1.2 MiB
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/10.1007/978-3-030-05843-2
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 4.0 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 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
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 (CO1:) 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 CO1: emitted will increase the atmospheric CO1: concentration over hundreds or even thousands of years. Since the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the finding that cumulative CO1: emissions are roughly linearly related to temperature has shaped scientific and political debate. The remaining permissible CO1: 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 CO2 emissions of roughly 1000 GtCO1: are permissible for a “likely below 2.0 °C” target
change, and approximately 400 GtCO1: 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 GtCO1: 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 GtCO1: 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 (CO1:) emissions from fossil fuels have remained relatively flat. Early estimates based on preliminary data suggest that this changed in 2017, with global CO1: 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 CO2 pathways with non-CO2 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-CO2 gas emission levels every 5 years—conditional on the energy-related CO2 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 CO2
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
emissions2
annual
energy-relatedCO
emissions2
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. 1:). The second most important pathway in terms of the amount of CO1: 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. 1:, would result in the sequestration of 151.9 GtC. This is approximately equivalent to all historical land-use-related CO1: 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
1,000
1,500
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 CO1: 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 CO1: 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 CO1: 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
Executive Summary
xxi
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. 2:).
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%
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Powertrain split of world passenger car fleet[^2015][^2020][^2025][^2030][^2035][^2040][^2045][^2050]
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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. 3:). 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
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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/m1 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 CO1: 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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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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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. 4:).
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 CO1: emissions from bunker fuels accounted for 1.3 Gt in 2015, approximately 4% of the global energy-related CO1: 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
- Because no substitution with “green” fuels is assumed, CO2 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°CREF 2.0°C1.5°C REF2.0°C1.5°CREF 2.0°C1.5°C REF2.0°C1.5°C
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Fig. 5 Global projections of total primary energy demand (PED) by energy carrier in the various scenarios
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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 CO1: 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 CO1: 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 CO1: 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. 2:. 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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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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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 5: 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.
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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.
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202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050202020302050
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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
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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
- 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. 6:. 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 CO1: 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 7:, 1:, 2:, 3:, 4:, 5:, 6:, 8:, and
xxxix
Contents
Sven Teske and Thomas Pregger
Sven Teske, Malte Meinshausen, and Kate Dooley
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
Malte Meinshausen and Kate Dooley
Thomas Pregger, Sonja Simon, Tobias Naegler, and Sven Teske
Johannes Pagenkopf, Bent van den Adel, Özcan Deniz,
and Stephan Schmid
Sven Teske, Kriti Nagrath, Tom Morris, and Kate Dooley
Sven Teske, Thomas Pregger, Tobias Naegler, Sonja Simon,
Johannes Pagenkopf, Bent van den Adel, and Özcan Deniz
Sven Teske
xl
and 1.5 °C Scenarios............................................................................... 413
Elsa Dominish, Chris Briggs, Sven Teske, and Franziska Mey
Renewable Scenarios .............................................................................. 437
Damien Giurco, Elsa Dominish, Nick Florin, Takuma Watari,
and Benjamin McLellan
for Climate Change................................................................................. 459
Malte Meinshausen
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
emissions pathways from the other chapters are used until
2050, and then extended beyond 2050 by either keeping
the CO2 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
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 CO1: 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,
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:
- 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. 14:). 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 CO1: 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. 17:; Mohn 14:).
(c) Making finance flows consistent with a pathway towards low green-
house gas emissions and climate-resilient development.
- 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. 12:;
Kriegler et al. 15:). 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 13:). 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.
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,
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/m1) the incoming short-wave radiation (340 W/m1) 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 N1: and O1: (100 W/m1) or is absorbed by the troposphere (75 W/m1) (Stephens et al. 19:). 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/m1, the ‘curtain’
generated by human-induced increases in the concentrations of long-lived green-
house gases (CO1:, CH3:, halocarbons, N1:O and fluorinated gases) appears to be of little importance, as it “only” amounts to 2.83 W/m1. The addition and subtraction
of many other smaller human influences results in a slightly reduced net current (year 2011) forcing of 2.29 W/m1.
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 (CO1:) emissions.
S. Teske et al.
7
It is not only the magnitude of the anthropogenic emissions of CO1: that makes it such a significant driver of human-induced climate change. There is also an inherent difference between CO1: and almost all other GHGs and aerosols. Over the time scales of interest here, CO1: 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 CO1: 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 CO1: 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 CO1: is consumed by plants during the photosynthesis process and then built into plant tissue as carbon, this same carbon is released again as CO2 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 CO1: emissions will increase the atmospheric CO1: concentration for hundreds or even thousands of years. Initially, the average CO1: 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 CO2 and other GHGs. The airborne fraction of CO1: emissions diminishes over time, as for other GHGs. However, the airborne CO1: fraction does not decline to zero over 100 years, 1000 years, or even longer periods. Furthermore, carbon-cycle feedback mechanisms mean that higher CO1: concentrations cause more carbon to remain in the atmosphere. Acting in the other direction, any extra amount of CO1: in the atmo- sphere will have less and less effect on radiative forcing, i.e., how much each CO2 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 CO1: emissions. In other words, every extra kilogram of CO1: 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 CO1: 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 CO1: that has ever been emitted, largely independent of when a certain amount of CO1: 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-
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 CO1:-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 GtCO1: before reaching 2.0 °C warming (66% chance) and 770 GtCO2 before reaching 1.5 °C warming (50% chance). These figures must be reduced by a further 100 GtCO1: 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. 13:, 12:; Hawkins et al. 13:). 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-CO1: 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 13:). 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.
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 12:). 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
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 CO1: emissions from fossil fuels have remained relatively flat. Early estimates based on preliminary data suggest that this changed in 2017, with global CO1: emissions growing by around 1.4% (REN21- GSR 12:). 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
S. Teske et al.
11
included around 1.7 billion tonnes of CO1: in 2016 (EC13). 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 12:). The global investment in renewable energy in 2017 (excluding hydropower plants larger than 50 megawatts [MW]) was USD 280 billion (REN21-GSR 12:), 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 12:).
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 13:). 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 12:). Bank finance for fossil fuels increased in 2017 by
11% relative to that in 2016, after a significant decline in 2016 (RAN 12:).
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 13:).
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 12:).
In 2017, renewables accounted for an estimated 70% of net additions to the
global power-generating capacity, up from 63% in 2016 (REN21-GSR 12:). 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 12:). 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 13:). 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 12:). 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 12:). Integration challenges have led to high curtailment rates in China, the world’s largest wind and solar PV market (ECNS. CN 12:). 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 12:). 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
12.9.13:).
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
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 14:; IEA-EAO
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 13:). 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 12:). Off-grid solar devices, such as solar lanterns and
SHS, displayed annual growth rates of 60% between 2013 and 2017 (Dahlberg
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 13:). 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 13:). Renewable energy—excluding
traditional biomass—supplied approximately 9% of the total global heat production
in 2017, up from about 6% in 2008 (REN21-GSR 12:).
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 12:). 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 13:). 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. 13:; USA-EPA
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 12:).
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
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 13:). 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 13:). Switching existing districting heating systems
from fossil fuels to renewables has considerable potential (IRENA-RE-H 13:).
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 12:). 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 12:) 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 13:). 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 12:) of the total world final energy consumption, and is supplied almost entirely by electricity (IEA-RE 13:). Solar-based space-cooling systems are still in the minority compared with conven- tional air-conditioning systems.
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 13:). 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 13:).
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 13:).
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 12:). Biodiesel production remained relatively stable in 2017, following
a 9% increase in 2016 relative to 2015 (IEA OIL 12:).
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-13:). The largest
producers of biogas for vehicle fuel in 2016 were Germany, Sweden, Switzerland,
the UK, and the USA (IRENA-RV-13:). 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 (m2), compared with natural gas prices, which are as low as USD 0.13 per m2.
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 13:). The number of electric vehicles (EVs) on the road passed the three million mark in 2017 (Guardian 25.12.13:). 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 11.13:). 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 13:). 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 13:).
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 13:), Delhi (Times of India 13:), and
Santiago de Chile (CT 13:).
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-13:). 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
6.10.14:). 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 12:). In 2017,
Norway announced a target of 100% electric short-haul flights by 2040 (Guardian
18.1.12:).
Shipping consumes around 12% of the global energy used in transport
(US-EIA-13:) and is responsible for approximately 2.0% of global CO1: 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 14.11.13:). 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 13:). 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 13:). Just over a third of the electricity (9% of rail energy) is estimated to be derived from renewable sources (IEA-UIC 13:). 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-
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 13:), 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
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 12:).
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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.
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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 15:) will require the total decarbonisation of the energy system by 2050, with a global emissions peak no later than 2020 (Hare and Roming 14:) and a drastic reduction in non- energy-related greenhouse gases (GHGs), including land-use-related emissions (Rogelj and den Elzen 14:). 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 17:). Whereas climate models analyse the effects of a variety of GHG emissions, energy scenarios only cover energy-related CO1:. 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 CO1: 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 16:; Teske and Dominish 14:; Klaus et al. [^2010]:; Teske and Brown 19:), 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 CO2 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
- 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. 4:).
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 CO1: pathways with the non-CO1: 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-CO1: gas emission levels every 5 years, conditional on the energy-related CO1: 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 CO1: 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 15:). 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-CO1: emissions modules provide information on additional gases based on the
energy-related CO1: 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
30
t upt uO
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 energydemand power, heat & transport,supply structure, primary energy
demand by fuel, emission, investmen
t
balanced RE power
system, storage
demand,curtailment
total climatechange effects
energy demand
by
transport
mode
RE
generationcurves
budget
energy
-related
CO
emissions2
annual
energ
y-
related
CO
emission2
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 inall 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
32
Wind speed data at different levels, in metres per second (m/s), were obtained
from Vaisala 13:. 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 15:), 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 14:), 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
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
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 CO1: emissions from biofuels are given a GHG emis- sion factor of zero, because the downstream emissions level out with the upstream emissions. The CO1: emissions from synthetic fuels are also given a value of zero,
because the CO1: 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 CO1: 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. 5:.
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;
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 CO1: 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:
– SECFmi t
wr
, (): specific freight mode energy consumption of powertrain i and
mode m in world region wr at time step t [MJ/tkm]
– SECPmi t
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 CO1: 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 19:; 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. (12:). The model calculates the energy flows of a system on an
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:
FDss tUED tMSt t
fe
et
ss ss
et
fe
()= ∑ ()⋅ ()⋅η_et_ ()
FD tFDt
fe
ss
ss
()= ∑ fe ()
TFDt FD tFDt UEDtMS
fe
fe
ss fe
ss
fe
ss fe et
ss ss
()= ∑∑()= ∑ ()= ∑∑∑ ()⋅ et tttfe
()⋅ηet ()
with:
• FDss, fe(t): demand of (final) energy carrier fe in sub-sector ss7: 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]
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
CO2-emissions
Drivers:
GDP,
population
Technology database:
efficiency, emission
factors, allocation factors,
costs (power sector)
LCOE
Fuel production:
•H1:-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] • MSss et(t): market share of end-sector technology et in sub-sector ss [dimensionless] • ηfe et(t): efficiency of end-sector technology et using energy carrier fe1: 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
()= ∑∑ ()⋅ ()⋅η_ct_ ()
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] • MSfe ct(t): market share of conversion technology ct in the generation of final energy carrier fe [dimensionless] • ηfe ct(t): efficiency of conversion technology2: ct using the final energy carrier fe at time step t [dimensionless]
The indices denote:
• pe: (primary) energy carrier • ct: conversion sector technology3
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;
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 CO1: 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:
- a flow calculation module, which balances the energy supply and demand; and
- 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 (15:) 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
44
and statistical data on renewable power generation from IRENA (REN21-GSR
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:
- Calculation of each cluster
- Identifies over/under supply
Capex, Opex, Fuel costs
LCOE
StandardReport
- Result Analysis
Base year, 2020, 2030, 2040, 2050
in hourly resolution
- Records results
- Connects clusters
Macro
- 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
Interconnection
Storage
Capacity by region
Calculation of load curves
Power Generation
and Infrastructur
- Graphs and tables
- Key results costs
- Hourly results - Demand
storage capacity
ResultingDemand
by addition of 3 load curces
> by technology
to sub-regions (clusters)
Capacity by technology
installed capacities
- 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
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.
C1 C5
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC1 CapacityC5
C2 C6
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC2 CapacityC6
C3 C7
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC3 CapacityC7
C4 C8
LocalGeneration LocalGeneration
Interconnection- Interconnection-
CapacityC4 CapacityC8
Data:USAStatistics Data:Mexico Statistics
INTERCONNECTION OF CLUSTERS
Clusters im/export from all clusters: -Total
interconnec on capacity limited to x% of total
genera on capacity (individuel input)
Power Plant Capacity Power Plant Capacity
Demand Demand
Popula on Popula on
GDPGDP
C5 C8
Cluster= X US States Cluster= Country
Data:USAStatistics Data:CANADAStatistics
Power Plant Capacity Power Plant Capacity
Demand Demand
Popula on Popula on
GDPGDP
C[^1] C[^2]
Cluster=US State Cluster=4 Canadian States
- Oceania Pacific
- IndiaC7. South East USA
- China C8 Mexico
- Eurasia C5. South West USA
- Non OECD Asia C6. North East USA
- Africa C3. East Canada
- Middle East C4. North West USA
- La n America C1. Alaska
- Europe C2. West Canada
Regions Sub-Regions (Cluster)
- OECD North America e.g. North America
Fig. 3.8 Spatial concept of the [R]E 24/7 model
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–
- 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 14:), the Australian Storage Requirements (Rutovitz
and James 13:), and a 100% Renewable Energy Analysis for Tanzania (Teske and
Morris 13:). 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]
50
INPUT OUTPUT
Equation1
Equation2
Equation3
Equation4
Result
Dispatch Order for each Sluppy Category exchangeable
DispatchOrder
L Cluster [MW/h]
L Interconnec on [MW/h]
Load
LIni al [MW/h]
LIni al [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-
connec on
LPost_IN [MW/h]
CapFactActual_IN [h/yr]
ion1
ion2
ion3
ion4
LIni al [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 connec on
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 12:), 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
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, 15:; SARAH
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 13:), 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.
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
under different 100% global renewable energy scenarios (Rutovitz and Dominish
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 15:). 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
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 per
year x Construction
employment factor x Regional job multiplier
for year
Operation and
Maintenance =Cumulative capacityxO&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 multiplier
for year x % of local
production
Heat supply = MW installed per
year x Employment factor for
heat x Regional job multiplier
for year
Jobs in region = Manufacturing+Construction+Operation and maintenance
(O&M) + Fuel + Heat
Employment factors at 2020,
2030or 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 13:). 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. 5:.
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 13:)
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 ()
=
7: ∑ −
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).
Primaryproductionpt,,=−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
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-CO1: GHG Pathways
This section provides an overview of the methodology that has been used to com- plement the energy-related CO1: emission pathways for non-energy-related CO2 emissions, other GHG emissions, and aerosols. The energy-related CO1: emissions were derived using energy-system modelling frameworks, but two different approaches have been used to derive the land-use CO2 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-CO1: pathways
that are consistent with the relevant emission mitigation levels, the non-CO1: emis-
sions were regressed against the fossil fuel and industrial CO1: emissions. These regression characteristics were then used to derive the non-CO1: 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-CO1: 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 CO1: 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. 14:). Table 3.9 provides an overview to the regional definitions used in this study. The top row indicates the regions for the CO1: fossil and industrial emissions, and the various rows refer to the five regions used in IAMs. To derive the non-CO1: emis- sions, we used the IAM’s five RCP regions. The numbers indicate the fossil fuel and industrial emissions in the year 2015 in MtCO1:, 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 CO1: emissions. For example, the first row indicates that the largest sub-region in the RCP5_Asia group is China, with 8,826 MtCO1: of emissions. The transfer of the energy-related CO1: 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 CO1: 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 CO1: versus non-CO1: 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_OECD9
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
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 CO1: fossil and industrial emission categories (such as waste- related emissions) and that were outside the scope of emissions in the energy-related
CO1: emission chapters. The scenarios from which these ‘other’ energy-related CO2 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 CO1: emissions were compared with the overall fossil and industrial sum of CO1: 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 CO1: 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 CO2 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-CO1: Gases
The completed fossil and industrial CO1: 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 CO1:-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 CO1:, 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 CO1: 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 CO1: and various other gases. More specifically, we derived the non-CO1: emissions in a particular year by ranking all the scenarios against the indicator of fossil and industrial CO1: 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 CO1: 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 CO1: 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 CO1: and the non-CO1: gases. To derive the non-CO1: gases, the 2050 percentile location was assumed constant for the remainder of the twenty-first century. For the fossil and
industrial CO1: emissions in the 2.0 °C and 1.5 °C Scenarios, which do not assume
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 CO1: Extensions Beyond 2050
By the end of the century, almost 40% of all of 811 scenarios will feature net nega- tive fossil and industrial CO1: 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 CO1: emissions until 2050. The energy-related CO1: emissions pathways from the other chapters are used until 2050, and then extended beyond 2050 by either keeping the CO1: 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-CO1: emissions that involve a level of effort that is comparable to the mitigation effort involved in reducing energy-related CO1: emissions, we assumed that the energy scenarios developed for this study were comparable to other scenarios that share zero emissions around
- This percentile ‘stringency’ level was then held constant for the remainder of the twenty-first century. Therefore, whereas the actual fossil and industrial CO2 emissions in the LDF scenarios are assumed to remain constant at zero, the non-CO2 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 CO1: emission levels throughout the twenty- first century for each of the three scenarios, and have complemented these with the ‘pseudo’ CO1: emission levels for the second half of the twenty-first century. Therefore, we can derive the corresponding non-CO1: emissions. In the first step, we derived the total non-CO1: 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 CO1: 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 CO1: and other gases can also be taken into account. By performing all quantile regressions in the space defined by the global fossil and industrial CO2 emissions in a particular year, any kind of non-linear, positive or negative relation- ship with other non-CO1: 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 CO1: 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 CO1: emissions
66
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Global Emissions (kt)
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Global Emissions (kt) Global Emissions (kt)
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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 CO1: emission categories.
Therefore, the quantile regression approach can also be applied to the land-use CO2 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. 12:) 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 CO2 (DeCicco and Schlesinger 12:; Law et al. 12:; Mackey et al. 17:; Mackey
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. 15:), 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. 12:)
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. (14:). 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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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 CO1: emissions derived in Chap. 8:.
In this section, we present the results for the land-use CO1: and non-CO1: emissions
pathways that complement the 2.0 °C and 1.5 °C energy-related CO1: scenarios.
4.1 Land-Use CO1: 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 CO1: 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 CO1: 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
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achieved in the tropics (Martin et al. 15:), 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.
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 16:).
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 CO1: emissions to date (Houghton and Nassikas
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 16:). 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
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risk (DellaSala, [^2019]:; Lindenmayer and Sato 12:), 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 CO1: 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 CO1: 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 CO1: 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 CO1: 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 N1: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 (CH3:) 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
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Fig. 4.2
Land-use-related CO
emission and sequestration rates2
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the lower end of the fossil fuel CO1: emissions is relatively narrow, and there is a strong correlation between the fossil CO1: and total CH3: 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 CH3: pathways for 1.5 °C and 2.0 °C track towards the lower of the scenario distributions. Nitrous oxide (N1:O) is one of the longer-lived GHGs, although the overall amounts in the atmosphere are much smaller than those of methane or CO1:. The relatively high plateau of global emissions, around 5 MtN1:O-N for N1: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 N1:O, it means that the N1: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, CF3:) 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. SF5: 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 SF6 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
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Fig. 4.3
Global and regional methane emissions from fossil, industrial, and land-use-related sources
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Fig. 4.4
Global and regional methane emissions from fossil, industrial, and land-use-related sources
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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 CO1: emissions and black or
Fig. 4.5 Global SF5: emission levels from literature-reported scenarios and the LDF pathways derived in this study
Fig. 4.6 Global tetrafluoromethane (CF3:) emissions from the collection of assessed literature- reported scenarios and the LDF pathways derived in this study
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Fig. 4.7
Global and regional sulfate dioxide (SO
) emissions in the literature-reported scenarios considered and the LDF pathways derived in this studyX
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Fig. 4.8
Global and regional nitrate aerosol (NO
) emissions in the literature-reported scenarios considered and the LDF pathways derived in this studyX
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Fig. 4.9
Global and regional black carbon BC emissions in the literature-reported scenarios considered and the LDF pathways derived in this study
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Fig. 4.10
Global and regional organic carbon OC emissions in the literature-reported scenarios considered and the LDF pathways derived in this study
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organic carbon is less pronounced than the correlations of fossil CO1: 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
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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.
© The Author(s) 2019 93 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 13:), 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 CO1: 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. 15:). 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 CO1: 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
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 CO1: 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.
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
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. 14:; Hess 12:), the solar market is
T. Pregger et al.
99
taking off. Projects with a capacity of 11 GW are planned for 2018 (MESIA 12:).
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 16:). 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. 12:). 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. 12:).
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 16:). 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 16:), 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.
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 16:).
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 CO2 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 13: (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
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 2025 2030 2035 2040 2045 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 1447 1450 1442 1426 1403 1374 −2%
Africa 1194 1353 1522 1704 1897 2100 2312 2528 112%
India 1309 1383 1452 1513 1565 1605 1636 1659 27%
Non-OECD Asia 1132 1203 1269 1329 1382 1428 1467 1499 32%
Global 7383 7795 8185 8551 8893 9210 9504 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 115,108 136,578 166,646 196,715 231,758 266,801 306,519 346,236 201%
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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 13:). Therefore, fossil fuel price projections have also seen considerable variations (IEA
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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. (12:)
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An evaluation of the oil price projections of the IEA since 2000 by Wachtmeister et al. (12:) 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.
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]:, 16:, 13:). 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
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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 CO1: 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.
T. Pregger et al.
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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
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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. 13:), 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
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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
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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. (15:) 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 16:) (and
up to $17/GJ for an potential extending to 147 EJ).
Table 5.6 Development projections for fossil fuel prices in $2015 (IEA 13:)
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
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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 16:)
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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 CO1: Costs
The WEO 2017 (IEA 13:) considers the future price of CO1: 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 CO1: 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. 14:), but on the other hand, decarbonisation may limit the costs of CO1: emissions if an efficient pricing measure is in place (Jacobson et al. 13:). Because the scenarios in this study rely heavily on effective reductions in CO1: emissions, we used the CO1: 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 CO1: emission costs. Instead, we applied CO2 costs equivalent to the cost of the resulting climate damage. Based on existing stud- ies of fossil-energy-induced damage (Anthoff and Tol 17:; Stern et al. [^2006]:), we assumed that $78/t of CO1: is a plausible cost estimate in the wide range of estimates
of the social costs of CO1: emissions (Table 5.8).
Table 5.7 Biomass price projections for 2030 at 108 EJ of the biomass demand (IRENA 16:) 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
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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. (18:), Thrän et al. ([^2011]:), and Schueler et al. (17:). 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 CO1: cost assumptions in the scenarios CO1: costs 2020 2025 2030 2040 2050 Reference All regions $/t CO2[^0][^42][^69][^78][^78] 2.0 °C and 1.5 °C OECD Economies $/t CO1: [^0]: [^62]: 87.6 [^138]: 189.0 Other regions $/t CO1: [^0]: [^42]: 69.5 [^124]: 177.5
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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 13:). 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. (16:), and recently published low-energy-demand scenarios devel- oped by Grubler et al. (12:).
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%
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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%
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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 15: [^2020]: [^2025]: 20: [^2035]: [^2040]: [^2045]: 21: 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%
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. (12:) for the specific final
energy demands in northern and southern world regions, the assumptions made in
this study are conservative. In Grubler et al. (12:), 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
2,500
3,000
3,500
4,000
4,500
2010 2020 2030 2040 2050
PDG
$rep
ygrenelanif
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
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)
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 19:; Stetter 16:; Pietzcker
et al. 16:). 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 (16:). 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.
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
erahs
noitarenegre
woplabolg
egareva
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
- The largest shares are achieved in the Middle East (25%) and Africa (20%)
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 CO1: emissions depend
on the CO1: 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
erahs
noitarenegtaehlabolg
egareva
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
erahs
noitarenegtaehlabolg
egareva
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
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 regions 108.8 101.2 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 regions 108.8 101.1 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 efficiency7 of electrolytic hydrogen generation, from 66% today to 77% by 2050 (ratio of energy output [H1:] to energy input [electricity]). The generation of syn- thetic fuels (such as Fischer-Tropsch fuels) from hydrogen, using CO1: 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 efficiency1 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.
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.
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© The Author(s) 2019 131 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 CO1: 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. 16:). 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 CO1: budgets. We structured our scenario designs around the following key CO1:-reducing measures7:
• 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 PJ1 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
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
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
ecna
mrofreptropsnartreptilps
niartre
woP
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 CO1: 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 12:), 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.
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 CO1: 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 TruckPassengerTrainFreightTrain
Powertrain split per transport performance
InternalCombustionEngine 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
OECD Pacific China India Africa
Powertrain split per transport performance
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
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 12:). 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
Tr
olle
y el
ec
tr
ic
sh
ar
e of
to
ta
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]
OECDEurope Eurasia
Africa MiddleEast India China Non-OECDAsia OECDPacific
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
1,000
1,500
2,000
2,500
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
- 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. 5:, 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
MiddleEast
India
China
Non-OECD Asia
OECD Pacific
Fig. 6.9 Electricity-performed pkm in domestic aviation under the 2.0 °C Scenario
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 fleet2015 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 13:). 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 15:).
Maximum efficiencies of 38–40% can be reached by ICE (Schäfer 14:), 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)
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. 12:). 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 13:; Lischke 13:). 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
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1.1
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0.0
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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 13:).
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 12:). 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. 12:) and 26 in the USA (Eudy and Post 13:). 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
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0
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1.5
2
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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
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
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0.43 0.43
0.34
0.51
0.20
0.33
0.0
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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 12:;
Reddy et al. 13:). Thailand has announced that it plans to convert all existing two-
stroke-powered tuk-tuks to battery electric powertrains within 5 years (Coconuts
Bangkok 13:). In India, too, plans are repeatedly announced to electrify all new
two- and three-wheel vehicles within the next two decades (Ghoshal 13:).
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
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
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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)
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 15:). 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 14:; Vyas et al. 17:). We
project a 1% annual increase in efficiency on a per pkm basis.
0.31
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D - FreightNG - FreightE - FreightE - Intermodal
Freight
E - LDHV Freight
MJ/tkm
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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
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rotcaf1.4
ycneiciffecificeps
noigeR
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1
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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 CO2 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%
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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
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. 12:). 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. 12:). 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 12:), (multi-brand) truck platooning has become possible, which is a
driver-assistance technology (Janssen et al. 15:). 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 12:).
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.
150
6.4.1 Passenger Transport Modes
To reduce transport-related CO1: 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%
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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%
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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%
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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
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 CO1: 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
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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
2015 2020 2025 2030 2035
Passenger Train Passenger Car Bus 2- & 3- wheeler Aviaon (Domesc)Aviaon (Internaonal)
2040 2045 2050
pk
m
in trillion
2015 2020 2025 2030 2035 2040 2045 2050
2015 2020 2025 2030 2035 2040 2045 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
154
0
1
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4
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6
2015 2020 2025 2030 2035 2040 2045 2050
tk
m
x 10
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LDF ADV REF
Fig. 6.26 World tkm development in all scenarios
2015
400%
350%
300%
250%
200%
tkm-Evolution150%
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
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%
2015 2020 2025 2030 2035 2040 2045 2050
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
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.
© The Author(s) 2019 161 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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. 2:)—[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. 2:)—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. 19:), 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 15:). 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 19:; Smith et al. 16:). 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
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
16 4
regrows (EASAC 13:). 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. 15:, 13:). 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 CO1: concentration (Sterman et al.
intact forests can represent a better climate mitigation strategy (DeCicco and
Schlesinger 12:), because increased atmospheric concentrations of CO1: from the burning of bioenergy may worsen the irreversible impacts of climate change before
the forests can grow back to compensate the increase (EASAC 13:; Booth 12:; Schlesinger 12:). 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. 13:). The supply of waste and residues as a bioenergy source is always inherently limited (Miyake et al. 19:). 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 12:), 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 13:). The use of secondary residues (cascade utiliza- tion) may reduce the logistical costs and trade-offs associated with waste use (Smith et al. 16:).
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. 4: were analysed with the [R] E-SPACE methodology described in Sect. 3 of Chap. 2:. 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/m1 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 (12:) 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
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
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. 6:). The installed capacities are calculated based on the following space requirements (Table 7.2):
• Solar photovoltaic: 1 MW = 0.04 km1 • Concentrated solar power: 1 MW = 0.04 km1 • Onshore wind: 1 MW = 0.2 km1
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)
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 – Africa 8,336,960 208,424 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
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.
© The Author(s) 2019 175 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 CO1: 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
- 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
- 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)
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
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 CO1: 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
- 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°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. 8.7 Global: development of electricity-generation structure in the scenarios
182
Compared with these results, the generation costs (without including CO1: 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
CO1: 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: 2015-2050
total 20,400
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
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 aExcluding direct electric heating
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 CO1: Emissions
In the 5.0 °C Scenario, the annual global energy-related CO1: 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 CO1: 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
188
Thus, the cumulative CO1: 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
emissions [Mt/yr]2
'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C
Fig. 8.13 Global: development of CO1: emissions by sector and cumulative CO1: emissions (since
- 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 CO1: emissions from bunker fuels accounted for 1.3 Gt in 2015, approxi-
mately 4% of global energy-related CO1: 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, CO2 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
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 CO1: 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 CO1: 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
emissions2
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)
192
Table 8.5
(continued)
World bunkers 5.0 °C scenario
Unit
2015
2020
2025
2030
2035
2040
2045
2050
CO
emissions2
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
emissions2
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. 2:.
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.
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. 2:. 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 12:) 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 12:). 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 12:). 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- tions7—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
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
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 °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] 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
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%
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:
- Diversity;
- Flexibility;
- Inter-sectorial connectivity;
- 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
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
- 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)
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
- The 2.0 °C Scenario will require approximately 1400 TWh/year of electricity
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
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
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 CO1: 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
- 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 CO1: 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 CO1: 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
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
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
aExcluding 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
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 CO1: Emissions
In the 5.0 °C Scenario, OECD North America’s annual CO1: 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 CO1: 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 CO1: 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°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
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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
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
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. 8.24 OECD North America: development of CO1: emissions by sector and cumulative CO2
emissions (after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)
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. 2:) 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 13:) 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 13:). 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
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
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)
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
- In the 1.5 °C Scenario, a significant level of overproduction is achieved by
- 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 °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] 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%
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
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
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
- 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
1,000
1,500
2,000
2,500
3,000
3,500
4,000
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/
PJ yr
/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
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°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. 8.27 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 CO1: 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
billi
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
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 CO1: 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 CO1: 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
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
2,000
4,000
6,000
8,000
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 20402050
PJ
/y
r
Efficiency (compared
to 5.0°C)
Hydrogen
Electricheating
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
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 CO1: Emissions
In the 5.0 °C Scenario, Latin America’s annual CO1: 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 aExcluding 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 CO1: 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 CO1: 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°C 5.0°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
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
emi2
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 CO1: emissions by sector and cumulative CO1: 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 15:). 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.
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 12:). 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 °C 2.0 °C 1.5 °C 2.0 °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)
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 °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] 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%
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
248
load was 1.7 GW in 2012 according to IDB (17:). 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)
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
10,000
20,000
30,000
40,000
50,000
60,000
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 202520302040 2050
TW
h/
PJ yr
/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
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 CO1: 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
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 CO1: 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 CO1: 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
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
5,000
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: 2015-2050
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
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
- 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 CO1: Emissions
In the 5.0 °C Scenario, OECD Europe’s annual CO1: 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 aExcluding 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 CO1: 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 CO1: 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
10,000
12,000
14,000
16,000
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. 8.41 OECD Europe: final energy consumption by transport in the scenarios
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
1,000
1,500
2,000
2,500
3,000
3,500
4,000
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 20252030 2040 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. 8.42 OECD Europe: development of CO1: emissions by sector and cumulative CO1: emissions
(after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)
S. Teske et al.
261
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
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
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 12:) and produces the Ten-
Year-Network Development Plan (TYNDP), which aims to integrate 60% renewable
electricity by 2040 (TYNDP 14:). 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.
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 12:) 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 °C 2.0 °C 1.5 °C 2.0 °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)
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 12:). 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 °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] 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%
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)
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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
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°
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
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
- 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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
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 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
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 CO1: 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 CO1: 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 CO2 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.
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
10,000
15,000
20,000
25,000
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
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
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: 2015-2050
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
aExcluding 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 CO1: Emissions
In the 5.0 °C Scenario, Africa’s annual CO1: 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
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 CO1: 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 CO1: 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
2,000
4,000
6,000
8,000
10,000
12,000
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
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 CO1: emissions by sector and cumulative CO1: emissions (after
- 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
80,000
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. 8.52 Africa: projection of total primary energy demand (PED) by energy carrier in the sce-
narios (including electricity import balance)
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 16:).
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 12:).
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 °C 2.0 °C 1.5 °C 2.0 °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
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%
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)
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
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2015 2025 2030 2040 2050
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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
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
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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
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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
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 CO1: 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.
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2.0°C efficiency measures 2.0°C
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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
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The generation costs without the CO1: 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 CO1: 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.
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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
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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
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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).
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: 2015-2050
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
aExcluding direct electric heating
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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
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8.9.1.8 The Middle East: Development of CO1: Emissions
In the 5.0 °C Scenario, the Middle East’s annual CO1: 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 CO1: 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 CO1: 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.
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Biofuels
Natural Gas
Oil products
Fig. 8.59 Middle East: final energy consumption by transport in the scenarios
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'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 CO1: emissions by sector and cumulative CO1: emissions
(after 2015) in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)
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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)
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 12:) (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 12:). The vast solar resources in the Middle
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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 °C 2.0 °C 1.5 °C 2.0 °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.
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%
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 °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] 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
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
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
- 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.
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 CO1: 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
1,000
2,000
3,000
4,000
5,000
6,000
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/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
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 CO1: 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 CO1: 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.
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
10,000
15,000
20,000
25,000
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. 8.66 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
aExcluding 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-
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 CO1: Emissions
In the 5.0 °C Scenario, Eastern Europe/Eurasia’s annual CO1: 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 CO1: 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 CO1: 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).
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
1,000
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
2015 2025 203020402050
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. 8.69 Eastern Europe/Eurasia: development of CO1: emissions by sector and cumulative CO2
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 3-12:)
and solar industries (PVM 3-12:).
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
- 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).
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 °C 2.0 °C 1.5 °C 2.0 °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)
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 °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] 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.
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)
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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0
10,000
20,000
30,000
40,000
50,000
60,000
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
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
- In the 2.0 °C Scenario, the electricity demand for heating will be approxi-
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 CO1: 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°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. 8.72 Non-OECD Asia: development of electricity-generation structure in the scenarios
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 CO1: 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 CO1: 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
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
10,000
15,000
20,000
25,000
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
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
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
aExcluding 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
10,000
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 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
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 CO1: Emissions
In the 5.0 °C Scenario, Non-OECD Asia’s annual CO1: 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 CO1: 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 CO1: 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
1,000
2,000
3,000
4,000
5,000
6,000
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
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 CO1: emissions by sector and cumulative CO1: 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)
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 12:) 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 °C 2.0 °C 1.5 °C 2.0 °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
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%
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 14:) (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 °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] 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)
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)
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
- 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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
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 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
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 CO1: 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
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 CO1: 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 CO1: 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
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
5,000
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
- 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 aExcluding direct electric heating
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 CO1: Emissions
In the 5.0 °C Scenario, India’s annual CO1: 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 CO1: 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 CO1: 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°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. 8.86 India: final energy consumption by transport in the scenarios
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 CO1: emissions by sector and cumulative CO1: emissions (after
- 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 12:) 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 12:):
[...] “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
100,000
120,000
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. 8.88 India: projection of total primary energy demand (PED) by energy carrier in the sce-
narios (including electricity import balance)
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 °C 1.5 °C 2.0 °C 1.5 °C 2.0 °C 1.5 °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.
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 °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] 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
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
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
- 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
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 CO1: 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 CO1: 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 CO1: 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
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
- 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
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 CO1: Emissions
In the 5.0 °C Scenario, China’s annual CO1: 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 CO1: 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 aExcluding 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 CO1: 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
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 CO1: emissions by sector and cumulative CO1: emissions (after
- in the scenarios (‘Savings’ = reduction compared with the 5.0 °C Scenario)
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
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. 8.97 China: projection of total primary energy demand (PED) by energy carrier in the sce-
narios (including electricity import balance)
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
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 12:). 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 12:). 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 1-12:) 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 °C 2.0 °C 1.5 °C 2.0 °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
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%
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 °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] 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)
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)
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
- 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
1,000
1,500
2,000
2,500
3,000
3,500
0
5,000
10,000
15,000
20,000
25,000
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
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
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 CO1: 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
1,000
1,500
2,000
2,500
3,000
3,500
4,000
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 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
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 CO1: 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 CO1: 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
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 aExcluding direct electric heating
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
- 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
1,000
2,000
3,000
4,000
5,000
6,000
7,000
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. 8.104 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
390
8.14.1.8 OECD Pacific: Development of CO1: Emissions
In the 5.0 °C Scenario, OECD Pacific’s annual CO1: 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 CO1: 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 CO1: 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
emissions [Mt/yr]2
'Power generation' 'Other Conversion'
'Transport' 'Industry'
'Residential & other sectors' Savings
5.0°C 2.0°C
1.5°C
Fig. 8.105 OECD Pacific: development of CO1: emissions by sector and cumulative CO1: 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
5,000
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)
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 12:) 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 12:). 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 °C 2.0 °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
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%
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 12:). Japan’s peak demand was 152 GW in 2015 according to the Tokyo
Electric Power Company (TEPCO -12:) 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 °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] 54.8% 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
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
400
References
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:// 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.
© The Author(s) 2019 403 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 19:), 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 15:).
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 12:), 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
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
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
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. 4:, 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
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 15:), 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 14:).
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.
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; www.globalenergyassessment.org ILO (2015), International Labour Organization, Just Transition – A report for the OECD, May 2017, Just Transition Centre, 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.
© The Author(s) 2019 413 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 14:; ILO 15:).
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
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. 2: ff. This study is funded by the German Greenpeace Foundation and builds on the methodology developed by UTS/ISF (Rutovitz et al. 15:), 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. 12:).
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. 2:). 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. (12:)
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
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 (19:) 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. 4:) 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
- 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.
418
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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,
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:
- 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.
- 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.
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 17:).
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 13:). 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 13:). 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 13:). 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
Technology Component
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
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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428
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000,
000
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
20
15
20
25
MA
NA
GE
RS
PR
OF
ES
SI
ON
AL
S
EN
GI
NE
ER
S
TE
CH
NI
CI
AN
S
CL
ER
IC
AL
&
AD
MI
NI
ST
RA
TI
VE
CO
NS
TRUC
TI
ON
TR
AD
ES
ME
TA
L
TR
AD
ES
EL
EC
TR
IC
IA
NS
OP
ER
AT
OR
S
&
AS
SE
MB
LE
RS
LA
BO
UR
ER
S
SH
IP
CR
EW
Fo
ssil
fu
el
jo
bs
PV
jo
bs
On
sh
ore
wi
nd
jobs
Of
fs
ho
re
wi
nd
jobs
Ch
an
ge
in
fo
ss
il fuel
jobs
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%
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%
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
434
Fig. 10.6 Employment changes between 2015 and 2025 by occupational breakdown under the 1.5 °C Scenario
E. Dominish et al.
435
References
Australian Bureau of Statistics (2017), Employment in Renewable Energy Activities – Explanatory 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 Standard Classification of Occupations, http://www.abs.gov.au/ANZSCO, Accessed September 16 2018 Clean Energy Manufacturing Analysis Center (CEMAC), 2017. Benchmarks of global clean energy manufacturing. Available at: https://www.nrel.gov/docs/fy17osti/65619.pdf Dominish, E., Teske S., Briggs, C., Mey, F., and Rutovitz, J. (2018). Just Transition: A global social plan for the fossil fuel industry. Report prepared by ISF for German Greenpeace Foundation, November 2018. 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 IRENA (2017a) Renewable Energy Benefits: Leveraging Local Capacity for Onshore Wind, IRENA, Abu Dhabi; IRENA (2017b) Renewable Energy Benefits: Leveraging Local Capacity for Solar PV, IRENA, Abu Dhabi. IRENA (2018) Renewable Energy Benefits: Leveraging Local Capacity for Offshore Wind, IRENA, Abu Dhabi. 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. erss.2015.10.004 Rutovitz, J., Dominish, E., & Downes, J. (2015). Calculating global energy sector jobs: 2015 methodology. Prepared for Greenpeace International by the Institute for Sustainable Futures, University of Technology Sydney. Smith, S. (2017). Just Transition – A Report for the OECD. Brussels. Retrieved from https://www. oecd.org/environment/cc/g20-climate/collapsecontents/Just-Transition-Centre-report-just- transition.pdf Sovacool, B. K., & Dworkin, M. H. (2014). Global energy justice: Problems, principles, and practices. Global energy justice: Problems, principles, and practices. https://doi.org/10.1017/ CBO9781107323605 UNFCCC. (2016). Just transition of the workforce, and the creation of decent work and quality jobs: Technical paper by the Secretariat. Paris**.** https://doi.org/10.1186/1750-9378-6-S2-S6
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.
© The Author(s) 2019 437 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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
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. 13:; Valero et al. 12:). 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. (19:) 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. 15:) or globally (Valero et al. 12:; Watari et al. 12:), and some have explored the role of technol- ogy mixes and material substitution in detail (Månberger and Stenqvist 12:). In addition to issues of resource availability, environmental and social issues have also been explored (Giurco et al. 16:; Florin and Dominish 13:) and the need for improved resource governance has been highlighted (Prior et al. 17:; Ali et al. 13:). 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.
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. 14:). 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. 17:). It is estimated that about 20% of all installed wind turbines (both onshore and offshore) use rare earth mag- nets (CEMAC 13:).
Fig. 11.1 Overview of key metal requirements and supply chain for solar PV
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. 16:).
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. 12:). 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 12:) 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. 12:).
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. 15:).
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
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
- 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. 12:). 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. 12:). 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. 19:). 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
444
route (King et al. 12:). 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. 16:).7 However, some losses during processing seem unavoidable.1
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 12:), and the future material efficiency is based on an assumed minimum amount of silver (Kavlak et al. 15:). 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.2 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-
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 share 95.8% 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
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
(500,000)
500,000
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
5,000,000
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
aldemand
(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
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
talde
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)
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
0
100,000
200,000
300,000
400,000
500,000
600,000
Cumula
tiv
ep
rima
ry
meta
ld
em
and(
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 12:), 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
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. 13:; Watari et al. 12:). 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 12:; Watari et al. 12:; Valero et al. 12:; Watari et al. 12:), 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 12:).
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. 15:), whereas lithium’s production is expected to grow rapidly (Mohr et al. 19:). 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 13:). 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)
454
court ruling that the Xinca Indigenous peoples were not adequately consulted before
a mine licence was granted (Jamasmie 12:).
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
vanadium, and zinc (Tchetvertakov 12:). 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. 17:).
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. 13:). 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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© The Author(s) 2019 459 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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 CO1: 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 CO1: emissions developed here, complemented by land-use CO1: 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 CO1: 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 CO1: concentrations have only oscillated between approximately 180 ppm and 280 ppm. In fact, for the last 10 million years on this planet, the CO1: concentrations have probably not
exceeded the CO1: 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 CO1: concentrations are disregarded, the consequences will be dramatic. At atmo- spheric CO1: 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. 17:). 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
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12.1 Background on the Investigated Scenarios
The international community uses various scenarios to explore these future changes to CO1:, 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 CO1: levels. This is the key difference from the scenarios developed in the present study. Whereas we use a substantial amount of negative CO1: 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 CO1: 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 CO1: 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-CO1: gases can be reduced even further to make negative CO2 emissions unnecessary. The scenarios developed in this study use all three options,
as outlined in the previous chapters. Not only will energy-related CO1: emissions be radically reduced, CO1: uptake via reforestation and forest restoration will also play an important role. This study takes a more conservative approach to non-CO1: 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 CO1: 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 CO1: 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 CO1: 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.
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Figure 12.1 shows the global CO1:, CH3:, and N1: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 CO1: 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 CO1: emis- sions in the SSP1_19 scenario bring the CO1: 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 CO1: yields the so-called ‘CO1: equivalence concentrations’. In Fig. 12.2, the CO2 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 CO1: 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 CO1: 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 CO1: 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 CO1: Emissions
Since the IPCC Fifth Assessment Report, cumulative CO1: emissions have been introduced as a key metric into the international climate debate. Every tonne of additional CO1: 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 CO1: emissions, which means bringing the annual CO2 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 CO1: 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 CO1: emissions, we have used a range of land-use-based sequestration options. These are not unambitious, as
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Fig. 12.2 CO1: 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
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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 CO1: emissions beyond levels will constitute around 300 GtCO1: (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 CO1: 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 CO1: 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 CO1: emissions are reduced again in SSP1_19 with large-scale net negative CO1: emissions via bioen- ergy with carbon capture and storage. In contrast, the reduction of cumulative CO2 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
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Fig. 12.3 Global cumulative CO2 emissions – 2.0 °C and 1.5 °C scenarios
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budget range is the effective central value presented in the recent IPCC Special Report on 1.5 °C warming.7
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. (13:) 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).
730 GtCO1: 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 GtCO1: 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).
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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 CO1: 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 CO1: emissions. Therefore, whereas temperatures are relatively
agnostic about when CO1: emissions occurred, sea-level rise will be higher the lon- ger ago the CO1: 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 CO1: emissions, drawing back out of the atmosphere a lot of the CO1: 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).
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Fig. 12.4 Global-mean surface air temperature projections
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Fig. 12.5 Global-mean sea level rise projections under the three scenarios developed in this study
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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.
© The Author(s) 2019 471 S. Teske (ed.), Achieving the Paris Climate Agreement Goals,
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.
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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. 3:). 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, N1: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.
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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
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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
$GDP in the ‘Industry’ sector, which will decrease by 65% in OECD regions and by
80% in non-OECD regions, an ambitious pathway that will require stringent
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technology change and replacement strategies and supporting regulatory and gover-
nance measures to trigger huge investments for its realization.
On the energy supply side, considerable contributions in the future are assumed
from wind and solar power, electrification and synthetic fuel use in transport and
heating, sustainable biomass use, especially for co-generation and biofuels, and dis-
trict heating systems that integrate solar, geothermal, and ambient heat potentials.
The exploitation of renewable energy potentials depends strongly on regional con-
ditions. In regions such as India and China, a 100% renewable electricity supply
will require the extensive use of existing potentials. The global installed capacities
of renewable power generation technologies will increase by a factor of more than
12, from 2000 GW in 2015 to more than 25,000 GW in 2050. Cumulative invest-
ments for power generation are estimated to increase between 2015 and 2050 by up
to around US$ 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 CO1: 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.
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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
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 CO1: 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 CO1: 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 CO1: emission potentials towards the second half of this century.
Going beyond the land-use CO1: 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-CO1: pathways from this rich scenario database—in a way that respects the correlations and dependencies between energy-related CO1: 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 CO2 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-CO1: 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 N1: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-CO1: 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.
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:
- Define the maximum carbon budget and other targets, milestones, and con-
straints to achieve the climate goal;
- Define the renewable energy resource potentials and limits within a space-
constrained environment;
482
- Identify the economic and societal drivers of demand;
- Define the efficiency potentials and energy intensities by energy service and
sector;
- Establish time lines and narratives for the technology implementation on the
end-user and supply sides;
- Estimate the infrastructure needs, generation costs, and other effects;
- 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-12:). 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 12:). 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 12:). 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 12:).
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 16:) 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.
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 12:) 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. CO1: 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 CO1:, 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,
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
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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:// 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.
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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,
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
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State of Research..................................................................................... 5 ↩︎
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Methodology............................................................................................ 25 ↩︎
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Mitigation Scenarios for Non-energy GHG.......................................... 79 ↩︎
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Main Assumptions for Energy Pathways.............................................. 93 ↩︎
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Transport Transition Concepts.............................................................. 131 ↩︎
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Renewable Energy Resource Assessment ............................................. 161 ↩︎
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Introduction............................................................................................. 1 ↩︎
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Energy Scenario Results......................................................................... 175 ↩︎
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Implications of the Developed Scenarios ↩︎
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Just Transition: Employment Projections for the 2.0 °C ↩︎
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Requirements for Minerals and Metals for 100% ↩︎
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). However, bioenergy (including traditional biomass) remains the leading ↩︎
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). Distributed renewables for energy access (DREA) systems were serving an ↩︎
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. In December 2017 (REN21-GSR 12:), 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 CO1: annually (Xu and Mason 13:). For comparison, the emissions trading scheme of the European Union ↩︎
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), and an increasing body of research is estimating the jobs created by ↩︎
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; Nabuurs et al. 13:). 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. 16:; Houghton and Nassikas 12:; 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 CO1: over the time scales of interest here (Houghton and Nassikas 12:). 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- ↩︎
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, 13:) 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 12:). ↩︎
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). The ‘forest ecosystem restoration’ pathway is also important, which basi- ↩︎
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, 15:), 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 (16:), 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. 15:). 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. (12:), 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. 13:). 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 ↩︎
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39% 32% 29% 47% 32% 21% ↩︎
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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 ↩︎