario, the gross electricity demand will increase to a maximum of 6300 TWh/

year in 2050.

The efficiency gains in the heating sector could be even larger than in the elec-

tricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 3600 PJ/year is avoided through efficiency gains by 2050 compared

with the 5.0 °C Scenario.

8.8.1.2 Africa: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in Africa will come from

renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—mainly wind,

solar, and geothermal energy—will contribute 92% of the total electricity genera-

tion. Renewable electricity’s share of total production will be 61% by 2030 and 96%

by 2040. The installed capacity of renewables will reach about 360 GW by 2030 and

2040 GW by 2050. In the 1.5 °C Scenario, the share of renewable electricity genera-

tion in 2030 is assumed to be 73%. The 1.5  °C Scenario will have a generation

capacity from renewable energy of approximately 2280 GW in 2050.

Table 8.41 shows the development of different renewable technologies in Africa

over time. Figure 8.45 provides an overview of the overall power-generation struc-

ture in Africa. From 2020 onwards, the continuing growth of wind and PV, up to 610

GW and 980 GW, respectively, will be complemented by up to 230 GW of solar

thermal generation, as well as limited biomass, geothermal, and ocean energy, in the

2.0 °C Scenario. Both the 2.0 °C and 1.5 °C Scenarios will lead to high proportions

of variable power generation (PV, wind, and ocean) of 40% and 49%, respectively,

by 2030, and 71% by 2050.

S. Teske et al.

271

Table 8.41 Africa: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 28 47 58 84 117
2.0 °C 28 46 49 51 54
1.5 °C 28 46 48 51 54
Biomass 5.0 °C 1 2 4 8 13
2.0 °C 1 8 17 33 48
1.5 °C 1 8 25 42 72
Wind 5.0 °C 3 11 14 20 29
2.0 °C 3 42 132 415 609
1.5 °C 3 87 197 453 633
Geothermal 5.0 °C 1 2 3 7 14
2.0 °C 1 7 16 33 64
1.5 °C 1 7 16 33 64
PV 5.0 °C 2 17 27 52 89
2.0 °C 2 38 134 611 983
1.5 °C 2 70 166 757 1162
CSP 5.0 °C 0 2 3 10 17
2.0 °C 0 0 1 80 235
1.5 °C 0 2 19 108 257
Ocean 5.0 °C 0 0 0 0 0
2.0 °C 0 2 10 20 43
1.5 °C 0 2 10 20 43
Total 5.0 °C 35 81 110 180 279
2.0 °C 35 144 359 1243 2036
1.5 °C 35 223 481 1464 2284

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 20252030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.45 Africa: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

272

8.8.1.3 Africa: Future Costs of Electricity Generation

Figure 8.46 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated

electricity- generation costs in 2015 (referring to full costs) were around 5.4 ct/kWh.

In the 5.0 °C case, generation costs will increase until 2030, when they reach 11 ct/

kWh, and will then stabilize at 10.8 ct/kWh by 2050. In the 2.0  °C and 1.5  °C

Scenarios, the generation costs will increase until 2030, when they reach 8.4 ct/kWh

and 8.2 ct/kWh, respectively. They will then drop to 5.6 ct/kWh by 2050  in both

scenarios, 5.2 ct/kWh lower than in the 5.0  °C case. Note that these estimates of

generation costs do not take into account integration costs such as power grid expan-

sion, storage, or other load-balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

cause the total electricity supply costs to increase from today’s $40 billion/year to

more than $290 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs

will be $350 billion/year, and in the 1.5 °C Scenario, they will be $380 billion/year

The long-term costs of electricity supply will be more than 23% higher under the

2.0 °C Scenario than under the 5.0 °C Scenario as a result of the estimated genera-

tion costs and the electrification of heating and mobility. Further electrification and

synthetic fuel generation in the 1.5 °C Scenario will result in total power generation

costs that are 34% higher than in the 5.0 °C case.

0

2

4

6

8

10

12

0

50

100

150

200

250

300

350

400

450

2015 2025 2030 2040 2050

billion ct/kWh

$

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.46 Africa: development of total electricity supply costs and specific electricity-generation costs in the scenarios

S. Teske et al.

273

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0  °C case to 8.1 ct/kWh. In the 2.0  °C

Scenario, they will increase until 2030, when they reach 6.8 ct/kWh, and then drop

to 5.6 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 7.2 ct/kWh and

then drop to 5.6 ct/kWh by 2050. Therefore, the generation costs in both alternative

scenarios are, at maximum, 2.5 ct/kWh lower than in the 5.0  °C case. If the CO 2

costs are not considered, the total electricity supply costs in the 5.0  °C case will

increase to about $220 billion/year in 2050.

8.8.1.4 Africa: Future Investments in the Power Sector

An investment of around $3500 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments in plant replace-

ment at the ends of their economic lives. This value is equivalent to approximately

$97 billion per year, on average, and is $2590 billion more than in the 5.0 ° C case

($910 billion). An investment of around $3910 billion for power generation will be

required between 2015 and 2050  in the 1.5  °C Scenario. On average, this is an

investment of $109 billion per year. In the 5.0 °C Scenario, the investment in con-

ventional power plants will be around 45% of the total cumulative investments, and

approximately 55% will be invested in renewable power generation and co-genera-

tion (Fig. 8.47).

However, in the 2.0 °C (1.5 °C) Scenario, Africa will shift almost 93% (94%) of

its entire investments to renewables and co-generation. By 2030, the fossil fuel

share of power sector investments will focus predominantly on gas power plants

that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $1510 billion in 2050,

equivalent to $42 billion per year. Therefore, the total fuel cost savings will be

equivalent to 60% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $1610 billion,

or $45 billion per year.

8.8.1.5 Africa: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 166%,

from 7600 PJ/year in 2015 to 20,200 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 18% in 2050 in both alterna-

tive scenarios, relative to the 5.0 °C case. Today, renewables supply around 61% of

Africa’s final energy demand for heating, with the main contribution from biomass.

Renewable energy will provide 71% of Africa’s total heat demand in 2030 under the

2.0 °C Scenario and 79% under the 1.5 °C Scenario. In both scenarios, renewables

will provide 100% of the total heat demand from renewable energy in 2050.

8 Energy Scenario Results

274

Figure 8.48 shows the development of different technologies for heating in

Africa over time, and Table 8.42 provides the resulting renewable heat supply for all

scenarios. Biomass will remain the main contributor. The growing use of solar, geo-

thermal, and environmental heat will lead, in the long term, to a reduced biomass

share of 51% in the 2.0 °C Scenario and 40% in the 1.5 °C Scenario.

0

5,000

10,00 0

15,00 0

20,00 0

25,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electric heating
Geothermal heat
and heat pumps
Solar heating
Biomass

Fig. 8.48 Africa: development of heat supply by energy carrier in the scenarios

Fossil
Nuclear 41%
4%

CHP 1%

Renewable
54%

5.0°C: 2015-2050

total 915
billion $

Fossil (incl. H2)
7%

CHP 3%
Renewable
90%

2.0°C: 2015-2050

total 3,500
billion $

Fossil (incl. H2)
6%
CHP 3%

Renewable
91%

1.5°C: 2015-2050

total 3,900
billion $

Fig. 8.47 Africa: investment shares for power generation in the scenarios

S. Teske et al.

275

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 720 PJ/year in

both the 2.0 °C Scenario and 1.5 °C Scenario. The direct use of electricity for heat-

ing will also increase by a factor of 21–34 between 2015 and 2050, and will attain a

final energy share of 23% in 2050  in the 2.0  °C Scenario and 37% in the 1.5  °C

Scenario.

8.8.1.6 Africa: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $790 billion in the 2.0 °C Scenario (including investments in

plant replacement after their economic lifetimes), or approximately $22 billion per

year. The largest share of investment in Africa is assumed to be for heat pumps

(around $370 billion), followed by solar collectors and biomass technologies. The

1.5  °C Scenario assumes an even faster expansion of renewable technologies.

However, the lower heat demand (compared with the 2.0 °C Scenario) will result in

a lower average annual investment of around $21 billion per year (Table 8.43,

Fig. 8.49).

Table 8.42 Africa: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 4586 5761 6317 7211 8203
2.0 °C 4586 5308 6047 7039 6551
1.5 °C 4586 5748 6448 6938 4222
Solar heating 5.0 °C 7 37 86 228 481
2.0 °C 7 204 786 2066 3416
1.5 °C 7 203 783 2109 3416
Geothermal heat and heat pumps 5.0 °C 0 0 0 0 0
2.0 °C 0 86 215 559 2106
1.5 °C 0 86 213 591 2106
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 0 397 720
1.5 °C 0 0 0 429 720
Total 5.0 °C 4593 5797 6404 7440 8684
2.0 °C 4593 5598 7047 10,061 12,793
1.5 °C 4593 6037 7444 10,067 10,464

8 Energy Scenario Results

276

biomass
technologies
97%

geothermal
heat use
0%

solar
collectors
3%

heat
pumps
0%

5.0°C: 2015-2050

total 490 billion $

biomass
technologies
15%

geothermal
heat use
10%

solar
collectors
28%

heat
pumps
47%

2.0°C: 2015-205 0

total 790 billion $

biomass
technologies
12%

geothermal
heat use
11%

solar
collectors
29 %

heat
pumps
48%

1.5°C: 2015-2050

total 760 billion $

Fig. 8.49 Africa: development of investments for renewable heat-generation technologies in the scenarios

Table 8.43 Africa: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5 0 °C 3655 4036 4100 3973 3870 2 0 °C 3655 3276 3063 2792 2251 1 5 °C 3655 3562 3069 2440 1307 Geothermal 5 0 °C 0 0 0 0 0 2 0 °C 0 5 9 15 37 1 5 °C 0 5 8 15 37 Solar heating 5 0 °C 1 7 16 44 92 2 0 °C 1 39 150 396 654 1 5 °C 1 39 150 404 654 Heat pumps 5 0 °C 0 0 0 0 0 2 0 °C 0 3 16 51 227 1 5 °C 0 3 16 54 227 Totala 5 0 °C 3656 4043 4116 4017 3962 2 0 °C 3656 3324 3239 3253 3169 1 5 °C 3656 3610 3244 2912 2225 a Excluding direct electric heating

S. Teske et al.

277

8.8.1.7 Africa: Transport

The energy demand in the transport sector in Africa is expected to increase by 131%

in the 5.0 °C Scenario, from around 4400 PJ/year in 2015 to 10,100 PJ/year in 2050.

In the 2.0 °C Scenario, assumed technical, structural, and behavioural changes will

save 53% (5410 PJ/year) by 2050 compared with the 5.0 °C Scenario. Additional

modal shifts, technology switches, and a reduction in the transport demand will lead

to even higher energy savings in the 1.5 °C Scenario of 63% (or 6360 PJ/year) in

2050 compared with the 5.0 °C case (Table 8.44, Fig. 8.50).

By 2030, electricity will provide 4% (50 TWh/year) of the transport sector’s total

energy demand in the 2.0 °C Scenario, whereas by 2050, the share will be 28% (370

TWh/year). In 2050, up to 410 PJ/year of hydrogen will be used in the transport

sector as a complementary renewable option. In the 1.5  °C Scenario, the annual

electricity demand will be 360 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 340 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 2300 PJ/year.

Therefore, around 2030, synthetic fuels based on power-to-liquid will be introduced,

with a maximum amount of 700 PJ/year in 2050. With the lower overall energy

demand by transport, biofuel use will be reduced in the 1.5 °C Scenario to a maxi-

mum of 1700 PJ/year The maximum synthetic fuel demand will amount to 470 PJ/

year.

8.8.1.8 Africa: Development of CO 2 Emissions

In the 5.0 °C Scenario, Africa’s annual CO 2 emissions will increase by 126%, from

1140 Mt. in 2015 to 2585 Mt. in 2050. The stringent mitigation measures in both

alternative scenarios will cause annual emissions to fall to 400 Mt. in 2040 in the

Table 8.44 Africa: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 46 52 58 67 74
2.0 °C 46 58 71 96 110
1.5 °C 46 69 88 125 186
Road 5.0 °C 4182 5000 5812 7522 9635
2.0 °C 4182 4688 4828 4651 4488
1.5 °C 4182 4493 4422 3925 3482
Domestic aviation 5.0 °C 105 159 198 256 272
2.0 °C 105 114 110 90 71
1.5 °C 105 110 102 74 54
Domestic navigation 5.0 °C 32 35 37 40 44
2.0 °C 32 35 37 40 44
1.5 °C 32 35 37 40 44
Total 5.0 °C 4366 5246 6105 7885 10,027
2.0 °C 4366 4895 5045 4877 4714
1.5 °C 4366 4707 4648 4164 3765

8 Energy Scenario Results

278

2.0 °C Scenario and to 200 Mt. in the 1.5 °C Scenario, with further reductions to

almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions from 2015

until 2050 will add up to 66 Gt. In contrast, in the 2.0 °C and 1.5 °C Scenarios, the

cumulative emissions for the period from 2015 until 2050 will be 27 Gt and 22 Gt,

respectively.

Therefore, the cumulative CO 2 emissions will decrease by 59% in the 2.0  °C

Scenario and by 67% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario, this reduction will be greatest in ‘Power generation’, followed by the

‘Industry’ and ‘Residential and other’ sectors (Fig. 8.51).

8.8.1.9 Africa: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.52. In the 2.0 °C

Scenario, the primary energy demand will increase by 50% from around 33,200 PJ/

year in 2015 to around 50,000 PJ/year in 2050. Compared with the 5.0 °C Scenario,

the overall primary energy demand will decrease by 26% by 2050  in the 2.0  °C

Scenario (5.0  °C: 67700 PJ in 2050). In the 1.5  °C Scenario, the primary energy

demand will be even lower (48,000 PJ in 2050) because the final energy demand

and conversion losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 56% in 2030 and

98% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 98% in 2050 (including non-energy consump-

tion, which will still include fossil fuels). Nuclear energy will be phased-out by

0

2,00 0

4,00 0

6,00 0

8,00 0

10,00 0

12,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.50 Africa: final energy consumption by transport in the scenarios

S. Teske et al.

279

0

10

20

30

40

50

60

70

0

500

1,000

1,500

2,000

2,500

3,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025 203020402050

cumulated

emissions

CO [Gt]

2

emissions

[Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.51 Africa: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

PJ/yr

net electricity
imports
Efficiency

Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal
Nuclear

Fig. 8.52 Africa: projection of total primary energy demand (PED) by energy carrier in the sce- narios (including electricity import balance)

8 Energy Scenario Results

280

2035 under both the 2.0 °C Scenario and 1.5 °C Scenario. The cumulative primary

energy consumption of natural gas in the 5.0  °C case will add up to 290 EJ, the

cumulative coal consumption to about 210 EJ, and the crude oil consumption to 390

EJ. In contrast, in the 2.0 °C Scenario, the cumulative gas demand will amount to

130 EJ, the cumulative coal demand to 70 EJ, and the cumulative oil demand to 180

EJ. Even lower fossil fuel use will achieved in the 1.5 °C Scenario: 110 EJ for natu-

ral gas, 50 EJ for coal, and 150 EJ for oil.

8.8.2 Africa: Power Sector Analysis

The African continent has 54 countries and its geographic, economic, and climatic

diversity are significant. Its regional breakdown into sub-regions tries to reflect this

diversity, but still requires a level of simplification. There is no pan-African power

grid yet, although it is currently under discussion. The African Clean Energy

Corridor (ACEC) is the most prominent regional initiative and aims to connect the

Eastern Africa Power Pool (EAPP) with the Southern Africa Power Pool (SAPP). It

was politically endorsed in January 2014 at the Assembly of the International

Renewable Energy Agency (IRENA 2014 ).

8.8.2.1 Africa: Development of Power Plant Capacities

In 2050, Africa’s most important renewable power-generation technology in both

scenarios will be solar PV. In the 1.5 °C Scenario, solar PV will provide just over

40% of the total generation capacity, followed by onshore wind (with 24%), hydro-

gen power (15%), and CSP plants (located in the desert regions), with 10% of the

total capacity. All other renewable power plant technologies will have only 2%–3%

shares. The 2.0 °C Scenario will arrive at similar capacities by 2050, although the

transition times in the two scenarios differ. Africa must build up solar PV and

onshore wind markets equal to the market sizes in China in 2017: 50 GW of solar

PV installation (REN21-GSR2018) and 23 GW of onshore wind (GWEC 2018 ).

The market for CSP plants must reach about 1 GW per year by 2025, increasing

rapidly to 3 GW per year in 2029 and 15 GW per year in 2035 (Table 8.45).

8.8.2.2 Africa: Utilization of Power-Generation Capacities

Africa’s sub-regions are assumed to have an interconnection capacity of 5% at the

beginning of the calculation period (2015). This capacity is not required for any

exchange of variable electricity production, because currently, shares are only at or

below 2% of the total generation capacity (Table 8.46). However, the variable gen-

eration capacity will increase rapidly towards 2030. We assume that the intercon-

nection capacity between sub-regions will increase and that initiatives such as the

African Clean Energy Corridor (ACEC) will be implemented successfully.

S. Teske et al.

281

The development of average capacity factors for each generation type will follow

the same trend as in most world regions. Table 8.47 shows the significant drop in the

capacity factors of limited dispatchable power plants under the 1.5 °C Scenario.

8.8.2.3 Africa: Development of Load, Generation, and Residual Load

Table 8.48 shows that under the 2.0 °C Scenario, the transmission capacities need

not exceed the assumed 25% interconnection capacity. If the exchange capacity

between Africa’s sub-regions is 20%—as calculated under the 1.5  °C Scenario—

additional capacity will be required. Therefore, a 25% interconnection capacity

seems a good target for high renewable penetration scenarios in Africa. The load in

all sub-regions—from North Africa to South Africa—will increase significantly.

The greatest increase is calculated for Southern Africa, with the load increasing by

a factor of 7, followed by Central Africa (a factor of 6.5), East Africa (6), West

Africa (5.5), and North Africa (4). The load increase in the Republic of South Africa

will follow the patterns of other industrialized countries, more than doubling, due

mainly to increases in electric mobility. The load increases in other parts of Africa

will be first and foremost due to universal access to energy services for all house-

holds and favourable economic development.

Table 8.49 provides an overview of the calculated storage and dispatch power

requirements by African sub-region. East and West Africa will require the highest

battery capacity, due to the very high share of solar PV battery systems in rural and

residential areas with low power grid availability. Like the Middle East, Africa is

Table 8.45 Africa: average annual change in installed power plant capacity

Africa power generation: average annual
change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal 2 0 − 2 − 7 − 4 0
Lignite 0 0 0 0 0 0
Gas 6 3 10 16 13 14
Hydrogen-gas 0 0 1 3 15 32
Oil/diesel − 1 − 2 − 2 − 2 − 1 − 1
Nuclear 0 0 0 0 0 0
Biomass 1 3 2 3 2 3
Hydro 2 1 1 1 0 0
Wind (onshore) 5 20 21 21 23 21
Wind (offshore) 0 2 5 10 7 4
PV (roof top) 3 12 29 31 41 48
PV (utility scale) 1 4 10 10 14 16
Geothermal 1 2 2 2 3 3
Solar thermal power plants 0 2 4 9 18 16
Ocean energy 0 1 1 1 3 3
Renewable fuel based co-generation 1 2 2 2 1 1

8 Energy Scenario Results

282

Table 8.46

Africa: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

North Africa

2015

2%

25%

73%

5%

2030

56%

23%

21%

20%

60%

8%

32%

5%

2050

75%

25%

0%

25%

61%

10%

29%

20%

West Africa

2015

1%

26%

73%

5%

2030

38%

24%

38%

20%

41%

18%

41%

5%

2050

67%

33%

0%

25%

63%

23%

14%

20%

Central Africa

2015

0%

26%

74%

5%

2030

20%

29%

50%

20%

19%

30%

52%

5%

2050

42%

58%

0%

25%

39%

44%

17%

20%

East Africa

2015

2%

26%

72%

5%

2030

50%

22%

28%

20%

59%

10%

31%

5%

2050

75%

25%

0%

25%

68%

13%

18%

20%

Southern Africa

2015

1%

25%

73%

5%

2030

46%

20%

34%

20%

52%

17%

31%

5%

2050

81%

19%

0%

25%

70%

12%

17%

20%

South Africa

2015

2%

25%

73%

5%

2030

63%

0%

36%

20%

54%

8%

38%

5%

2050

67%

33%

0%

25%

49%

9%

42%

20%

Africa

2015

2%

26%

73%

2030

47%

21%

32%

52%

13%

35%

2050

73%

27%

0%

64%

15%

21%

S. Teske et al.

283

Table 8.47

Africa: capacity factors by generation type

Utilization of Variable and Dispatchable power generation:

2015

2020

2020

2030

2030

2040

2040

2050

2050

Africa

2.0 °C

1.5 °C

2.0 °C

1.5 °C

2.0 °C

1.5 °C

2.0 °C

1.5 °C

Capacity factor – average

[%/yr]

54.7%

33%

33%

29%

25%

40%

23%

36%

23%

Limited dispatchable: fossil and nuclear

[%/yr]

69.4%

31%

5%

19%

8%

20%

4%

10%

5%

Limited dispatchable: renewable

[%/yr]

29.7%

52%

32%

35%

24%

51%

17%

36%

17%

Dispatchable: fossil

[%/yr]

49.2%

32%

37%

16%

23%

36%

15%

16%

17%

Dispatchable: renewable

[%/yr]

43.7%

39%

28%

27%

20%

41%

12%

49%

14%

Variable: renewable

[%/yr]

12.2%

12%

12%

38%

28%

34%

27%

35%

27%

8 Energy Scenario Results

284

Table 8.48

Africa: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

Africa

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

North Africa

2020

23.8

19.3

8.4

23.8

20.2

7.8

2030

31.0

33.9

4.2

0

34.0

43.6

6.0

4

2050

99.3

161.9

63.6

0

109.8

186.1

28.5

48

West Africa

2020

38.7

19.5

19.7

38.6

22.9

16.5

2030

64.7

56.7

25.3

0

66.5

58.5

24.5

0

2050

214.4

310.3

164.6

0

216.1

355.7

118.0

22

Central Africa

2020

4.2

3.4

0.8

4.2

3.9

0.3

2030

8.2

7.3

2.6

0

8.5

7.7

2.6

0

2050

27.0

38.6

26.4

0

27.3

46.8

26.6

0

East Africa

2020

44.0

34.8

11.9

44.0

39.5

7.0

2030

86.5

75.0

30.0

0

88.5

82.9

28.5

0

2050

265.1

369.8

197.4

0

267.1

425.1

101.7

56

Southern Africa

2020

27.8

24.2

4.0

27.7

25.4

2.3

2030

67.2

57.9

35.9

0

68.3

74.5

36.6

0

2050

199.3

359.3

169.9

0

199.6

407.3

111.5

96

South Africa

2020

25.3

23.5

1.7

25.3

23.5

2.7

2030

22.4

30.0

3.3

4

30.4

37.5

7.0

0

2050

70.1

122.9

24.7

28

94.9

141.5

25.4

21

S. Teske et al.

285

Table 8.49

Africa: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

Africa

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

North Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

1456

44

857

901

0

4611

65

1500

1565

0

2050

59,499

1959

2904

4864

37,284

77,546

1976

2994

4969

2904

West Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

0

1

11

12

0

18

10

126

136

0

2050

62,015

2525

3154

5679

41,842

125,281

2552

3797

6349

10,940

Central Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

0

0

0

0

0

2050

4938

293

298

590

6107

10,557

323

391

714

3879

East Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

54

45

827

872

0

1960

78

1787

1865

0

2050

104,983

3467

4976

8444

65,953

182,399

3573

5673

9246

6375

Southern Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

609

33

640

673

0

3268

49

1053

1102

0

2050

110,532

2122

3371

5493

42,521

177,898

2189

3818

6008

19,886

South Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

2757

113

2155

2268

0

1407

46

877

923

0

2050

25,233

2659

3245

5904

19,194

11,741

2038

1886

3924

0

(continued)

8 Energy Scenario Results

286

Storage and dispatch

2.0 °C

1.5 °C

Africa

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Africa

2020

0

0

0

0

0

0

0

0

0

0

2030

4877

237

4489

4726

0

11,264

248

5343

5591

0

2050

367,201

13,026

17,948

30,974

212,902

585,423

12,651

18,558

31,210

43,984

Table 8.49

(continued)

S. Teske et al.

287

one of the global renewable fuel production regions and it is assumed that all sub-

regions of Africa have equal amounts of energy export potential. However, a more

detailed examination of export energy is required, which is beyond the scope of this

project.

8.9 The Middle East

8.9.1 The Middle East: Long-Term Energy Pathways

8.9.1.1 The Middle East: Final Energy Demand by Sector

The future development pathways for the Middle East’s final energy demand when

the assumptions on population growth, GDP growth, and energy intensity are com-

bined are shown in Fig. 8.53 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the

5.0 °C Scenario, the total final energy demand will increase by 133% from the cur-

rent 17,100 PJ/year to around 40,000 PJ/year in 2050. In the 2.0 °C Scenario, the

final energy demand will decrease by 8% compared with current consumption and

will reach 15,800 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario

will reach 13,600 PJ, 20% below the 2015 demand level. In the 1.5 °C Scenario, the

final energy demand in 2050 will be 14% lower than in the 2.0 °C Scenario. The

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

4,00 0

4,50 0

5,00 0

0

5,00 0

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

TWh/yr

PJ/yr

Transport fuelsTransport electricity
Industry fuelsIndustry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.53 Middle East: development of the final energy demand by sector in the scenarios

8 Energy Scenario Results

288

electricity demand for ‘classical’ electrical devices (without power-to-heat or

e-mobility) will increase from 650 TWh/year in 2015 to 1230 TWh/year (2.0 °C)

and 1160 TWh/year (1.5 °C) by 2050. Compared with the 5.0 °C case (2330 TWh/

year in 2050), the efficiency measures in the 2.0 °C and 1.5 °C Scenarios will save

a maximum of 1100 TWh/year and 1170 TWh/year, respectively.

Electrification will lead to a significant increase in the electricity demand. In the

2.0 °C Scenario, the electricity demand for heating will rise to approximately 800

TWh/year due to electric heaters and heat pumps, and in the transport sector, the

demand will rise to approximately 1700 TWh/year due to the increase in electric

mobility. The generation of hydrogen (for transport and high-temperature process

heat) and the manufacture of synthetic fuels (mainly for transport) will add an addi-

tional power demand of 1900 TWh/year. The gross power demand will thus rise

from 1100 TWh/year in 2015 to 4700 TWh/year in 2050  in the 2.0  °C Scenario,

57% higher than in the 5.0  °C case. In the 1.5  °C Scenario, the gross electricity

demand will increase to a maximum of 4100 TWh/year by 2045.

The efficiency gains could be even larger in the heating sector than in the elec-

tricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 10,100 PJ/year and 10,500 PJ/year, respectively, will be avoided

through efficiency gains by 2050 compared with the 5.0 °C Scenario.

8.9.1.2 The Middle East: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in the Middle East will

come from renewable energy sources under the 2.0  °C Scenario. ‘New’ renew-

ables—mainly wind, solar, and geothermal energy—will contribute 96% of the total

electricity generation. Renewable electricity’s share of the total production will be

49% by 2030 and 91% by 2040. The installed capacity of renewables will reach

about 430 GW by 2030 and 1910 GW by 2050. The share of renewable electricity

generation in 2030  in the 1.5  °C Scenario is assumed to be 58%. In the 1.5  °C

Scenario, the generation capacity from renewable energy will be approximately

1700 GW in 2050.

Table 8.50 shows the development of different renewable technologies in the

Middle East over time. Figure 8.54 provides an overview of the overall power-

generation structure in the Middle East. From 2020 onwards, the continuing growth

of wind and PV, up to 480 GW and 1070 GW, respectively, will be complemented

by up to 250 GW of solar thermal generation, as well as limited biomass, geother-

mal, and ocean energy, in the 2.0 °C Scenario. Both the 2.0 °C Scenario and 1.5 °C

Scenario will lead to high proportions of variable power generation (PV, wind, and

ocean) of 39% and 46%, respectively, by 2030, and 64% and 66%, respectively, by

S. Teske et al.

289

Table 8.50 Middle East: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 16 20 22 25 29
2.0 °C 16 22 22 25 29
1.5 °C 16 22 22 25 29
Biomass 5.0 °C 0 0 1 3 7
2.0 °C 0 2 3 4 4
1.5 °C 0 3 3 4 4
Wind 5.0 °C 0 4 9 23 49
2.0 °C 0 54 156 371 481
1.5 °C 0 60 175 432 456
Geothermal 5.0 °C 0 0 0 0 0
2.0 °C 0 5 7 20 25
1.5 °C 0 5 7 20 21
PV 5.0 °C 0 7 10 21 40
2.0 °C 0 76 187 560 1069
1.5 °C 0 92 236 587 928
CSP 5.0 °C 0 2 3 6 7
2.0 °C 0 10 43 270 252
1.5 °C 0 10 47 342 216
Ocean 5.0 °C 0 0 0 0 0
2.0 °C 0 5 10 40 50
1.5 °C 0 5 10 40 45
Total 5.0 °C 16 32 45 79 132
2.0 °C 16 174 427 1290 1911
1.5 °C 16 197 500 1449 1699

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.54 Middle East: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

290

8.9.1.3 The Middle East: Future Costs of Electricity Generation

Figure 8.55 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated

electricity- generation costs in 2015 (referring to full costs) were around 7.1 ct/kWh.

In the 5.0 °C case, the generation costs will increase until 2030, when they reach

14.8 ct/kWh, and then drop to 13.7 ct/kWh by 2050. The generation costs in the

2.0  °C Scenario will increase until 2030, when they reach 11.1 ct/kWh, and then

drop to 6.1 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 10.7 ct/

kWh, and then drop to 7.3 ct/kWh by 2050. In the 2.0 °C Scenario, the generation

costs in 2050 will be 7.6 ct/kWh lower than in the 5.0 °C case. In the 1.5 °C Scenario,

the generation costs in 2050 will be 6.4 ct/kWh lower than in the 5.0 °C case. Note

that these estimates of generation costs do not take into account integration costs

such as power grid expansion, storage, or other load-balancing measures.

In the 5.0 °C case, growth in demand and increasing fossil fuel prices will cause

the total electricity supply costs to rise from today’s $70 billion/year to more than

$410 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs will be $300

billion/year and in the 1.5  °C Scenario, they will be $310 billion/year. The long-

term cost of electricity supply will be more than 27% lower in the 2.0 °C Scenario

than in the 5.0  °C Scenario as a result of the estimated generation costs and the

electrification of heating and mobility. Further demand reductions in the 1.5  °C

Scenario will result in total power-generation costs that are 24% lower than in the

5.0 °C case.

0

2

4

6

8

10

12

14

16

0

50

100

150

200

250

300

350

400

450

2015 2025 2030 2040 2050

billion ct/kWh

$

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.55 Middle East: development of total electricity supply costs and specific electricity- generation costs in the scenarios

S. Teske et al.

291

The generation costs without the CO 2 emission costs will increase in the 5.0 °C

case to 11.1 ct/kWh by 2030, and then stabilize at 10.8 ct/kWh by 2050. In the

2.0 °C Scenario and the 1.5 °C Scenario, they will increase to a maximum of 9 ct/

kWh in 2030, before they drop to 6.1 ct/kWh and 7.3 ct/kWh by 2050, respectively.

In the 2.0  °C Scenario, the generation costs will be 4.7 ct/kWh lower than in the

5.0 °C case and this maximum difference will occur in 2050. In the 1.5 °C Scenario,

the maximum difference in generation costs compared with the 5.0 °C case will be

3.5 ct/kWh in 2050. If the CO 2 costs are not considered, the total electricity supply

costs in the 5.0 °C case will rise to about $320 billion/year by 2050.

8.9.1.4 The Middle East: Future Investments in the Power Sector

An investment of around $3450 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments in plant replace-

ment at the ends of their economic lives. This value will be equivalent to approxi-

mately $96 billion per year on average, and this is $2720 billion more than in the

5.0 °C case ($730 billion). An investment of around $3470 billion for power genera-

tion will be required between 2015 and 2050 in the 1.5 °C Scenario, or on average,

$96 billion per year. In the 5.0 °C Scenario, the investment in conventional power

plants will be around 68% of the total cumulative investments, whereas approxi-

mately 32% will be invested in renewable power generation and co-generation

(Fig. 8.56). However, in both alternative scenarios, the Middle East will shift almost

94% of its entire investments to renewables and co-generation. By 2030, the fossil

fuel share of power sector investment will predominantly focus on gas power plants

that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $2900 billion in 2050,

equivalent to $81 billion per year. Therefore, the total fuel cost savings will be

equivalent to 110% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $3100 billion,

or $86 billion per year.

8.9.1.5 The Middle East: Energy Supply for Heating

The final energy demand for heating will increase by 139% in the 5.0 °C Scenario,

from 7100 PJ/year in 2015 to 17,100 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 59% in 2050  in the 2.0  °C

Scenario, relative to the 5.0  °C case, and by 62% in the 1.5  °C Scenario. Today,

renewables supply almost none of the Middle East’s final energy demand for heat-

ing. Renewable energy will provide 23% of the Middle East’s total heat demand in

2030  in the 2.0  °C Scenario and 25% in the 1.5  °C Scenario. In both scenarios,

renewables will provide 100% of the total heat demand in 2050.

8 Energy Scenario Results

292

Figure 8.57 shows the development of different technologies for heating in the

Middle East over time, and Table 8.51 provides the resulting renewable heat supply

for all scenarios. The growing use of solar, geothermal, and environmental heat will

0

2,00 0

4,00 0

6,00 0

8,00 0

10,000

12,000

14,000

16,000

18,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electric heating
Geothermal heat
and heat pumps
Solar heating
Biomass
Fossil

Fig. 8.57 Middle East: development of heat supply by energy carrier in the scenarios

Fossil
60%

Nuclear
8%

CHP
0%

Renewable
32%

5.0°C: 2015-2050

total 733
billion $

Fossil (incl. H2) 6% CHP 0% Renewable 94%

2.0°C: 2015-2050

total 3,450
billion $

Fossil
(incl. H2)
6%

CHP
Renewable 0%
94%

1.5°C: 2015-2050

total 3,470
billion $

Fig. 8.56 Middle East: investment shares for power generation in the scenarios

S. Teske et al.

293

Table 8.51 Middle East: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 20 56 86 169 291
2.0 °C 20 101 132 200 196
1.5 °C 20 92 124 183 155
Solar heating 5.0 °C 8 92 284 778 1113
2.0 °C 8 404 932 1535 1961
1.5 °C 8 393 909 1475 1619
Geothermal heat and heat pumps 5.0 °C 0 0 0 0 0
2.0 °C 0 118 232 565 1387
1.5 °C 0 115 226 540 1057
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 51 488 946
1.5 °C 0 0 48 828 915
Total 5.0 °C 28 149 370 947 1404
2.0 °C 28 624 1346 2788 4489
1.5 °C 28 601 1307 3025 3746

supplement electrification, with solar heat becoming the main direct renewable heat

source in the 2.0 °C Scenario and 1.5 °C Scenario.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 950 PJ/year in the

2.0 °C Scenario and 920 PJ/year in the 1.5 °C Scenario. The direct use of electricity

for heating will also increase by a factor of 9–10 between 2015 and 2050, and its

final energy share will be 36% in 2050 in the 2.0 °C Scenario and 43% in the 1.5 °C

Scenario (Fig. 8.57).

8.9.1.6 The Middle East: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies to 2050 will

amount to less than $440 billion in the 2.0 °C Scenario (including investments for

plant replacement after their economic lifetimes), or approximately $12 billion per

year. The largest share of investments in the Middle East is assumed to be for heat

pumps (more than $200 billion), followed by solar collectors and geothermal heat

use. The 1.5 °C Scenario assumes an even faster expansion of renewable technolo-

gies. However, the lower heat demand (compared with the 2.0  °C Scenario) will

result in a lower average annual investment of around $10 billion per year

(Table 8.52, Fig. 8.58).

8 Energy Scenario Results

biomass
technologies
47%

geothermal
heat use
0%

solar
collectors
53%

heat
pumps
0%

5.0°C: 2015-2050

total 62 billion $

biomass
technologies
3%

geothermal
heat use
16%

solar
collectors
33%

heat
pumps
48%

2.0°C: 2015-2050

total 445 billion $

biomass
technologies
4%

geothermal
heat use
22%

solar
collectors
35%

heat
pumps
39%

1.5°C: 2015-205 0

total 360 billion $

Fig. 8.58 Middle East: development of investments for renewable heat-generation technologies in the scenarios

Table 8.52 Middle East: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 4 10 14 25 38 2.0 °C 4 13 15 18 14 1.5 °C 4 12 15 17 13 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 2 8 19 30 1.5 °C 0 2 8 18 35 Solar heating 5.0 °C 1 17 51 139 198 2.0 °C 1 72 142 217 252 1.5 °C 1 71 139 209 206 Heat pumps 5.0 °C 0 0 0 0 0 2.0 °C 0 12 17 43 122 1.5 °C 0 12 17 42 76 Totala 5.0 °C 6 26 65 164 237 2.0 °C 6 99 183 297 418 1.5 °C 6 96 178 286 330 a Excluding direct electric heating

295

8.9.1.7 The Middle East: transport

Energy demand in the transport sector in the Middle East is expected to increase in

the 5.0 °C Scenario by 133%, from around 5700 PJ/year in 2015 to 13,300 PJ/year

in 2050. In the 2.0  °C Scenario, assumed technical, structural, and behavioural

changes will save 67% (8860 PJ/year) by 2050 compared with the 5.0 °C Scenario.

Additional modal shifts, technology switches, and a reduction in the transport

demand will lead to even higher energy savings in the 1.5 °C Scenario of 79% (or

10,400 PJ/year) in 2050 compared with the 5.0 °C case (Table 8.53, Fig. 8.59).

By 2030, electricity will provide 4% (70 TWh/year) of the transport sector’s total

energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 39% (480

TWh/year). In 2050, up to 620 PJ/year of hydrogen will be used in the transport

sector as a complementary renewable option. In the 1.5  °C Scenario, the annual

electricity demand will be 350 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 450 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 370 PJ/year.

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

duced, with a maximum consumption of 1670 PJ/year in 2050. Biofuel use in the

1.5 °C Scenario with have a maximum of 430 PJ/year. The maximum synthetic fuel

demand will amount to 920 PJ/year.

Table 8.53 Middle East: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 184 38 48 65 75
2.0 °C 184 64 103 169 157
1.5 °C 184 89 117 161 194
Road 5.0 °C 5425 6613 7802 10,999 12,992
2.0 °C 5425 5928 5732 4510 4194
1.5 °C 5425 5246 4528 2899 2618
Domestic aviation 5.0 °C 57 83 103 136 146
2.0 °C 57 60 57 47 37
1.5 °C 57 57 52 36 28
Domestic navigation 5.0 °C 0 0 0 0 0
2.0 °C 0 0 0 0 0
1.5 °C 0 0 0 0 0
Total 5.0 °C 5666 6734 7954 11,200 13,213
2.0 °C 5666 6051 5893 4726 4388
1.5 °C 5666 5392 4697 3096 2840

8 Energy Scenario Results

296

8.9.1.8 The Middle East: Development of CO 2 Emissions

In the 5.0  °C Scenario, the Middle East’s annual CO 2 emissions will increase by

76% from 1760 Mt. in 2015 to 3094 Mt. in 2050. The stringent mitigation measures

in both alternative scenarios will cause the annual emissions to fall to 510 Mt. in

2040  in the 2.0  °C Scenario and to 220 Mt. in the 1.5  °C Scenario, with further

reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions

from 2015 until 2050 will add up to 90 Gt. In contrast, in the 2.0  °C and 1.5  °C

Scenarios, the cumulative emissions for the period from 2015 until 2050 will be 38

Gt and 31 Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 58% in the 2.0  °C

Scenario and by 66% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario, this reduction will be greatest in ‘Industry’ followed by the ‘Power gen-

eration’ and ‘Transport’ sectors (Fig. 8.60).

8.9.1.9 The Middle East: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.61. In the 2.0 °C

Scenario, the primary energy demand will decrease by 16%, from around 30,300

PJ/year in 2015 to 25,400 PJ/year in 2050. Compared with the 5.0 °C Scenario, the

overall primary energy demand will decrease by 59% by 2050 in the 2.0 °C Scenario

(5.0 °C: 61,700 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand will

be even lower (22,300 PJ in 2050) because the final energy demand and conversion

losses will be lower.

0

2,00 0

4,00 0

6,00 0

8,00 0

10,000

12,000

14,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.59 Middle East: final energy consumption by transport in the scenarios

S. Teske et al.

297

0

10

20

30

40

50

60

70

80

90

100

0

500

1,000

1,500

2,000

2,500

3,000

3,500

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025 2030 2040 2050

cumulated

emissions

CO [Gt]

2

emissions

[Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.60 Middle East: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

0

10,00 0

20,000

30,000

40,00 0

50,00 0

60,00 0

70,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

PJ/yr

net electricity
imports
Efficiency

Ocean energy

Geothermal

Solar
Biomass

Wind

Hydro

Natural gas

Crude oil

Coal
Nuclear

Fig. 8.61 Middle East: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

8 Energy Scenario Results

298

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 18% in 2030 and

88% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 86% in 2050 (including non-energy consump-

tion, which will still include fossil fuels). Nuclear energy will be phased-out in

2035 in both the 2.0 °C and the 1.5 °C Scenarios. The cumulative primary energy

consumption of natural gas in the 5.0 °C case will add up to 830 EJ, the cumulative

coal consumption to about 10 EJ, and the crude oil consumption to 630 EJ. In the

2.0 °C Scenario, the cumulative gas demand will amount to 330 EJ, the cumulative

coal demand to 1 EJ, and the cumulative oil demand to 310 EJ. Even lower fossil

fuel use will be achieved in the 1.5 °C Scenario: 280 EJ for natural gas, 0.9 EJ for

coal, and 270 EJ for oil.

8.9.2 The Middle East: Power Sector Analysis

The Middle East has significant renewable energy potential. The region’s solar radi-

ation is among the highest in the world and it has good wind conditions in coastal

areas and in its mountain ranges. The electricity market is fragmented, and policies

differ significantly. However, most countries are connected to their neighbours by

transmission lines. Saudi Arabia, the geographic centre of the region, has connec-

tions to most neighbouring countries. Both the 2.0  °C Scenario and the 1.5  °C

Scenario assume that the Middle East will remain a significant player in the energy

market, moving from oil and gas to solar, and that it will play an important role in

producing synthetic fuels and hydrogen for export.

8.9.2.1 The Middle East: Development of Power Plant Capacities

The overwhelming majority of fossil-fuel-based power generation in the Middle

East is from gas-fired power plants. Both scenarios assume that this gas capacity (in

GW) will remain on the same level until 2050, but will be converted to hydrogen.

The annual market for solar PV must increase to 2.5 GW in 2020 and to 28.5 GW

by 2030 in the 2.0 °C Scenario, and to 35 GW in the 1.5 °C Scenario. The onshore

wind market must expand to 10 GW by 2025 in both scenarios. This represents a

very ambitious target because the market for wind power plants in the Middle East

has never been higher than 117 MW (GWEC 2018 ) (in 2015). Parts of the offshore

oil and gas industry can be transitioned into an offshore wind industry. The total

capacity assumed for the Middle East by 2050 is 20–25 GW under both scenarios.

For comparison, the UK had an installed capacity for offshore wind of 6.8 GW and

Germany of 5.4 GW in 2017 (GWEC 2018 ). The vast solar resources in the Middle

S. Teske et al.

299

Table 8.54 Middle East: average annual change in installed power plant capacity

Middle East – power generation: average
annual change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal 0.0 0.0 0.0 0.0 0.0 0.0
Lignite 0.0 0.0 0.0 0.0 0.0 0.0
Gas 1.5 7.0 1.9 6.2 −19.1 3.0
Hydrogen-gas 0.0 0.3 1.5 1.7 20.3 24.2
Oil/Diesel −0.1 −4.0 −8.9 −8.1 −0.8 −0.5
Nuclear −0.1 0.0 −0.1 −0.1 0.0 0.0
Biomass 0.2 0.3 0.2 0.1 0.2 0.0
Hydro 1.0 0.5 0.2 0.2 0.5 0.5
Wind (onshore) 6.5 19.3 28.3 35.5 14.7 7.6
Wind (offshore) 0.2 0.5 0.8 0.8 1.4 1.2
PV (roof top) 7.3 19.0 26.2 29.9 46.4 32.3
PV (utility scale) 2.4 6.3 8.7 10.0 15.5 10.8
Geothermal 0.6 0.8 1.1 1.1 1.0 0.6
Solar thermal power plants 1.3 5.4 13.1 20.3 11.4 3.7
Ocean energy 0.3 1.3 1.3 2.5 1.0 1.7
Renewable fuel based co-generation 0.0 0.0 0.1 0.1 0.0 0.0

East make it suitable for CSP plants—the total capacity by 2050 is calculated to be

252 GW (2.0 °C Scenario), equal to the gas power plant capacity in the Middle East

in 2017 (Table 8.54).

8.9.2.2 Middle East: Utilization of Power-Generation Capacities

In 2015, the base year of the scenario calculations, the Middle East had less than

0.5% variable power generation. Table 8.55 shows the rapidly increasing shares of

variable renewable power generation across the Middle East. Israel is included in

the Middle East region (as opposed to the IEA region used for the long-term sce-

nario) to reflect its current and possible future interconnection with the regional

power system. The current interconnection capacity between all sub-regions is

assumed to be 5%, increasing to 20% in 2030 and 25% in 2050. Dispatchable

renewables will have a stable market share of around 15%–20% over the entire

modelling period in the 2.0 °C Scenario and 15%–20% in the 1.5 °C Scenario.

Average capacity factors correspond to the results for the other world regions.

Table 8.56 shows that the limited dispatchable fossil and nuclear generation will

drop quickly, whereas the significant gas power plant capacity within the region can

increase capacity factors to take over their load and reduce carbon emissions at an

early stage. The calculation results are attributed to the assumed dispatch order,

which prioritizes gas over coal and nuclear.

8 Energy Scenario Results

300

Table 8.55

Middle East: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

Middle East

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Israel

2015

0%

12%

88%

5%

2030

41%

18%

41%

20%

46%

18%

36%

15%

2050

81%

18%

0%

25%

64%

15%

21%

20%

North-ME

2015

0%

12%

88%

5%

2030

50%

20%

30%

20%

55%

19%

26%

15%

2050

83%

17%

0%

25%

74%

15%

10%

20%

Saudi Arabia-ME

2015

0%

12%

88%

5%

2030

51%

17%

32%

20%

55%

17%

28%

15%

2050

83%

17%

0%

25%

72%

16%

12%

20%

UAE-ME

2015

0%

12%

88%

5%

2030

36%

20%

45%

20%

40%

20%

40%

15%

2050

76%

24%

0%

25%

53%

18%

28%

20%

East-ME

2015

0%

12%

88%

5%

2030

42%

20%

38%

20%

47%

21%

32%

15%

2050

80%

20%

0%

25%

63%

17%

20%

20%

Iraq-ME

2015

0%

12%

88%

5%

2030

60%

18%

21%

20%

65%

17%

18%

15%

2050

82%

18%

0%

25%

76%

16%

7%

20%

S. Teske et al.

301

Iran-ME

2015

0%

12%

88%

5%

2030

57%

19%

24%

20%

62%

18%

21%

15%

2050

81%

19%

0%

25%

73%

17%

9%

20%

Middle East

2015

0%

12%

88%

2030

51%

19%

31%

56%

18%

27%

2050

81%

19%

0%

70%

16%

13%

8 Energy Scenario Results

302

8.9.2.3 The Middle East: Development of Load, Generation,

and Residual Load

The Middle East is assumed to be one of the exporters of solar electricity into the

EU, so the calculated solar installation capacities throughout the region will be sig-

nificantly higher than required for self-supply.

Table 8.57 shows a negative residual load in almost all sub-regions for every year

and in both scenarios. This is attributable to substantial oversupply, so the produc-

tion of renewables is almost constantly higher than the demand. This electricity has

been calculated as exports from the Middle East and imports to Europe.

The Middle East will be one of three dedicated renewable energy export regions.

These exports are in the form of renewable fuels and electricity. The [R]E 24/7

model does not calculate electricity exchange in 1 h steps between the world regions,

and therefore the amount of electricity exported accumulates from year to year. The

load curves for the Middle East and European regions are not calculated

separately.

Table 8.58 provides an overview of the calculated storage and dispatch power

requirements by sub-region in the Middle East. Iran and Saudi Arabia West Africa

will require the highest storage capacity, due to the very high share of solar PV

systems in residential areas. Like the Africa, the Middle East is one of the global

renewable fuel production regions and it is assumed that all sub-regions of the

Middle East have equal amounts of energy export potential. However, a more

detailed examination of export energy is required, which is beyond the scope of this

project.

Table 8.56 Middle East: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
Middle East 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr]52.6% 45% 43% 27% 24% 34% 21% 29% 25%

Limited
dispatchable:
fossil and
nuclear

[%/yr]31.1% 13% 13% 5% 2% 19% 3% 10% 5%

Limited
dispatchable:
renewable

[%/yr]26.3% 34% 34% 47% 42% 50% 21% 28% 30%

Dispatchable:
fossil

[%/yr]52.9% 41% 40% 15% 10% 45% 8% 17% 16%

Dispatchable:
renewable

[%/yr]38.9% 83% 83% 66% 57% 43% 20% 36% 38%

Variable:
renewable

[%/yr] 6.6% 12% 12% 24% 23% 27% 23% 29% 25%

S. Teske et al.

303

Table 8.57

Middle East: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

Middle East

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Israel

2020

11.5

11.0

−

2.9

11.5

11.9

−

2.9

2030

14.0

17.3

−

1.8

5

14.3

19.9

−

1.4

7

2050

29.7

62.7

−

3.7

37

29.3

55.2

−

10.3

36

North-ME

2020

33.7

25.7

−

12.1

33.8

29.4

−

11.6

2030

39.7

44.6

−

12.1

17

40.6

52.6

−

11.2

23

2050

83.6

196.5

−

11.5

124

77.5

172.8

−

19.9

115

Saudi Arabia-ME

2020

59.5

45.9

−

3.4

59.6

45.4

−

2.4

2030

72.4

85.9

2.3

11

75.1

99.8

5.6

19

2050

173.6

380.9

−

21.7

229

168.6

334.0

−

59.4

225

UAE-ME

2020

21.2

29.8

−

0.4

21.2

29.6

1.3

2030

26.0

44.1

2.2

16

27.0

50.7

3.4

20

2050

62.2

120.2

−

7.4

65

61.3

105.4

−

23.4

67

East-Middle East

2020

12.0

23.3

−

2.5

12.0

22.6

−

2.6

2030

14.8

31.3

−

1.2

18

15.1

35.9

−

0.8

22

2050

32.5

63.4

−

3.9

35

31.2

55.6

−

7.2

32

Iraq-ME

2020

20.1

13.8

−

7.6

20.2

13.8

−

7.3

2030

26.0

30.4

−

7.4

12

26.8

35.8

−

8.1

17

2050

64.3

137.5

−

7.8

81

57.7

119.9

−

20.0

82

Iran-ME

2020

49.4

56.9

−

12.2

49.4

56.4

−

12.2

2030

76.1

88.1

−

14.3

26

78.3

103.9

−

11.4

37

2050

188.5

399.1

−

22.7

233

174.3

348.0

−

40.5

214

8 Energy Scenario Results

304

Table 8.58

Middle East: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

Middle East

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Israel

2020

0

0

0

0

0

0

0

0

0

0

2030

29

0

10

10

0

226

0

36

36

8

2050

24,725

11

379

390

0

14,244

11

320

331

529

North-ME

2020

0

0

0

0

0

0

0

0

0

0

2030

1164

0

193

194

0

3596

1

355

356

20

2050

109,498

32

1409

1441

0

84,974

31

1434

1465

1193

Saudi Arabia

2020

0

0

0

0

0

0

0

0

0

0

2030

3366

1

513

514

0

11,457

2

900

902

39

2050

231,140

73

2685

2757

0

159,949

74

2429

2503

2624

UAE

2020

0

0

0

0

0

0

0

0

0

0

2030

9

0

5

5

0

233

0

45

45

17

2050

35,463

24

679

703

0

17,093

23

507

531

1075

S. Teske et al.

305

East-ME

2020

0

0

0

0

0

0

0

0

0

0

2030

2

0

3

3

0

117

0

29

29

9

2050

21,916

12

410

421

0

12,920

12

350

362

490

Iraq

2020

0

0

0

0

0

0

0

0

0

0

2030

3941

0

330

330

0

8185

0

446

447

10

2050

87,343

18

892

910

0

74,252

17

920

937

684

Iran

2020

0

0

0

0

0

0

0

0

0

0

2030

9576

1

831

833

0

21,130

1

1127

1128

30

2050

242,799

50

2508

2558

0

190,790

47

2443

2490

2036

Middle East

2020

0

0

0

0

0

0

0

0

0

0

2030

18,088

4

1886

1890

0

44,945

4

2939

2943

132

2050

752,882

218

8962

9180

0

554,222

215

8404

8618

8630

8 Energy Scenario Results

306

8.10 Eastern Europe/Eurasia

8.10.1 Eastern Europe/Eurasia: Long-Term Energy Pathways

8.10.1.1 Eastern Europe/Eurasia: Final Energy Demand by Sector

The future development pathways for Eastern Europe/Eurasia’s final energy demand

when the assumptions on population growth, GDP growth, and energy intensity are

combined are shown in Fig. 8.62 for the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios. In the

5.0 °C Scenario, the total final energy demand will increase by 45%, from the cur-

rent 25,500 PJ/year to 37,000 PJ/year in 2050. In the 2.0  °C Scenario, the final

energy demand will decrease by 25% compared with current consumption and will

reach 19,100 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will

reach 17,800 PJ, 30% below the 2015 level. In the 1.5 °C Scenario, the final energy

demand in 2050 will be 7% lower than in the 2.0  °C Scenario. The electricity

demand for ‘classical’ electrical devices (without power-to-heat or e-mobility) will

increase from 910 TWh/year in 2015 to 1000 TWh/year (2.0 °C) or 940 TWh/year

(1.5  °C) by 2050. Compared with the 5.0  °C case (1600 TWh/year in 2050), the

efficiency measures in the 2.0 °C and 1.5 °C Scenarios will save a maximum of 600

TWh/year and 660 TWh/year, respectively.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
20152025 2030 2040 2050

TWh/yr

PJ/yr

Transport fuels Transport electricity
Industry fuels Industry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.62 Eastern Europe/Eurasia: development of the final energy demand by sector in the scenarios

S. Teske et al.

307

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0  °C Scenario, the electricity demand for heating will be approxi-

mately 700 TWh/year due to electric heaters and heat pumps, and in the transport

sector, the electricity demand will be approximately 2300 TWh/year due to increased

electric mobility. The generation of hydrogen (for transport and high-temperature

process heat) and the manufacture of synthetic fuels (mainly for transport) will add

an additional power demand of 2300 TWh/year. Therefore, the gross power demand

will rise from 1700 TWh/year in 2015 to 4900 TWh/year in 2050  in the 2.0  °C

Scenario, 88% higher than in the 5.0 °C case. In the 1.5 °C Scenario, the gross elec-

tricity demand will increase to a maximum of 4800 TWh/year in 2050.

Efficiency gains could be even larger in the heating sector than in the electricity

sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equivalent to

more than 10,700 PJ/year is avoided by 2050 compared with the 5.0 °C Scenario

through efficiency gains.

8.10.1.2 Eastern Europe/Eurasia: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in Eastern Europe/Eurasia

will come from renewable energy sources in the 2.0  °C Scenario. ‘New’ renew-

ables—mainly wind, solar, and geothermal energy—will contribute 75% of the total

electricity generation. Renewable electricity’s share of the total production will be

55% by 2030 and 84% by 2040. The installed capacity of renewables will reach

about 560 GW by 2030 and 1900 GW by 2050. The share of renewable electricity

generation in 2030  in the 1.5  °C Scenario is assumed to be 66%. In the 1.5  °C

Scenario, the generation capacity from renewable energy will be approximately

1870 GW in 2050.

Table 8.59 shows the development of different renewable technologies in Eastern

Europe/Eurasia over time. Figure 8.63 provides an overview of the overall power-

generation structure in Eastern Europe/Eurasia. From 2020 onwards, the continuing

growth of wind and PV, up to 740 GW and 820 GW, respectively, will be comple-

mented by up to 30 GW of solar thermal generation, as well as limited biomass,

geothermal, and ocean energy, in the 2.0 °C Scenario. Both the 2.0 °C Scenario and

1.5  °C Scenario will lead to a high proportion of variable power generation (PV,

wind, and ocean) of 28% and 32%, respectively, by 2030, and 62% and 61%,

respectively, by 2050.

8 Energy Scenario Results

308

8.10.1.3 Eastern Europe/Eurasia: Future Costs of Electricity Generation

Figure 8.64 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated

electricity- generation costs in 2015 (referring to full costs) were around 4.5 ct/kWh.

In the 5.0 °C case, the generation costs will increase until 2050, when they reach 10

ct/kWh. In the 2.0 °C Scenario, the generation costs will increase until 2050, when

they will reach 8.6 ct/kWh. In the 1.5 °C Scenario, they will increase to 9.3 ct/kWh,

and then drop to 8.8 ct/kWh by 2050. In the 2.0 °C Scenario, the generation costs in

2050 will be 1.4 ct/kWh lower than in the 5.0 °C case. In the 1.5 °C Scenario, the

generation costs in 2050 will be 1.1 ct/kWh lower than in the 5.0 °C case. Note that

these estimates of generation costs do not take into account integration costs such as

power grid expansion, storage, or other load-balancing measures.

Table 8.59 Eastern Europe/Eurasia: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 98 107 112 123 136
2.0 °C 98 107 112 115 116
1.5 °C 98 107 112 115 116
Biomass 5.0 °C 1 4 6 9 14
2.0 °C 1 21 45 64 96
1.5 °C 1 40 74 86 109
Wind 5.0 °C 6 9 10 17 23
2.0 °C 6 70 176 469 744
1.5 °C 6 74 196 531 697
Geothermal 5.0 °C 0 1 1 2 4
2.0 °C 0 5 12 38 71
1.5 °C 0 7 21 46 71
PV 5.0 °C 4 5 6 8 10
2.0 °C 4 108 209 502 817
1.5 °C 4 132 294 678 821
CSP 5.0 °C 0 0 0 0 0
2.0 °C 0 0 1 16 33
1.5 °C 0 0 1 22 34
Ocean 5.0 °C 0 0 0 0 0
2.0 °C 0 0 1 13 19
1.5 °C 0 0 1 13 19
Total 5.0 °C 108 126 136 159 186
2.0 °C 108 310 555 1216 1896
1.5 °C 108 360 698 1492 1869

S. Teske et al.

309

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

TWh/y

r

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.63 Eastern Europe/Eurasia: development of electricity-generation structure in the scenarios

0

2

4

6

8

10

12

0

100

200

300

400

500

600

2015 2025203020402050

billion ct/kWh

$

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.64 Eastern Europe/Eurasia: development of total electricity supply costs and specific
electricity- generation costs in the scenarios

8 Energy Scenario Results

310

In the 5.0 °C case, the growth of demand and increasing fossil fuel prices will

cause the total electricity supply costs to rise from today’s $120 billion/year to more

than $320 billion/year in 2050. In both alternative scenarios, the total supply costs

will be $490 billion/year in 2050. The long-term costs of electricity supply will be

more than 54% higher in the 2.0 °C Scenario than in the 5.0 °C Scenario as a result

of the estimated generation costs and the electrification of heating and mobility.

Further electrification and synthetic fuel generation in the 1.5  °C Scenario will

result in total power generation costs that are 55% higher than in the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0  °C case to 6.9 ct/kWh. In the 2.0  °C

Scenario, the generation costs will increase continuously until 2050, when they

reach 8.6 ct/kWh. They will increase to 8.8 ct/kWh in the 1.5 °C Scenario. In the

2.0 °C Scenario, the generation costs will reach a maximum, at 1.7 ct/kWh higher

than in the 5.0 °C case, and this will occur in 2050. In the 1.5 °C Scenario, the maxi-

mum difference in generation costs compared with the 5.0 °C case will be 2.6 ct/

kWh in 2040. The generation costs in 2050 will still be 2 ct/kWh higher than in the

5.0 °C case. If the CO 2 costs are not considered, the total electricity supply costs in

the 5.0 °C case will rise to about $240 billion in 2050.

8.10.1.4 Eastern Europe/Eurasia: Future Investments in the Power

Sector

An investment of around $3600 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments in plant replace-

ment at the end of their economic lives. This value is equivalent to approximately

$100 billion per year on average, and is $2660 billion more than in the 5.0 °C case

($940 billion). An investment of around $3770 billion for power generation will be

required between 2015 and 2050  in the 1.5  °C Scenario. On average, this is an

investment of $105 billion per year. In the 5.0 °C Scenario, the investment in con-

ventional power plants will be around 40% of the total cumulative investments,

whereas approximately 60% will be invested in renewable power generation and

co-generation (Fig. 8.65).

However, in the 2.0  °C (1.5  °C) scenario, Eastern Europe/Eurasia will shift

almost 97% (98%) of its entire investments to renewables and co-generation. By

2030, the fossil fuel share of the power sector investments will predominantly focus

on gas power plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $1730 billion in 2050,

equivalent to $48 billion per year. Therefore, the total fuel cost savings will be

equivalent to 70% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $1900 billion,

or $53 billion per year.

S. Teske et al.

311

Fossil
18%

Nuclear
22%

CHP
26%

Renewable
34%

5.0°C: 2015-2050

total 938
billion $

Fossil (incl. H2)
3%
Nuclear
1%

CHP
Renewable 22%
74%

2.0°C: 2015-2050

total 3,600
billion $

Fossil (incl.
H2)
2% Nuclear
1%

CHP
24 %

Renewable
73%

1.5°C: 2015-2050

total 3,770
billion $

Fig. 8.65 Eastern Europe/Eurasia: investment shares for power generation in the scenarios

8.10.1.5 Eastern Europe/Eurasia: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 46%,

from 15,700 PJ/year in 2015 to 22,900 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 47% in 2050 in both alterna-

tive scenarios. Today, renewables supply around 4% of Eastern Europe/Eurasia’s

final energy demand for heating, with the main contribution from biomass.

Renewable energy will provide 29% of Eastern Europe/Eurasia’s total heat demand

in 2030 in the 2.0 °C Scenario and 42% in the 1.5 °C Scenario. In both scenarios,

renewables will provide 100% of the total heat demand in 2050.

Figure 8.66 shows the development of different technologies for heating in

Eastern Europe/Eurasia over time, and Table 8.60 provides the resulting renewable

heat supply for all scenarios. Until 2030, biomass will remain the main contributor.

In the long term, the growing use of solar, geothermal, and environmental heat will

lead to a biomass share of 28% in both alternative scenarios.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 1900 PJ/year in

the 2.0 °C Scenario and 2000 PJ/year in the 1.5 °C Scenario.

8 Energy Scenario Results

312

The direct use of electricity for heating will also increases by a factor of 2.7

between 2015 and 2050, and its final energy share will be 18% in 2050 in the 2.0 °C

Scenario and 19% in the 1.5 °C Scenario.

8.10.1.6 Eastern Europe/Eurasia: Future Investments in the Heating

Sector

The roughly estimated investment in renewable heating technologies up to 2050

will amount to around $1070 billion in the 2.0 °C Scenario (including investments

in plant replacement after their economic lifetimes), or approximately $30 billion

Table 8.60 Eastern Europe/Eurasia: development of renewable heat supply in the scenarios
(excluding the direct use of electricity)
in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 537 810 873 1005 1164
2.0 °C 537 1604 2199 2971 2819
1.5 °C 537 1869 2684 2734 2722
Solar heating 5.0 °C 5 10 13 24 41
2.0 °C 5 277 706 1560 1662
1.5 °C 5 351 768 1395 1620
Geothermal heat and heat pumps 5.0 °C 6 9 11 15 21
2.0 °C 6 265 780 2314 3493
1.5 °C 6 410 1163 2434 3393
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 42 152 795 1934
1.5 °C 0 155 494 1344 2032
Total 5.0 °C 548 829 897 1044 1226
2.0 °C 548 2187 3837 7640 9908
1.5 °C 548 2786 5110 7906 9767

0

5,000

10,00 0

15,00 0

20,00 0

25,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electric heating

Geothermal heat
and heat pumps
Solar heating

Biomass

Fossil

Fig. 8.66 Eastern Europe/Eurasia: development of heat supply by energy carrier in the scenarios

S. Teske et al.

313

Table 8.61 Eastern Europe/Eurasia: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 107 150 157 169 183 2.0 °C 107 230 249 263 172 1.5 °C 107 241 252 230 162 Geothermal 5.0 °C 0 0 0 1 1 2.0 °C 0 14 26 64 61 1.5 °C 0 12 30 52 54 Solar heating 5.0 °C 1 2 3 5 9 2.0 °C 1 56 145 330 359 1.5 °C 1 74 163 300 352 Heat pumps 5.0 °C 1 1 2 2 3 2.0 °C 1 25 64 184 248 1.5 °C 1 33 76 175 236 Totala 5.0 °C 109 154 162 177 196 2.0 °C 109 325 483 841 839 1.5 °C 109 361 522 758 805 a Excluding direct electric heating

per year. The largest share of the investments in Eastern Europe/Eurasia is assumed

to be for heat pumps (around $490 billion), followed by solar collectors and bio-

mass technologies. The 1.5 °C Scenario assumes an even faster expansion of renew-

able technologies. However, the lower heat demand (compared with the 2.0  °C

Scenario) will result in a lower average annual investment of around $29 billion per

year (Table 8.61, Fig. 8.67).

8.10.1.7 Eastern Europe/Eurasia: Transport

Energy demand in the transport sector in Eastern Europe/Eurasia is expected to

increase in the 5.0 °C Scenario by 34%, from around 6000 PJ/year in 2015 to 8000

PJ/year in 2050. In the 2.0 °C Scenario, assumed technical, structural, and behav-

ioural changes will save 48% (3840 PJ/year) by 2050 compared with the 5.0  °C

Scenario. Additional modal shifts, technology switches, and a reduction in the

transport demand will lead to even higher energy savings in the 1.5 °C Scenario of

62% (or 4970 PJ/year) in 2050 compared with the 5.0 °C case (Table 8.62, Fig. 8.68).

By 2030, electricity will provide 14% (240 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 54%

(630 TWh/year). In 2050, up to 410 PJ/year of hydrogen will be used in the trans-

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand will be 510 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 330 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 720 PJ/year

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

8 Energy Scenario Results

314

duced, with a maximum amount of 880 PJ/year in 2050. With the lower overall

energy demand in transport, biofuel use will also be reduced in the 1.5 °C Scenario

to a maximum of 700 PJ/year The maximum synthetic fuel demand will amount to

540 PJ/year.

8.10.1.8 Eastern Europe/Eurasia: Development of CO 2 Emissions

In the 5.0 °C Scenario, Eastern Europe/Eurasia’s annual CO 2 emissions will increase

by 14%, from 2420 Mt. in 2015 to 2768 Mt. in 2050. The stringent mitigation mea-

sures in both alternative scenarios will cause the annual emissions to fall to 590 Mt.

in 2040 in the 2.0 °C Scenario and to 340 Mt. in the 1.5 °C Scenario, with further

reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions

from 2015 until 2050 will add up to 95 Gt. In contrast, in the 2.0  °C and 1.5  °C

Scenarios, the cumulative emissions for the period from 2015 until 2050 will be 45

Gt and 36 Gt, respectively.

biomass
technologies
92%

geothermal
heat use 1%
solar collectors
4%
heat pumps
3%

5.0°C: 2015-2050

total 165 billion $

biomass
technologies
14%

geothermal
heat use
12%

solar
collectors
29%

heat
pumps
45%

2.0°C: 2015-2050

total 1,075 billion $

biomass
technologies
14%

geothermal
heat use
11%

solar
collectors
29%

heat pumps
46%

1.5°C: 2015-2050

total 1,060 billion $

Fig. 8.67 Eastern Europe/Eurasia: development of investments for renewable heat-generation technologies in the scenarios

S. Teske et al.

315

Table 8.62 Eastern Europe/Eurasia: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 434 498 528 599 674
2.0 °C 434 509 544 646 712
1.5 °C 434 449 470 620 796
Road 5.0 °C 3873 4321 4680 5181 5319
2.0 °C 3873 4336 4403 3923 3195
1.5 °C 3873 3593 2963 2346 2016
Domestic aviation 5.0 °C 232 336 403 482 471
2.0 °C 232 247 228 188 150
1.5 °C 232 237 207 146 114
Domestic navigation 5.0 °C 34 35 36 38 40
2.0 °C 34 35 36 38 40
1.5 °C 34 35 36 38 40
Total 5.0 °C 4573 5191 5647 6301 6504
2.0 °C 4573 5127 5210 4795 4097
1.5 °C 4573 4313 3677 3150 2966

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025203020402050

PJ/yr

Efficiency
(compared to
5.0°C)

Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.68 Eastern Europe/Eurasia: final energy consumption by transport in the scenarios

Therefore, the cumulative CO 2 emissions will decrease by 53% in the 2.0  °C

Scenario and by 62% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario, this reduction will be greatest in ‘Power generation’, followed by the

‘Residential and other’ and ‘Industry’ sectors (Fig. 8.69).

8 Energy Scenario Results

316

8.10.1.9 Eastern Europe/Eurasia: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.70. In the 2.0 °C

Scenario, the primary energy demand will decrease by 25%, from around 46,000

PJ/year in 2015 to 34,600 PJ/year in 2050. Compared with the 5.0 °C Scenario, the

overall primary energy demand will decrease by 40% by 2050 in the 2.0 °C Scenario

(5.0 °C: 57,700 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand will

be even lower (33,600 PJ in 2050) because the final energy demand and conversion

losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 26% in 2030 and

91% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 90% in 2050 (including non-energy consump-

tion, which will still include fossil fuels). Nuclear energy will be phased-out by

2040  in both the 2.0  °C Scenario and 1.5  °C Scenario. The cumulative primary

energy consumption of natural gas in the 5.0  °C case will add up to 840 EJ, the

cumulative coal consumption to about 290 EJ, and the crude oil consumption to 340

EJ. In contrast, in the 2.0 °C Scenario, the cumulative gas demand will amount to

510 EJ, the cumulative coal demand to 100 EJ, and the cumulative oil demand to

160 EJ. Even lower fossil fuel use will be achieved in the 1.5 °C Scenario: 450 EJ

for natural gas, 70 EJ for coal, and 120 EJ for oil.

0

20

40

60

80

100

120

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

cumulated

emissions

[Gt]
CO

2
emissions

[Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.69 Eastern Europe/Eurasia: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

317

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 203020402050

PJ/yr

net electricity
imports
Efficiency

Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal

Nuclear

Fig. 8.70 Eastern Europe/Eurasia: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

8.10.2 Eastern Europe/Eurasia: Power Sector Analysis

This region sits between the strong economic hubs of the EU, China, and India.

Russia, by far the largest country within this region, is an important producer of oil

and gas, and supplies all surrounding countries. Therefore, Eurasia will be key in

future energy developments. Its renewable energy industry is among the smallest in

the world, but recent developments indicate growth in both the wind (WPM 3- 2018 )

and solar industries (PVM 3- 2018 ).

8.10.2.1 Eurasia: Development of Power Plant Capacities—2.0 °C

Scenario

The northern part of Eurasia and Mongolia have significant wind potential, whereas

the southern part, especially in Central Asia, has substantial possibilities for utility-

scale solar power plants—both for solar PV and concentrated solar. The annual

market for solar PV and onshore wind—as for all other renewable power generation

technologies—must develop from a very low MW range in 2017 to a GW market by

2025. Besides solar PV and onshore wind, bioenergy has significant potential in

Eurasia, especially in the European part, Russia, and the agricultural regions around

the Caspian Sea (Table 8.63).

8 Energy Scenario Results

318

8.10.2.2 Eurasia: Utilization of Power-Generation Capacities

Variable power generation starts at almost zero, but increases rapidly to over 30% in

most sub-regions of Eurasia, as shown in Table 8.64.

Table 8.64 shows that dispatchable renewables will experience stable market

conditions throughout the entire modelling period across the whole region. Both

scenarios assume that the interconnections between Eastern Europe and Russia will

increase significantly, whereas the power transmission capacities for Kazakhstan,

Central Asia, the area around the Caspian Sea, and Mongolia will remain low due to

geographic distances.

Compared with other world regions, it will take longer for the capacity factor of

the limited dispatchable power plants to drop below economic viability, as shown in

Table 8.65.

Table 8.65. The capacity factor of variable renewables will rise by 2030, mainly

due to increased deployment of wind and concentrated solar power with storage.

The average capacity factor of the power-generation fleet will be around 35% by

2050 and will therefore be on the same level as it was 2015 in both scenarios.

8.10.2.3 Eurasia: Development of Load, Generation, and Residual Load

The modelling of both scenarios predicts small increases in interconnection beyond

those assumed to occur by 2030 (see Table 8.64).

Table 8.63 Eurasia: average annual change in installed power plant capacity

Eurasia power generation: average annual
change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal − 1 − 6 − 6 − 4 0 0
Lignite − 3 − 4 − 2 − 1 0 0
Gas 4 1 0 − 2 − 17 − 5
Hydrogen-gas 0 2 2 4 20 17
Oil/Diesel − 2 − 2 − 1 − 1 0 0
Nuclear − 2 − 3 − 2 − 4 − 1 0
Biomass 3 8 3 5 4 2
Hydro 2 1 1 1 0 0
Wind (onshore) 7 20 26 28 24 21
Wind (offshore) 1 3 6 6 11 8
PV (roof top) 9 25 21 32 31 22
PV (utility scale) 3 8 7 11 10 7
Geothermal 1 3 2 4 4 3
Solar thermal power plants 0 0 1 1 1 2
Ocean energy 0 0 1 1 1 1
Renewable fuel based co-generation 2 7 4 7 5 3

S. Teske et al.

319

Table 8.64

Eurasia: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

Eurasia

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Eastern Europe

2015

1%

35%

63%

5%

2030

37%

45%

18%

10%

41%

46%

13%

10%

2050

70%

22%

7%

20%

66%

24%

10%

20%

Russia

2015

1%

35%

63%

5%

2030

35%

43%

22%

5%

39%

47%

14%

5%

2050

68%

24%

8%

5%

64%

26%

10%

5%

Kazakhstan

2015

2%

35%

63%

5%

2030

44%

42%

14%

5%

49%

42%

9%

5%

2050

80%

16%

4%

5%

77%

18%

5%

5%

Mongolia

2015

2%

35%

63%

5%

2030

43%

43%

13%

5%

48%

43%

10%

5%

2050

74%

20%

6%

10%

71%

22%

8%

10%

West Caspian Sea

2015

1%

35%

63%

5%

2030

43%

41%

16%

5%

47%

40%

12%

5%

2050

77%

17%

6%

10%

72%

19%

9%

10%

East Caspian Sea

2015

1%

35%

63%

5%

2030

37%

44%

19%

5%

41%

45%

14%

5%

2050

71%

22%

7%

10%

67%

24%

10%

10%

(continued)

8 Energy Scenario Results

320

Power generation structure and interconnection

2.0 °C

1.5 °C

Eurasia

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

Central Asia

2015

0%

35%

64%

5%

2030

18%

50%

31%

5%

23%

50%

27%

5%

2050

43%

39%

18%

5%

38%

37%

26%

5%

Eurasia

2015

1%

35%

63%

2030

36%

43%

21%

40%

46%

14%

2050

69%

23%

7%

65%

25%

10%

Table 8.64

(continued)

S. Teske et al.

321

Table 8.65 Eurasia: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
Eurasia 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor –
average

[%/yr]36.8% 31% 40% 48% 47% 34% 34% 34% 34%

Limited
dispatchable:
fossil and
nuclear

[%/yr]43.8% 31% 30% 22% 18% 19% 0% 7% 4%

Limited
dispatchable:
renewable

[%/yr]39.3% 42% 42% 57% 54% 60% 39% 39% 40%

Dispatchable:
fossil

[%/yr]27.6% 18% 17% 7% 6% 31% 8% 12% 15%

Dispatchable:
renewable

[%/yr]38.7% 48% 73% 73% 68% 41% 49% 50% 51%

Variable:
renewable

[%/yr]10.5% 11% 11% 40% 39% 25% 32% 32% 33%

Table 8.64. However, after 2030, significant increases will be required by 2050,

especially in Russia. The export of renewable electricity can also take place via

existing gas pipelines with power-to-gas technologies. Between 2030 and 2050, the

loads for all regions will double, due to the increased electrification of the heating,

industry, and transport sectors (Table 8.66).

In Eurasia, the main storage technology for both scenarios is pumped hydro,

whereas hydrogen plays a major role for the grid integration of variable generation

(Table 8.67). Hydrogen production can also be used for load management, although

not for short peak loads. Due to the technical and economic limitations associated

with the increased interconnection via transmission lines and pumped hydro storage

systems, curtailment will be higher than the scenario target (a maximum of 10% by

2050). For Eastern Europe, Kazakhstan, Mongolia, and the East Caspian Sea, the

calculated curtailment will be between 10% and 14%, whereas the West Caspian

Region will have the highest curtailment of 19% in the 2.0 °C Scenario and 17% in

the 1.5 °C Scenario. Further research and optimization are required.

8 Energy Scenario Results

322

Table 8.66

Eurasia: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

Eurasia

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Eastern Europe

2020

32.9

30.8

3.9

32.9

33.0

4.6

2030

38.1

45.0

12.4

0

40.8

56.2

14.2

1

2050

77.2

179.6

30.9

71

77.5

174.3

31.6

65

Russia

2020

172.7

95.8

83.6

172.7

100.6

81.8

2030

214.2

218.6

103.5

0

221.4

275.2

94.9

0

2050

428.3

887.6

191.7

268

429.2

859.0

194.4

235

Kazakhstan

2020

14.7

18.8

0.9

14.7

17.9

0.9

2030

17.9

18.6

8.3

0

18.9

23.1

7.7

0

2050

34.3

74.5

14.1

26

34.4

72.2

14.4

23

Mongolia

2020

1.7

2.0

0.1

1.7

2.0

0.1

2030

2.0

2.3

0.9

0

2.1

2.9

0.9

0

2050

3.7

8.5

1.2

4

3.7

8.4

1.2

3

West Caspian Sea

2020

10.7

6.2

4.6

10.7

6.9

4.2

2030

12.5

13.9

6.4

0

13.4

17.3

5.9

0

2050

24.3

55.8

9.8

22

24.4

54.1

10.0

20

East Caspian Sea

2020

21.6

7.5

14.2

21.6

7.8

13.8

2030

25.2

28.2

12.7

0

26.9

35.0

12.5

0

2050

50.0

113.4

18.6

45

50.2

109.7

19.1

40

Central Asia

2020

2.5

2.3

0.2

2.5

2.3

0.2

2030

6.0

5.8

2.8

0

6.7

6.5

3.0

0

2050

12.0

18.2

4.2

2

12.1

18.2

4.4

2

S. Teske et al.

323

Table 8.67

Eurasia: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

Eurasia

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Eastern Europe

2020

0

0

0

0

0

0

0

0

0

0

2030

373

1

137

138

1720

1674

2

317

319

5920

2050

52,516

274

2626

2900

49,057

43,933

267

2303

2570

49,858

Russia

2020

0

0

0

0

0

0

0

0

0

0

2030

36

0

41

41

9711

2290

3

517

520

33,707

2050

147,854

1132

9342

10,474

282,100

123,490

1049

7895

8944

287,188

Kazakhstan

2020

0

0

0

0

0

0

0

0

0

0

2030

7

0

7

7

690

281

1

84

85

2223

2050

28,094

133

1444

1577

13,192

23,926

127

1271

1398

13,544

Mongolia

2020

0

0

0

0

0

0

0

0

0

0

2030

24

0

11

11

78

131

0

25

25

258

2050

3177

17

152

169

1997

2938

16

139

155

1971

West Caspian Sea

2020

0

0

0

0

0

0

0

0

0

0

2030

163

0

78

79

472

882

1

173

174

1558

2050

30,281

96

1207

1303

12,025

26,053

94

1120

1214

12,088

East Caspian Sea

2020

0

0

0

0

0

0

0

0

0

0

2030

134

0

65

65

1125

773

1

170

170

3759

2050

32,074

202

1785

1988

30,493

27,253

195

1580

1775

30,852
(continued)

8 Energy Scenario Results

324

Table 8.67

(continued)

Storage and dispatch

2.0 °C

1.5 °C

Eurasia

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Central Asia

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

309

0

0

1

1

1090

2050

2495

39

211

250

12,181

2391

39

207

245

12,037

Eurasia Eastern Europe

2020

0

0

0

0

0

0

0

0

0

0

2030

736

2

339

341

14,106

6031

7

1287

1295

48,516

2050

296,490

1894

16,767

18,661

401,044

249,984

1788

14,515

16,303

407,537

S. Teske et al.

325

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

0

10,00 0

20,00 0

30,000

40,00 0

50,00 0

60,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015202520302040 2050

TWh/y

PJ/yr r

Transport fuelsTransport electricity
Industry fuelsIndustry electricity
Residential & other sectors fuels Residential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.71 Non-OECD Asia: development of the final energy demand by sector in the scenarios

8.11 Non-OECD Asia

8.11.1 Non-OECD Asia: Long-Term Energy Pathways

8.11.1.1 Non-OECD Asia: Final Energy Demand by Sector

The future development pathways for Non-OECD Asia’s final energy demand when

the assumptions on population growth, GDP growth, and energy intensity are com-

bined are shown in Fig. 8.71 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the

5.0 °C Scenario, the total final energy demand will increase by 111% from the cur-

rent 24,500 PJ/year to 51,800 PJ/year in 2050. In the 2.0  °C Scenario, the final

energy demand will increase at a much lower rate by 16% compared with current

consumption, and will reach 28,300 PJ/year by 2050. The final energy demand in

the 1.5 °C Scenario will reach 25,700 PJ, 5% above the 2015 demand. In the 1.5 °C

Scenario, the final energy demand in 2050 will be 9% lower than in the 2.0  °C

Scenario. The electricity demand for ‘classical’ electrical devices (without power-

to- heat or e-mobility) will increase from 830 TWh/year in 2015 to 2480 TWh/year

in 2050 in both alternative scenarios. Compared with the reference case (3880 TWh/

year in 2050), the efficiency measures in the 2.0 °C and 1.5 °C scenarios will save

1400 TWh/year in 2050.

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0  °C Scenario, the electricity demand for heating will be approxi-

8 Energy Scenario Results

326

mately 1500 TWh/year due to electric heaters and heat pumps, and in the transport

sector, the electricity demand will be approximately 1700 TWh/year due to electric

mobility. The generation of hydrogen (for transport and high-temperature process

heat) and the manufacture of synthetic fuels (mainly for transport) will add an addi-

tional power demand of 1700 TWh/year. Therefore, the gross power demand will

rise from 1400 TWh/year in 2015 to 6400 TWh/year in 2050 in the 2.0 °C Scenario,

33% higher than in the 5.0  °C case. In the 1.5  °C Scenario, the gross electricity

demand will increase to a maximum of 6000 TWh/year in 2050.

The efficiency gains in the heating sector could be even larger than those in the

electricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption

equivalent to about 6900 PJ/year and 8100 PJ/year, respectively, will be avoided by

2050 compared with the 5.0 °C Scenario, through efficiency gains.

8.11.1.2 Non-OECD Asia: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in Non-OECD Asia will

come from renewable energy sources in the 2.0  °C Scenario. ‘New’ renewables—

mainly wind, solar, and geothermal energy—will contribute 87% of the total electric-

ity generation. Renewable electricity’s share of the total production will be 59% by

2030 and 87% by 2040. The installed capacity of renewables will reach about 610

GW by 2030 and 2430 GW by 2050. The share of renewable electricity generation

in 2030  in the 1.5  °C Scenario is assumed to be 74%. In the 1.5  °C Scenario, the

generation capacity from renewable energy will be approximately 2320 GW in 2050.

Table 8.68 shows the development of different renewable technologies in Non-

OECD Asia over time. Figure 8.72 provides an overview of the overall power-

generation structure in Non-OECD Asia. From 2020 onwards, the continuing

growth of wind and PV up to 635 GW and 1280 GW, respectively, will be comple-

mented by up to 275 GW solar thermal generation, as well as limited biomass,

geothermal, and ocean energy in the 2.0 °C Scenario. Both the 2.0 °C Scenario and

1.5  °C Scenario will lead to a high proportion of variable power generation (PV,

wind, and ocean) of 34% and 48%, respectively, by 2030, and 64% and 66%,

respectively, by 2050.

8.11.1.3 Non-OECD Asia: Future Costs of Electricity Generation

Figure 8.73 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated elec-

tricity generation costs in 2015 (referring to full costs) were around 5.2 ct/kWh. In

the 5.0 °C case, the generation costs will increase until 2050, when they reach 11.7

ct/kWh. The generation costs will increase in the 2.0 °C Scenario until 2030, when

they will reach 8.1 ct/kWh, and will drop to 6.3 ct/kWh by 2050. In the 1.5  °C

S. Teske et al.

327

Table 8.68 Non-OECD Asia: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 63 85 124 151 183
2.0 °C 63 86 86 90 91
1.5 °C 63 86 86 90 91
Biomass 5.0 °C 7 10 17 22 31
2.0 °C 7 19 19 30 36
1.5 °C 7 19 20 31 39
Wind 5.0 °C 2 5 17 32 54
2.0 °C 2 53 148 389 635
1.5 °C 2 98 229 458 631
Geothermal 5.0 °C 3 4 6 8 10
2.0 °C 3 6 23 50 63
1.5 °C 3 7 26 47 54
PV 5.0 °C 3 9 26 44 70
2.0 °C 3 107 287 806 1282
1.5 °C 3 157 396 907 1256
CSP 5.0 °C 0 0 0 0 0
2.0 °C 0 5 45 134 275
1.5 °C 0 5 45 110 224
Ocean 5.0 °C 0 0 0 0 0
2.0 °C 0 0 2 20 50
1.5 °C 0 0 2 15 30
Total 5.0 °C 78 113 191 257 348
2.0 °C 78 276 610 1518 2432
1.5 °C 78 373 804 1658 2325

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.72 Non-OECD Asia: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

328

0

2

4

6

8

10

12

14

0

100

200

300

400

500

600

2015 2025 2030 2040 2050

billion ct/kWh

$

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.73 Non-OECD Asia: development of total electricity supply costs and specific electricity generation costs in the scenarios

Scenario, they will increase to 7.9 ct/kWh, and drop to 6.1 ct/kWh by 2050. In both

alternative scenarios, the generation costs in 2050 will be around 5.5 ct/kWh lower

than in the 5.0 °C case. Note that these estimates of generation costs do not take into

account integration costs such as power grid expansion, storage, or other load-

balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

cause the total electricity supply costs to rise from today’s $70 billion/year to more

than $560 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs will be

$430 billion/year and in the 1.5  °C Scenario they will be $390 billion/year. The

long-term costs for electricity supply will be more than 24% lower in the 2.0  °C

Scenario than in the 5.0 °C Scenario as a result of the estimated generation costs and

the electrification of heating and mobility. Further reductions in demand in the

1.5 °C Scenario will result in total power generation costs that are 30% lower than

in the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0  °C case to 7.4 ct/kWh. In the 2.0  °C

Scenario, they still increase until 2030, when they reach 6.5 ct/kWh, and then drop

to 6.3 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 6.9 ct/kWh and

then drop to 6.1 ct/kWh by 2050. In the 2.0 °C case, the generation costs will be

maximum in 2050, and 1.1 ct/kWh lower than in the 5.0 °C, whereas they will be

1.3 ct/kWh in the 1.5  °C Scenario. If the CO 2 costs are not considered, the total

electricity supply costs in the 5.0 °C case will increase to about $360 billion/year in

S. Teske et al.

329

8.11.1.4 Non-OECD Asia: Future Investments in the Power Sector

An investment of $4030 billion will be required for power generation between 2015

and 2050 in the 2.0 °C Scenario—including investment in additional power plants

for the production of hydrogen and synthetic fuels and investments in plant replace-

ment at the end of their economic lifetimes. This value is equivalent to approxi-

mately $112 billion per year on average, and is $2660 billion more than in the

5.0 °C case ($1370 billion). An investment of around $3950 billion for power gen-

eration will be required between 2015 and 2050 in the 1.5 °C Scenario. On average,

this is an investment of $110 billion per year. In the 5.0 °C Scenario, the investment

in conventional power plants will be around 55% of the total cumulative invest-

ments, whereas approximately 45% will be invested in renewable power generation

and co-generation (Fig. 8.74).

However, in the 2.0 °C (1.5 °C) Scenario, Non-OECD Asia will shift almost 93%

(95%) of its entire investment to renewables and co-generation. By 2030, the fossil

fuel share of power sector investment will predominantly focus on gas power plants

that can also be operated with hydrogen.

Fossil
54%

Nuclear
1%

CHP
1%

Renewable
44%

5.0°C: 2015-2050

total 1,366
billion $

Fossil
(incl. H2)
7%

CHP
5%

Renewable
88%

2.0°C: 2015-2050

total 4,030
billion $

Fossil
(incl. H2)
5%

CHP
6%

Renewable
89%

1.5°C: 2015-2050

total 3,950
billion $

Fig. 8.74 Non-OECD Asia: investment shares for power generation in the scenarios

8 Energy Scenario Results

330

Because renewable energy has no fuel costs, other than biomass, the cumulative fuel

cost savings in the 2.0 °C Scenario will reach a total of $2610 billion in 2050, equiva-

lent to $73 billion per year. Therefore, the total fuel cost savings will be equivalent to

98% of the total additional investments compared to the 5.0 °C Scenario. The fuel cost

savings in the 1.5 °C Scenario will add up to $2770 billion, or $77 billion per year.

8.11.1.5 Non-OECD Asia: Energy Supply for Heating

The final energy demand for heating will increase by 103% in the 5.0 °C scenario,

from 10,800 PJ/year in 2015 to 21,900 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 32% by 2050 in the 2.0 °C

Scenario, relative to the 5.0  °C case, and by 37% in the 1.5  °C Scenario. Today,

renewables supply around 43% of Non-OECD Asia’s final energy demand for heat-

ing, with the main contribution from biomass. Renewable energy will provide 57%

of Non-OECD Asia’s total heat demand in 2030 in the 2.0 °C Scenario and 70% in

the 1.5 °C Scenario. In both scenarios, renewables will provide 100% of the total

heat demand in 2050.

Figure 8.75 shows the development of different technologies for heating in Non-

OECD Asia over time, and Table 8.69 provides the resulting renewable heat supply

for all scenarios. Up to 2030, biomass remains the main contributor. In the long

term, the growing use of solar, geothermal, and environmental heat will lead to a

biomass share of 40% in the 2.0 °C Scenario and 38% in the 1.5 °C Scenario. The

heat from renewable hydrogen will further reduce the dependence on fossil fuels in

both scenarios. The hydrogen consumption in 2050 will be around 900 PJ/year in

the 2.0 °C Scenario and 1300 PJ/year in the 1.5 °C Scenario. The direct use of elec-

tricity for heating will also increase by a factor of 5-5.7 between 2015 and 2050.

Energy for heating will have a final energy share of 34% in 2050  in the 2.0  °C

Scenario and 32% in the 1.5 °C Scenario.

0

5,000

10,00 0

15,00 0

20,00 0

25,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025 203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electric heating

Geothermal heat
and heat pumps
Solar heating

Biomass

Fossil

Fig. 8.75 Non-OECD Asia: development of heat supply by energy carrier in the scenarios

S. Teske et al.

331

8.11.1.6 Non-OECD Asia: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $1120 billion in the 2.0 °C Scenario (including investments

for the replacement of plants after their economic lifetimes), or approximately $31

billion per year. The largest share of investment in Non-OECD Asia is assumed to

be for solar collectors (around $480 billion), followed by heat pumps and geother-

mal heat use. The 1.5 °C Scenario assumes an even faster expansion of renewable

technologies. However, the lower heat demand (compared with the 2.0 °C Scenario)

will results in a lower average annual investment of around $28 billion per year

(Table 8.70, Fig. 8.76).

8.11.1.7 Non-OECD Asia: Transport

The energy demand in the transport sector in Non-OECD Asia is expected to

increase in 2015  in the 5.0  °C Scenario from around 6500 PJ/year by 102% to

13,200 PJ/year in 2050. In the 2.0 °C Scenario, assumed technical, structural, and

behavioural changes will save 63% (8320 PJ/year) by 2050 compared to the 5.0 °C

Scenario. Additional modal shifts, technology switches, and a reduction in the

transport demand will lead to even higher energy savings in the 1.5 °C Scenario of

73% (or 9660 PJ/year) by 2050 compared to the 5.0 °C case (Table 8.71, Fig. 8.77).

By 2030, electricity will provide 6% (120 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 36%

(480 TWh/year). In 2050, up to 650 PJ/year of hydrogen will be used in the trans-

Table 8.69 Non-OECD Asia: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 4459 4800 4787 4878 4919
2.0 °C 4459 4680 4529 4232 3948
1.5 °C 4459 4772 4890 4054 3549
Solar heating 5.0 °C 4 12 33 70 128
2.0 °C 4 401 1129 2252 2723
1.5 °C 4 509 1221 2141 2389
Geothermal heat and heat pumps 5.0 °C 0 0 0 0 0
2.0 °C 0 141 740 1563 2410
1.5 °C 0 262 839 1587 2198
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 0 454 862
1.5 °C 0 0 133 735 1274
Total 5.0 °C 4464 4811 4821 4948 5047
2.0 °C 4464 5222 6398 8501 9942
1.5 °C 4464 5542 7083 8516 9411

8 Energy Scenario Results

332

biomass
technologies
95%

geothermal
heat use
0%

solar
collectors
5%

heat
pumps
0%

5.0°C: 2015-2050

total 176 billion $

biomass
technologies
13%

geothermal
heat use
14%

solar
collectors
43%

heat
pumps
30%

2.0°C: 2015-2050

total 1,120 billion $

biomass
technologies
9%

geothermal
heat use
14%

solar
collectors
44%

heat pumps
33%

1.5°C: 2015-2050

total 1,005 billion $

Fig. 8.76 Non-OECD Asia: development of investments for renewable heat-generation technolo- gies in the scenarios

Table 8.70 Non-OECD Asia: installed capacities for renewable heat generation in the scenarios

Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 1886 1925 1767 1610 1459 2.0 °C 1886 1850 1557 1150 821 1.5 °C 1886 1829 1693 1084 713 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 4 18 51 73 1.5 °C 0 4 15 44 64 Solar heating 5.0 °C 1 3 10 20 37 2.0 °C 1 114 321 639 772 1.5 °C 1 145 349 609 678 Heat pumps 5.0 °C 0 0 0 0 0 2.0 °C 0 13 58 103 159 1.5 °C 0 27 70 110 144 Totala 5.0 °C 1888 1928 1777 1631 1496 2.0 °C 1888 1981 1954 1944 1825 1.5 °C 1888 2004 2127 1847 1598 a Excluding direct electric heating

S. Teske et al.

333

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand will be 350 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 500 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 1940 PJ/year

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

duced, with a maximum amount of 530 PJ/year in 2050. Due to the lower overall

energy demand in transport, biofuel use will be reduced in the 1.5 °C Scenario to a

Table 8.71 Non-OECD Asia: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 76 81 81 83 83
2.0 °C 76 96 116 158 183
1.5 °C 76 115 124 148 212
Road 5.0 °C 6023 7139 9256 11,061 12,181
2.0 °C 6023 6694 6489 5251 4245
1.5 °C 6023 5493 4217 3258 2903
Domestic aviation 5.0 °C 225 353 447 581 621
2.0 °C 225 240 220 180 143
1.5 °C 225 230 200 139 108
Domestic navigation 5.0 °C 196 216 227 246 267
2.0 °C 196 216 227 246 267
1.5 °C 196 216 227 246 267
Total 5.0 °C 6521 7789 10,010 11,970 13,153
2.0 °C 6521 7246 7051 5834 4838
1.5 °C 6521 6053 4769 3791 3489

0

2,000

4,000

6,000

8,000

10,00 0

12,00 0

14,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.77 Non-OECD Asia: final energy consumption by transport in the scenarios

8 Energy Scenario Results

334

maximum of 1540 PJ/year. The maximum synthetic fuel demand will amount to

280 PJ/year.

8.11.1.8 Non-OECD Asia: Development of CO 2 Emissions

In the 5.0 °C Scenario, Non-OECD Asia’s annual CO 2 emissions will increase by

160%, from 1880 Mt. in 2015 to 4880 Mt. in 2050. The stringent mitigation mea-

sures in both alternative scenarios will cause the annual emissions to fall to 630 Mt.

in 2040 in the 2.0 °C Scenario and to 330 Mt. in the 1.5 °C Scenario, with further

reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions

from 2015 until 2050 will add up to 121 Gt. In contrast, in the 2.0 °C and 1.5 °C

Scenarios, the cumulative emissions for the period from 2015 until 2050 will be 42

Gt and 32 Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 65% in the 2.0  °C

Scenario and by 74% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario, this reduction will be greatest in ‘Power generation’, followed by the

‘Residential and other’ and ‘Industry’ sectors (Fig. 8.78).

0

20

40

60

80

100

120

140

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015202520302040 2050

cumulated

emissions

[Gt]
CO

emissions 2

[Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.78 Non-OECD Asia: development of CO 2 emissions by sector and cumulative CO 2 emis- sions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

335

8.11.1.9 Non-OECD Asia: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.79. In the 2.0 °C

Scenario, the primary energy demand will increase by 13%, from around 38,100 PJ/

year in 2015 to 43,200 PJ/year. Compared with the 5.0  °C Scenario, the overall

primary energy demand will decrease by 47% by 2050  in the 2.0  °C Scenario

(5.0 °C: 81600 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand will

be even lower (39,300 PJ in 2050) because the final energy demand and conversion

losses will be lower.

Both the 2.0 °C Scenario and 1.5 °C Scenario aim to rapidly phase-out coal and

oil. This will cause renewable energy to have a primary energy share of 40% in 2030

and 93% in 2050  in the 2.0  °C Scenario. In the 1.5  °C Scenario, renewables will

have a primary energy share of more than 92% in 2050 (including non-energy con-

sumption, which will still include fossil fuels). Nuclear energy will be phased out by

2045  in both the 2.0  °C Scenario and 1.5  °C Scenario. The cumulative primary

energy consumption of natural gas in the 5.0  °C case will add up to 430 EJ, the

cumulative coal consumption to about 530 EJ, and the crude oil consumption to 580

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

net electricity
imports
Efficiency

Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal

Nuclear

Fig. 8.79 Non-OECD Asia: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

8 Energy Scenario Results

336

EJ. In contrast, in the 2.0 °C Scenario, the cumulative gas demand will amount to

260 EJ, the cumulative coal demand to 120 EJ, and the cumulative oil demand to

270 EJ. Even lower fossil fuel use will be achieved in the 1.5 °C Scenario: 230 EJ

for natural gas, 70 EJ for coal, and 190 EJ for oil.

8.11.2 Non-OECD Asia: Power Sector Analysis

Non-OECD Asia is the most heterogeneous region of all IEA world energy regions

because it includes not only all the ASEAN countries (ASEAN 2018 ) of South East

Asia, but also central and south Asian nations, as well all 16 Pacific Island states. As

for the Caribbean Islands, a power system assessment—especially with regard to

possible storage demand—that examines all Pacific Island states together rather

than individually, is not sufficient to provide the actual required storage demand.

However, with this is in mind, the ratio of solar PV generation to storage require-

ments does provide some indication. A specific assessment for each of the Pacific

Island states is required, but is beyond the scope of this study. Indonesia and the

Philippines are selected as sub-regions because they are island states with some

interconnection between islands.

8.11.2.1 Non-OECD Asia: Development of Power Plant Capacities

Non-OECD Asia’s renewable power market can be subdivided into the following

categories: technologies for small and medium islands (mainly solar PV–battery

systems, mini-hydro and small-scale bioenergy systems); and utility-scale solar and

onshore wind for all major economies in mainland Asia or on the large islands of

the Philippines and Indonesia. Several countries in this region are on the Pacific

Ring of Fire and have significant geothermal energy resources. The annual market

for geothermal power plants is one of the world’s largest, with a projected 3–4 GW

each year for almost two decades between 2025 and 2045  in both scenarios

(Table 8.72).

8.11.2.2 Non-OECD Asia: Utilization of Power-Generation Capacities

Due to the geographic diversity and wide distribution of all sub-regions of the Non-

OECD Asia region, it is assumed that there are no interconnection capacities avail-

able, and that there will not be any at the end of the modelling period (Table 8.73).

S. Teske et al.

337

In both scenarios, variable power generation will jump from only 1% today to

around 25% in all sub-regions, whereas dispatchable renewables will remain stable

at around 25%–30% until 2050.

Compared with other world regions, the capacity factors for limited dispatchable

fossil and nuclear energy will remain relatively high until 2030, as shown in

Table 8.74. The time required for variable power generation to replace fossil and

nuclear generation will be greater than it is in other regions. In the 1.5 °C Scenario,

all coal capacities across the region will be phased out by 2030, except for 4 GW

(equivalent to 4–5 power plants), which will be off-line 5 years later.

8.11.2.3 Non-OECD Asia: Development of Load, Generation,

and Residual Load

Because both scenarios were calculated under the assumption that there are no

interconnection capacities at the sub-regional level, more dispatch capacity will be

deployed. Table 8.75 shows that only Asia North-West and Asia South-West will

require some interconnection to avoid curtailment. The development of the maxi-

mum load, generation, and the resulting residual load—the load remaining after

Table 8.72 Non-OECD Asia: average annual change in installed power plant capacity

Non-OECD-Asia power generation: average
annual change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal 2 − 6 − 7 − 4 − 1 0
Lignite − 2 − 4 − 1 − 2 0 0
Gas 4 10 19 14 − 26 − 22
Hydrogen-gas 0 1 0 6 33 24
Oil/diesel 0 − 5 − 4 − 5 − 1 0
Nuclear 0 0 0 0 0 0
Biomass 2 1 1 1 1 1
Hydro 3 2 0 0 0 0
Wind (onshore) 4 21 20 24 26 20
Wind (offshore) 3 7 6 7 5 4
PV (roof top) 10 36 40 47 50 37
PV (utility scale) 3 12 13 16 17 12
Geothermal 0 3 4 4 2 1
Solar thermal power plants 1 6 9 8 17 13
Ocean energy 0 0 1 1 3 2
Renewable fuel based co-generation 1 2 1 1 1 1

8 Energy Scenario Results

338

Table 8.73

Non-OECD Asia: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

Variable RE

Dispatch RE

Dispatch Fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch Fossil

Inter-
connection

Asia West: Pakistan, Afghanistan, Nepal, Bhutan

2015

1%

35%

63%

0%

2030

31%

31%

38%

0%

44%

29%

28%

0%

2050

62%

25%

13%

0%

64%

24%

12%

0%

Sri Lanka

2015

1%

35%

64%

0%

2030

30%

37%

33%

0%

41%

34%

25%

0%

2050

58%

27%

15%

0%

59%

26%

14%

0%

Pacific Island State

2015

1%

35%

64%

0%

2030

29%

34%

37%

0%

39%

30%

30%

0%

2050

55%

25%

20%

0%

55%

25%

20%

0%

Asia North West: Bangladesh, Myanmar, Thailand

2015

1%

35%

64%

0%

2030

23%

37%

40%

0%

33%

35%

32%

0%

2050

48%

31%

21%

0%

50%

30%

20%

0%

Asia Central North: Viet Nam, Laos and Cambodia

2015

1%

35%

64%

0%

2030

27%

36%

36%

0%

38%

33%

29%

0%

2050

53%

28%

20%

0%

56%

27%

17%

0%

Asia South West: Malaysia, Brunei

2015

1%

35%

64%

0%

2030

26%

40%

34%

0%

36%

37%

27%

0%

2050

52%

29%

19%

0%

57%

28%

15%

0%

S. Teske et al.

339

Indonesia

2015

1%

35%

64%

0%

2030

21%

34%

45%

0%

31%

35%

35%

0%

2050

47%

30%

23%

0%

48%

30%

22%

0%

Philippines

2015

1%

35%

64%

0%

2030

34%

34%

32%

0%

48%

30%

22%

0%

2050

63%

23%

13%

0%

65%

22%

13%

0%

Non-OECD Asia

2015

1%

35%

64%

2030

26%

35%

39%

36%

34%

30%

2050

52%

28%

19%

55%

28%

17%

8 Energy Scenario Results

340

variable renewable generation. According to the Philippine Department of Energy,

the peak demand in the Philippines in 2016 was 13.3 GW (PR-DoE 2016 ) (9.7 GW

in Luzon, 1.9 GW in the Visayas, and 1.7 GW in Mindanao). The calculated load for

the Philippines in 2020 was 16.3 GW, which seems realistic. The load will increase

to 75.5 GW by 2050 under the 2.0  °C Scenario. The results for all Asian regions

show a quadrupling of load by 2050.

The lack of interconnection potential between or even within most sub-regions

will lead to some curtailment.

Table 8.76 shows that whereas countries on the Asian mainland will use and

increase their capacity for hydro pump storage electricity, batteries will be used for

most of the storage requirements of islands and island states. Where available, gas

infrastructure must be converted to hydrogen-operated systems.

Table 8.74 Non-OECD Asia: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
Non-OECD
Asia 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr] 55.4% 52% 53% 45% 42% 33% 33% 34% 32%

Limited
dispatchable:
fossil and
nuclear

[%/yr]71.4% 52% 53% 44% 33% 31% 13% 25% 0%

Limited
dispatchable:
renewable

[%/yr]40.5% 61% 61% 59% 56% 58% 53% 45% 49%

Dispatchable:
fossil

[%/yr]50.2% 32% 33% 23% 27% 37% 13% 28% 12%

Dispatchable:
renewable

[%/yr]34.4% 75% 75% 74% 69% 41% 58% 53% 51%

Variable:
renewable

[%/yr]13.1% 19% 19% 36% 35% 26% 31% 30% 29%

S. Teske et al.

341

Table 8.75

Non-OECD Asia: load, generation, and residual load development—2.0 °C Scenario

Power generation structure

2.0 °C

1.5 °C

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Asia West: Pakistan, Afghanistan, Nepal, Bhutan

2020

38.1

22.4

17.4

38.1

22.3

17.5

2030

65.1

58.2

44.4

0

67.1

64.2

47.9

0

2050

145.6

194.0

117.5

0

137.5

185.6

112.2

0

Sri Lanka

2020

5.6

2.7

3.2

5.6

2.7

3.2

2030

9.0

8.6

6.3

0

9.2

10.5

6.5

0

2050

19.7

31.1

15.2

0

18.2

29.7

14.1

0

Pacific Island State

2020

1.6

1.0

0.6

1.6

1.0

0.6

2030

2.6

2.3

1.8

0

2.7

2.6

1.9

0

2050

5.6

8.2

4.2

0

5.5

7.9

4.2

0

Asia North West: Bangladesh, Myanmar, Thailand

2020

57.8

18.9

41.8

57.8

18.8

41.9

2030

97.4

88.8

67.1

0

99.6

101.2

71.5

0

2050

218.9

321.0

171.4

0

198.1

306.3

155.8

0

Asia Central North: Viet Nam, Laos and Cambodia

2020

29.4

26.3

3.5

29.4

26.2

3.6

2030

47.0

44.4

29.8

0

47.9

61.2

32.2

0

2050

109.6

191.0

83.1

0

93.7

182.6

70.2

19

Asia South West: Malaysia, Brunei

2020

38.2

16.0

25.0

38.2

15.1

25.5

2030

53.6

54.0

28.1

0

53.9

68.4

34.0

0

2050

121.0

216.7

89.1

7

99.2

206.9

71.0

37

(continued)

8 Energy Scenario Results

342

Power generation structure

2.0 °C

1.5 °C

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

Indonesia

2020

60.9

34.3

26.6

60.9

33.0

27.9

2030

106.7

99.8

59.9

0

108.7

114.5

77.4

0

2050

239.4

363.8

188.3

0

218.6

348.2

173.2

0

Philippines

2020

16.3

13.7

3.9

2030

33.5

33.0

19.0

0

34.3

42.6

24.1

0

2050

75.5

133.1

58.8

0

70.3

127.3

55.5

2

Table 8.75

(continued)

S. Teske et al.

343

Table 8.76

Non-OECD Asia: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

Non-OECD Asia

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Asia West: Pakistan, Afghanistan, Nepal, Bhutan

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

434

4

78

82

3356

2050

36,251

767

716

1483

42,533

37,649

407

774

1181

44,157

Sri Lanka

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

72

1

9

10

564

2050

4755

135

125

260

7380

5471

74

144

218

7330

Pacific Island State

2020

0

0

0

0

0

0

0

0

0

0

2030

12

0

2

2

0

183

1

14

14

142

2050

2178

44

43

87

2101

1932

22

42

65

2211

Asia North West: Bangladesh, Myanmar, Thailand

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

194

1

27

28

6617

2050

19,992

1114

824

1938

93,720

29,141

657

1113

1770

92,309

Asia Central North: Viet Nam, Laos and Cambodia

2020

0

0

0

0

0

0

0

0

0

0

2030

6

0

3

4

0

1031

5

121

126

3346

2050

26,401

727

708

1435

49,483

40,048

416

919

1335

45,848(continued)

8 Energy Scenario Results

344

Storage and dispatch

2.0 °C

1.5 °C

Non-OECD Asia

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

Asia South West: Malaysia, Brunei

2020

0

0

0

0

0

0

0

0

0

0

2030

7

0

2

3

0

1036

5

120

125

4151

2050

32,422

942

893

1835

59,371

55,862

610

1406

2016

51,750

Indonesia

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

0

176

1

21

22

7391

2050

11,890

720

530

1250

107,913

17,040

478

717

1195

107,330

Philippines

2020

0

0

0

0

0

0

0

0

0

0

2030

112

3

22

25

0

3723

6

232

239

1917

2050

38,084

507

670

1177

23,954

41,017

266

743

1009

24,126

Other Asia

2020

0

0

0

0

0

0

0

0

0

0

2030

137

4

30

34

0

6848

23

622

646

27,484

2050

171,973

4955

4510

9465

386,454

228,160

2930

5859

8789

375,061

Table 8.76

(continued)

S. Teske et al.

345

8.12 India

8.12.1 India: Long-Term Energy Pathways

8.12.1.1 India: Final Energy Demand by Sector

The future development pathways for India’s final energy demand when the assump-

tions on population growth, GDP growth, and energy intensity are combined are

shown in Fig. 8.80 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the 5.0  °C

Scenario, the total final energy demand will increase by 201% from the current

22,200 PJ/year to 66,800 PJ/year by 2050. In the 2.0 °C Scenario, the final energy

demand will increase at a much slower rate by 57% compared with current con-

sumption and will reach 34,900 PJ/year by 2050. The final energy demand in the

1.5  °C Scenario will reach 31,900 PJ, 44% above the 2015 level. In the 1.5  °C

Scenario, the final energy demand in 2050 will be 9% lower than in the 2.0  °C

Scenario. The electricity demand for ‘classical’ electrical devices (without power-

to- heat or e-mobility) will increase from 750 TWh/year in 2015 to 3200 TWh/year

in 2050 in both alternative scenarios. Compared with the 5.0 °C case (4720 TWh/

year in 2050), efficiency measures in the 2.0  °C and 1.5  °C Scenarios will save

around 1520 TWh/year by 2050.

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0 °C Scenario, the electricity demand for heating will be approximately

1900 TWh/year due to electric heaters and heat pumps, and in the transport sector,

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

8,00 0

9,00 0

0

10,00 0

20,00 0

30,00 0

40,00 0

50,00 0

60,00 0

70,00 0

80,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

TWh/yr

PJ/yr

Transport fuelsTransport electricity
Industry fuelsIndustry electricity
Residential & other sectors fuelsResidential & other sectors electricity
total power demand (incl. synfuels & H2)

0

PJ/yr

Fig. 8.80 India: development of final energy demand by sector in the scenarios

8 Energy Scenario Results

346

the electricity demand will be approximately 3400 TWh/year due to electric mobil-

ity. The generation of hydrogen (for transport and high-temperature process heat)

and the manufacture of synthetic fuels (mainly for transport) will add an additional

power demand of 1700 TWh/year. Therefore, the gross power demand will increase

from 1400 TWh/year in 2015 to 8400 TWh/year in 2050  in the 2.0  °C Scenario,

31% higher than in the 5.0  °C case. In the 1.5  °C Scenario, the gross electricity

demand will increases to a maximum of 7700 TWh/year in 2050.

Efficiency gains in the heating sector could be even larger than in the electricity

sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equivalent to

about 9500 PJ/year and 9800 PJ/year, respectively, will be avoided through effi-

ciency gains by 2050 compared with the 5.0 °C Scenario.

8.12.1.2 India: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in India will come from

renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—mainly wind,

solar, and geothermal energy—will contribute 90% of the total electricity genera-

tion. Renewable electricity’s share of the total production will be 66% by 2030 and

89% by 2040. The installed capacity of renewables will reach about 1060 GW by

2030 and 3360 GW by 2050. The share of renewable electricity generation in

2030 in the 1.5 °C Scenario is assumed to be 77%. In the 1.5 °C Scenario, the gen-

eration capacity from renewable energy will be approximately 3040 GW in 2050.

Table 8.77 shows the development of different renewable technologies in India

over time. Figure 8.81 provides an overview of the overall power-generation struc-

ture in India. From 2020 onwards, the continuing growth of wind and PV up to 1270

GW and 1570 GW, respectively, is complemented by up to 210 GW solar thermal

generation, as well as limited biomass, geothermal, and ocean energy, in the 2.0 °C

Scenario. Both the 2.0 °C Scenario and 1.5 °C Scenario will lead to a high propor-

tion of variable power generation (PV, wind, and ocean) of 48% and 60%, respec-

tively, by 2030, and 75% and 72%, respectively, by 2050.

8.12.1.3 India: Future Costs of Electricity Generation

Figure 8.82 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated elec-

tricity generation costs in 2015 (referring to full costs) were around 5.4 ct/kWh. In

the 5.0 °C case, the generation costs will increase until 2040, when they reach 11 ct/

kWh, and then drop to 10.7 ct/kWh by 2050. The generation costs will increase in

the 2.0 °C Scenario until 2030, when they reach 8.4 ct/kWh, and then drop to 5.7 ct/

kWh by 2050. In the 1.5  °C Scenario, they will increase to 7.8 ct/kWh, and then

drop to 5.8 ct/kWh by 2050. In the 2.0 °C Scenario, the generation costs in 2050 will

be 5 ct/kWh lower than in the 5.0 °C case. In the 1.5 °C Scenario, the generation

S. Teske et al.

347

Table 8.77 India: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 46 68 81 97 117
2.0 °C 46 68 72 80 87
1.5 °C 46 68 72 80 87
Biomass 5.0 °C 8 13 16 20 25
2.0 °C 8 23 31 60 93
1.5 °C 8 23 31 60 93
Wind 5.0 °C 25 82 119 185 246
2.0 °C 25 200 421 938 1273
1.5 °C 25 275 543 1002 1110
Geothermal 5.0 °C 0 0 0 0 0
2.0 °C 0 3 8 42 68
1.5 °C 0 3 8 42 68
PV 5.0 °C 5 115 198 345 545
2.0 °C 5 230 469 1090 1572
1.5 °C 5 365 648 1185 1412
CSP 5.0 °C 0 0 1 1 2
2.0 °C 0 8 48 138 209
1.5 °C 0 8 48 138 209
Ocean 5.0 °C 0 0 0 0 0
2.0 °C 0 1 11 33 59
1.5 °C 0 1 11 33 59
Total 5.0 °C 84 279 415 648 936
2.0 °C 84 532 1061 2381 3360
1.5 °C 84 742 1361 2540 3037

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025203020402050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.81 India: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

348

costs in 2050 will be 4.9 ct/kWh lower than in the 5.0 °C case. Note that these esti-

mates of generation costs do not take into account integration costs such as power

grid expansion, storage, or other load-balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

cause the total electricity supply costs to rise from today’s $75 billion/year to more

than $690 billion/year in 2050. In the 2.0 °C case, the total supply costs will be $500

billion/year and in the 1.5 °C Scenario, they will be $470 billion/year. The long- term

costs for electricity supply will be more than 27% lower in the 2.0 °C Scenario than

in the 5.0 °C Scenario as a result of the estimated generation costs and the electrifi-

cation of heating and mobility. Further demand reductions in the 1.5  °C Scenario

will result in total power generation costs that are 32% lower than in the 5.0 °C case.

Compared with these results, the generation costs, when the CO 2 emission costs

are not considered, will increase in the 5.0 °C case to only 6.9 ct/kWh. In both alter-

native scenarios, they will still increase until 2030, when they reach 6.7 ct/kWh, and

then drop to around 5.8 ct/kWh by 2050. The maximum difference in generation

costs will be around 1 ct/kWh in 2050. If the CO 2 costs are not considered, the total

electricity supply costs in the 5.0 °C case will rise to about $430 billion/year in 2050.

8.12.1.4 India: Future Investments in the Power Sector

An investment of around $5640 billion will be required for power generation between

2015 and 2050 in the 2.0 °C Scenario—including additional power plants for the pro-

duction of hydrogen and synthetic fuels and investments in the replacement of plants

after the end of their economic lifetimes. This value is equivalent to approximately $157

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

800

2015 2025 2030 2040 2050

billion $ ct/kWh

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.82 India: development of total electricity supply costs and specific electricity generation costs in the scenarios

S. Teske et al.

349

billion per year on average, and is $3310 billion more than in the 5.0 °C case ($2330

billion). An investment of around $5560 billion for power generation will be required

between 2015 and 2050 in the 1.5 °C Scenario. On average, this will be an investment

of $154 billion per year. In the 5.0 °C Scenario, the investment in conventional power

plants will be around 48% of the total cumulative investments, whereas approximately

52% will be invested in renewable power generation and co-generation (Fig. 8.83).

However, in the 2.0 °C (1.5 °C) Scenario, India will shift almost 94% (95%) of

its entire investment to renewables and co-generation. By 2030, the fossil fuel share

of the power sector investment will predominantly focus on gas power plants that

can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $3110 billion in 2050,

equivalent to $86 billion per year. Therefore, the total fuel cost savings will be

equivalent to 90% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $3330 billion,

or $93 billion per year.

8.12.1.5 India: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 133%,

from 11,900 PJ/year in 2015 to 27,800 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 34% in 2050  in the 2.0  °C

Fossil
Nuclear 43%
5%

CHP
0%

Renewable
52%

5.0°C: 2015-2050

total 2,330
billion $

Fossil
(incl. H2)
6%
CHP
4%

Renewable
90%

2.0°C: 2015-2050

total 5,640
billion $

Fossil
(incl. H2)
5%

CHP
4%

Renewable
91%

1.5°C: 2015-2050

total 5,560
billion $

Fig. 8.83 India: investment shares for power generation in the scenarios

8 Energy Scenario Results

350

Scenario, relative to the 5.0  °C case, and by 35% in the 1.5  °C Scenario. Today,

renewables supply around 47% of India’s final energy demand for heating, with the

main contribution from biomass. Renewable energy will provide 53% of India’s

total heat demand in 2030 in the 2.0 °C Scenario and 68% in the 1.5 °C Scenario. In

both scenarios, renewables will provide 100% of the total heat demand in 2050.

Figure 8.84 shows the development of different technologies for heating in India

over time, and Table 8.78 provides the resulting renewable heat supply for all sce-

narios. Up to 2030, biomass will remain the main contributor. In the long term, the

increasing use of solar, geothermal, and environmental heat will lead to a biomass

share of 38% in the 2.0 °C Scenario and 36% in the 1.5 °C Scenario.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

under both scenarios. Hydrogen consumption in 2050 will be around 1400 PJ/year

0

5,00 0

10,000

15,000

20,000

25,000

30,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025203020402050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electric heating
Geothermal heat
and heat pumps
Solar heating
Biomass
Fossil

Fig. 8.84 India: development of heat supply by energy carrier in the scenarios

Table 8.78 India: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 5544 5633 5666 5595 5341
2.0 °C 5544 5726 5600 4854 4366
1.5 °C 5544 5600 5444 4758 4078
Solar heating 5.0 °C 28 77 115 200 310
2.0 °C 28 589 1537 2964 3693
1.5 °C 28 887 2271 3107 3626
Geothermal heat and heat pumps 5.0 °C 0 1 1 1 2
2.0 °C 0 164 647 1627 2136
1.5 °C 0 189 725 1497 2103
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 2 299 1409
1.5 °C 0 0 2 437 1613
Total 5.0 °C 5572 5711 5781 5796 5653
2.0 °C 5572 6478 7787 9743 11,603
1.5 °C 5572 6675 8442 9800 11,420

S. Teske et al.

351

in the 2.0 °C Scenario and 1600 PJ/year in the 1.5 °C Scenario. The direct use of

electricity for heating will also increase by a factor of about 21 between 2015 and

2050, and the electricity for heating will have a final energy share of 36% in 2050 in

both alternative scenarios.

8.12.1.6 India: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

amount to around $930 billion in the 2.0  °C Scenario (including investments for

replacement after the economic lifetimes of the plants), or approximately $26 billion

per year. The largest share of investment in India is assumed to be for solar collectors

(around $490 billion), followed by heat pumps and biomass technologies. The 1.5 °C

Scenario assumes an even faster expansion of renewable technologies and results in a

higher average annual investment of around $29 billion per year (Table 8.79, Fig. 8.85).

8.12.1.7 India: Transport

The energy demand in the transport sector in India is expected to increase in the

5.0 °C Scenario by 377%, from around 3600 PJ/year in 2015 to 17,200 PJ/year in

2050. In the 2.0 °C Scenario, assumed technical, structural, and behavioural changes

will save 66% (11,280 PJ/year) by 2050 compared to the 5.0 °C Scenario. Additional

modal shifts, technology switches, and a reduction in the transport demand will lead

to even higher energy savings in the 1.5 °C Scenario of 81% (or 13,930 PJ/year) in

2050 compared with the 5.0 °C case (Table 8.80, Fig. 8.86).

By 2030, electricity will provide 10% (160 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 58%

Table 8.79 India: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 2049 1923 1836 1633 1432 2.0 °C 2049 1954 1798 1311 856 1.5 °C 2049 1916 1756 1276 785 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 2 9 32 38 1.5 °C 0 5 12 28 37 Solar heating 5.0 °C 6 17 25 43 67 2.0 °C 6 126 327 619 777 1.5 °C 6 191 486 653 763 Heat pumps 5.0 °C 0 0 0 0 0 2.0 °C 0 12 42 90 131 1.5 °C 0 11 46 82 129 Totala 5.0 °C 2055 1940 1861 1676 1499 2.0 °C 2055 2094 2177 2052 1802 1.5 °C 2055 2122 2300 2039 1715 a Excluding direct electric heating

8 Energy Scenario Results

352

(950 TWh/year). In 2050, up to 860 PJ/year of hydrogen will be used in the trans-

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand will be 560 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 590 PJ/year by 2050.

Biofuel use is limited in the 2.0 °C Scenario to a maximum of around 1000 PJ/year.

Therefore, around 2030, synthetic fuels based on power-to-liquid will be introduced,

with a maximum amount of 610 PJ/year in 2050. Due to the lower overall energy

demand in transport, biofuel use will be reduced in the 1.5 °C Scenario to a maximum

of 510 PJ/year. The maximum synthetic fuel demand will amount to 310 PJ/year.

8.12.1.8 India: Development of CO 2 Emissions

In the 5.0 °C Scenario, India’s annual CO 2 emissions will increase by 236%, from

2060 Mt. in 2015 to 6950 Mt. in 2050. The stringent mitigation measures in both

alternative scenarios will cause the annual emissions to fall to 930 Mt. in 2040 in the

2.0 °C Scenario and to 200 Mt. in the 1.5 °C Scenario, with further reductions to

biomass
technologies
84%

geothermal
heat use
0%

solar
collectors
15%

heat
pumps
1%

5.0°C: 2015-2050

total 190 billion $

biomass
technologies
14%

geothermal
heat use
9%

solar
collectors
52%

heat
pumps
25%

2.0°C: 2015-2050

total 930 billion $

biomass
technologies
11%

geothermal
heat use
9%

solar
collectors
54%

heat
pumps
26%

1.5°C: 2015-2050

total 1,040 billion $

Fig. 8.85 India: development of investments for renewable heat-generation technologies in the scenarios

S. Teske et al.

353

almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions from 2015

until 2050 will add up to 169 Gt. In contrast, in the 2.0 °C and 1.5 °C Scenarios, the

cumulative emissions for the period from 2015 until 2050 will be 55 Gt and 38 Gt,

respectively.

Therefore, the cumulative CO 2 emissions will decrease by 67% in the 2.0  °C

Scenario and by 78% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in the annual emissions will occur in both alternative scenarios. In the

Table 8.80 India: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 180 238 278 353 423
2.0 °C 180 270 325 421 526
1.5 °C 180 219 234 332 446
Road 5.0 °C 3294 5861 7880 12,152 16,455
2.0 °C 3294 5017 5562 5301 5285
1.5 °C 3294 4253 3125 2977 2730
Domestic aviation 5.0 °C 84 131 166 216 231
2.0 °C 84 89 81 66 52
1.5 °C 84 85 74 52 40
Domestic navigation 5.0 °C 29 34 36 40 52
2.0 °C 29 34 36 40 52
1.5 °C 29 34 36 40 52
Total 5.0 °C 3587 6263 8360 12,762 17,161
2.0 °C 3587 5410 6006 5828 5914
1.5 °C 3587 4590 3470 3401 3268

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electricity

Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.86 India: final energy consumption by transport in the scenarios

8 Energy Scenario Results

354

2.0 °C Scenario, the reduction will be greatest in the ‘Residential and other’ sector,

followed by the ‘Power generation’ and ‘Industry’ sectors (Fig. 8.87).

8.12.1.9 India: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.88. In the 2.0 °C

Scenario, the primary energy demand will increase by 43%, from around 35,600 PJ/

year in 2015 to 50,900 PJ/year in 2050. Compared with the 5.0  °C Scenario, the

overall primary energy demand will decrease by 51% by 2050 in the 2.0 °C Scenario

(5.0 °C: 104,800 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand

will be even lower (47,100 PJ in 2050) because the final energy demand and conver-

sion losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 40% in 2030 and

94% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 94% in 2050 (including non-energy consump-

tion, which will still include fossil fuels). Nuclear energy will be phased out by

2050 in both the 2.0 °C and 1.5 °C Scenarios. The cumulative primary energy con-

sumption of natural gas in the 5.0 °C case will add up to 160 EJ, the cumulative coal

consumption to about 1180 EJ, and the crude oil consumption to 570 EJ. In contrast,

0

20

40

60

80

100

120

140

160

180

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

cumulated emissions [Gt]

CO

emissions [Mt/yr] 2

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.87 India: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

355

in the 2.0 °C Scenario, the cumulative gas demand will amount to 120 EJ, the cumu-

lative coal demand to 360 EJ, and the cumulative oil demand to 220 EJ. Even lower

fossil fuel use will be achieved in the 1.5 °C Scenario: 130 EJ for natural gas, 220

EJ for coal, and 150 EJ for oil.

8.12.2 India: Power Sector Analysis

The electricity market in India is in dynamic development. The government of India

is making great efforts to increase the reliability of the power supply and at the same

time, it is developing universal access to electric power. In 2017, about 300 million

Indians (RF 2018 ) had no power or inadequate power. In 2017, the Indian

Government launched The Third National Electricity Plan , which covers two 5-year

periods: 2017–2022 and 2022–2027. According to the International Energy Agency

(IEA) Policies and Measures Database (IEA P + M DB 2018 ):

[...] “the plan covers short- and long-term demand forecasts in different regions and recom-
mend areas for transmission and generation capacity additions ... However, as India sets to
meet its first nationally-determined contribution (NDC) under the Paris Agreement ...
Highlights of the plan include, that during the period 2017–22, no additional capacity of
coal will be added – except for the coal power plants under construction [...]”.

0

20,000

40,000

60,000

80,000

100,00 0

120,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

net electricity
imports

Efficiency
Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal
Nuclear

Fig. 8.88 India: projection of total primary energy demand (PED) by energy carrier in the sce- narios (including electricity import balance)

8 Energy Scenario Results

356

In terms of renewable power generation, India aims to have a total capacity of 275

GW for solar and wind and 72 GW for hydro, with no further increase in the coal

power plant capacity until at least 2027.

8.12.2.1 India: Development of Power Plant Capacities

The Third National Electricity Plan for India is an important foundation for strength-

ening India’s renewable power market in order to achieve the levels envisaged in

both alternative scenarios. Whereas the hydropower target is consistent with the

2.0 °C and 1.5 °C targets, the solar and wind capacity of 275 GW must be reached

between 2020 and 2025 for both scenarios. The annual installation rates for solar

PV installations must increase to around 50 GW—the market size in China in

2017—and remain at that level until 2040 to implement either the 2.0 °C or 1.5 °C

Scenario. The installation rates for onshore wind must be equally high. In 2017,

4.15 GW of new wind turbines were installed, and significant growth is required.

Offshore wind and concentrated solar power plants have significant potential for

selected regions of India. Both technologies are vital to achieving the 2.0  °C or

1.5 °C targets (Table 8.81).

Table 8.81 India: average annual change in installed power plant capacity

India power generation: average annual change
of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Hard coal 7 − 7 − 6 − 7 − 15 − 6
Lignite 0 − 1 − 1 − 2 − 2 − 1
Gas 9 13 7 7 − 14 17
Hydrogen-Gas 0 0 1 1 32 32
Oil/Diesel 0 − 1 − 1 − 1 0 0
Nuclear 1 0 0 0 − 1 − 1
Biomass 2 2 2 2 4 4
Hydro 3 2 1 1 1 1
Wind (onshore) 20 55 54 59 44 21
Wind (offshore) 2 6 7 7 5 4
PV (roof top) 21 55 49 53 51 30
PV (utility scale) 7 18 16 18 17 10
Geothermal 0 1 3 3 4 4
Solar thermal power plants 1 6 11 11 10 10
Ocean energy 0 1 3 3 3 3
Renewable fuel based co-generation 0 1 2 2 3 3

S. Teske et al.

357

8.12.2.2 India: Utilization of Power-Generation Capacities

The division of India into five sub-regions is intended to reflect the main grid zones

and it is assumed that interconnection will continue to increase to 15% in 2030 and

20% in 2050. Both scenarios aim for an even distribution of variable power plant

capacities across all Indian sub-regions. By 2030, the variable power generation

will reach 40% in most regions, whereas dispatchable renewables will supply about

one quarter of the demand by 2030 (Table 8.82).

India’s average capacity factors for the entire power plant fleet remain at around

35% over the entire modelling period, as the calculation results in Table 8.83 show.

Contributions from limited dispatchable fossil and nuclear power plants will remain

high until 2030 and indicate that a significant replacement of coal for electricity

must occur after 2030 in the 2.0 °C Scenario. In the 1.5 °C Scenario, coal will be

phased-out just after 2035.

8.12.2.3 India: Development of Load, Generation, and Residual Load

Table 8.84 shows that India’s load is predicted to quadruple in all five sub-regions

between 2020 and 2050. Under the 2.0 °C Scenario, additional interconnection will

increase—beyond the assumed 20% target—but may only be required for the west-

ern and southern sub-regions of India. However, for the 1.5 °C Scenario, intercon-

nections must increase in four of the five regions. In the northern region, the

calculated generation increases faster than the demand. This region has significant

potential for concentrated solar power plants and could supply neighbouring

regions.

Table 8.85 shows the storage and dispatch requirements under the 2.0  °C and

1.5 °C Scenarios. All the regions remain within the maximum curtailment target of

10%. Table 8.71 provides an overview of the calculated storage and dispatch power

requirements by sub-region. Charging capacities are moderate compared with other

world regions. Compared to all other world regions, hydrogen dispatch utilization is

very low due to a relatively moderate increase in the gas and hydrogen capacities in

India.

8 Energy Scenario Results

358

Table 8.83 India: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
India 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr] 60.8% 53% 57% 35% 26% 33% 30% 37% 34%

Limited
dispatchable:
fossil and
nuclear

[%/yr]67.7% 57% 61% 48% 38% 37% 27% 37% 12%

Limited
dispatchable:
renewable

[%/yr]17.1% 24% 26% 38% 34% 58% 39% 44% 42%

Dispatchable:
fossil

[%/yr]44.7% 12% 19% 11% 12% 30% 29% 24% 29%

Dispatchable:
renewable

[%/yr]39.8% 60% 68% 57% 45% 40% 52% 65% 57%

Variable:
renewable

[%/yr] 9.0% 8% 8% 19% 20% 27% 25% 29% 28%

Table 8.82 India: power system shares by technology group

Power
generation
structure and
interconnection

2.0 °C 1.5 °C

Variable
RE

Dispatch
RE

Dispatch
Fossil

Inter-
connection

Variable
RE

Dispatch
RE

Dispatch
fossil

Inter-
connection
India-Northern
Region

2015 4% 32% 64% 10%
2030 41% 28% 31% 15% 56% 24% 20% 15%
2050 60% 38% 2% 20% 48% 35% 17% 20%
India-North-
Eastern Region

2015 4% 32% 64% 10%
2030 44% 26% 30% 15% 58% 21% 21% 15%
2050 95% 5% 0% 20% 92% 5% 3% 20%
India-Eastern
Region

2015 4% 32% 64% 10%
2030 51% 26% 23% 15% 68% 22% 10% 15%
2050 73% 26% 1% 20% 69% 29% 2% 20%
India-Western
Region

2015 4% 32% 64% 10%
2030 44% 26% 30% 15% 57% 21% 22% 15%
2050 70% 29% 1% 20% 49% 24% 27% 20%
India-Southern
Region

2015 4% 32% 64% 10%
2030 48% 23% 29% 15% 60% 18% 22% 15%
2050 78% 21% 1% 20% 62% 19% 19% 20%
India 2015 4% 32% 64%
2030 45% 26% 29% 60% 21% 19%
2050 72% 27% 1% 58% 26% 16%

S. Teske et al.

359

Table 8.84

India: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

India-Northern Region

2020

87.8

85.6

11.1

87.8

78.2

19.9

2030

150.1

147.3

41.2

0

149.6

240.4

57.2

34

2050

372.2

397.2

265.7

0

366.8

381.7

211.1

0

India-North-
Eastern Region

2020

10.7

10.4

0.6

10.7

10.4

0.6

2030

18.3

21.7

2.7

1

18.3

30.4

2.7

9

2050

45.4

69.1

32.7

0

44.8

223.9

9.3

170

India-Eastern Region

2020

64.5

47.5

25.3

64.5

38.5

34.2

2030

110.8

118.0

43.1

0

110.4

198.8

53.1

35

2050

276.9

364.6

183.6

0

273.0

409.7

174.8

0

India-Western Region

2020

64.6

62.9

3.5

64.6

62.9

3.5

2030

111.0

173.5

19.4

43

110.6

196.4

20.0

66

2050

277.4

542.0

207.2

57

273.4

401.3

86.4

42

India-Southern Region

2020

60.6

59.1

3.5

60.6

59.1

3.2

2030

103.0

163.4

5.2

55

102.6

195.0

15.2

77

2050

252.8

507.5

164.8

90

249.1

448.0

76.7

122

8 Energy Scenario Results

360

Table 8.85

India: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

India

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

India-
Northern Region

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

507

24,533

57

3063

3121

160

2050

1244

42

51

93

9873

734

38

51

89

8647

India-
North-
Eastern Region

2020

1

0

1

1.1

0

1

0

1

1.1

0

2030

307

1

65

66

0

3862

8

471

478

0

2050

4923

126

332

457

1025

258,992

219

1896

2115

11

India-
Eastern Region

2020

0

0

0

0

0

0

0

0

0

0

2030

1657

10

427

437

476

54,903

95

4933

5028

156

2050

27,180

729

2154

2884

6813

46,793

1519

3163

4682

5715

India-
Western Region

2020

0

0

0

0

0

0

0

0

0

0

2030

29,610

51

2978

3028

448

41,348

84

3928

4012

310

2050

174,263

1709

5618

7327

5037

28,209

1228

2263

3491

2020

India-
Southern Region

2020

0

0

0

0

0

0

0

0

0

0

2030

27,824

42

2496

2537

328

57,916

88

4759

4847

144

2050

165,200

1643

5274

6917

5365

103,156

1891

4931

6822

2066

India

2020

1

0

1

1

0

1

0

1

1

0

2030

59,399

104

5966

6069

1759

182,561

333

17,154

17,487

769

2050

372,809

4248

13,430

17,678

28,113

437,884

4895

12,304

17,199

18,459

S. Teske et al.

361

8.13 China

8.13.1 China: Long-Term Energy Pathways

8.13.1.1 China: Final Energy Demand by Sector

The future development pathways for China’s final energy demand when the assump-

tions on population growth, GDP growth and energy intensity are combined are

shown in Fig. 8.89 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the 5.0  °C

Scenario, the total final energy demand will increase by 56% from the current 73,600

PJ/year to 114,600 PJ/year in 2050. In the 2.0 °C Scenario, the final energy demand

will decreases by 26% compared with current consumption and will reach 54,400

PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will reach 49,200

PJ, 33% below the 2015 demand. In the 1.5 °C Scenario, the final energy demand in

2050 will be 10% lower than in the 2.0  °C Scenario. The electricity demand for

‘classical’ electrical devices (without power-to-heat or e-mobility) will increase

from 3470 TWh/year in 2015 to around 5230 TWh/year in both alternative scenarios

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

TWh/yr

PJ/yr

Transport fuelsTransport electricity
Industry fuelsIndustry electricity
Residential & other sectors fuelsResidential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.89 China: development of final energy demand by sector in the scenarios

8 Energy Scenario Results

362

by 2050. Compared with the 5.0 °C case (9480 TWh/year in 2050), the efficiency

measures in the 2.0 °C and 1.5 °C Scenarios save around 4250 TWh/year by 2050.

Electrification will lead to a significant increase in the electricity demand by

2050. In the 2.0  °C Scenario, the electricity demand for heating will be approxi-

mately 2800 TWh/year due to electric heaters and heat pumps and in the transport

sector, the electricity demand will be approximately 4200 TWh/year due to electric

mobility. The generation of hydrogen (for transport and high-temperature process

heat) and the manufacture of synthetic fuels (mainly for transport) will add an addi-

tional power demand of 3900 TWh/year. Therefore, the gross power demand will

rise from 5900 TWh/year in 2015 to 13,800 TWh/year in 2050  in the 2.0  °C

Scenario, 11% higher than in the 5.0 °C case. In the 1.5 °C Scenario, the gross elec-

tricity demand will increase to a maximum of 13,300 TWh/year in 2050.

The efficiency gains in the heating sector could be even larger than in the elec-

tricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 24,400 PJ/year and 27,600 PJ/year, respectively, will be avoided

through efficiency gains by 2050 compared to the 5.0 °C Scenario.

8.13.1.2 China: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power from renew-

able sources. By 2050, 100% of the electricity produced in China will come from

renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—mainly wind,

solar, and geothermal energy—will contribute 77% of the total electricity genera-

tion. Renewable electricity’s share of the total production will be 54% by 2030 and

84% by 2040. The installed capacity of renewables will reach about 2170 GW by

2030 and 5420 GW by 2050. The share of renewable electricity generation in

2030 in the 1.5 °C Scenario is assumed to be 63%. In the 1.5 °C Scenario, the gen-

eration capacity from renewable energy will be approximately 5310 GW in 2050.

Table 8.86 shows the development of different renewable technologies in China

over time. Figure 8.90 provides an overview of the overall power-generation struc-

ture in China. From 2020 onwards, the continuing growth of wind and PV, up to

1670 GW and 2220 GW, respectively, will be complemented by up to 680 GW solar

thermal generation, as well as limited biomass, geothermal, and ocean energy, in the

2.0 °C Scenario. Both the 2.0 °C and 1.5 °C Scenarios will lead to a high proportion

of variable power generation (PV, wind, and ocean) of 28% and 34%, respectively,

by 2030, and 51% and 52%, respectively, by 2050.

S. Teske et al.

363

Table 8.86 China: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 320 395 424 477 525
2.0 °C 320 383 396 420 450
1.5 °C 320 383 396 420 450
Biomass 5.0 °C 11 24 29 39 48
2.0 °C 11 57 101 158 195
1.5 °C 11 72 106 160 195
Wind 5.0 °C 132 343 408 536 667
2.0 °C 132 428 678 1299 1674
1.5 °C 132 508 877 1460 1652
Geothermal 5.0 °C 0 0 0 1 3
2.0 °C 0 4 19 77 134
1.5 °C 0 7 29 77 119
PV 5.0 °C 43 265 330 430 565
2.0 °C 43 504 889 1614 2218
1.5 °C 43 604 1036 1781 2215
CSP 5.0 °C 0 3 5 7 11
2.0 °C 0 11 84 413 677
1.5 °C 0 16 103 391 614
Ocean 5.0 °C 0 0 0 1 1
2.0 °C 0 1 7 33 74
1.5 °C 0 1 7 33 62
Total 5.0 °C 505 1029 1196 1490 1819
2.0 °C 505 1390 2175 4015 5421
1.5 °C 505 1592 2555 4322 5307

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025203020402050

TWh/yr

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.90 China: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

364

8.13.1.3 China: Future Costs of Electricity Generation

Figure 8.91 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated elec-

tricity generation costs in 2015 (referring to full costs) were around 4.7 ct/kWh. In

the 5.0 °C case, the generation costs will increase until 2030, when they reach 9.2

ct/kWh, and then drop to 8.8 ct/kWh by 2050. The generation costs will increase in

the alternative scenarios until 2030, when they reach around 8 ct/kWh, and will then

drop to 6.5 ct/kWh by 2050, 2.3 ct/kWh lower than in the 5.0 °C Scenario. Note that

these estimates of generation costs do not take into account integration costs such as

power grid expansion, storage, or other load-balancing measures.

In the 5.0 °C case, the growth in demand and increasing fossil fuel prices will

cause total electricity supply costs to rise from today’s $310 billion/year to more

than $1230 billion/year in 2050. In the 2.0 °C case, the total supply costs will be

$1030 billion/year and $1010 billion/year in the 1.5  °C Scenario. Therefore, the

long-term costs for electricity supply will be more than 16% lower in the alternative

scenarios than in the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0 °C case to 5.7 ct/kWh in 2030 and stabi-

lize at 5.5 ct/kWh in 2050. In the 2.0 °C Scenario, they increase continuously until

2050, when they reach 6.6 ct/kWh. In the 1.5 °C Scenario, they will increase to 7 ct/

kWh and then drop to 6.6 ct/kWh by 2050. In the 2.0 °C Scenario, the generation

costs will be a maximum of 1 ct/kWh higher than in the 5.0 °C case, and this will

0

1

2

3

4

5

6

7

8

9

10

0

200

400

600

800

1000

1200

1400

2015 2025 2030 2040 2050

billion $ ct/kWh

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.91 China: development of total electricity supply costs and specific electricity-generation costs in the scenarios

S. Teske et al.

365

occur in 2050. In the 1.5 °C Scenario, compared to the 5.0 °C Scenario, the maxi-

mum difference in generation costs will be 1.6 ct/kWh in 2040. The generation costs

in 2050 will be 1.1 ct/kWh higher than in the 5.0 °C case. If the CO 2 costs are not

considered, the total electricity supply costs in the 5.0  °C case will rise to about

$810 billion/year in 2050.

8.13.1.4 China: Future Investments in the Power Sector

An investment of around $9740 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments for plant replace-

ment at the end of their economic lifetimes. This value will be equivalent to approx-

imately $271 billion per year on average and will be $5680 billion more than in the

5.0 °C case ($4060 billion). An investment of around $9840 billion for power gen-

eration will be required between 2015 and 2050 in the 1.5 °C Scenario. On average,

this will be an investment of $273 billion per year. In the 5.0 °C Scenario, the invest-

ment in conventional power plants will be around 29% of the total cumulative

investments, whereas approximately 71% will be invested in renewable power gen-

eration and co-generation (Fig. 8.92).

However, in the 2.0 °C (1.5 °C) Scenario, China will shift almost 97% (98%) of

its entire investment to renewables and co-generation. By 2030, the fossil fuel share

of the power sector investment will predominantly focus on gas power plants that

can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in both alternative scenarios will reach a total of more than $6200

billion in 2050, equivalent to $173 billion per year. Therefore, the total fuel cost

savings will be equivalent to 110% of the total additional investments compared to

the 5.0 °C Scenario.

8.13.1.5 China: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 38%

from 42,300 PJ/year in 2015 to 58,200 PJ/year in 2050. Energy efficiency measures

will help to reduce the energy demand for heating by 42% in 2050  in the 2.0  °C

Scenario, relative to the 5.0  °C case, and by 47% in the 1.5  °C Scenario. Today,

renewables supply around 11% of China’s final energy demand for heating, with the

main contribution from biomass. Renewable energy will provide 32% of China’s

total heat demand in 2030 in the 2.0 °C Scenario and 46% in the 1.5 °C Scenario. In

both scenarios, renewables will provide 100% of the total heat demand in 2050.

Figure 8.93 shows the development of different technologies for heating in China

over time, and Table 8.87 provides the resulting renewable heat supply for all sce-

narios. Up to 2030, biomass will remain the main contributor. In the long term, the

8 Energy Scenario Results

366

growing use of solar, geothermal, and environmental heat will lead to a biomass

share of 24% in both alternative scenarios.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. Hydrogen consumption in 2050 will be around 4100 PJ/year in

Fossil
19%

Nuclear
10 %

CHP
8%

Renewable
63%

5.0°C: 2015-2050

total 4,065
billion $

Fossil
(incl.H2)
3%Nuclear
1%
CHP
14 %

Renewable
82%

2.0°C: 2015-2050

total 9,740
billion $

Fossil (incl. H2)
2%
Nuclear
1%

CHP
14%

Renewable
83 %

1.5°C: 2015-2050

total 9,850
billion $

Fig. 8.92 China: investment shares for power generation in the scenarios

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electric heating
Geothermal heat
and heat pumps
Solar heating

Biomass

Fossil

Fig. 8.93 China: development of heat supply by energy carrier in the scenarios

S. Teske et al.

367

the 2.0 °C Scenario and to 4500 PJ/year in the 1.5 °C Scenario. The direct use of

electricity for heating will also increase by a factor of 3.7–4 between 2015 and 2050

and electricity for heating will have a final energy share of 27% in 2050 in both the

2.0 °C Scenario and 1.5 °C Scenario.

8.13.1.6 China: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $2780 billion in the 2.0 °C Scenario (including investments

for the replacement of plants after their economic lifetimes), or approximately $77

billion per year. The largest share of investment in China is assumed to be for heat

pumps (around $1200 billion), followed by solar collectors and geothermal heat

use. The 1.5 °C Scenario assumes an even faster expansion of renewable technolo-

gies. However, the lower heat demand (compared with the 2.0  °C Scenario) will

result in a lower average annual investment of around $67 billion per year

(Table 8.88, Fig. 8.94).

8.13.1.7 China: Transport

The energy demand in the transport sector in China is expected to increase in the

5.0 °C Scenario by 107% from around 12,600 PJ/year in 2015 to 26,100 PJ/year in

2050. In the 2.0 °C Scenario, assumed technical, structural, and behavioural changes

will save 68% (17,840 PJ/year) by 2050 compared with the 5.0  °C Scenario.

Table 8.87 China: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 2776 2095 2079 2291 2877
2.0 °C 2776 4609 5603 6254 5967
1.5 °C 2776 5378 6263 6055 5385
Solar heating 5.0 °C 892 1297 1515 1962 2535
2.0 °C 892 2066 2906 5454 5417
1.5 °C 892 2364 3242 4381 4360
Geothermal heat and heat pumps 5.0 °C 306 452 526 743 1026
2.0 °C 306 1304 2720 6690 9225
1.5 °C 306 1269 2884 5706 7943
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 0 7 1020 4118
1.5 °C 0 0 7 1890 4549
Total 5.0 °C 3974 3844 4120 4996 6438
2.0 °C 3974 7978 11,237 19,417 24,727
1.5 °C 3974 9011 12,396 18,031 22,237

8 Energy Scenario Results

368

Additional modal shifts, technology switches, and a reduction in transport demand

will lead to even higher energy savings in the 1.5 °C Scenario of 76% (or 19,900 PJ/

year) in 2050 compared with the 5.0 °C case (Table 8.89, Fig. 8.95).

By 2030, electricity will provide 21% (680 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 51%

(1170 TWh/year). In 2050, up to 1600 PJ/year of hydrogen will be used in the trans-

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand is 860 TWh in 2050. The 1.5 °C Scenario also assumes a hydro-

gen demand of 1100 PJ/year by 2050.

Biofuel use is limited in the 2.0  °C Scenario to a maximum of 1900 PJ/year.

Therefore, around 2030, synthetic fuels based on power-to-liquid will be intro-

duced, with a maximum amount of 560 PJ/year in 2050. Due to the lower overall

energy demand in transport, biofuel use will be reduced in the 1.5 °C Scenario to a

maximum of around 1400 PJ/year. The maximum synthetic fuel demand will

amount to 720 PJ/year.

8.13.1.8 China: Development of CO 2 Emissions

In the 5.0 °C Scenario, China’s annual CO 2 emissions will increase by 25%, from

9060 Mt. in 2015 to 11,320 Mt. in 2050. The stringent mitigation measures in both

alternative scenarios will cause annual emissions to fall to 1990 Mt. in 2040 in the

2.0 °C Scenario and to 760 Mt. in the 1.5 °C Scenario, with further reductions to

almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions from 2015

until 2050 will add up to 392 Gt. In contrast, in the 2.0 °C and 1.5 °C Scenarios, the

Table 8.88 China: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 1194 764 648 519 468 2.0 °C 1194 1284 1214 921 578 1.5 °C 1194 1267 1280 808 481 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 20 46 187 272 1.5 °C 0 20 42 139 161 Solar heating 5.0 °C 281 409 478 618 799 2.0 °C 281 592 843 1546 1539 1.5 °C 281 688 956 1252 1275 Heat pumps 5.0 °C 52 76 89 126 174 2.0 °C 52 151 251 449 565 1.5 °C 52 136 213 349 446 Totala 5.0 °C 1527 1250 1214 1263 1441 2.0 °C 1527 2048 2355 3103 2954 1.5 °C 1527 2111 2491 2549 2361 a Excluding direct electric heating

S. Teske et al.

369

cumulative emissions for the period from 2015 until 2050 will be 174 Gt and 132

Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 56% in the 2.0  °C

Scenario and by 66% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in annual emissions will occur in both alternative scenarios. In the 2.0 °C

Scenario the reduction will be greatest in the ‘Residential and other’ sector, fol-

lowed by ‘Power generation’ and ‘Transport’ sectors (Fig. 8.96).

8.13.1.9 China: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.97. In the 2.0 °C

Scenario, the primary energy demand will decrease by 30%, from around 125,000

PJ/year in 2015 to 87,800 PJ/year in 2050. Compared with the 5.0 °C Scenario, the

biomass
technologies
25%

geothermal
heat use
0%

solar
collectors
24 %

heat
pumps
51 %

5.0°C: 2015-2050

total 640 billion $

biomass
technologies
5%

geothermal
heat use
18%

solar
collectors
34 %

heat
pumps
43%

2.0C: 2015-2050

total 2,800 billion $

biomass
technologies
8%

geothermal
heat use
13%

solar
collectors
40 %

heat
pumps
39 %

1.5°C: 2015-2050

total 2,400 billion $

Fig. 8.94 China: development of investments for renewable heat-generation technologies in the scenarios

8 Energy Scenario Results

370

overall primary energy demand will decrease by 54% by 2050 in the 2.0 °C Scenario

(5.0 °C: 192,300 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand

will be even lower (80,700 PJ in 2050) because the final energy demand and conver-

sion losses will be lower.

Both the 2.0 °C and 1.5 °C Scenarios aim to rapidly phase-out coal and oil. This

will cause renewable energy to have a primary energy share of 28% in 2030 and

92% in 2050 in the 2.0 °C Scenario. In the 1.5 °C Scenario, renewables will have a

primary energy share of more than 91% in 2050 (including non-energy consump-

Table 8.89 China: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 539 567 593 644 672
2.0 °C 539 589 637 687 762
1.5 °C 539 580 597 622 662
Road 5.0 °C 10,421 15,629 17,651 19,664 22,073
2.0 °C 10,421 11,509 9395 7143 5894
1.5 °C 10,421 9607 7372 4576 4020
Domestic aviation 5.0 °C 754 1234 1590 2070 2213
2.0 °C 754 814 742 592 470
1.5 °C 754 777 653 463 366
Domestic navigation 5.0 °C 877 984 1035 1113 1157
2.0 °C 877 984 1035 1113 1157
1.5 °C 877 984 1035 1113 1157
Total 5.0 °C 12,591 18,413 20,870 23,490 26,115
2.0 °C 12,591 13,895 11,809 9535 8284
1.5 °C 12,591 11,948 9657 6773 6206

0

5,000

10,000

15,000

20,000

25,000

30,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electricity

Synfuels
Biofuels
Natural Gas
Oil products

Fig. 8.95 China: final energy consumption by transport in the scenarios

S. Teske et al.

371

0

50

100

150

200

250

300

350

400

450

0

2,000

4,000

6,000

8,000

10,000

12,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

cumulated emissions [Gt]

CO

emissions [Mt/yr] 2

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.96 China: development of CO 2 emissions by sector and cumulative CO 2 emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

0

20,000

40,000

60,000

80,000

100,00 0

120,00 0

140,00 0

160,00 0

180,00 0

200,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

net electricity
imports
Efficiency
Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal

Nuclear

Fig. 8.97 China: projection of total primary energy demand (PED) by energy carrier in the sce- narios (including electricity import balance)

8 Energy Scenario Results

372

tion, which will still include fossil fuels). Nuclear energy will be phased-out by

2050  in the 2.0  °C Scenario and by 2045  in the 1.5  °C Scenario. The cumulative

primary energy consumption of natural gas in the 5.0 °C case will add up to 570 EJ,

the cumulative coal consumption to about 3000 EJ, and the crude oil consumption

to 1080 EJ.  In contrast, in the 2.0  °C Scenario, the cumulative gas demand will

amount to 360 EJ, the cumulative coal demand to 1360 EJ, and the cumulative oil

demand to 430 EJ. Even lower fossil fuel use will be achieved in the 1.5 °C Scenario:

440 EJ for natural gas, 930 EJ for coal, and 340 EJ for oil.

8.13.2 China: Power Sector Analysis

China has by far the largest power sector of all world regions—about one quarter of

the world’s total electricity generation. China’s National Energy Administration

(NEA) released the 13th Energy Five-Year Plan (FYP) in January 2016 (IEA RED

2016 ). The FYP that is in force from 2016 to 2020 introduces framework legislation

that defines energy development for the next 5 years in China. In parallel to the main

Energy FYP, there are 14 additional supporting FYPs, such as the Renewable

Energy 13th FYP, the Wind FYP, and the Electricity FYP, which were all released

at about the same time (GWEC-NL 2018 ). According to the Renewable Energy 13th

FYP, by 2020, the total RE electricity installations will reach 680 GW, with electric-

ity production of 1900 TWh/year This will account for 27% of electricity produc-

tion. The wind power target is set to reach 210 GW by 2020, with electricity

production of 420 TWh, supplying 6% of China’s total electricity demand. The

target for offshore wind is 5 GW by 2020 (GWEC-NL 2018 ). For other renewable

power-generation technologies, the 2020 targets are 150 GW for solar PV, 10 GW

for concentrated solar power (CSP), 15 GW for bioenergy, and 380 GW for hydro-

power, including 40 GW hydro pump storage (IEA-RED 2016). The renewable tar-

gets are consistent, to large extent, with both the 2.0 °C and 1.5 °C Scenarios. The

onshore wind and solar PV capacities in both scenarios will increase to 50 GW and

are within the current market size range. The targets for the 2.0  °C and 1.5  °C

Scenarios for CSP, bioenergy, and offshore wind are slightly higher than current

market volumes. However, the first decade of the 2.0 °C and 1.5 °C Scenarios will

reflect the existing trends in China’s power sector.

8.13.2.1 China: Development of Power Plant Capacities

China’s solar PV and wind power markets are the largest in the world and represent

about half the global annual market for solar PV (in 2017) and a third of the market

for onshore wind. The continued growth of the annual renewable power market—

for all technologies—for the Chinese market will continue to have a significant

impact on other world regions. To implement the project’s 2.0 °C Scenario, the cur-

rent solar PV market in China must remain at the 2017 level, and to achieve the

S. Teske et al.

373

1.5 °C Scenario, it must double. The onshore wind market must increase by 50%

compared with 2015 for the 2.0 °C Scenario and must triple to meet the 1.5 °C tra-

jectory. All these annual market volumes must be maintained until 2035, before a

moderate reduction in the annual market sizes can occur (Table 8.90).

8.13.2.2 China: Utilization of Power Generation Capacities

Across all regions, an interconnection capacity of 10% is assumed for the base year

calculation. The interconnection capacity will increase to 20% by 2030, with no

further increase thereafter. For the entire modelling period, it is assumed that Taiwan

is not connected to any other region. Under the 2.0 °C Scenario, variable renewables

will attain a share of around 30% in all sub-regions, whereas the 1.5 °C Scenario

will lead to shares of over 40% in five of the seven sub-regions (Table 8.91).

Table 8.92 shows the results of the capacity factor calculations done under the

assumption that variable and dispatchable power plants will have priority access to

the grid and priority dispatch. The average capacity factors for limited dispatchable

power plants will remain at around 30% until 2030 under the 2.0 °C Scenario. This

relatively low factor indicates an overcapacity in China’s power market. The curtail-

ment rates of 20% (REW 1- 2018 ) and more in 2017—mainly for wind farms—con-

firm this.

Table 8.90 China: average annual change in installed power plant capacity

China power generation: average annual change
of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5 °C2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal 5 − 51 − 55 − 81 − 41 − 5
Lignite 0 0 0 0 0 0
Gas 4 28 6 30 − 16 − 17
Hydrogen-Gas 0 0 1 3 24 38
Oil/Diesel 0 − 1 0 − 1 0 0
Nuclear 3 0 − 2 0 − 3 − 4
Biomass 6 10 9 8 5 5
Hydro 8 5 3 3 3 3
Wind (onshore) 31 65 46 64 36 29
Wind (offshore) 2 12 20 22 11 9
PV (roof top) 41 77 69 76 62 50
PV (utility scale) 14 26 23 25 21 17
Geothermal 1 4 5 6 8 6
Solar thermal power plants 1 13 34 29 40 30
Ocean energy 0 1 2 2 5 4
Renewable fuel based co-generation 4 9 10 9 8 8

8 Energy Scenario Results

374

Table 8.91

China: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

China

Variable RE

Dispatch RE

Dispatch Fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch fossil

Inter-
connection

China-North

2015

7%

35%

58%

10%

2030

32%

21%

47%

20%

43%

29%

28%

20%

2050

53%

43%

4%

20%

53%

37%

9%

20%

China-Northwest

2015

7%

35%

58%

10%

2030

29%

22%

49%

20%

40%

31%

29%

20%

2050

49%

47%

3%

20%

54%

44%

2%

20%

China-Northeast

2015

6%

35%

60%

10%

2030

34%

24%

43%

20%

45%

31%

24%

20%

2050

54%

43%

4%

20%

54%

45%

2%

20%

China-Tibet

2015

7%

35%

58%

10%

2030

37%

34%

29%

20%

49%

37%

14%

20%

2050

43%

49%

7%

20%

42%

53%

5%

20%

China-Central

2015

6%

35%

60%

10%

2030

28%

26%

47%

20%

36%

32%

32%

20%

2050

41%

52%

7%

20%

44%

48%

9%

20%

China-East

2015

6%

35%

60%

10%

2030

30%

25%

45%

20%

36%

29%

35%

20%

2050

48%

47%

5%

20%

48%

38%

14%

20%

S. Teske et al.

375

China-South

2015

6%

35%

60%

10%

2030

30%

28%

43%

20%

38%

31%

31%

20%

2050

49%

47%

4%

20%

48%

46%

6%

20%

Taiwan

2015

7%

35%

59%

0%

2030

31%

24%

46%

0%

39%

29%

31%

0%

2050

57%

40%

3%

0%

51%

37%

12%

0%

China

2015

6%

35%

59%

2030

30%

24%

46%

39%

30%

31%

2050

49%

47%

5%

49%

42%

9%

8 Energy Scenario Results

376

8.13.2.3 China: Development of Load, Generation, and Residual Load

The load for China is calculated to continue to increase. Table 8.93 shows that the

maximum load will double across all regions. However, the assumed interconnec-

tion rates of 20% are sufficient for the 2.0 °C Scenario, whereas significantly higher

interconnection capacities will be required under the 1.5 °C Scenario. By 2050, all

regions will have an oversupply under the 1.5 °C Scenario. This surplus electricity

will be used to produce synthetic fuels and hydrogen. The [R]E 24/7 model does not

interface with other world regions, so surplus generation will result in a negative

residual load.

Finally, Table 8.94 provides an overview of the calculated storage and dispatch

power requirements in the Chinese region. The calculated hydro pump storage

increase by 2050 is consistent with the Thirteenth Five-Year Plan’s requirement for

40 GW additional capacity. Furthermore, curtailment is within the acceptable range,

at significantly below 10% in both scenarios by 2050. Battery capacities must

increase significantly after 2030. The central, southern, and eastern sub-regions of

mainland China have by far the highest storage requirements.

Table 8.92 China: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
China 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr] 42.0% 30% 28% 26% 21% 37% 24% 37% 26%

Limited
dispatchable:
fossil and
nuclear

[%/yr]39.2% 34% 29% 32% 25% 20% 17% 9% 16%

Limited
dispatchable:
renewable

[%/yr]47.3% 20% 17% 21% 14% 68% 19% 47% 27%

Dispatchable:
fossil

[%/yr]30.7% 28% 40% 46% 34% 24% 37% 11% 37%

Dispatchable:
renewable

[%/yr]59.1% 27% 31% 28% 23% 47% 34% 62% 39%

Variable:
renewable

[%/yr]17.9% 15% 15% 17% 16% 22% 17% 22% 17%

S. Teske et al.

377

Table 8.93

China: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

China

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

China-North

2020

168.7

168.7

3.6

167.9

167.9

3.6

2030

215.6

222.5

22.5

0

213.0

292.8

25.8

54

2050

364.2

504.4

246.3

0

368.2

587.9

−
133.4

353

China-Northwest

2020

77.4

80.5

6.1

77.1

82.4

6.1

2030

95.6

99.5

11.8

0

94.5

126.7

13.3

19

2050

135.3

206.2

114.3

0

136.9

246.4

−
48.1

158

China-Northeast

2020

67.8

67.7

1.9

67.4

67.3

1.9

2030

83.9

96.3

12.9

0

82.7

126.3

13.8

30

2050

133.2

219.9

103.7

0

135.0

255.9

−
22.8

144

China-Tibet

2020

0.8

0.8

0.0

0.8

0.8

0.0

2030

1.0

1.0

0.4

0

1.0

1.3

0.2

0

2050

2.3

2.4

1.4

0

2.4

2.8

−

0.9

1

China-Central

2020

208.7

208.7

5.9

207.2

207.2

5.9

2030

262.7

260.5

44.9

0

258.4

329.5

34.5

37

2050

445.3

536.2

299.8

0

451.7

642.0

−

218.4

409

China-East

2020

226.8

201.9

47.9

225.9

214.1

31.0

2030

286.3

284.3

40.1

0

283.6

372.4

41.5

47

2050

454.4

633.5

320.4

0

458.5

739.3

−

132.0

413

(continued)

8 Energy Scenario Results

378

Table 8.93

(continued)

Power generation structure

2.0 °C

1.5 °C

China

Max demand

Max generation

Max residual load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

China-South

2020

173.6

173.6

9.0

173.6

173.6

9.0

2030

242.3

238.6

36.2

0

239.6

312.0

44.6

28

2050

368.8

529.6

282.0

0

372.8

622.7

−
49.1

299

Taiwan

2020

33.0

33.2

0.0

2030

46.0

45.9

3.8

0

45.7

52.5

5.9

1

2050

63.7

92.0

47.1

0

64.1

105.7

−
4.0

46

S. Teske et al.

379

Table 8.94

China: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

China

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

China-
North

2020

0

0

0

0

0

0

0

0

0

0

2030

45

3

38

41

11

6734

62

2363

2425

0

2050

14,152

14,255

641

14,896

96,848

39,562

2958

6350

9308

17,528

China-
Northwest

2020

158

2

302

304

0

326

3

547

550

0

2030

7

1

9

10

1

3401

38

1240

1278

0

2050

12,360

15,511

661

16,172

39,433

31,642

2171

4847

7018

10,080

China-
Northeast

2020

0

0

0

0

0

0

0

0

0

0

2030

912

22

563

585

143

11,430

57

2362

2418

1

2050

24,955

22,345

1465

23,809

39,793

49,329

2238

5393

7631

10,012

China-
Tibet

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

4

43

0

15

15

0

2050

0

0

0

0

754

3

1

1

3

230

China-
Central Baltic

2020

0

0

0

0

0

0

0

0

0

0

2030

6

1

10

11

576

6013

74

2305

2379

1

2050

4763

7167

44

7211

167,132

23,175

2609

4372

6981

47,112

China-East

2020

0

0

0

0

0

0

0

0

0

0

2030

59

4

79

83

797

8720

95

3042

3137

0

2050

17,604

21,928

1036

22,964

148,351

50,402

3884

8341

12,225

18,866(continued)

8 Energy Scenario Results

380

Table 8.94

(continued)

Storage and dispatch

2.0 °C

1.5 °C

China

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

China-
South

2020

0

0

0

0

0

0

0

0

0

0

2030

74

7

89

96

961

8676

93

3086

3179

0

2050

21,703

28,028

1143

29,171

116,735

56,742

4139

9307

13,446

22,281

Taiwan

2020

0

0

0

0

0

0

0

0

0

0

2030

0

0

0

0

89

202

5

121

126

0

2050

6506

5734

943

6677

14,209

13,873

426

2985

3411

0

China

2020

158

2

302

304

0

326

3

547

550

0

2030

1102

39

789

827

2582

45,217

424

14,533

14,957

2

2050

102,042

114,967

5932

120,899

623,254

264,729

18,427

41,596

60,022

126,108

S. Teske et al.

381

8.14 OECD Pacific

8.14.1 OECD Pacific: Long-Term Energy Pathways

8.14.1.1 OECD Pacific: Final Energy demand by Sector

The future development pathways for OECD Pacific’s final energy demand when

the assumptions on population growth, GDP growth, and energy intensity are com-

bined are shown in Fig. 8.98 for the 5.0  °C, 2.0  °C, and 1.5  °C Scenarios. In the

5.0 °C Scenario, the total final energy demand will decrease by 2%, from the current

20,100 PJ/year to 19,600 PJ/year in 2050. In the 2.0 °C Scenario, the final energy

demand will decrease by 46% compared with current consumption and will reach

10,800 PJ/year by 2050. The final energy demand in the 1.5 °C Scenario will reach

10,200 PJ, 49% below the 2015 demand. In the 1.5 °C Scenario, the final energy

demand in 2050 will be 6% lower than in the 2.0  °C Scenario. The electricity

demand for ‘classical’ electrical devices (without power-to-heat or e-mobility) will

decrease from 1520 TWh/year in 2015 to 1150 TWh/year in 2050 in both alternative

scenarios. Compared with the 5.0 °C case (1890 TWh/year in 2050), the efficiency

measures in the 2.0 °C and 1.5 °C Scenarios will save 740 TWh/year in 2050.

Electrification will lead to a significant increase in the electricity demand by

2050. The 2.0 °C Scenario has an electricity demand for heating of approximately

400 TWh/year due to electric heaters and heat pumps, and in the transport sector,

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

0

5,00 0

10,00 0

15,00 0

20,00 0

25,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

20152025203020402050

TWh/yr

PJ/yr

Transport fuels Transport electricity
Industry fuelsIndustry electricity
Residential & other sectors fuelsResidential & other sectors electricity
total power demand (incl. synfuels & H2)

Fig. 8.98 OECD Pacific: development of final energy demand by sector in the scenarios

8 Energy Scenario Results

382

the electricity demand will be approximately 1100 TWh/year due to electric mobil-

ity. The generation of hydrogen (for transport and high-temperature process heat)

and the manufacture of synthetic fuels (mainly for transport) will add an additional

power demand of 1000 TWh/year. Therefore, the gross power demand will rise

from 1900 TWh/year in 2015 to 3000 TWh/year in 2050  in the 2.0  °C Scenario,

25% higher than in the 5.0  °C case. In the 1.5  °C Scenario, the gross electricity

demand will increase to a maximum of 3400 TWh/year in 2050.

The efficiency gains in the heating sector could be even larger than in the elec-

tricity sector. In the 2.0 °C and 1.5 °C Scenarios, a final energy consumption equiva-

lent to about 3000 PJ/year and 3100 PJ/year, respectively, will be avoided by 2050

through efficiency gains compared with the 5.0 °C Scenario.

8.14.1.2 OECD Pacific: Electricity Generation

The development of the power system is characterized by a dynamically growing

renewable energy market and an increasing proportion of total power coming from

renewable sources. By 2050, 100% of the electricity produced in OECD Pacific will

come from renewable energy sources in the 2.0 °C Scenario. ‘New’ renewables—

mainly wind, solar, and geothermal energy—will contribute 82% of total electricity

generation. Renewable electricity’s share of the total production will be 60% by

2030 and 89% by 2040. The installed capacity of renewables will reach about 680

GW by 2030 and 1420 GW by 2050. The share of renewable electricity generation

in 2030 in the 1.5 °C Scenario is assumed to be 68%. The 1.5 °C Scenario will have

a generation capacity from renewable energy of approximately 1590 GW in 2050.

Table 8.95 shows the development of different renewable technologies in OECD

Pacific over time. Figure 8.99 provides an overview of the overall power-generation

structure in OECD Pacific. From 2020 onwards, the continuing growth of wind and

PV, up to 320 GW and 830 GW, respectively, will complemented by up to 60 GW

solar thermal generation, as well as limited biomass, geothermal, and ocean energy,

in the 2.0  °C Scenario. Both the 2.0  °C and 1.5  °C Scenarios will lead to a high

proportion of variable power generation (PV, wind, and ocean) of 40% and 47% by

2030, respectively, and of 68% in both scenarios by 2050.

8.14.1.3 OECD Pacific: Future Costs of Electricity Generation

Figure 8.100 shows the development of the electricity-generation and supply costs

over time, including the CO 2 emission costs, in all scenarios. The calculated

electricity- generation costs in 2015 (referring to full costs) were around 8 ct/kWh.

In the 5.0 °C case, the generation costs will increase until 2030, when they reach

11.1 ct/kWh, and then drop to 10.9 ct/kWh by 2050. The generation costs will

increase in the 2.0 °C Scenario until 2030, when they reach 10.5 ct/kWh, and then

drop to 8.3 ct/kWh by 2050. In the 1.5 °C Scenario, they will increase to 10.7 ct/

kWh, and then drop to 8.5 ct/kWh by 2050. In the 2.0 °C Scenario, the generation

S. Teske et al.

383

Table 8.95 OECD Pacific: development of renewable electricity-generation capacity in the scenarios

in GW Case 2015 2025 2030 2040 2050
Hydro 5.0 °C 69 73 76 78 78
2.0 °C 69 76 78 82 84
1.5 °C 69 76 78 82 84
Biomass 5.0 °C 9 13 15 16 18
2.0 °C 9 23 26 35 43
1.5 °C 9 23 29 42 47
Wind 5.0 °C 9 23 28 40 56
2.0 °C 9 77 145 263 322
1.5 °C 9 84 198 335 384
Geothermal 5.0 °C 2 4 5 7 11
2.0 °C 2 4 14 27 37
1.5 °C 2 4 14 27 37
PV 5.0 °C 43 84 96 102 107
2.0 °C 43 225 394 701 831
1.5 °C 43 253 427 782 932
CSP 5.0 °C 0 0 0 1 1
2.0 °C 0 1 15 39 57
1.5 °C 0 1 20 49 67
Ocean 5.0 °C 0 1 1 2 4
2.0 °C 0 3 8 27 42
1.5 °C 0 3 8 27 42
Total 5.0 °C 132 197 221 246 275
2.0 °C 132 409 681 1176 1416
1.5 °C 132 444 774 1345 1594

0

500

1,00 0

1,50 0

2,00 0

2,50 0

3,00 0

3,50 0

4,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 20252030 2040 2050

TWh/y

r

Ocean Energy
CSP
Geothermal
Biomass
PV
Wind
Hydro
Hydrogen
Nuclear
Diesel
Oil
Gas
Lignite
Coal

Fig. 8.99 OECD Pacific: development of electricity-generation structure in the scenarios

8 Energy Scenario Results

384

costs in 2050 will be 2.6 ct/kWh lower than in the 5.0 °C case, and in the 1.5 °C

Scenario, this difference will 2.4 ct/kWh. Note that these estimates of generation

costs do not take into account integration costs such as power grid expansion, stor-

age, or other load-balancing measures.

In the 5.0 ° C case, the growth in demand and increasing fossil fuel prices will

cause the total electricity supply costs to rise from today’s $160 billion/year to more

than $270 billion/year in 2050. In the 2.0 °C Scenario, the total supply costs will be

$270 billion/year, and in the 1.5  °C Scenario, they will be $310 billion/year. The

long-term costs for electricity supply will be only 2% higher in the 2.0 °C Scenario

than in the 5.0  °C Scenario as a result of the estimated generation costs and the

electrification of heating and mobility. Further electrification and synthetic fuel gen-

eration in the 1.5  °C Scenario will result in total power generation costs that are

17% higher than in the 5.0 °C case.

Compared with these results, the generation costs when the CO 2 emission costs

are not considered will increase in the 5.0 °C case to 8.3 ct/kWh in 2050. The gen-

eration costs in the 2.0 °C Scenario will increase until 2030, when they will reach

9.3 ct/kWh, and then drop to 8.3 ct/kWh by 2050. In the 1.5 °C Scenario, they will

increase to 9.9 ct/kWh, and then drop to 8.5 ct/kWh by 2050. In the 2.0 °C Scenario,

the generation costs will be a maximum of 1 ct/kWh higher than in the 5.0 °C case

and this will occur in 2040. In the 1.5  °C Scenario, compared with the 5.0  °C

Scenario, the maximum difference in the generation costs will be 1.4 ct/kWh, again

in 2040. If the CO 2 costs are not considered, the total electricity supply costs in the

5.0 °C case will rise to about $200 billion/year in 2050.

0

2

4

6

8

10

12

0

50

100

150

200

250

300

350

400

2015 2025 203020402050

billion $ ct/kWh

2.0°C efficiency measures 2.0°C
1.5°C efficiency measures 1.5°C
Spec. Electricity Generation Costs 5.0°C 5.0°C
Spec. Electricity Generation Costs 1.5°C Spec. Electricity Generation Costs 2.0°C

Fig. 8.100 OECD Pacific: development of total electricity supply costs and specific electricity- generation costs in the scenarios

S. Teske et al.

385

8.14.1.4 OECD Pacific: Future Investments in the Power Sector

An investment of around $2780 billion will be required for power generation

between 2015 and 2050 in the 2.0 °C Scenario—including additional power plants

for the production of hydrogen and synthetic fuels and investments in the replace-

ment of plants at the end of their economic lifetimes. This value will be equivalent

to approximately $77 billion per year on average, and will be $1520 billion more

than in the 5.0 °C case ($1260 billion). An investment of around $3100 billion for

power generation will required between 2015 and 2050 in the 1.5 °C Scenario. On

average, this is an investment of $86 billion per year. In the 5.0  °C Scenario, the

investment in conventional power plants will be around 56% of the total cumulative

investments, whereas approximately 44% will be invested in renewable power gen-

eration and co-generation (Fig. 8.101).

However, in the 2.0 °C (1.5 °C) Scenario, OECD Pacific will shift almost 93%

(95%) of its entire investment to renewables and co-generation. By 2030, the fossil

fuel share of the power sector investment will predominantly focused on gas power

plants that can also be operated with hydrogen.

Because renewable energy has no fuel costs, other than biomass, the cumulative

fuel cost savings in the 2.0 °C Scenario will reach a total of $1420 billion in 2050,

equivalent to $39 billion per year. Therefore, the total fuel cost savings will be

equivalent to 90% of the total additional investments compared to the 5.0  °C

Scenario. The fuel cost savings in the 1.5 °C Scenario will add up to $1510 billion,

or $42 billion per year.

Fossil
Nuclear 31%
24%

CHP
5%

Renewable
40%

total 1,260 billion $

Fossil
(incl. H2)
6%
Nuclear
4%

CHP
7%

Renewable
83%

2.0°C: 2015-2050

5.0°C: 2015-2050

total 2,780
billion $

Fossil
(incl. H2)
5%
Nuclear
3%

CHP
7%

Renewable
85%

1.5°C: 2015-2050

total 3,100
billion $

Fig. 8.101 OECD Pacific: investment shares for power generation in the scenarios

8 Energy Scenario Results

386

8.14.1.5 OECD Pacific: Energy Supply for Heating

The final energy demand for heating will increase in the 5.0 °C Scenario by 17%,

from 7100 PJ/year in 2015 to 8300 PJ/year in 2050. Energy efficiency measures will

help to reduce the energy demand for heating by 35% in 2050 in the 2.0 °C Scenario,

relative to the 5.0 °C case, and by 37% in the 1.5 °C Scenario. Today, renewables

supply around 7% of OECD Pacific’s final energy demand for heating, with the main

contribution from biomass. Renewable energy will provide 33% of OECD Pacific’s

total heat demand in 2030 in the 2.0 °C Scenario and 42% in the 1.5 °C Scenario. In

both scenarios, renewables will provide 100% of the total heat demand in 2050.

Figure 8.102 shows the development of different technologies for heating in

OECD Pacific over time, and Table 8.96 provides the resulting renewable heat sup-

ply for all scenarios. Up to 2030, biomass will remain the main contributor. The

growing use of solar, geothermal, and environmental heat will lead, in the long term,

to a biomass share of 37% in the 2.0 °C Scenario and of 35% in the 1.5 °C Scenario.

Heat from renewable hydrogen will further reduce the dependence on fossil fuels

in both scenarios. The hydrogen consumption in 2050 will be around 700 PJ/year in

the 2.0 °C Scenario and 800 PJ/year in the 1.5 °C Scenario. The direct use of elec-

tricity for heating will also increases by a factor of 1.6 between 2015 and 2050, and

will achieves a final energy share of 21% in 2050 in the 2.0 °C Scenario and 22% in

the 1.5 °C Scenario.

8.14.1.6 OECD Pacific: Future Investments in the Heating Sector

The roughly estimated investments in renewable heating technologies up to 2050

will amount to around $530 billion in the 2.0  °C Scenario (including the invest-

ments for the replacement of plants after their economic lifetimes), or approxi-

mately $15 billion per year. The largest share of the investment in OECD Pacific is

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen
Electric heating
Geothermal heat
and heat pumps
Solar heating
Biomass
Fossil

Fig. 8.102 OECD Pacific: development of heat supply by energy carrier in the scenarios

S. Teske et al.

387

assumed to be for solar collectors (around $240 billion), followed by heat pumps

and biomass technologies. The 1.5 °C Scenario assumes an even faster expansion of

renewable technologies, but with a similar average annual investment of around $15

billion per year (Table 8.97, Fig. 8.103).

Table 8.96 OECD Pacific: development of renewable heat supply in the scenarios (excluding the direct use of electricity)

in PJ/year Case 2015 2025 2030 2040 2050
Biomass 5.0 °C 314 471 504 584 714
2.0 °C 314 633 815 1250 1579
1.5 °C 314 650 823 1229 1463
Solar heating 5.0 °C 45 76 92 150 236
2.0 °C 45 221 452 737 819
1.5 °C 45 252 543 772 795
Geothermal heat and heat pumps 5.0 °C 30 33 34 36 38
2.0 °C 30 157 307 737 1119
1.5 °C 30 197 420 830 1094
Hydrogen 5.0 °C 0 0 0 0 0
2.0 °C 0 6 16 251 728
1.5 °C 0 9 160 642 772
Total 5.0 °C 390 580 629 769 988
2.0 °C 390 1017 1591 2975 4245
1.5 °C 390 1107 1946 3473 4124

Table 8.97 OECD Pacific: installed capacities for renewable heat generation in the scenarios

in GW Case 2015 2025 2030 2040 2050 Biomass 5.0 °C 44 60 63 69 75 2.0 °C 44 77 92 117 94 1.5 °C 44 79 91 113 80 Geothermal 5.0 °C 0 0 0 0 0 2.0 °C 0 3 8 20 28 1.5 °C 0 3 7 22 26 Solar heating 5.0 °C 13 22 27 43 69 2.0 °C 13 64 128 207 230 1.5 °C 13 73 152 215 224 Heat pumps 5.0 °C 5 5 5 5 6 2.0 °C 5 11 23 54 74 1.5 °C 5 16 36 63 71 Totala 5.0 °C 62 87 95 117 150 2.0 °C 62 156 250 397 426 1.5 °C 62 171 287 413 401 a Excluding direct electric heating

8 Energy Scenario Results

388

8.14.1.7 OECD Pacific: Transport

Energy demand in the transport sector in OECD Pacific is expected to decrease by

37% in the 5.0 °C Scenario, from around 6200 PJ/year in 2015 to 3900 PJ/year in

2050. In the 2.0 °C Scenario, assumed technical, structural, and behavioural changes

will save 49% (around 1900 PJ/year) by 2050 compared with the 5.0 °C Scenario.

Additional modal shifts, technology switches, and a reduction in the transport

demand will lead to even higher energy savings in the 1.5 °C Scenario of 59% (or

2300 PJ/year) in 2050 compared with the 5.0 °C case (Table 8.98, Fig. 8.104).

By 2030, electricity will provide 20% (200 TWh/year) of the transport sector’s

total energy demand in the 2.0 °C Scenario, whereas in 2050, the share will be 53%

(300 TWh/year). In 2050, up to 480 PJ/year of hydrogen will be used in the trans-

port sector as a complementary renewable option. In the 1.5 °C Scenario, the annual

electricity demand will be 240 TWh in 2050. The 1.5 °C Scenario also assumes a

hydrogen demand of 360 PJ/year by 2050.

Biofuel use is limited in the 2.0 °C Scenario and the 1.5 °C Scenario to a maxi-

mum of approximately 200 PJ/year. Therefore, around 2030, synthetic fuels based

on power-to-liquid will be introduced, with a maximum amount of 270 PJ/year in

2050 in the 2.0 °C Scenario. Due to the lower overall energy demand in transport,

the maximum synthetic fuel demand will amount to 210  PJ/year in the 1.5  °C

Scenario.

biomass
technologies
42%

geothermal
heat
use
0%

solar
collectors
52%

heat
pumps
6%

total 132 billion $

biomass
technologies
16%

geothermal
heat use
12%
solar
collectors
45%

heat
pumps
27%

2.0°C: 2015-2050

5.0°C: 2015-2050

total 533 billion $

biomass
technologies
15%

geothermal
heat use
11%
solar
collectors
48%

heat
pumps
26%

1.5°C: 2015-2050

total 545 billion $

Fig. 8.103 OECD Pacific: development of investments for renewable heat-generation technolo- gies in the scenarios

S. Teske et al.

389

0

1,00 0

2,00 0

3,00 0

4,00 0

5,00 0

6,00 0

7,00 0

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

Efficiency
(compared to
5.0°C)
Hydrogen

Electricity
Synfuels

Biofuels

Natural Gas

Oil products

Fig. 8.104 OECD Pacific: final energy consumption by transport in the scenarios

Table 8.98 OECD Pacific: projection of transport energy demand by mode in the scenarios

in PJ/year Case 2015 2025 2030 2040 2050
Rail 5.0 °C 158 162 163 162 161
2.0 °C 158 154 156 154 159
1.5 °C 158 156 156 162 161
Road 5.0 °C 5515 4317 3902 3365 2614
2.0 °C 5515 3961 2979 1837 1456
1.5 °C 5515 2891 1975 1399 1123
Domestic aviation 5.0 °C 331 524 663 863 922
2.0 °C 331 338 308 242 194
1.5 °C 331 307 240 147 109
Domestic navigation 5.0 °C 173 178 181 186 193
2.0 °C 173 178 181 186 193
1.5 °C 173 178 181 186 193
Total 5.0 °C 6176 5182 4908 4576 3890
2.0 °C 6176 4631 3624 2419 2002
1.5 °C 6176 3533 2551 1893 1586

8 Energy Scenario Results

390

8.14.1.8 OECD Pacific: Development of CO 2 Emissions

In the 5.0  °C Scenario, OECD Pacific’s annual CO 2 emissions will decrease by

21%, from 2080 Mt in 2015 to 1640 Mt in 2050. The stringent mitigation measures

in both alternative scenarios will cause the annual emissions to fall to 280  Mt in

2040  in the 2.0  °C Scenario and to 160 Mt. in the 1.5  °C Scenario, with further

reductions to almost zero by 2050. In the 5.0 °C case, the cumulative CO 2 emissions

from 2015 until 2050 will add up to 67  Gt. In contrast, in the 2.0  °C and 1.5  °C

Scenarios, the cumulative emissions for the period from 2015 until 2050 will be

31 Gt and 26 Gt, respectively.

Therefore, the cumulative CO 2 emissions will decrease by 54% in the 2.0  °C

Scenario and by 61% in the 1.5 °C Scenario compared with the 5.0 °C case. A rapid

reduction in the annual emissions will occur under both alternative scenarios. In the

2.0 °C Scenario, this reduction will be greatest in ‘Power generation’, followed by

‘Transport’ and ‘Industry’ (Fig. 8.105).

0

10

20

30

40

50

60

70

80

0

500

1,000

1,500

2,000

2,500

5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C 5.0°C2.0°C1.5°C
2015 2025 2030 2040 2050

cumulated emissions [Gt]

CO

2
emissions [Mt/yr]

'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C

Fig. 8.105 OECD Pacific: development of CO 2 emissions by sector and cumulative CO 2 emis- sions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)

S. Teske et al.

391

8.14.1.9 OECD Pacific: Primary Energy Consumption

The levels of primary energy consumption in the three scenarios when the assump-

tions discussed above are taken into account are shown in Fig. 8.106. In the 2.0 °C

Scenario, the primary energy demand will decrease by 48%, from around 36,300 PJ/

year in 2015 to 18,900  PJ/year in 2050. Compared with the 5.0  °C Scenario, the

overall primary energy demand will decrease by 45% by 2050 in the 2.0 °C Scenario

(5.0 °C: 34,700 PJ in 2050). In the 1.5 °C Scenario, the primary energy demand will

be even lower (19,900 PJ in 2050) because the final energy demand and conversion

losses will be lower.

Both the 2.0 °C Scenario and 1.5 °C Scenario aim to rapidly phase-out coal and

oil. This will cause renewable energy to have a primary energy share of 33% in 2030

and 88% in 2050  in the 2.0  °C Scenario. In the 1.5  °C Scenario, renewables will

have a primary energy share of more than 89% in 2050 (including non-energy con-

sumption, which will still include fossil fuels). Nuclear energy will be phased-out in

2040 in both the 2.0 °C and 1.5 °C Scenarios. The cumulative primary energy con-

sumption of natural gas in the 5.0 °C case will add up to 230 EJ, the cumulative coal

consumption to about 300 EJ, and the crude oil consumption to 380 EJ. In contrast,

in the 2.0 °C Scenario, the cumulative gas demand will amount to 150 EJ, the cumu-

lative coal demand to 100 EJ, and the cumulative oil demand to 230 EJ. Even lower

0

5,00 0

10,000

15,000

20,000

25,000

30,000

35,000

40,000

5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C
5.0°C2.0°

C
1.5°

C

2015 2025 2030 2040 2050

PJ/yr

net electricity
imports
Efficiency
Ocean energy

Geothermal

Solar

Biomass

Wind

Hydro

Natural gas

Crude oil

Coal

Nuclear

Fig. 8.106 OECD Pacific: projection of total primary energy demand (PED) by energy carrier in the scenarios (including electricity import balance)

8 Energy Scenario Results

392

fossil fuel use will be achieved in the 1.5 °C Scenario: 150 EJ for natural gas, 70 EJ

for coal, and 190 EJ for oil.

8.14.2 OECD Pacific: Power Sector Analysis

South Korea, Japan, Australia, and New Zealand form the OECD Pacific region

(also referred to as OECD Asia Pacific or OECD Asia Oceania). Like Non-OECD

Asia, a regional interconnected power market with regular electricity exchange is

unlikely. Therefore, the region is broken down into seven sub-regions: (1) South

Korea; (2) the north of Japan; (3) the south of Japan; (4) Australia’s National

Electricity Market (NEM) (covering the entire east coast); (5) the SWIS-NT grid

region (comprising Western Australia and the Northern Territory); (6) the North

Island of New Zealand; and (7) the South Island of New Zealand. The sub-regions

have very different electricity policies, power-generation structures, and demand

patterns. In this analysis, simplifications that may not reflect the local conditions are

made to ensure that the results comparable on a global level. Therefore, the results

for specific countries are only estimates.

8.14.2.1 OECD Pacific: Development of Power Plant Capacities

The region has significant potential for all renewables, including the dominant

renewable power technologies of solar PV and onshore wind. Japan has significant

geothermal power resources, and offshore wind potentials are substantial across the

region. There is also potential for ocean energy across the region, although it is cur-

rently a niche technology. Australia has one of the best solar resources in the world,

so concentrated solar power plants will be an important part of both scenarios in

Australia. Coal and nuclear capacities will be phased-out as plants come to the end

of their lifetimes. In the 1.5 °C Scenario, the last coal power plant will be phased out

just after 2030.

The solar PV market will reach 8 GW in 2020 under the 2.0 °C Scenario—the

same level as the actual regional market of 8.3 GW (REN21-GSR 2018 ) in 2017—

and increase rapidly to 43 GW by 2030. The 1.5 °C Scenario requires that solar PV

will achieve an equal market size by 2030 and remain at this level until 2040.

However, the onshore market must increase significantly compared with the mar-

ket in 2017, which was only 0.54 GW (GWEC-NL 2018 ). By 2025, 12  GW of

onshore wind capacity must be installed annually across the region under the 2.0 °C

S. Teske et al.

393

Scenario, and 17 GW under the 1.5 °C Scenario. By 2030, geothermal, concentrated

solar power, and ocean energy must increase by around 2 GW each (Table 8.99).

8.14.2.2 OECD Pacific: Utilization of Power Generation Capacities

The very different developments of variable and dispatch power plants in all sub-

regions reflect the diversity the Pacific region. Table 8.100 shows that because there

is no interconnection between the northern and southern parts of Japan, we assume

that even within Japan, the separate electricity markets of the 50  Hz and 60  Hz

regions will remain as they are. For Australia, it is assumed that the east- and west-

coast electricity markets will have limited interconnection capacities by 2030. The

North and South Islands of New Zealand are calculated to have an increased inter-

connection capacity by 2050.

Table 8.101 shows that for the region as a whole, the limited dispatchable power

plants will retain a relatively high capacity factor, compared with other regions,

until after 2020 and decrease thereafter. The average capacity factors from 2030

onwards will be consistent with all other regions.

Table 8.99 OECD Pacific: average annual change in installed power plant capacity

OECD Pacific power generation: average annual
change of installed capacity [GW/a]

2015–2025 2026–2035 2036–2050
2.0 °C 1.5C°2.0 °C 1.5 °C2.0 °C 1.5 °C
Hard coal − 4 − 9 − 5 − 4 − 1 0
Lignite 0 − 1 − 2 − 2 0 0
Gas 2 − 2 − 1 − 3 − 14 0
Hydrogen-gas 0 1 1 5 12 12
Oil/diesel − 3 − 2 − 2 − 2 − 1 − 1
Nuclear 0 − 5 − 3 − 3 − 2 − 2
Biomass 2 1 1 1 1 1
Hydro 2 1 0 1 0 0
Wind (onshore) 7 18 12 17 7 6
Wind (offshore) 1 4 5 5 2 2
PV (roof top) 17 33 33 33 16 21
PV (utility scale) 6 11 11 11 5 7
Geothermal 0 2 2 2 2 2
Solar thermal power plants 1 2 3 4 2 3
Ocean energy 0 1 2 2 2 2
Renewable fuel based co-generation 1 1 1 2 1 1

8 Energy Scenario Results

394

Table 8.100

OECD Pacific: power system shares by technology group

Power generation structure and interconnection

2.0 °C

1.5 °C

OECD Pacific

Variable RE

Dispatch RE

Dispatch Fossil

Inter-
connection

Variable RE

Dispatch RE

Dispatch Fossil

Inter-
connection

South Korea

2015

4%

34%

61%

5%

2030

40%

18%

43%

20%

46%

17%

38%

0%

2050

70%

28%

2%

25%

62%

24%

14%

0%

Japan – North (50 Hz)

2015

4%

34%

61%

5%

2030

46%

26%

28%

20%

51%

23%

26%

0%

2050

72%

26%

2%

25%

64%

21%

15%

0%

Japan – South (60 Hz)

2015

4%

34%

61%

5%

2030

36%

38%

26%

20%

41%

35%

24%

0%

2050

72%

25%

2%

25%

64%

20%

15%

0%

Australia – East and South (NEM)

2015

5%

34%

61%

5%

2030

17%

82%

0%

20%

17%

83%

0%

10%

2050

73%

26%

2%

25%

67%

21%

12%

20%

Australia West and North (SWIS + NT)

2015

5%

34%

61%

5%

2030

41%

37%

22%

20%

46%

33%

21%

10%

2050

73%

25%

2%

25%

67%

21%

12%

20%

S. Teske et al.

395

New Zealand – North Island

2015

5%

34%

61%

5%

2030

39%

61%

0%

20%

45%

55%

0%

10%

2050

77%

22%

2%

25%

70%

18%

12%

20%

New Zealand – South Island

2015

5%

34%

61%

5%

2030

39%

61%

0%

20%

45%

55%

0%

10%

2050

77%

22%

2%

25%

70%

18%

12%

20%

OECD Pacific

2015

4%

34%

61%

2030

40%

31%

30%

45%

29%

27%

2050

71%

26%

2%

64%

22%

14%

8 Energy Scenario Results

396

8.14.2.3 OECD Pacific: Development of Load, Generation,

and Residual Load

Table 8.102 shows the development of the maximum load, generation, and resulting

residual load in the Pacific region. To verify the calculation results, we compared

the peak demands in Australia and Japan.

The peak load for Australia’s NEM was calculated to be 32.6 GW in 2020, which

corresponds to the reported summer peak of 32.5 GW in the summer of 2017/2018

(AER 2018 ). Japan’s peak demand was 152  GW in 2015 according to the Tokyo

Electric Power Company (TEPCO - 2018 ) and TEPCO predicts that it will be

136 GW in 2020, which is 11% lower.

In the long term, the Pacific region will be a renewable fuel producer for the

export market. Therefore, the calculated increased interconnection capacities indi-

cate overproduction, which will be used for international bunker fuels.

The storage and dispatch requirements for all sub-regions are shown in

Table 8.103. The Pacific region has vast solar and wind resources and will therefore

be one of the production hubs for synthetic fuels and hydrogen, which may be used

for industrial processes, for bunker fuels, or to replace natural gas. Therefore, the

storage and dispatch demand may vary significantly because they depend on the

extent to which renewable fuel production is integrated into the national power sec-

tors or used for dispatch and demand-side management. The more integrated the

fuel production is, the lower the overall requirement for battery or hydro pump stor-

age technologies. Further research is required to develop a dedicated plan to pro-

duce renewable bunker fuels in Australia.

Table 8.101 OECD Pacific: capacity factors by generation type

Utilization of
variable and
dispatchable
power
generation: 2015 2020 2020 2030 2030 2040 2040 2050 2050
OECD Pacific 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C 2.0 °C1.5 °C
Capacity
factor – average

[%/yr]54.8% 55% 55% 29% 29% 29% 29% 34% 31%

Limited
dispatchable:
fossil and
nuclear

[%/yr]65.1% 54% 54% 26% 31% 19% 29% 25% 32%

Limited
dispatchable:
renewable

[%/yr]42.7% 63% 54% 29% 30% 54% 25% 27% 25%

Dispatchable:
fossil

[%/yr]48.6% 48% 50% 20% 23% 35% 21% 19% 26%

Dispatchable:
renewable

[%/yr]43.1% 73% 73% 50% 52% 37% 46% 49% 46%

Variable:
renewable

[%/yr]23.2% 17% 17% 20% 20% 27% 27% 31% 28%

S. Teske et al.

397

Table 8.102

OECD Pacific: load, generation, and residual load development

Power generation structure

2.0 °C

1.5 °C

OECD Pacific

Max demand

Max generation

Max Residual Load

Max interconnection requirements

Max demand

Max generation

Max residual load

Max interconnection requirements

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

[GW]

South Korea

2020

86.8

86.8

1.6

86.8

86.8

1.6

2030

92.3

145.0

6.6

46

94.5

167.5

14.9

58

2050

116.4

298.0

58.3

123

134.7

339.9

62.4

143

Japan – North (50 Hz)

2020

130.6

97.9

35.1

130.6

97.4

36.0

2030

79.1

125.0

4.1

42

81.6

145.4

4.1

60

2050

106.0

252.2

49.4

97

120.1

288.5

33.9

134

Japan – South (60 Hz)

2020

83.6

83.6

3.5

83.7

83.6

3.5

2030

87.5

126.1

4.8

34

90.4

148.0

11.3

46

2050

116.2

287.9

51.2

121

132.6

329.3

38.8

158

Australia – East and South (NEM)

2020

6.7

7.0

1.2

6.7

7.0

1.2

2030

4.3

6.4

3.8

0

4.4

6.6

3.9

0

2050

5.7

12.6

2.6

4

6.4

14.4

2.5

5

Australia West and North (SWIS + NT)

2020

32.6

32.6

1.1

32.6

32.6

1.1

2030

33.9

49.6

1.1

15

34.8

58.3

1.1

22

2050

44.7

111.5

21.3

45

51.4

127.4

22.5

53

New Zealand – North Island

2020

5.5

5.5

3.9

5.5

5.5

3.9

2030

5.0

6.9

0.2

2

5.1

8.2

0.2

3

2050

6.5

15.9

3.0

6

7.5

18.1

2.1

9

New Zealand – South Island

2020

1.3

4.4

0.0

2030

1.5

2.1

0.0

1

1.5

2.4

0.0

1

2050

2.0

4.8

0.9

2

2.2

5.4

0.6

3

8 Energy Scenario Results

398

Table 8.103

OECD Pacific: storage and dispatch service requirements

Storage and dispatch

2.0 °C

1.5 °C

OECD Pacific

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total Storage demand (incl. H2)

Dispatch Hydrogen-
based

Required to avoid curtailment

Utilization battery-through-put-

Utilization PSH-through-put-

Total storage demand (incl. H2)

Dispatch hydrogen-
based

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

[GWh/year]

South Korea

2020

0

0

0

0

70

0

0

0

0

51

2030

13,803

275

1968

2242

241

27,635

400

3197

3596

890

2050

156,658

46,248

8717

54,965

22,747

176,909

45,195

8599

53,793

16,906

Japan – North (50 Hz)

2020

0

0

0

0

85

0

0

0

0

62

2030

25,236

357

2820

3177

200

44,298

418

3819

4238

131

2050

156,580

32,626

6784

39,411

21,744

185,902

32,676

6917

39,594

15,831

Japan – South (60 Hz)

2020

0

0

0

0

121

0

0

0

0

88

2030

21,734

343

2320

2663

303

37,937

439

3490

3929

774

2050

199,561

38,310

8309

46,618

24,062

233,815

38,207

8381

46,588

17,382

Australia – East and South (NEM)

2020

114

0

0

0

15

202

0

0

0

11

2030

850

0

55

55

0

1696

0

86

86

0

2050

9375

1983

457

2440

924

11,304

1981

472

2453

538

Australia West and North (SWIS + NT)

2020

4

0

0

0

0

49

0

0

0

0

2030

11,311

219

1621

1840

87

19,866

255

2289

2544

79

2050

90,062

18,266

4625

22,891

8053

103,839

18,187

4637

22,824

5140

S. Teske et al.

399

New Zealand – North Island

2020

0

0

0

0

4

0

0

0

0

3

2030

1223

20

142

162

0

2165

26

221

247

0

2050

12,361

2316

546

2862

1090

14,474

2304

548

2852

779

New Zealand – South Island

2020

0

0

0

0

0

0

0

0

0

0

2030

374

6

43

49

0

658

8

67

74

0

2050

3733

695

164

859

328

4371

691

164

855

235

OECD Pacific

2020

118

0

0

0

295

251

0

0

0

215

2030

84,079

1246

9157

10,403

831

146,440

1564

13,290

14,855

1874

2050

654,287

140,807

29,623

170,431

81,215

760,962

139,369

29,724

169,093

59,243

8 Energy Scenario Results

400

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AER (2018), Australian Energy Regulator (AER), Seasonal peak demand (NEM), website, viewed in September 2018, https://www.aer.gov.au/wholesale-markets/wholesale-statistics/ seasonal-peak-demand-nem ASEAN-CE (2018) ASEAN Centre for Energy, ASEAN POWER GRID initiative, http://www. aseanenergy.org/programme-area/apg/ (viewed September 2018) ASEAN (2018), The Association of Southeast Asian Nations, or ASEAN, was established on 8 August 1967  in Bangkok, Thailand, with the signing of the ASEAN Declaration (Bangkok Declaration) by the Founding Fathers of ASEAN, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Brunei Darussalam then joined on 7 January 1984, Viet Nam on 28 July 1995, Lao PDR and Myanmar on 23 July 1997, and Cambodia on 30 April 1999, making up what is today the ten Member States of ASEAN. (Source: https://asean.org/asean/ about-asean/) C2ES (2017), INTERCONNECTED: CANADIAN AND U.S. ELECTRICITY, March 2017, Doug Vine, C2ES, Center for Climate and Energy Solutions, 2101 WILSON BLVD.  SUITE 550 ARLINGTON, VA 22201 703-516-4146, https://www.c2es.org/site/assets/uploads/2017/05/ canada-interconnected.pdf - total cumulative current interconnection capacity between Canada and the USA kV4,606 ENTSO-E (2018), Statistical Factsheet 2017, Electricity system data of member TSO countries, https://docstore.entsoe.eu/Documents/Publications/Statistics/Factsheet/entsoe_sfs_2017.pdf EU-EG (2017), Towards a sustainable and integrated Europe, Report of the Commission Expert Group on electricity interconnection targets, November 2017, Page 25, https://ec.europa.eu/ energy/sites/ener/files/documents/report_of_the_commission_expert_group_on_electricity_ interconnection_targets.pdf GWEC (2018), Global Wind Report: Annual Market Update 2017, Global Wind Energy Council, (GWEC), Rue d’Arlon 80, 1040 Brussels, Belgium, http://files.gwec.net/files/GWR2017. pdf?ref=PR GWEC-NL (2018), GWEC 2016, Newsletter  – November 2016, China’s new Five-Year-Plan, http://gwec.net/chinas-new-five-year-energy-plan/ Hohmeyer (2015) A 100% renewable Barbados and lower energy bills: A plan to change Barbados’ power supply to 100% renewables and its possible benefits, January, 2015, Prof. Dr. Olav Hohmeyer, Europa University Flensburg, Discussion Papers, CENTER FOR SUSTAINABLE ENERGY SYSTEMS (CSES/ZNES); System Integration Department, ISSN: 2192–4597(Internet Version), https://www.uni-flensburg.de/fileadmin/content/abteilungen/ industrial/dokumente/downloads/veroeffentlichungen/diskussionsbeitraege/znes-discussions- papers-005-barbados.pdf IDB (2013) Uruguay – Rapid Assessment and Gap Analysis, UNDP, Inter-American Development Bank (IDB), 2013, Sustainable Energy For All, https://www.seforall.org/sites/default/files/ Uruguay_RAGA_EN_Released.pdf IEA RED (2016) – Renewable Policy Updated, Issue 11, 17 November 2016, https://www.iea.org/ media/topics/renewables/repolicyupdate/REDRenewablePolicyUpdateNo11FINAL20161117. pdf IEA P+M DB (2018), International Energy Agency, Policies and Measure Database, viewed September 2018, http://www.iea.org/policiesandmeasures/pams/india/name-168047- en.php?s=dHlwZT1jYyZzdGF0dXM9T2s,&return=PG5hdiBpZD0iYnJlYWRjcnVtYiI-PGE gaHJlZj0iLyI-SG9tZTwvYT4gJnJhcXVvOyA8YSBocmVmPSIvcG9saWNpZXNhbmRtZW- FzdXJlcy8iPlBvbGljaWVzIGFuZCBNZWFzdXJlczwvYT4gJnJhcXVvOyA8YSBocmVmP- SIvcG9saWNpZXNhbmRtZWFzdXJlcy9jbGltYXRlY2hhbmdlLyI-Q2xpbWF0ZSBDaGFu- Z2U8L2E-PC9uYXY IRENA (2014), African Clean Energy Corridor  – internet page viewed September 2018, http:// http://www.irena.org/cleanenergycorridors/Africa-Clean-Energy-Corridor

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PVM-(3-2018), PV-Magazine, Kazahkstan opened a tender for 290  MW solar and 620  MW wind power plants in March 2018, Kazahkstan tenders 290  MW of solar, 19th March 2018, Emilliano Bellini, PV-Magazine, https://www.pv-magazine.com/2018/03/09/ kazahkstan-tenders-290-mw-of-solar/ PR-DoE (2016), Republic of the Philippines, Department of Energy, government website, viewed in September 2018, 2016 Philippines Power Situation Report, https://asean.org/asean/ about-asean/ REN21-GSR (2018). 2018; Renewables 2018 Global Status Report, (Paris: REN21 Secretariat), ISBN 978-3-9818911-3-3, http://www.ren21.net/status-of-renewables/global-status-report/ REW (1-2018), Renewable Energy World, Wind Power Curtailment in China on the Mend, January 26, 2018, Liu Yuanyuan, https://www.renewableenergyworld.com/articles/2018/01/ wind-power-curtailment-in-china-on-the-mend.html RF (2018), Rockefeller Foundation, 24x7 Power is About “Access”, Not “Electrification”, Jaideep Mukherji, 22nd January 2018, https://www.rockefellerfoundation.org/ blog/24x7-power-access-not-electrification/ TEPCO (2018) Tokyo Electric Power Company (TEPCO), companies website, viewed September 2018, https://www4.tepco.co.jp/en/corpinfo/illustrated/power-demand/peak-demand-interna- tional-e.html TYNDP (2016) ENTSO-E, TYNDP 2016 (published) and TYNDP 2018 (in consultation) are pub- lished online only; https://tyndp.entsoe.eu/ WB-DB (2018), World Bank  – Database, World Development Indicators, The per capita elec- tricity demand of Turkey has been 2850 kWh (2014), compared to 6352 kWh in the Euro area, http://databank.worldbank.org/data/reports.aspx?source=2&series=EG.USE.ELEC. KH.PC&country= WPM (3-2018), Wind Power monthly, The first commercial wind farm in Russia opened in 2018, and a project pipeline of more than 1000  MW is reported in Wind Power monthly; Russian partners plan Leningrad wind farm, 27th March 2018, https://www.windpowermonthly.com/ article/1460599/russian-partners-plan-leningrad-wind-farm

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

8 Energy Scenario Results

© The Author(s) 2019 403 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_9

Chapter 9

Trajectories for a Just Transition

of the Fossil Fuel Industry

Sven Teske

Abstract This section provides historical production data for coal, oil and gas

between 1980 and 2015. The 2.0 °C and 1.5 °C scenario lead to specific phase-out

pathways for each of the fossil fuel types. Current regional production volumes are

compared with future demands. The results provide the input for the employment

analysis in the following chapter for the fossil fuel sector. This section discusses the

need to shift the current political debate about coal, oil and gas which is focused on

security of supply and price security towards an open debate about an orderly with-

drawal from coal, oil and gas extraction industries.

The implementation of the 2.0 °C and 1.5 °C climate mitigation pathways presented

here will have a significant impact on the global fossil fuel industry. Although this

may appear to be stating the obvious, current climate debates have not yet involved

open discussion of the orderly withdrawal from the coal, oil, and gas extraction

industries. Instead, the political debate about coal, oil, and gas has focused on the

security of supply and price security. However, mitigating climate change is only

possible when fossil fuels are phased-out. This section provides an overview of the

time-frame of this phase-out under the 2.0 °C and 1.5 °C Scenarios compared with

the 5.0 °C pathway.

9.1 Fossil Energy Resources—The Sky Is the Limit

An unrelenting increase in fossil fuel extraction conflicts with the finite nature of

these resources. At the same time, the global distribution of oil and gas resources

does not match the distribution of demand. Therefore, some countries currently rely

almost entirely on imported fossil fuels. Therefore, is the relative scarcity of fossil

fuels an additional reason an energy transition? The Global Energy Assessment

S. Teske (*) Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au

404

(GEA 2012 ), an integrated assessment of the global energy system, has published a

comprehensive overview of estimated available fossil fuel reserves and resources.

Table 9.1 shows the estimates for conventional and unconventional coal, oil, and gas

reserves and resources. The distinction between reserves and resources is based on

the current technology (exploration and production) and market conditions. The

resource data are not cumulative and do not include reserves (GEA 2012).

The assessment shows that there is no shortage of fossil fuels. There might be a

shortage of conventional oil and gas, but unconventional resources are still signifi-

cantly larger than our climate can cope with. Reducing global fossil fuel consump-

tion for reasons of resource scarcity alone is not essential, even though there may be

substantial price fluctuations and regional or structural shortages, as we have seen

in the past (Teske and Pregger 2015 ).

9.2 Coal—Past Production and Future Trajectories

Under Three Scenarios

Global coal production is dominated by China, which in 2017, produced over 3.5

billion tonnes of coal, 45% of the world volume, followed by India with 716 million

tonnes, the USA with 702 million tons, and Australia with 481 million tons. The top

10 producers, in order of annual production, are China, India, USA, Australia,

Indonesia, the Russian Federation, South Africa, Germany (mainly lignite), Poland,

and Kazakhstan. These countries account for 90% of the global coal production.

Figure 9.1 shows the historical time series for global coal production. The data

are based on the BP Statistical Review 2018 (BP 2018 ), as are the following over-

views of oil and gas. Production volumes have declined in recent years, mainly due

to changes in demand in China, but they rose again in 2017.

Under the 5.0  °C scenario, the required production of thermal coal (excluding

coal for non-energy uses, such as steel production) will remain at the 2015 level,

with an annual increase of around 1% per year until 2050. As shown in Fig. 9.2,

under the 2.0 °C Scenario, coal production will decline sharply between 2020 and

2030 at a rate of around 6% per year. By 2030, the global coal production will be

equal to China’s annual production in 2017, at 3.7 billion tons, whereas that volume

will be reached in 2025 under the 1.5 °C Scenario.

Table 9.1 Fossil reserves, resources, and additional occurrences

Energy carrier

Reserves
[EJ/year]

Resources
[EJ/year]

Demand in 2015
[EJ/year]
Conventional oil 4900–7610 4170–6150 41.9
Unconventional oil 3750–5600 11,280–14,800
Conventional gas 5000–7100 7200–8900 33.8
Unconventional gas 20,100–67,100 40,200–121,900
Coal 17,300–21,000 291,000–435,000 16.5

S. Teske

405

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0
1981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920192011201220132014201520162017
OECD North America Latin America OECD Europe Eurasia Middle East
Africa Other Asia India China OECD Pacific

Fig. 9.1 Global coal production in 1981–2017 (BP 2018—Statistical Review)

0

2,000

4,000

6,000

8,000

10,000

12,000

201520162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050
Global - 5.0C Global 2.0C Global 1.5C

Fig. 9.2 Global coal production until 2050 under the three scenarios

9 Trajectories for a Just Transition of the Fossil Fuel Industry

406

9.3 Oil—Past Production and Future Trajectories

Under the Three Scenarios

Oil production almost doubled between 1965 and 1975. After the early 1990s, it

grew almost constantly and by 2017, the production volume was about three times

higher than in 1965 and twice as high as in 1985 (Fig. 9.3). Unlike coal, there is no

sign of a decline in oil production in response to reduced demand. Oil production is

more widely distributed than coal production. Three countries, the USA, Russia,

and Saudi Arabia, have global market shares of around 12–14% each, whereas four

countries, Canada, Iran, Iraq, and China, produce around 5% each. The other oil-

producing countries have significantly lower market shares.

Figure 9.4 shows the global oil production levels required by the three calculated

scenarios. Oil for non-energy uses, such as the petrochemical industry, is not

included in this graph. Again, oil production in the 5.0  °C Scenario will grow

steadily by 1% until the end of the modelling period in 2050. Under the 2.0  °C

Scenario, oil production will decline by 5% per year until 2030 and by 3% annually

until 2025. After 2030, production will decline by around 7% per year, on average,

until oil production for energy is phased-out entirely. The oil production capacity of

the USA, Saudi Arabia, and Russia in 2017 would be sufficient to supply the global

demand calculated for the 2.0 °C Scenario in 2035. The 1.5 °C Scenario will cut the

required production volumes in half by 2030, reducing them further to the equivalent

of the production volume of just one of the three largest oil producers (the USA,

Saudi Arabia, or Russia) by 2040.

0

10,00 0

20,00 0

30,00 0

40,00 0

50,00 0

60,00 0

70,00 0

80,00 0

90,00 0

100,000

19651967196919711973197519771979198119831985198719

89
199119931995199

7
19992001200320052007200

9
201120132015201

7

OECD North America
Africa Other Asia

OECD Europe
IndiaChina

EurasiaMiddle East
OECD Pacific

Latin America

Fig. 9.3 Global oil production in 1965–2017 (BP 2018—Statistical Review)

S. Teske

407

9.4 Gas—Past Production and Future Trajectories

Under the Three Scenarios

Gas production has grown steadily over the past four decades, leading to an overall

production of 3500 billion cubic meters—3.5 times higher than in 1970. The pro-

duction of natural gas is even more widely distributed than oil production. According

to 2017 figures, by far the largest producers are the USA, with 20% of the global

volume, and Russia with 17%. Four countries have market share of around 5% each:

Canada (4.8%), Iran (6.1%), Qatar (4.8%), and China (4.1%). The remaining 43%

of global gas production is distributed over 42 countries (Fig. 9.5).

In the 5.0 °C Scenario, gas production will increase steadily by 2% a year for the

next two decades, leading to an overall production increase of about 50% by 2050.

Compared with coal and oil, the gas phase-out will be significantly slower in the

2.0 °C and 1.5 °C Scenarios. Furthermore, these scenarios assume that infrastruc-

ture, such as gas pipelines and power plants, will be used after this phase-out for

hydrogen and/or renewable methane produced with electricity from renewable

sources (see Chap. 5, Sect 5.2). Under the 2.0 °C Scenario, gas production will only

decrease by 0.2% per year until 2025, then by 1% until 2030, and on average by 4%

annually until 2040. This represents a rather slow phase-out and will allow the gas

industry to gradually transfer to hydrogen. The phase-out under the 1.5 °C Scenario

will be equally slow, and a 4%/year reduction will occur after 2025 (Fig. 9.6).

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

201520162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050
OIL Global 5.0C 5.0 C OIL Global 2.0C 2.0 C OIL Global 1.5C 1.5 C

Fig. 9.4 Global oil production until 2050 under the three scenarios

9 Trajectories for a Just Transition of the Fossil Fuel Industry

408

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

197019721974197619781980198219841986198819901992199419961998200020022004200620082010201220142016
OECD North America Latin America OECD Europe Eurasia Middle East
Africa Other Asia India China OECD Pacific

Fig. 9.5 Global gas production in 1970–2017 (BP 2018—Statistical Review)

0

1,000

2,000

3,000

4,000

5,000

6,000

201520162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050
Global 5.0C Global 2.0C Global 1.5C

Fig. 9.6 Global gas production until 2050 under the three scenarios

S. Teske

409

9.5 Overview: Required Fossil Fuel Resources

Under the 5.0 °C, 2.0 °C, and 1.5 °C Trajectories

In summary, the global fossil fuel extraction industry must reduce production at a

rate of 2% per annum under the 2.0 °C Scenario and 3% per annum under the 1.5 °C

Scenario. A constant reduction in production seems unlikely if no international

measures are taken to organize the economic and social transitions in the producing

countries, and for the communities and workers involved. The idea of a ‘just transi-

tion’ is well documented in the international literature. According to the International

Labour Organization (ILO 2015 ), the concept was first mentioned in the 1990s,

when North American unions began developing the concept of just transition.

Initially, trade unionists understood ‘just transition’ to be a program of support

for workers who lost their jobs due to environmental protection policies. Since then,

several UNFCCC Climate Conferences have referred to the ‘just transition’ con-

cept. The Paris Climate Agreement 2015, during the 21st session of the Conference

of the Parties (COP 21) “ decided to continue and improve the forum on the impact

of the implementation of response measures (hereinafter referred to as the improved

forum), and adopted the work programme, comprising two areas: (1) economic

diversification and transformation; and (2) just transition of the workforce, and the

creation of decent work and quality jobs ” (UNFCCC-JT 2016 ).

Table 9.2 provides possible trajectories for global coal, oil, and gas production,

consistent with the Paris Agreement targets. These trajectories are the results of the

2.0 °C and 1.5 °C Scenarios, documented in detail over the previous six chapters of

this book. Chapter 10 uses these trajectories to calculate possible employment

effects, both in terms of job losses in the fossil fuel industry, job gains in the renew-

able energy industry, and options for transitioning the gas industry towards a renew-

ably produced hydrogen industry.

9 Trajectories for a Just Transition of the Fossil Fuel Industry

410

Table 9.2 Summary—coal, oil, and gas trajectories for a just transition under the 5.0 °C, 2.0 °C, and 1.5 °C Scenarios

2015 2020 2025 2030 2035 2040 2045 2050
5.0 °C: Primary energy [PJ/a]
Coal 140,895 147,324 153,529 167,795 179,666 191,078 196,692 200,680
Lignite 19,835 18,836 18,550 18,573 18,597 19,028 19,546 19,562
Natural gas 123,673 133,732 145,075 162,132 178,213 194,467 207,273 214,702
Crude oil 166,465 173,082 181,520 190,294 197,300 204,563 208,561 210,970
5.0 °C production units
Coal [million tons per
year]

5871 6138 6397 6991 7486 7962 8196 8362

Lignite [million tons
per year]

2088 1983 1953 1955 1958 2003 2058 2059

Natural gas [billion
cubic meters]

3171 3429 3720 4157 4570 4986 5315 5505

Oil [thousand barrels
per day]

94,836 98,606 103,414 108,412 112,403 116,541 118,819 120,191

2.0 °C—primary energy [PJ/a]
Coal 140,624 136,111 114,647 77,766 45,445 25,594 12,480 7568
Lignite 19,835 16,779 8333 3203 1630 912 288 0
Natural gas 123,770 132,209 130,797 126,054 105,321 78,390 42,535 9949
Crude oil 166,472 164,438 141,523 109,213 71,812 45,013 26,649 15,461
2.0 °C—production units
Coal [million tons per
year]

5859 5671 4777 3240 1894 1066 520 315

Lignite [million tons
per year]

2088 1766 877 337 172 96 30 0

Natural gas [billion
cubic meters]

3174 3390 3354 3232 2701 2010 1091 255

Oil [thousand barrels
per day]

94,840 93,682 80,627 62,219 40,912 25,644 15,182 8808

1.5 °C— primary energy [PJ/a]
Coal 141,275 125,431 84,267 41,360 14,243 9134 9363 9759
Lignite 19,835 16,956 5006 2056 777 0 0 0
Natural gas 123,426 132,241 125,494 104,349 80,940 50,883 23,202 7315
Crude oil 166,472 163,957 114,986 68,449 36,541 22,923 16,772 14,794
1.5 °C production units
Coal [million tons per
year]

5886 5226 3511 1723 593 381 390 407

Lignite [million tons
per year]

2088 1785 527 216 82 0 0 0

Natural gas [billion
cubic meters]

3165 3391 3218 2676 2075 1305 595 188

Oil [thousand barrels
per day]

94,840 93,408 65,509 38,996 20,818 13,059 9555 8428

S. Teske

411

References

BP (2018), British Petrol, Statistical review, website with statistical data for download, down- loaded in September 2018, https://www.bp.com/en/global/corporate/energy-economics/statis- tical-review-of-world-energy/downloads.html GEA (2012): Global Energy Assessment  - Toward a Sustainable Future, Cambridge University Press, Cambridge, UK and New  York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria; http://www.globalenergyassessment.org ILO (2015), International Labour Organization, Just Transition  – A report for the OECD, May 2017, Just Transition Centre, http://www.ituc-csi.org/just-transition-centre Teske, Pregger (2015), Teske, S, Pregger, T., Naegler, T., Simon, S., Energy [R]evolution  - A sustainable World Energy Outlook 2015, Greenpeace International with the German Aerospace Centre (DLR), Institute of Engineering Thermodynamics, System Analysis and Technology Assessment, Stuttgart, Germany https://www.scribd.com/document/333565532/ Energy-Revolution-2015-Full UNFCCC-JT (2016) Just Transition of the Workforce, and the Creation of Decent Work and Quality Jobs – Technical paper, United Nations – Framework Convention on Climate Change (UNFCCC) 20 https://unfccc.int/sites/default/files/resource/Just%20transition.pdf

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

9 Trajectories for a Just Transition of the Fossil Fuel Industry

© The Author(s) 2019 413 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_10

Chapter 10

Just Transition: Employment Projections

for the 2.0 °C and 1.5 °C Scenarios

Elsa Dominish, Chris Briggs, Sven Teske, and Franziska Mey

Abstract This section provides the input data for two different employment devel-

opment calculation methods: The quantitative analysis, which looks into the overall

number of jobs in renewable and fossil fuel industries and the occupational analysis

which looks into specific job categories required for the solar and wind sector as

well as the oil, gas, and coal industry. Results are given with various figures and

tables.

10.1 Introduction: Employment Modelling for a Just

Transition

The transition to a 100% renewable energy system is not just a technical task. It is

also a socially and economically challenging process, and it is imperative that the

transition is managed in a fair and equitable way. One of the key concerns is the

employment of workers in the affected industries (UNFCCC 2016 ; ILO 2015 ).

However, it should be noted that the ‘just transition’ concept is concerned not only

with workers’ rights, but also with the well-being of the broader community (Smith

2017 ; Jenkins et al. 2016 ; Sovacool and Dworkin 2014 ). This includes, for example,

community participation in decision-making processes, public dialogue, and policy

mechanisms to create an enabling environment for new industries, to ensure local

economic development.

Although it is acknowledged that a just transition is important, there are limited

data on the impacts that the transition will have on employment. There is even less

information on the types of occupations that will be affected by the transition, either

by project growth or a decline in employment. This study provides projections for

jobs in construction, manufacturing, operations and maintenance (O&M), and fuel

and heat supply across 12 technologies and 10 world regions, based on the energy

E. Dominish (*) · C. Briggs · S. Teske · F. Mey Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: elsa.dominish@uts.edu.au; chris.briggs@uts.edu.au; sven.teske@uts.edu.au; franziska.mey@uts.edu.au

414

scenario from the Leonardo Di Caprio project (see Chap. 3 ff. This study is funded

by the German Greenpeace Foundation and builds on the methodology developed

by UTS/ISF (Rutovitz et al. 2015 ), with an updated framework that disaggregates

jobs by specific occupations. Projected employment is calculated regionally, but in

this chapter, we present an overview of the global data, which are an aggregate of the

results for the 10 world regions. Further details, including a further regional break-

down of employment data, are provided in the full report (Dominish et al. 2018 ).

10.2 Quantitative Employment Modelling

This section discusses the calculation factors used for the quantitative employment

modelling (an overview of the methodology is given in Sect. 3.6 of Chap. 3). The

factors were analysed on a regional basis where possible, to take into account the

significant economic differences between world regions. The results are then pre-

sented in the following section.

10.2.1 Employment Factors

Employment factors were used to calculate the number of jobs required per unit of

electrical or heating capacity, or per unit of fuel. The employment factors differ

depending on whether they involve manufacturing, construction, operation and

maintenance, or fuel supply. Information about these factors usually comes from

OECD countries because that is where most data are collected, although local data

were used wherever possible. For job calculations in non-OECD regions, regional

adjustments were made when a local factor was not available (see Sect. 10.2.2). The

employment factors used in the calculations are shown in Table 10.1.

The employment factors were based on coal supplies, because employment per

tonne varies significantly across the world regions and because coal plays a signifi-

cant role in energy production in many countries. In Australia and the USA, coal is

extracted at an average rate of more than 9000 tonnes per person per year, whereas

in Europe, the average coal miner is responsible for less than 1000 tonnes per year.

China has relatively low per capita productivity at present, with 650  tonnes per

worker per year, but the annual increases in productivity are very high. India and

Eurasia have significantly increased their productivity since a similar analysis was

performed in 2015. Local data were also used for gas extraction in every region

except India, the Middle East, and Non-OECD-Asia. The calculation of coal and

gas employment per petajoule (PJ) drew on data from national statistics and com-

pany reports, combined with production figures from the BP Statistical Review of

World Energy 2018 (BP-SR 2018) or other sources. Data were collected for as

many major coal-producing countries as possible, and coverage was obtained for

90% of the world coal production (Table 10.2).

E. Dominish et al.

415

Table 10.1 Summary of employment factors used in a global analysis in 2012

Construction/installation Manufacturing

Operations &
maintenance

Fuel –
PRIMARY
energy demand
Job years/ MW Job years/
MW

Jobs/MW

Coal 11.4 5.1 0.14 Regional
Gas 1.8 2.9 0.14 Regional
Nuclear 11.8 1.3 0.6 0.001 jobs per
GWh final
energy demand
Biomass 14.0 2.9 1.5 29.9 jobs/PJ
Hydro-large 7.5 3.9 0.2
Hydro-small 15.8 11.1 4.9
Wind onshore 3.0 3.4 0.3
Wind offshore 6.5 13.6 0.15
PV 13.0 6.7 0.7
Geothermal 6.8 3.9 0.4
Solar thermal 8.9 4.0 0.7
Ocean 10.3 10.3 0.6
Geothermal – heat 6.9 jobs/ MW (construction and manufacturing)
Solar – heat 8.4 jobs/ MW (construction and manufacturing)
Nuclear
decommissioning

0.95 jobs per MW decommissioned

Combined heat
and power

CHP technologies use the factor for the technology, i.e. coal, gas, biomass,
geothermal, etc., increased by a factor of 1.5 for O&M only.

Note: For details of sources and derivation of factors see Dominish et al. ( 2018 )

Table 10.2 Employment factors used for coal fuel supply (mining and associated jobs)

Employment factor
Jobs per PJ Tonnes per person per year (coal equivalent)
World average 36.2 943
OECD North America 3.5 9613
OECD Europe 36.2 942
OECD Asia-Oceania 3.6 9455
India 33.6 1016
China 52.9 645
Africa 13.7 2482
Eastern Europe/Eurasia 36.0 948
Developing Asia 6.5 5273
Latin America 12.5 2725
Middle East Used world average because no employment data were available

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

416

10.2.2 Regional Adjustments

The employment factors used in this model for energy technologies other than coal

mining were usually for OECD regions, which are typically wealthier than other

regions. A regional multiplier was applied to make the jobs per MW more realistic

for other parts of the world. In developing countries, there are generally more jobs

per unit of electricity because those countries have more labour-intensive practices.

The multipliers change over the study period, consistent with the projections for

GDP per worker. This reflects the fact that as prosperity increases, labour intensity

tends to fall. The multipliers are shown in Table 10.3.

10.2.2.1 Local Employment Factors

Local employment factors were used where possible. These region-specific factors

were:

• OECD Americas— gas and coal fuel, photovoltaics (PV) and offshore wind (all

factors), and solar thermal power (construction and operation and maintenance
(O&M)

• OECD Europe— gas and coal fuel, offshore wind (all factors), solar thermal

power (construction and O&M), and solar heating

• OECD Pacific— gas and coal fuel
• Africa —gas, coal, and biomass fuel
• China— gas and coal fuel, and solar heating
• Eastern Europe/Eurasia— gas and coal fuel
• Developing Asia —coal fuel
• India  – coal fuel and solar heating
• Latin America— coal and biomass fuels, onshore wind (all factors), nuclear (con-

struction and O&M), large hydro (O&M), and small hydro (construction and
O&M).

Table 10.3 Regional multipliers used for the quantitative calculation of employment

2015 2020 2030 2040 2050
OECD (North America, Europe, Pacific) 1.0 1.0 1.0 1.0 1.0
Latin America 3.4 3.4 3.4 3.1 2.9
Africa 5.7 5.7 5.6 5.2 4.9
Middle East 1.4 1.5 1.5 1.4 1.3
Eastern Europe/Eurasia 2.4 2.4 2.2 2.0 1.8
India 7.0 5.6 3.7 2.7 2.0
Developing Asia 6.1 5.3 4.2 3.5 2.9
China 2.6 2.2 1.6 1.3 1.1

Source: Derived from ILO ( 2012 ) Key Indicators of the Labour Market, eighth edition software, with growth in GDP per capita derived from IEA World Energy Outlook 2018 and World Bank data

E. Dominish et al.

417

10.2.2.2 Local Manufacturing and Fuel Production

Some regions do not manufacture the equipment (e.g., wind turbines or solar PV

panels) required for the introduction of renewable technologies. This model includes

estimates of the percentages of renewable technology that are made locally and

assumes that the percentage of local manufacturing will increase over time as the

industry matures. Based on this, the jobs involving the manufacture of components

for export were calculated for the region in which the manufacturing occurs. The

same applies to coal and gas, because they are traded internationally, so the jobs in

fuel supply were calculated regionally, based on historical data.

10.2.2.3 Learning Adjustments or ‘Decline Factors’

Learning adjustments are used to account for the projected reductions in the costs of

renewables over time, as technologies and companies become more efficient and

production processes are scaled up. Generally, jobs per MW are projected to fall in

parallel with this trend. The cost projections for each of the calculated energy sce-

nario regions (see Sect. 5.3 of Chap. 5) were used to derive these factors.

10.2.3 Results of Quantitative Employment Modelling

The 2.0 °C and 1.5 °C Scenarios will result in an increase in energy-sector jobs in

the world as a whole at every stage of the projection. The 1.5  °C Scenario will

increase the renewable energy capacities faster, so employment will increase faster

than in the 2.0 °C Scenario. By 2050, employment in te energy sector will be within

the same range in both scenarios, at around 48–50 million jobs.

• In 2025, there will be 29.6 million energy-sector jobs in the 5.0  °C Scenario,

42.3 million in the 2.0 °C Scenario, and 48.1 million in the 1.5 °C Scenario.

• In 2030, there will be 30.3 million energy-sector jobs in the 5.0  °C Scenario,

49.2 million in the 2.0 °C Scenario, and 53.8 million in the 1.5 °C Scenario.

• In 2050, there will be 29.6 million energy-sector jobs in the 5.0  °C Scenario,

50.4 million in the 2.0 °C Scenario, and 47.8 million in the 1.5 °C Scenario.

Figure 10.1 shows the changes in job numbers under the 5.0  °C, 2.0  °C, and

1.5 °C Scenarios for each technology between 2015 and 2030. the 5.0 °C Scenario,

jobs will drop to 4% below the 2015 levels by 2020 and then remain quite stable

until 2030. Strong growth in renewable energy will lead to an increase of 44% in the

total energy-sector jobs in the 2.0 °C Scenario and 66% in the 1.5 °C Scenario by

2025. In the 2.0 °C (1.5 °C) Scenario the renewable energy sector will account for

81% (86%) in 2025 and 87% (89%) in 2030, with PV having the greatest share of

24% (26%), followed by biomass, wind, and solar heating.

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

418

0.

0
10

.0
20

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2020

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Fig. 10.1

World employment in the energy sector under the 5.0 °C and 2.0 °C Scenarios (

left

) and the 5.0 °C and 1.5 °C Scenarios (

right

)

E. Dominish et al.

419

10.3 Occupational Employment Modelling

To plan for a just transition, it is important to understand the occupations and loca-

tions at which jobs are likely to be lost or created. The modelling of employment by

type of occupation is based on a new framework developed by UTS/ISF and financed

by the German Greenpeace Foundation. The framework is applied to the results of

the employment modelling discussed in Sect. 10.2. This information can be used to

attempt to understand where labour is likely to be required in the renewable energy

transition, and where job losses are likely to occur.

10.3.1 Background: Development of Occupational

Employment Modelling

The occupational employment modelling framework used in this study was devel-

oped for renewable energy (solar PV, onshore wind, offshore wind) and fossil fuels

(coal and gas). The three primary studies that classified and measured the occupa-

tional composition of renewable energy industries were conducted by the

International Renewable Energy Agency (IRENA). Through surveys of around 45

industry participants across a range of developed and developing nations, IRENA

estimated the percentages of person–days for the various occupations across the

solar PV and onshore and offshore wind farm supply chains (IRENA 2017a, b,

2018 ). Figure 10.2 is an example (in this case, for solar PV manufacturing).

IRENA’s studies are the most detailed estimates of the occupational composi-

tions of the solar PV and onshore wind industries to date. ISF has extended the

application of IRENA’s work in two key ways:

1. Mapping IRENA’s job categories against the International Standard

Classification of Occupations (ISCO): IRENA uses its own occupational clas-
sification system, which does not match the ISCO, which is the basis for national
statistical agency data. For example, ‘regulation and standardization experts’ is
not a category in the ISCO. Consequently, the IRENA job categories have been
mapped and translated across to the ISCO to facilitate comparisons between
renewable energy technologies and fossil fuel sectors. The best fit for each of the
occupations in the IRENA studies has been identified at one-digit, two-digit,
three-digit, and four-digit levels of the ISCO.

2. Unpacking mid- and low-skill job categories in IRENA’s study : Some of the

categories in the IRENA studies containe jobs that are of interest from a just
transition perspective. Specifically, IRENA combines:

• ‘Factory workers’ for solar PV and onshore and offshore wind manufacturing
• ‘Ship crews’ for offshore wind construction and operation and maintenance
• ‘Construction workers’ for solar PV, onshore wind farm construction, and operation and maintenance.

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

420

These categories combine a range of technicians, trades, machinery operators,

drivers, and assemblers, and labourers.

For factory workers, industry data from the Benchmarks of Global Clean Energy

Manufacturing study by the Clean Energy Manufacturing Analysis Centre was

combined with the occupational framework from the Australian census. The inter-

national standard classification of industries used for the Benchmarks of Global

Clean Energy Manufacturing study were translated across to the Australian New

Zealand Standard Classification of Industry framework (which is based on the inter-

national classification standard) (Australian Bureau of Statistics & Statistics New

Zealand 2013 ).

Data from the Australian census on the occupational composition of these manu-

facturing sectors were then used to derive the breakdown of employment (Australian

Bureau of Statistics 2017 ). The census includes a comprehensive stocktake of

employment, with data at one-, two-, three- and four-digit levels for each industry.

The Australian–New Zealand Standard Classification of Occupations is based on

the ISCO. Clean Energy Manufacturing Analysis data on the relative share of the

value added by component was used to weight the shares of employment. The

Australian manufacturing sectors used are not wind or solar PV manufacturing

activities, but as the Clean Energy Manufacturing Analysis Centre notes, ‘Large

portions of the wind energy supply chain connect well to core manufacturing indus-

Health and Safety
4%
Quality control
4%
Regulation and
standardisation experts
4%

Factory workers and
technicians
62%

Engineers
12%

Admin & Management 5%

Marketing and sales
5%

Logistics
4%

Fig. 10.2 Distribution of human resources required to manufacture the main components of a 50 MW solar photovoltaic power plant. (IRENA 2017a)

E. Dominish et al.

421

tries’ (CEMAC 2017 ). To clarify construction worker categories for onshore wind

and PV construction, and for the operation and maintenance of solar PV, interviews

were conducted with project managers who were currently overseeing or had

recently completed construction projects (Table 10.4).

The IRENA studies are the richest data source on employment in solar PV and

onshore wind projects, but further work is required to directly match renewable

energy job levels against existing fossil fuel sectors and to generate data on mid- and

low-skill jobs, which are of primary interest from a just transition perspective.

As an example, Table 10.5 shows the occupational hierarchy for solar PV con-

struction and how it is matched against ISCO. ISCO classifies occupations from a

one-digit level (left) to a four-digit level (right). Each level is more detailed than the

previous one in terms of the labour force required for the type of work. This meth-

odology has been transferred to an occupational hierarchy that has been constructed

for solar PV, onshore wind, and offshore wind using IRENA and other data sources

to map jobs against the ISCO. The result is a matrix with percentages allocated to

each occupation at the one-, two-, three-, and four-digit levels of aggregation.

The framework for fossil fuels was derived from labour statistics from the

Australian 2016 national census for coal mining, gas supply, and coal and gas gen-

eration (Australian Bureau of Statistics 2017 ). Although these data are specific to

Australia, these statistics provide the best source of data, and regional multipliers in

the quantitative modelling can adjust the results to account for economic differences

between regions.

Table 10.4 Wind and solar PV manufacturing–study methodology

TechnologyComponent

I-O Industry
Category Equivalent ANZSIC Classification
Wind Nascelles Machinery &
equipment
manufacturing, not
elsewhere classified

22 fabricated metal product manufacturing
222 structural metal product manufacturing
2221 structural steel fabricating

Blades Manufacturing not
elsewhere classified

22 fabricated metal product manufacturing
222 structural metal product manufacturing
2221 structural steel fabricating
Towers Fabricated metal
product
manufacturing

22 fabricated metal product manufacturing
222 structural metal product manufacturing
2221 structural steel fabricating
Steel Basic metal
manufacturing

21 primary metal and metal product
manufacturing
211 basic ferrous metal manufacturing
2110 Iron Smelting & Steel Manufacturing
Generators Electrical
machinery &
apparatus
manufacturing,

24 Machinery & Equipment Manufacturing
243 electrical equipment manufacturing
2439 other electrical equipment manufacturing

Solar PV Modules Computers,
electronic and
optical equipment
manufacturing

24 Machinery & Equipment Manufacturing
243 electrical equipment manufacturing
2439 other electrical equipment manufacturing

Cells
Wafers

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

422

Table 10.5

Occupational hierarchy, solar PV construction

ILO 1-digit

ILO 2-digit

ILO 3-digit

ILO 4-digit

1 MANAGERS

1.7%

Production and specialised service MANAGERS (13)

1.7%

Manufacturing, mining, construction and distribution MANAGERS (132)

1.7%

Supply, distribution and related MANAGERS (1324)

1.7%

2 PROFESSIONALS

12.0%

Science & engineering PROFESSIONALS (21)

4.4%

Physical, life & earth science PROFESSIONALS (211)

0.1%

Geotechnical experts (2114)

0.1%

Life science PROFESSIONALS (213)

2.1%

Environmental protection professionals (2133)

2.1%

Engineering PROFESSIONALS (214)

1.0%

Mechanical engineers (2144)

0.8%

Health professionals (22)

4.4%

Electrotechnology engineers (215)

0.8%

Civil engineers (2142)

0.3%

Other health professionals (226)

4.4%

Electrical engineers (2151)

0.8%

Environmental and occupational health and hygiene professionals (2263)

4.4%

Business & administrative PROFESSIONALS (24)

3.4%

Finance PROFESSIONALS (241)

2.6%

Financial analysts (2413)

1.9%

Administration PROFESSIONALS (242)

0.8%

Accountants (2411)

0.7%

Policy administration professionals (2422)

0.8%

3 TECHNICIANS and Associate professionals

27.8%

Business and administration Associate professionals (33)

0.6%

Business services agents (333)

0.7%

Real estate agents and property managers (3334)

0.7%

Science and engineering TECHNICIANS and supervisors (31)

24.9%

Physical and science engineering TECHNICIANS (311)

11.9%

Civil engineering technicians (3112)

7.0%

E. Dominish et al.

423

ILO 1-digit

ILO 2-digit

ILO 3-digit

ILO 4-digit

Information & communications technicians (35)

2.1%

Electrical engineering technicians (3113)

4.9%

Mining, manufacturing & construction supervisors (312)

13.0%

Construction supervisors (3123)

13.0%

ICT Operations & Support Technicians (351)

2.1%

ICT operations technicians (3511)

2.1%

4 Clerical Support workers

0.3%

Numerical and material recording clerks (43)

0.3%

Numerical clerks (431)

0.3%

Accounting and bookkeeping clerks (4311)

0.2%

Payroll clerks (4313)

0.2%

7 Craft and related TRADES workers

31.6%

Electrical and electronics TRADES workers (74)

31.6%

Electrical equipment installers and repairers (741)

31.6%

Building Frame & Finisher Trades (711 & 712)

9.9%

Sheet & Structural Metal Workers (721)

7.9%

Electricial equipment installers (741)

13.8%

8 Plant and machine operators and assemblers

22.0%

Assemblers 821

9.8%

Assemblers 821

9.8%

Mechanical machinery assemblers (8211)

4.2%

Drivers and mobile plant OPERATORS (83)

12.1%

Heavy truck and bus drivers (833)

4.3%

Electrical assemblers (8212)

5.6%

Truck and lorry drivers (8332)

4.3%

Mobile plant OPERATORS (834)

7.8%

Earthmoving plant operators (8342)

3.5%

Crane, hoist and related plant operators (8343)

4.3%

9 Elementary occupations

4.3%

Labourers in mining, construction, manufacturing and transport (93)

4.3%

Mining & construction labourers (931)

4.3%

Civil engineering Labourers (9312)

4.3%

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

424

For each modelling run, the results for the installed capacity of the renewable

energy technologies (MW) and full-time-equivalent jobs (FTE/MW) were used to

generate an aggregate level of employment for construction, manufacturing, and

operation and maintenance. The matrix was then used to calculate the number of

jobs at different levels of disaggregation (Sect. 3.6.1.)

The final framework is shown in Table 10.4. This is based on a composite profile

for each technology using a mix of one-, two-, three-, and four-digit levels of occu-

pation, depending on which best illustrates the breakdown of jobs and allows com-

parison to be made across technologies. Choices have been made based on the

proportion of jobs and the labels that are most readily understandable to readers

(noting that the categories used in the ISCO do not always correspond to popularly

used titles). Note, for example, that ‘Managers’ is a one-digit category, but trades

are broken down into construction trades, metal trades, and electricians because this

provides more meaningful descriptions.

In the example shown in Table 10.6, it is notable that wind and solar farms

employ higher proportions of professionals and technicians for their operations and

maintenance than coal mining, and similar or higher proportions of elementary

occupations, but much lower proportions of machinery operators and drivers.

10.3.1.1 Methodology and Limitations

• At the aggregate level, it is assumed that rising labour productivity over time will

reduce the labour intensity (i.e., less FTE/MW) that is applied in the construc-
tion, manufacture, and operation and maintenance of each renewable energy
technology. No assumptions have been made about changes to the relative labour
intensity between occupations. Over time, we would expect that the proportion
of less-skilled jobs would fall as a result of mechanization. Therefore, the share
of less-skilled jobs is likely to be overestimated.

• IRENA estimates a single global figure for each occupation, averaged from sur-

veys of industry participants across different global markets. In practice, there
are variations in labour intensity and the compositions of jobs across supply
chains between different regions (broadly speaking, supply chains in lower-wage
nations are more labour intensive). ISF takes account of regional conditions in
the job factors applied at the level of major sub-sectors (construction, manufac-
turing, operation and maintenance), but not at the disaggregated level. Therefore,
it is likely that the proportion of less-skilled jobs is overestimated for rich econo-
mies and underestimated for less-developed economies.

• The breakdown of the category of ‘construction workers’ is based on interviews

with some Australian solar and wind project managers. The project managers
had overseen recent projects and provided detailed estimates of the contributions
of different jobs. Nonetheless, the breakdowns are based on a limited sample,
and further research is required to generate more-accurate estimates.

E. Dominish et al.

Table 10.6

Occupational compositions for renewable and fossil fuel technologies

Solar PV

Onshore Wind

Offshore wind

Fossil fuels

ISCO category

Name

Construction

Manufacturing

O&M

Construction

Manufacturing

O&M

Construction

Manufacturing

O&M

Coal mining

Gas supply

Coal and gas generation

1

Managers

1.0%

4.2%

6.3%

1.7%

7.6%

1.5%

2.2%

4.6%

3.0%

6.7%

16.8%

13.0%

2

Other professionals (Legal, finance, scientific)

5.0%

12.7%

4.4%

10.6%

11.3%

11.6%

7.0%

26.0%

15.8%

10.0%

14.7%

6.7%

2

Engineers (Industrial, electrical & civil)

3.8%

14.3%

14.7%

1.8%

8.7%

27.0%

6.2%

6.3%

7.7%

0.0%

5.6%

8.5%

3

Technicians & associate professionals

7.2%

6.3%

26.2%

27.8%

6.5%

46.9%

0.1%

3.5%

25.1%

7.5%

10.5%

22.5%

4

Clerical support workers

3.3%

4.9%

1.3%

0.3%

4.6%

4.7%

0.2%

9.2%

8.4%

5.1%

18.8%

12.1%

7

Construction trades

0.8%

0.0%

0.0%

9.9%

2.5%

0.0%

0.0%

2.0%

5.5%

0.0%

13.9%

1.6%

7

Metal trades

1.8%

7.9%

0.0%

7.9%

28.4%

0.0%

0.0%

23.3%

0.0%

16.3%

0.0%

12.1%

7

Electricians

14.2%

21.6%

32.3%

13.8%

4.0%

4.1%

0.2%

3.3%

5.5%

5.5%

0.0%

11.2%

8

Plant & machine operators & assemblers

55.6%

10.6%

0.0%

21.9%

18.3%

0.0%

12.9%

15.0%

20.0%

46.4%

13.2%

6.0%

9

Elementary occupations (Labourers)

7.4%

17.5%

14.7%

4.3%

8.2%

4.1%

0.8%

6.7%

8.9%

2.6%

6.5%

6.4%

Ship crew

70.3%

426

10.3.2 Results of Occupational Employment Modelling

There will be an increase in jobs in the 1.5 °C Scenario across all occupations

between 2015 and 2025, except in metal trades, which will display a minor decline

of 2%, as shown in Fig. 10.3.

There will be an increase in jobs across all occupations between 2015 and 2025 in

the 2.0 °C Scenario, as shown in Fig. 10.4. The occupations with the highest number

of jobs will be plant and machine operators and assemblers, followed by technicians

(including electrical, mechanical, civil, and IT technicians) and electricians. The

occupations that will have the largest percentage increase in jobs from 2015 to 2025

will be labourers, engineers, electricians, and construction trades. The results are

similar in the 1.5  °C Scenario, except for managers and metal trades, which will

experience minor reductions in overall jobs (3% each) (Table 10.7).

However, the results are not uniform across regions. For example, China and

India will both experience a reduction in the number of jobs for managers and cleri-

cal and administrative workers between 2015 and 2025, as shown in Table 10.8.

Table 10.8 and Fig. 10.6 show the employment changes between 2015 and 2025

under the 1.5 °C Scenario. Across all eight employment groups, the net effect of the

energy transition will positive or stable (Fig. 10.5).

However, the results are not uniform across regions. For example, China and

India both foresee a reduction in the number of jobs for managers and clerical and

administrative workers between 2015 and 2025, as shown in Table 10.9.

Table 10.10 and Fig. 10.5 show the employment changes between 2015 and 2025

under the 2.0 °C Scenario. Across all eight employment groups, the net effect of the

energy transition is positive. Further research is required to identify the training

needs for all employment groups.

10.4 Conclusions

Under both the 1.5 °C and 2.0 °C Scenarios, the renewable energy transition is pro-

jected to increase employment. Importantly, this analysis has reviewed the locations

and types of occupations and found that the jobs created in wind and solar PV alone

are enough to replace the jobs lost in the fossil fuel industry across all occupation

types. Further research is required to identify the training needs and supportive poli-

cies needed to ensure a just transition for all employment groups.

E. Dominish et al.

427

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-2,0

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-1,0

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428

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2,000,

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3,000,

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5,000,

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6,000,

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20
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20
25

20

15

20
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15

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25

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15

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25

20
15

20
25

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15

20
25

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15

20
25

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15

20
25

20
15

20
25

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15

20
25

20
15

20
25

MA

NA

GE

RS

PR

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CL
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ICAL

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EL

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OR

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Fig. 10.4

Division of occupations between fossil fuels and renewable energy in 2015 and 2025 under the 2.0 °C Scenario

E. Dominish et al.

429

Table 10.7

Jobs created and lost between 2015 and 2025 under the 1.5 °C Scenario

Jobs created or lost

Total jobs

Difference in jobs

Fossil fuels

PV

Wind – onshore

Wind – offshore

Total jobs in 2015

Total jobs in 2025

Total difference

% difference

Managers

−
505,000

355,000

130,000

75,000

1,345,000

1,380,000

35,000

3%

Professionals (Social, Legal, finance, scientific)

−
430,000

770,000

375,000

385,000

1,330,000

2 430,000

1,100,000

83%

Engineers (Industrial, electrical & civil)

−
225,000

990,000

365,000

125,000

790,000

2,050,000

1,260,000

159%

Technicians (Electrical, mechanical, civil & IT)

−
900,000

1,240,000

840,000

55,000

2 130,000

3 365,000

1 235,000

58%

Clerical & administrative workers

−
395,000

375,000

100,000

120,000

1 235,000

1,440,000

200,000

16%

Construction trades

80,000

45,000

155,000

30,000

290,000

600,000

310,000

107%

Metal trades

−
970,000

335,000

450,000

295,000

1,750,000

1,860,000

110,000

6%

Electricians

−
560,000

2,250,000

260,000

45,000

1 335,000

3 330,000

1,995,000

150%

Plant & machine operators & assemblers

−
1,700,000

3,360,000

505,000

290,000

3,820,000

6 560,000

2,740,000

72%

Labourers (Manufacturing, construction & transport)

−
235,000

1,300,000

190,000

95,000

835,000

2,185,000

1,350,000

162%

Total

−

5 845,000

11,300,000

3,370,000

1,510,000

14 900,000

25 195,000

10 335,000

70%

Ship crew

490,000

12,000

500,000

490,000

4000%

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

430

Table 10.8

Jobs created or lost between 2015 and 2025 by region under the 1.5 °C Scenario

OECD North America

Latin America

OECD Europe

Africa

Middle East

Eastern Europe/Eurasia

India

Developing Asia

China

OECD Pacific

Global

Managers

138%

57%

26%

13%

42%

42%

−

14%

36%

−
24%

40%

3%

Professionals (Social, legal, finance, scientific)

383%

185%

113%

186%

119%

119%

133%

194%

1%

172%

83%

Engineers (Industrial, electrical & civil)

414%

306%

199%

201%

179%

179%

140%

200%

82%

214%

159%

Technicians (Electrical, mechanical, civil & IT)

236%

189%

65%

140%

153%

153%

46%

125%

−
5%

138%

58%

Clerical & administrative workers

165%

63%

50%

24%

47%

47%

−

7%

53%

−
13%

52%

16%

Construction trades

145%

75%

137%

202%

64%

64%

107%

93%

124%

115%

107%

Metal trades

258%

76%

44%

50%

112%

112%

33%

83%

−
36%

76%

6%

Electricians

489%

556%

165%

237%

500%

500%

157%

346%

24%

206%

150%

Plant & machine operators & assemblers

390%

456%

53%

326%

502%

502%

166%

649%

−
29%

178%

72%

Labourers (Manufacturing, construction & transport)

475%

395%

209%

220%

221%

221%

181%

266%

48%

210%

162%

Total

327%

237%

87%

159%

168%

168%

90%

199%

−

10%

152%

70%

Ship crew

4150%

731%

68,465%

1462%

8742%

4000%

E. Dominish et al.

431

Table 10.9

Jobs created and lost between 2015 and 2025 under the 2.0 °C Scenario

Jobs created or dlost

Total jobs

Difference in jobs

Fossil fuels

PV

Wind – onshore

Wind – offshore

Total jobs in 2015

Total jobs in 2025

Total difference

% difference

Managers

−

382,067

255,887

89,967

56,041

1,444,621

1,464,449

19,828

1%

Professionals (Social, legal, finance, scientific)

−

305,186

609,373

260,542

288,834

1,421,732

2 275,295

853,563

60%

Engineers (Industrial, electrical & civil)

−

191,753

764,217

250,117

94,195

830,073

1,746,850

916,776

110%

Technicians (Electrical, mechanical, civil & IT)

−

778,902

945,091

580,639

41,848

2,195,839

2,984,516

788,677

36%

Clerical & administrative workers

−

278,592

297,860

69,929

91,610

1,350,727

1,531,534

180,807

13%

Construction trades

129,278

37,123

109,843

21,101

371,818

669,163

297,345

80%

Metal trades

−

844,156

267,032

315,134

222,511

1,752,952

1,713,473

−
39,479

−

2%

Electricians

−

504,687

1,740,194

182,757

34,307

1,342,336

2,794,906

1,452,570

108%

Plant & machine operators & assemblers

−

1,350,554

2,949,489

354,152

215,137

3,903,199

6,071,423

2,168,224

56%

Labourers (Manufacturing, construction & transport)

−

186,954

1,013,583

133,271

71,034

881,189

1,912,124

1,030,935

117%

Total

−

4,693,573

8,879,851

2,346,350

1,136,618

15,494,487

23,163,733

7,669,246

49%

Ship crew

355,791

12,199

367,990

355,791

2917%

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

432

Table 10.10

Jobs created or lost between 2015 and 2025 by region under the 2.0 °C Scenario

OECD North America

Latin America

OECD Europe

Africa

Middle East

Eastern Europe/Eurasia

India

Developing Asia

China

OECD Pacific

Global

Managers

132%

35%

14%

16%

40%

40%

−

20%

36%

−

26%

26%

1%

Professionals (Social, legal, finance, scientific)

308%

106%

80%

151%

82%

82%

102%

120%

−

5%

137%

60%

Engineers (Industrial, electrical & civil)

369%

187%

134%

144%

126%

126%

90%

124%

53%

172%

110%

Technicians (Electrical, mechanical, civil & it)

223%

111%

39%

100%

114%

114%

18%

82%

−

13%

105%

36%

Clerical & administrative workers

154%

38%

33%

29%

43%

43%

−

11%

46%

−

16%

38%

13%

Construction Trades

140%

50%

100%

163%

52%

52%

87%

72%

101%

54%

80%

Metal trades

202%

26%

29%

48%

101%

101%

20%

56%

−

38%

61%

−

2%

Electricians

456%

378%

100%

203%

416%

416%

102%

239%

11%

180%

108%

Plant & machine operators & assemblers

369%

258%

30%

317%

289%

289%

130%

398%

−

27%

164%

56%

Labourers (Manufacturing, construction & transport)

436%

230%

136%

191%

143%

143%

125%

167%

31%

176%

117%

Total

298%

134%

55%

138%

113%

113%

60%

129%

−

14%

127%

49%

Ship crew

1724%

644%

43,668%

1098%

8610%

2917%

E. Dominish et al.

433

Fig. 10.5 Employment changes between 2015 and 2025 by occupational breakdown under the 2.0 °C Scenario

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

434

Fig. 10.6 Employment changes between 2015 and 2025 by occupational breakdown under the 1.5 °C Scenario

E. Dominish et al.

435

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Notes. http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/4631.0Explanatory+Notes12015-16.
Accessed September 16 2018.
Australian Bureau of Statistics & Statistics New Zealand (2013), Australian and New Zealand
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International Labour Office (2012), International Standard Classification of Occupations, Geneva,
ILO
International Labour Office. (2015). Guidelines for a just transition towards environmentally sus-
tainable economies and societies for all. Geneva
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IRENA, Abu Dhabi ;
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IRENA (2018) Renewable Energy Benefits: Leveraging Local Capacity for Offshore Wind, IRENA,
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Jenkins, K., McCauley, D., Heffron, R., Stephan, H., & Rehner, R. (2016). Energy justice: A con-
ceptual review. Energy Research and Social Science , 11 , 174–182. https://doi.org/10.1016/j.
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Rutovitz, J., Dominish, E., & Downes, J.  (2015). Calculating global energy sector jobs: 2015
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Smith, S. (2017). Just Transition – A Report for the OECD. Brussels. Retrieved from https://www.
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Sovacool, B.  K., & Dworkin, M.  H. (2014). Global energy justice: Problems, principles, and
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Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

10 Just Transition: Employment Projections for the 2.0 °C and 1.5 °C Scenarios

© The Author(s) 2019 437 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_11

Chapter 11

Requirements for Minerals and Metals

for 100% Renewable Scenarios

Damien Giurco, Elsa Dominish, Nick Florin, Takuma Watari,

and Benjamin McLellan

Abstract This chapter explores the magnitude of the changes in patterns of material

use that will be associated with the increasing deployment of renewable energy and

discusses the implications for sustainable development. In particular, this chapter

focuses on the increased use of lithium and cobalt, metals which are used extensively

in battery technologies, and silver used in solar cells. Consistent with the strong

growth in renewable energy and electrification of the transport system required in a

1.5°C scenario, the material requirements also rise dramatically, particularly for

cobalt and lithium. Scenarios developed for this study show that increasing recycling

rates and material efficiency can significantly reduce primary demand for metals.

11.1 Introduction

Globally, recent investments in new renewable energy infrastructure have been dou-

ble the investments in new energy from fossil fuels and nuclear power (REN21

2018 ). This is strong evidence of the increasing momentum of the energy transition

away from fossil fuels. A rapid transition to 100% renewables offers hope for reduc-

ing carbon emissions and increasing the chance that global warming will be main-

tained to below 2.0 °C. However, the transition to 100% renewables also comes with

requirements for new patterns of material use to support the renewable energy infra-

structure, including wind turbines, solar cells, batteries, and other technologies.

This chapter explores the magnitude of the changes in patterns of material use

that will be associated with the increasing deployment of renewable energy, and

discusses the implications for sustainable development. In particular, this chapter

focuses on the increased use of lithium (used extensively in battery technology),

D. Giurco (*) · E. Dominish · N. Florin Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: damien.giurco@uts.edu.au; elsa.dominish@uts.edu.au; nick.florin@uts.edu.au

T. Watari · B. McLellan Graduate School of Energy Science, Kyoto University, Kyoto, Japan

438

cobalt (again used in batteries, particularly for vehicles), and silver (used in solar

cells because it is an excellent conductor of electricity).

The importance of understanding how material use is affected by renewable

energy technologies is already established (Ali et  al. 2017 ; Valero et  al. 2018 ). A

range of studies have identified different aspects of the challenges associated with

the supply of the materials needed for renewable energy. The first aspect is the avail-

ability of mineral supplies. For example, Mohr et al. ( 2012 ) developed commodity-

focused supply projections for lithium, with an emphasis on the origins and locations

of available lithium deposits over time, which are contrasted with a simple demand

function. Other studies have placed more emphasis on demand scenarios, either for

a specific geographic location, such as Germany (Viebahn et al. 2015 ) or globally

(Valero et al. 2018 ; Watari et al. 2018 ), and some have explored the role of technol-

ogy mixes and material substitution in detail (Månberger and Stenqvist 2018 ).

In addition to issues of resource availability, environmental and social issues

have also been explored (Giurco et  al. 2014 ; Florin and Dominish 2017 ) and the

need for improved resource governance has been highlighted (Prior et al. 2013 ; Ali

et al. 2017 ). This chapter evaluates the total demand to determine which metals may

present bottlenecks to the supply of renewable energy technologies. The potential to

offset primary demand is explored through a range of scenarios for technology type,

material efficiency, and recycling rates. This chapter draws on the results of a larger

study funded by Earthworks (see Dominish et al. 2019).

This chapter has five sections. Following this introductory section, the second

section outlines the material requirements for key renewable energy infrastructure

technologies, namely solar photovoltaics (PV), wind turbines, and batteries. The

key assumptions, and the energy and resource scenarios are described in Sect. 11.3,

together with a summary of the methodology, which is described in detail in Chap.

3. The requirements for selected materials (lithium, cobalt, and silver) are presented

in Sect. 11.4. Following the results, a discussion is presented in Sect. 11.5.

11.2 Overview of Metal Requirements for each Technology

Renewable energy and storage technologies typically have high and diverse metal

requirements. Moreover, there are often competing technologies or component

technologies, which add to the complexity of material considerations. The key met-

als used for solar PV, wind power, batteries, and EV are discussed below.

11.2.1 Solar PV

A typical crystalline silicon (c-Si) PV panel, which is currently the dominant tech-

nology, with over 95% of the global market, contains about 76% glass (panel sur-

face), 10% polymer (encapsulant and back-sheet foil), 8% aluminium (frame), 5%

silicon (solar cells), 1% copper (interconnectors), and less than 0.1% silver (contact

lines) and other metals (e.g., tin and lead). Thin film technologies,

D. Giurco et al.

439

copper–indium–gallium–(di)selenide (CIGS) and cadmium telluride (CdTe), make

up the remainder of the market. These technologies require less material overall

than crystalline silicon. For CdTe panels, the composition is 96–97% glass, 3%–4%

polymer, and less than 1% semi-conductor materials (CdTe) and other metals (e.g.,

nickel, zinc, tin). CIGS contain about 88%–89% glass, 7% aluminium, 4% polymer,

and less than 1% semi-conductor material (indium, gallium, selenium) and other

metals (e.g., copper) (Weckend et al. 2016 ). Figure 11.1 provides a simplified dia-

gram of the PV supply chain, including key materials and sub-components.

11.2.2 Wind

The major raw materials required for the manufacture of wind turbine components

are bulk commodities: iron ore, copper, aluminium, limestone, and carbon. Wind

turbines use steel for the towers, nacelle structural components, and the drivetrain,

accounting for about 80% of the total weight. Some turbine generator designs use

direct-drive magnetics, which contain the rare earth metals neodymium and dyspro-

sium ( Fig. 11.2 ). The development of direct-drive permanent magnet generators

(PMG) by major producers (e.g., Siemens and General Electric) simplifies the

design by eliminating the gearbox, and this is attractive for offshore applications

because it reduces maintenance (Zimmermann et al. 2013 ). It is estimated that about

20% of all installed wind turbines (both onshore and offshore) use rare earth mag-

nets (CEMAC 2017 ).

Fig. 11.1 Overview of key metal requirements and supply chain for solar PV

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

440

11.2.3 Batteries and Electric Vehicles

This study focuses on lithium ion batteries (LIBs), which power almost all electric

vehicles (EVs) on the market today and are also an important technology for sta-

tionary energy-storage applications. LIBs are made of two electrodes (anode and

cathode), current collectors, a separator, electrolyte, a container, and sealing parts.

The anode is typically made of graphite, with a copper foil current collector. The

cathode is typically a layered transition metal oxide, with an aluminium foil current

collector. In between the electrodes is a porous separator and electrolyte. All of

these components are typically housed in an aluminium container. LIBs are gener-

ally referred to by the material content of the cathode, which accounts for 90% of

the material value and about 25% of the total weight (Gratz et al. 2014 ).

The size and type of the LIB has the greatest impact on the material require-

ments. Since commercialization in the 1990s, a range of different types (‘chemis-

tries’) have been developed for different applications, named according to the metals

in the cathode. The most common LIB types for EV applications are nickel–manga-

nese–cobalt (NMC), lithium–iron phosphate (LFP), nickel–cobalt–aluminium

(NCA), and lithium–manganese oxide (LMO) (Vaalma et  al. 2018 ). NMC is the

most common battery type for passenger vehicles, and NCA is also common, with

a small share for LMO. However, in China, LFP is the dominant chemistry. Electric

buses have traditionally used LFP batteries (BNEF 2018 ) and lead–acid batteries are

most commonly used for two-wheel vehicles. However, the application of LIBs in

this market sector is growing (Yan et al. 2018 ).

For energy storage, NMC and NCA are the most commonly used chemistries. A

simplified overview of the lithium-ion battery supply chain, including its key metals

Fig. 11.2 Overview of key metal requirements and supply chain for wind power

D. Giurco et al.

4 41

(for the NMC chemistry) and sub-components, is shown in Fig. 11.3. Rare earth

permanent magnets using neodymium and dysprosium are common in most electric

vehicle motors. Other motor technologies, or those that replace rare earths with

lower-cost materials, are under development and are already used in some vehicles.

However, rare earth magnets are expected to remain the standard for the foreseeable

future because of their higher performance characteristics (Widmer et al. 2015 ).

11.3 Scenarios and Key Assumptions

11.3.1 Electricity and Transport Scenarios

The energy scenarios presented here were developed to achieve the climate target in

the 2015 Paris Climate Agreement of limiting anthropogenic climate change to a

maximum of 1.5 °C above pre-industrial levels. This projection is more ambitious

than the IEA’s annual World Energy Outlook (WEO) scenarios, which project that

current policies, and therefore the global development of renewable power and elec-

tric mobility, will not change. In contrast, many scenarios have been proposed in the

Fig. 11.3 Overview of key metal requirements and supply chain for LIB and EV

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

442

academic literature that extend as far as zero emissions, whereas the IEA’s own

“Energy Technology Perspectives” (ETP) scenarios include both a 2.0 °C scenario

(2DS) and a more ambitious “Beyond 2.0 °C” scenario (B2DS), which aim to achieve

the Paris targets. The scenario proposed here includes both a shift to 100% renewable

electricity and a shift to renewable electricity and fuels in the transport sector.

In this scenario, solar PV will account for more than one-third of the installed

capacity by 2050, with the remainder from wind and other renewables. Lithium-ion

batteries will account for approximately 6% of energy storage (which will be domi-

nated by pumped hydro and hydrogen).

In the transport system, we focus on the material requirements for the batteries

used in road transport, because other types of transport do not require batteries to

power drivetrains or are assumed to rely on other forms of energy (e.g., biofuels for

aviation). In 2050, most of the energy for road transport will come from electricity

(55%) and hydrogen (22%), and the remainder will be from biofuels and synfuels.

In the 1.5 °C Scenario, the batteries required to electrify road transport are specified

for electric buses and passenger cars, including battery electric vehicles (BEV),

plug-in hybrid electric vehicles (PHEV), and commercial vehicles. Passenger cars

will account for 92% of vehicles and 51% of battery capacity, whereas commercial

vehicles are projected to account for 48% of battery capacity, although they will

make up only 8% of the total fleet of vehicles. This is because the battery sizes for

commercial vehicles (assumed to be 250  kWh in 2015 and rising to 600  kWh in

2050. are larger than those for passenger vehicles (5–15 kWh for PHEV and 38–62

kWh for BEV). Buses will account for a small percentage (1%) of both vehicles and

batteries. Electric bikes and scooters have been excluded, because although they are

currently a growing market in Asia, by 2050, their share of electricity consumption

will be negligible compared with the predicted uptake of electric passenger and

commercial vehicles. Lead–acid batteries are also the main type of battery for elec-

tric bikes and scooters, although lithium-ion may replace these in future.

11.3.2 Resource Scenario Development

Five resource scenarios were developed to estimate the metal demand for 100%

renewables, as shown in Table 11.1. The scenarios were developed based on the

current market trends and the likelihood of changes in material efficiency or tech-

nology. Our aim is to understand how primary demand can be offset through changes

in technology or recycling rates.

11.3.3 Technology Assumptions

The total metal demand for renewable energy and battery technology each year is

estimated based on the metal intensity of a specific technology and the capacity of

each technology introduced in a specific year. This introduced stock will accounts

D. Giurco et al.

443

for the new capacity and the replacement of technologies at the end of their lives,

based on a lifetime distribution curve for the average lifetime.

The values for metal intensity are given in tonnes/GW (for solar PV) or tonnes/

GWh (for batteries) of capacity, and two values are given for each metal to evaluate

the impact of improving the material efficiency (for solar PV) and technology shifts

(for batteries). To evaluate the impact of recycling, the primary demand is estimated

by multiplying the discarded products by the recycling rate. The recycling rate is

obtained by multiplying the collection rate by the recovery efficiency of a metal

from a specific technology. This recycling rate is also varied to obtain a current rate

and a potential rate. The potential recycling rate is technologically possible, but is

not currently applicable because it is not economic. The detailed assumptions for

batteries and solar PV are explained in detail in the following section.

11.3.4 Batteries

The ‘current materials intensity’ for LIB for EVs and storage (Table 11.2) is esti-

mated based on the assumed market shares of the LIB technologies: NMC (60%),

LMO (20%), NCA (15%), and LFP (5%) (Vaalma et al. 2018 ). The dominant bat-

tery technologies in the future are not likely to be the same as those commercialized

today. Therefore, for the ‘future technology scenario’, we assume that lithium–sul-

fur batteries will replace LIB for EVs (Cano et al. 2018 ). We have modelled a future

market (Table 11.3) in which Li–S will achieve a 50% market share for EVs by

2050, with deployment scaling up at a linear rate, assuming the first commercializa-

tion in 2030. In this scenario, the technology does not change for storage batteries.

We have assumed a collection efficiency of 100% for all batteries and a recovery

rate of 90% for Co and Ni (Georgi-Maschler et al. 2012 ). A 10% recovery rate is

assumed for Li, acknowledging that pyrometallurgical processing routes account

for most of the current global capacity, and Li recovery may not be possible by this

Table 11.1 Summary of metal scenarios

Scenario name Market share/ materials efficiency Recycling

Colour in
figures
Total demand Current materials intensity and
current market share

No recycling Red

Current recycling Current materials intensity and
current market share

Current
recycling rates

Pink

Potential recycling Current materials intensity and
current market share

Improved
recycling rates

Orange

Future technology Improved materials efficiency for
PV and current market share
Technology shift for batteries

No recycling Dark blue

Future technology &
potential recycling

Improved materials efficiency for
PV and current market share
Technology shift for batteries

Improved
recycling rates

Light blue

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

444

route (King et  al. 2018 ). For scenarios that assume a ‘potential future recycling’

rate, we have assumed 95% recovery for all metals. This is reasonable given that

100% recovery has been reported in the laboratory (Gratz et al. 2014 ).^1 However,

some losses during processing seem unavoidable.^2

11.3.5 Solar PV

For solar PV, we assume that the technology types do not change until 2050, and

that they retain their current market shares, so that crystalline silicon will remain the

dominant technology. We have modelled the potential to offset demand through

increases in material efficiency and increases in recycling.

A high and a low value are given for silver to show the impact of material effi-

ciency on silver demand. The current data on silver intensity are from a survey of the

PV industry (ITRPV 2018 ), and the future material efficiency is based on an assumed

minimum amount of silver (Kavlak et  al. 2015 ). The ‘current recycling rate’ sce-

narios assume a current collection rate of 85% for all panels, consistent with the

target of the EU WEEE Directive.^3 This should be considered an average rate, not-

ing that remote and/or distributed roof-top systems will be more costly to collect and

transport than large utility-scale PV. We assume that no recycling is currently occur-

(^1) See: https://americanmanganeseinc.com/investor-info-3/investment-proposition/ (^2) Pers comms Boxall, N. (^3) More details available here: http://ec. europa.eu/environment/waste/weee/index_en.htm Table 11.2 Material intensity and recycling rates Solar PV Batteries Materials Silver Lithium Cobalt Current materials intensity 20 t/GW 113 t/GWh 124 t/GWh Future technology 4 t/GW 411 t/GWh Current recycling rate [%] 0% 0% 90% Potential recycling rate [%] 81% 95% 95% Table 11.3 Market share Solar PV Batteries Technology c-Si Li-ion Li-S Current market share95.8% 100% Future market share Decreases to 50% by 2050 50% by 2050, beginning from 2030 D. Giurco et al.

445

ring for silver from PV, but that 95% recovery may be possible. Therefore, for the

‘potential recycling rate’ scenarios, we assume a 95% recovery efficiency and an

85% collection rate, which will result in an 81% metal recycling rate.

11.3.6 Metal Assumptions

For each scenario, the annual primary demand is compared with the current produc-

tion (2017 data), and the cumulative demand to 2050 is compared with current

reserves. The data presented (Table 11.4) highlight the annual production, total

resources, and reserves. ‘Reserves’ are the subset of the total resources that can be

economically mined under current conditions. They are dependent on a multitude of

factors and can change over time. By contrast, resources are less certain economi-

cally and there may be no firm plan to mine them. Over time, new resources can be

discovered and as economic conditions change, resources may be upgraded to

reserves (for example, where the price for the metal increases, thus making lower-

grade or more- challenging ores profitable or where a new technology for extraction

allows lower cost processing). In contrast, reserve estimates can also be downgraded

over time (as occurred with coal reserve estimates in UK and Germany).

11.4 Results for Lithium, Cobalt, and Silver

The cumulative demand from renewable energy technologies for cobalt, lithium, and

silver by 2050 has been modelled, and is compared to current reserves in Fig. 11.4.

The cumulative demand for cobalt from renewable energy and transport exceeds the

current reserves in all scenarios, and for lithium, the cumulative demand is exceeded

in all scenarios, except the ‘potential recycling scenario’. For silver, the total demand

for silver from renewable energy will reach around 50% of current reserves.

The annual demand in 2050 is compared with the current rates of production

(based on 2017 data). Both cobalt and lithium have annual demands that far exceed

the current rates of production—particularly lithium in the ‘future technology’ sce-

nario. However, the annual demand for silver will remain below current production

levels (Fig. 11.5).

The detailed results for each metal are shown in the following section.

Table 11.4 Metal assumptions

Metal Production 2017 (tonnes/year) Reserve (tonnes) Resources (tonnes)
Cobalt 110,000 7,100,000 25,000,000
Lithium 46,500 16,000,000 53,000,000
Silver 25,000 530,000 N/A

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

446

11.4.1 Cobalt

The annual demand for cobalt from EVs and storage could exceed the current pro-

duction rates in around 2023 (in all scenarios). In the ‘future technology’ scenario,

shifting to Li–S instead of LIB will reduce the demand for cobalt. However, recy-

cling, rather than shifting technologies, will have the greatest impact on reducing

the primary demand in both the current technology and future technology

scenarios.

The cumulative total demand to 2050 (with current technology and no recycling)

could exceed current reserves by 400%, and exceed current resources by 20%. Even

with recycling and a shift to technologies that use less cobalt, the cumulative demand

will still exceed reserves. However, in these scenarios, the demand will remain

below the resource levels (Figs. 11.6 and 11.7).

0%

100%

200%

300%

400%

500%

Silver

% of reserves

0%

100%

200%

300%

400%

500%

Lithium

% of reserves

0%

100%

200%

300%

400%

500%

Cobalt

% of reserves

Total demand (no recycling) Current recycling Potential recycling
Future technology (no recycling) New technology & potential recycling

Fig. 11.4 Cumulative demand from renewable energy and transport technologies to 2050 com- pared with reserves

Total demand (no recycling) Current recycling Potential recycling
Future technology (no recycling) New technology & potential recycling

0%

20%

40%

60%

80%

100%

Silver

% of annual production

0%

2,000%

4,000%

6,000%

8,000%

10,000%

Lithium

% of annual production

0%

500%

1,000%

1,500%

2,000%

Cobalt

% of annual production

Fig. 11.5 Annual demand from renewable energy and storage technologies in 2050 compared with current production rates (note that scale varies across the metals)

D. Giurco et al.

447

(5 00,000)

500,00 0

1,000, 000

1,500, 000

2,000, 000

2,500, 000

2015 202020252030203520402045 2050

P
rimary

meta

ld

eman

d(

tonnes

)

Total demand (no recycling) Current recycling
Potential recycling Future technology & no recycling
Future technology & potential recycling Current production

Fig. 11.6 Annual primary demand for cobalt from EVs and storage

0

5,000,00 0

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

C

umu

lative

primar

y

m

et

al

demand

(tonnes

)

Resources Reserves
Total demand (no recycling) Current recycling
Potential recycling Future technology & no recycling
Future technology & potential recycling

Fig. 11.7 Cumulative primary demand for cobalt from EVs and storage by 2050

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

448

11.4.2 Lithium

The annual demand for lithium for EVs and storage could exceed the current pro-

duction rates by around 2022 (in all scenarios). In the ‘future technology and no

recycling’ scenario, a shift to Li–S will increase the demand for lithium, because

these batteries have a higher amount of lithium. Increasing recycling from its cur-

rent low levels (which are assumed to be 10%) will offer the greatest potential to

offset the primary demand for lithium.

The cumulative demand for lithium by 2050 will be below the resource levels for

all scenarios, but will exceed the reserves unless there is a shift to a high recycling

rate. The cumulative demand could be as high as 170% of the current reserves with

the current technology, and could be 280% of current reserves with a switch to Li–S

batteries (Figs. 11.8 and 11.9).

11.4.3 Silver

The total annual demand for silver could reach more than 40% of the current pro-

duction rates by 2050, assuming no recycling and that the materials efficiency does

not change (Fig. 11.10). The cumulative demand to 2050 could reach around half

the current reserves with the current technology, and around one-quarter if the tech-

nology improves (Fig. 11.11). The reduction in material intensity in the ‘future tech-

nology’ scenario, in which silver use decreases from 20 to 4 tonnes/GW, has the

greatest potential to reduce demand.

500, 000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

2015 2020202520302035204020452050

Primar

y m

etal

demand

(tonne

s / y

ear

)

Total demand (no recycling) Current recycling
Potential recycling Future technology & no recycling
Future technology & potential recycling Current production

Fig. 11.8 Annual primary demand for lithium from EVs and storage

D. Giurco et al.

449

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

Cumulati

ve

primar

y

m

eta

l d

eman

d (

tonnes

)

Resources Reserves
Total demand (no recycling) Current recycling
Potential recycling Future technology & no recycling
Future technology & potential recycling

Fig. 11.9 Cumulative primary demand for lithium from EVs and storage by 2050

5,000

10,000

15,000

20,000

25,000

30,000

2015 2020 2025 2030 2035 2040 2045 2050

Prim

ary

me

ta

lde

m
an

d (

tonnes

/ye

ar

)

Total demand (no recycling) Potential recycling
Future technology & no recycling Future technology & potential recycling
Current production

Fig. 11.10 Annual primary demand for silver from solar PV (c-Si)

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

450

11.5 Discussion

Within the context of the increasing metal resource requirements for the renewable

energy and storage technologies, the rapid increase in the demand for both cobalt

and lithium is of greatest concern, and the demand for both metals will exceed the

current production rates by 2023 and 2022, respectively. The demand for these met-

als will increase more rapidly than it does for silver, partly because solar PV is a

more established technology and silver use has become very efficient, whereas the

electrification of the transport system and the rapid expansion of lithium battery

usage has only begun to accelerate in the last few years.

The potential to offset the primary demand differs, depending on the technology.

Offsetting demand through secondary sources of cobalt and lithium has the most

potential to reduce total primary demand for these metals, as batteries have a com-

paratively short lifetime of approximately 10 years. The cumulative demand for both

metals will exceed current reserves, but with high recycling rates, they can remain

below resource levels. However, there is a delay until the period in which recycling

will offset demand, because there must be enough batteries in use and reaching the

ends of their lives to be collected and recycled. This delay could be further extended

by strategies for the reuse of vehicular batteries as stationary storage, which might

save costs in the short term and increase the uptake of PV. The efficiency of cobalt

use in batteries also significantly reduces demand, and this is already happening as

manufacturers shift towards lower-cobalt chemistries (Energy Insights by McKinsey

2018 ). However, this is also likely to lead to an increase in the demand for lithium.

Increasing the efficiency of material use has the biggest potential utility in offset-

ting the demand for PV metals, whereas recycling will have a smaller impact on

0

100,00 0

200,00 0

300,00 0

400,00 0

500,00 0

600,00 0

Cumula

tiv

ep

rima

ry

meta

ld

em

an

d(

ton

nes

)

Reserves Total demand (no recycling)
Current recycling Potential recycling
Future technology & no recycling Future technology & potential recycling

Fig. 11.11 Cumulative primary demand for silver from solar PV (c-Si) by 2050

D. Giurco et al.

451

demand. This is attributable to the long life-span of solar PV panels, and their lower

potential for recycling. Although not as extreme as the growth of cobalt and lithium,

the growth of silver and its demand in 2050 are still considerable. This is important,

especially when considering that solar PV currently consumes approximately 9% of

end-use silver (The Silver Institute & Thomson Reuters 2018 ), and although it is

possible to create silver-less solar panels, these panels are not expected to be on the

market in the near future (ITRPV 2018).

11.5.1 Limitations

This study only focuses on the metal demand for renewable energy and transport. It

does not take into account other demands for these metals. It is expected that with

the increase in renewable energy, renewable energy technologies will consume a

greater share of these metals. In our modelling, the recycled content used in tech-

nologies comes only from metals from the same technologies at the end of their

lives. However, the demand could potentially be offset by accessing other secondary

sources of the metal. At the same time, these technologies are only one end-use

within the overall economic demand for these metals. The expansion of renewable

energy and storage technologies could have a significant effect on the overall market

dynamics, including influencing prices, which may feed back to the efforts to reduce

material intensity. These results only focus on cobalt, lithium, and silver because of

their importance. However, further analysis is required of other metals that will be

important in the renewable energy transition.

Another important limitation is that this analysis does not consider the impact of

the demand for a mineral on the mining of that mineral and therefore on the amount

of energy required for this mining and processing activity. In particular, as the

potential ore grade declines, and polymetallic ore processing and the mining of

deeper ore bodies increase, it is possible that this feedback loop could have a more-

than- marginal influence on the overall sectoral energy consumption. To examine

this is an area accurately will require much more-complex modelling.

11.5.2 Comparison with Other Studies

A large number of studies have examined scenarios for renewable energy and stor-

age technologies that will mitigate climate change. In recent years, there has also

been an upsurge in studies of mineral ‘criticality’, which have paralleled the present

study in terms of the high penetration of renewable and storage technologies and the

potential constraints that certain minerals may impose. This increased interest has

been prompted to some extent by China’s rare earth export restrictions of

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

452

2009–2011, which reflect the sense that mineral supply chains are still quite inse-

cure. Most of these studies have addressed specific technologies or specific coun-

tries or regions, rather than global climate targets. A number of studies have

specifically and directly addressed the Paris Agreement targets (ensuring that the

temperature rise does not exceed 2.0 °C), although the modelling frameworks have

been slightly different. Some of the authors of the present chapter have been

involved in these studies (Tokimatsu et al. 2017 ; Watari et al. 2018 ).

A range of variables affect the results, including the installed capacity and tech-

nology type in the energy scenario; the assumed market demand of technology

types (e.g., type of battery or solar panel); and the material intensity assumptions.

The projected future demand for lithium is higher than in previous studies, and that

for cobalt is similar or higher (Månberger and Stenqvist 2018 ; Watari et al. 2018 ;

Valero et  al. 2018 ; Watari et  al. 2018 ), as shown in Table 11.5. This is primarily

attributable to the ambitious renewable energy scenario used in this study, which

includes achieving a 100% renewable transport system by 2050, whereas the other

studies have still included a large share of gasoline-powered cars in 2050. This

study also includes batteries for stationary energy storage, whereas the other studies

have only included batteries for road transport. Moreover, this study assumes a

shorter battery life of 10 years, based on current warranties, whereas some scenarios

assume a longer life.

The results for silver are in the middle of the range of results given by other stud-

ies. Our scenario includes a higher installed capacity of PV in 2050 than the other

studies. However, this study also assumes a lower metal intensity than previous

studies, because new data have been published based on the current material use by

the PV industry (ITRPV 2018 ).

11.6 Supply Impacts and Challenges

In addition to the material requirements for renewable technologies explored earlier

in this chapter, it is important to understand the changes in the available supply, the

geopolitical landscape, and the associated social and environmental impacts, which

are outlined below.

Table 11.5 Comparison of results with other studies

Study Energy Scenario

Metals (% of reserves up
to 2050)
Cobalt Lithium Silver
This study 1.5 °C100% renewable energy
scenario

420% 170% 50%

Watari et al. ( 2018 ) Beyond 2 degree scenario (IEA) 180% 130% 78%
Månberger and Stenqvist
(2018)

Beyond 2 degree scenario (IEA) 440% 100% 18%

Valero et al. ( 2018 ) 2 degree scenario 70% 40% 70%

D. Giurco et al.

453

11.6.1 Geopolitical Landscape

The geopolitical shift underway in the supply of the resources required for the

globe’s future energy mix is clearly illustrated in Fig. 11.12. Whereas the value of

the lithium industry is much less than the value of the oil industry, this comparison

highlights a distinct shift in the energy commodities that society values. Oil’s rate of

use is projected to decline somewhat in the decade ahead (Mohr et al. 2015 ), whereas

lithium’s production is expected to grow rapidly (Mohr et al. 2012 ).

Although it is not a key focus of this particular chapter, the dominance of China

in the supply of rare earths has encouraged manufacturing countries to look at diver-

sifying their supply sources, including through the recovery of rare earths from

recycled material. A similar situation of heavily concentrated supply occurs for

cobalt, where the Democratic Republic of the Congo is the largest supplier, at

around 66,000 tonnes per year. The next four largest cobalt-producing countries

(China, Canada, Russia, and Australia) only produce 5–7000 tonnes per year each.

The supply chain is also concentrated downstream, with around 50% of cobalt

smelted and refined in China.

11.6.2 Social and Environmental Impacts

The mining and supply chain for these metals can have adverse social and environ-

mental consequences for workers, local communities, and the environment. These

impacts are most significant for the cobalt mined in the Democratic Republic of

Congo, where there are human rights violations, child labour, and severe environ-

mental pollution affecting health (Florin and Dominish 2017 ).

These types of impacts at mine sites and along the supply chain also influence the

availability of primary resources. For example, whereas Australia and Chile are

large producers of lithium, large deposits remain undeveloped in Bolivia, due in part

to local concerns over the social and environmental impacts. The global silver mar-

ket receives less media attention than the market for lithium, but the world’s second-

largest silver mine (Escobal) in Guatemala is currently closed by a constitutional

Top 5 Oil
producing countries:
Northern hemisphere
features heavily

Top 5 Lithium
producing countries:
Southern hemisphere
features heavily

Fig. 11.12 Top five oil-producing countries (left) versus lithium-producing countries (right)

11 Requirements for Minerals and Metals for 100% Renewable Scenarios

454

court ruling that the Xinca Indigenous peoples were not adequately consulted before

a mine licence was granted (Jamasmie 2018 ).

The increased use of materials such as lithium, cobalt, and silver has economic

implications for the future of battery manufacture. For example, the cost of cobalt

has risen dramatically from US$20000/t in 2016 to US$80000 in 2018 (Tchetvertakov

2018 ). This is prompting manufacturers to look for alternatives, such as nickel,

vanadium, and zinc (Tchetvertakov 2018 ). At the same time, a significant propor-

tion of these price fluctuations are attributable to non-industrial factors, such as

investment and speculation in metal markets, which may also require greater regula-

tion to avoid unnecessary restrictions on renewable energy and storage

technologies.

11.6.2.1 Recycling Challenges

Whilst recycling can help to offset primary material demand through recycled

sources, there are technological, social and environmental challenges to increasing

recycling. The collection systems and infrastructure required to recycle metals from

renewable energy technologies are not well established. For example, although sil-

ver has an overall recycling rate of 30–50% (Graedel et al. 2011 ), almost no recy-

cling of silver from PV panels occurs, because most recycling of PV panels focuses

on recycling the glass, aluminium, and copper. Most of the processes used to recycle

lithium-ion batteries focus on the recovery of cobalt and nickel, because of their

higher price, so that lithium is downcycled into less valuable products, such as

cement. It should also be noted that the recycling of some key energy materials,

such as rare earths in magnets, does not offer significant cost savings or environ-

mental benefits over their extraction from primary resources (McLellan et al. 2013 ).

The establishment of effective collection networks is important for recycling.

Collection networks must be easily understood and must provide easily accessible

deposit mechanisms. Recycling can be both informal and formal, and whereas in

some cases, informal collection networks offer greater rates of recycling, the social

and environmental impacts can be higher. For example, recycling electronic waste

on open fires creates hazardous fumes for informal recyclers and has detrimental

effects on the environment. There is also a potential tension between reducing mate-

rial intensity and the ability to effectively separate, recover, and recycle materials.

Increasingly, thin layers of material are being utilized because of improved manu-

facturing processes, which can reduce the costs of products in their first lifetimes.

However, as the material content decreases, the value of the secondary production

decreases, making it a less attractive option for investment. Again, the use of

advanced, complex materials can sometimes make valuable materials difficult to

separate from each other, and this again increases the costs and reduces the profits

for investors in recovery processes.

To ensure that social and environmental issues will be addressed, several initia-

tives across supply chains are being developed, including the China-led Responsible

Cobalt Initiative, the Cobalt Institute, the Initiative for Responsible Mining

D. Giurco et al.

455

Assurance, Solving the Ewaste Problem (StEP), and the R2 standard for sustainable

electronics recycling. In the USA, the Dodd-Frank Act has mandated the traceabil-

ity of the gold, tin, tungsten, and tantalum from the Democratic Republic of Congo.

This has arisen from the international concern over the social impacts and implica-

tions of sourcing metals from that country, and in particular, to avoid supporting

conflict and environmental damage. However, cobalt is not explicitly mentioned in

the Dodd-Frank Act.

International resource governance that looks beyond renewable energy and

beyond a single commodity focus is increasingly recognised as a missing area in

environmental policy (Ali et al. 2017 ). The transition to renewable energy and the

associated requirements for resources to support this transition could be a catalyst

for advancing such policies.

11.7 Concluding Remarks

This chapter has given an overview of the material requirements for key renewable

energy technologies and the projected demands for lithium, cobalt, and silver.

Consistent with the strong growth in renewable energy and the electrification of the

transport system required by the 1.5 °C scenario, these material requirements will

also increase dramatically, particularly those for cobalt and lithium. The high total

demand requirements for these metals emphasize the importance of recycling, alter-

native chemistries, and transport planning and practices (including city design to

encourage active and public transport, as well as car sharing and pooling). The key

messages of this chapter are: it is important to design for both renewable energy and

resource cycles; and it is important to adopt a systems view that considers the avail-

able supply and also social and environmental factors. To support sustainable devel-

opment goals, both the primary and secondary sources of the resources required to

underpin the renewable energy transformation must be stewarded effectively as the

supply chains develop.

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11 Requirements for Minerals and Metals for 100% Renewable Scenarios

© The Author(s) 2019 459 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_12

Chapter 12

Implications of the Developed Scenarios

for Climate Change

Malte Meinshausen

Abstract This section provides a summary of the implications of the developed

2.0 °C and 1.5 °C scenarios for global mean climate change. Specifically, we con-

sider atmospheric CO 2 concentrations, radiative forcing, global-mean surface air

temperatures and sea level rise.

The question addressed in this section is what the implications are for future climate

change if the world were to follow the energy-related CO 2 emissions developed here,

complemented by land-use CO 2 emissions and those of other greenhouses gases

(GHGs). According to the high- emission scenario with unabated fossil fuel use, the

world could experience 1400 ppm CO 2 concentrations by the end of 2100, which is

five times higher than the pre-industrial background concentration of 278 ppm. Our

ice-core records have shown that over the last 850,000 years, the CO 2 concentrations

have only oscillated between approximately 180 ppm and 280 ppm. In fact, for the

last 10  million years on this planet, the CO 2 concentrations have probably not

exceeded the CO 2 concentrations that our thirst for fossil fuels would propel the

world into in just two centuries.

That is a dramatic change to the thin layer of atmosphere that wraps our planet.

Even if any climate change consequences that follow from this dramatic change in

CO 2 concentrations are disregarded, the consequences will be dramatic. At atmo-

spheric CO 2 concentrations above 900 ppm, the acidity level in the oceans will drop

below the so-called ‘aragonite saturation level’, which is the level required for coral

reefs and other organisms with calciferous shells to sustain their structures (Ricke

et al. 2013 ). Therefore, without even considering ocean warming and the associated

bleaching, this would mean the end of much marine life as we know it.

M. Meinshausen (*) Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: malte.meinshausen@unimelb.edu.au

460

12.1 Background on the Investigated Scenarios

The international community uses various scenarios to explore these future changes

to CO 2 , involving other GHGs and ultimately, temperature, precipitation changes,

and changes involving extreme events. Here, we compare the scenarios developed

in this study with the standard scenarios developed for the forthcoming

Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. The

scenarios used in the forthcoming IPCC Sixth Assessment Report are the so-called

‘SSP scenarios’. The full range of those nine SSP scenarios spans cases of unabated

fossil fuel use at the higher-emissions end to two scenarios at the lower-emission

end that are considered to be consistent with a 2  °C and 1.5  °C warming target.

These two scenarios are called ‘SSP1_26’ and ‘SSP1_19’, respectively.

Both of those lower-emissions scenarios imply a substantial amount of biomass

use for energy, combined with CCS, to draw down atmospheric CO 2 levels. This is

the key difference from the scenarios developed in the present study. Whereas we

use a substantial amount of negative CO 2 emissions in the reforestation and forest

restoration options, our analysis does not depend on the assumption that biomass,

combined with CCS, must be used on large scale to achieve either the 2 °C or 1.5 °C

target.

If we do not rely on some negative CO 2 emission options, in particular biomass

and CCS, the questions are: (1) to what extent are those negative emissions created

by other means (e.g., reforestation); or (2) whether CO 2 emissions can be reduced

in the first place so that we do not need to rely on negative emissions options; or (3)

whether other non-CO 2 gases can be reduced even further to make negative CO 2

emissions unnecessary. The scenarios developed in this study use all three options,

as outlined in the previous chapters. Not only will energy-related CO 2 emissions be

radically reduced, CO 2 uptake via reforestation and forest restoration will also play

an important role. This study takes a more conservative approach to non-CO 2 gases

by being consistent with other stringent mitigation scenarios. Thereby, we ensure

that the feasibility constraints implicitly or explicitly set by other modelling frame-

works are not violated.

12.2 Comparison of Atmospheric CO 2 Concentrations

and Radiative Forcing

As mentioned above, unabated fossil fuel use over the last century and the twenty-

first century will dramatically change the oceans, simply by creating an atmospheric

CO 2 concentration beyond that present on the Earth for the last 10 million years.

Currently, we just exceeded the historical maximum level of 400 ppm atmospheric

CO 2 and are continuing to add 2–3 ppm a year. If we consider the most stringent

mitigation scenario used in the preparation of the forthcoming IPCC Sixth

Assessment Report, SSP1_19, then we will reach 400 ppm concentrations again in

the latter half of the century. Staying below 400 ppm is a prerequisite in the long

term for remaining below 2 °C or reaching 1.5 °C warming.

M. Meinshausen

461

Figure 12.1 shows the global CO 2 , CH 4 , and N 2 O concentrations under the key

RCP and SSP scenarios and the three scenarios developed as part of this study. Our

1.5 °C scenario is clearly lower in terms of its CO 2 concentrations than the lowest

SSP scenario, SSP1_19, for practically the entire twenty-first century (upper panel).

Only towards the end of the twenty-first century do the strongly negative CO 2 emis-

sions in the SSP1_19 scenario bring the CO 2 concentration closer towards our

1.5 °C scenario.

Aggregating all the greenhouse gas and aerosol emissions by their radiative forc-

ing, and expressing the resulting radiative forcing again as if it were only caused by

CO 2 yields the so-called ‘CO 2 equivalence concentrations’. In Fig. 12.2, the CO 2

equivalence concentrations for the four RCP scenarios, the nine SSP scenarios, and

three scenarios developed in this study are shown. The reference scenario in this

study is quite similar to both the RCP6.0 and SSP4_60 scenarios, providing a

medium-high reference case. The radiative forcing and CO 2 equivalence concentra-

tion of the 2 °C scenario of this study is actually quite closely aligned with the lower

SSP1_19 scenario, at least initially until the middle of the second half of this cen-

tury. Thereafter, the net negative CO 2 emissions implied by the SSP1_19 scenario

lead to stronger reductions in radiative forcing than the 2.0 °C scenario of our study.

However, our lower 1.5 °C scenario first undercuts the radiative forcing trajectory

of the SSP1_19 scenario, but then ends up at a similar radiative forcing level by

Figure 12.2 shows CO 2 equivalence concentrations (upper panel) and radiative

forcing (lower panel) of the main scenarios used in IPCC Assessment Reports and

this study’s scenarios. The RCP scenarios (shown in thin dotted lines) underlie the

IPCC Fifth Assessment Report and the so-called ‘SSP scenarios’ provide the main

basis for the scenarios considered in the IPCC Sixth Assessment Report. The three

scenarios developed in this study are shown in thick blue lines.

12.3 Comparison of Cumulative CO 2 Emissions

Since the IPCC Fifth Assessment Report, cumulative CO 2 emissions have been

introduced as a key metric into the international climate debate. Every tonne of

additional CO 2 emitted will add to that cumulative burden and warm the planet for

the next hundreds and in fact thousands of years. The only way to halt further global

warming is to halt cumulative CO 2 emissions, which means bringing the annual CO 2

emissions to basically zero levels. Only with net negative emissions can the tem-

perature thermostat of the Earth be dialled back again. Therefore, while achieving

such net negative emissions is tremendously challenging, it is the only way to main-

tain long-term climate change at levels close to those of today or well below

1.5 °C. Few scenarios include very strong near-term reductions and no substantially

negative CO 2 emissions and can limit the temperature increase to 1.5 °C, with no or

only a slight overshoot (IPCC Special Report on 1.5 °C). In this study, as well as

very strong near-term reductions in energy-related CO 2 emissions, we have used a

range of land-use-based sequestration options. These are not unambitious, as

12 Implications of the Developed Scenarios for Climate Change

462

1850 1900 1950 2000 2050 2100
250

300

350

400

450

500

550

600

650

700

750
Global CO 2 Concentrations

Global mean atmospheric CO

concentrations (ppm) 2

Global mean atmospheric CH

concentrations (ppb) 4

Global mean atmospheric N

O concentrations (ppb) 2

1850 1900 1950 2000 2050 2100
500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500
Global CH 4 Concentrations

1850 1900 1950 2000 2050 2100
260

280

300

320

340

360

380

400

420
Global N 2 O Concentrations

1.5C2.0C
5.0CRCP26
RCP45RCP60
RCP85

SSP1_19SSP1_26
SSP2_45
SSP3_70SSP3_70_LowNTCF
SSP4_34SSP4_60
SSP5_34_OSSSP5_85

Fig. 12.1 Global CO 2 , CH 4 and N 2 O concentrations under various scenarios. The so-called SSP scenarios are going to inform the Sixth Assessment Report by the IPCC, the RCP scenarios are the previous generation of scenarios and the LDF scenarios are those developed in this study

M. Meinshausen

463

1850 1900 1950 2000 2050 2100

200

400

600

800

1,000

1,200

1,400

1,600

Co2 Equivalence Concentrations (ppm)

CO 2 Equivalence concentrations

1850 1900 1950 2000 2050 2100

0

1

2

3

4

5

6

7

8

9

10

Effecive Radiative Forcing (W/m2)

Radiative Forcing

1.5C
2.0C
5.0C
RCP26
RCP45
RCP60
RCP85

SSP1_19
SSP1_26
SSP2_45
SSP3_70
SSP3_70_LowNTCF
SSP4_34
SSP4_60
SSP5_34_OS
SSP5_85

Fig. 12.2 CO 2 equivalence concentrations and radiative forcing of main IPCC scenarios for the forthcoming Sixth Assessment (so-called SSP scenarios), the RCP scenarios underlying the Fifth IPCC Assessment Report and the LDF scenarios developed in this study

12 Implications of the Developed Scenarios for Climate Change

464

outlined in previous chapters. In fact, in the case of the 1.5 °C scenario, they practi-

cally require that the Earth’s forests be returned to their pre-industrial coverage—a

substantial challenge given the widespread deforestation that has occurred over the

last 100  years. By the end of the century, the cumulative CO 2 emissions beyond

levels will constitute around 300 GtCO 2 (see Fig. 12.3).

Without going into too much detail about why cumulative emission numbers dif-

fer between various studies, the key point is that CO 2 emissions must be reduced to

zero. Otherwise, cumulative emissions and global warming will continue to increase.

Therefore, there is no way to avoid emissions-free electricity, transport, and indus-

trial systems, that either learn to completely get rid themselves of fossil fuels or to

compensates for any remaining use, either by CCS or land-based sequestration.

Because the latter negative emissions option must be used to offset some remaining

agricultural emissions, the story becomes simpler. There is no way around the fast

and complete cessation of all fossil fuel use. This is the simple and powerful logic

of carbon budgets.

Global cumulative CO 2 emissions for the scenarios developed in this study (thick

blue lines) and other literature-reported scenarios from the RCP and SSP sets. It is

clear that this study’s 2.0 °C pathway initially reaches a similar cumulative emis-

sions level as the SSP1_19 scenario, before the cumulative CO 2 emissions are

reduced again in SSP1_19 with large-scale net negative CO 2 emissions via bioen-

ergy with carbon capture and storage. In contrast, the reduction of cumulative CO 2

emissions in this study’s 1.5 °C scenarios starts to plateau much earlier (by 2035)

and are then reduced by land -based sequestration options, such as reforestation and

forest restoration (thick blue line at the lower end). The stated blue 1.5 °C carbon

1850 1900 1950 2000 2050 2100

-400

-200

0

200

400

600

Cumulative Carbon Emissions (GtC

)

-1,500

-1,000

-500

0

500

1,000

1,500

2,000

2,500

Global Cumulative CO 2 Emissions LDF_REF

RCP26

RCP45

SSP1_19

SSP1_26

SSP4_34

SSP5_34_OS

1.5C
2.0C
5.0C
RCP26
RCP45
RCP60
RCP85

SSP1_19
SSP1_26
SSP2_45
SSP3_70
SSP3_70_LowNTCF
SSP4_34
SSP4_60
SSP5_34_OS
SSP5_85

Cumulative Carbon Emissions (GtCO

) 2

1.5C Carbon Budget
(incl. additional Earth System feedbacks)

Cumulative Carbon Emissions so far (until 2011)

Fig. 12.3 Global cumulative CO2 emissions – 2.0 °C and 1.5 °C scenarios

M. Meinshausen

465

budget range is the effective central value presented in the recent IPCC Special

Report on 1.5 °C warming.^1

12.4 Implications for Temperature and Sea-Level Rise

This section examines the implications of the three scenarios developed in this

study for the probabilistic global mean temperature and sea level rise. Based on the

latest version 7 of the reduced complexity carbon cycle and climate model MAGICC,

we can derive a range of projections for every scenarios that provides a good mea-

sure of the projection uncertainties over the twenty-first century. We ran this model

600 times for each scenario, varying wide ranges of feedback and forcing parame-

ters. For the sea-level rise projections, we used the new sea-level rise module of

MAGICC7, as described by Nauels et al. ( 2017 ) and drove that with our probabilis-

tic temperature projections. We did not include the recent finding that possible

Antarctic ice sheet instability could lead to a much greater sea-level rise this cen-

tury. These extra contributions to sea-level rise are also assumed to affect the higher

end of the projections for high-emission scenarios, and do not therefore change the

sea-level rise projections of the lower-emissions scenarios.

A key question regarding future temperature projections is whether the scenarios

will stay below the envisaged 1.5  °C and 2.0  °C warming levels relative to pre-

industrial levels. When answering this question in terms of the consistency of the

1.5 °C and 2.0 °C warming levels, the uncertainty in historical reconstructions of

temperatures must play a role. The latest IPCC Special Report on 1.5 °C warming

estimated from an average of four studies that the difference between early- industrial

(1850–1900) and recent surface air temperature levels (2006–2015) was 0.97 °C. If

we accept here the slightly oversimplified assumption that the 1850–1900 tempera-

ture levels can be equated with pre-industrial temperatures, we can evaluate the

difference towards a 1.5 °C warming level, namely 0.53 °C. Note that this distance

could be shorter—a matter of ongoing scientific debate.

Anyway, the median temperatures for the (lowest) 1.5 °C Scenario in this study

do indeed—somewhat by design—reach the 1.5 °C target level by 2100. This means

that after a slight overshoot, there will be a 50% chance by 2100 that the global

mean temperature is at or below 1.5  °C warming—and without the widespread

deployment of bioenergy with carbon capture and storage (BECCS).

(^1) The IPCC Special Report estimates for a 0.53 °C distance of the 1.5 °C degree target from the 2006–2015 level, a carbon budget of 560 GtCO 2 from 1 January 2018 onwards. Reduced by 100 GtCO 2 for additional Earth System Feedbacks (see Table 2.2 in the IPCC Special Report on 1.5 °C) and adding approximately 270 GtCO 2 emissions for the period from 2011 to 2017, the central estimate for a 50% chance of staying below 1.5 °C warming as stated by IPCC is approximately 730 GtCO 2 from 2011 onwards. This is for a definition of historical warming that is based on a consistent surface air temperature estimate over the land and oceans. A further reduction of approximately 150 GtCO 2 would result, if we take into account that the IPCC carbon budget esti- mate refer to a 1850–1900 early industrial reference period for the 1.5 °C warming, rather than a 1750 pre-industrial reference period (which makes approximately a 0.1 °C difference, with +− 0.1 °C uncertainty). 12 Implications of the Developed Scenarios for Climate Change

466

In the case of our 2 °C Scenario, the centric 66% range is almost entirely below

the 2 °C warming level, which means that the chance of maintaining temperature

change below 2 °C is 80%–85%, in the modelling framework used. This warming

level and likelihood fits well with the adapted Paris Agreement target, which shifted

from a ‘below 2 °C’ to a ‘well below 2°C’ formulation.

Of course, there are several uncertainties that are not addressed in those probabi-

listic temperature projections. Therefore, future investigations might shift the

estimates for a 50%, lower, or higher chance of staying below 1.5 °C warming level

relative to the pre-industrial level.

Figure 12.4 shows the global mean surface air temperature projections, and their

66% and 90% ranges, for the three scenarios developed in this study. The reference

scenario is shown in red, the 2.0 °C pathway is shown in blue, and the 1.5 °C path-

way is shown in green. For historical temperatures, a mixture between ocean- surface

and land-surface air temperatures is shown, namely the HadCRUT4 dataset. The

most recent estimate by the IPCC of the pre-industrial air surface temperature levels

is that they were around 0.97 °C below the 2006–2015 levels. The median projec-

tions of the three scenarios are shown in thick solid lines.

As a first approximation, the global mean temperature is proportional to the sum

of all historical CO 2 emissions. In a similar vein, sea-level rise is the sum over all

past temperature rises relative to the pre-industrial level. Combining these two

approximations yields the rule of thumb that sea-level rise is proportional to the

double integral of CO 2 emissions. Therefore, whereas temperatures are relatively

agnostic about when CO 2 emissions occurred, sea-level rise will be higher the lon-

ger ago the CO 2 emission occurred.

This proportionality has implications for our scenarios. As we saw previously,

our scenarios are relatively low in terms of radiative forcing early in the twenty-first

century compared with the lower scenarios of the SSP and RCP sets. Towards the

end of this century, the strongly net negative emissions of the SSP1_19 scenario will

cause radiative forcing (and approximate temperature) to be similar under SSP1_19

and our 1.5 °C scenario. That our scenarios will first entail lower and then similar

forcing and temperature levels suggests that the implied sea-level rise will be lower

for the second half of the twenty-first century and beyond, even though the 2100

temperature level might be similar. This is a clear benefit of our scenarios and should

be investigated further in the future.

Thus, there is clearly an advantage in undertaking concerted early action rather a

slower decline in emissions followed by strong negative emissions. This is impor-

tant. However, in the larger scheme of things, it is clearly a second-order effect.

Even under the strongest mitigation scenarios, we cannot expect sea-level rise to

stop any time soon. To halt sea-level rise in the 21st or even the twenty-second cen-

tury, we will require massively negative CO 2 emissions, drawing back out of the

atmosphere a lot of the CO 2 that we emitted this century. Therefore, even under our

low 1.5 °C and 2.0 °C scenarios, the expected sea-level rise by 2100 will be above

30 cm relative to the 2010 levels—and will continue to rise to 2100 (Fig. 12.5).

M. Meinshausen

467

Fig. 12.4 Global-mean surface air temperature projections

12 Implications of the Developed Scenarios for Climate Change

468

Fig. 12.5 Global-mean sea level rise projections under the three scenarios developed in this study

M. Meinshausen

469

References

Ricke, K.L., Orr, J.C., Schneider, K. and Caldeira, K., 2013. Risks to coral reefs from ocean car- bonate chemistry changes in recent earth system model projections. Environmental Research Letters, 8(3), p.034003. Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K. and Wigley, T.M., 2017. Synthesizing long-term sea level rise projections-the MAGICC sea level model v2. 0. Geoscientific Model Development, 10(6).

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

12 Implications of the Developed Scenarios for Climate Change

© The Author(s) 2019 471 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2_13

Chapter 13

Discussion, Conclusions

and Recommendations

Sven Teske, Thomas Pregger, Johannes Pagenkopf, Bent van den Adel,

Özcan Deniz, Malte Meinshausen, and Damien Giurco

Abstract The following section focuses on the main findings in all parts of the

research, with priority given to high-level lessons, to avoid the repetition of previous

chapters. The key findings as well as the research limitations and further research

requirements are given for following topics:

Renewable energy potential mapping, Transport scenario and long-term energy

scenario development, power sector analysis, employment and mineral resource

implications for the 2.0C and 1.5C scenarios and non-energy GHG scenarios,

Policy recommendations for the energy sector with a focus on policies for build-

ings sector decarbonisation, for the transport and industry sector as well as a recom-

mended political framework for power markets are provided.

S. Teske (*) · D. Giurco Institute for Sustainable Futures, University of Technology Sydney, Sydney, NSW, Australia e-mail: sven.teske@uts.edu.au; damien.giurco@uts.edu.au

T. Pregger Department of Energy Systems Analysis, German Aerospace Center (DLR), Institute for Engineering Thermodynamics (TT), Pfaffenwaldring, Germany e-mail: thomas.pregger@dlr.de

J. Pagenkopf · B. van den Adel · Ö. Deniz Department of Vehicle Systems and Technology Assessment, German Aerospace Center (DLR), Institute of Vehicle Concepts (FK), Pfaffenwaldring, Germany e-mail: johannes.pagenkopf@dlr.de; Bent.vandenAdel@dlr.de; oezcan.deniz@dlr.de

M. Meinshausen Australian-German Climate and Energy College, University of Melbourne, Parkville, Victoria, Australia e-mail: malte.meinshausen@unimelb.edu.au

The Paris Agreement central aim is to strengthen the global
response to the threat of climate change by keeping a global
temperature rise this century well below 2 °C above pre-
industrial levels and to pursue efforts to limit the temperature
increase even further to 1.5 °C.

UNFCCC ( 2015 )

472

The Paris Agreement’s goals require significant change in how we use and produce

energy on a global level. This energy transition must be started immediately and

without further delay, and concerns energy-producing industry and utilities as well

as every energy consumer—from the industry level down to the residential sector. A

combination of energy efficiency and the use of renewable energies will involve

new business concepts for the energy sector, which will require entirely new poli-

cies that provide a new market framework. The implementation of new technologies

and business concepts will include policy changes on the community level as well

as by national governments and international organizations. While the 2.0  °C

Scenario allows for a 3–5 years transition period in which to implement policy mea-

sures, the 1.5 °C Scenario allows no further delay but requires an immediate start,

at the latest in 2020. Therefore, the 1.5 °C Scenario presented in this book provides

a technical pathway that assumes and allows no political delay, and may therefore

be seen as a technical benchmark scenario.

However, this energy transition cannot be seen in isolation. To stay within the

Paris Agreement Goal requires reductions in greenhouse gas (GHG) emission that

are greater than can be delivered by the energy transition alone (see Chap. 4).

However, there are technical and logistical limits to how fast new technologies can

be implemented. The establishment of efficient transport systems and industry pro-

cesses, or simply the provision of required renewable energy and storage technolo-

gies will require a minimum time. Production capacities must increase significantly,

people must be trained, and existing buildings must be retro-fitted. Infrastructural

changes, such as smart power grids, must be planed, construction permits must be

issued. The energy transition will require political determination and public accep-

tance. Therefore, the planning process will require resolute stakeholder involve-

ment. Furthermore, a fundamental shift in today’s resource-intensive lifestyles

seems unavoidable if we are to limit global warming. In particular, the way con-

sumption and mobility are organized in developed countries today will challenge

planetary boundaries to the extreme if they remain at their current levels.

During the development of the energy scenario pathways, it became clear that

even the most ambitious ‘man-on-the-moon’ program—if focussed only on the

energy sector—would not be enough. Therefore, the political framework required to

implement the Paris Agreement on national levels must also take the land-use sec-

tor, as well as the main GHGs, methane, N 2 O, and fluorinated gases, into account.

The following sections present the main findings and lessons of this research proj-

ect, and highlight its limitations and further research requirements. Finally, we pro-

vide policy recommendations for the energy sector in order to implement the -energy

scenarios.

13.1 Findings and Limitations—Modelling

The following section focuses on the main findings in all parts of the research, with

priority given to high-level lessons, to avoid the repetition of previous chapters.

S. Teske et al.

473

13.1.1 Key Findings—Renewable Energy Potential Mapping

Various research projects have analysed renewable energy potentials and all have in

common that the renewable energy potential exceeded the current and projected

energy demands over the next decades by an order of magnitude. However, regional

distributions are uneven, and especially in densely populated regions, such as urban

areas, the local renewable energy resource might be unable to supply the demand.

Therefore, renewable energies might have to be transported by power lines or in the

form of gasified or liquid renewable fuels to the demand centre. However, all 10

regions and 75 sub-regions examined had sufficient renewable energy resources to

meet the regional needs.

13.1.2 Limitations and Further Research—Renewable Energy

Potential

The quality of data varies significantly across the regions and especially detailed

high-resolution surveys have their limits. Due to the limited available data, detailed

mapping of the Eurasia region—especially for Russia and parts of central Asian

countries—was not possible. Therefore, in this case, we relied on research published

in the scientific literature. Onshore wind data were generally only available for 80 m

above the ground, whereas no data for 100–120  m were available in most cases.

However, modern wind turbines operate at those higher levels and wind resources

are generally better at these elevations. Therefore, further research is required using

open source data for onshore and offshore wind data at the 100 m level.

13.1.3 Key Findings—Transport Scenario

Transport modelling has shown that the 2.0  °C and 1.5  °C pathways can be met

when strong and determined measures are taken, starting immediately. They include

rapid electrification across passenger and freight transport modes, a shift towards

more energy-efficient transport modes, and a build-up of biomass and synfuel

capacities for the transport modes that are less inclined towards electrification due

to range or constructional constraints, as is the case in aviation. In the road sub-

sector, which is the most relevant emission source in the transport area, battery

electric and fuel-cell electric vehicles must be widely introduced, which will also

require a stringent parallel build-up of recharging and refuelling infrastructures.

In general, we found that beyond pure technical measures with regard to pow-

ertrain shifts and overall efficiency enhancements, fundamental changes in today’s

mobility patterns will also be required to meet the 2.0 °C Scenario, and even more

so for the 1.5 °C pathway. This will apply particularly to the car use habits in the

OECD countries. In essence, definite limitations on transport activities and modal

13 Discussion, Conclusions and Recommendations

474

shifts towards mainly buses and railways in some world regions and sub-sectors will

be required to meet the Paris goals. However, the Non-OECD world regions will

mainly increase their overall transport activity until 2050. The 2.0  °C and 1.5  °C

transport scenario pathways will not be achieved automatically, but will require

long-sighted infrastructural and transport policy framework settings on both inter-

and intra-governmental levels.

13.1.4 Limitations and Further Research

Requirement—Transport

The statistical databases for several world regions on transport activities and fleet

and powertrain shares are limited, and in those cases, projections, conclusions by

analogy, and estimations were required in our modelling. Therefore, further studies

should focus on enhancing these databases and specify the modelling in more detail,

which could also include case studies of countries instead of regions, to better

address spatial particularities in the transport models. Detailed investigations of

mode shift potentials, based on infrastructure capacity constraints, were considered

to some extent, but deserve more in-depth modelling in future works. Further

research is required to refine the coupling of renewable energy potentials, transport

infrastructure upgrades, and the expansion of on-board energy storage usage.

13.1.5 Key Findings—Long-Term Energy Scenario

The 2.0 °C and the 1.5 °C scenarios both represent ambitious pathways that require

fundamental changes in current energy consumption and supply. The key strategies

of these pathways are the implementation of renewable energy technologies and

efficiency improvements in all sectors. The electrification of the transport and heat-

ing sectors and the diversification of supply technologies are core elements of both

alternative scenarios. Besides numerous technical and structural improvements,

behavioural changes among end-users and major changes in investment activities

and strategies must be achieved. This applies, for example, to the per capita electric-

ity consumption (electric appliances without heating) of ‘residential and other sec-

tors’, which will decrease in OECD countries by one third between 2015 and

2050 in the 1.5 °C Scenario, but will grows in the non-OECD regions by only 70%

in the same period. This could imply limitations on personal consumption compared

with today’s standards, particularly in OECD countries—at least for as long as fos-

sil fuels still play a significant role. Another example is the final energy demand per

$GDP in the ‘Industry’ sector, which will decrease by 65% in OECD regions and by

80% in non-OECD regions, an ambitious pathway that will require stringent

S. Teske et al.

475

technology change and replacement strategies and supporting regulatory and gover-

nance measures to trigger huge investments for its realization.

On the energy supply side, considerable contributions in the future are assumed

from wind and solar power, electrification and synthetic fuel use in transport and

heating, sustainable biomass use, especially for co-generation and biofuels, and dis-

trict heating systems that integrate solar, geothermal, and ambient heat potentials.

The exploitation of renewable energy potentials depends strongly on regional con-

ditions. In regions such as India and China, a 100% renewable electricity supply

will require the extensive use of existing potentials. The global installed capacities

of renewable power generation technologies will increase by a factor of more than

12, from 2000 GW in 2015 to more than 25,000 GW in 2050. Cumulative invest-

ments for power generation are estimated to increase between 2015 and 2050 by up

to around US$ 50,000 billion compared with around US$20000 billion in the refer-

ence case. Fuel cost savings could offset around 90% of the additional investment

costs as consequence of the fossil fuel phase-out and reductions in demand, but

without considering the additional infrastructure demands of the transition arising

from grid expansion, storage, and other flexibility demands. In the scenarios, large-

scale and long-range electricity transport between Europe and the MENA countries

is assumed to be a possible and promising example of supra-regional exchange

between regions of production and regions of demand. Many of these import/export

relationships must also be realized among countries within individual world regions

(which are thus not resolved in our model) to increase the security and cost-

efficiency of the energy supply. Decentralization and digitalization, but also the

efficient implementation of new respectively the expansion of existing central infra-

structures, are other implicit core elements of the scenario narratives. The large-

scale generation and use of synthetic fuels are expected to play key roles in the

deep-decarbonisation scenario, at least if the intensity of today’s freight transport

and air traffic is to be maintained, despite the huge energy losses this option will

have. Both alternative scenarios, especially the 1.5  °C Scenario, require a rapid

reduction in the final energy demand and, as far as possible, stagnation in the strong

global growth in the demand for energy services, at least for as long as fossil ener-

gies dominate the energy supply structures.

Besides the structural similarities between both the 2.0 °C and 1.5 °C Scenarios,

one main difference between them is the rate of transformation: To maintain the

average global temperature increase due to climate change below 1.5 °C, the trans-

formation must be accomplished as fast as technically possible. The trend in increas-

ing global energy-related CO 2 emissions must be reversed as soon as possible (in

the early 2020s, at the latest) and emissions must be reduced by 70% in 2035 and by

85% in 2040 (compared with emission levels in 1990). Every single year without

significant emission reductions on the global level will dramatically reduce our

chance to confine global warming to 1.5  °C.  In contrast, in the 2.0  °C Scenario,

emission reductions in 2035 may be in the order of 40% and 65% in 2040, leaving

a little more time for the transition process.

13 Discussion, Conclusions and Recommendations

476

13.1.6 Limitations and Further Research Requirement—

Long-Term Energy Scenario

The 1.5  °C Scenario may seem more difficult than similarly ambitious scenarios

that were made 10 to even 20 years ago. However, the opportunities to respond to

climate change have been largely wasted in the last two decades, and all transition

processes have faced huge obstacles in the past due to the inertia and conflicting

aims of societies, governments, and most relevant stakeholders. Too often, more

attention has been paid to doubters than to facts. Therefore, the scenarios also show

that the longer the governments wait, the more difficult it will be to prevent severe

climate damage and the greater will be the technical and economic challenges that

will be encountered in the energy system transformation.

The coarse regional resolution of such global scenarios does not allow sufficient

account to be taken of sub-regional differences in energy demand and the character-

istic and favourable possibilities of sustainable supply. However, it can provide

rather fundamental insight into basic technical and structural possibilities and

requirements of a target-oriented pathway. Our results clearly reveal and quantita-

tively describe that the coming years will be most critical regarding a successful

energy transition because for both parts of the energy transition—efficiency

improvement/demand reduction and the implementation of new technologies—

huge investments and fundamental changes in producing, distributing, and consum-

ing energy will be needed. Such transformation processes must be analysed and

planned carefully under the complex economic and societal framework conditions

of each region, down to the country, sub-country, and community levels. Such anal-

yses can then form the basis for the further investigation of the economic implica-

tions of these pathways.

Another limitation of this approach is that the economic, technical, and market

assumptions made probably have limited consistency. Carbon, fuel, and technology

costs are assumed independently of the assumptions regarding overall economic

development and the final energy demand. It also remains unclear to what extent the

energy transition will change the overall material demand and activity of the manu-

facturing industry. Furthermore, which economic framework conditions and market

mechanisms will be necessary for rapid decarbonisation remain largely unclear, as

is whether the current market mechanisms are capable of supporting the fundamen-

tal paradigm shift of this target-oriented energy transition.

13.1.7 Key Findings—Power Sector Analysis

Although there are significant differences across all the regions and sub-regions

analysed, there are some similarities:

Increasing loads: The loads in all regions will increase significantly between 2020

and 2050, due to increased electrification of the transport and, to some extent, the
heating sectors. Higher loads will require the adaptation of power-lines and

S. Teske et al.

477

transformer stations, especially on distribution grids where electric vehicles are
most likely to be charged.

Increasing reverse power flows: Almost all regions will have periods of negative

residual load, when generation is higher than the required load within a specific
period of time. This leads to reversed power flow, and the generated electricity
must be transported to other regions. The dominant power-generation technolo-
gies are wind and solar photovoltaic (PV). Solar PV is mainly connected to the
distribution grid and export requires that the electricity must be able to flow from
low- to medium-voltage levels, which requires adaptation at transformer
stations.

Ratio between maximum variable generation capacity and maximum load: The

results presented in Chap. 8 suggest that a ratio of 180% variable renewable
capacity to the maximum load represents the optimum relationship. If the capac-
ity were higher, short-term peaks would appear more frequently, which would
lead to higher curtailment, or high storage and transmission demands. Example:
The maximum load of a region is 100 GW and the installed capacity of wind and
solar PV combined is 180 GW. Short-term peaks are relatively rare, and curtail-
ment is below 10% of the total annual generation potential. If the variable power
generation were above 200 GW, while the load remained at 100 GW, the curtail-
ment rates would increase significantly, to 15% and higher.

Technology variety reduces storage demand: The combination of solar PV and wind

leads to a lower storage demand than does a solar- or wind-dominated supply
scenario. The relative interaction between the available wind and solar resources,
not just in regard to day and night, but also seasonally, will reduce the storage
demand in all regions. Therefore, the levelized cost of electricity generation can-
not be seen in isolation. Even if, for example, wind is more expensive than solar
PV in one region, a combination of wind and solar will lead to a reduction in
storage demand and lower systems costs.

Variety in storage technology and sector coupling: The combination of battery and

hydro pump storage technology and the conversion of the gas sector to hydrogen
and synthetic fuels will be beneficial. The hydrogen produced can be used for the
management of demand, and hydrogen-fuelled power plants can provide valu-
able dispatch services.

13.1.8 Limitations and Further Research Requirement—

Power Sector Analysis

Measured load curves for regions, countries, or states/provinces are often unavail-

able and, in some countries, are even classified information. Therefore, it was not

always possible to compare the calculated loads with actual current actual. However,

the comparison of 2020 calculations with current maximum loads for regions and

countries with published data showed that the calculations were within a  ±  10%

range. However, verification was not possible for several regions. Therefore, our

analysis may over- or underestimate the current loads and therefore the future

13 Discussion, Conclusions and Recommendations

478

projections of loads as well. Therefore, the optimal ratio of maximum load to

installed variable capacity requires further research.

More research is also required to verify the thesis of an optimal ratio between the

variable generation and maximum load, because the sample size of this study was

not sufficient to ensure validity. Furthermore, the optimal mix of solar and wind

requires better meteorological data and actual measured load profiles. Access to

more detailed data to calculate more case studies is vital to determining the possible

optimal combination of wind and solar.

13.1.9 Key Findings—Non-energy Scenarios

The key finding of the land-use-related emission scenarios is that a dedicated con-

certed effort to sequester carbon by reforestation and forest restoration could re-

establish the terrestrial carbon stock of pre-industrial times. That would undoubtedly

come with multiple co-benefits, but would not be without challenges. After all, there

is a reason why humans in the various corners of the planet pursued deforestation,

whether for short-term and short-sighted gains or to establish agricultural areas that

fed an increasing population. Therefore, land-use conflicts and trade offs are an

inherent part of future mitigation actions, whether CO 2 sequestration is pursued by

reforestation sequestration or by some biomass and CCS use. Nevertheless, the

important result of this study is that the addition of land-use CO 2 and other GHG

emission pathways to energy-related scenarios yields scenarios that stay below or

get below 1.5 °C warming without a reliance on massive net negative CO 2 emission

potentials towards the second half of this century.

Going beyond the land-use CO 2 emission pathways that we sketched for a series

of sequestration options, we also designed trajectories for all the other GHGs and

aerosols. An unprecedented wealth of scenario information is now available thanks

to the recent concerted efforts of the larger integrated assessment community.

Designing a novel method here, the Generalized Quantile Walk method, we were

able to distil non-CO 2 pathways from this rich scenario database—in a way that

respects the correlations and dependencies between energy-related CO 2 and other

gas emissions. This is not only a new methodological advance in scenario research,

but also key to the proper estimation of the climate effects of the energy-related CO 2

scenarios designed in the main part of this study.

13.1.10 Limitations and Further Research Requirement—

Non-energy Scenarios

There are a number of limitations associated with the derived non-energy-related

emission trajectories. Possibly the most important opportunity for future research

will involve a more fine-grained look at land-use-based sequestration options in

S. Teske et al.

479

various countries and biomes. This study assumed only a rather coarse approxima-

tion of the available land areas, sequestration rates, and cumulative changes in land

carbon stocks to estimate the potential and time trajectories of those reforestation,

forest restoration, agroforestry, and other land-based sequestration options.

In terms of the non-CO 2 emission trajectories, this study relied heavily on the

collective wisdom embodied within a large set of literature-reported scenarios.

Although we have designed probably the most advanced method to distil that

knowledge into emission trajectories that are consistent with our energy-related

pathways, this meta-analytical approach is not without its limitations. In particular,

a bottom-up energy-system and land-use/agricultural model must be able to esti-

mate methane and N 2 O emissions from various agricultural activities in a more

coherent way, which could provide results on a regional level. Such regionally and

sectorally specific information would, in turn, allow the examination of various

mitigation options for non-CO 2 emissions. This bottom-up modelling capacity is

missing from our meta-analytical approach.

13.1.11 Key Findings—Employment Analysis

The occupational employment analysis was developed in 2018 to improve the data-

base for the ‘just transition’ concept. Not only is the number of jobs that will be

created or lost as a result of a global or regional energy transition important, but also

the specific occupations that will be to develop a socially sound transition. This

analysis breaks new grounds because very little information has been available.

However, the results indicate that even within the seven occupation types, job losses

are the exception and almost all trades will gain more jobs.

Very specialized jobs, such as machine operators in coal mines, will be lost and

there will be no replacement. Therefore, a detailed analysis of all sectors is required

to identify those highly specialized tasks and to develop re-training possibilities.

13.1.12 Limitations and Further Research Requirement—

Employment Analysis

The data available on the detailed employment requirements for renewable energies

are very limited. Although there are some data for solar PV and onshore and off-

shore wind, there are almost none for concentrated solar power plants or geothermal

energy. Furthermore, occupational surveys of the heating and energy efficiency sec-

tors are required.

13 Discussion, Conclusions and Recommendations

480

13.1.13 Key Findings—Mineral Resource Analysis

Lithium sees the highest projected increase from mined ore of around 40 times cur-

rent production to above 80 times current production should future technologies be

introduced without recycling.

Cumulative primary lithium demand by 2050 for the majority of scenarios is

above current reserves of lithium except for the “potential recycling” scenario but

less than known resources. The scenarios anticipate a scale up in resource explora-

tion, discovery and production for primary resources to meet demand—assuming

lithium-ion batteries continue to dominate as the chemistry of choice. It is important

that future mines be responsibly developed and that battery designs be compatible

with circular-economy thinking. Significant infrastructure for reuse and recycling

will need to be developed to achieve high rates of lithium recycling.

For cobalt, future scenarios exceed currently known reserves and approach cur-

rently known resources in 2050. Given the concentrated supply source from the

Democratic Republic of Congo, this will continue to keep pressure on exploring

alternative battery chemistries and on increasing cobalt recycling. Attention must

continue to be paid to reducing the social and environmental impacts of supply

whilst supporting development noting the significant adverse impacts on human and

environmental health associated with cobalt mining. While the value of cobalt in EV

battery recycling is already an important component of the recycling economics

because of supply limitations, the social and environmental challenges provide a

further driver for increasing recycling.

For silver, the potential of materials efficiency (using less silver per GW solar PV

panel) has potential to reduce demand owing to the long lifetimes of PV panels.

Under some scenarios using future technology with recycling, the levels of silver

demand are similar to current production.

13.1.14 Limitations and Further Research Requirement—

Mineral Resource Analysis

This study focuses only on the metal demand for renewable energy (generation and

storage) and transport and does not consider other demands for these metals.

However, it is expected that with the increase in renewable energy, renewable energy

technologies will consume a greater share of these metals and it is anticipated that

this growth will have significant influence on overall market dynamics, including

influencing prices, which may feedback to efforts to reduce material intensity and

invest in reuse and recycling infrastructure.

Promoting the transition to circular economy for both renewable energy and

resource cycles; and adopting a systems view that considers available supply as well

as social and environmental factors is critically important. To support sustainable

development goals, both the primary and secondary sources of the resources

S. Teske et al.

481

required to underpin this renewable energy transformation needs to be stewarded as

the supply chains develop. The high total demand requirements for energy metals,

demonstrates the importance of redesigning technologies and systems to eliminate

the adverse social and environmental supply chain impacts, to promote long-life

products, and to actively encourage efficient material use in both energy and trans-

port sectors.

13.2 Policy Recommendations by Sector

To implement the 2.0 °C or 1.5 °C Scenario will require a significant shift in current

policies. This section documents the policy measures that have been assumed for

the scenarios presented in the previous chapters, as well as policy measures known

to be successful. The global legal frameworks and regulations differ significantly on

national and community levels. Therefore, only the functions and aims of suggested

policy measures can be discussed, but not how they can be integrated into current

jurisdictions. That said, it is assumed that the Paris Agreement will be the global

basis.

13.2.1 Energy

The energy sector is not a homogeneous sector but is highly diverse. Therefore,

policy measures can follow different strategies and can address different aspects and

stakeholders. The supply side can be divided into the main sub-sectors of electricity,

heating, and fuel supply. The demand side can be broken down further into build-

ings, industry, and transport. However, because all these sub-sectors are directly or

indirectly connected via resources and energy markets, policy interventions can

have different effects.

The basic principles for the development of the 2.0  °C and 1.5  °C Scenarios

derive from the long-term experiences with scenario development of the authorship

team, and have led to a ‘seven-step logic’.

This logic extends from the definition of the final state of the energy systems in

the long-term future to the key drivers of the energy demand and the energy effi-

ciency potentials, a technological analysis of supply and demand and the market

development potential, and the specific policy measures required to implement a

theoretical concept in the real market-place.

The seven steps are:

1. Define the maximum carbon budget and other targets, milestones, and con-

straints to achieve the climate goal;

2. Define the renewable energy resource potentials and limits within a space-

constrained environment;

13 Discussion, Conclusions and Recommendations

482

3. Identify the economic and societal drivers of demand;
4. Define the efficiency potentials and energy intensities by energy service and

sector;

5. Establish time lines and narratives for the technology implementation on the

end-user and supply sides;

6. Estimate the infrastructure needs, generation costs, and other effects;
7. Identify the required policies and discuss the policy options.

13.2.1.1 General Energy Policies

The energy sector requires both very specific measures, such as grid codes and effi-

ciency standards, and overarching measures. International collaboration and co-

operation are required to define and implement mandatory standards and to

effectively develop energy policy and its regulative interventions. The most impor-

tant interventions to accelerate the energy transition are:

• Renewable energy targets and incentives for their deployment and expansion;
• Internalization of external costs by carbon tax or surcharge;
• Phase-out of fossil fuel subsidies;
• Accelerated replacement of fossil and inefficient technologies.

Renewable energy targets are vital to accelerate the deployment of renewable

energy. Experiences of the past two decades clearly show the effectiveness of renew-

able energy policy development. The Renewable Policy Network for the 21st

Century (REN21) states in their annual market analysis, Renewables 2018, that

“Targets remain one of the primary means for policy makers to express their com-

mitment to renewable energy deployment. Targets are enacted for economy-wide

energy development as well as for specific sectors” (REN21-GSR- 2018 ). To achieve

these goals, innovation processes must be initiated, markets developed, and

investment stimulated. For the latter, auctions and feed-in tariffs have proven suit-

able. It is important in this context to guarantee investment security and to enable

long-term but appropriate revenues.

Climate change leads to a number of types of environmental damage. Carbon

emissions lead to climate change. Therefore, it is vital to put a price on carbon to

internalize the external costs. Carbon-pricing schemes can be established as cap-

and- trade schemes or taxes. Carbon pricing is not sufficient on its own to achieve

the objective of the Paris Agreement, and many leading international agencies and

institutions argue that a much more concerted and widespread global take-up of

carbon pricing will be necessary (Carbon Tracker 2018 ). To make carbon pricing an

efficient measure, the price of carbon must be sufficient to reflect the environmental

damage it causes and it must be reliable. Therefore, a minimum price should be

implemented to provide planning security.

Subsidies of fossil fuels counteract any efforts to make energy efficiency and

renewable energy competitive. According to the International Energy Agency, the

total amount of global fossil fuel subsidies was estimated to be around US$260 bil-

S. Teske et al.

483

lion in 2016 (IEA-DB 2018 ). The governments of the G20 and the Asia-Pacific

Economic Cooperation (APEC) reached an agreement to “ rationalize and phase out

over the medium term inefficient fossil fuel subsidies that encourage wasteful con-

sumption ” (OECD-IEA 2018 ).

Setting legally binding national targets for 100% renewable energy pathways

will lead to an orderly phase-out of fossil fuels. This is vital in planning the socio-

economic effects of the energy transition (see Chap. 10). Supporting measures to

achieve the replacement of fossil and inefficient technologies and energy sources

will be necessary because targets and economic incentives may not be sufficient in

all areas. Regulatory interventions for the decommissioning and replacement of

facilities are a way to stop opposing business scenarios and to overcome the inertia

of consumers and investors. Moreover, economic policy measures and the clearer

definitions and stringent enforcement of international standards will accelerate the

implementation of the best available technologies in the industry.

Both a minimum price on carbon and the immediate phase-out of fossil fuel

subsidies must be implemented in order to support the global energy transition.

Policy support for technology innovation is another important measure to create the

basis for the energy transition processes.

13.2.1.2 Policies for Buildings Sector Decarbonisation

To reduce the energy demands of existing building stock and new buildings, con-

stantly most stringent energy demand standards (= building energy codes ) are

required for all building types and across all countries. The goal must be to achieve

(near) zero-energy buildings, so that each building reduces its heating and cooling

demand to the lowest possible level and aims to supply the remaining energy with

on-site renewable energy technologies, such as solar collectors, electric heaters,

advanced bioenergy and heat pumps, or with low-temperature heating networks.

Mandatory municipal heating plans are an appropriate way to define an efficient

strategy that balances insulation, local heating systems, and the grid-connected heat

supply in regions with a significant space-heating demand. An analysis of building

energy codes in 15 countries (Young 2014 ) distinguished between residential and

commercial buildings and listed six technical requirement categories, ranging from

heating and cooling requirements and the insulation of the building envelope to the

building design. However, building energy codes must be mandatory and include

existing building stock as well as new buildings. The most urgent need for action is

in developing countries, where the rapid growth in building construction can be

expected over the next decades. Economic measures to increase heating/cooling

costs, e.g., by introducing fossil energy taxes or surcharges or by phasing-out fossil

fuel subsidies, could support efforts to save energy. However, they must be accom-

panied by social policies.

13 Discussion, Conclusions and Recommendations

484

13.2.1.3 Policies for more-Efficient Electrical Appliances

To reduce the electricity demand of consumer goods in households and equipment

in buildings, the efficiency standards for electrical appliances, all forms of informa-

tion and communication technologies (computers, smart devices, screens, televi-

sions, etc.), white goods (washing machines, dryers, dishwasher, fridges, and

freezers), electrical building equipment for thermal comfort, and lighting technolo-

gies are required. These efficiency standards must be dynamic and designed to sup-

port competition for the most efficient design. The Japanese front runner system

(IEA-PM 2018 ) is a positive example of dynamic efficiency standards. Labelling

programmes and purchase subsidies for the best available technologies can support

the replacement of old devices with the most efficient technologies. Measures for

training and capacity building are also essential, most importantly in non-OECD

countries.

13.2.1.4 Policies for the Transport Sector

As well as resolute electrification and further technical advances in all transport

modes, decision-makers in politics and the urban planning context must carefully

steer transport habits and infrastructure development toward a climate-friendly

transport system. Their aim should be to promote the use of less ecologically prob-

lematic transport modes. This can be done by, for example, the introduction of fiscal

and regulatory measures that effectively reduce the subsidization of currently

untaxed and internalisation of external costs.

In parallel, environmentally less harmful transportation modes should be incen-

tivized. Investments must also be channelled towards highly productive and energy-

efficient passenger and freight railway systems and towards a dense network of

battery recharging and hydrogen refuelling infrastructures for road vehicles. In the

passenger car and truck context, direct subsidies or tax incentives for electric

vehicles will speed up the electrification of fleets. CO 2 taxation, road tolls, and con-

gestion charges could be applied, in addition to parking-space management schemes

to reduce road traffic and thus internal combustion engines in a transition to car-

reduced cities. The assignment of parking lots and driving lanes exclusively to elec-

tric cars will speed the phase-out of internal combustion engines.

In aviation, measures could include the taxation of jet fuel and CO 2 , the applica-

tion of an emission trading scheme on the direct and indirect climate effects of flight

at high altitudes. Direct and indirect public subsidies for carriers and airports should

be abolished (investment and operational grants should be reduced and funding

should be allocated to a competitive and attractive rail system).

All measures curtailing the use of individual passenger transport should be

accompanied by the promotion of ubiquitous, fast, comfortable, and price-

competitive public transport systems, ride and car sharing and on-demand services

(especially for less densely populated semi-urban and rural areas). Last but not

least, an attractive and safe infrastructure for bicycles and e-bikes will help to reduce

S. Teske et al.

485

emissions and other unwanted side-effects of transport. In this arena, Copenhagen

and Amsterdam are at the cycling forefront and inspiring more and more cities in

following their path. Cities must also curtail tendencies to urban sprawl and ‘rein-

vent’ the compact city ideal, which means becoming pedestrian-friendly cities, thus

reducing the need for motorized individual mobility and freeing up space for recre-

ation and green spaces.

Cities in developed countries should aim to transform their transport systems

(often) from passenger-car-centred urban structures and policies towards pedes-

trian-, bike-, and mass-transport-friendly environments. The often densely popu-

lated emerging megacities in the upcoming economic powerhouses of Africa, Latin

America, and Asia should invest, right from the start, in resilient public-transport-

oriented urban structures instead of relying too strongly on individual passenger car

traffic, as the OECD countries have done in the past.

13.2.1.5 Policies for the Industry Sector

Policies to achieve the implementation of new highly efficient technologies and to

replace fossil fuel use in industry must be defined region-wide or even on the global

level, and will require stringent and regulatory implementation. Economic incen-

tives, national initiatives, and voluntary agreements with industrial branches will

most probably not, by themselves, see the achievement of a rapid technological

change. Concrete standards and requirements must be defined at a very detailed

level, covering as far as possible all technologies and their areas of application. The

systematic implementation of already-identified best-available technologies could

begin in the next few years. Mandatory energy management systems should be

introduced to identify efficiency potentials and to monitor efficiency progress. The

sustainability features along process chains and material flows must also be taken

into account when designing political measures. Particular attention must be paid to

the material efficiency of both production processes and their products, because this

can open up major energy efficiency potentials and reduce other environmental

effects. Public procurement policies and guidelines can help to establish new mar-

kets and to demonstrate new more-efficient products and opportunities. The effec-

tiveness of policy interventions must be assessed by independent experts and the

further development of efficiency programs and measures will require ongoing

coordination by independent executive agencies. The public provision of low-

interest loans, investment risk management, and tax exemptions for energy-efficient

technologies and processes will significantly support technological changes and

incentivize the huge investments required. Knowledge transfer between sectors and

countries can be achieved through networks initiated and coordinated by govern-

ments. Public funding for research and development activities with regard to tech-

nological innovation, low-carbon solutions, and their process integration will be

vital to push the technological limits further. Innovative approaches to the realiza-

tion of material cycles and recycling options, the recovery of industrial waste heat,

13 Discussion, Conclusions and Recommendations

486

and low-carbon raw materials and process routes in industry must also be identified

and implemented.

13.2.1.6 Political Framework for Power Markets

The 2.0  °C and 1.5  °C Scenarios will lead to 100% renewable electricity supply,

with significant shares of variable power generation. The traditional electricity mar-

ket framework has been developed for central suppliers operating dispatchable and

limited dispatchable (‘ base load ’) thermal power plants. The electricity markets of

the future will be dominated by variable generation without marginal/fuel costs. The

power system will also require the built-up and economic operation of a combina-

tion of dispatch generation, storage, and other system services whose operation will

be conditioned by renewable electricity feed-ins. For both reasons, a significantly

different market framework is urgently needed, in which the technologies can be

operated economically and refinanced. Renewable electricity should be guaranteed

priority access to the grid. Access to the exchange capacity available at any given

moment should be fully transparent and the transmission of renewable electricity

must always have preference. Furthermore, the design of distribution and transmis-

sion networks, particularly for interconnections and transformer stations, should be

guided by the objective of facilitating the integration of renewables and to achieve a

100% renewable electricity system.

To establish fair and equal market conditions, the ownership of electrical grids

should be completely disengaged from the ownership of power-generation and sup-

ply companies. To encourage new businesses, relevant grid data must be made avail-

able from transmission and distribution system operators. This will require

establishing communication standards and data protection guidelines for smart

grids. Legislation to support and expand demand-side management is required to

create new markets for the flexibility services for renewable electricity integration.

Public funding for research and development is required to further develop and

implement technologies that allow variable power integration, such as smart grid

technology, virtual power stations, low-cost storage solutions, and responsive

demand-side management. Finally, a policy framework that supports the electrifica-

tion and sector coupling of the heating and transport sectors is urgently needed for

a successful and cost-efficient transition process.

References

Carbon Tracker (2018), Closing the Gap to a Paris-compliant EU-ETS, 25th April 2018, website, viewed October 2018, https://www.carbontracker.org/reports/carbon-clampdown/ IEA-DB (2018), International Energy Agency Agency Database 2018, Energy Subsidies, webbased database, viewed October 2018, https://www.iea.org/statistics/resources/energysubsidies/

S. Teske et al.

487

IEA-PM (2018), International Energy Agency  – Policy and Measures database, Energy Conservation Frontrunner Plan-Japan, website, viewed October 2018, https://www.iea.org/pol- iciesandmeasures/pams/japan/name-22959-en.php OECD-IEA (2018), OECD website, OECD-IEA analysis of fossil fuels and other support, viewed October 2018, http://www.oecd.org/site/tadffss/ UNFCCC (2015), The Paris Agreement, website, viewed October 2018, https://unfccc.int/ process-and-meetings/the-paris-agreement/the-paris-agreement REN21-GSR (2018), REN21, 2018, Renewables 2018, Global Status Report, Paris/France, http:// http://www.ren21.net/status-of-renewables/global-status-report/ page 20 Young, R. (2014), Young, Rachel, Global Approaches: A Comparison of Building Energy Codes in 15 Countries, American Council for an Energy-Efficient Economy, 2014 ACEEE Summer Study on Energy Efficiency in Buildings, https://aceee.org/files/proceedings/2014/data/ papers/3-606.pdf

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

13 Discussion, Conclusions and Recommendations

489

Annex

Glossary, regions, and countries included in the scenarios, further detailed descrip-

tions of the assumptions (tables) of all models and methodologies, are detailed

results tables.

Acronyms

2DS 2.0 °C Scenario – published in the IEA ETP

ACEC Africa Clean Energy Corridor

ACOLA Australian Council of Learned Academies

B2DS beyond 2.0 °C Scenario – published in the IEA ETP

BC (emissions) Black carbon emission

BECCS Bioenergy with carbon capture and storage

BEV Battery electric vehicles

BP British Petrol

CCGT Closed-Cycle Gas Turbine (gas power plant technology)

CCS Carbon Capture and Sequestration

CdTd Cadmium Telluride (Solar photovoltaic cell technology)

CEDS Community Emissions Data System (for Historical Climate rel-

evant Emissions)

CHP Combine Heat and Power (=co-generation)

CIGS Copper Indium Gallium Di-Selenide (Solar photovoltaic cell

technology)

CMIP6 Coupled Model Inter-comparison Project Phase 6 (computer cli-

mate model)

CO2 Carbon Dioxide

CSP Concentrated Solar Power

DBFZ Deutsches Biomasse Forschungs-Zentrum (German Bio Mass

Research Centre)

© The Author(s) 2019 S. Teske (ed.), Achieving the Paris Climate Agreement Goals , https://doi.org/10.1007/978-3-030-05843-2

490

DLR Deutsche Luft und Raumfahrt, German Aero Space Centre

DREA Distributed renewables for energy access

EAPP East Africa Power Pool

EJ/year Exa-Joule per year

EJ Exa-Joule

ENTSO-E European Network of Transmission System Operators for

Electricity

ESM Earth System Model (computer climate model)

ETP Energy Technology Perspectives (publication of the International

Energy Agency)

EU-WEEE Waste electronic equipment  – Environment, Directive of the

European Commission

EV Electric Vehicles

FAO Food and Agriculture Organization of the United Nations

FCEV Fuel-Cell-Electric-Vehicle

FYP Five-Year-Plan

G20 Group of 20 (The G20 is an international forum for the govern-

ments and central bank governors from Argentina, Australia,
Brazil, Canada, China, the European Union, France, Germany,
India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia,
South Africa, South Korea, Turkey, the United Kingdom, and the
United States.)

GDP Gross Domestic Product

GEA Global Energy Assessment

GIS Global Information System

GLOBIOM GLObal BIOsphere Model (computer model for land-use)

GW Gigawatt

GWh/year Gigawatthours per year

HDV High Duty Vehicle (= truck)

HOV High Occupancy Vehicle lanes (transport modelling)

HST High Speed Trains

HWP Harvested Wood Product (computer model for carbon flows)

IAM Integrated Assessment Modelling

ICAO International Civil Aviation Organization

IEA International Energy Agency

ILO International Labour Organization

IMAGE Integrated Model to Assess the Global Environment (computer

climate model)

IMO International Maritime Organization

INDC Intended Nationally Determined Contributions

IRENA International Renewable Energy Agency

ITRPV International Technology Roadmap for Photovoltaic

LCOE Levelized Cost of Electricity

LDF Leonardo DiCaprio Foundation

LDV Light Duty Vehicle (= passenger car)

Annex

491

LIB lithium Ion Batteries

MAGICC Model for the Assessment of Greenhouse-gas Induced Climate

Change

MEPC Marine Environment Protection Committee

MESSAGE Model for Energy Supply Strategy Alternatives and their General

Environmental Impact (computer energy model)

MJ/year Mega-Joule per year

MW Megawatt

MWh/year Megawatthours per year

NDC Nationally Determined Contributions

NEA National Energy Administration (China)

NGV Natural gas vehicles

OC (emissions) Organic Carbon emission

OD Origin Destination (transport modelling)

OECD Organization for Economic Co-operation and Development

PJ/year Peta-Joule per year

PMG Permanent magnet generators

PPMC Paris Process on Mobility and Climate

PPP Purchasing Power Parity (economic term)

PRIMAP Potsdam Real-time Integrated Model for probabilistic Assessment

of emissions Paths (computer climate model)

PV Photovoltaic

QGIS Quantum - Global Information System (open source software)

RCP Representative Concentration Pathway

REF Reference scenario

REN21 Renewable Energy Network for the 21st Century

SAPP South Africa Power Pool

SF6 Sulphur Hexafluoride

SHS solar home systems

SOx Sulphur Oxides

SSP Shared Socio Economic Pathways (for climate scenarios)

TWh/year Terawatthours per year

TYNDP Ten-Year-Network Development Plan

UIC Union Internationale des Chemins de fer; International union of

raiways

UNFCCC United Nations Framework Convention on Climate Change

USA-EPA United States of America – Environmental Protection Agency

USD US Dollar

UTCE Union for the Coordination of the Transmission of Electricity

UTS-ISF University of Technology Sydney  – Institute for Sustainable

Futures

VRE Variable Renewable Energy

WEO World Energy Outlook (publication of the International Energy

Agency)

Annex