Technical Summary
33
TS
Technical Summary
Coordinating Lead Authors:
Ottmar Edenhofer (Germany), Ramón Pichs-Madruga (Cuba), Youba Sokona (Mali / Switzerland),
Susanne Kadner (Germany), Jan C. Minx (Germany), Steffen Brunner (Germany)
Lead Authors:
Shardul Agrawala (France), Giovanni Baiocchi (UK / Italy), Igor Alexeyevich Bashmakov (Russian
Federation), Gabriel Blanco (Argentina), John Broome (UK), Thomas Bruckner (Germany), Mercedes
Bustamante (Brazil), Leon Clarke (USA), Mariana Conte Grand (Argentina), Felix Creutzig
(Germany), Xochitl Cruz-Núñez (Mexico), Shobhakar Dhakal (Nepal / Thailand), Navroz K. Dubash
(India), Patrick Eickemeier (Germany), Ellie Farahani (Canada / Switzerland / Germany), Manfred
Fischedick (Germany), Marc Fleurbaey (France / USA), Reyer Gerlagh (Netherlands), Luis Gómez-
Echeverri (Austria / Colombia), Sujata Gupta (India / Philippines), Jochen Harnisch (Germany), Kejun
Jiang (China), Frank Jotzo (Germany / Australia), Sivan Kartha (USA), Stephan Klasen (Germany),
Charles Kolstad (USA), Volker Krey (Austria / Germany), Howard Kunreuther (USA), Oswaldo Lucon
(Brazil), Omar Masera (Mexico), Yacob Mulugetta (Ethiopia / UK), Richard Norgaard (USA), Anthony
Patt (Austria / Switzerland), Nijavalli H. Ravindranath (India), Keywan Riahi (IIASA / Austria),
Joyashree Roy (India), Ambuj Sagar (USA / India), Roberto Schaeffer (Brazil), Steffen Schlömer
(Germany), Karen Seto (USA), Kristin Seyboth (USA), Ralph Sims (New Zealand), Pete Smith (UK),
Eswaran Somanathan (India), Robert Stavins (USA), Christoph von Stechow (Germany), Thomas
Sterner (Sweden), Taishi Sugiyama (Japan), Sangwon Suh (Republic of Korea / USA), Kevin Urama
(Nigeria / UK / Kenya), Diana Ürge-Vorsatz (Hungary), Anthony Venables (UK), David G. Victor (USA),
Elke Weber (USA), Dadi Zhou (China), Ji Zou (China), Timm Zwickel (Germany)
Contributing Authors:
Adolf Acquaye (Ghana / UK), Kornelis Blok (Netherlands), Gabriel Chan (USA), Jan Fuglestvedt
(Norway), Edgar Hertwich (Austria / Norway), Elmar Kriegler (Germany), Oliver Lah (Germany),
Sevastianos Mirasgedis (Greece), Carmenza Robledo Abad (Switzerland / Colombia), Claudia
Sheinbaum (Mexico), Steven J. Smith (USA), Detlef van Vuuren (Netherlands)
Review Editors:
Tomás Hernández-Tejeda (Mexico), Roberta Quadrelli (IEA / Italy)
34
TSTS
Technical Summary
This summary should be cited as:
Edenhofer O., R. Pichs-Madruga, Y. Sokona, S. Kadner, J. C. Minx, S. Brunner, S. Agrawala, G. Baiocchi, I. A. Bashmakov,
G. Blanco, J. Broome, T. Bruckner, M. Bustamante, L. Clarke, M. Conte Grand, F. Creutzig, X. Cruz-Núñez, S. Dhakal, N. K.
Dubash, P. Eickemeier, E. Farahani, M. Fischedick, M. Fleurbaey, R. Gerlagh, L. Gómez-Echeverri, S. Gupta, J. Harnisch, K.
Jiang, F. Jotzo, S. Kartha, S. Klasen, C. Kolstad, V. Krey, H. Kunreuther, O. Lucon, O. Masera, Y. Mulugetta, R. B. Norgaard, A.
Patt, N. H. Ravindranath, K. Riahi, J. Roy, A. Sagar, R. Schaeffer, S. Schlömer, K. C. Seto, K. Seyboth, R. Sims, P. Smith, E. Som-
anathan, R. Stavins, C. von Stechow, T. Sterner, T. Sugiyama, S. Suh, D. Ürge-Vorsatz, K. Urama, A. Venables, D. G. Victor, E.
Weber, D. Zhou, J. Zou, and T. Zwickel, 2014: Technical Summary. In: Climate Change 2014: Mitigation of Climate Change.
Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
[Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier,
B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J. C. Minx (eds.)]. Cambridge University Press, Cam-
bridge, United Kingdom and New York, NY, USA.
3535
Technical Summary
TSTSTS
Contents
TS.1 Introduction and framing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
TS.2 Trends in stocks and flows of greenhouse gases and their drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
TS.2.1 Greenhouse gas emission trends
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42
TS.2.2 Greenhouse gas emission drivers
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47
TS.3 Mitigation pathways and measures in the context of sustainable development. . . . . . . . . . . . . . . . . . .50
TS.3.1 Mitigation pathways
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50
TS.3.1.1 Understanding mitigation pathways in the context of multiple objectives
. . . . . . . . . . . . . . . . . .50
TS.3.1.2 Short- and long-term requirements of mitigation pathways
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51
TS.3.1.3 Costs, investments and burden sharing
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
TS.3.1.4 Implications of mitigation pathways for other objectives
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61
TS.3.2 Sectoral and cross-sectoral mitigation measures
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64
TS.3.2.1 Cross-sectoral mitigation pathways and measures
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64
TS.3.2.2 Energy supply
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69
TS.3.2.3 Transport
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72
TS.3.2.4 Buildings
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78
TS.3.2.5 Industry
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81
TS.3.2.6 Agriculture, Forestry and Other Land Use (AFOLU)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86
TS.3.2.7 Human settlements, infrastructure, and spatial planning
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90
3636
TS
Technical Summary
TS.4 Mitigation policies and institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93
TS.4.1 Policy design, behaviour and political economy
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94
TS.4.2 Sectoral and national policies
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95
TS.4.3 Development and regional cooperation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99
TS.4.4 International cooperation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
TS.4.5 Investment and finance
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3737
Technical Summary
TS
TS.1 Introduction and framing
‘Mitigation’, in the context of climate change, is a human interven-
tion to reduce the sources or enhance the sinks of greenhouse gases
(GHGs). One of the central messages from Working Groups I and II
of the Intergovernmental Panel on Climate Change (IPCC) is that the
consequences of unchecked climate change for humans and natural
ecosystems are already apparent and increasing. The most vulnerable
systems are already experiencing adverse effects. Past GHG emissions
have already put the planet on a track for substantial further changes
in climate, and while there are many uncertainties in factors such as
the sensitivity of the climate system many scenarios lead to substantial
climate impacts, including direct harms to human and ecological well-
being that exceed the ability of those systems to adapt fully.
Because mitigation is intended to reduce the harmful effects of climate
change, it is part of a broader policy framework that also includes
adaptation to climate impacts. Mitigation, together with adaptation to
climate change, contributes to the objective expressed in Article 2 of
the United Nations Framework Convention on Climate Change
(UNFCCC) to stabilize “greenhouse gas concentrations in the atmo-
sphere at a level to prevent dangerous anthropogenic interference
with the climate system […] within a time frame sufficient to allow
ecosystems to adapt […] to ensure that food production is not threat-
ened and to enable economic development to proceed in a sustainable
manner”. However, Article 2 is hard to interpret, as concepts such as
‘dangerous’ and ‘sustainable’ have different meanings in different
decision contexts (see Box TS.1).
1
Moreover, natural science is unable
to predict precisely the response of the climate system to rising GHG
1
Boxes throughout this summary provide background information on main research
concepts and methods that were used to generate insight.
Box TS.1 | Many disciplines aid decision making on climate change
Something is dangerous if it leads to a significant risk of consider-
able harm. Judging whether human interference in the climate sys-
tem is dangerous therefore divides into two tasks. One is to esti-
mate the risk in material terms: what the material consequences of
human interference might be and how likely they are. The other is
to set a value on the risk: to judge how harmful it will be.
The first is a task for natural science, but the second is not [Section
3.1]. As the Synthesis Report of AR4 states, “Determining what
constitutes ‘dangerous anthropogenic interference with the cli-
mate system’ in relation to Article 2 of the UNFCCC involves value
judgements”. Judgements of value (valuations) are called for,
not just here, but at almost every turn in decision making about
climate change [3.2]. For example, setting a target for mitigation
involves judging the value of losses to people’s well-being in the
future, and comparing it with the value of benefits enjoyed now.
Choosing whether to site wind turbines on land or at sea requires
a judgement of the value of landscape in comparison with the
extra cost of marine turbines. To estimate the social cost of carbon
is to value the harm that GHG emissions do [3.9.4].
Different values often conflict, and they are often hard to weigh
against each other. Moreover, they often involve the conflicting
interests of different people, and are subject to much debate and
disagreement. Decision makers must therefore find ways to medi-
ate among different interests and values, and also among differing
viewpoints about values. [3.4, 3.5]
Social sciences and humanities can contribute to this process by
improving our understanding of values in ways that are illustrated
in the boxes contained in this summary. The sciences of human
and social behaviour — among them psychology, political science,
sociology, and non-normative branches of economics — investi-
gate the values people have, how they change through time, how
they can be influenced by political processes, and how the process
of making decisions affects their acceptability. Other disciplines,
including ethics (moral philosophy), decision theory, risk analysis,
and the normative branch of economics, investigate, analyze, and
clarify values themselves [2.5, 3.4, 3.5, 3.6]. These disciplines offer
practical ways of measuring some values and trading off conflict-
ing interests. For example, the discipline of public health often
measures health by means of ‘disability-adjusted life years’ [3.4.5].
Economics uses measures of social value that are generally based
on monetary valuation but can take account of principles of
distributive justice [3.6, 4.2, 4.7, 4.8]. These normative disciplines
also offer practical decision-making tools, such as expected util-
ity theory, decision analysis, cost-benefit and cost-effectiveness
analysis, and the structured use of expert judgment [2.5, 3.6, 3.7,
3.9].
There is a further element to decision making. People and
countries have rights and owe duties towards each other.
These are matters of justice, equity, or fairness. They fall within
the subject matter of moral and political philosophy, jurispru-
dence, and economics. For example, some have argued that
countries owe restitution for the harms that result from their
past GHG emissions, and it has been debated, on jurispruden-
tial and other grounds, whether restitution is owed only for
harms that result from negligent or blameworthy GHG emis-
sions. [3.3, 4.6]
3838
TS
Technical Summary
concentrations nor fully understand the harm it will impose on indi-
viduals, societies, and ecosystems. Article 2 requires that societies bal-
ance a variety of considerations — some rooted in the impacts of cli-
mate change itself and others in the potential costs of mitigation and
adaptation. The difficulty of that task is compounded by the need to
develop a consensus on fundamental issues such as the level of risk
that societies are willing to accept and impose on others, strategies for
sharing costs, and how to balance the numerous tradeoffs that arise
because mitigation intersects with many other goals of societies. Such
issues are inherently value-laden and involve different actors who
have varied interests and disparate decision-making power.
The Working Group III (WGIII) contribution to the IPCC’s Fifth Assessment
Report (AR5) assesses literature on the scientific, technological, environ-
mental, economic and social aspects of mitigation of climate change.
It builds upon the WGIII contribution to the IPCC’s Fourth Assessment
Report (AR4), the Special Report on Renewable Energy Sources and Cli-
mate Change Mitigation (SRREN) and previous reports and incorporates
subsequent new findings and research. Throughout, the focus is on the
implications of its findings for policy, without being prescriptive about
the particular policies that governments and other important partici-
pants in the policy process should adopt. In light of the IPCC’s mandate,
authors in WGIII were guided by several principles when assembling this
assessment: (1) to be explicit about mitigation options, (2) to be explicit
about their costs and about their risks and opportunities vis-à-vis other
development priorities, (3) and to be explicit about the underlying crite-
ria, concepts, and methods for evaluating alternative policies.
The remainder of this summary offers the main findings of this report.
The degree of certainty in findings, as in the reports of all three IPCC
Working Groups, is based on the author teams’ evaluations of underly-
ing scientific understanding and is expressed as a qualitative level of
confidence (from very low to very high) and, when possible, proba-
bilistically with a quantified likelihood (from exceptionally unlikely to
virtually certain). Confidence in the validity of a finding is based on the
type, amount, quality, and consistency of evidence (e. g., data, mecha-
nistic understanding, theory, models, expert judgment) and the degree
of agreement. Probabilistic estimates of quantified measures of uncer-
tainty in a finding are based on statistical analysis of observations or
model results, or both, and expert judgment.
2
Where appropriate, find-
2
The following summary terms are used to describe the available evidence: limited,
medium, or robust; and for the degree of agreement: low, medium, or high. A level
of confidence is expressed using five qualifiers: very low, low, medium, high, and
very high, and typeset in italics, e. g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increas-
ing levels of evidence and degrees of agreement are correlated with increasing
confidence. The following terms have been used to indicate the assessed likeli-
hood of an outcome or a result: virtually certain 99 – 100 % probability, very likely
90 – 100 %, likely 66 – 100 %, about as likely as not 33 – 66 %, unlikely 0 – 33 %,
very unlikely 0 – 10 %, exceptionally unlikely 0 – 1 %. Additional terms (more likely
than not >50 – 100 %, and more unlikely than likely 0 –<50 %) may also be used
when appropriate. Assessed likelihood is typeset in italics, e. g., very likely. For
more details, please refer to the Guidance Note for Lead Authors of the IPCC Fifth
Assessment Report on Consistent Treatment of Uncertainties, available at http://
www.ipcc.ch/pdf/supporting-material/uncertainty-guidance-note.pdf.
ings are also formulated as statements of fact without using uncer-
tainty qualifiers. Within paragraphs of this summary, the confidence,
evidence, and agreement terms given for a bolded finding apply to
subsequent statements in the paragraph, unless additional terms are
provided. References in [square brackets] indicate chapters, sections,
figures, tables, and boxes where supporting evidence in the underlying
report can be found.
This section continues with providing a framing of important con-
cepts and methods that help to contextualize the findings presented
in subsequent sections. Section TS.2 presents evidence on past trends
in stocks and flows of GHGs and the factors that drive emissions at the
global, regional, and sectoral scales including economic growth, tech-
nology, or population changes. Section TS.3.1 provides findings from
studies that analyze the technological, economic, and institutional
requirements of long-term mitigation scenarios. Section TS.3.2 provides
details on mitigation measures and policies that are used within and
across different economic sectors and human settlements. Section TS.4
summarizes insights on the interactions of mitigation policies between
governance levels, economic sectors, and instrument types.
Climate change is a global commons problem that implies the
need for international cooperation in tandem with local,
national, and regional policies on many distinct matters. Because
the GHG emissions of any agent (individual, company, country) affect
every other agent, an effective outcome will not be achieved if indi-
vidual agents advance their interests independently of others. Interna-
tional cooperation can contribute by defining and allocating rights and
responsibilities with respect to the atmosphere [Sections 1.2.4, 3.1,
4.2, 13.2.1]. Moreover, research and development (R&D) in support of
mitigation is a public good, which means that international coopera-
tion can play a constructive role in the coordinated development and
diffusion of technologies [1.4.4, 3.11, 13.9, 14.4.3]. This gives rise to
separate needs for cooperation on R&D, opening up of markets, and
the creation of incentives to encourage private firms to develop and
deploy new technologies and households to adopt them.
International cooperation on climate change involves ethical
considerations, including equitable effort-sharing. Countries have
contributed differently to the build-up of GHG in the atmosphere, have
varying capacities to contribute to mitigation and adaptation, and have
different levels of vulnerability to climate impacts. Many less developed
countries are exposed to the greatest impacts but have contributed least
to the problem. Engaging countries in effective international cooperation
may require strategies for sharing the costs and benefits of mitigation
in ways that are perceived to be equitable [4.2]. Evidence suggests that
perceived fairness can influence the level of cooperation among individ-
uals, and that finding may suggest that processes and outcomes seen as
fair will lead to more international cooperation as well [3.10, 13.2.2.4].
Analysis contained in the literature of moral and political philosophy
can contribute to resolving ethical questions raised by climate change
[3.2, 3.3, 3.4]. These questions include how much overall mitigation is
needed to avoid ‘dangerous interference with the climate system’ (Box
Box TS.2 | Mitigation brings both market and non-market benefits to humanity
The impacts of mitigation consist in the reduction or elimination
of some of the effects of climate change. Mitigation may improve
people’s livelihood, their health, their access to food or clean water,
the amenities of their lives, or the natural environment around them.
Mitigation can improve human well-being through both market
and non-market effects. Market effects result from changes in
market prices, in people’s revenues or net income, or in the quality
or availability of market commodities. Non-market effects result
from changes in the quality or availability of non-marketed goods
such as health, quality of life, culture, environmental quality,
natural ecosystems, wildlife, and aesthetic values. Each impact
of climate change can generate both market and non-market
damages. For example, a heat wave in a rural area may cause heat
stress for exposed farm labourers, dry up a wetland that serves as
a refuge for migratory birds, or kill some crops and damage others.
Avoiding these damages is a benefit of mitigation. [3.9]
Economists often use monetary units to value the damage
done by climate change and the benefits of mitigation. The
monetized value of a benefit to a person is the amount of
income the person would be willing to sacrifice in order to get
it, or alternatively the amount she would be willing to accept
as adequate compensation for not getting it. The monetized
value of a harm is the amount of income she would be will-
ing to sacrifice in order to avoid it, or alternatively the amount
she would be willing to accept as adequate compensation for
suffering it. Economic measures seek to capture how strongly
individuals care about one good or service relative to another,
depending on their individual interests, outlook, and economic
circumstances. [3.9]
Monetary units can be used in this way to measure costs and
benefits that come at different times and to different people. But
it cannot be presumed that a dollar to one person at one time
can be treated as equivalent to a dollar to a different person or
at a different time. Distributional weights may need to be applied
between people [3.6.1], and discounting (see Box TS.10) may be
appropriate between times. [3.6.2]
3939
Technical Summary
TS
ings are also formulated as statements of fact without using uncer-
tainty qualifiers. Within paragraphs of this summary, the confidence,
evidence, and agreement terms given for a bolded finding apply to
subsequent statements in the paragraph, unless additional terms are
provided. References in [square brackets] indicate chapters, sections,
figures, tables, and boxes where supporting evidence in the underlying
report can be found.
This section continues with providing a framing of important con-
cepts and methods that help to contextualize the findings presented
in subsequent sections. Section TS.2 presents evidence on past trends
in stocks and flows of GHGs and the factors that drive emissions at the
global, regional, and sectoral scales including economic growth, tech-
nology, or population changes. Section TS.3.1 provides findings from
studies that analyze the technological, economic, and institutional
requirements of long-term mitigation scenarios. Section TS.3.2 provides
details on mitigation measures and policies that are used within and
across different economic sectors and human settlements. Section TS.4
summarizes insights on the interactions of mitigation policies between
governance levels, economic sectors, and instrument types.
Climate change is a global commons problem that implies the
need for international cooperation in tandem with local,
national, and regional policies on many distinct matters. Because
the GHG emissions of any agent (individual, company, country) affect
every other agent, an effective outcome will not be achieved if indi-
vidual agents advance their interests independently of others. Interna-
tional cooperation can contribute by defining and allocating rights and
responsibilities with respect to the atmosphere [Sections 1.2.4, 3.1,
4.2, 13.2.1]. Moreover, research and development (R&D) in support of
mitigation is a public good, which means that international coopera-
tion can play a constructive role in the coordinated development and
diffusion of technologies [1.4.4, 3.11, 13.9, 14.4.3]. This gives rise to
separate needs for cooperation on R&D, opening up of markets, and
the creation of incentives to encourage private firms to develop and
deploy new technologies and households to adopt them.
International cooperation on climate change involves ethical
considerations, including equitable effort-sharing. Countries have
contributed differently to the build-up of GHG in the atmosphere, have
varying capacities to contribute to mitigation and adaptation, and have
different levels of vulnerability to climate impacts. Many less developed
countries are exposed to the greatest impacts but have contributed least
to the problem. Engaging countries in effective international cooperation
may require strategies for sharing the costs and benefits of mitigation
in ways that are perceived to be equitable [4.2]. Evidence suggests that
perceived fairness can influence the level of cooperation among individ-
uals, and that finding may suggest that processes and outcomes seen as
fair will lead to more international cooperation as well [3.10, 13.2.2.4].
Analysis contained in the literature of moral and political philosophy
can contribute to resolving ethical questions raised by climate change
[3.2, 3.3, 3.4]. These questions include how much overall mitigation is
needed to avoid ‘dangerous interference with the climate system’ (Box
Box TS.2 | Mitigation brings both market and non-market benefits to humanity
The impacts of mitigation consist in the reduction or elimination
of some of the effects of climate change. Mitigation may improve
people’s livelihood, their health, their access to food or clean water,
the amenities of their lives, or the natural environment around them.
Mitigation can improve human well-being through both market
and non-market effects. Market effects result from changes in
market prices, in people’s revenues or net income, or in the quality
or availability of market commodities. Non-market effects result
from changes in the quality or availability of non-marketed goods
such as health, quality of life, culture, environmental quality,
natural ecosystems, wildlife, and aesthetic values. Each impact
of climate change can generate both market and non-market
damages. For example, a heat wave in a rural area may cause heat
stress for exposed farm labourers, dry up a wetland that serves as
a refuge for migratory birds, or kill some crops and damage others.
Avoiding these damages is a benefit of mitigation. [3.9]
Economists often use monetary units to value the damage
done by climate change and the benefits of mitigation. The
monetized value of a benefit to a person is the amount of
income the person would be willing to sacrifice in order to get
it, or alternatively the amount she would be willing to accept
as adequate compensation for not getting it. The monetized
value of a harm is the amount of income she would be will-
ing to sacrifice in order to avoid it, or alternatively the amount
she would be willing to accept as adequate compensation for
suffering it. Economic measures seek to capture how strongly
individuals care about one good or service relative to another,
depending on their individual interests, outlook, and economic
circumstances. [3.9]
Monetary units can be used in this way to measure costs and
benefits that come at different times and to different people. But
it cannot be presumed that a dollar to one person at one time
can be treated as equivalent to a dollar to a different person or
at a different time. Distributional weights may need to be applied
between people [3.6.1], and discounting (see Box TS.10) may be
appropriate between times. [3.6.2]
TS.1) [3.1], how the effort or cost of mitigating climate change should
be shared among countries and between the present and future [3.3,
3.6, 4.6], how to account for such factors as historical responsibility for
GHG emissions [3.3, 4.6], and how to choose among alternative policies
for mitigation and adaptation [3.4, 3.5, 3.6, 3.7]. Ethical issues of well-
being, justice, fairness, and rights are all involved. Ethical analysis can
identify the different ethical principles that underlie different viewpoints,
and distinguish correct from incorrect ethical reasoning [3.3, 3.4].
Evaluation of mitigation options requires taking into account
many different interests, perspectives, and challenges between
and within societies. Mitigation engages many different agents, such
as governments at different levels — regionally [14.1], nationally and
locally [15.1], and through international agreements [13.1] — as well
as households, firms, and other non-governmental actors. The intercon-
nections between different levels of decision making and among dif-
ferent actors affect the many goals that become linked with climate
policy. Indeed, in many countries the policies that have (or could have)
the largest impact on emissions are motivated not solely by concerns
surrounding climate change. Of particular importance are the interac-
tions and perceived tensions between mitigation and development
[4.1, 14.1]. Development involves many activities, such as enhancing
access to modern energy services [7.9.1, 14.3.2, 16.8], the building of
infrastructures [12.1], ensuring food security [11.1], and eradicating
poverty [4.1]. Many of these activities can lead to higher emissions,
if achieved by conventional means. Thus, the relationships between
development and mitigation can lead to political and ethical conun-
drums, especially for developing countries, when mitigation is seen as
exacerbating urgent development challenges and adversely affecting
the current well-being of their populations [4.1]. These conundrums
are examined throughout this report, including in special boxes high-
lighting the concerns of developing countries.
Economic evaluation can be useful for policy design and be
given a foundation in ethics, provided appropriate distribu-
tional weights are applied. While the limitations of economics are
widely documented [2.4, 3.5], economics nevertheless provides use-
ful tools for assessing the pros and cons of mitigation and adaptation
options. Practical tools that can contribute to decision making include
cost-benefit analysis, cost-effectiveness analysis, multi-criteria analysis,
expected utility theory, and methods of decision analysis [2.5, 3.7.2].
Economic valuation (see Box TS.2) can be given a foundation in ethics,
provided distributional weights are applied that take proper account
of the difference in the value of money to rich and poor people [3.6].
Few empirical applications of economic valuation to climate change
have been well-founded in this respect [3.6.1]. The literature provides
significant guidance on the social discount rate for consumption (see
Box TS.10), which is in effect inter-temporal distributional weighting. It
suggests that the social discount rate depends in a well-defined way
primarily on the anticipated growth in per capita income and inequal-
ity aversion [3.6.2].
Most climate policies intersect with other societal goals, either
positively or negatively, creating the possibility of ‘co-benefits’
4040
TS
Technical Summary
or ‘adverse side-effects’. Since the publication of AR4, a substantial
body of literature has emerged looking at how countries that engage
in mitigation also address other goals, such as local environmental
protection or energy security, as a ‘co-benefit’ and conversely [1.2.1,
6.6.1, 4.8]. This multi-objective perspective is important because it
helps to identify areas where political, administrative, stakeholder, and
other support for policies that advance multiple goals will be robust.
Moreover, in many societies the presence of multiple objectives may
make it easier for governments to sustain the political support needed
for mitigation [15.2.3]. Measuring the net effect on social welfare (see
Box TS.11) requires examining the interaction between climate policies
and pre-existing other policies [3.6.3, 6.3.6.5].
Mitigation efforts generate tradeoffs and synergies with other
societal goals that can be evaluated in a sustainable develop-
ment framework. The many diverse goals that societies value are
often called ‘sustainable development’. A comprehensive assessment
of climate policy therefore involves going beyond a narrow focus on
distinct mitigation and adaptation options and their specific co-bene-
fits and adverse side-effects. Instead it entails incorporating climate
issues into the design of comprehensive strategies for equitable and
sustainable development at regional, national, and local levels [4.2,
4.5]. Maintaining and advancing human well-being, in particular over-
coming poverty and reducing inequalities in living standards, while
avoiding unsustainable patterns of consumption and production, are
fundamental aspects of equitable and sustainable development [4.4,
4.6, 4.8]. Because these aspects are deeply rooted in how societies for-
mulate and implement economic and social policies generally, they are
critical to the adoption of effective climate policy.
Variations in goals reflect, in part, the fact that humans perceive
risks and opportunities differently. Individuals make their decisions
based on different goals and objectives and use a variety of different
methods in making choices between alternative options. These choices
and their outcomes affect the ability of different societies to cooperate
and coordinate. Some groups put greater emphasis on near-term eco-
nomic development and mitigation costs, while others focus more on
the longer-term ramifications of climate change for prosperity. Some
are highly risk averse while others are more tolerant of dangers. Some
have more resources to adapt to climate change and others have
fewer. Some focus on possible catastrophic events while others ignore
extreme events as implausible. Some will be relative winners, and
some relative losers from particular climate changes. Some have more
political power to articulate their preferences and secure their interests
and others have less. Since AR4, awareness has grown that such con-
siderations — long the domain of psychology, behavioural economics,
political economy, and other disciplines — need to be taken into
account in assessing climate policy (see Box TS.3). In addition to the
different perceptions of climate change and its risks, a variety of norms
can also affect what humans view as acceptable behaviour. Awareness
has grown about how such norms spread through social networks and
ultimately affect activities, behaviours and lifestyles, and thus develop-
ment pathways, which can have profound impacts on GHG emissions
and mitigation policy. [1.4.2, 2.4, 3.8, 3.10, 4.3]
Box TS.4 | ‘Fat tails’: unlikely vs. likely outcomes in understanding the value of mitigation
What has become known as the ‘fat-tails’ problem relates to uncer-
tainty in the climate system and its implications for mitigation and
adaptation policies. By assessing the chain of structural uncertain-
ties that affect the climate system, the resulting compound probabil-
ity distribution of possible economic damage may have a fat right
tail. That means that the probability of damage does not decline
with increasing temperature as quickly as the consequences rise.
The significance of fat tails can be illustrated for the distribution
of temperature that will result from a doubling of atmospheric
carbon dioxide (CO
2
) (climate sensitivity). IPCC Working Group
I (WGI) estimates may be used to calibrate two possible dis-
tributions, one fat-tailed and one thin-tailed, that each have a
median temperature change of 3 °C and a 15 % probability of a
temperature change in excess of 4.5 °C. Although the probability
of exceeding 4.5 °C is the same for both distributions, likelihood
drops off much more slowly with increasing temperature for the
fat-tailed compared to the thin-tailed distribution. For example,
the probability of temperatures in excess of 8 °C is nearly ten
times greater with the chosen fat-tailed distribution than with
the thin-tailed distribution. If temperature changes are character-
ized by a fat tailed distribution, and events with large impact may
occur at higher temperatures, then tail events can dominate the
computation of expected damages from climate change.
In developing mitigation and adaptation policies, there is value in
recognizing the higher likelihood of tail events and their con-
sequences. In fact, the nature of the probability distribution of
temperature change can profoundly change how climate policy
is framed and structured. Specifically, fatter tails increase the
importance of tail events (such as 8 °C warming). While research
attention and much policy discussion have focused on the most
likely outcomes, it may be that those in the tail of the probability
distribution are more important to consider. [2.5, 3.9.2]
Box TS.3 | Deliberative and intuitive thinking are inputs to effective risk management
When people — from individual voters to key decision makers in
firms to senior government policymakers — make choices that
involve risk and uncertainty, they rely on deliberative as well intui-
tive thought processes. Deliberative thinking is characterized by
the use of a wide range of formal methods to evaluate alternative
choices when probabilities are difficult to specify and / or outcomes
are uncertain. They can enable decision makers to compare choices
in a systematic manner by taking into account both short and
long-term consequences. A strength of these methods is that they
help avoid some of the well-known pitfalls of intuitive thinking,
such as the tendency of decision makers to favour the status quo.
A weakness of these deliberative decision aids is that they are
often highly complex and require considerable time and attention.
Most analytically based literature, including reports such as this
one, is based on the assumption that individuals undertake delib-
erative and systematic analyses in comparing options. However,
when making mitigation and adaptation choices, people are also
likely to engage in intuitive thinking. This kind of thinking has the
advantage of requiring less extensive analysis than deliberative
thinking. However, relying on one’s intuition may not lead one to
characterize problems accurately when there is limited past expe-
rience. Climate change is a policy challenge in this regard since it
involves large numbers of complex actions by many diverse actors,
each with their own values, goals, and objectives. Individuals are
likely to exhibit well-known patterns of intuitive thinking such
as making choices related to risk and uncertainty on the basis
of emotional reactions and the use of simplified rules that have
been acquired by personal experience. Other tendencies include
misjudging probabilities, focusing on short time horizons, and
utilizing rules of thumb that selectively attend to subsets of goals
and objectives. [2.4]
By recognizing that both deliberative and intuitive modes of deci-
sion making are prevalent in the real world, risk management pro-
grammes can be developed that achieve their desired impacts. For
example, alternative frameworks that do not depend on precise
specification of probabilities and outcomes can be considered in
designing mitigation and adaptation strategies for climate change.
[2.4, 2.5, 2.6]
4141
Technical Summary
TS
Effective climate policy involves building institutions and
capacity for governance. While there is strong evidence that a tran-
sition to a sustainable and equitable path is technically feasible, chart-
ing an effective and viable course for climate change mitigation is not
merely a technical exercise. It will involve myriad and sequential deci-
sions among states and civil society actors. Such a process benefits
from the education and empowerment of diverse actors to participate
in systems of decision making that are designed and implemented
with procedural equity as a deliberate objective. This applies at the
national as well as international levels, where effective governance
relating to global common resources, in particular, is not yet mature.
Any given approach has potential winners and losers. The political
feasibility of that approach will depend strongly on the distribution of
power, resources, and decision-making authority among the potential
winners and losers. In a world characterized by profound disparities,
procedurally equitable systems of engagement, decision making and
governance may help enable a polity to come to equitable solutions to
the sustainable development challenge. [4.3]
Effective risk management of climate change involves consider-
ing uncertainties in possible physical impacts as well as human
and social responses. Climate change mitigation and adaptation is
a risk management challenge that involves many different decision-
making levels and policy choices that interact in complex and often
unpredictable ways. Risks and uncertainties arise in natural, social, and
technological systems. As Box TS.3 explains, effective risk management
strategies not only consider people’s values, and their intuitive decision
processes but utilize formal models and decision aids for systemati-
cally addressing issues of risk and uncertainty [2.4, 2.5]. Research on
other such complex and uncertainty-laden policy domains suggest the
importance of adopting policies and measures that are robust across
a variety of criteria and possible outcomes [2.5]. As detailed in Box
TS.4, a special challenge arises with the growing evidence that cli-
mate change may result in extreme impacts whose trigger points
and outcomes are shrouded in high levels of uncertainty [2.5, 3.9.2].
A risk management strategy for climate change will require integrat-
ing responses in mitigation with different time horizons, adaptation to
an array of climate impacts, and even possible emergency responses
such as ‘geoengineering’ in the face of extreme climate impacts [1.4.2,
3.3.7, 6.9, 13.4.4]. In the face of potential extreme impacts, the ability
to quickly offset warming could help limit some of the most extreme
climate impacts although deploying these geoengineering systems
could create many other risks (see Section TS.3.1.3). One of the cen-
tral challenges in developing a risk management strategy is to have it
adaptive to new information and different governing institutions [2.5].
TS.2 Trends in stocks and
flows of greenhouse
gases and their drivers
This section summarizes historical GHG emissions trends and their
underlying drivers. As in most of the underlying literature, all aggre-
gate GHG emissions estimates are converted to CO
2
-equivalents based
on Global Warming Potentials with a 100-year time horizon (GWP
100
)
(Box TS.5). The majority of changes in GHG emissions trends that are
observed in this section are related to changes in drivers such as eco-
mulate and implement economic and social policies generally, they are
critical to the adoption of effective climate policy.
Variations in goals reflect, in part, the fact that humans perceive
risks and opportunities differently. Individuals make their decisions
based on different goals and objectives and use a variety of different
methods in making choices between alternative options. These choices
and their outcomes affect the ability of different societies to cooperate
and coordinate. Some groups put greater emphasis on near-term eco-
nomic development and mitigation costs, while others focus more on
the longer-term ramifications of climate change for prosperity. Some
are highly risk averse while others are more tolerant of dangers. Some
have more resources to adapt to climate change and others have
fewer. Some focus on possible catastrophic events while others ignore
extreme events as implausible. Some will be relative winners, and
some relative losers from particular climate changes. Some have more
political power to articulate their preferences and secure their interests
and others have less. Since AR4, awareness has grown that such con-
siderations — long the domain of psychology, behavioural economics,
political economy, and other disciplines — need to be taken into
account in assessing climate policy (see Box TS.3). In addition to the
different perceptions of climate change and its risks, a variety of norms
can also affect what humans view as acceptable behaviour. Awareness
has grown about how such norms spread through social networks and
ultimately affect activities, behaviours and lifestyles, and thus develop-
ment pathways, which can have profound impacts on GHG emissions
and mitigation policy. [1.4.2, 2.4, 3.8, 3.10, 4.3]
Box TS.4 | ‘Fat tails’: unlikely vs. likely outcomes in understanding the value of mitigation
What has become known as the ‘fat-tails’ problem relates to uncer-
tainty in the climate system and its implications for mitigation and
adaptation policies. By assessing the chain of structural uncertain-
ties that affect the climate system, the resulting compound probabil-
ity distribution of possible economic damage may have a fat right
tail. That means that the probability of damage does not decline
with increasing temperature as quickly as the consequences rise.
The significance of fat tails can be illustrated for the distribution
of temperature that will result from a doubling of atmospheric
carbon dioxide (CO
2
) (climate sensitivity). IPCC Working Group
I (WGI) estimates may be used to calibrate two possible dis-
tributions, one fat-tailed and one thin-tailed, that each have a
median temperature change of 3 °C and a 15 % probability of a
temperature change in excess of 4.5 °C. Although the probability
of exceeding 4.5 °C is the same for both distributions, likelihood
drops off much more slowly with increasing temperature for the
fat-tailed compared to the thin-tailed distribution. For example,
the probability of temperatures in excess of 8 °C is nearly ten
times greater with the chosen fat-tailed distribution than with
the thin-tailed distribution. If temperature changes are character-
ized by a fat tailed distribution, and events with large impact may
occur at higher temperatures, then tail events can dominate the
computation of expected damages from climate change.
In developing mitigation and adaptation policies, there is value in
recognizing the higher likelihood of tail events and their con-
sequences. In fact, the nature of the probability distribution of
temperature change can profoundly change how climate policy
is framed and structured. Specifically, fatter tails increase the
importance of tail events (such as 8 °C warming). While research
attention and much policy discussion have focused on the most
likely outcomes, it may be that those in the tail of the probability
distribution are more important to consider. [2.5, 3.9.2]
Box TS.3 | Deliberative and intuitive thinking are inputs to effective risk management
When people — from individual voters to key decision makers in
firms to senior government policymakers — make choices that
involve risk and uncertainty, they rely on deliberative as well intui-
tive thought processes. Deliberative thinking is characterized by
the use of a wide range of formal methods to evaluate alternative
choices when probabilities are difficult to specify and / or outcomes
are uncertain. They can enable decision makers to compare choices
in a systematic manner by taking into account both short and
long-term consequences. A strength of these methods is that they
help avoid some of the well-known pitfalls of intuitive thinking,
such as the tendency of decision makers to favour the status quo.
A weakness of these deliberative decision aids is that they are
often highly complex and require considerable time and attention.
Most analytically based literature, including reports such as this
one, is based on the assumption that individuals undertake delib-
erative and systematic analyses in comparing options. However,
when making mitigation and adaptation choices, people are also
likely to engage in intuitive thinking. This kind of thinking has the
advantage of requiring less extensive analysis than deliberative
thinking. However, relying on one’s intuition may not lead one to
characterize problems accurately when there is limited past expe-
rience. Climate change is a policy challenge in this regard since it
involves large numbers of complex actions by many diverse actors,
each with their own values, goals, and objectives. Individuals are
likely to exhibit well-known patterns of intuitive thinking such
as making choices related to risk and uncertainty on the basis
of emotional reactions and the use of simplified rules that have
been acquired by personal experience. Other tendencies include
misjudging probabilities, focusing on short time horizons, and
utilizing rules of thumb that selectively attend to subsets of goals
and objectives. [2.4]
By recognizing that both deliberative and intuitive modes of deci-
sion making are prevalent in the real world, risk management pro-
grammes can be developed that achieve their desired impacts. For
example, alternative frameworks that do not depend on precise
specification of probabilities and outcomes can be considered in
designing mitigation and adaptation strategies for climate change.
[2.4, 2.5, 2.6]
4242
TS
Technical Summary
nomic growth, technological change, human behaviour, or population
growth. But there are also some smaller changes in GHG emissions
estimates that are due to refinements in measurement concepts and
methods that have happened since AR4. There is a growing body of
literature on uncertainties in global GHG emissions data sets. This sec-
tion tries to make these uncertainties explicit and reports variations in
estimates across global data sets wherever possible.
TS.2.1 Greenhouse gas emission trends
Total anthropogenic GHG emissions have risen more rapidly
from 2000 to 2010 than in the previous three decades (high
confidence). Total anthropogenic GHG emissions were the highest in
human history from 2000 to 2010 and reached 49 (± 4.5) gigatonnes
CO
2
-equivalents per year (GtCO
2
eq / yr) in 2010.
3
Current trends are at
the high end of levels that had been projected for this last decade.
GHG emissions growth has occurred despite the presence of a wide
array of multilateral institutions as well as national policies aimed at
mitigation. From 2000 to 2010, GHG emissions grew on average by
1.0 GtCO
2
eq (2.2 %) per year compared to 0.4 GtCO
2
eq (1.3 %) per
year over the entire period from 1970 to 2000 (Figure TS.1). The global
economic crisis 2007 / 2008 has only temporarily reduced GHG emis-
sions. [1.3, 5.2, 13.3, 15.2.2, Figure 15.1]
3
In this summary, uncertainty in historic GHG emissions data is reported using
90 % uncertainty intervals unless otherwise stated. GHG emissions levels are
rounded to two significant digits throughout this document; as a consequence,
small differences in sums due to rounding may occur.
4
FOLU (Forestry and Other Land Use) — also referred to as LULUCF (Land Use,
Land-Use Change, and Forestry) — is the subset of Agriculture, Forestry, and Other
Land Use (AFOLU) emissions and removals of GHGs related to direct human-
induced land use, land-use change and forestry activities excluding agricultural
emissions (see WGIII AR5 Glossary).
5
In this report, data on non-CO
2
GHGs, including fluorinated gases, are taken from
the EDGAR database (see Annex II.9), which covers substances included in the
Kyoto Protocol in its first commitment period.
Figure TS.1 | Total annual anthropogenic GHG emissions (GtCO
2
eq / yr) by groups of gases 1970 – 2010: carbon dioxide (CO
2
) from fossil fuel combustion and industrial processes;
CO
2
from Forestry and Other Land Use
4
(FOLU); methane (CH
4
); nitrous oxide (N
2
O); fluorinated gases
5
covered under the Kyoto Protocol (F-gases). At the right side of the figure,
GHG emissions in 2010 are shown again broken down into these components with the associated uncertainties (90 % confidence interval) indicated by the error bars. Total anthro-
pogenic GHG emissions uncertainties are derived from the individual gas estimates as described in Chapter 5 [5.2.3.6]. Emissions are converted into CO
2
-equivalents based on
Global Warming Potentials with a 100-year time horizon (GWP
100
) from the IPCC Second Assessment Report (SAR). The emissions data from FOLU represents land-based CO
2
emis-
sions from forest and peat fires and decay that approximate to the net CO
2
flux from FOLU as described in Chapter 11 of this report. Average annual GHG emissions growth rates
for the four decades are highlighted with the brackets. The average annual growth rate from 1970 to 2000 is 1.3 %. [Figure 1.3]
27 Gt
33 Gt
55%
17%
19%
7.9%
0.44%
58%
15%
18%
7.9%
0.67%
62%
13%
16%
6.9%
1.3%
38 Gt
40 Gt
59%
16%
18%
7.4%
0.81%
49 Gt
65%
11%
16%
6.2%
2.0%
2005
Gas
CO
2
Fossil Fuel and
Industrial Processes
CO
2
FOLU
CH
4
N
2
O
F-Gases
GHG Emissions [GtCO
2
eq/yr]
0
10
20
30
40
50
2010200520001995199019851980
19751970
+2.2%/yr
2000-10
+0.6%/yr
1990-00
+1.4%/yr
1980-90
+2.0%/yr
1970-80
2010
Total Annual Anthropogenic GHG Emissions by Groups of Gases 1970-2010
4343
Technical Summary
TS
Figure TS.2 | Historical anthropogenic CO
2
emissions from fossil fuel combustion, flaring, cement, and Forestry and Other Land Use (FOLU)
4
in five major world regions: OECD-
1990 (blue); Economies in Transition (yellow); Asia (green); Latin America and Caribbean (red); Middle East and Africa (brown). Emissions are reported in gigatonnes of CO
2
per
year (Gt CO
2
/ yr). Left panels show regional CO
2
emissions 1750 – 2010 from: (a) the sum of all CO
2
sources (c+e); (c) fossil fuel combustion, flaring, and cement; and (e) FOLU.
The right panels report regional contributions to cumulative CO
2
emissions over selected time periods from: (b) the sum of all CO
2
sources (d+f); (d) fossil fuel combustion, flaring
and cement; and (f) FOLU. Error bars on panels (b), (d) and (f) give an indication of the uncertainty range (90 % confidence interval). See Annex II.2.2 for definitions of regions.
[Figure 5.3]
4444
TS
Technical Summary
Figure TS.3 | Total anthropogenic GHG emissions (GtCO
2
eq/yr) by economic sectors and country income groups. Upper panel: Circle shows direct GHG emission shares (in % of
total anthropogenic GHG emissions) of five major economic sectors in 2010. Pull-out shows how indirect CO
2
emission shares (in % of total anthropogenic GHG emissions) from
electricity and heat production are attributed to sectors of final energy use. ‘Other Energy’ refers to all GHG emission sources in the energy sector other than electricity and heat
production. Lower panel: Total anthropogenic GHG emissions in 1970, 1990 and 2010 by five major economic sectors and country income groups. ‘Bunkers’ refer to GHG emissions
from international transportation and thus are not, under current accounting systems, allocated to any particular nation’s territory. The emissions data from Agriculture, Forestry and
Other Land Use (AFOLU) includes land-based CO
2
emissions from forest and peat fires and decay that approximate to the net CO
2
flux from the Forestry and Other Land Use (FOLU)
sub-sector as described in Chapter 11 of this report. Emissions are converted into CO
2
-equivalents based on Global Warming Potentials with a 100-year time horizon (GWP
100
) from
the IPCC Second Assessment Report (SAR). Assignment of countries to income groups is based on the World Bank income classification in 2013. For details see Annex II.2.3. Sector
definitions are provided in Annex II.9.1. [Figure 1.3, Figure 1.6]
Greenhouse Gas Emissions by Economic Sectors
Indirect CO
2
Emissions
Direct Emissions
Buildings
6.4%
Transport
14%
Industry
21%
Other
Energy
9.6%
Electricity
and Heat Production
25%
49 Gt CO
2
eq
(2010)
AFOLU
24%
Buildings
12%
Transport
0.3%
Industry
11%
Energy
1.4%
AFOLU
0.87%
0.48Gt
0.62Gt
1.1Gt
3.2Gt
3.4Gt
3.4Gt
7.9Gt
18Gt
19Gt
18Gt
14Gt
9.8Gt
5.9Gt
5.6Gt
3.5Gt
Energy
Transport
Buildings
Industry
AFOLU
201019901970201019901970201019901970201019901970201019901970
GHG Emissions [GtCO
2
eq/yr]
0
5
10
15
20
High IncomeUpper Mid IncomeLower Mid IncomeLow IncomeBunkers
4545
Technical Summary
TS
CO
2
emissions from fossil fuel combustion and industrial pro-
cesses contributed about 78 % to the total GHG emissions
increase from 1970 to 2010, with similar percentage contribu-
tion for the period 2000 – 2010 (high confidence). Fossil fuel-related
CO
2
emissions reached 32 (± 2.7) GtCO
2
/ yr in 2010 and grew further
by about 3 % between 2010 and 2011 and by about 1 – 2 % between
2011 and 2012. Since AR4, the shares of the major groups of GHG
emissions have remained stable. Of the 49 (± 4.5) GtCO
2
eq / yr in total
anthropogenic GHG emissions in 2010, CO
2
remains the major GHG
accounting for 76 % (38± 3.8 GtCO
2
eq / yr) of total anthropogenic GHG
emissions. 16 % (7.8± 1.6 GtCO
2
eq / yr) come from methane (CH
4
),
6.2 % (3.1± 1.9 GtCO
2
eq / yr) from nitrous oxide (N
2
O), and 2.0 %
(1.0± 0.2 GtCO
2
eq / yr) from fluorinated gases (Figure TS.1).
5
Using the
most recent GWP
100
values from the AR5 [WGI 8.7] global GHG emis-
sions totals would be slightly higher (52 GtCO
2
eq / yr) and non-CO
2
emission shares would be 20 % for CH
4
, 5.0 % for N
2
O and 2.2 % for
F-gases. Emission shares are sensitive to the choice of emission metric
and time horizon, but this has a small influence on global, long-term
trends. If a shorter, 20-year time horizon were used, then the share
of CO
2
would decline to just over 50 % of total anthropogenic GHG
emissions and short-lived gases would rise in relative importance. As
detailed in Box TS.5, the choice of emission metric and time horizon
involves explicit or implicit value judgements and depends on the pur-
pose of the analysis. [1.2, 3.9, 5.2]
Over the last four decades total cumulative CO
2
emissions have
increased by a factor of 2 from about 910 GtCO
2
for the period
1750 – 1970 to about 2000 GtCO
2
for 1750 – 2010 (high confi-
dence). In 1970, the cumulative CO
2
emissions from fossil fuel combus-
tion, cement production and flaring since 1750 was 420 (± 35) GtCO
2
;
in 2010 that cumulative total had tripled to 1300 (± 110) GtCO
2
(Fig-
ure TS.2). Cumulative CO
2
emissions associated with FOLU
4
since 1750
increased from about 490 (± 180) GtCO
2
in 1970 to approximately 680
(± 300) GtCO
2
in 2010. [5.2]
Regional patterns of GHG emissions are shifting along with
changes in the world economy (high confidence). Since 2000,
GHG emissions have been growing in all sectors, except Agriculture,
Forestry and Other Land Use (AFOLU)
4
where positive and negative
emission changes are reported across different databases and uncer-
tainties in the data are high. More than 75 % of the 10 Gt increase in
annual GHG emissions between 2000 and 2010 was emitted in the
energy supply (47 %) and industry (30 %) sectors (see Annex II.9.I
for sector definitions). 5.9 GtCO
2
eq of this sectoral increase occurred
in upper-middle income countries,
6
where the most rapid economic
development and infrastructure expansion has taken place. GHG
emissions growth in the other sectors has been more modest in abso-
lute (0.3 – 1.1 Gt CO
2
eq) as well as in relative terms (3 % – 11 %). [1.3,
5.3, Figure 5.18]
6
When countries are assigned to income groups in this summary, the World Bank
income classification for 2013 is used. For details see Annex II.2.3.
Figure TS.4 | Trends in GHG emissions by country income groups. Left panel: Total annual anthropogenic GHG emissions from 1970 to 2010 (GtCO
2
eq / yr). Middle panel: Trends in
annual per capita mean and median GHG emissions from 1970 to 2010 (tCO
2
eq / cap/ yr). Right panel: Distribution of annual per capita GHG emissions in 2010 of countries within
each country income group (tCO
2
/ cap/ yr). Mean values show the GHG emissions levels weighed by population. Median values describe GHG emissions levels per capita of the
country at the 50th percentile of the distribution within each country income group. Emissions are converted into CO
2
-equivalents based on Global Warming Potentials with a 100-
year time horizon (GWP
100
) from the IPCC Second Assessment Report (SAR). Assignment of countries to country income groups is based on the World Bank income classification in
2013. For details see Annex II.2.3. [Figures 1.4, 1.8]
0
Per Capita GHG Emissions [(tCO
2
eq/cap)/yr]
1970 1980 1990 2000 2010
Per Capita GHG Emissions 2010 [(tCO
2
eq/cap)/yr]
0
5
10
15
25
20
LIC LMC UMC HIC
0
10
20
30
40
60
50
1970 1980 1990 2000 2010
GHG Emissions [GtCO
2
eq/yr]
2
8
6
4
10
12
14
16
22
24
18
20
Mean
Median
Total GHG Emissions
Lower Middle Income
Lower Income
High Income
Upper Middle Income
Mean
25
th
Percentile
75
th
Percentile
90
th
Percentile
10
th
Percentile
Median
4646
TS
Technical Summary
Current GHG emission levels are dominated by contributions
from the energy supply, AFOLU, and industry sectors; indus-
try and buildings gain considerably in importance if indirect
emissions are accounted for (robust evidence, high agreement).
Of the 49 (± 4.5) GtCO
2
eq emissions in 2010, 35 % (17 GtCO
2
eq)
of GHG emissions were released in the energy supply sector, 24 %
(12 GtCO
2
eq, net emissions) in AFOLU, 21 % (10 GtCO
2
eq) in indus-
try, 14 % (7.0 GtCO
2
eq) in transport, and 6.4 % (3.2 GtCO
2
eq) in
buildings. When indirect emissions from electricity and heat produc-
tion are assigned to sectors of final energy use, the shares of the
industry and buildings sectors in global GHG emissions grow to 31 %
and 19 %,
3
respectively (Figure TS.3 upper panel). [1.3, 7.3, 8.2, 9.2,
10.3, 11.2]
Per capita GHG emissions in 2010 are highly unequal (high confi-
dence). In 2010, median per capita GHG emissions (1.4 tCO
2
eq / cap / yr)
for the group of low-income countries are around nine times lower
than median per capita GHG emissions (13 tCO
2
eq / cap / yr) of high-
income countries (Figure TS.4).
6
For low-income countries, the largest
part of GHG emissions comes from AFOLU; for high-income countries,
GHG emissions are dominated by sources related to energy supply and
industry (Figure TS.3 lower panel). There are substantial variations in
per capita GHG emissions within country income groups with emis-
sions at the 90th percentile level more than double those at the 10th
percentile level. Median per capita emissions better represent the
typical country within a country income group comprised of heteroge-
neous members than mean per capita emissions. Mean per capita GHG
emissions are different from median mainly in low-income countries
as individual low-income countries have high per capita emissions due
to large CO
2
emissions from land-use change (Figure TS.4, right panel).
[1.3, 5.2, 5.3]
A growing share of total anthropogenic CO
2
emissions is
released in the manufacture of products that are traded across
international borders (medium evidence, high agreement). Since
AR4, several data sets have quantified the difference between tradi-
tional ‘territorial’ and ‘consumption-based’ emission estimates that
assign all emission released in the global production of goods and
services to the country of final consumption (Figure TS.5). A growing
share of CO
2
emissions from fossil fuel combustion in middle income
countries is released in the production of goods and services exported,
notably from upper middle income countries to high income countries.
Total annual industrial CO
2
emissions from the non-Annex I group now
exceed those of the Annex I group using territorial and consumption-
based accounting methods, but per-capita emissions are still markedly
higher in the Annex I group. [1.3, 5.3]
Regardless of the perspective taken, the largest share of
anthropogenic CO
2
emissions is emitted by a small number
of countries (high confidence). In 2010, 10 countries accounted for
about 70 % of CO
2
emissions from fossil fuel combustion and industrial
processes. A similarly small number of countries emit the largest share
of consumption-based CO
2
emissions as well as cumulative CO
2
emis-
sions going back to 1750. [1.3]
The upward trend in global fossil fuel related CO
2
emissions is
robust across databases and despite uncertainties (high confi-
dence). Global CO
2
emissions from fossil fuel combustion are known
within 8 % uncertainty. CO
2
emissions related to FOLU have very large
uncertainties attached in the order of 50 %. Uncertainty for global
emissions of methane (CH
4
), nitrous oxide (N
2
O), and the fluorinated
gases has been estimated as 20 %, 60 %, and 20 %. Combining these
values yields an illustrative total global GHG uncertainty estimate
of about 10 % (Figure TS.1). Uncertainties can increase at finer spa-
tial scales and for specific sectors. Attributing GHG emissions to the
country of final consumption increases uncertainties, but literature on
this topic is just emerging. GHG emissions estimates in the AR4 were
5 – 10 % higher than the estimates reported here, but lie within the
estimated uncertainty range.
3
[5.2]
Figure TS.5 | Total annual CO
2
emissions (GtCO
2
/ yr) from fossil fuel combustion for
country income groups attributed on the basis of territory (solid line) and final con-
sumption (dotted line). The shaded areas are the net CO
2
trade balances (differences)
between each of the four country income groups and the rest of the world. Blue shading
indicates that the country income group is a net importer of embodied CO
2
emissions,
leading to consumption-based emission estimates that are higher than traditional ter-
ritorial emission estimates. Orange indicates the reverse situation — the country income
group is a net exporter of embodied CO
2
emissions. Assignment of countries to country
income groups is based on the World Bank income classification in 2013. For details see
Annex II.2.3. [Figure 1.5]
Net Import
Net Export
Territory-Based
Consumption-Based
Attribution Principle
Transfer of Embodied CO
2
1990 1995 2000 2005 2010
0
2
4
6
8
10
12
14
16
18
Annual CO
2
Emissions [Gt/yr]
HIC
UMC
LMC
LIC
4747
Technical Summary
TS
TS.2.2 Greenhouse gas emission drivers
This section examines the factors that have, historically, been associated
with changes in GHG emissions levels. Typically, such analysis is based
on a decomposition of total GHG emissions into various components
such as growth in the economy (Gross Domestic Product (GDP) / capita),
growth in the population (capita), the energy intensity needed per unit of
economic output (energy / GDP) and the GHG emissions intensity of that
energy (GHGs / energy). As a practical matter, due to data limitations and
the fact that most GHG emissions take the form of CO
2
from industry and
energy, almost all this research focuses on CO
2
from those sectors.
Globally, economic and population growth continue to be the
most important drivers of increases in CO
2
emissions from
fossil fuel combustion. The contribution of population growth
between 2000 and 2010 remained roughly identical to the
previous three decades, while the contribution of economic
growth has risen sharply (high confidence). Worldwide popula-
tion increased by 86 % between 1970 and 2010, from 3.7 to 6.9
billion. Over the same period, income as measured through pro-
duction and/ or consumption per capita has grown by a factor of
about two. The exact measurement of global economic growth is
difficult because countries use different currencies and converting
Box TS.5 | Emissions metrics depend on value judgements and contain wide uncertainties
Emission metrics provide ‘exchange rates’ for measuring
the contributions of different GHGs to climate change. Such
exchange rates serve a variety of purposes, including apportion-
ing mitigation efforts among several gases and aggregating
emissions of a variety of GHGs. However, there is no metric that
is both conceptually correct and practical to implement. Because
of this, the choice of the appropriate metric depends on the
application or policy at issue. [3.9.6]
GHGs differ in their physical characteristics. For example, per
unit mass in the atmosphere, methane (CH
4
) causes a stronger
instantaneous radiative forcing than CO
2
, but it remains in the
atmosphere for a much shorter time. Thus, the time profiles of
climate change brought about by different GHGs are different and
consequential. Determining how emissions of different GHGs are
compared for mitigation purposes involves comparing the result-
ing temporal profiles of climate change from each gas and making
value judgments about the relative significance to humans of
these profiles, which is a process fraught with uncertainty. [3.9.6;
WGI 8.7]
A commonly used metric is the Global Warming Potential (GWP).
It is defined as the accumulated radiative forcing within a specific
time horizon (e. g., 100 years — GWP
100
), caused by emitting one
kilogram of the gas, relative to that of the reference gas CO
2
. This
metric is used to transform the effects of different GHG emissions
to a common scale (CO
2
-equivalents).
1
One strength of the GWP is
1
In this summary, all quantities of GHG emissions are expressed in CO
2
-equiva-
lent (CO
2
eq) emissions that are calculated based on GWP
100
. Unless otherwise
stated, GWP values for different gases are taken from IPCC Second Assess-
ment Report (SAR). Although GWP values have been updated several times
since, the SAR values are widely used in policy settings, including the Kyoto
Protocol, as well as in many national and international emission accounting
systems. Modelling studies show that the changes in GWP
100
values from
SAR to AR4 have little impact on the optimal mitigation strategy at the global
level. [6.3.2.5, Annex II.9.1]
that it can be calculated in a relatively transparent and straight-
forward manner. However, there are also limitations, including the
requirement to use a specific time horizon, the focus on cumula-
tive forcing, and the insensitivity of the metric to the temporal
profile of climate effects and its significance to humans. The choice
of time horizon is particularly important for short-lived gases,
notably methane: when computed with a shorter time horizon for
GWP, their share in calculated total warming effect is larger and
the mitigation strategy might change as a consequence. [1.2.5]
Many alternative metrics have been proposed in the scientific
literature. All of them have advantages and disadvantages, and
the choice of metric can make a large difference for the weights
given to emissions from particular gases. For instance, methane’s
GWP
100
is 28 while its Global Temperature Change Potential
(GTP), one alternative metric, is 4 for the same time horizon (AR5
values, see WGI Section 8.7). In terms of aggregate mitigation
costs alone, GWP
100
may perform similarly to other metrics (such
as the time-dependent Global Temperature Change Potential or
the Global Cost Potential) of reaching a prescribed climate target;
however, there may be significant differences in terms of the
implied distribution of costs across sectors, regions, and over time.
[3.9.6, 6.3.2.5]
An alternative to a single metric for all gases is to adopt a ‘multi-
basket’ approach in which gases are grouped according to their
contributions to short and long term climate change. This may
solve some problems associated with using a single metric, but
the question remains of what relative importance to attach to
reducing GHG emissions in the different groups. [3.9.6; WGI 8.7]
4848
TS
Technical Summary
individual national economic figures into global totals can be done
in various ways. With rising population and economic output, emis-
sions of CO
2
from fossil fuel combustion have risen as well. Over
the last decade, the importance of economic growth as a driver of
global CO
2
emissions has risen sharply while population growth has
remained roughly steady. Due to changes in technology, changes
in the economic structure and the mix of energy sources as well
as changes in other inputs such as capital and labour, the energy
intensity of economic output has steadily declined worldwide. This
decline has had an offsetting effect on global CO
2
emissions that
is nearly of the same magnitude as growth in population (Figure
TS.6). There are only a few countries that combine economic growth
and decreasing territorial CO
2
emissions over longer periods of time.
Such decoupling remains largely atypical, especially when consider-
ing consumption-based CO
2
emissions. [1.3, 5.3]
Between 2000 and 2010, increased use of coal relative to other
energy sources has reversed a long-standing pattern of gradual
decarbonization of the world’s energy supply (high confidence).
Increased use of coal, especially in developing Asia, is exacerbating
the burden of energy-related GHG emissions (Figure TS.6). Estimates
Box TS.6 | The use of scenarios in this report
Scenarios of how the future might evolve capture key factors of
human development that influence GHG emissions and our ability
to respond to climate change. Scenarios cover a range of plausible
futures, because human development is determined by a myriad
of factors including human decision making. Scenarios can be
used to integrate knowledge about the drivers of GHG emissions,
mitigation options, climate change, and climate impacts.
One important element of scenarios is the projection of the level
of human interference with the climate system. To this end, a set
of four ‘representative concentration pathways’ (RCPs) has been
developed. These RCPs reach radiative forcing levels of 2.6, 4.5,
6.0, and 8.5 Watts per square meter (W / m
2
) (corresponding to
concentrations of 450, 650, 850, and 1370 ppm CO
2
eq), respec-
tively, in 2100, covering the range of anthropogenic climate forc-
ing in the 21st century as reported in the literature. The four RCPs
are the basis of a new set of climate change projections that have
been assessed by WGI AR5. [WGI 6.4, WGI 12.4]
Scenarios of how the future develops without additional and
explicit efforts to mitigate climate change (‘baseline scenarios’)
and with the introduction of efforts to limit GHG emissions (‘miti-
gation scenarios’), respectively, generally include socio-economic
projections in addition to emission, concentration, and climate
change information. WGIII AR5 has assessed the full breadth of
baseline and mitigation scenarios in the literature. To this end, it
has collected a database of more than 1200 published mitigation
and baseline scenarios. In most cases, the underlying socio-eco-
nomic projections reflect the modelling teams’ individual choices
about how to conceptualize the future in the absence of climate
policy. The baseline scenarios show a wide range of assump-
tions about economic growth (ranging from threefold to more
than eightfold growth in per capita income by 2100), demand for
energy (ranging from a 40 % to more than 80 % decline in energy
intensity by 2100) and other factors, in particular the carbon
intensity of energy. Assumptions about population are an excep-
tion: the vast majority of scenarios focus on the low to medium
population range of nine to 10 billion people by 2100. Although
the range of emissions pathways across baseline scenarios in the
literature is broad, it may not represent the full potential range of
possibilities (Figure TS.7). [6.3.1]
The concentration outcomes of the baseline and mitigation
scenarios assessed by WGIII AR5 cover the full range of RCPs.
However, they provide much more detail at the lower end, with
many scenarios aiming at concentration levels in the range of 450,
500, and 550 ppm CO
2
eq in 2100. The climate change projections
of WGI based on RCPs, and the mitigation scenarios assessed
by WGIII AR5 can be related to each other through the climate
outcomes they imply. [6.2.1]
Figure TS.6 | Decomposition of the change in total annual CO
2
emissions from fos-
sil fuel combustion by decade and four driving factors: population, income (GDP) per
capita, energy intensity of GDP and carbon intensity of energy. Total emissions changes
are indicated by a triangle. The change in emissions over each decade is measured in
gigatonnes of CO
2
per year (GtCO
2
/yr); income is converted into common units using
purchasing power parities. [Figure 1.7]
1970-1980 1980-1990 1990-2000 2000-2010
-6
-4
-2
0
2
4
6
8
10
12
Change in Annual CO
2
Emissions by Decade [GtCO
2
/yr]
CarbonIntensity of Energy
EnergyIntensity of GDP
GDPperCapita
Population
TotalChange
6.8
2.5
2.9
4.0
4949
Technical Summary
TS
indicate that coal and unconventional gas and oil resources are large;
therefore reducing the carbon intensity of energy may not be primar-
ily driven by fossil resource scarcity, but rather by other driving forces
such as changes in technology, values, and socio-political choices. [5.3,
7.2, 7.3, 7.4; SRREN Figure 1.7]
Technological innovations, infrastructural choices, and behav-
iour affect GHG emissions through productivity growth, energy-
and carbon-intensity and consumption patterns (medium con-
fidence). Technological innovation improves labour and resource
productivity; it can support economic growth both with increasing
and with decreasing GHG emissions. The direction and speed of tech-
nological change depends on policies.Technology is also central to
the choices of infrastructure and spatial organization, such as in cit-
ies, which can have long-lasting effects on GHG emissions. In addi-
tion, a wide array of attitudes, values, and norms can inform different
lifestyles, consumption preferences, and technological choices all of
which, in turn, affect patterns of GHG emissions. [5.3, 5.5, 5.6, 12.3]
Without additional efforts to reduce GHG emissions beyond
those in place today, emissions growth is expected to persist,
driven by growth in global population and economic activities
despite improvements in energy supply and end-use technolo-
gies (high confidence). Atmospheric concentrations in baseline sce-
narios collected for this assessment (scenarios without explicit addi-
tional efforts to reduce GHG emissions) exceed 450 parts per million
Figure TS.7 | Global baseline projection ranges for four emissions driving factors. Scenarios harmonized with respect to a particular factor are depicted with individual lines. Other
scenarios are depicted as a range with median emboldened; shading reflects interquartile range (darkest), 5th – 95th percentile range (lighter), and full range (lightest), excluding
one indicated outlier in panel a). Scenarios are filtered by model and study for each indicator to include only unique projections. Model projections and historic data are normalized
to 1 in 2010. GDP is aggregated using base-year market exchange rates. Energy and carbon intensity are measured with respect to total primary energy. [Figure 6.1]
Index (2010=1)
Index (2010=1)
Index (2010=1)
Index (2010=1)
1 Outlier
History
History
History
History
Default
Fast
1970 1990 2010 2030 2050 2070 2090 1970 1990 2010 2030 2050 2070 2090
0
0.5
1.0
2.0
1.5
0
2
4
8
6
10
0
1.0
2.0
1.5
2.5
0
0.5
1.0
2.0
1.5
1970 1990 2010 2030 2050 2070 2090 1970 1990 2010 2030 2050 2070 2090
Historic Trend:
Average Rate
of Growth
1970-2010 =
1.4%
Historic Trend:
Average Rate
of Decline
1970-2010 =
0.8%
a) Population b) GDP Per Capita
c) Energy Intensity of GDP d) Carbon Intensity of Energy
0.5
Harmonized Default
UN Variants
(High, Medium, Low)
Harmonized High
Harmonized Low
0-100
th
5-95
th
25-75
th
Percentile
5050
TS
Technical Summary
(ppm) CO
2
eq by 2030.
7
They reach CO
2
eq concentration levels from
750 to more than 1300 ppm CO
2
eq by 2100 and result in projected
global mean surface temperature increases in 2100 from 3.7 to 4.8 °C
compared to pre-industrial levels
8
(range based on median climate
response; the range is 2.5 °C to 7.8 °C when including climate uncer-
tainty, see Table TS.1).
9
The range of 2100 concentrations corresponds
roughly to the range of CO
2
eq concentrations in the Representative
Concentration Pathways (RCP) 6.0 and RCP8.5 pathways (see Box
TS.6), with the majority of scenarios falling below the latter. For com-
parison, the CO
2
eq concentration in 2011 has been estimated to be
430 ppm (uncertainty range 340 – 520 ppm).
10
The literature does not
systematically explore the full range of uncertainty surrounding devel-
opment pathways and possible evolution of key drivers such as popu-
lation, technology, and resources. Nonetheless, the scenarios strongly
suggest that absent any explicit mitigation efforts, cumulative CO
2
emissions since 2010 will exceed 700 GtCO
2
by 2030, 1,500 GtCO
2
by
2050, and potentially well over 4,000 GtCO
2
by 2100. [6.3.1; WGI Fig-
ure SPM.5, WGI 8.5, WGI 12.3]
TS.3 Mitigation pathways and
measures in the context of
sustainable development
This section assesses the literature on mitigation pathways and mea-
sures in the context of sustainable development. Section TS 3.1 first
examines the anthropogenic GHG emissions trajectories and potential
temperature implications of mitigation pathways leading to a range
of future atmospheric CO
2
eq concentrations. It then explores the tech-
nological, economic, and institutional requirements of these pathways
along with their potential co-benefits and adverse side-effects. Section
TS 3.2 examines mitigation options by sector and how they may inter-
act across sectors.
7
These CO
2
eq concentrations represent full radiative forcing, including GHGs,
halogenated gases, tropospheric ozone, aerosols, mineral dust and albedo change.
8
Based on the longest global surface temperature dataset available, the observed
change between the average of the period 1850 – 1900 and of the AR5 reference
period (1986 – 2005) is 0.61 °C (5 – 95 % confidence interval: 0.55 to 0.67 °C)
[WGI SPM.E], which is used here as an approximation of the change in global
mean surface temperature since pre-industrial times, referred to as the period
before 1750.
9
Provided estimates reflect the 10th to the 90th percentile of baseline scenarios
collected for this assessment. The climate uncertainty reflects the 5th to 95th
percentile of climate model calculations described in Table TS.1 for each scenario.
10
This is based on the assessment of total anthropogenic radiative forcing for 2011
relative to 1750 in WGI AR5, i. e., 2.3 W m
– 2
, uncertainty range 1.1 to 3.3 W m
– 2
.
[WGI Figure SPM.5, WGI 8.5, WGI 12.3]
TS.3.1 Mitigation pathways
TS.3.1.1 Understanding mitigation pathways in the
context of multiple objectives
The world’s societies will need to both mitigate and adapt to cli-
mate change if it is to effectively avoid harmful climate impacts
(robust evidence, high agreement). There are demonstrated examples
of synergies between mitigation and adaptation [11.5.4, 12.8.1] in
which the two strategies are complementary. More generally, the two
strategies are related because increasing levels of mitigation imply less
future need for adaptation. Although major efforts are now underway
to incorporate impacts and adaptation into mitigation scenarios, inher-
ent difficulties associated with quantifying their interdependencies
have limited their representation in models used to generate mitiga-
tion scenarios assessed in WGIII AR5 (Box TS.7). [2.6.3, 3.7.2.1, 6.3.3]
There is no single pathway to stabilize CO
2
eq concentrations at
any level; instead, the literature points to a wide range of mitiga-
tion pathways that might meet any concentration level (high confi-
dence). Choices, whether deliberated or not, will determine which of these
pathways is followed. These choices include, among other things, the
emissions pathway to bring atmospheric CO
2
eq concentrations to a par-
ticular level, the degree to which concentrations temporarily exceed (over-
shoot) the long-term level, the technologies that are deployed to reduce
emissions, the degree to which mitigation is coordinated across countries,
the policy approaches used to achieve mitigation within and across coun-
tries, the treatment of land use, and the manner in which mitigation is
meshed with other policy objectives such as sustainable development.
A society’s development pathway — with its particular socioeconomic,
institutional, political, cultural and technological features — enables and
constrains the prospects for mitigation. At the national level, change is
considered most effective when it reflects country and local visions and
approaches to achieving sustainable development according to national
circumstances and priorities. [4.2, 6.3 – 6.8, 11.8]
Mitigation pathways can be distinguished from one another by
a range of outcomes or requirements (high confidence). Decisions
about mitigation pathways can be made by weighing the requirements
of different pathways against each other. Although measures of aggre-
gate economic costs and benefits have often been put forward as key
decision-making factors, they are far from the only outcomes that mat-
ter. Mitigation pathways inherently involve a range of synergies and
tradeoffs connected with other policy objectives such as energy and
food security, energy access, the distribution of economic impacts,
local air quality, other environmental factors associated with different
technological solutions, and economic competitiveness (Box TS.11).
Many of these fall under the umbrella of sustainable development.
In addition, requirements such as the rates of up-scaling of energy
technologies or the rates of reductions in GHG emissions may provide
important insights into the degree of challenge associated with meet-
ing a particular long-term goal. [4.5, 4.8, 6.3, 6.4, 6.6]
5151
Technical Summary
TS
TS.3.1.2 Short- and long-term requirements of mitigation
pathways
Mitigation scenarios point to a range of technological and
behavioral measures that could allow the world’s societies to
follow GHG emissions pathways consistent with a range of dif-
ferent levels of mitigation (high confidence). As part of this assess-
ment, about 900 mitigation and 300 baseline scenarios have been
collected from integrated modelling research groups around the world
(Box TS.7). The mitigation scenarios span atmospheric concentration
levels in 2100 from 430 ppm CO
2
eq to above 720 ppm CO
2
eq, which
is roughly comparable to the 2100 forcing levels between the RCP2.6
and RCP6.0 scenarios (Figure TS.8, left panel). Scenarios have been
constructed to reach mitigation goals under very different assump-
tions about energy demands, international cooperation, technologies,
the contributions of CO
2
and other forcing agents to atmospheric
CO
2
eq concentrations, and the degree to which concentrations tem-
porarily exceed the long-term goal (concentration overshoot, see Box
TS.8). Other scenarios were also assessed, including some scenarios
with concentrations in 2100 below 430 ppm CO
2
eq (for a discussion of
these scenarios see below). [6.3]
Limiting atmospheric peak concentrations over the course of
the century — not only reaching long-term concentration lev-
els — is critical for limiting transient temperature change (high
confidence). Scenarios reaching concentration levels of about 500 ppm
CO
2
eq by 2100 are more likely than not to limit temperature change
to less than 2 °C relative to pre-industrial levels, unless they temporar-
ily ‘overshoot’ concentration levels of roughly 530 ppm CO
2
eq before
2100. In this case, they are about as likely as not to achieve that goal.
The majority of scenarios reaching long-term concentrations of about
450 ppm CO
2
eq in 2100 are likely to keep temperature change below
2 °C over the course of the century relative to pre-industrial levels
(Table TS.1, Box TS.8). Scenarios that reach 530 to 650 ppm CO
2
eq
concentrations by 2100 are more unlikely than likely to keep tempera-
ture change below 2 °C relative to pre-industrial levels. Scenarios that
exceed about 650 ppm CO
2
eq by 2100 are unlikely to limit tempera-
ture change to below 2 °C relative to pre-industrial levels. Mitigation
Box TS.7 | Scenarios from integrated models can help to understand how actions affect outcomes
in complex systems
The long-term scenarios assessed in this report were generated
primarily by large-scale computer models, referred to here as
‘integrated models’, because they attempt to represent many of
the most important interactions among technologies, relevant
human systems (e. g., energy, agriculture, the economic system),
and associated GHG emissions in a single integrated framework.
A subset of these models is referred to as ‘integrated assessment
models’, or IAMs. IAMs include not only an integrated representa-
tion of human systems, but also of important physical processes
associated with climate change, such as the carbon cycle, and
sometimes representations of impacts from climate change. Some
IAMs have the capability of endogenously balancing impacts
with mitigation costs, though these models tend to be highly
aggregated. Although aggregate models with representations
of mitigation and damage costs can be very useful, the focus in
this assessment is on integrated models with sufficient sectoral
and geographic resolution to understand the evolution of key
processes such as energy systems or land systems.
Scenarios from integrated models are invaluable to help under-
stand how possible actions or choices might lead to different
future outcomes in these complex systems. They provide quan-
titative, long-term projections (conditional on our current state
of knowledge) of many of the most important characteristics
of mitigation pathways while accounting for many of the most
important interactions between the various relevant human and
natural systems. For example, they provide both regional and
global information about emissions pathways, energy and land-
use transitions, and aggregate economic costs of mitigation.
At the same time, these integrated models have particular
characteristics and limitations that should be considered when
interpreting their results. Many integrated models are based
on the rational choice paradigm for decision making, exclud-
ing the consideration of some behavioural factors. The models
approximate cost-effective solutions that minimize the aggregate
economic costs of achieving mitigation outcomes, unless they
are specifically constrained to behave otherwise. Scenarios from
these models capture only some of the dimensions of develop-
ment pathways that are relevant to mitigation options, often only
minimally treating issues such as distributional impacts of mitiga-
tion actions and consistency with broader development goals. In
addition, the models in this assessment do not effectively account
for the interactions between mitigation, adaptation, and climate
impacts. For these reasons, mitigation has been assessed indepen-
dently from climate impacts. Finally, and most fundamentally, inte-
grated models are simplified, stylized, numerical approaches for
representing enormously complex physical and social systems, and
scenarios from these models are based on uncertain projections
about key events and drivers over often century-long timescales.
Simplifications and differences in assumptions are the reason why
output generated from different models — or versions of the same
model — can differ, and projections from all models can differ
considerably from the reality that unfolds. [3.7, 6.2]
5252
TS
Technical Summary
scenarios in which temperature increase is more likely than not to be
less than 1.5 °C relative to pre-industrial levels by 2100 are character-
ized by concentrations in 2100 of below 430 ppm CO
2
eq. Temperature
peaks during the century and then declines in these scenarios. [6.3]
Mitigation scenarios reaching about 450 ppm CO
2
eq in 2100
typically involve temporary overshoot of atmospheric concen-
trations, as do many scenarios reaching about 500 ppm or about
550 ppm CO
2
eq in 2100 (high confidence). Concentration overshoot
means that concentrations peak during the century before descend-
ing toward their 2100 levels. Overshoot involves less mitigation in the
near term, but it also involves more rapid and deeper emissions reduc-
tions in the long run. The vast majority of scenarios reaching about
450 ppm CO
2
eq in 2100 involve concentration overshoot, since most
models cannot reach the immediate, near-term emissions reductions
that would be necessary to avoid overshoot of these concentration
levels. Many scenarios have been constructed to reach about 550 ppm
CO
2
eq by 2100 without overshoot.
Depending on the level of overshoot, many overshoot sce-
narios rely on the availability and widespread deployment of
bioenergy with carbon dioxide capture and storage (BECCS)
and / or afforestation in the second half of the century (high con-
fidence). These and other carbon dioxide removal (CDR) technologies
and methods remove CO
2
from the atmosphere (negative emissions).
Scenarios with overshoot of greater than 0.4 W / m
2
(> 35 – 50 ppm
CO
2
eq concentration) typically deploy CDR technologies to an extent
that net global CO
2
emissions become negative in the second-half of
the century (Figure TS.8, right panel). CDR is also prevalent in many
scenarios without concentration overshoot to compensate for residual
emissions from sectors where mitigation is more expensive. The avail-
ability and potential of BECCS, afforestation, and other CDR technolo-
gies and methods are uncertain and CDR technologies and methods
are, to varying degrees, associated with challenges and risks. There is
uncertainty about the potential for large-scale deployment of BECCS,
large-scale afforestation, and other CDR technologies and methods.
[6.3, 6.9]
Reaching atmospheric concentration levels of about 450 to about
500 ppm CO
2
eq by 2100 will require substantial cuts in anthro-
pogenic GHG emissions by mid-century (high confidence). Scenarios
reaching about 450 ppm CO
2
eq by 2100 are associated with GHG emis-
sions reductions of about 40 % to 70 % by 2050 compared to 2010 and
emissions levels near zero GtCO
2
eq or below in 2100.
11
Scenarios with
GHG emissions reductions in 2050 at the lower end of this range are
characterized by a greater reliance on CDR technologies beyond mid-
century. The majority of scenarios that reach about 500 ppm CO
2
eq in
2100 without overshooting roughly 530 ppm CO
2
eq at any point during
the century are associated with GHG emissions reductions of 40 % to
55 % by 2050 compared to 2010 (Figure TS.8, left panel; Table TS.1). In
contrast, in some scenarios in which concentrations rise to well above
530 ppm CO
2
eq during the century before descending to concentrations
below this level by 2100, emissions rise to as high as 20 % above 2010
levels in 2050. However, these high-overshoot scenarios are character-
ized by negative global emissions of well over 20 GtCO
2
per year in the
second half of the century (Figure TS.8, right panel). Cumulative CO
2
11
This range differs from the range provided for a similar concentration category in
AR4 (50 % to 85 % lower than 2000 for CO
2
only). Reasons for this difference
include that this report has assessed a substantially larger number of scenarios
than in AR4 and looks at all GHGs. In addition, a large proportion of the new
scenarios include Carbon Dioxide Removal (CDR) technologies and associated
increases in concentration overshoot. Other factors include the use of 2100 con-
centration levels instead of stabilization levels and the shift in reference year from
2000 to 2010.
Figure TS.8 | Development of total GHG emissions for different long-term concentration levels (left panel) and for scenarios reaching about 450 to about 500 (430 – 530) ppm
CO
2
eq in 2100 with and without net negative CO
2
emissions larger than 20 GtCO
2
/ yr (right panel). Ranges are given for the 10th – 90th percentile of scenarios. [Figure 6.7]
GHG Emissions with Different Assumptions for Negative Emissions
Annual GHG Emissions [GtCO
2
eq/yr]
430-530 ppm CO
2
eq
> 20 GtCO
2
/yr
All AR5 Scenarios
< 20 GtCO
2
/yr
Net Negative Emissions
RCP2.6
2000 2020 2040 2060 2080 2100
-40
-20
0
20
40
60
80
Total GHG Emissions in all AR5 Scenarios
Baseline Range (2100)
2000 2020 2040 2060 2080 2100
-20
0
20
40
60
80
100
120
140
RCP8.5
RCP6.0
RCP4.5
RCP2.6
Annual GHG Emissions [GtCO
2
eq/yr]
> 1000
720 - 1000
580 - 720
530 - 580
480 - 530
430 - 480
Full AR5 Database Range
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
5353
Technical Summary
TS
emissions between 2011 and 2100 are 630 – 1180 GtCO
2
in scenarios
reaching about 450 ppm CO
2
eq in 2100; they are 960 – 1550 GtCO
2
in
scenarios reaching about 500 ppm CO
2
eq in 2100. The variation in cumu-
lative CO
2
emissions across scenarios is due to differences in the contri-
bution of non-CO
2
GHGs and other radiatively active substances as well
as the timing of mitigation (Table TS.1). [6.3]
In order to reach atmospheric concentration levels of about 450
to about 500 ppm CO
2
eq by 2100, the majority of mitigation
relative to baseline emissions over the course of century will
occur in the non-Organisation for Economic Co-operation and
Development (OECD) countries (high confidence). In scenarios that
attempt to cost-effectively allocate emissions reductions across coun-
tries and over time, the total CO
2
eq emissions reductions from baseline
emissions in non-OECD countries are greater than in OECD countries.
This is, in large part, because baseline emissions from the non-OECD
countries are projected to be larger than those from the OECD coun-
tries, but it also derives from higher carbon intensities in non-OECD
countries and different terms of trade structures. In these scenarios,
GHG emissions peak earlier in the OECD countries than in the non-
OECD countries. [6.3]
Reaching atmospheric concentration levels of about 450 to
about 650 ppm CO
2
eq by 2100 will require large-scale changes
to global and national energy systems over the coming decades
(high confidence). Scenarios reaching atmospheric concentrations lev-
els of about 450 to about 500 ppm CO
2
eq by 2100 are characterized by
a tripling to nearly a quadrupling of the global share of zero- and low-
carbon energy supply from renewables, nuclear energy, fossil energy
with carbon dioxide capture and storage (CCS), and bioenergy with
CCS (BECCS), by the year 2050 relative to 2010 (about 17 %) (Figure
TS.10, left panel). The increase in total global low-carbon energy sup-
Box TS.8 | Assessment of temperature change in the context of mitigation scenarios
Long-term climate goals have been expressed both in terms of
concentrations and temperature. Article 2 of the UNFCCC calls
for the need to ‘stabilize’ concentrations of GHGs. Stabilization of
concentrations is generally understood to mean that the CO
2
eq
concentration reaches a specific level and then remains at that level
indefinitely until the global carbon and other cycles come into a new
equilibrium. The notion of stabilization does not necessarily preclude
the possibility that concentrations might exceed, or ‘overshoot’
the long-term goal before eventually stabilizing at that goal. The
possibility of ‘overshoot’ has important implications for the required
GHG emissions reductions to reach a long-term concentration level.
Concentration overshoot involves less mitigation in the near term
with more rapid and deeper emissions reductions in the long run.
The temperature response of the concentration pathways assessed
in this report focuses on transient temperature change over the
course of the century. This is an important difference with WGIII
AR4, which focused on the long-term equilibrium temperature
response, a state that is reached millennia after the stabilization
of concentrations. The temperature outcomes in this report are
thus not directly comparable to those presented in the WGIII AR4
assessment. One reason that this assessment focuses on transient
temperature response is that it is less uncertain than the equilib-
rium response and correlates more strongly with GHG emissions
in the near and medium term. An additional reason is that the
mitigation pathways assessed in WGIII AR5 do not extend beyond
2100 and are primarily designed to reach specific concentration
goals for the year 2100. The majority of these pathways do not
stabilize concentrations in 2100, which makes the assessment of
the equilibrium temperature response ambiguous and dependent
on assumptions about post-2100 emissions and concentrations.
Transient temperature goals might be defined in terms of the
temperature in a specific year (e. g., 2100), or based on never
exceeding a particular level. This report explores the implications
of both types of goals. The assessment of temperature goals are
complicated by the uncertainty that surrounds our understanding
of key physical relationships in the earth system, most notably
the relationship between concentrations and temperature. It is
not possible to state definitively whether any long-term con-
centration pathway will limit either transient or equilibrium
temperature change to below a specified level. It is only possible
to express the temperature implications of particular concentra-
tion pathways in probabilistic terms, and such estimates will
be dependent on the source of the probability distribution of
different climate parameters and the climate model used for
analysis. This report employs the MAGICC model and a distribu-
tion of climate parameters that results in temperature outcomes
with dynamics similar to those from the Earth System Models
assessed in WGI AR5. For each emissions scenario, a median
transient temperature response is calculated to illustrate the
variation of temperature due to different emissions pathways.
In addition, a transient temperature range for each scenario is
provided, reflecting the climate system uncertainties. Information
regarding the full distribution of climate parameters was utilized
for estimating the likelihood that the scenarios would limit tran-
sient temperature change to below specific levels (Table TS.1).
Providing the combination of information about the plausible
range of temperature outcomes as well as the likelihood of meet-
ing different targets is of critical importance for policymaking,
since it facilitates the assessment of different climate objectives
from a risk management perspective. [2.5.7.2, 6.3.2]
5454
TS
Technical Summary
Table TS.1 | Key characteristics of the scenarios collected and assessed for WGIII AR5. For all parameters, the 10th to 90th percentile of the scenarios is shown.
1, 2
[Table 6.3]
CO
2
eq
Concentrations
in 2100 [ppm
CO
2
eq]
Category label
(concentration
range)
9
Subcategories
Relative
position of
the RCPs
5
Cumulative CO
2
emissions
3
[GtCO
2
]
Change in CO
2
eq emissions
compared to 2010 in [%]
4
Temperature change (relative to 1850 – 1900)
5, 6
2011 – 2050 2011 – 2100 2050 2100
2100
Temperature
change [°C]
7
Likelihood of staying below temperature
level over the 21st century
8
1.5 °C 2.0 °C 3.0 °C 4.0 °C
< 430 Only a limited number of individual model studies have explored levels below 430 ppm CO
2
eq
450
(430 – 480)
Total range
1, 10
RCP2.6 550 – 1300 630 – 1180 − 72 to − 41 − 118 to − 78
1.5 – 1.7
(1.0 – 2.8)
More unlikely
than likely
Likely
Likely
Likely
500
(480 – 530)
No overshoot of
530 ppm CO
2
eq
860 – 1180 960 – 1430 − 57 to − 42 − 107 to − 73
1.7 – 1.9
(1.2 – 2.9)
Unlikely
More likely
than not
Overshoot of
530 ppm CO
2
eq
1130 – 1530 990 – 1550 − 55 to − 25 − 114 to − 90
1.8 – 2.0
(1.2 – 3.3)
About as
likely as not
550
(530 – 580)
No overshoot of
580 ppm CO
2
eq
1070 – 1460 1240 – 2240 − 47 to − 19 − 81 to − 59
2.0 – 2.2
(1.4 – 3.6)
More unlikely
than likely
12
Overshoot of
580 ppm CO
2
eq
1420 – 1750 1170 – 2100 − 16 to 7 − 183 to − 86
2.1 – 2.3
(1.4 – 3.6)
(580 – 650) Total range
RCP4.5
1260 – 1640 1870 – 2440 − 38 to 24 − 134 to − 50
2.3 – 2.6
(1.5 – 4.2)
(650 – 720) Total range 1310 – 1750 2570 – 3340 − 11 to 17 − 54 to − 21
2.6 – 2.9
(1.8 – 4.5)
Unlikely
More likely
than not
(720 – 1000)
2
Total range RCP6.0 1570 – 1940 3620 – 4990 18 to 54 − 7 to 72
3.1 – 3.7
(2.1 – 5.8)
Unlikely
11
More unlikely
than likely
> 1000
2
Total range RCP8.5 1840 – 2310 5350 – 7010 52 to 95 74 to 178
4.1 – 4.8
(2.8 – 7.8)
Unlikely
11
Unlikely
More unlikely
than likely
Notes:
1
The ‘total range’ for the 430 – 480 ppm CO
2
eq scenarios corresponds to the range of the 10th – 90th percentile of the subcategory of these scenarios shown in Table 6.3.
2
Baseline scenarios (see TS.2.2) fall into the > 1000 and 720 – 1000 ppm CO
2
eq categories. The latter category also includes mitigation scenarios. The baseline scenarios in the
latter category reach a temperature change of 2.5 – 5.8 °C above preindustrial in 2100. Together with the baseline scenarios in the > 1000 ppm CO
2
eq category, this leads to
an overall 2100 temperature range of 2.5 – 7.8 °C (range based on median climate response: 3.7 – 4.8 °C) for baseline scenarios across both concentration categories.
3
For comparison of the cumulative CO
2
emissions estimates assessed here with those presented in WGI AR5, an amount of 515 [445 – 585] GtC (1890 [1630 – 2150] GtCO
2
),
was already emitted by 2011 since 1870 [WGI 12.5]. Note that cumulative CO
2
emissions are presented here for different periods of time (2011 – 2050 and 2011 – 2100)
while cumulative CO
2
emissions in WGI AR5 are presented as total compatible emissions for the RCPs (2012 – 2100) or for total compatible emissions for remaining below a
given temperature target with a given likelihood [WGI Table SPM.3, WGI SPM.E.8].
4
The global 2010 emissions are 31 % above the 1990 emissions (consistent with the historic GHG emissions estimates presented in this report). CO
2
eq emissions include the
basket of Kyoto gases (CO
2
, CH
4
, N
2
O as well as F-gases).
5
The assessment in WGIII AR5 involves a large number of scenarios published in the scientific literature and is thus not limited to the RCPs. To evaluate the CO
2
eq concen-
tration and climate implications of these scenarios, the MAGICC model was used in a probabilistic mode (see AnnexII). For a comparison between MAGICC model results
and the outcomes of the models used in WGI, see Sections WGI 12.4.1.2, WGI 12.4.8 and 6.3.2.6. Reasons for differences with WGI SPM Table.2 include the difference in
reference year (1986 – 2005 vs. 1850 – 1900 here), difference in reporting year (2081 – 2100 vs 2100 here), set-up of simulation (CMIP5 concentration-driven versus MAGICC
emission-driven here), and the wider set of scenarios (RCPs versus the full set of scenarios in the WGIII AR5 scenario database here).
6
Temperature change is reported for the year 2100, which is not directly comparable to the equilibrium warming reported in WGIII AR4 [Table 3.5, Chapter 3; see also WGIII
AR5 6.3.2]. For the 2100 temperature estimates, the transient climate response (TCR) is the most relevant system property. The assumed 90 % range of the TCR for MAGICC
is 1.2 – 2.6 °C (median 1.8 °C). This compares to the 90 % range of TCR between 1.2 – 2.4 °C for CMIP5 [WGI 9.7] and an assessed likely range of 1 – 2.5 °C from multiple
lines of evidence reported in the WGI AR5 [Box 12.2 in Section 12.5].
7
Temperature change in 2100 is provided for a median estimate of the MAGICC calculations, which illustrates differences between the emissions pathways of the scenarios
in each category. The range of temperature change in the parentheses includes in addition the carbon cycle and climate system uncertainties as represented by the MAGICC
model [see 6.3.2.6 for further details]. The temperature data compared to the 1850 – 1900 reference year was calculated by taking all projected warming relative to
1986 – 2005, and adding 0.61 °C for 1986 – 2005 compared to 1850 – 1900, based on HadCRUT4 [see WGI Table SPM.2].
8
The assessment in this table is based on the probabilities calculated for the full ensemble of scenarios in WGIII AR5 using MAGICC and the assessment in WGI AR5 of the
uncertainty of the temperature projections not covered by climate models. The statements are therefore consistent with the statements in WGI AR5, which are based on the
CMIP5 runs of the RCPs and the assessed uncertainties. Hence, the likelihood statements reflect different lines of evidence from both WGs. This WGI method was also applied
for scenarios with intermediate concentration levels where no CMIP5 runs are available. The likelihood statements are indicative only [6.3], and follow broadly the terms used
by the WGI AR5 SPM for temperature projections: likely 66 – 100 %, more likely than not > 50 – 100 %, about as likely as not 33 – 66 %, and unlikely 0 – 33 %. In addition the
term more unlikely than likely 0 – < 50 % is used.
9
The CO
2
-equivalent concentration includes the forcing of all GHGs including halogenated gases and tropospheric ozone, as well as aerosols and albedo change (calculated on
the basis of the total forcing from a simple carbon cycle / climate model, MAGICC).
10
The vast majority of scenarios in this category overshoot the category boundary of 480 ppm CO
2
eq concentrations.
11
For scenarios in this category no CMIP5 run [WGI Chapter 12, Table 12.3] as well as no MAGICC realization [6.3] stays below the respective temperature level. Still, an
unlikely assignment is given to reflect uncertainties that might not be reflected by the current climate models.
12
Scenarios in the 580 – 650 ppm CO
2
eq category include both overshoot scenarios and scenarios that do not exceed the concentration level at the high end of the category
(like RCP4.5). The latter type of scenarios, in general, have an assessed probability of more unlikely than likely to stay below the 2 °C temperature level, while the former are
mostly assessed to have an unlikely probability of staying below this level.
5555
Technical Summary
TS
ply is from three-fold to seven-fold over this same period. Many mod-
els could not reach 2100 concentration levels of about 450 ppm CO
2
eq
if the full suite of low-carbon technologies is not available. Studies
indicate a large potential for energy demand reductions, but also indi-
cate that demand reductions on their own would not be sufficient to
bring about the reductions needed to reach levels of about 650 ppm
CO
2
eq or below by 2100. [6.3, 7.11]
Mitigation scenarios indicate a potentially critical role for land-
related mitigation measures and that a wide range of alter-
native land transformations may be consistent with similar
concentration levels (medium confidence). Land-use dynamics in
mitigation scenarios are heavily influenced by the production of bioen-
ergy and the degree to which afforestation is deployed as a negative-
emissions, or CDR option. They are, in addition, influenced by forces
independent of mitigation such as agricultural productivity improve-
ments and increased demand for food. The range of land-use trans-
formations depicted in mitigation scenarios reflects a wide range of
differing assumptions about the evolution of all of these forces. Many
scenarios reflect strong increases in the degree of competition for land
between food, feed, and energy uses. [6.3, 6.8, 11.4.2]
Delaying mitigation efforts beyond those in place today
through 2030 will increase the challenges of, and reduce the
options for, limiting atmospheric concentration levels from
about 450 to about 500 ppm CO
2
eq by the end of the century
(high confidence). Cost-effective mitigation scenarios leading to atmo-
spheric concentration levels of about 450 to about 500 ppm CO
2
eq at
the end of the 21st century are typically characterized by annual GHG
emissions in 2030 of roughly between 30 GtCO
2
eq and 50 GtCO
2
eq.
Scenarios with emissions above 55 GtCO
2
eq in 2030 are character-
ized by substantially higher rates of emissions reductions from 2030
to 2050 (median emissions reductions of about 6 % / yr as compared to
just over 3 % / yr) (Figure TS.9, right panel); much more rapid scale-up of
low-carbon energy over this period (more than a tripling compared to
a doubling of the low-carbon energy share) (Figure TS.10, right panel);
Figure TS.9 | The implications of different 2030 GHG emissions levels for the rate of CO
2
emissions reductions from 2030 to 2050 in mitigation scenarios reaching about 450 to
about 500 (430 – 530) ppm CO
2
eq concentrations by 2100. The scenarios are grouped according to different emissions levels by 2030 (coloured in different shades of green). The
left panel shows the pathways of GHG emissions (GtCO
2
eq / yr) leading to these 2030 levels. The black bar shows the estimated uncertainty range of GHG emissions implied by the
Cancún Pledges. Black dot with whiskers gives historic GHG emission levels and associated uncertainties in 2010 as reported in Figure TS.1. The right panel denotes the average
annual CO
2
emissions reduction rates for the period 2030 – 2050. It compares the median and interquartile range across scenarios from recent intermodel comparisons with explicit
2030 interim goals to the range of scenarios in the Scenario Database for WGIII AR5. Annual rates of historical emissions change between 1900 – 2010 (sustained over a period
of 20 years) and the average annual emissions change between 2000 – 2010 are shown in grey. Note: Scenarios with large net negative global emissions (>20 GtCO
2
/ yr) are not
included in the WGIII AR5 scenario range, but rather shown as independent points. Only scenarios that apply the full, unconstrained mitigation technology portfolio of the underlying
models (default technology assumption) are shown. Scenarios with exogenous carbon price assumptions or other policies affecting the timing of mitigation (other than 2030 interim
targets) as well as scenarios with 2010 emissions significantly outside the historical range are excluded. [Figure 6.32, 13.13.1.3]
2005 2010 2015 2020 2025 2030
20
25
30
35
40
45
50
55
60
65
Annual GHG Emissions [GtCO
2
eq/yr]
GHG Emissions Pathways to 2030 of Mitigation
Scenarios Reaching 430-530 ppm CO
2
eq in 2100
Implications for the Pace of Annual Average
CO
2
Emissions Reductions from 2030 to 2050
Depending on Different 2030 GHG Emissions Levels
Annual Rate of Change in CO
2
Emissions (2030-2050) [%]
AR5 Scenario Range
Interquartile Range
and Median of Model
Comparisons with
2030 Targets
Scenarios with High
Net Negative Emissions
>20 GtCO
2
/yr in 2100
History
1900-2010
2000-2010
n=76
Cancún
Pledges
<50 GtCO
2
eq
Annual
GHG Emissions
in 2030
50-55 GtCO
2
eq
>55 GtCO
2
eq
−12
−9
−6
−3
0
3
6
n = 76
5656
TS
Technical Summary
a larger reliance on CDR technologies in the long-term (Figure TS.8,
right panel); and higher transitional and long term economic impacts
(Table TS.2, orange segments, Figure TS.13, right panel). Due to these
increased challenges, many models with 2030 GHG emissions in this
range could not produce scenarios reaching atmospheric concentra-
tions levels of about 450 to about 500 ppm CO
2
eq in 2100. [6.4, 7.11]
Estimated global GHG emissions levels in 2020 based on the
Cancún Pledges are not consistent with cost-effective long-
term mitigation trajectories that reach atmospheric concen-
trations levels of about 450 to about 500 ppm CO
2
eq by 2100,
but they do not preclude the option to meet that goal (robust
evidence, high agreement). The Cancún Pledges are broadly consis-
tent with cost-effective scenarios reaching about 550 ppm CO
2
eq to
650 ppm CO
2
eq by 2100. Studies confirm that delaying mitigation
through 2030 has a substantially larger influence on the subsequent
challenges of mitigation than do delays through 2020 (Figures TS.9,
TS.11). [6.4]
Only a limited number of studies have explored scenarios that
are more likely than not to bring temperature change back to
below 1.5 °C by 2100 relative to pre-industrial levels; these
scenarios bring atmospheric concentrations to below 430 ppm
CO
2
eq by 2100 (high confidence). Assessing this goal is currently dif-
ficult because no multi-model study has explored these scenarios. The
limited number of published studies exploring this goal have produced
associated scenarios that are characterized by (1) immediate mitiga-
tion; (2) the rapid up-scaling of the full portfolio of mitigation technol-
ogies; and (3) development along a low-energy demand trajectory.
12
[6.3, 7.11]
TS.3.1.3 Costs, investments and burden sharing
Globally comprehensive and harmonized mitigation actions
would result in significant economic benefits compared to frag-
mented approaches, but would require establishing effective
institutions (high confidence). Economic analysis of mitigation scenar-
ios demonstrates that globally comprehensive and harmonized mitiga-
tion actions achieve mitigation at least aggregate economic cost, since
they allow mitigation to be undertaken where and when it is least
expensive (see Box TS.7, Box TS.9). Most of these mitigation scenarios
assume a global carbon price, which reaches all sectors of the econ-
omy. Instruments with limited coverage of GHG emissions reductions
among sectors and climate policy regimes with fragmented regional
12
In these scenarios, the cumulative CO
2
emissions range between 680 – 800 GtCO
2
for the period 2011 – 2050 and between 90 – 310 GtCO
2
for the period
2011 – 2100. Global CO
2
eq emissions in 2050 are between 70 – 95 % below 2010
emissions, and they are between 110 – 120 % below 2010 emissions in 2100.
Figure TS.10 | The up-scaling of low-carbon energy in scenarios meeting different 2100 CO
2
eq concentration levels (left panel). The right panel shows the rate of up-scaling subject
to different 2030 GHG emissions levels in mitigation scenarios reaching about 450 to about 500 (430 – 530) ppm CO
2
eq concentrations by 2100. Colored bars show the inter-
quartile range and white bars indicate the full range across the scenarios, excluding those with large, global net negative CO
2
emissions (>20 GtCO
2
/ yr). Scenarios with large net
negative global emissions are shown as individual points. The arrows indicate the magnitude of zero- and low-carbon energy supply up-scaling from 2030 to 2050. Zero- and low-
carbon energy supply includes renewables, nuclear energy, fossil energy with carbon dioxide capture and storage (CCS), and bioenergy with CCS (BECCS). Note: Only scenarios that
apply the full, unconstrained mitigation technology portfolio of the underlying models (default technology assumption) are shown. Scenarios with exogenous carbon price assump-
tions are excluded in both panels. In the right panel, scenarios with policies affecting the timing of mitigation other than 2030 interim targets are also excluded. [Figure 7.16]
2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100
2030 2050 2100 2030 2050 2100 2030 2050 2100
Low-Carbon Energy Share of Primary Energy [%]
Low-Carbon Energy Share of Primary Energy [%]
GHG Emission Levels in 2030:
0
20
40
60
80
100
+105%
+135%
+135%
+145%
2010
2010
0
20
40
60
80
100
+90%
+160%
+240%
430−480 ppm CO
2
eq
480−530 ppm CO
2
eq
530−580 ppm CO
2
eq
580−650 ppm CO
2
eq
< 50 GtCO
2
eq 50-55 GtCO
2
eq > 55 GtCO
2
eq
Min
75
th
Percentile
Max
Median
25
th
Scenarios with High Net
Negative Emissions
>20 GtCO
2
/yr
Scenarios with High Net Negative Emissions >20 GtCO
2
/yr
5757
Technical Summary
TS
Figure TS.11 | Near-term GHG emissions from mitigation scenarios reaching about 450 to about 500 (430 – 530) ppm CO
2
eq concentrations by 2100. The Figure includes only
scenarios for which temperature exceedance probabilities were calculated. Individual model results are indicated with a data point when 2 °C exceedance probability is below 50 %
as assessed by a simple carbon cycle/climate model (MAGICC). Colours refer to scenario classification in terms of whether net CO
2
emissions become negative before 2100 (nega-
tive vs. no negative) and the timing of international participation in climate mitigation (immediate vs. delay until 2020 vs. delay until 2030). Number of reported individual results
is shown in legend. The range of global GHG emissions in 2020 implied by the Cancún Pledges is based on analysis of alternative interpretations of national pledges. Note: In the
WGIII AR5 scenario database, only four reported scenarios were produced based on delayed mitigation without net negative emissions while still lying below 530 ppm CO
2
eq by
2100. They do not appear in the figure, because the model had insufficient coverage of non-gas species to enable a temperature calculation. Delay in these scenarios extended
only to 2020, and their emissions fell in the same range as the ‘No Negative / Immediate’ category. Delay scenarios include both delayed global mitigation and fragmented action
scenarios. [Figure 6.31, 13.13.1.3]
<50
GtCO
2
eq
Full Range for All Scenarios with
Calculated 2°C Exceedance
Probability
0
10
20
30
40
50
60
70
80
2000 2010 2020 2030
Annual GHG Emissions [GtCO
2
eq/yr]
No Negative/Immediate (17)
No Negative/Delay 2020 (0)
No Negative/Delay 2030 (0)
Negative/Immediate (116)
Negative/Delay 2020 (21)
Negative/Delay 2030 (27)
Ranges for 530-650 ppm CO
2
eq
Range for Cancún Pledges
Base Year Variation
in Model Scenarios
>55
GtCO
2
eq
50-55
GtCO
2
eq
Interquartile Range for Scenarios
with 2°C Exceedance Probability
<50%
430-530 ppm CO
2
eq in 2100
History
action increase aggregate economic costs. These cost increases are
higher at more ambitious levels of mitigation. [6.3.6]
Estimates of the aggregate economic costs of mitigation vary
widely, but increase with stringency of mitigation (high confi-
dence). Most cost-effective scenarios collected for this assessment that
are based on the assumptions that all countries of the world begin
mitigation immediately, there is a single global carbon price applied to
well-functioning markets, and key technologies are available, estimate
that reaching about 450 ppm CO
2
eq by 2100 would entail global con-
sumption losses of 1 % to 4 % in 2030 (median: 1.7 %), 2 % to 6 % in
2050 (median: 3.4 %), and 3 % to 11 % in 2100 (median: 4.8 %) relative
to consumption in baseline scenarios (those without additional miti-
gation efforts) that grows anywhere from 300 % to more than 900 %
between 2010 and 2100 (baseline consumption growth represents the
full range of corresponding baseline scenarios; Figure TS.12; Table TS.2
yellow segments). The consumption losses correspond to an annual
average reduction of consumption growth by 0.06 to 0.2 percentage
points from 2010 through 2030 (median: 0.09), 0.06 to 0.17 percentage
points through 2050 (median: 0.09), and 0.04 to 0.14 percentage points
over the century (median: 0.06). These numbers are relative to annual
average consumption growth rates in baseline scenarios between 1.9 %
and 3.8 % per year through 2050 and between 1.6 % and 3 % per year
over the century (Table TS.2, yellow segments). These mitigation cost
estimates do not consider the benefits of reduced climate change or
co-benefits and adverse side-effects of mitigation (Box TS.9). Costs for
maintaining concentrations in the range of 530 – 650 ppm CO
2
eq are
estimated to be roughly one-third to two-thirds lower than for associ-
ated 430 – 530 ppm CO
2
eq scenarios. Cost estimates from scenarios can
vary substantially across regions. Substantially higher cost estimates
have been obtained based on assumptions about less idealized policy
implementations and limits on technology availability as discussed
below. Both higher and lower estimates have been obtained based on
interactions with pre-existing distortions, non-climate market failures,
or complementary policies. [6.3.6.2]
Delaying mitigation efforts beyond those in place today through
2030 or beyond could substantially increase mitigation costs
in the decades that follow and the second half of the century
(high confidence). Although delays in mitigation by any major emitter
will reduce near-term mitigation costs, they will also result in more
investment in carbon-intensive infrastructure and then rely on future
5858
TS
Technical Summary
decision makers to undertake a more rapid, deeper, and costlier future
transformation of this infrastructure. Studies have found that aggre-
gate costs, and associated carbon prices, rise more rapidly to higher
levels in scenarios with delayed mitigation compared to scenarios
where mitigation is undertaken immediately. Recent modelling stud-
ies have found that delayed mitigation through 2030 can substantially
increase the aggregate costs of meeting 2100 concentrations of about
450 to about 500 ppm CO
2
eq, particularly in scenarios with emissions
greater than 55 GtCO
2
eq in 2030. (Figure TS.13, right panel; Table TS.2,
orange segments) [6.3.6.4]
The technological options available for mitigation greatly influ-
ence mitigation costs and the challenges of reaching atmo-
spheric concentration levels of about 450 to about 550 ppm
CO
2
eq by 2100 (high confidence). Many models in recent model inter-
comparisons could not produce scenarios reaching atmospheric con-
centrations of about 450 ppm CO
2
eq by 2100 with broadly pessimistic
assumptions about key mitigation technologies. In these studies, the
character and availability of CCS and bioenergy were found to have a
particularly important influence on the mitigation costs and the chal-
lenges of reaching concentration levels in this range. For those mod-
els that could produce such scenarios, pessimistic assumptions about
these increased discounted global mitigation costs of reaching concen-
tration levels of about 450 and about 550 ppm CO
2
eq by the end of
the century significantly, with the effect being larger for more strin-
gent mitigation scenarios (Figure TS.13, left panel; Table TS.2, grey seg-
ments). The studies also showed that reducing energy demand could
potentially decrease mitigation costs significantly. [6.3.6.3]
The distribution of mitigation costs among different countries
depends in part on the nature of effort-sharing frameworks
and thus need not be the same as the distribution of mitiga-
tion efforts. Different effort-sharing frameworks draw upon
different ethical principles (medium confidence). In cost-effective
scenarios reaching concentrations of about 450 to about 550 ppm
CO
2
eq in 2100, the majority of mitigation investments over the course
Table TS.2 | Global mitigation costs in cost-effective scenarios
1
and estimated cost increases due to assumed limited availability of specific technologies and delayed additional mit-
igation. Cost estimates shown in this table do not consider the benefits of reduced climate change as well as co-benefits and adverse side-effects of mitigation. The yellow columns
show consumption losses (Figure TS.12, right panel) and annualized consumption growth reductions in cost-effective scenarios relative to a baseline development without climate
policy. The grey columns show the percentage increase in discounted costs
2
over the century, relative to cost-effective scenarios, in scenarios in which technology is constrained
relative to default technology assumptions (Figure TS.13, left panel).
3
The orange columns show the increase in mitigation costs over the periods 2030 – 2050 and 2050 – 2100, rela-
tive to scenarios with immediate mitigation, due to delayed additional mitigation through 2030 (see Figure TS.13, right panel).
4
These scenarios with delayed additional mitigation
are grouped by emission levels of less or more than 55 GtCO
2
eq in 2030, and two concentration ranges in 2100 (430 – 530 ppm CO
2
eq and 530 – 650 ppm CO
2
eq). In all figures,
the median of the scenario set is shown without parentheses, the range between the 16th and 84th percentile of the scenario set is shown in the parentheses, and the number of
scenarios in the set is shown in square brackets.
5
[Figures TS.12, TS.13, 6.21, 6.24, 6.25, Annex II.10]
Consumption losses in cost-effective scenarios
1
Increase in total discounted mitigation costs in
scenarios with limited availability of technologies
Increase in medium- and long-term
mitigation costs due to delayed
additional mitigation until 2030
[% reduction in consumption
relative to baseline]
[percentage point reduction in
annualized consumption growth rate]
[% increase in total discounted
mitigation costs (2015 – 2100) relative
to default technology assumptions]
[% increase in mitigation costs
relative to immediate mitigation]
Concentration
in 2100
[ppm CO
2
eq]
2030 2050 2100
2010
– 2030
2010
– 2050
2010
– 2100
No CCS
Nuclear
phase
out
Limited
Solar /
Wind
Limited
Bioenergy
≤ 55 GtCO
2
eq > 55 GtCO
2
eq
2030 –
2050
2050 –
2100
2030 –
2050
2050 –
2100
450 (430 – 480)
1.7
(1.0 – 3.7)
[N: 14]
3.4
(2.1 – 6.2)
4.8
(2.9 – 11.4)
0.09
(0.06–0.2)
0.09
(0.06–0.17)
0.06
(0.04–0.14)
138
(29 – 297)
[N: 4]
7
(4 – 18)
[N: 8]
6
(2 – 29)
[N: 8]
64
(44 – 78)
[N: 8]
28
(14 – 50)
[N: 34]
15
(5 – 59)
44
(2 – 78)
[N: 29]
37
(16 – 82)
500 (480 – 530)
1.7
(0.6 – 2.1)
[N: 32]
2.7
(1.5 – 4.2)
4.7
(2.4 – 10.6)
0.09
(0.03–0.12)
0.07
(0.04–0.12)
0.06
(0.03–0.13)
N / A N / A N / A N / A
550 (530 – 580)
0.6
(0.2 – 1.3)
[N: 46]
1.7
(1.2 – 3.3)
3.8
(1.2 – 7.3)
0.03
(0.01–0.08)
0.05
(0.03–0.08)
0.04
(0.01–0.09)
39
(18 – 78)
[N: 11]
13
(2 – 23)
[N: 10]
8
(5 – 15)
[N: 10]
18
(4 – 66)
[N: 12]
3
(− 5 – 16)
[N: 14]
4
(− 4 – 11)
15
(3 – 32)
[N: 10]
16
(5 – 24)
580 – 650
0.3
(0 – 0.9)
[N: 16]
1.3
(0.5 – 2.0)
2.3
(1.2 – 4.4)
0.02
(0–0.04)
0.03
(0.01–0.05)
0.03
(0.01–0.05)
N / A N / A N / A N / A
Notes:
1
Cost-effective scenarios assume immediate mitigation in all countries and a single global carbon price. In this analysis, they also impose no additional limitations on technol-
ogy relative to the models’ default technology assumptions.
2
Percentage increase of net present value of consumption losses in percent of baseline consumption (for scenarios from general equilibrium models) and abatement costs in
percent of baseline GDP (for scenarios from partial equilibrium models) for the period 2015 – 2100, discounted (see Box TS.10) at 5 % per year.
3
No CCS: CCS is not included in these scenarios. Nuclear phase out: No addition of nuclear power plants beyond those under construction, and operation of existing plants
until the end of their lifetime. Limited Solar / Wind: a maximum of 20 % global electricity generation from solar and wind power in any year of these scenarios. Limited Bioen-
ergy: a maximum of 100 EJ / yr modern bioenergy supply globally (modern bioenergy used for heat, power, combinations, and industry was around 18 EJ / yr in 2008 [11.13.5]).
4
Percentage increase of total undiscounted mitigation costs for the periods 2030 – 2050 and 2050 – 2100.
5
The range is determined by the central scenarios encompassing the 16th and 84th percentile of the scenario set. Only scenarios with a time horizon until 2100 are included.
Some models that are included in the cost ranges for concentration levels above 530 ppm CO
2
eq in 2100 could not produce associated scenarios for concentration levels
below 530 ppm CO
2
eq in 2100 with assumptions about limited availability of technologies and / or delayed additional mitigation (see caption of Figure TS.13 for more details).
5959
Technical Summary
TS
Box TS.9 | The meaning of ‘mitigation cost’ in the context of mitigation scenarios
Mitigation costs represent one component of the change in
human welfare from climate change mitigation. Mitigation costs
are expressed in monetary terms and generally are estimated
against baseline scenarios, which typically involve continued, and
sometimes substantial, economic growth and no additional and
explicit mitigation efforts [3.9.3, 6.3.6]. Because mitigation cost
estimates focus only on direct market effects, they do not take
into account the welfare value (if any) of co-benefits or adverse
side-effects of mitigation actions (Box TS.11) [3.6.3]. Further, these
costs do not capture the benefits of reducing climate impacts
through mitigation (Box TS.2).
There are a wide variety of metrics of aggregate mitigation
costs used by economists, measured in different ways or at
different places in the economy, including changes in GDP,
consumption losses, equivalent variation and compensating
variation, and loss in consumer and producer surplus. Consump-
tion losses are often used as a metric because they emerge from
many integrated models and they directly impact welfare. They
can be expressed as a reduction in overall consumption relative
to consumption in the corresponding baseline scenario in a
given year or as a reduction of the average rate of consumption
growth in the corresponding baseline scenario over a given time
period.
Mitigation costs need to be distinguished from emissions prices.
Emissions prices measure the cost of an additional unit of emis-
sions reduction; that is, the marginal cost. In contrast, mitigation
costs usually represent the total costs of all mitigation. In addition,
emissions prices can interact with other policies and measures, such
as regulatory policies directed at GHG reduction. If mitigation is
achieved partly by these other measures, emissions prices may not
reflect the actual costs of an additional unit of emissions reductions
(depending on how additional emissions reductions are induced).
In general, estimates of global aggregate mitigation costs over
the coming century from integrated models are based on largely
stylized assumptions about both policy approaches and existing
markets and policies, and these assumptions have an important
influence on cost estimates. For example, cost-effective idealized
implementation scenarios assume a uniform price on CO
2
and
other GHGs in every country and sector across the globe, and
constitute the least cost approach in the idealized case of largely
efficient markets without market failures other than the climate
change externality. Most long-term, global scenarios do not
account for the interactions between mitigation and pre-existing
or new policies, market failures, and distortions. Climate policies
can interact with existing policies to increase or reduce the actual
cost of climate policies. [3.6.3.3, 6.3.6.5]
Figure TS.12 | Global carbon prices (left panel) and consumption losses (right panel) over time in cost-effective, idealized implementation scenarios. Consumption losses are
expressed as the percentage reduction from consumption in the baseline. The number of scenarios included in the boxplots is indicated at the bottom of the panels. The 2030 num-
bers also apply to 2020 and 2050. The number of scenarios outside the figure range is noted at the top. Note: The figure shows only scenarios that reported consumption losses (a
subset of models with full coverage of the economy) or carbon prices, respectively, to 2050 or 2100. Multiple scenarios from the same model with similar characteristics are only
represented by a single scenario in the sample. [Figure 6.21]
9 28 60 60 34 9 28 60 54 32
2020 2030 2050 2100
10
0
10
1
10
2
10
3
10
4
CO
2
Price [USD
2010
/tCO
2
]
Carbon Prices 2020−2100
2020 2030 2050 2100
-2
0
2
4
6
8
10
12
7 16 46 40 14 7 16 46 32 14
Consumption Loss [% Baseline Consumption]
Consumption Losses 2020−2100
9 28 60 60 34 9 28 60 60 34 7 16 46 40 14 7 16 46 40 14
1 1 1 11
Min
75
th
Percentile
Max
Median
25
th
Percentile
650 - 720
580 - 650
530 - 580
480 - 530
430 - 480
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
ppm CO
2
eq
6060
TS
Technical Summary
of century occur in the non-OECD countries. Some studies exploring
particular effort-sharing frameworks, under the assumption of a global
carbon market, estimate that the associated financial flows could be
in the order of hundred billions of USD per year before mid-century to
bring concentrations to between about 450 and about 500 ppm CO
2
eq
in 2100. Most studies assume efficient mechanisms for international
carbon markets, in which case economic theory and empirical research
suggest that the choice of effort sharing allocations will not meaning-
fully affect the globally efficient levels of regional abatement or aggre-
gate global costs. Actual approaches to effort-sharing can deviate from
this assumption. [3.3, 6.3.6.6, 13.4.2.4]
Geoengineering denotes two clusters of technologies that are
quite distinct: carbon dioxide removal (CDR) and solar radia-
tion management (SRM). Mitigation scenarios assessed in AR5
do not assume any geoengineering options beyond large-scale
CDR due to afforestation and BECCS. CDR techniques include affor-
estation, using bioenergy along with CCS (BECCS), and enhancing
uptake of CO
2
by the oceans through iron fertilization or increasing
alkalinity. Most terrestrial CDR techniques would require large-scale
land-use changes and could involve local and regional risks, while
maritime CDR may involve significant transboundary risks for ocean
ecosystems, so that its deployment could pose additional challenges
for cooperation between countries. With currently known technologies,
CDR could not be deployed quickly on a large scale. SRM includes vari-
ous technologies to offset crudely some of the climatic effects of the
build-up of GHGs in the atmosphere. It works by adjusting the planet’s
heat balance through a small increase in the reflection of incoming
sunlight such as by injecting particles or aerosol precursors in the
upper atmosphere. SRM has attracted considerable attention, mainly
Figure TS.13 | Left panel shows the relative increase in net present value mitigation costs (2015 – 2100, discounted at 5 % per year) from technology portfolio variations relative to
a scenario with default technology assumptions. Scenario names on the horizontal axis indicate the technology variation relative to the default assumptions: No CCS = unavailabil-
ity of carbon dioxide capture and storage (CCS); Nuclear phase out = No addition of nuclear power plants beyond those under construction; existing plants operated until the end
of their lifetime; Limited Solar / Wind = a maximum of 20 % global electricity generation from solar and wind power in any year of these scenarios; Limited Bioenergy = a maximum
of 100 exajoules per year (EJ / yr) modern bioenergy supply globally. [Figure 6.24] Right panel shows increase in long-term mitigation costs for the period 2050 – 2100 (sum over
undiscounted costs) as a function of reduced near-term mitigation effort, expressed as the relative change between scenarios implementing mitigation immediately and those that
correspond to delayed additional mitigation through 2020 or 2030 (referred to here as ‘mitigation gap’). The mitigation gap is defined as the difference in cumulative CO
2
emis-
sions reductions until 2030 between the immediate and delayed additional mitigation scenarios. The bars in the lower right panel indicate the mitigation gap range where 75 %
of scenarios with 2030 emissions above (dark blue) and below (red) 55 GtCO
2
, respectively, are found. Not all model simulations of delayed additional mitigation until 2030 could
reach the lower concentration goals of about 450 or 500 (430 – 530) ppm CO
2
eq (for 2030 emissions above 55 GtCO
2
eq, 29 of 48 attempted simulations could reach the goal; for
2030 emissions below 55 GtCO
2
eq, 34 of 51 attempted simulations could reach the goal). [Figure 6.25]
430-530 ppm CO
2
eq Obs.
530-650 ppm CO
2
eq Obs.
430-650 ppm CO
2
eq Range
Median
+1 Standard Deviation
-1 Standard Deviation
‡
Scenarios from two models reach concentration levels in 2100 that are slightly above the 430-480 ppm CO
2
eq category
†
Scenarios from one model reach concentration levels in 2100 that are slightly below the 530-580 ppm CO
2
eq category
530
†
- 580 ppm CO
2
eq
Scenario
430 - 480
‡
ppm CO
2
eq
Min
75
th
Percentile
Max
Median
25
th
Percentile
Mitigation Gap till 2030 [%]
0
20
40
60
80
100
-20
0
+20
+40
+60
+80
+100
+120
+160
+140
−100
−50
0
+50
+100
+150
+200
+250
+300
Mitigation Cost Increase Relative to Default Technology Assumptions [%]
No CCS Nuclear
Phase Out
Limited
Solar/Wind
Limited
Bioenergy
Mitigation Costs Increase (% Difference w.r.t. Immediate Mitigation)
* Number of models successfully vs. number of models attempting running the respective technology variation scenario
8/10*12/12*8/9*10/10*8/9*10/10*4/10*11/11*
< 55 Gt CO
2
in 2030
> 55 Gt CO
2
in 2030
6161
Technical Summary
TS
because of the potential for rapid deployment in case of climate emer-
gency. The suggestion that deployment costs for individual technolo-
gies could potentially be low could result in new challenges for inter-
national cooperation because nations may be tempted to prematurely
deploy unilaterally systems that are perceived to be inexpensive. Con-
sequently, SRM technologies raise questions about costs, risks, gover-
nance, and ethical implications of developing and deploying SRM, with
special challenges emerging for international institutions, norms and
other mechanisms that could coordinate research and restrain testing
and deployment. [1.4, 3.3.7, 6.9, 13.4.4]
Knowledge about the possible beneficial or harmful effects of
SRM is highly preliminary. SRM would have varying impacts on
regional climate variables such as temperature and precipitation, and
might result in substantial changes in the global hydrological cycle
with uncertain regional effects, for example on monsoon precipita-
tion. Non-climate effects could include possible depletion of strato-
spheric ozone by stratospheric aerosol injections. A few studies have
begun to examine climate and non-climate impacts of SRM, but there
is very little agreement in the scientific community on the results or
on whether the lack of knowledge requires additional research or
eventually field testing of SRM-related technologies. [1.4, 3.3.7, 6.9,
13.4.4]
TS.3.1.4 Implications of mitigation pathways for other
objectives
Mitigation scenarios reaching about 450 to about 500 ppm
CO
2
eq by 2100 show reduced costs for achieving energy secu-
rity and air quality objectives (medium confidence) (Figure TS.14,
lower panel). The mitigation costs of most of the scenarios in this
assessment do not consider the economic implications of the cost
reductions for these other objectives (Box TS.9). There is a wide range
of co-benefits and adverse side-effects other than air quality and
energy security (Tables TS.4 – 8). The impact of mitigation on the over-
all costs for achieving many of these other objectives as well as the
associated welfare implications are less well understood and have
not been assessed thoroughly in the literature (Box TS.11). [3.6.3,
4.8, 6.6]
Box TS.10 | Future goods should be discounted at an appropriate rate
Investments aimed at mitigating climate change will bear fruit
far in the future, much of it more than 100 years from now. To
decide whether a particular investment is worthwhile, its future
benefits need to be weighed against its present costs. In doing
this, economists do not normally take a quantity of commodities
at one time as equal in value to the same quantity of the same
commodities at a different time. They normally give less value
to later commodities than to earlier ones. They ‘discount’ later
commodities, that is to say. The rate at which the weight given to
future goods diminishes through time is known as the ‘discount
rate’ on commodities.
There are two types of discount rates used for different purposes.
The market discount rate reflects the preferences of presently
living people between present and future commodities. The social
discount rate is used by society to compare benefits of present
members of society with those not yet born. Because living people
may be impatient, and because future people do not trade in
the market, the market may not accurately reflect the value of
commodities that will come to future people relative to those that
come to present people. So the social discount rate may differ
from the market rate.
The chief reason for social discounting (favouring present people
over future people) is that commodities have ‘diminishing
marginal benefit’ and per capita income is expected to increase
over time. Diminishing marginal benefit means that the value of
extra commodities to society declines as people become better
off. If economies continue to grow, people who live later in time
will on average be better off — possess more commodities — than
people who live earlier. The faster the growth and the greater the
degree of diminishing marginal benefit, the greater should be the
discount rate on commodities. If per capita growth is expected to
be negative (as it is in some countries), the social discount rate
may be negative.
Some authors have argued, in addition, that the present genera-
tion of people should give less weight to later people’s well-being
just because they are more remote in time. This factor would add
to the social discount rate on commodities.
The social discount rate is appropriate for evaluating mitigation
projects that are financed by reducing current consumption. If a
project is financed partly by ‘crowding out’ other investments, the
benefits of those other investments are lost, and their loss must
be counted as an opportunity cost of the mitigation project. If a
mitigation project crowds out an exactly equal amount of other
investment, then the only issue is whether or not the mitiga-
tion investment produces a greater return than the crowded-out
investment. This can be tested by evaluating the mitigation
investment using a discount rate equal to the return that would
have been expected from the crowded out investment. If the
market functions well, this will be the market discount rate.
[3.6.2]
6262
TS
Technical Summary
Min
75
th
Percentile
Max
Median
25
th
Percentile
Stringent
Climate Policy
Stringent
Climate Policy
Baseline Baseline
Change from 2005 [%]
IPCC AR5 Scenario Ensemble
Impact of Climate Policy on Air Pollutant Emissions (Global, 2005-2050)
Air Quality Levels of GEA Scenarios in Bottom PanelEnergy Security Levels of GEA Scenarios in Bottom Panel
Increased
Pollution
Decreased
Pollution
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Only Energy Security Only Air Quality Only Mitigation All Three Objectives
Policy Choices
Total Global Policy Costs 2010-2050 [% of Global GDP]
w)
Costs of Achieving
Energy Security
Levels Shown in
Top Left Panel
x)
Costs of Achieving
Air Pollution Levels
Shown in Top Right
Panel
w + x + y > z
y)
Costs of Achieving
Stringent
Mitigation Targets
(430-530 ppm
CO
2
eq in 2100)
z)
Costs of Integrated
Approaches that
Achieve all Three
Objectives
Simultaneosly;
Highest Cost-
Effectiveness
Global Energy Assessment Scenario Ensemble (n=624)
Policy Costs of Achieving Different Objectives
Co-Benefits of Climate Change Mitigation for Energy Security and Air Quality
0
50
100
150
200
250
300
350
400
450
5
6
7
8
9
10
11
12
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
[EJ/yr]
Baseline Stringent
Climate
Policy
Baseline Stringent
Climate
Policy
Baseline Stringent
Climate
Policy
Shannon-Wiener-Diversity Index
[ZJ]
LIMITS Model Inter-Comparison
Impact of Climate Policy on Energy Security
Energy Trade
(Global, 2050)
Cumulative Oil Extraction
(Global, 2010-2050)
Electricity Diversity
(Global, 2050)
Improved
Energy
Security
Improved
Energy
Security
Improved
Energy
Security
-100
-50
0
50
Black Carbon Sulfur Dioxide
⇒
6363
Technical Summary
TS
Figure TS.14 | Co-benefits of mitigation for energy security and air quality in scenarios with stringent climate policies reaching about 450 to about 500 (430 – 530) ppm CO
2
eq
concentrations in 2100. Upper panels show co-benefits for different security indicators and air pollutant emissions. Lower panel shows related global policy costs of achieving the
energy security, air quality, and mitigation objectives, either alone (w, x, y) or simultaneously (z). Integrated approaches that achieve these objectives simultaneously show the high-
est cost-effectiveness due to synergies (w + x + y > z). Policy costs are given as the increase in total energy system costs relative to a baseline scenario without additional efforts to
reduce GHG emissions beyond those in place today. Costs are indicative and do not represent full uncertainty ranges. [Figure 6.33]
Mitigation scenarios reaching about 450 to about 500 ppm
CO
2
eq by 2100 show co-benefits for energy security objectives,
enhancing the sufficiency of resources to meet national energy
demand as well as the resilience of the energy system (medium
confidence). These mitigation scenarios show improvements in terms
of the diversity of energy sources and reduction of energy imports,
resulting in energy systems that are less vulnerable to price volatility
and supply disruptions (Figure TS.14, upper left panel). [6.3.6, 6.6, 7.9,
8.7, 9.7, 10.8, 11.13.6, 12.8]
Mitigation policy could devalue fossil fuel assets and reduce
revenues for fossil fuel exporters, but differences between
regions and fuels exist (high confidence). Most mitigation scenarios
are associated with reduced revenues from coal and oil trade for major
exporters (high confidence). However, a limited number of studies find
that mitigation policies could increase the relative competitiveness of
conventional oil vis-à-vis more carbon-intensive unconventional oil
and ‘coal-to-liquids’. The effect of mitigation on natural gas export rev-
enues is more uncertain, with some studies showing possible benefits
for export revenues in the medium term until about 2050 (medium
confidence). The availability of CCS would reduce the adverse effect
of mitigation on the value of fossil fuel assets (medium confidence).
[6.3.6, 6.6, 14.4.2]
Fragmented mitigation policy can provide incentives for emis-
sion-intensive economic activity to migrate away from a region
that undertakes mitigation (medium confidence). Scenario studies
have shown that such ‘carbon leakage’ rates of energy-related emis-
sions are relatively contained, often below 20 % of the emissions
reductions. Leakage in land-use emissions could be substantial, though
fewer studies have quantified it. While border tax adjustments are
seen as enhancing the competitiveness of GHG- and trade-intensive
industries within a climate policy regime, they can also entail welfare
losses for non-participating, and particularly developing, countries.
[5.4, 6.3, 13.8, 14.4]
Mitigation scenarios leading to atmospheric concentration lev-
els of about 450 to about 500 ppm CO
2
eq in 2100 are associated
with significant co-benefits for air quality and related human
health and ecosystem impacts. The benefits from major cuts in
air pollutant emissions are particularly high where currently
legislated and planned air pollution controls are weak (high con-
fidence). Stringent mitigation policies result in co-controls with major
cuts in air pollutant emissions significantly below baseline scenarios
(Figure TS.14, upper right panel). Co-benefits for health are particularly
high in today’s developing world. The extent to which air pollution
policies, targeting for example black carbon (BC), can mitigate climate
change is uncertain. [5.7, 6.3, 6.6, 7.9, 8.7, 9.7, 10.8, 11.7, 11.13.6,
12.8; WGII 11.9]
There is a wide range of possible adverse side-effects as well
as co-benefits and spillovers from climate policy that have not
been well-quantified (high confidence). Whether or not side-effects
materialize, and to what extent side-effects materialize, will be case-
and site-specific, as they will depend on local circumstances and the
scale, scope, and pace of implementation. Important examples include
biodiversity conservation, water availability, food security, income dis-
tribution, efficiency of the taxation system, labour supply and employ-
ment, urban sprawl, and the sustainability of the growth of developing
countries. (Box TS.11)
Some mitigation policies raise the prices for some energy
services and could hamper the ability of societies to expand
access to modern energy services to underserved populations
(low confidence). These potential adverse side-effects can be
avoided with the adoption of complementary policies (medium
confidence). Most notably, about 1.3 billion people worldwide do not
have access to electricity and about 3 billion are dependent on tradi-
tional solid fuels for cooking and heating with severe adverse effects
on health, ecosystems and development. Providing access to modern
energy services is an important sustainable development objective.
The costs of achieving nearly universal access to electricity and clean
fuels for cooking and heating are projected to be between 72 to 95
billion USD per year until 2030 with minimal effects on GHG emis-
sions (limited evidence, medium agreement). A transition away from
the use of traditional biomass
13
and the more efficient combustion of
solid fuels reduce air pollutant emissions, such as sulfur dioxide (SO
2
),
nitrogen oxides (NO
x
), carbon monoxide (CO), and black carbon (BC),
and thus yield large health benefits (high confidence). [4.3, 6.6, 7.9,
9.3, 9.7, 11.13.6, 16.8]
The effect of mitigation on water use depends on technologi-
cal choices and the portfolio of mitigation measures (high con-
fidence). While the switch from fossil energy to renewable energy like
photovoltaic (PV) or wind can help reducing water use of the energy
system, deployment of other renewables, such as some forms of hydro-
power, concentrated solar power (CSP), and bioenergy may have
adverse effects on water use. [6.6, 7.9, 9.7, 10.8, 11.7, 11.13.6]
13
Traditional biomass refers to the biomass — fuelwood, charcoal, agricultural resi-
dues, and animal dung — used with the so-called traditional technologies such as
open fires for cooking, rustic kilns and ovens for small industries (see Glossary).
6464
TS
Technical Summary
Box TS.11 | Accounting for the co-benefits and adverse side-effects of mitigation
A government policy or a measure intended to achieve one objec-
tive (such as mitigation) will also affect other objectives (such as
local air quality). To the extent these side-effects are positive, they
can be deemed ‘co-benefits’; otherwise they are termed ‘adverse
side-effects’. In this report, co-benefits and adverse side-effects
are measured in non-monetary units. Determining the value of
these effects to society is a separate issue. The effects of co-ben-
efits on social welfare are not evaluated in most studies, and one
reason is that the value of a co-benefit depends on local circum-
stances and can be positive, zero, or even negative. For example,
the value of the extra tonne of sulfur dioxide (SO
2
) reduction
that occurs with mitigation depends greatly on the stringency
of existing SO
2
control policies: in the case of weak existing SO
2
policy, the value of SO
2
reductions may be large, but in the case
of stringent existing SO
2
policy it may be near zero. If SO
2
policy
is too stringent, the value of the co-benefit may be negative
(assuming SO
2
policy is not adjusted). While climate policy affects
non-climate objectives (Tables TS.4 – 8) other policies also affect
climate change outcomes. [3.6.3, 4.8, 6.6, Glossary]
Mitigation can have many potential co-benefits and adverse
side-effects, which makes comprehensive analysis difficult. The
direct benefits of climate policy include, for example, intended
effects on global mean surface temperature, sea level rise, agri-
cultural productivity, biodiversity, and health effects of global
warming [WGII TS]. The co-benefits and adverse side-effects of
climate policy could include effects on a partly overlapping set
of objectives such as local air pollutant emissions reductions
and related health and ecosystem impacts, biodiversity con-
servation, water availability, energy and food security, energy
access, income distribution, efficiency of the taxation system,
labour supply and employment, urban sprawl, and the sustain-
ability of the growth of developing countries [3.6, 4.8, 6.6,
15.2].
All these side-effects are important, because a comprehensive
evaluation of climate policy needs to account for benefits and
costs related to other objectives. If overall social welfare is to
be determined and quantified, this would require valuation
methods and a consideration of pre-existing efforts to attain
the many objectives. Valuation is made difficult by factors such
as interaction between climate policies and pre-existing non-
climate policies, externalities, and non-competitive behaviour.
[3.6.3]
Mitigation scenarios and sectoral studies show that overall the
potential for co-benefits of energy end-use measures outweigh
the potential adverse side-effects, whereas the evidence sug-
gests this may not be the case for all energy supply and AFOLU
measures (high confidence). (Tables TS.4 – 8) [4.8, 5.7, 6.6, 7.9, 8.7,
9.7, 10.8, 11.7, 11.13.6, 12.8]
TS.3.2 Sectoral and cross-sectoral mitigation
measures
Anthropogenic GHG emissions result from a broad set of human
activities, most notably those associated with energy supply and con-
sumption and with the use of land for food production and other
purposes. A large proportion of emissions arise in urban areas. Miti-
gation options can be grouped into three broad sectors: (1) energy
supply, (2) energy end-use sectors including transport, buildings,
industry, and (3) AFOLU. Emissions from human settlements and
infrastructures cut across these different sectors. Many mitigation
options are linked. The precise set of mitigation actions taken in any
sector will depend on a wide range of factors, including their relative
economics, policy structures, normative values, and linkages to other
policy objectives. The first section examines issues that cut across
the sectors and the following subsections examine the sectors them-
selves.
TS.3.2.1 Cross-sectoral mitigation pathways and
measures
Without new mitigation policies GHG emissions are projected
to grow in all sectors, except for net CO
2
emissions in the
AFOL U
14
sector (robust evidence, medium agreement). Energy sup-
ply sector emissions are expected to continue to be the major source
of GHG emissions in baseline scenarios, ultimately accounting for the
significant increases in indirect emissions from electricity use in the
buildings and the industry sectors. Deforestation decreases in most of
the baseline scenarios, which leads to a decline in net CO
2
emissions
from the AFOLU sector. In some scenarios the AFOLU sector changes
from an emission source to a net emission sink towards the end of the
century. (Figure TS.15) [6.3.1.4, 6.8]
Infrastructure developments and long-lived products that lock
societies into GHG-intensive emissions pathways may be dif-
ficult or very costly to change, reinforcing the importance of
early action for ambitious mitigation (robust evidence, high agree-
ment). This lock-in risk is compounded by the lifetime of the infrastruc-
ture, by the difference in emissions associated with alternatives, and
14
Net AFOLU CO
2
emissions include emissions and removals of CO
2
from the AFOLU
sector, including land under forestry and, in some assessments, CO
2
sinks in agri-
cultural soils.
6565
Technical Summary
TS
the magnitude of the investment cost. As a result, lock-in related to
infrastructure and spatial planning is the most difficult to eliminate,
and thus avoiding options that lock high emission patterns in perma-
nently is an important part of mitigation strategies in regions with rap-
idly developing infrastructure. In mature or established cities, options
are constrained by existing urban forms and infrastructure, and limits
on the potential for refurbishing or altering them. However, materials,
products and infrastructure with long lifetimes and low lifecycle emis-
sions can ensure positive lock-in as well as avoid emissions through
dematerialization (i. e., through reducing the total material inputs
required to deliver a final service). [5.6.3, 6.3.6.4, 9.4, 10.4, 12.3, 12.4]
Systemic and cross-sectoral approaches to mitigation are
expected to be more cost-effective and more effective in cut-
ting emissions than sector-by-sector policies (medium confi-
dence). Cost-effective mitigation policies need to employ a system
perspective in order to account for inter-dependencies among differ-
ent economic sectors and to maximize synergistic effects. Stabiliz-
ing atmospheric CO
2
eq concentrations at any level will ultimately
require deep reductions in emissions and fundamental changes to
both the end-use and supply-side of the energy system as well as
changes in land-use practices and industrial processes. In addition,
many low-carbon energy supply technologies (including CCS) and
their infrastructural requirements face public acceptance issues lim-
iting their deployment. This applies also to the adoption of new tech-
nologies, and structural and behavioural change, in the energy end-
use sectors (robust evidence, high agreement) [7.9.4, 8.7, 9.3.10,
9.8, 10.8, 11.3, 11.13]. Lack of acceptance may have implications
not only for mitigation in that particular sector, but also for wider
mitigation efforts.
Integrated models identify three categories of energy system
related mitigation measures: the decarbonization of the energy
supply sector, final energy demand reductions, and the switch to
low-carbon energy carriers, including electricity, in the energy
end-use sectors (robust evidence, high agreement) [6.3.4, 6.8, 7.11].
The broad range of sectoral mitigation options available mainly relate
to achieving reductions in GHG emissions intensity, energy intensity
and changes in activity (Table TS.3) [7.5, 8.3, 8.4, 9.3, 10.4, 12.4]. Direct
options in AFOLU involve storing carbon in terrestrial systems (for
example, through afforestation) and providing bioenergy feedstocks
[11.3, 11.13]. Options to reduce non-CO
2
GHG emissions exist across
all sectors, but most notably in agriculture, energy supply, and industry.
Demand reductions in the energy end-use sectors, due to, e.g.,
efficiency enhancement and behavioural change, are a key miti-
Figure TS.15 | Direct (left panel) and direct and indirect emissions (right panel) of CO
2
and non-CO
2
GHGs across sectors in baseline scenarios. Non-CO
2
GHGs are converted to
CO
2
-equivalents based on Global Warming Potentials with a 100-year time horizon from the IPCC Second Assessment Report (SAR) (see Box TS.5). Note that in the case of indirect
emissions, only electricity generation emissions are allocated from energy supply to end-use sectors. In the left panel electricity sector emissions are shown (Electricity*) in addition
to energy supply sector emissions which they are part of, to illustrate their large role on the energy supply side. The numbers at the bottom refer to the number of scenarios included
in the ranges that differ across sectors and time due to different sectoral resolutions and time horizons of models. [Figure 6.34]
n=
0
20
40
60
80
Direct Emissions
Direct Emissions [GtCO
2
eq/yr]
93 93 78 80 80 65 80 80 65 103 103 88 131 131 118 121 121 107
2030
2050
2100
103 103 88
0
20
40
60
80
Direct and Indirect Emissions
Direct and Indirect Emissions [GtCO
2
eq/yr]
77 77 68 68 68 59 68 68 59
2030
2050
2100
CO
2
Electricity
CO
2
Transport
CO
2
Buildings
CO
2
Industry
CO
2
Energy Supply
CO
2
Net AFOLU
Non−CO
2
(All Sectors)
Actual 2010 Level
Min
75
th
Percentile
Max
Median
25
th
Percentile
CO
2
Energy Supply
excl. Electricity Generation
CO
2
Transport
CO
2
Buildings
CO
2
Industry
Actual 2010 Level
147 147 127
Transport Buildings Industry Energy
Supply
Energy
Supply
Net AFOLU Non−CO
2
Transport Buildings Industry
Electricity*
6666
TS
Technical Summary
gation strategy and affect the scale of the mitigation challenge
for the energy supply side (high confidence). Limiting energy demand:
(1) increases policy choices by maintaining flexibility in the technology
portfolio; (2) reduces the required pace for up-scaling low-carbon energy
supply technologies and hedges against related supply-side risks (Fig-
ure TS.16); (3) avoids lock-in to new, or potentially premature retirement
of, carbon-intensive infrastructures; (4) maximizes co-benefits for other
policy objectives, since the potential for co-benefits of energy end-use
measures outweighs the potential for adverse side-effects which may
not be the case for all supply-side measures (see Tables TS.4 – 8); and
(5) increases the cost-effectiveness of the transformation (as compared
to mitigation strategies with higher levels of energy demand) (medium
confidence). However, energy service demand reductions are unlikely in
developing countries or for poorer population segments whose energy
service levels are low or partially unmet. [6.3.4, 6.6, 7.11, 10.4]
Behaviour, lifestyle, and culture have a considerable influence
on energy use and associated emissions, with a high mitigation
potential in some sectors, in particular when complementing
technological and structural change (medium evidence, medium
agreement). Emissions can be substantially lowered through: changes
in consumption patterns (e. g., mobility demand and mode, energy use
in households, choice of longer-lasting products); dietary change and
reduction in food wastes; and change of lifestyle (e. g., stabilizing / low-
ering consumption in some of the most developed countries, sharing
economy and other behavioural changes affecting activity) (Table
TS.3). [8.1, 8.9, 9.2, 9.3, Box 10.2, 10.4, 11.4, 12.4, 12.6, 12.7]
Evidence from mitigation scenarios indicates that the decar-
bonization of energy supply is a key requirement for stabiliz-
ing atmospheric CO
2
eq concentrations below 580 ppm (robust
evidence, high agreement). In most long-term mitigation scenarios not
exceeding 580 ppm CO
2
eq by 2100, global energy supply is fully decar-
bonized at the end of the 21st century with many scenarios relying on
a net removal of CO
2
from the atmosphere. However, because exist-
ing supply systems are largely reliant on carbon-intensive fossil fuels,
energy intensity reductions can equal or outweigh decarbonization of
energy supply in the near term. In the buildings and industry sector, for
example, efficiency improvements are an important strategy for reduc-
ing indirect emissions from electricity generation (Figure TS.15). In the
long term, the reduction in electricity generation emissions is accom-
panied by an increase in the share of electricity in end uses (e. g., for
Figure TS.16 | Influence of energy demand on the deployment of energy supply technologies in 2050 in mitigation scenarios reaching about 450 to about 500 (430 – 530) ppm
CO
2
eq concentrations by 2100. Blue bars for ‘low energy demand’ show the deployment range of scenarios with limited growth of final energy of < 20 % in 2050 compared
to 2010. Red bars show the deployment range of technologies in case of ‘high energy demand’ (>20 % growth in 2050 compared to 2010). For each technology, the median,
interquartile, and full deployment range is displayed. Notes: Scenarios assuming technology restrictions and scenarios with final energy in the base-year outside ± 5 % of 2010
inventories are excluded. Ranges include results from many different integrated models. Multiple scenario results from the same model were averaged to avoid sampling biases; see
Chapter 6 for further details. [Figure 7.11]
Min
75
th
Max
Median
25
th
Percentile
4321
Secondary Energy Supply [EJ/yr]
0
10
20
30
40
50
60
Secondary Energy Supply [EJ/yr]
0
10
20
30
40
50
60
Secondary Energy Supply [EJ/yr]
0
10
20
30
40
50
60
Secondary Energy Supply [EJ/yr]
0
20
40
60
80
100
120
140
160
High Energy Demand
Low Energy Demand
In 430-530 ppm CO
2
eq
Mitigation Scenarios
Oil Products
Liquids Coal
Liquids Gas
Liquids Biomass
Hydrogen
Nuclear
Biomass w/o CCS
Biomass w/ CCS
Solar
Wind
Geothermal
Hydro
Coal w/o CCS
Coal w/ CCS
Gas w/o CCS
Gas w/ CCS
Electricity Generation
Coal and Natural Gas
Non-Fossil
Liquids and Hydrogen
Oil
Other Liquids and H
2
High energy
demand scenarios
show higher levels
of oil supply.
In high energy demand scenarios, alternative liquid
and hydrogen technologies are scaled up more
rapidly.
High energy demand scenarios show
a more rapid up-scaling of CCS
technologies but a more rapid phase-
out of unabated fossil fuel conversion
technologies.
In high energy demand scenarios non-fossil electricity
generation technologies are scaled up more rapidly.
6767
Technical Summary
TS
space and process heating, and potentially for some modes of trans-
port). Deep emissions reductions in transport are generally the last to
emerge in integrated modelling studies because of the limited options
to switch to low-carbon energy carriers compared to buildings and
industry (Figure TS.17). [6.3.4, 6.8, 8.9, 9.8, 10.10, 7.11, Figure 6.17]
The availability of CDR technologies affects the size of the miti-
gation challenge for the energy end-use sectors (robust evidence,
high agreement) [6.8, 7.11]. There are strong interdependencies in
mitigation scenarios between the required pace of decarbonization of
energy supply and end-use sectors. The more rapid decarbonization of
supply generally provides more flexibility for the end-use sectors. How-
ever, barriers to decarbonizing the supply side, resulting for example
from a limited availability of CCS to achieve negative emissions when
combined with bioenergy, require a more rapid and pervasive decar-
bonisation of the energy end-use sectors in scenarios achieving low-
CO
2
eq concentration levels (Figure TS.17). The availability of mature
large-scale biomass supply for energy, or carbon sequestration tech-
nologies in the AFOLU sector also provides flexibility for the develop-
ment of mitigation technologies in the energy supply and energy end-
use sectors [11.3] (limited evidence, medium agreement), though there
may be adverse impacts on sustainable development.
Spatial planning can contribute to managing the development
of new infrastructure and increasing system-wide efficiencies
across sectors (robust evidence, high agreement). Land use, transport
choice, housing, and behaviour are strongly interlinked and shaped by
infrastructure and urban form. Spatial and land-use planning, such as
mixed-zoning, transport-oriented development, increasing density, and
co-locating jobs and homes can contribute to mitigation across sectors
by (1) reducing emissions from travel demand for both work and lei-
sure, and enabling non-motorized transport, (2) reducing floor space for
housing, and hence (3) reducing overall direct and indirect energy use
through efficient infrastructure supply. Compact and in-fill development
of urban spaces and intelligent densification can save land for agricul-
ture and bioenergy and preserve land carbon stocks. [8.4, 9.10, 10.5,
11.10, 12.2, 12.3]
Interdependencies exist between adaptation and mitigation at
the sectoral level and there are benefits from considering adap-
tation and mitigation in concert (medium evidence, high agree-
ment). Particular mitigation actions can affect sectoral climate vulner-
ability, both by influencing exposure to impacts and by altering the
capacity to adapt to them [8.5, 11.5]. Other interdependencies include
climate impacts on mitigation options, such as forest conservation or
hydropower production [11.5.5, 7.7], as well as the effects of particular
adaptation options, such as heating or cooling of buildings or estab-
lishing more diversified cropping systems in agriculture, on GHG emis-
sions and radiative forcing [11.5.4, 9.5]. There is a growing evidence
base for such interdependencies in each sector, but there are substan-
tial knowledge gaps that prevent the generation of integrated results
at the cross-sectoral level.
29 29 29 22 22 22 22 22 22 36 36 36 32 32 32 36 36 36
2030
2050
2100
Transport Buildings Industry Electricity Net
AFOLU
Non−CO
2
Transport Buildings Industry Electricity Net
AFOLU
Non−CO
2
5 5 5 3 3 3 3 3 3 5 5 5 6 6 6 6 6 6
20
-20
10
-10
0
Direct GHG Emissions [GtCO
2
eq/yr]
Direct GHG Emissions [GtCO
2
eq/yr]
n=
450 ppm CO
2
eq with CCS 450 ppm CO
2
eq without CCS
2030
2050
2100
20
-20
10
-10
0
CO
2
Transport
CO
2
Buildings
CO
2
Industry
CO
2
Electricity
CO
2
Net AFOLU
Non−CO
2
(All Sectors)
Actual 2010 Level
Individual
Scenarios
Min
75
th
Percentile
Max
Median
25
th
Percentile
Figure TS.17 | Direct emissions of CO
2
and non-CO
2
GHGs across sectors in mitigation scenarios that reach about 450 (430–480) ppm CO
2
eq concentrations in 2100 with using
carbon dioxide capture and storage (CCS) (left panel) and without using CCS (right panel). The numbers at the bottom of the graphs refer to the number of scenarios included in the
ranges that differ across sectors and time due to different sectoral resolutions and time horizons of models. White dots in the right panel refer to emissions of individual scenarios to
give a sense of the spread within the ranges shown due to the small number of scenarios. [Figures 6.35]
6868
TS
Technical Summary
Table TS.3 | Main sectoral mitigation measures categorized by key mitigation strategies (in bold) and associated sectoral indicators (highlighted in yellow) as discussed in
Chapters 7 – 12.
GHG emissions
intensity reduction
Energy intensity reduction by
improving technical efficiency
Production and resource
efficiency improvement
Structural and systems
efficiency improvement
Activity indicator change
Energy [Section 7.5]
Emissions / secondary
energy output
Energy input / energy output Embodied energy / energy output
–
Final energy use
Greater deployment of renewable
energy (RE), nuclear energy,
and (BE)CCS; fuel switching
within the group of fossil fuels;
reduction of fugitive (methane)
emissions in the fossil fuel chain
Extraction, transport and
conversion of fossil fuels;
electricity / heat / fuel transmission,
distribution, and storage;
Combined Heat and Power (CHP)
or cogeneration (see Buildings
and Human Settlements)
Energy embodied in manufacturing
of energy extraction,
conversion, transmission and
distribution technologies
Addressing integration needs Demand from end-use sectors
for different energy carriers (see
Transport, Buildings and Industry)
Transport [8.3]
Emissions / final energy Final energy / transport service
–
Shares for each mode Total distance per year
Fuel carbon intensity
(CO
2
eq / megajoule (MJ)):
Fuel switching to low-carbon
fuels e. g., electricity / hydrogen
from low-carbon sources (see
Energy); specific biofuels in
various modes (see AFOLU)
Energy intensity
(MJ / passenger-km, tonne-
km): Fuel-efficient engines and
vehicle designs; more advanced
propulsion systems and designs;
use of lighter materials in vehicles
Embodied emissions during
vehicle manufacture; material
efficiency; and recycling of
materials (see Industry);
infrastructure lifecycle emissions
(see Human Settlements)
Modal shifts from light-duty
vehicles (LDVs) to public transit,
cycling / walking, and from aviation
and heavy-duty vehicles (HDVs)
to rail; eco-driving; improved
freight logistics; transport
(infrastructure) planning
Journey avoidance; higher
occupancy / loading rates; reduced
transport demand; urban planning
(see Human Settlements)
Buildings [9.3]
Emissions / final energy Final energy / useful energy Embodied energy /
operating energy
Useful energy / energy service Energy service demand
Fuel carbon intensity
(CO
2
eq / MJ): Building-
integrated RE technologies; fuel
switching to low-carbon fuels,
e. g., electricity (see Energy)
Device efficiency: heating /
cooling (high-performance boilers,
ventilation, air-conditioning,
heat pumps); water heating;
cooking (advanced biomass
stoves); lighting; appliances
Building lifetime; component,
equipment, and appliance
durability; low(er) energy and
emission material choice for
construction (see Industry)
Systemic efficiency: integrated
design process; low / zero energy
buildings; building automation
and controls; urban planning;
district heating / cooling and CHP;
smart meters / grids; commissioning
Behavioural change (e. g.,
thermostat setting, appliance use);
lifestyle change (e. g., per capita
dwelling size, adaptive comfort)
Industry [10.4]
Emissions / final energy Final energy / material production Material input / product output Product demand / service demand Service demand
Emissions intensity: Process
emissions reductions; use of
waste (e. g., municipal solid waste
(MSW) / sewage sludge in cement
kilns) and CCS in industry; HFCs
replacement and leak repair;
fuel switching among fossil fuels
to low-carbon electricity (see
Energy) or biomass (see AFOLU)
Energy efficiency / best
available technologies:
Efficient steam systems;
furnace and boiler systems;
electric motor (pumps, fans,
air compressor, refrigerators,
and material handling) and
electronic control systems; (waste)
heat exchanges; recycling
Material efficiency:
Reducing yield losses;
manufacturing / construction:
process innovations, new design
approaches, re-using old material
(e. g., structural steel); product
design (e. g., light weight car
design); fly ash substituting clinker
Product-service efficiency:
More intensive use of products
(e. g., car sharing, using products
such as clothing for longer, new
and more durable products)
Reduced demand for, e. g.,
products such as clothing;
alternative forms of travel
leading to reduced demand
for car manufacturing
Human
Settlements
[12.4]
Emissions / final energy Final energy / useful energy Material input in infrastructure Useful energy / energy service Service demand per capita
Integration of urban
renewables; urban-scale fuel
switching programmes
Cogeneration, heat cascading,
waste to energy
Managed infrastructure supply;
reduced primary material
input for infrastructure
Compact urban form; increased
accessibility; mixed land use
Increasing accessibility:
shorter travel time, and more
transport mode options
Agriculture, Forestry and Other
Land Use (AFOLU) [11.3]
Supply-side improvements Demand-side measures
Emissions / area or unit product (conserved, restored) Animal / crop product consumption per capita
Emissions reduction: of methane (e. g.,
livestock management) and nitrous oxide
(fertilizer and manure management)
and prevention of emissions to the
atmosphere by conserving existing carbon
pools in soils or vegetation (reducing
deforestation and forest degradation, fire
prevention / control, agroforestry); reduced
emissions intensity (GHG / unit product).
Sequestration: Increasing the
size of existing carbon pools,
thereby extracting CO
2
from the
atmosphere (e. g., afforestation,
reforestation, integrated systems,
carbon sequestration in soils)
Substitution: of biological
products for fossil fuels or
energy-intensive products,
thereby reducing CO
2
emissions,
e. g., biomass co-firing / CHP (see
Energy), biofuels (see Transport),
biomass-based stoves, and
insulation products (see Buildings)
Demand-side measures: Reducing losses
and wastes of food; changes in human diets
towards less emission-intensive products;
use of long-lived wood products
6969
Technical Summary
TS
TS.3.2.2 Energy supply
The energy supply sector is the largest contributor to global
GHG emissions (robust evidence, high agreement). Annual GHG emis-
sions from the global energy supply sector grew more rapidly between
2000 and 2010 than in the previous decade; their growth accelerated
from 1.7 % / yr from 1990 – 2000 to 3.1 % / yr from 2000 – 2010. The main
contributors to this trend are an increasing demand for energy services
and a growing share of coal in the global fuel mix. The energy supply
sector, as defined in this report, comprises all energy extraction, con-
version, storage, transmission, and distribution processes that deliver
final energy to the end-use sectors (industry, transport, buildings, agri-
culture and forestry). [7.2, 7.3]
In the baseline scenarios assessed in AR5, direct CO
2
emissions
from the energy supply sector increase from 14.4 GtCO
2
/ yr
in 2010 to 24 – 33 GtCO
2
/ yr in 2050 (25 – 75th percentile; full
range 15 – 42 GtCO
2
/ yr), with most of the baseline scenarios
assessed in WGIII AR5 showing a significant increase (medium
evidence, medium agreement) (Figure TS.15). The lower end of the
full range is dominated by scenarios with a focus on energy inten-
sity improvements that go well beyond the observed improvements
over the past 40 years. The availability of fossil fuels alone will not
be sufficient to limit CO
2
eq concentration to levels such as 450 ppm,
550 ppm, or 650 ppm. [6.3.4, 6.8, 7.11, Figure 6.15]
The energy supply sector offers a multitude of options to reduce
GHG emissions (robust evidence, high agreement). These options
include: energy efficiency improvements and fugitive emission reduc-
tions in fuel extraction as well as in energy conversion, transmission,
and distribution systems; fossil fuel switching; and low-GHG energy
supply technologies such as renewable energy (RE), nuclear power, and
CCS (Table TS.3). [7.5, 7.8.1, 7.11]
The stabilization of GHG concentrations at low levels requires
a fundamental transformation of the energy supply system,
including the long-term phase-out of unabated fossil fuel con-
version technologies and their substitution by low-GHG alter-
natives (robust evidence, high agreement). Concentrations of CO
2
in
the atmosphere can only be stabilized if global (net) CO
2
emissions
peak and decline toward zero in the long term. Improving the energy
efficiencies of fossil fuel power plants and / or the shift from coal to
gas will not by themselves be sufficient to achieve this. Low-GHG
energy supply technologies would be necessary if this goal were to be
achieved (Figure TS.19). [7.5.1, 7.8.1, 7.11]
Decarbonizing (i. e., reducing the carbon intensity of) electric-
ity generation is a key component of cost-effective mitigation
strategies in achieving low-stabilization levels (430 – 530 ppm
CO
2
eq); in most integrated modelling scenarios, decarboniza-
tion happens more rapidly in electricity generation than in
the buildings, transport, and industry sectors (medium evidence,
high agreement) (Figure TS.17). In the majority of mitigation scenar-
ios reaching about 450 ppm CO
2
eq concentrations by 2100, the share
of low-carbon electricity supply (comprising RE, nuclear, fossil fuels
with CCS, and BECCS) increases from the current share of around
30 % to more than 80 % by 2050, and fossil fuel power generation
without CCS is phased out almost entirely by 2100 (Figures TS.17 and
TS.18) [7.14].
Since AR4, many RE technologies have demonstrated substantial
performance improvements and cost reductions, and a growing
number of RE technologies have achieved a level of maturity
to enable deployment at significant scale (robust evidence, high
agreement). Some technologies are already economically competitive in
various settings. Levelized costs of PV systems fell most substantially
between 2009 and 2012, and a less extreme trend has been observed
for many others RE technologies. Regarding electricity generation alone,
RE accounted for just over half of the new electricity-generating capacity
added globally in 2012, led by growth in wind, hydro, and solar power.
Decentralized RE to meet rural energy needs has also increased, includ-
ing various modern and advanced traditional biomass options as well
as small hydropower, PV, and wind. Nevertheless, many RE technologies
still need direct support (e. g., feed-in tariffs (FITs), RE quota obligations,
and tendering / bidding) and / or indirect support (e. g., sufficiently high
carbon prices and the internalization of other externalities), if their mar-
ket shares are to be significantly increased. RE technology policies have
been successful in driving the recent growth of RE. Additional enabling
policies are needed to address their integration into future energy sys-
tems. (medium evidence, medium agreement) (Figure TS.19) [7.5.3,
7.6.1, 7.8.2, 7.12, 11.13]
The use of RE is often associated with co-benefits, including
the reduction of air pollution, local employment opportunities,
few severe accidents compared to some other energy supply
technologies, as well as improved energy access and security
(medium evidence, medium agreement) (Table TS.4). At the same time,
however, some RE technologies can have technology and location-spe-
cific adverse side-effects, which can be reduced to a degree through
appropriate technology selection, operational adjustments, and siting
of facilities. [7.9]
Infrastructure and integration challenges vary by RE technology
and the characteristics of the existing energy system (medium
evidence, medium agreement). Operating experience and studies of
medium to high penetrations of RE indicate that integration issues can
be managed with various technical and institutional tools. As RE pen-
etrations increase, such issues are more challenging, must be carefully
considered in energy supply planning and operations to ensure reliable
energy supply, and may result in higher costs. [7.6, 7.8.2]
Nuclear energy is a mature low-GHG emission source of base-
load power, but its share of global electricity generation has
been declining (since 1993). Nuclear energy could make an
increasing contribution to low-carbon energy supply, but a
variety of barriers and risks exist (robust evidence, high agree-
7070
TS
Technical Summary
ment) (Figure TS.19). Nuclear electricity accounted for 11 % of the
world’s electricity generation in 2012, down from a high of 17 % in
1993. Pricing the externalities of GHG emissions (carbon pricing)
could improve the competitiveness of nuclear power plants. [7.2,
7.5.4, 7.8.1, 7.12]
Barriers and risks associated with an increasing use of nuclear
energy include operational risks and the associated safety
concerns, uranium mining risks, financial and regulatory risks,
unresolved waste management issues, nuclear weapon prolif-
eration concerns, and adverse public opinion (robust evidence,
high agreement) (Table TS.4). New fuel cycles and reactor technologies
addressing some of these issues are under development and progress
has been made concerning safety and waste disposal. Investigation of
mitigation scenarios not exceeding 580 ppm CO
2
eq has shown that
excluding nuclear power from the available portfolio of technologies
would result in only a slight increase in mitigation costs compared to
the full technology portfolio (Figure TS.13). If other technologies, such
as CCS, are constrained the role of nuclear power expands. [6.3.6,
7.5.4, 7.8.2, 7.9, 7.11]
GHG emissions from energy supply can be reduced signifi-
cantly by replacing current world average coal-fired power
plants with modern, highly efficient natural gas combined
cycle power plants or combined heat and power (CHP) plants,
provided that natural gas is available and the fugitive emis-
sions associated with its extraction and supply are low or mit-
igated (robust evidence, high agreement). In mitigation scenarios
reaching about 450 ppm CO
2
eq concentrations by 2100, natural gas
power generation without CCS typically acts as a bridge technology,
with deployment increasing before peaking and falling to below
current levels by 2050 and declining further in the second half of
the century (robust evidence, high agreement). [7.5.1, 7.8, 7.9, 7.11,
7.12]
Carbon dioxide capture and storage (CCS) technologies could
reduce the lifecycle GHG emissions of fossil fuel power plants
(medium evidence, medium agreement). While all components of inte-
grated CCS systems exist and are in use today by the fossil fuel extrac-
tion and refining industry, CCS has not yet been applied at scale to
a large, commercial fossil fuel power plant. CCS power plants could
be seen in the market if they are required for fossil fuel facilities by
regulation or if they become competitive with their unabated coun-
terparts, for instance, if the additional investment and operational
costs faced by CCS plants, caused in part by efficiency reductions, are
compensated by sufficiently high carbon prices (or direct financial sup-
port). Beyond economic incentives, well-defined regulations concern-
ing short- and long-term responsibilities for storage are essential for a
large-scale future deployment of CCS. [7.5.5]
Barriers to large-scale deployment of CCS technologies include
concerns about the operational safety and long-term integrity
of CO
2
storage, as well as risks related to transport and the
required up-scaling of infrastructure (limited evidence, medium
agreement) (Table TS.4). There is, however, a growing body of liter-
ature on how to ensure the integrity of CO
2
wells, on the potential
consequences of a CO
2
pressure build-up within a geologic formation
(such as induced seismicity), and on the potential human health and
environmental impacts from CO
2
that migrates out of the primary
injection zone (limited evidence, medium agreement). [7.5.5, 7.9,
7.11]
Combining bioenergy with CCS (BECCS) offers the prospect of
energy supply with large-scale net negative emissions, which
plays an important role in many low-stabilization scenarios,
while it entails challenges and risks (limited evidence, medium
agreement). Until 2050, bottom-up studies estimate the economic
potential to be between 2 – 10 GtCO
2
per year [11.13]. Some mitiga-
tion scenarios show higher deployment of BECCS towards the end of
the century. Technological challenges and risks include those associ-
ated with the upstream provision of the biomass that is used in the
CCS facility, as well as those associated with the CCS technology itself.
Currently, no large-scale projects have been financed. [6.9, 7.5.5, 7.9,
11.13]
Figure TS.18 | Share of low-carbon energy in total primary energy, electricity and liquid fuels supply sectors for the year 2050. Dashed horizontal lines show the low-carbon share
for the year 2010. Low-carbon energy includes nuclear, renewables, fossil fuels with carbon dioxide capture and storage (CCS) and bioenergy with CCS. [Figure 7.14]
0
20
40
60
80
100
2010
2010
Low Carbon Share of Primary Energy (2050) [%]
Primary Energy
0
20
40
60
80
100
Low Carbon Share of Electricity (2050) [%]
Electricity
n.a.
2010
0
20
40
60
80
100
Low Carbon Share of Liquids Supply (2050) [%]
Liquid Fuels Supply
430-480 ppm CO
2
eq
480-530 ppm CO
2
eq
530-580 ppm CO
2
eq
580-650 ppm CO
2
eq
Baselines
650-720 ppm CO
2
eq
Min
75
th
Max
Percentile
Median
25
th
7171
Technical Summary
TS
Figure TS.19 | Specific direct and lifecycle emissions (gCO
2
eq / kilowatt hour (kWh)) and levelized cost of electricity (LCOE in USD
2010
/ MWh) for various power-generating technolo-
gies (see Annex III.2 for data and assumptions and Annex II.3.1 and II.9.3 for methodological issues). The upper left graph shows global averages of specific direct CO
2
emissions
(gCO
2
/ kWh) of power generation in 2030 and 2050 for the set of about 450 to about 500 (430 – 530)ppm CO
2
eq scenarios that are contained in the WG III AR5 Scenario Database
(see Annex II.10). The global average of specific direct CO
2
emissions (gCO
2
/ kWh) of power generation in 2010 is shown as a vertical line. Note: The inter-comparability of LCOE is
limited. For details on general methodological issues and interpretation see Annexes as mentioned above. CCS: CO
2
capture and storage; IGCC: Integrated coal gasification com-
bined cycle; PC: Pulverized hard coal; PV: Photovoltaic; WACC: Weighted average cost of capital. [Figure 7.7]
Global Average Direct Emission Intensity, 2010
Emission Intensity of Electricity [gCO
2
/kWh]
Emission Intensity of Electricity [gCO
2
eq/kWh]
Levelized Cost of Electricity at 10% Weighted Average Cost of Capital (WACC) [USD
2010
/MWh]
Pre-commercial Technologies
0 -2002004006008001000
0200 0 100 200 300 400 500 600 700 8004006008001000
Scenarios Reaching 430-530 ppm CO
2
eq in 2100 in Integrated Models
Currently Commercially Available Technologies
High Full Load Hours
Low Full Load Hours
High Full Load Hours, 100 USD
2010
/tCO
2
eq*
Low Full Load Hours, 100 USD
2010
/tCO
2
eq*
Conditions of Operation
Direct Emissions
Lifecycle Emissions
Emission Intensity Based on:
Global Average, 2030
Global Average, 2050
Direct Emission Intensity
2200
1
Assuming biomass feedstocks are dedicated energy plants and crop residues and 80-95% coal input.
2
Assuming feedstocks are dedicated energy plants and crop residues.
*
Carbon price levied on direct emissions. Effects shown where significant.
3
Direct emissions of biomass power plants are not shown explicitly, but included in the lifecycle
emissions. Lifecycle emissions include albedo effect.
4
LCOE of nuclear include front and back-end fuel costs as well as decommissioning costs.
5
Transport and storage costs of CCS are set to 10 USD
2010
/tCO
2
.
Coal - PC
Gas -
Combined Cycle
Biomass
Cofiring
1,3
Biomass
Dedicated
2,3
Geothermal -
Electricity
Hydropower
Nuclear
4
Concentrated
Solar Power
Solar PV -
Rooftop
Solar PV -
Utility
Wind Onshore
Wind Offshore
CCS - Coal
- Oxyfuel
5
CCS - Coal - PC
5
CCS - Coal - IGCC
5
CCS - Gas -
Combined Cycle
5
Ocean -
Wave & Tidal
Minimum
75
th
percentile
Maximum Median
25
th
percentile
7272
TS
Technical Summary
TS.3.2.3 Transport
Since AR4, emissions in the global transport sector have grown
in spite of more efficient vehicles (road, rail, watercraft, and
aircraft) and policies being adopted (robust evidence, high agree-
ment). Road transport dominates overall emissions but aviation could
play an increasingly important role in total CO
2
emissions in the future.
[8.1, 8.3, 8.4]
The global transport sector accounted for 27 % of final energy
use and 6.7 GtCO
2
direct emissions in 2010, with baseline CO
2
emissions projected to increase to 9.3 – 12 GtCO
2
/ yr in 2050
(25 – 75th percentile; full range 6.2 – 16 GtCO
2
/ yr); most of the
baseline scenarios assessed in WGIII AR5 foresee a significant
increase (medium evidence / medium agreement) (Figure TS.15). With-
out aggressive and sustained mitigation policies being implemented,
transport sector emissions could increase faster than in the other
energy end-use sectors and could lead to more than a doubling of CO
2
emissions by 2050. [6.8, 8.9, 8.10]
While the continuing growth in passenger and freight activity
constitutes a challenge for future emission reductions, analyses
of both sectoral and integrated studies suggest a higher mitiga-
tion potential in the transport sector than reported in the AR4
(medium evidence, medium agreement). Transport energy demand
per capita in developing and emerging economies is far lower than
in OECD countries but is expected to increase at a much faster rate in
the next decades due to rising incomes and the development of infra-
structure. Baseline scenarios thus show increases in transport energy
demand from 2010 out to 2050 and beyond. However, sectoral and
Table TS.4 | Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the energy supply sector; arrows pointing
up / down denote a positive / negative effect on the respective objective or concern; a question mark (?) denotes an uncertain net effect. Co-benefits and adverse side-effects depend
on local circumstances as well as on the implementation practice, pace, and scale. For possible upstream effects of biomass supply for bioenergy, see Table TS.8. For an assessment
of macroeconomic, cross-sectoral effects associated with mitigation policies (e. g., on energy prices, consumption, growth, and trade), see e. g., Sections 3.9, 6.3.6, 13.2.2.3 and
14.4.2. The uncertainty qualifiers in brackets denote the level of evidence and agreement on the respective effects (see TS.1). Abbreviations for evidence: l=limited, m=medium,
r=robust; for agreement: l=low, m=medium, h=high. [Table 7.3]
Energy Supply
Effect on additional objectives / concerns
Economic Social Environmental Other
Nuclear
replacing
coal power
↑
↑
↑
Energy security (reduced exposure
to fuel price volatility) (m / m)
Local employment impact (but
uncertain net effect) (l / m)
Legacy cost of waste and
abandoned reactors (m / h)
↓
↑
↑
Health impact via
Air pollution and coal
mining accidents (m / h)
Nuclear accidents and waste
treatment, uranium
mining and milling (m / l)
Safety and waste concerns (r / h)
↓
↑
Ecosystem impact via
Air pollution (m / h) and
coal mining (l / h)
Nuclear accidents (m / m)
Proliferation
risk (m / m)
RE (wind, PV,
concentrated
solar power
(CSP), hydro,
geothermal,
bioenergy)
replacing coal
↑
↑
↑
↑
Energy security (resource
sufficiency, diversity in the
near / medium term) (r / m)
Local employment impact (but
uncertain net effect) (m / m)
Irrigation, flood control,
navigation, water availability (for
multipurpose use of reservoirs
and regulated rivers) (m / h)
Extra measures to match demand
(for PV, wind and some CSP) (r / h)
↓
↓
↑
?
↑
Health impact via
Air pollution (except
bioenergy) (r / h)
Coal mining accidents (m / h)
Contribution to (off-grid)
energy access (m / l)
Project-specific public acceptance
concerns (e. g., visibility of wind) (l / m)
Threat of displacement (for
large hydro) (m / h)
↓
↓
↑
↑
↓
↑
Ecosystem impact via
Air pollution (except
bioenergy) (m / h)
Coal mining (l / h)
Habitat impact (for some
hydro) (m / m)
Landscape and wildlife
impact (for wind) m / m)
Water use (for wind and PV) (m / m)
Water use (for bioenergy, CSP,
geothermal, and reservoir hydro) (m / h)
Higher use of critical
metals for PV and
direct drive wind
turbines (r / m)
Fossil CCS
replacing coal
↑ ↑
Preservation vs. lock-in of
human and physical capital in
the fossil industry (m / m)
↑
↑
↑
Health impact via
Risk of CO
2
leakage (m / m)
Upstream supply-chain
activities (m / h)
Safety concerns (CO
2
storage
and transport) (m / h)
↑
↑
Ecosystem impact via upstream
supply-chain activities (m / m)
Water use (m / h)
Long-term
monitoring of CO
2
storage (m / h)
BECCS
replacing coal
See fossil CCS where applicable. For possible upstream effect of biomass supply, see Table TS.8.
Methane
leakage
prevention,
capture or
treatment
↑
Energy security (potential to
use gas in some cases) (l / h)
↓
↑
Health impact via reduced
air pollution (m / m)
Occupational safety at
coal mines (m / m)
↓
Ecosystem impact via reduced
air pollution (l / m)
7373
Technical Summary
TS
integrated mitigation scenarios indicate that energy demand reduc-
tions of 10 – 45 % are possible by 2050 relative to baseline (Figure
TS.20, left panel) (medium evidence, medium agreement). [6.8.4, 8.9.1,
8.9.4, 8.12, Figure 8.9.4]
A combination of low-carbon fuels, the uptake of improved
vehicle and engine performance technologies, behavioural
change leading to avoided journeys and modal shifts, invest-
ments in related infrastructure and changes in the built environ-
ment, together offer a high mitigation potential (high confidence)
[8.3, 8.8]. Direct (tank-to-wheel) GHG emissions from passenger and
freight transport can be reduced by:
• using fuels with lower carbon intensities (CO
2
eq / megajoule (MJ));
• lowering vehicle energy intensities
(MJ / passenger-km or MJ / tonne-km);
• encouraging modal shift to lower-carbon passenger and freight
transport systems coupled with investment in infrastructure and
compact urban form; and
• avoiding journeys where possible (Table TS.3).
Other short-term mitigation strategies include reducing black carbon
(BC), aviation contrails, and nitrogen oxides (NO
x
) emissions. [8.4]
Strategies to reduce the carbon intensities of fuel and the rate
of reducing carbon intensity are constrained by challenges
associated with energy storage and the relatively low energy
density of low-carbon transport fuels; integrated and sectoral
studies broadly agree that opportunities for fuel switching
exist in the short term and will grow over time (medium evi-
dence, medium agreement) (Figure TS.20, right panel). Electric, hydro-
gen, and some biofuel technologies could help reduce the carbon
intensity of fuels, but their total mitigation potentials are very uncer-
tain (medium evidence, medium agreement). Methane-based fuels
are already increasing their share for road vehicles and waterborne
craft. Electricity produced from low-carbon sources has near-term
potential for electric rail and short- to medium-term potential as elec-
tric buses, light-duty and 2-wheel road vehicles are deployed. Hydro-
gen fuels from low-carbon sources constitute longer-term options.
Commercially available liquid and gaseous biofuels already provide
co-benefits together with mitigation options that can be increased
by technology advances, particularly drop-in biofuels for aircraft.
Reducing transport emissions of particulate matter (including BC),
tropospheric ozone and aerosol precursors (including NO
x
) can have
human health and mitigation co-benefits in the short term (medium
evidence, medium agreement). Up to 2030, the majority of integrated
studies expect a continued reliance on liquid and gaseous fuels, sup-
ported by an increase in the use of biofuels. During the second half
of the century, many integrated studies also show substantial shares
of electricity and / or hydrogen to fuel electric and fuel-cell light-duty
vehicles (LDVs). [8.2, 8.3, 11.13]
Energy efficiency measures through improved vehicle and
engine designs have the largest potential for emissions reduc-
Figure TS.20 | Final energy demand reduction relative to baseline (left panel) and development of final low-carbon energy carrier share in final energy (including electricity, hydro-
gen, and liquid biofuels; right panel) in transport by 2030 and 2050 in mitigation scenarios from three different CO
2
eq concentrations ranges shown in boxplots (see Section 6.3.2)
compared to sectoral studies shown in shapes assessed in Chapter 8. Filled circles correspond to sectoral studies with full sectoral coverage. [Figures 6.37 and 6.38]
Min
75
th
Percentile
Max
Median
25
th
Percentile
N= 161 225 161 225
2030 2050
Transport
Final Energy Demand Reduction Relative to Baseline [%]
100
80
60
40
20
0
Sectoral Studies (Base)
Sectoral Studies (Policy)
Historic Data 2010
Baselines
530−650 ppm CO
2
eq
430−530 ppm CO
2
eq
Sectoral Studies (Full)
N=
154 130 182 154 130 182
2030 2050
0
20
40
60
100
80
Transport
Low Carbon Energy Carrier Share in Final Energy [%]
7474
TS
Technical Summary
tions in the short term (high confidence). Potential energy efficiency
and vehicle performance improvements range from 30 – 50 % relative
to 2010 depending on transport mode and vehicle type (Figures TS.21,
TS.22). Realizing this efficiency potential will depend on large invest-
ments by vehicle manufacturers, which may require strong incentives
and regulatory policies in order to achieve GHG emissions reduction
goals (medium evidence, medium agreement). [8.3, 8.6, 8.9, 8.10]
Shifts in transport mode and behaviour, impacted by new
infrastructure and urban (re)development, can contribute to
the reduction of transport emissions (medium evidence, low
agreement). Over the medium term (up to 2030) to long term (to
2050 and beyond), urban redevelopment and investments in new
infrastructure, linked with integrated urban planning, transit-oriented
development, and more compact urban form that supports cycling
and walking can all lead to modal shifts. Such mitigation measures
are challenging, have uncertain outcomes, and could reduce trans-
port GHG emissions by 20 – 50 % compared to baseline (limited evi-
dence, low agreement). Pricing strategies, when supported by pub-
lic acceptance initiatives and public and non-motorized transport
infrastructures, can reduce travel demand, increase the demand for
more efficient vehicles (e. g., where fuel economy standards exist)
and induce a shift to low-carbon modes (medium evidence, medium
agreement). While infrastructure investments may appear expensive
at the margin, the case for sustainable urban planning and related
policies is reinforced when co-benefits, such as improved health,
accessibility, and resilience, are accounted for (Table TS.5). Busi-
ness initiatives to decarbonize freight transport have begun but will
need further support from fiscal, regulatory, and advisory policies to
encourage shifting from road to low-carbon modes such as rail or
waterborne options where feasible, as well as improving logistics
(Figure TS.22). [8.4, 8.5, 8.7, 8.8, 8.9, 8.10]
Sectoral and integrated studies agree that substantial, sus-
tained, and directed policy interventions could limit transport
emissions to be consistent with low concentration goals, but
the societal mitigation costs (USD / tCO
2
eq avoided) remain
uncertain (Figures TS.21, TS.22, TS.23). There is good potential to
reduce emissions from LDVs and long-haul heavy-duty vehicles (HDVs)
from both lower energy intensity vehicles and fuel switching, and the
levelized costs of conserved carbon (LCCC) for efficiency improvements
can be very low and negative (limited evidence, low agreement). Rail,
buses, two-wheel motorbikes, and waterborne craft for freight already
have relatively low emissions so their emissions reduction potential is
limited. The mitigation cost of electric vehicles is currently high, espe-
cially if using grid electricity with a high emissions factor, but their
LCCC are expected to decline by 2030. The emissions intensity of avia-
tion could decline by around 50 % in 2030 but the LCCC, although
uncertain, are probably over USD 100 / tCO
2
eq. While it is expected
that mitigation costs will decrease in the future, the magnitude of such
reductions is uncertain. (limited evidence, low agreement) [8.6, 8.9]
Barriers to decarbonizing transport for all modes differ across
regions but can be overcome, in part, through economic
incentives (medium evidence, medium agreement). Financial, insti-
tutional, cultural, and legal barriers constrain low-carbon technol-
ogy uptake and behavioural change. They include the high invest-
ment costs needed to build low-emissions transport systems, the
slow turnover of stock and infrastructure, and the limited impact of
a carbon price on petroleum fuels that are already heavily taxed.
Regional differences are likely due to cost and policy constraints. Oil
price trends, price instruments on GHG emissions, and other mea-
sures such as road pricing and airport charges can provide strong
economic incentives for consumers to adopt mitigation measures.
[8.8]
There are regional differences in transport mitigation pathways
with major opportunities to shape transport systems and infra-
structure around low-carbon options, particularly in develop-
ing and emerging countries where most future urban growth
will occur (robust evidence, high agreement). Possible transforma-
tion pathways vary with region and country due to differences in the
dynamics of motorization, age and type of vehicle fleets, existing infra-
structure, and urban development processes. Prioritizing infrastructure
for pedestrians, integrating non-motorized and transit services, and
managing excessive road speed for both urban and rural travellers can
create economic and social co-benefits in all regions. For all econo-
mies, especially those with high rates of urban growth, investments
in public transport systems and low-carbon infrastructure can avoid
lock-in to carbon-intensive modes. Established infrastructure may limit
the options for modal shift and lead to a greater reliance on advanced
vehicle technologies; a slowing of growth in LDV demand is already
evident in some OECD countries. (medium evidence, medium agree-
ment) [8.4, 8.9]
A range of strong and mutually supportive policies will be
needed for the transport sector to decarbonize and for the
co-benefits to be exploited (robust evidence, high agreement).
Transport mitigation strategies associated with broader non-climate
policies at all government levels can usually target several objec-
tives simultaneously to give lower travel costs, improved access and
mobility, better health, greater energy security, improved safety, and
increased time savings. Activity reduction measures have the largest
potential to realize co-benefits. Realizing the co-benefits depends on
the regional context in terms of economic, social, and political fea-
sibility as well as having access to appropriate and cost-effective
advanced technologies (Table TS.5). (medium evidence, high agree-
ment) Since rebound effects can reduce the CO
2
benefits of efficiency
improvements and undermine a particular policy, a balanced package
of policies, including pricing initiatives, could help to achieve stable
price signals, avoid unintended outcomes, and improve access, mobil-
ity, productivity, safety, and health (medium evidence, medium agree-
ment). [8.4, 8.7, 8.10]
7575
Technical Summary
TS
Figure TS.21 | Indicative emissions intensity (tCO
2
eq / p-km) and levelized costs of conserved carbon (LCCC in USD
2010
/ tCO
2
eq saved) of selected passenger transport technologies.
Variations in emissions intensities stem from variation in vehicle efficiencies and occupancy rates. Estimated LCCC for passenger road transport options are point estimates ± 100
USD
2010
/ tCO
2
eq based on central estimates of input parameters that are very sensitive to assumptions (e. g., specific improvement in vehicle fuel economy to 2030, specific biofuel CO
2
eq
intensity, vehicle costs, fuel prices). They are derived relative to different baselines (see legend for colour coding) and need to be interpreted accordingly. Estimates for 2030 are based
on projections from recent studies, but remain inherently uncertain. LCCC for aviation are taken directly from the literature. Table 8.3 provides additional context (see Annex III.3 for data
and assumptions on emissions intensities and cost calculations and Annex II.3.1 for methodological issues on levelized cost metrics). WACC: Weighted average cost of capital. [Table 8.3]
2010 Electric, 600 g CO
2
eq/kWh
el
2010 Electric, 200 g CO
2
eq/kWh
el
2010 Diesel
2010 Hybrid Diesel
2010 Gasoline
2010 Gasoline
2010 Gasoline
2030 Gasoline
2030 Gasoline
2030 Diesel
2030 Compressed Natural Gas
2010 Narrow and Wide Body
2030 Narrow Body
2030 Narrow Body, Open Rotor Engine
2010 Hybrid Gasoline
2030 Hybrid Gasoline
2010 Hybrid Gasoline
2010 Diesel
2010 Compressed Natural Gas
2010 Electric, 600 g CO
2
eq/kWh
el
2010 Electric, 200 g CO
2
eq/kWh
el
2030 Hybrid Gasoline
2030 Hybrid Gasoline/Biofuel* (50/50 Share)
050100150200250
2010 Stock Average
Rail (Light Rail Car)
Road
2010 Stock Average SUV
2010 Stock Average LDV
2010 Stock Average 2 Wheeler
New Buses, Large Size
New Sport Utility Vehicles (SUV),
Mid-Size
New Light Duty Vehicles (LDV), Mid-Size
New 2 Wheeler (Scooter Up to 200 cm
3
Cylinder Capacity)
Aviation
(Commercial, Medium- to Long-Haul)
*Assuming 70% less CO
2
eq/MJ of Biofuel than per MJ of Gasoline
Currently Commercially Available and Future (2030) Expected Technologies
Emissions Intensity (gCO
2
eq/p-km) Levelized Cost of Conserved Carbon at 5% WACC [USD
2010
/t CO
2
eq]
Passenger Transport
Optimized Gasoline LDV (2030)
Optimized Gasoline SUV (2030)
Average New Aircraft (2010)
New Gasoline SUV (2010)
New Gasoline LDV (2010)
Baselines for LCCC Calculation
-600 -400 -200 0 200 400 600 800 1000 1200
2030 Electric, 200 g CO
2
eq/kWh
el
7676
TS
Technical Summary
Figure TS.22 | Indicative emissions intensity (tCO
2
eq / t-km) and levelized costs of conserved carbon (LCCC in USD
2010
/ tCO
2
eq saved) of selected freight transport technologies.
Variations in emissions intensities largely stem from variation in vehicle efficiencies and load rates. Levelized costs of conserved carbon are taken directly from the literature and are
very sensitive to assumptions (e. g., specific improvement in vehicle fuel economy to 2030, specific biofuel CO
2
eq intensity, vehicle costs, and fuel prices). They are expressed relative
to current baseline technologies (see legend for colour coding) and need to be interpreted accordingly. Estimates for 2030 are based on projections from recent studies but remain
inherently uncertain. Table 8.3 provides additional context (see Annex III.3 for data and assumptions on emissions intensities and cost calculations and Annex II.3.1 for method-
ological issues on levelized cost metrics). LNG: Liquefied natural gas; WACC: Weighted average cost of capital. [Table 8.3]
Aviation
(Commercial, Medium- to Long-Haul)
Rail (Freight Train)
Waterborne
Road
New Heavy Duty, Long-Haul Trucks
New Medium Duty Trucks
Average New Aircraft (2010)
New Diesel Medium Duty (2010)
New Diesel Long-Haul (2010)
New Bulk Carrier/
Container Vessel (2010)
Baselines for LCCC Calculation
02004006008001000
2010 Belly-Hold
2010 Diesel, Light Goods
2010 Diesel, Heavy Goods
2010 Electric, 200g CO
2
eq/kWh
el
2010 New Large International
Container Vessel
2010 Large Bulk Carrier/Tanker
2010 LNG Bulk Carrier
2010 Diesel
2010 Diesel Hybrid
2010 Compressed Natural Gas
2010 Diesel
2010 Compressed Natural Gas
2030 Diesel/Biofuel (50/50 Share)*
-100-200 1000 300200 400
2010 Dedicated Airfreighter
2030 Improved Aircraft
2030 Improved, Open Rotor Engine
2030 Optimized Container Vessel
2030 Optimized Bulk Carrier
2030 Diesel
2030 Diesel
*Assuming 70% Less CO
2
eq/MJ Biofuel than /MJ Diesel
Freight Transport
Levelized Cost of Conserved Carbon at 5% WACC [USD
2010
/t CO
2
eq]
2010 Stock Average
2010 Stock Average International Shipping
2010 Stock Average
2010 Stock Average
Currently Commercially Available and Future (2030) Expected Technologies
Emissions Intensity (gCO
2
eq/t-km)
7777
Technical Summary
TS
Figure TS.23 | Direct global CO
2
emissions from all passenger and freight transport are indexed relative to 2010 values for each scenario with integrated model studies grouped by
CO
2
eq concentration levels by 2100, and sectoral studies grouped by baseline and policy categories. [Figure 8.9]
n=
166 513193233 166 513193233 166 411193233 161 163198
430-530 ppm CO
2
eq
>650 ppm CO
2
eq
Policy
530-650 ppm CO
2
eq
Baseline
Units in Comparison to 2010 [2010 = 1]
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2100205020302020
IAM Sectoral IAM Sectoral SectoralIAM IAM
Min
75
th
Percentile
Max
Median
25
th
Percentile
Min
Max
Table TS.5 | Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the transport sector; arrows pointing
up / down denote a positive / negative effect on the respective objective or concern; a question mark (?) denotes an uncertain net effect. Co-benefits and adverse side-effects depend
on local circumstances as well as on implementation practice, pace and scale. For possible upstream effects of low-carbon electricity, see Table TS.4. For possible upstream effects
of biomass supply, see Table TS.8. For an assessment of macroeconomic, cross-sectoral effects associated with mitigation policies (e. g., on energy prices, consumption, growth,
and trade), see e. g., Sections 3.9, 6.3.6, 13.2.2.3 and 14.4.2. The uncertainty qualifiers in brackets denote the level of evidence and agreement on the respective effects (see TS.1).
Abbreviations for evidence: l = limited, m = medium, r = robust; for agreement: l = low, m = medium, h = high. [Table 8.4]
Transport
Effect on additional objectives / concerns
Economic Social Environmental
Reduction of fuel
carbon intensity:
electricity,
hydrogen (H
2
),
compressed natural
gas (CNG), biofuels,
and other fuels
↑
↑
Energy security (diversification,
reduced oil dependence and exposure
to oil price volatility) (m / m)
Technological spillovers (e. g., battery
technologies for consumer electronics) (l / l)
?
↓
↑
↓
↓
Health impact via urban air pollution by
CNG, biofuels: net effect unclear (m / l)
Electricity, H
2
: reducing most pollutants (r / h)
Shift to diesel: potentially
increasing pollution (l / m)
Health impact via reduced noise
(electricity and fuel cell LDVs) (l / m)
Road safety (silent electric LDVs at low speed) (l / l)
↓
↑
?
Ecosystem impact of electricity
and hydrogen via
Urban air pollution (m / m)
Material use (unsustainable
resource mining) (l / l)
Ecosystem impact of biofuels: see AFOLU
Reduction of
energy intensity
↑
Energy security (reduced oil dependence
and exposure to oil price volatility) (m / m)
↓
↑
Health impact via reduced urban air pollution (r / h)
Road safety (via increased crash-worthiness) (m / m)
↓
Ecosystem and biodiversity impact via
reduced urban air pollution (m / h)
Compact urban
form and improved
transport
infrastructure
Modal shift
↑
↑
?
Energy security (reduced oil dependence
and exposure to oil price volatility) (m / m)
Productivity (reduced urban congestion
and travel times, affordable and
accessible transport) (m / h)
Employment opportunities in the public
transport sector vs. car manufacturing (l / m)
↓
↑
↓
↑
↑
Health impact for non-motorized modes via
Increased physical activity (r / h)
Potentially higher exposure to air pollution (r / h)
Noise (modal shift and travel reduction) (r / h)
Equitable mobility access to
employment opportunities, particularly
in developing countries (r / h)
Road safety (via modal shift and / or infrastructure
for pedestrians and cyclists) (r / h)
↓
↓
Ecosystem impact via
Urban air pollution (r / h)
Land-use competition (m / m)
Journey distance
reduction and
avoidance
↑
↑
Energy security (reduced oil dependence
and exposure to oil price volatility) (r / h)
Productivity (reduced urban congestion,
travel times, walking) (r / h)
↓
Health impact (for non-motorized
transport modes) (r / h)
↓
↑
↓
Ecosystem impact via
Urban air pollution (r / h)
New / shorter shipping routes (r / h)
Land-use competition from
transport infrastructure (r / h)
7878
TS
Technical Summary
TS.3.2.4 Buildings
GHG emissions from the buildings secto r
15
have more than dou-
bled since 1970, accounting for 19 % of global GHG emissions
in 2010, including indirect emissions from electricity genera-
tion. The share rises to 25 % if AFOLU emissions are excluded from the
total. The buildings sector also accounted for 32 % of total global final
energy use, approximately one-third of black carbon emissions, and an
eighth to a third of F-gases, with significant uncertainty (medium evi-
dence, medium agreement). (Figure TS.3) [9.2]
Direct and indirect CO
2
emissions from buildings are projected
to increase from 8.8 GtCO
2
/ yr in 2010 to 13 – 17 GtCO
2
/ yr in
2050 (25 – 75th percentile; full range 7.9 – 22 GtCO
2
/ yr) in base-
line scenarios; most of the baseline scenarios assessed in WGIII
AR5 show a significant increase (medium evidence, medium agree-
ment) (Figure TS.15) [6.8]. The lower end of the full range is dominated
by scenarios with a focus on energy intensity improvements that go
well beyond the observed improvements over the past 40 years. With-
out further policies, final energy use of the buildings sector may grow
from approximately 120 exajoules per year (EJ / yr) in 2010 to 270 EJ / yr
in 2050 [9.9].
Significant lock-in risks arise from the long lifespans of build-
ings and related infrastructure (robust evidence, high agreement).
If only currently planned policies are implemented, the final energy use
in buildings that could be locked-in by 2050, compared to a scenario
where today’s best practice buildings become the standard in newly
built structures and retrofits, is equivalent to approximately 80 % of
the final energy use of the buildings sector in 2005. [9.4]
Improvements in wealth, lifestyle change, the provision of
access to modern energy services and adequate housing, and
urbanization will drive the increases in building energy demand
(robust evidence, high agreement). The manner in which those without
access to adequate housing (about 0.8 billion people), modern energy
carriers, and sufficient levels of energy services including clean cooking
and heating (about 3 billion people) meet these needs will influence
the development of building-related emissions. In addition, migration
to cities, decreasing household size, increasing levels of wealth, and
lifestyle changes, including increasing dwelling size and number and
use of appliances, all contribute to considerable increases in building
energy services demand. The substantial amount of new construction
taking place in developing countries represents both a risk and oppor-
tunity from a mitigation perspective. [9.2, 9.4, 9.9]
Recent advances in technologies, know-how, and policies in the
buildings sector, however, make it feasible that the global total
sector final energy use stabilizes or even declines by mid-century
(robust evidence, medium agreement). Recent advances in technology,
15
The buildings sector covers the residential, commercial, public and services sectors;
emissions from construction are accounted for in the industry sector.
design practices and know-how, coupled with behavioural changes, can
achieve a two to ten-fold reduction in energy requirements of individual
new buildings and a two to four-fold reduction for individual existing
buildings largely cost-effectively or sometimes even at net negative
costs (see Box TS.12) (robust evidence, high agreement). [9.6]
Advances since AR4 include the widespread demonstration
worldwide of very low, or net zero energy buildings both in
new construction and retrofits (robust evidence, high agreement).
In some jurisdictions, these have already gained important market
shares with, for instance, over 25 million m
2
of building floorspace in
Europe complying with the ‘Passivehouse’ standard in 2012. However,
zero energy / carbon buildings may not always be the most cost-optimal
solution, nor even be feasible in certain building types and locations.
[9.3]
High-performance retrofits are key mitigation strategies in
countries with existing building stocks, as buildings are very
long-lived and a large fraction of 2050 developed country
buildings already exists (robust evidence, high agreement). Reduc-
tions of heating / cooling energy use by 50 – 90 % have been achieved
using best practices. Strong evidence shows that very low-energy con-
struction and retrofits can be economically attractive. [9.3]
With ambitious policies it is possible to keep global building
energy use constant or significantly reduce it by mid-century
compared to baseline scenarios which anticipate an increase of
more than two-fold (medium evidence, medium agreement) (Figure
TS.24). Detailed building sector studies indicate a larger energy sav-
ings potential by 2050 than do integrated studies. The former indicate
a potential of up to 70 % of the baseline for heating and cooling only,
and around 35 – 45 % for the whole sector. In general, deeper reduc-
tions are possible in thermal energy uses than in other energy services
mainly relying on electricity. With respect to additional fuel switching
as compared to baseline, both sectoral and integrated studies find
modest opportunities. In general, both sectoral and integrated studies
indicate that electricity will supply a growing share of building energy
demand over the long term, especially if heating demand decreases
due to a combination of efficiency gains, better architecture, and cli-
mate change. [6.8.4, 9.8.2, Figure 9.19]
The history of energy efficiency programmes in buildings shows
that 25 – 30 % efficiency improvements have been available at
costs substantially lower than those of marginal energy sup-
ply (robust evidence, high agreement). Technological progress enables
the potential for cost-effective energy efficiency improvements to be
maintained, despite continuously improving standards. There has been
substantial progress in the adoption of voluntary and mandatory stan-
dards since AR4, including ambitious building codes and targets, vol-
untary construction standards, and appliance standards. At the same
time, in both new and retrofitted buildings, as well as in appliances
and information, communication and media technology equipment,
there have been notable performance and cost improvements. Large
7979
Technical Summary
TS
Figure TS.24 | Final energy demand reduction relative to baseline (left panel) and development of final low-carbon energy carrier share in final energy (from electricity; right panel)
in buildings by 2030 and 2050 in mitigation scenarios from three different CO
2
eq concentrations ranges shown in boxplots (see Section 6.3.2) compared to sectoral studies shown
in shapes assessed in Chapter 9. Filled circles correspond to sectoral studies with full sectoral coverage while empty circles correspond to studies with only partial sectoral coverage
(e. g., heating and cooling). [Figures 6.37 and 6.38]
Min
75
th
Percentile
Max
Median
25
th
Percentile
N= 126 189 126 189
2030 2050
Buildings
Final Energy Demand Reduction Relative to Baseline [%]
100
80
60
40
20
0
N=
124 103 110 124 103 110
100
2030 2050
0
20
40
60
80
Buildings
Low Carbon Energy Carrier Share in Final Energy [%]
Sectoral Studies (Base)
Sectoral Studies (Policy)
Historic Data 2010
Baselines
530−650 ppm CO
2
eq
430−530 ppm CO
2
eq
Sectoral Studies (Partial)
Sectoral Studies (Full)
Box TS.12 | Negative private mitigation costs
A persistent issue in the analysis of mitigation options and costs
is whether there are mitigation opportunities that are privately
beneficial — generating private benefits that more than offset the
costs of implementation — but which consumers and firms do
not voluntarily undertake. There is some evidence of unrealized
mitigation opportunities that would have negative private cost.
Possible examples include investments in vehicles [8.1], lighting
and heating technology in homes and commercial buildings [9.3],
as well as industrial processes [10.1].
Examples of negative private costs imply that firms and indi-
viduals do not take opportunities to save money. This might be
explained in a number of ways. One is that status-quo bias can
inhibit the switch to new technologies or products [2.4, 3.10.1].
Another is that firms and individuals may focus on short-term
goals and discount future costs and benefits sharply; consumers
have been shown to do this when choosing energy conservation
measures or investing in energy-efficient technologies [2.4.3,
2.6.5.3, 3.10.1]. Risk aversion and ambiguity aversion may also
account for this behaviour when outcomes are uncertain [2.4.3,
3.10.1]. Other possible explanations include: insufficient informa-
tion on opportunities to conserve energy; asymmetric informa-
tion — for example, landlords may be unable to convey the value
of energy efficiency improvements to renters; split incentives,
where one party pays for an investment but another party reaps
the benefits; and imperfect credit markets, which make it difficult
or expensive to obtain finance for energy savings [3.10.1, 16.4].
Some engineering studies show a large potential for negative-cost
mitigation. The extent to which such negative-cost opportunities
can actually be realized remains a matter of contention in the
literature. Empirical evidence is mixed. [Box 3.10]
reductions in thermal energy use in buildings are possible at costs
lower than those of marginal energy supply, with the most cost-effec-
tive options including very high-performance new commercial build-
ings; the same holds for efficiency improvements in some appliances
and cooking equipment. [9.5, 9.6, 9.9]
Lifestyle, culture, and other behavioural changes may lead
to further large reductions in building and appliance energy
requirements beyond those achievable through technologies
and architecture. A three- to five-fold difference in energy use
has been shown for provision of similar building-related energy
8080
TS
Technical Summary
service levels in buildings. (limited evidence, high agreement) For
developed countries, scenarios indicate that lifestyle and behavioural
changes could reduce energy demand by up to 20 % in the short term
and by up to 50 % of present levels by mid-century (medium evidence,
medium agreement). There is a high risk that emerging countries
follow the same path as developed economies in terms of building-
related architecture, lifestyle, and behaviour. But the literature sug-
gests that alternative development pathways exist that provide high
levels of building services at much lower energy inputs, incorporating
strategies such as learning from traditional lifestyles, architecture, and
construction techniques. [9.3]
Most mitigation options in the building sector have consider-
able and diverse co-benefits (robust evidence, high agreement).
These include, but are not limited to: energy security; less need for
energy subsidies; health and environmental benefits (due to reduced
indoor and outdoor air pollution); productivity and net employment
gains; the alleviation of fuel poverty; reduced energy expenditures;
increased value for building infrastructure; and improved comfort and
services. (Table TS.6) [9.6, 9.7]
Especially strong barriers in this sector hinder the market-
based uptake of cost-effective technologies and practices; as
a consequence, programmes and regulation are more effective
than pricing instruments alone (robust evidence, high agreement).
Barriers include imperfect information and lack of awareness, princi-
pal / agent problems and other split incentives, transaction costs, lack
of access to financing, insufficient training in all construction-related
trades, and cognitive / behavioural barriers. In developing countries, the
large informal sector, energy subsidies, corruption, high implicit dis-
count rates, and insufficient service levels are further barriers. There-
fore, market forces alone are not expected to achieve the necessary
transformation without external stimuli. Policy intervention addressing
all stages of the building and appliance lifecycle and use, plus new
business and financial models, are essential. [9.8, 9.10]
A large portfolio of building-specific energy efficiency poli-
cies was already highlighted in AR4, but further considerable
advances in available instruments and their implementation
have occurred since (robust evidence, high agreement). Evidence
shows that many building energy efficiency policies worldwide have
Table TS.6 | Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the buildings sector; arrows pointing
up / down denote a positive / negative effect on the respective objective or concern. Co-benefits and adverse side-effects depend on local circumstances as well as on implementation
practice, pace and scale. For possible upstream effects of fuel switching and RE, see Tables TS.4 and TS.8. For an assessment of macroeconomic, cross-sectoral effects associated with
mitigation policies (e. g., on energy prices, consumption, growth, and trade), see e. g., Sections 3.9, 6.3.6, 13.2.2.3 and 14.4.2. The uncertainty qualifiers in brackets denote the level of
evidence and agreement on the respective effects (see TS.1). Abbreviations for evidence: l = limited, m = medium, r = robust; for agreement: l = low, m = medium, h = high. [Table 9.7]
Buildings
Effect on additional objectives / concerns
Economic Social Environmental Other
Fuel
switching, RES
incorporation,
green roofs,
and other
measures
reducing GHG
emissions
intensity
↑
↑
↑
↑
Energy security (m / h)
Employment impact (m / m)
Lower need for energy subsidies (l / l)
Asset values of buildings (l / m)
↓
↑
↓
↑
Fuel poverty (residential) via
Energy demand (m / h)
Energy cost (l / m)
Energy access (for higher
energy cost) (l / m)
Productive time for women / children (for
replaced traditional cookstoves) (m / h)
↓
↓
↓
↓
↑
Health impact in residential buildings via
Outdoor air pollution (r / h)
Indoor air pollution (in
developing countries) (r / h)
Fuel poverty (r / h)
Ecosystem impact (less outdoor
air pollution) (r / h)
Urban biodiversity (for
green roofs) (m / m)
Reduced Urban Heat
Island (UHI) effect (l / m)
Retrofits
of existing
buildings
(e. g., cool
roof, passive
solar, etc.)
Exemplary new
buildings
Efficient
equipment
↑
↑
↑
↑
↑
↑
Energy security (m / h)
Employment impact (m / m)
Productivity (for commercial
buildings) (m / h)
Lower need for energy subsidies (l / l)
Asset values of buildings (l / m)
Disaster resilience (l / m)
↓
↓
↑
↑
Fuel poverty (for retrofits and
efficient equipment) (m / h)
Energy access (higher cost for housing
due to the investments needed) (l / m)
Thermal comfort (for retrofits and
exemplary new buildings) (m / h)
Productive time for women
and children (for replaced
traditional cookstoves) (m / h)
↓
↓
↓
↓
↓
↓
↓
Health impact via
Outdoor air pollution (r / h)
Indoor air pollution (for
efficient cookstoves) (r / h)
Improved indoor environmental
conditions (m / h)
Fuel poverty (r / h)
Insufficient ventilation (m / m)
Ecosystem impact (less outdoor
air pollution) (r / h)
Water consumption and
sewage production (l / l)
Reduced UHI effect
(for retrofits and
new exemplary
buildings) (l / m)
Behavioural
changes
reducing
energy demand
↑
↑
Energy security (m / h)
Lower need for energy subsidies (l / l)
↓
↓
Health impact via less outdoor air
pollution (r / h) and improved indoor
environmental conditions (m / h)
Ecosystem impact (less outdoor
air pollution) (r / h)
8181
Technical Summary
TS
already been saving GHG emissions at large negative costs. Among the
most environmentally and cost-effective policies are regulatory instru-
ments such as building and appliance energy performance standards
and labels, as well as public leadership programmes and procurement
policies. Progress in building codes and appliance standards in some
developed countries over the last decade have contributed to stabi-
lizing or even reducing total building energy use, despite growth in
population, wealth, and corresponding energy service level demands.
Developing countries have also been adopting different effective
policies, most notably appliance standards. However, in order to reach
ambitious climate goals, these standards need to be substantially
strengthened and adopted in further jurisdictions, and to other build-
ing and appliance types. Due to larger capital requirements, financing
instruments are essential both in developed and developing countries
to achieve deep reductions in energy use. [9.10]
TS.3.2.5 Industry
In 2010, the industry sector accounted for around 28 % of final
energy use, and direct and indirect GHG emissions (the latter
being associated with electricity consumption) are larger than
the emissions from either the buildings or transport end-use
sectors and represent just over 30 % of global GHG emissions
in 2010 (the share rises to 40 % if AFOLU emissions are excluded
from the total) (high confidence). Despite the declining share of indus-
try in global GDP, global industry and waste / wastewater GHG emis-
sions grew from 10 GtCO
2
eq in 1990 to 13 GtCO
2
eq in 2005 and to
15 GtCO
2
eq in 2010 (of which waste / wastewater accounted for
1.4GtCO
2
eq). [10.3]
Carbon dioxide emissions from industry, including direct and
indirect emissions as well as process emissions, are projected
to increase from 13 GtCO
2
/ yr in 2010 to 20 – 24 GtCO
2
/ yr in 2050
(25 – 75th percentile; full range 9.5 – 34 GtCO
2
/ yr) in baseline
scenarios; most of the baseline scenarios assessed in WGIII AR5
show a significant increase (medium evidence, medium agreement)
(Figure TS.15) [6.8]. The lower end of the full range is dominated by
scenarios with a focus on energy intensity improvements that go well
beyond the observed improvements over the past 40 years.
The wide-scale upgrading, replacement and deployment of best
available technologies, particularly in countries where these are
not in practice, and in non-energy intensive industries, could
directly reduce the energy intensity of the industry sector by
about 25 % compared to the current level (robust evidence, high
agreement). Despite long-standing attention to energy efficiency in
industry, many options for improved energy efficiency still remain.
Through innovation, additional reductions of about 20 % in energy
intensity may potentially be realized (limited evidence, medium agree-
Figure TS.25 | A schematic illustration of industrial activity over the supply chain. Options for mitigation in the industry sector are indicated by the circled numbers: (1) energy
efficiency; (2) emissions efficiency; (3a) material efficiency in manufacturing; (3b) material efficiency in product design; (4) product-service efficiency; (5) service demand reduction.
[Figure 10.2]
Energy Use
Process Emissions
Energy-Related Emissions
Extractive
Industry
Materials
Industries
Energy (Ch.7)
Energy (Ch.7) Downstream Buildings/Transport (Chs. 8,9)
Demand
ServicesProductsMaterialFeedstocks
Home Scrap New Scrap Retirement
Discards
Re-Use
Recyclate
Manufacturing
and Construction
Extractive
Industry
Materials
Industries
Manufacturing
and Construction
Waste to Energy/
Disposal
Regional/
Domestic
Industry
Waste
Industry
Rest of the World/
Offshore Industry:
Traded Emissions
(See Ch. 5 and 14)
Use of Products
to Provide Services
Downstream
543b3a
2
1
Stock of
Products
8282
TS
Technical Summary
Figure TS.26 | Final energy demand reduction relative to baseline (left panel) and development of final low-carbon energy carrier share in final energy (including electricity, heat,
hydrogen, and bioenergy; right panel) in industry by 2030 and 2050 in mitigation scenarios from three different CO
2
eq concentration ranges shown in boxplots (see Section 6.3.2)
compared to sectoral studies shown in shapes assessed in Chapter 10. Filled circles correspond to sectoral studies with full sectoral coverage. [Figures 6.37 and 6.38]
Min
75
th
Percentile
Max
Median
25
th
Percentile
N= 126 189 126 189
2030 2050
100
80
60
40
20
0
Industry
Final Energy Demand Reduction Relative to Baseline [%]
N=
107 86 95 107 86 95
100
2030 2050
0
20
40
60
80
Industry
Low Carbon Energy Carrier Share in Final Energy [%]
Sectoral Studies (Base)
Sectoral Studies (Policy)
Historic Data 2010
Baselines
530−650 ppm CO
2
eq
430−530 ppm CO
2
eq
Sectoral Studies (Full)
ment). Barriers to implementing energy efficiency relate largely to
the initial investment costs and lack of information. Information pro-
grammes are a prevalent approach for promoting energy efficiency,
followed by economic instruments, regulatory approaches, and volun-
tary actions. [10.4, 10.7, 10.9, 10.11]
An absolute reduction in emissions from the industry sector will
require deployment of a broad set of mitigation options that
go beyond energy efficiency measures (medium evidence, high
agreement) [10.4, 10.7]. In the context of continued overall growth in
industrial demand, substantial reductions from the sector will require
parallel efforts to increase emissions efficiency (e. g., through fuel and
feedstock switching or CCS); material use efficiency (e. g., less scrap,
new product design); recycling and re-use of materials and products;
product-service efficiency (e. g., more intensive use of products through
car sharing, longer life for products); radical product innovations (e. g.,
alternatives to cement); as well as service demand reductions. Lack of
policy and experiences in material and product-service efficiency are
major barriers. (Table TS.3, Figure TS.25) [10.4, 10.7, 10.11]
While detailed industry sector studies tend to be more conser-
vative than integrated studies, both identify possible industrial
final energy demand savings of around 30 % by 2050 in mitiga-
tion scenarios not exceeding 650 ppm CO
2
eq by 2100 relative
to baseline scenarios (medium evidence, medium agreement) (Fig-
ure TS.26). Integrated models in general treat the industry sector in a
more aggregated fashion and mostly do not explicitly provide detailed
sub-sectoral material flows, options for reducing material demand,
and price-induced inter-input substitution possibilities. Due to the het-
erogeneous character of the industry sector, a coherent comparison
between sectoral and integrated studies remains difficult. [6.8.4, 10.4,
10.7, 10.10.1, Figure 10.14]
Mitigation in the industry sector can also be achieved by
reducing material and fossil fuel demand by enhanced waste
use, which concomitantly reduces direct GHG emissions from
waste disposal (robust evidence, high agreement). The hierarchy
of waste management places waste reduction at the top, followed
by re-use, recycling, and energy recovery. As the share of recycled or
reused material is still low, applying waste treatment technologies
and recovering energy to reduce demand for fossil fuels can result in
direct emission reductions from waste disposal. Globally, only about
20 % of municipal solid waste (MSW) is recycled and about 14 % is
treated with energy recovery while the rest is deposited in open dump-
sites or landfills. About 47 % of wastewater produced in the domestic
and manufacturing sectors is still untreated. The largest cost range is
for reducing GHG emissions from landfilling through the treatment
of waste by anaerobic digestion. The costs range from negative (see
Box TS.12) to very high. Advanced wastewater treatment technologies
may enhance GHG emissions reduction in wastewater treatment but
they are clustered among the higher cost options (medium evidence,
medium agreement). (Figure TS.29) [10.4, 10.14]
8383
Technical Summary
TS
Figure TS.27 | Indicative CO
2
emission intensities for cement (upper panel) and steel (lower panel) production, as well as indicative levelized cost of conserved carbon (LCCC)
shown for various production practices / technologies and for 450 ppm CO
2
eq scenarios of a limited selection of integrated models (for data and methodology, see Annex III). DRI:
Direct reduced iron; EAF: Electric arc furnace. [Figures 10.7, 10.8]
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
]
Scenarios Reaching 450 ppm CO
2
eq in 2100 in Integrated Models
Currently Commercially Available Technologies
Technologies in Pre-Commercial Stage
>15050-15020-500-20<0
0.0
0.10.20.30.40.50.60.7
0.8
Global Average (2010)
Emission Intensity [tCO
2
/t Cement]
Global Average, 2030
Global Average, 2050
Best Practice Energy Intensity
Best Practice Clinker Substitution
Improvements in Non-Electric Fuel Mix
Best Practice Energy Intensity and Clinker
Substitution Combined
Decarbonization of Electricity Supply
CCS
CCS and Fully Decarbonized Electricity
Supply Combined
Measure Affects Direct and Indirect Emissions
Measure Affects Indirect Emissions
Measure Affects Direct Emissions
Effect from Increased Use of Biomass as Non-Electric Fuel*
Data from Integrated Models
* Assuming for Simplicity that Biomass Burning is Carbon Neutral
Currently Commercially Available Technologies
Technologies in Pre-Commercial Stage
Scenarios Reaching 450 ppm CO
2
eq in 2100 in Integrated Models
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
]
Measure Affects Direct and Indirect EmissionsMeasure Affects Indirect EmissionsMeasure Affects Direct EmissionsData from Integrated Models
>15050-15020-500-20<00.01.01.52.02.5
0.5
Global Average (2010)
Emission Intensity [tCO
2
/t Crude Steel]
Global Average (2030)
Global Average (2050)
Advanced Blast Furnace Route
Natural Gas DRI Route
Scrap Based EAF
Decarbonization of
Electricity Supply
CCS
CCS and Fully Decarbonized
Electricity Supply Combined
Waste policy and regulation have largely influenced material
consumption, but few policies have specifically pursued mate-
rial efficiency or product-service efficiency (robust evidence, high
agreement) [10.11]. Barriers to improving material efficiency include
lack of human and institutional capacities to encourage management
decisions and public participation. Also, there is a lack of experience
and often there are no clear incentives either for suppliers or consum-
ers to address improvements in material or product-service efficiency,
or to reduce product demand. [10.9]
CO
2
emissions dominate GHG emissions from industry, but there
are also substantial mitigation opportunities for non-CO
2
gases
8484
TS
Technical Summary
Figure TS.28 | Indicative global CO
2
eq emissions for chemicals production (upper panel) and indicative global CO
2
emission intensities for paper production (lower panel) as well
as indicative levelized cost of conserved carbon (LCCC) shown for various production practices / technologies and for 450 ppm CO
2
eq scenarios of a limited selection of integrated
models (for data and methodology, see Annex III). [Figures 10.9, 10.10]
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
eq]
Measure Affects Direct and Indirect Emissions
Measure Affects Indirect Emissions
Measure Affects Direct Emissions
Effect from Increased Use of Biomass as Non-Electric Fuel*
Data from Integrated Models
* Assuming for Simplicity that Biomass Burning is Carbon Neutral
>15050-15020-500-20<0
Currently Commercially Available Technologies
Technologies in Pre-Commercial Stage
IEA ETP 2DS Scenario
Direct Emissions [GtCO
2
eq]Indirect Emissions
[GtCO
2
eq]
Global Average (2010)Global Average (2010)
0.00.51.01.52.00.00.5
Global Total (2030)
Global Total (2050)
Best Practice Energy Intensity
Enhanced Recycling, Cogeneration
and Process Intensification
Abatement of N
2
O from Nitric
and Adipic Acid
Abatement of HFC-23 Emissions
from HFC-22 Production
CCS for Ammonia Production
Improvements in Non-Electric Fuel Mix
Decarbonization of Electricity Supply
CCS Applied to Non-Electric
Fuel-Related Emissions
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
]
Measure Affects Direct and Indirect EmissionsMeasure Affects Indirect EmissionsMeasure Affects Direct EmissionsData from Integrated Models
>15050-15020-500-20<0
Currently Commercially Available Technologies
IEA ETP 2DS Scenario
Technologies in Pre-Commercial Stage
Global Average (2030)
Global Average (2050)
Best Practice Energy Intensity
Cogeneration
Decarbonization of Electricity Supply
CCS
0.00.10.20.30.40.50.60.00.10.20.30.40.50.6
Direct Emission Intensity [tCO
2
/t Paper]Indirect Emission Intensity [tCO
2
/t Paper]
Global Average (2010)Global Average (2010)
8585
Technical Summary
TS
(robust evidence, high agreement). Methane (CH
4
), nitrous oxide (N
2
O)
and fluorinated gases (F-gases) from industry accounted for emissions of
0.9 GtCO
2
eq in 2010. Key mitigation opportunities comprise, e. g., reduc-
tion of hydrofluorocarbon (HFC) emissions by leak repair, refrigerant
recovery and recycling, and proper disposal and replacement by alter-
native refrigerants (ammonia, HC, CO
2
). N
2
O emissions from adipic and
nitric acid production can be reduced through the implementation of
thermal destruction and secondary catalysts. The reduction of non-CO
2
GHGs also faces numerous barriers. Lack of awareness, lack of economic
incentives and lack of commercially available technologies (e. g., for HFC
recycling and incineration) are typical examples. [Table 10.2, 10.7]
Systemic approaches and collaborative activities across compa-
nies (large energy-intensive industries and Small and Medium
Enterprises (SMEs)) and sectors can help to reduce GHG emis-
sions (robust evidence, high agreement). Cross-cutting technologies
such as efficient motors, and cross-cutting measures such as reducing
air or steam leaks, help to optimize performance of industrial processes
and improve plant efficiency very often cost-effectively with both
energy savings and emissions benefits. Industrial clusters also help
to realize mitigation, particularly from SMEs. [10.4] Cooperation and
cross-sectoral collaboration at different levels — for example, sharing
of infrastructure, information, waste heat, cooling, etc. — may provide
further mitigation potential in certain regions / industry types [10.5].
Several emission-reducing options in the industrial sector are
cost-effective and profitable (medium evidence, medium agree-
ment). While options in cost ranges of 0 – 20 and 20 – 50 USD / tCO
2
eq
and even below 0 USD / tCO
2
eq exist, achieving near-zero emissions
intensity levels in the industry sector would require the additional real-
ization of long-term step-change options (e. g., CCS), which are asso-
ciated with higher levelized costs of conserved carbon (LCCC) in the
range of 50 – 150 USD / tCO
2
eq. Similar cost estimates for implement-
ing material efficiency, product-service efficiency, and service demand
reduction strategies are not available. With regard to long-term options,
some sector-specific measures allow for significant reductions in spe-
cific GHG emissions but may not be applicable at scale, e. g., scrap-
based iron and steel production. Decarbonized electricity can play an
important role in some subsectors (e. g., chemicals, pulp and paper,
and aluminium), but will have limited impact in others (e. g., cement,
iron and steel, waste). In general, mitigation costs vary regionally and
depend on site-specific conditions. (Figures TS.27, TS.28, TS.29) [10.7]
Mitigation measures are often associated with co-benefits (robust
evidence, high agreement). Co-benefits include enhanced competitive-
ness through cost-reductions, new business opportunities, better envi-
ronmental compliance, health benefits through better local air and water
quality and better work conditions, and reduced waste, all of which pro-
vide multiple indirect private and social benefits (Table TS.7). [10.8]
There is no single policy that can address the full range of miti-
gation measures available for industry and overcome associ-
ated barriers. Unless barriers to mitigation in industry are resolved,
the pace and extent of mitigation in industry will be limited and even
profitable measures will remain untapped (robust evidence, high
agreement). [10.9, 10.11]
Figure TS.29 | Indicative CO
2
eq emission intensities for waste (upper panel) and wastewater (lower panel) of various practices as well as indicative levelized cost of conserved
carbon (for data and methodology, see Annex III). MSW: Municipal solid waste. [Figures 10.19 and 10.20]
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
eq]
>15050-15020-500-20<0
Emission Intensity [tCO
2
eq/t MSW]
CH
4
Capture Plus Heat/Electricity Generation
CH
4
Flaring
In-Situ Aeration
Biocover
Anaerobic Digestion
Composting
Landfill at MSW Disposal Site
0.00.30.60.91.21.5
Indicative Cost of Conserved Carbon[USD
2010
/tCO
2
eq]
>15050-15020-500-20<0
0246810
Anaerobic Biomass Digester with
CH
4
Collection
Aerobic Wastewater Plant (WWTP)
Centralised Wastewater Collection
and WWTP
Untreated System: Stagnant Sewer
(Open and Warm)
Emission Intensity [tCO
2
eq/t BOD
5
]
8686
TS
Technical Summary
TS.3.2.6 Agriculture, Forestry and Other Land Use
(AFOLU)
Since AR4, GHG emissions from the AFOLU sector have sta-
bilized but the share of total anthropogenic GHG emissions
has decreased (robust evidence, high agreement). The average
annual total GHG flux from the AFOLU sector was 10 – 12 GtCO
2
eq in
2000 – 2010, with global emissions of 5.0 – 5.8 GtCO
2
eq / yr from agri-
culture on average and around 4.3 – 5.5 GtCO
2
eq / yr from forestry and
other land uses. Non-CO
2
emissions derive largely from agriculture,
dominated by N
2
O emissions from agricultural soils and CH
4
emissions
from livestock enteric fermentation, manure management, and emis-
sions from rice paddies, totalling 5.0 – 5.8 GtCO
2
eq / yr in 2010 (robust
evidence, high agreement). Over recent years, most estimates of FOLU
CO
2
fluxes indicate a decline in emissions, largely due to decreasing
deforestation rates and increased afforestation (limited evidence,
medium agreement). The absolute levels of emissions from deforesta-
tion and degradation have fallen from 1990 to 2010 (robust evidence,
high agreement). Over the same time period, total emissions for high-
income countries decreased while those of low-income countries
increased. In general, AFOLU emissions from high-income countries
are dominated by agriculture activities while those from low-income
countries are dominated by deforestation and degradation. [Figure
1.3, 11.2]
Net annual baseline CO
2
emissions from AFOLU are projected to
decline over time with net emissions potentially less than half of
the 2010 level by 2050, and the possibility of the AFOLU sector
becoming a net sink before the end of century. However, the uncer-
tainty in historical net AFOLU emissions is larger than for other sectors,
and additional uncertainties in projected baseline net AFOLU emissions
exist. (medium evidence, high agreement) (Figure TS.15) [6.3.1.4, 6.8,
Figure 6.5] As in AR4, most projections suggest declining annual net CO
2
emissions in the long run. In part, this is driven by technological change,
as well as projected declining rates of agriculture area expansion related
to the expected slowing in population growth. However, unlike AR4,
none of the more recent scenarios projects growth in the near-term.
There is also a somewhat larger range of variation later in the century,
with some models projecting a stronger net sink starting in 2050 (lim-
ited evidence, medium agreement). There are few reported projections
of baseline global land-related N
2
O and CH
4
emissions and they indicate
an increase over time. Cumulatively, land CH
4
emissions are projected to
be 44 – 53 % of total CH
4
emissions through 2030, and 41 – 59 % through
2100, and land N
2
O emissions 85 – 89 % and 85 – 90 %, respectively (lim-
ited evidence, medium agreement). [11.9]
Opportunities for mitigation in the AFOLU sector include sup-
ply- and demand-side mitigation options (robust evidence, high
agreement). Supply-side measures involve reducing emissions arising
Table TS.7 | Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the industry sector; arrows pointing
up / down denote a positive / negative effect on the respective objective or concern. Co-benefits and adverse side-effects depend on local circumstances as well as on the implemen-
tation practice, pace and scale. For possible upstream effects of low-carbon energy supply (includes CCS), see Table TS.4. For possible upstream effects of biomass supply, see Table
TS.8. For an assessment of macroeconomic, cross-sectoral, effects associated with mitigation policies (e. g., on energy prices, consumption, growth, and trade), see e. g., Sections 3.9,
6.3.6, 13.2.2.3 and 14.4.2. The uncertainty qualifiers in brackets denote the level of evidence and agreement on the respective effects (see TS.1). Abbreviations for evidence: l =
limited, m = medium, r = robust; for agreement: l = low, m = medium, h = high. [Table 10.5]
Industry
Effect on additional objectives / concerns
Economic Social Environmental
CO
2
and non-CO
2
GHG emissions
intensity reduction
↑
Competitiveness and productivity (m / h)
↓
Health impact via reduced local air
pollution and better work conditions (for
perfluorocarbons from aluminium) (m / m)
↓
↑
Ecosystem impact via reduced local air
pollution and reduced water pollution (m / m)
Water conservation (l / m)
Technical energy
efficiency improvements
via new processes
and technologies
↑
↑
↑
↑
Energy security (via lower
energy intensity) (m / m)
Employment impact (l / l)
Competitiveness and productivity (m / h)
Technological spillovers in developing
countries (due to supply chain linkages) (l / l)
↓
↑
↑
↑
Health impact via reduced
local pollution (l / m)
New business opportunities (m / m)
Water availability and quality (l / l)
Safety, working conditions and
job satisfaction (m / m)
↓
↓
Ecosystem impact via:
Fossil fuel extraction (l / l)
Local pollution and waste (m / m)
Material efficiency
of goods, recycling
↓
↑
↑
↑
National sales tax revenue
in medium term (l / l)
Employment impact in waste
recycling market (l / l)
Competitiveness in manufacturing (l / l)
New infrastructure for industrial clusters (l / l)
↓
↑
↓
Health impacts and safety concerns (l / m)
New business opportunities (m / m)
Local conflicts (reduced resource
extraction) (l / m)
↓
↓
Ecosystem impact via reduced local
air and water pollution and waste
material disposal (m / m)
Use of raw / virgin materials and
natural resources implying reduced
unsustainable resource mining (l / l)
Product demand
reductions
↓
National sales tax revenue
in medium term (l / l)
↑
Wellbeing via diverse lifestyle choices (l / l)
↓
Post-consumption waste (l / l)
8787
Technical Summary
TS
from land-use change, in particular reducing deforestation, and land
and livestock management, increasing carbon stocks by sequestration
in soils and biomass, or the substitution of fossil fuels by biomass for
energy production (Table TS.3). Further new supply-side technologies
not assessed in AR4, such as biochar or wood products for energy-
intensive building materials, could contribute to the mitigation poten-
tial of the AFOLU sector, but there are still few studies upon which to
make robust estimates. Demand-side measures include dietary change
and waste reduction in the food supply chain. Increasing forestry and
agricultural production without a commensurate increase in emissions
(i. e., one component of sustainable intensification; Figure TS.30) also
reduces emissions intensity (i. e., the GHG emissions per unit of prod-
uct), a mitigation mechanism largely unreported for AFOLU in AR4,
which could reduce absolute emissions as long as production volumes
do not increase. [11.3, 11.4]
Among supply-side measures, the most cost-effective forestry
options are afforestation, sustainable forest management and
reducing deforestation, with large differences in their relative
importance across regions; in agriculture, low carbon prices
16
(20 USD / tCO
2
eq) favour cropland and grazing land manage-
ment and high carbon prices (100 USD / tCO
2
eq) favour restora-
tion of organic soils (medium evidence, medium agreement). When
considering only studies that cover both forestry and agriculture and
include agricultural soil carbon sequestration, the economic mitiga-
tion potential in the AFOLU sector is estimated to be 7.18 to 10.6 (full
range of all studies: 0.49 – 10.6) GtCO
2
eq / yr in 2030 for mitigation
efforts consistent with carbon prices up to 100 USD / tCO
2
eq, about
a third of which can be achieved at <20 USD / tCO
2
eq (medium evi-
dence, medium agreement). The range of global estimates at a given
carbon price partly reflects uncertainty surrounding AFOLU mitigation
16
In many models that are used to assess the economic costs of mitigation, carbon
price is used as a proxy to represent the level of effort in mitigation policies (see
Glossary).
potentials in the literature and the land-use assumptions of the sce-
narios considered. The ranges of estimates also reflect differences in
the GHGs and options considered in the studies. A comparison of esti-
mates of economic mitigation potential in the AFOLU sector published
since AR4 is shown in Figure TS.31. [11.6]
While demand-side measures are under-researched, changes
in diet, reductions of losses in the food supply chain, and other
measures have a significant, but uncertain, potential to reduce
GHG emissions from food production (0.76 – 8.55 GtCO
2
eq / yr by
2050) (Figure TS.31) (limited evidence, medium agreement). Barriers to
implementation are substantial, and include concerns about jeopardizing
health and well-being, and cultural and societal resistance to behavioural
change. However, in countries with a high consumption of animal protein,
co-benefits are reflected in positive health impacts resulting from changes
in diet (robust evidence, high agreement). [11.4.3, 11.6, 11.7, 11.9]
The mitigation potential of AFOLU is highly dependent on
broader factors related to land-use policy and patterns (medium
evidence, high agreement). The many possible uses of land can com-
pete or work in synergy. The main barriers to mitigation are institu-
tional (lack of tenure and poor governance), accessibility to financ-
ing mechanisms, availability of land and water, and poverty. On the
other hand, AFOLU mitigation options can promote innovation, and
many technological supply-side mitigation options also increase agri-
cultural and silvicultural efficiency, and can reduce climate vulner-
ability by improving resilience. Multifunctional systems that allow the
delivery of multiple services from land have the capacity to deliver to
many policy goals in addition to mitigation, such as improving land
tenure, the governance of natural resources, and equity [11.8] (lim-
ited evidence, high agreement). Recent frameworks, such as those for
assessing environmental or ecosystem services, could provide tools for
valuing the multiple synergies and tradeoffs that may arise from miti-
gation actions (Table TS.8) (medium evidence, medium agreement).
[11.7, 11.8]
Figure TS.30 | GHG emissions intensities of selected major AFOLU commodities for decades 1960s – 2000s. (1) Cattle meat, defined as GHG (enteric fermentation + manure man-
agement of cattle, dairy and non-dairy) / meat produced; (2) pig meat, defined as GHG (enteric fermentation + manure management of swine, market and breeding) / meat produced;
(3) chicken meat, defined as GHG (manure management of chickens) / meat produced; (4) milk, defined as GHG (enteric fermentation + manure management of cattle, dairy) / milk
produced; (5) eggs, defined as GHG (manure management of chickens, layers) / egg produced; (6) rice, defined as GHG (rice cultivation) / rice produced; (7) cereals, defined as GHG
(synthetic fertilizers) / cereals produced; (8) wood, defined as GHG (carbon loss from harvest) / roundwood produced. [Figure 11.15]
0
1
2
3
4
5
6
7
8
GHG Emissions Intensities
[kgCO
2
eq/kg of Commodity;
kgCO
2
eq/m
3
Roundwood]
Cattle Meat
Pig Meat
Chicken
Eggs
Rice
Milk
Cereals
Roundwood
1960-1970 1970-1980 1980-1990 1990-2000 2000-2010
8888
TS
Technical Summary
Policies governing practices in agriculture as well as forest con-
servation and management need to account for the needs of
both mitigation and adaptation (medium evidence, high agree-
ment). Some mitigation options in the AFOLU sector (such as soil and
forest carbon stocks) may be vulnerable to climate change. Economic
incentives (e. g., special credit lines for low-carbon agriculture, sustain-
able agriculture and forestry practices, tradable credits, payment for
ecosystem services) and regulatory approaches (e. g., enforcement of
environmental law to protect forest carbon stocks by reducing defor-
estation, set-aside policies, air and water pollution control reducing
nitrate load and N
2
O emissions) have been effective in different cases.
Investments in research, development, and diffusion (e. g., increase of
resource use-efficiency (fertilizers), livestock improvement, better for-
estry management practices) could result in synergies between adap-
tation and mitigation. Successful cases of deforestation reduction in
different regions are found to combine different policies such as land
planning, regulatory approaches and economic incentives (limited evi-
dence, high agreement). [11.3.2, 11.10, 15.11]
Figure TS.31 | Estimates of economic mitigation potentials in the AFOLU sector published since AR4 (AR4 estimates shown for comparison, denoted by black arrows), includ-
ing bottom-up, sectoral studies, and top-down, multi-sector studies. Supply-side mitigation potentials are estimated for around 2030, ranging from 2025 to 2035, and are for
agriculture, forestry or both sectors combined. Studies are aggregated for potentials up to ~20 USD / tCO
2
eq (actual range 1.64 – 21.45), up to ~50 USD / tCO
2
eq (actual range
31.39 – 50.00), and up to ~100 USD / tCO
2
eq (actual range 70.0 – 120.91). Demand-side measures (shown on the right hand side of the figure) are for ~2050 and are not assessed
at a specific carbon price, and should be regarded as technical potentials. Smith etal. (2013) values are the mean of the range. Not all studies consider the same measures or the
same GHGs. [11.6.2, Figure 11.14]
Mitigation Potential [GtCO
2
eq/yr]
0
3
6
9
12
15
Smith et al. (2013) - Diet and All Measures
Smith et al. (2013) - Feed Improvement
Popp et al. (2011)
Stehfest et al. (2009) - High [No Animal Products]
Stehfest et al. (2009) - Low [Waste Reduction Only]
Kindermann et al. (2008)
Rose et al. (2012) - IMAGE 2.3 450 ppm
Sohngen (2009)
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
Smith et al. (2008)
IPCC AR4 (2007)
UNEP (2011)
McKinsey & Co (2009)
Kindermann et al. (2008)
Sohngen (2009)
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
Smith et al. (2008)
IPCC AR4 (2007)
Rose et al. (2012) - IMAGE 2.3 550 ppm
Rose et al. (2012) - GTEM EMF-21 4.5 W/m
2
Rose et al. (2012) - MESSAGE EMF-21 3.0 W/m
2
Kindermann et al. (2008)
Rose et al. (2012) - IMAGE 2.3 650 ppm
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
UNEP (2011)
Smith et al. (2008)
IPCC AR4 (2007)
Rose et al. (2012) - IMAGE 2.2 EMF-21 4.5 W/m
2
Rose et al. (2012) - MESSAGE A2r-21 4.5W/m
2
Rose et al. (2012) - MESSAGE EMF-21 4.5 W/m
2
Rose et al. (2012) - GRAPE EMF-21 4.5 W/m
2
Forestry
Agriculture
Up to 20 USD/tCO
2
eq Up to 50 USD/tCO
2
eq Up to 100 USD/tCO
2
eq Demand-Side
Measures -
Technical
Potentials
8989
Technical Summary
TS
Reducing Emissions from Deforestation and Forest Degradation
(REDD+)
17
can be a very cost-effective policy option for mitigat-
ing climate change, if implemented in a sustainable manner (lim-
ited evidence, medium agreement). REDD+ includes: reducing emissions
from deforestation and forest degradation; conservation of forest carbon
stocks; sustainable management of forests; and enhancement of forest
carbon stocks. It could supply a large share of global abatement of emis-
sions from the AFOLU sector, especially through reducing deforestation
in tropical regions, with potential economic, social and other environ-
mental co-benefits. To assure these co-benefits, the implementation of
national REDD+ strategies would need to consider financing mecha-
nisms to local stakeholders, safeguards (such as land rights, conserva-
tion of biodiversity and other natural resources), and the appropriate
scale and institutional capacity for monitoring and verification. [11.10]
Bioenergy can play a critical role for mitigation, but there are
issues to consider, such as the sustainability of practices and
the efficiency of bioenergy systems (robust evidence, medium
17
UN Programme on Reducing Emissions from Deforestation and Forest Degradation
in developing countries, including conservation, sustainable management of forests
and enhancement of forest carbon stocks.
agreement) [11.4.4, Box 11.5, 11.13.6, 11.13.7]. Barriers to large-
scale deployment of bioenergy include concerns about GHG emis-
sions from land, food security, water resources, biodiversity conserva-
tion and livelihoods. The scientific debate about the overall climate
impact related to land-use competition effects of specific bioenergy
pathways remains unresolved (robust evidence, high agreement).
[11.4.4, 11.13] Bioenergy technologies are diverse and span a wide
range of options and technology pathways. Evidence suggests that
options with low lifecycle emissions (e. g., sugar cane, Miscanthus,
fast growing tree species, and sustainable use of biomass residues),
some already available, can reduce GHG emissions; outcomes are
site-specific and rely on efficient integrated ‘biomass-to-bioenergy
systems’, and sustainable land-use management and governance.
In some regions, specific bioenergy options, such as improved cook-
stoves, and small-scale biogas and biopower production, could
reduce GHG emissions and improve livelihoods and health in the con-
text of sustainable development (medium evidence, medium agree-
ment). [11.13]
Table TS.8 | Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the AFOLU sector; arrows pointing
up / down denote a positive / negative effect on the respective objective or concern. These effects depend on the specific context (including bio-physic, institutional and socio-
economic aspects) as well as on the scale of implementation. For an assessment of macroeconomic, cross-sectoral effects associated with mitigation policies (e. g., on energy prices,
consumption, growth, and trade), see e. g., Sections 3.9, 6.3.6, 13.2.2.3 and 14.4.2. The uncertainty qualifiers in brackets denote the level of evidence and agreement on the
respective effects (see TS.1). Abbreviations for evidence: l = limited, m = medium, r = robust; for agreement: l = low, m = medium, h = high. [Tables 11.9 and 11.12]
AFOLU
Effect on additional objectives / concerns
Economic Social Environmental Institutional
Supply side:
Forestry, land-
based agriculture,
livestock,
integrated
systems, and
bioenergy
(marked by *)
Demand side:
Reduced losses
in the food
supply chain,
changes in human
diets, changes
in demand
for wood and
forestry products
*
↑
↓
↑*
↑*
↑*
↑*
↑
↑
Employment impact via
Entrepreneurship
development (m / h)
Use of less labour-
intensive technologies
in agriculture (m / m)
Diversification of income
sources and access
to markets (r / h)
Additional income to
(sustainable) landscape
management (m / h)
Income concentration (m / m)
Energy security (resource
sufficiency) (m / h)
Innovative financing
mechanisms for sustainable
resource management (m / h)
Technology innovation
and transfer (m / m)
↑*
↓*
↑
↑*
↓*
*
↑
↑
Food-crops production through
integrated systems and sustainable
agriculture intensification (r / m)
Food production (locally) due
to large-scale monocultures
of non-food crops (r / l)
Cultural habitats and recreational
areas via (sustainable) forest
management and conservation (m / m)
Human health and animal welfare e. g.,
through less pesticides, reduced burning
practices, and practices like agroforestry
and silvo-pastoral systems (m / h)
Human health when using
burning practices (in agriculture
or bioenergy) (m / m)
Gender, intra- and inter-
generational equity via
Participation and fair
benefit sharing (r / h)
Concentration of benefits (m / m)
↑
↓*
↑*
↑
↓
↑
↑
Provision of ecosystem
services via
Ecosystem
conservation and
sustainable
management as well
as sustainable
agriculture (r / h)
Large scale
monocultures (r / h)
Land-use competition (r / m)
Soil quality (r / h)
Erosion (r / h)
Ecosystem resilience (m / h)
Albedo and
evaporation (r / h)
↑ ↓*
↑ ↓
↑
Tenure and use rights
at the local level (for
indigenous people and
local communities)
especially when
implementing activities
in natural forests (r / h)
Access to participative
mechanisms for land
management decisions (r / h)
Enforcement of existing
policies for sustainable
resource management (r / h)
9090
TS
Technical Summary
TS.3.2.7 Human settlements, infrastructure, and spatial
planning
Urbanization is a global trend transforming human settlements,
societies, and energy use (robust evidence, high agreement). In
1900, when the global population was 1.6 billion, only 13 % of the
population, or some 200 million, lived in urban areas. As of 2011, more
than 52 % of the world’s population — roughly 3.6 billion — lives in
urban areas. By 2050, the urban population is expected to increase to
5.6 – 7.1 billion, or 64 – 69 % of the world population. [12.2]
Urban areas account for more than half of global primary energy
use and energy-related CO
2
emissions (medium evidence, high
agreement). The exact share of urban energy and GHG emissions varies
with emission accounting frameworks and definitions. Taking account
of direct and indirect emissions, urban areas account for 67 – 76 % of
global energy use (central estimate) and 71 – 76 % of global energy-
related CO
2
emissions. Taking account of direct emissions only, the
urban share of emissions is 44 % (Figure TS.32). [12.2, 12.3]
No single factor explains variations in per-capita emissions
across cities, and there are significant differences in per capita
GHG emissions between cities within a single country (robust
evidence, high agreement). Urban GHG emissions are influenced by a
variety of physical, economic and social factors, development levels,
and urbanization histories specific to each city. Key influences on urban
GHG emissions include income, population dynamics, urban form, loca-
tional factors, economic structure, and market failures. Per capita final
energy use and CO
2
emissions in cities of Annex I countries tend to be
lower than national averages, in cities of non-Annex I countries they
tend to be higher. [12.3]
The majority of infrastructure and urban areas have yet to be
built (limited evidence, high agreement). Accounting for trends in
declining population densities, and continued economic and popula-
tion growth, urban land cover is projected to expand by 56 – 310 %
between 2000 and 2030. If the global population increases to 9.3 bil-
lion by 2050 and developing countries expand their built environment
and infrastructure to current global average levels using available
technology of today, the production of infrastructure materials alone
would generate about 470 GtCO
2
emissions. Currently, average per
capita CO
2
emissions embodied in the infrastructure of industrialized
countries is five times larger than those in developing countries. [12.2,
12.3]
Infrastructure and urban form are strongly interlinked, and
lock in patterns of land use, transport choice, housing, and
behaviour (medium evidence, high agreement). Urban form and
infrastructure shape long-term land-use management, influence
individual transport choice, housing, and behaviour, and affect the
system-wide efficiency of a city. Once in place, urban form and
infrastructure are difficult to change (Figure TS.33). [12.2, 12.3,
12.4]
Mitigation options in urban areas vary by urbanization trajecto-
ries and are expected to be most effective when policy instru-
ments are bundled (robust evidence, high agreement). For rapidly
developing cities, options include shaping their urbanization and
infrastructure development towards more sustainable and low-carbon
pathways. In mature or established cities, options are constrained by
existing urban forms and infrastructure and the potential for refur-
bishing existing systems and infrastructures. Key mitigation strategies
include co-locating high residential with high employment densities,
Figure TS.32 | Estimated shares of direct (Scope 1) and indirect urban CO
2
emissions in
total emissions across world regions (GtCO
2
). Indirect emissions (Scope 2) allocate emis-
sions from thermal power plants to urban areas. CPA: Centrally Planned Asia and China;
EEU: Central and Eastern Europe; FSU: Former Soviet Union; LAM: Latin America and
Caribbean; MNA: Middle East and North Africa; NAM: North America; PAS: South-East
Asia and Pacific; POECD: Pacific OECD; SAS: South Asia; SSA: Sub Saharan Africa; WEU:
Western Europe. [12.2.2, Figure 12.4]
0 20 40 60 80 100
Urban CO
2
Emission Share by Region [%]
Total
CPA
SSA
EEU
FSU
LAM
MNA
NAM
POECD
PAS
SAS
WEU
Marcotullio et al., 2013 (Scope 1+2)
Marcotullio et al., 2013 (Scope 1)
Grübler et al., 2012
9191
Technical Summary
TS
achieving high diversity and integration of land uses, increasing acces-
sibility and investing in public transit and other supportive demand-
management measures (Figure TS.33). Bundling these strategies can
reduce emissions in the short term and generate even higher emissions
savings in the long term. [12.4, 12.5]
The largest opportunities for future urban GHG emissions
reduction might be in rapidly urbanizing countries where urban
form and infrastructure are not locked-in but where there are
often limited governance, technical, financial, and institutional
capacities (robust evidence, high agreement). The bulk of future
infrastructure and urban growth is expected in small- to medium-size
cities in developing countries, where these capacities can be limited or
weak. [12.4, 12.5, 12.6, 12.7]
Thousands of cities are undertaking climate action plans, but
their aggregate impact on urban emissions is uncertain (robust
evidence, high agreement). Local governments and institutions pos-
sess unique opportunities to engage in urban mitigation activities and
local mitigation efforts have expanded rapidly. However, little system-
atic assessment exists regarding the overall extent to which cities are
implementing mitigation policies and emissions reduction targets are
being achieved, or emissions reduced. Climate action plans include a
range of measures across sectors, largely focused on energy efficiency
rather than broader land-use planning strategies and cross-sectoral
measures to reduce sprawl and promote transit-oriented development
(Figure TS.34). [12.6, 12.7, 12.9]
The feasibility of spatial planning instruments for climate
change mitigation is highly dependent on a city’s financial and
governance capability (robust evidence, high agreement). Drivers
of urban GHG emissions are interrelated and can be addressed by a
number of regulatory, management, and market-based instruments.
Many of these instruments are applicable to cities in both developed
and developing countries, but the degree to which they can be imple-
mented varies. In addition, each instrument varies in its potential to
generate public revenues or require government expenditures, and the
administrative scale at which it can be applied (Figure TS.35). A bun-
Figure TS.33 | Four key aspects of urban form and structure (density, land-use mix, connectivity, and accessibility), their vehicle kilometers travelled (VKT) elasticities, commonly
used metrics, and stylized graphics. The dark blue row segments under the VKT elasticities column provide the range of elasticities for the studies included. CBD: Central business
district. [Figure 12.14]
Low CarbonHigh Carbon
Density
Land Use
Connectivity
Accessibility
Metrics to Measure
Co-Variance
With Density
RangesVKT Elasticities
- Household / Population
- Building /Floor-Area Ratio
- Job / Commercial
- Block / Parcel
- Dwelling Unit
- Population Centrality
- Distance to CBD
- Job Accessibility by Auto
and/or Transit
- Accessibility to Shopping
- Intersection Density
- Proportion of Quadrilateral
Blocks
- Sidewalk Dimension
- Street Density
- Land Use Mix
- Job Mix
- Job-Housing Balance
- Job-Population Balance
- Retail Store Count
- Walk Opportunities
–
1.00
0.16
0.39
Population and Job
Residential
Household
Job
Population
Regional Accessibility
Distance to CBD
Job Access by Auto
Job Access by Transit
Road-Induced Access (Short-Run)
Road-Induced Access (Long-Run)
Combined Design Metrics
Intersection Density
Diversity and Entropy Index
Land Use Mix
-0.4 -0.2 0.0 0.2
0.4 0.6 0.8 1.0
Road-Induced Access (short-run)
Road-Induced Access (long-run)
9292
TS
Technical Summary
dling of instruments and a high level of coordination across institu-
tions can increase the likelihood of achieving emissions reductions and
avoiding unintended outcomes. [12.6, 12.7]
For designing and implementing climate policies effectively,
institutional arrangements, governance mechanisms, and
financial resources should be aligned with the goals of reduc-
ing urban GHG emissions (high confidence). These goals will reflect
the specific challenges facing individual cities and local governments.
The following have been identified as key factors: (1) institutional
arrangements that facilitate the integration of mitigation with other
high-priority urban agendas; (2) a multilevel governance context that
empowers cities to promote urban transformations; (3) spatial plan-
ning competencies and political will to support integrated land-use
and transportation planning; and (4) sufficient financial flows and
incentives to adequately support mitigation strategies. [12.6, 12.7]
Successful implementation of urban climate change mitigation
strategies can provide co-benefits (robust evidence, high agree-
ment). Urban areas throughout the world continue to struggle with
challenges, including ensuring access to energy, limiting air and water
pollution, and maintaining employment opportunities and competi-
tiveness. Action on urban-scale mitigation often depends on the ability
to relate climate change mitigation efforts to local co-benefits. The co-
benefits of local climate change mitigation can include public savings,
air quality and associated health benefits, and productivity increases in
urban centres, providing additional motivation for undertaking mitiga-
tion activities. [12.5, 12.6, 12.7, 12.8]
Figure TS.34 | Common mitigation measures in Climate Action Plans. [Figure 12.22]
Efficiency / Retrofit Measures
Building Codes
On-Site Renewables
Building Performance Rating
Financing Mechanisms for Retrofit
Fuel Switching
Non-Motorized Transport
Accessibility to Public Transit
Vehicle Fuel Economy
Transport Demand Management
Bus / Light Rail Fuel Economy
Improve Bus Transit Times
Freight System Efficiency
Greenspace and/or Bio-Diversity
Compact Cities
Urban Agriculture
Brownfield Redevelopment
Transit-Oriented Development
Eco-District Development Strategy
Limiting Urban Sprawl
Building Energy Demand
Cities Undertaking this Mitigation Activity [%]
Transport
Waste
Energy Supply
Urban Land Use
Education
Water
Outdoor Lighting
0 100
908070605040302010
11
47
11
44
9
34
3
34
3
33
8
15
13
2
5
12
Absolute Number of Cities
Non-Annex I
Annex I
9393
Technical Summary
TS
Figure TS.35 | Key spatial planning tools and effects on government revenues and expenditures across administrative scales. Figure shows four key spatial planning tools (coded in
colours) and the scale of governance at which they are administered (x-axis) as well as how much public revenue or expenditure the government generates by implementing each
instrument (y-axis). [Figure 12.20]
Project
Country
District
City
Metropolis
Neutral
Revenue
Expenditures
Government Revenue Minus Expenditure
Government Scale
Public Transit Investment and Station Improvement
Zoning Change
Public Land Leasing/Sale (Land Bank)
Cordon Pricing
Property Tax
Air Right Sale/Tradable Development Rights
Parking Restriction
Sidewalk, Bikeway and Amenity Improvement
Public Housing Provision and Affordable Housing Program
Special Economic Zone
Business Improvement District
Land Acquisition & Assemblage Eminent Domain
Impact Fee and Connection Fee
Tax Increment Financing
Growth Boundary
Green Belt
Land Policy
Regulation
Investment
Taxation/Charge
Betterment Levy
Utility, IT & Access Road Improvement
Toll Lane
Public Campaign and Social Education Design Guideline
Local Feeder Service
Urban Green Preservation/Restoration
Tool Categories
TS.4 Mitigation policies
and institutions
The previous section shows that since AR4 the scholarship on mitiga-
tion pathways has begun to consider in much more detail how a variety
of real-world considerations — such as institutional and political con-
straints, uncertainty associated with climate change risks, the availabil-
ity of technologies and other factors — affect the kinds of policies and
measures that are adopted. Those factors have important implications
for the design, cost, and effectiveness of mitigation action. This sec-
tion focuses on how governments and other actors in the private and
public sectors design, implement, and evaluate mitigation policies. It
considers the ‘normative’ scientific research on how policies should
be designed to meet particular criteria. It also considers research on
how policies are actually designed and implemented a field known as
‘positive’ analysis. The discussion first characterizes fundamental con-
ceptual issues, and then presents a summary of the main findings from
WGIII AR5 on local, national, and sectoral policies. Much of the practical
policy effort since AR4 has occurred in these contexts. From there the
summary looks at ever-higher levels of aggregation, ultimately ending
at the global level and cross-cutting investment and finance issues.
9494
TS
Technical Summary
TS.4.1 Policy design, behaviour and political
economy
There are multiple criteria for evaluating policies. Policies are fre-
quently assessed according to four criteria [3.7.1, 13.2.2, 15.4.1]:
• Environmental effectiveness — whether policies achieve intended
goals in reducing emissions or other pressures on the environment
or in improving measured environmental quality.
• Economic effectiveness — the impact of policies on the overall
economy. This criterion includes the concept of economic effi-
ciency, the principle of maximizing net economic benefits. Eco-
nomic welfare also includes the concept of cost-effectiveness, the
principle of attaining a given level of environmental performance
at lowest aggregate cost.
• Distributional and social impacts — also known as ‘distributional
equity,’ this criterion concerns the allocation of costs and benefits
of policies to different groups and sectors within and across econo-
mies over time. It includes, often, a special focus on impacts on the
least well-off members of societies within countries and around
the world.
• Institutional and political feasibility — whether policies can be
implemented in light of available institutional capacity, the politi-
cal constraints that governments face, and other factors that are
essential to making a policy viable.
All criteria can be applied with regard to the immediate ‘static’ impacts
of policies and from a long-run ‘dynamic’ perspective that accounts for
the many adjustments in the economic, social and political systems.
Criteria may be mutually reinforcing, but there may also be conflicts
or tradeoffs among them. Policies designed for maximum environmen-
tal effectiveness or economic performance may fare less well on other
criteria, for example. Such tradeoffs arise at multiple levels of govern-
ing systems. For example, it may be necessary to design international
agreements with flexibility so that it is feasible for a large number of
diverse countries to accept them, but excessive flexibility may under-
mine incentives to invest in cost-effective long-term solutions.
Policymakers make use of many different policy instruments
at the same time. Theory can provide some guidance on the norma-
tive advantages and disadvantages of alternative policy instruments
in light of the criteria discussed above. The range of different policy
instruments includes [3.8, 15.3]:
• Economic incentives, such as taxes, tradable allowances, fines, and
subsidies
• Direct regulatory approaches, such as technology or performance
standards
• Information programmes, such as labelling and energy audits
• Government provision, for example of new technologies or in state
enterprises
• Voluntary actions, initiated by governments, firms, and non-gov-
ernmental organizations (NGOs)
Since AR4, the inventory of research on these different instruments
has grown, mostly with reference to experiences with policies adopted
within particular sectors and countries as well as the many interactions
between policies. One implication of that research has been that inter-
national agreements that aim to coordinate across countries reflect the
practicalities on the particular policy choices of national governments
and other jurisdictions.
The diversity in policy goals and instruments highlights dif-
ferences in how sectors and countries are organized eco-
nomically and politically as well as the multi-level nature of
mitigation. Since AR4, one theme of research in this area has been
that the success of mitigation measures depends in part on the pres-
ence of institutions capable of designing and implementing regu-
latory policies and the willingness of respective publics to accept
these policies. Many policies have effects, sometimes unanticipated,
across multiple jurisdictions — across cities, regions and coun-
tries — because the economic effects of policies and the technologi-
cal options are not contained within a single jurisdiction. [13.2.2.3,
14.1.3, 15.2, 15.9]
Interactions between policy instruments can be welfare-enhanc-
ing or welfare-degrading. The chances of welfare-enhancing inter-
actions are particularly high when policy instruments address multiple
different market failures — for example, a subsidy or other policy instru-
ment aimed at boosting investment in R&D on less emission-intensive
technologies can complement policies aimed at controlling emissions,
as can regulatory intervention to support efficient improvement of end-
use energy efficiency. By contrast, welfare-degrading interactions are
particularly likely when policies are designed to achieve identical goals.
Narrowly targeted policies such as support for deployment (rather
than R&D) of particular energy technologies that exist in tandem with
broader economy-wide policies aimed at reducing emissions (for exam-
ple, a cap-and-trade emissions scheme) can have the effect of shifting
the mitigation effort to particular sectors of the economy in ways that
typically result in higher overall costs. [3.8.6, 15.7, 15.8]
There are a growing number of countries devising policies for
adaptation, as well as mitigation, and there may be benefits
to considering the two within a common policy framework
(medium evidence, low agreement). However, there are divergent
views on whether adding adaptation to mitigation measures in the
policy portfolio encourages or discourages participation in interna-
tional cooperation [1.4.5, 13.3.3]. It is recognized that an integrated
approach can be valuable, as there exist both synergies and tradeoffs
[16.6].
Traditionally, policy design, implementation, and evaluation has
focused on governments as central designers and implementers
of policies, but new studies have emerged on government act-
ing in a coordinating role (medium confidence). In these cases, gov-
ernments themselves seek to advance voluntary approaches, especially
when traditional forms of regulation are thought to be inadequate or
9595
Technical Summary
TS
the best choices of policy instruments and goals is not yet apparent.
Examples include voluntary schemes that allow individuals and firms
to purchase emission credits that offset the emissions associated with
their own activities such as flying and driving. Since AR4, a substantial
new literature has emerged to examine these schemes from positive
and normative perspectives. [13.12, 15.5.7]
The successful implementation of policy depends on many fac-
tors associated with human and institutional behaviour (very
high confidence). One of the challenges in designing effective instru-
ments is that the activities that a policy is intended to affect — such as
the choice of energy technologies and carriers and a wide array of agri-
cultural and forestry practices — are also influenced by social norms,
decision-making rules, behavioural biases, and institutional processes
[2.4, 3.10]. There are examples of policy instruments made more effec-
tive by taking these factors into account, such as in the case of financ-
ing mechanisms for household investments in energy efficiency and
renewable energy that eliminate the need for up-front investment [2.4,
2.6.5.3]. Additionally, the norms that guide acceptable practices could
have profound impacts on the baselines against which policy interven-
tions are evaluated, either magnifying or reducing the required level of
policy intervention [1.2.4, 4.3, 6.5.2].
Climate policy can encourage investment that may otherwise
be suboptimal because of market imperfections (very high con-
fidence). Many of the options for energy efficiency as well as low-
carbon energy provision require high up-front investment that is often
magnified by high-risk premiums associated with investments in new
technologies. The relevant risks include those associated with future
market conditions, regulatory actions, public acceptance, and technol-
ogy cost and performance. Dedicated financial instruments exist to
lower these risks for private actors — for example, credit insurance,
feed-in tariffs (FITs), concessional finance, or rebates [16.4]. The design
of other mitigation policies can also incorporate elements to help
reduce risks, such as a cap-and-trade regime that includes price floors
and ceilings [2.6.5, 15.5, 15.6].
TS.4.2 Sectoral and national policies
There has been a considerable increase in national and sub-
national mitigation plans and strategies since AR4 (Figure TS.36).
These plans and strategies are in their early stages of development
and implementation in many countries, making it difficult to assess
whether and how they will result in appropriate institutional and
policy change, and therefore, their impact on future GHG emissions.
However, to date these policies, taken together, have not yet achieved
a substantial deviation in GHG emissions from the past trend. Theories
of institutional change suggest they might play a role in shaping incen-
tives, political contexts, and policy paradigms in a way that encourages
Figure TS.36 | National climate legislation and strategies in 2007 and 2012. Regions include NAI (Non Annex I countries — developing countries), AI (Annex I countries — devel-
oped countries), LAM (Latin America), MAF (Middle East and Africa), ASIA (Asia), EIT (Economies in Transition), OECD-1990; see Annex II.2 for more details. In this figure, climate
legislation is defined as mitigation-focused legislation that goes beyond sectoral action alone. Climate strategy is defined as a non-legislative plan or framework aimed at mitigation
that encompasses more than a small number of sectors, and that includes a coordinating body charged with implementation. International pledges are not included, nor are sub-
national plans and strategies. The panel shows proportion of GHG emissions covered. [Figure 15.1]
4
3
2
4
3
2
4
3
2
1 11
3 (16%)
9 (46%)
0 (1%)
15 (50%)
14 (46%)
1 (4%)
0 (0%)
12 (61%)
0 (1%)
8 (28%)
6 (19%)
1 (4%)
7 (38%)0 (0%) 7 (38%)14 (49%)
11 (23%)
15 (30%)
1 (3%)
22 (44%)
15 (30%)
25 (52%)
1 (3%)
7 (15%)
0 (3%)
7 (53%)
0 (0%)
1 (28%)
3 (70%)
0 (0%)
1 (13%)
1 (23%)
0 (0%)
1 (8%)
5 (76%)
1 (16%)
1 (19%)
4 (65%)
1 (16%)
13 (69%)
6 (30%)
0 (1%)
6 (34%)
1 (3%)
0 (1%)
0 (0%)
4 (76%)
0 (3%)
3 (50%)
1 (26%)
0 (3%)
0 (0%)
8 (55%)
0 (0%)
6 (44%)0 (2%) 2 (63%) 0 (0%) 0 (0%) 0 (0%) 12 (62%) 1 (20%) 1 (20%) 6 (44%)
2007 2012
2007 20122007 20122007 20122007 20122007 20122007 20122007 2012
0
00
5
10
15
20
4: Analysis Incomplete
3: No Climate Legislation
or Strategy/Coordinating Body
2: Climate Strategy and
Coordinating Body
1: Climate Legislation
5
10
15
20
25
30
20
40
60
80
100
GHG Emissions Covered [%]
GHG Emissions [GtCO
2
eq]
GHG Emissions [GtCO
2
eq]
OECD-1990EITASIAMAFAIGLOBAL NAI LAM
9696
TS
Technical Summary
GHG emissions reductions in the future [15.1, 15.2]. However, many
baseline scenarios (i. e., those without additional mitigation policies)
show concentrations that exceed 1000 ppm CO
2
eq by 2100, which is
far from a concentration with a likely probability of maintaining tem-
perature increases below 2 °C this century. Mitigation scenarios sug-
gest that a wide range of environmentally effective policies could be
enacted that would be consistent with such goals [6.3]. In practice,
climate strategies and the policies that result are influenced by politi-
cal economy factors, sectoral considerations, and the potential for real-
izing co-benefits. In many countries, mitigation policies have also been
actively pursued at state and local levels. [15.2, 15.5, 15.8]
Since AR4, there is growing political and analytical attention to
co-benefits and adverse side-effects of climate policy on other
objectives and vice versa that has resulted in an increased focus
on policies designed to integrate multiple objectives (high confi-
dence). Co-benefits are often explicitly referenced in climate and sectoral
plans and strategies and often enable enhanced political support [15.2].
However, the analytical and empirical underpinnings for many of these
interactive effects, and particularly for the associated welfare impacts,
are under-developed [1.2, 3.6.3, 4.2, 4.8, 6.6]. The scope for co-benefits
is greater in low-income countries, where complementary policies for
other objectives, such as air quality, are often weak [5.7, 6.6, 15.2].
The design of institutions affects the choice and feasibility of
policy options as well as the sustainable financing of mitigation
measures. Institutions designed to encourage participation by repre-
sentatives of new industries and technologies can facilitate transitions
to low-GHG emissions pathways [15.2, 15.6]. Policies vary in the extent
to which they require new institutional capabilities to be implemented.
Carbon taxation, in most settings, can rely mainly on existing tax infra-
structure and is administratively easier to implement than many other
alternatives such as cap-and-trade systems [15.5]. The extent of insti-
tutional innovation required for policies can be a factor in instrument
choice, especially in developing countries.
Sector-specific policies have been more widely used than econ-
omy-wide, market-based policies (medium evidence, high agree-
ment). Although economic theory suggests that market-based, economy-
wide policies for the singular objective of mitigation would generally
be more cost-effective than sector-specific policies, political economy
considerations often make economy-wide policies harder to design and
implement than sector-specific policies [15.2.3, 15.2.6, 15.5.1]. In some
countries, emission trading and taxes have been enacted to address the
market externalities associated with GHG emissions, and have contrib-
uted to the fulfilment of sector-specific GHG reduction goals (medium
evidence, medium agreement) [7.12]. In the longer term, GHG pricing
can support the adoption of low-GHG energy technologies. Even if
economy-wide policies were implemented, sector-specific policies may
be needed to overcome sectoral market failures. For example, building
codes can require energy-efficient investments where private invest-
ments would otherwise not exist [9.10]. In transport, pricing policies
that raise the cost of carbon-intensive forms of private transport are
more effective when backed by public investment in viable alternatives
[8.10]. Table TS.9 presents a range of sector-specific policies that have
been implemented in practice. [15.1, 15.2, 15.5, 15.8, 15.9]
Carbon taxes have been implemented in some countries
and — alongside technology and other policies — have contrib-
uted to decoupling of emissions from GDP (high confidence). Dif-
ferentiation by sector, which is quite common, reduces cost-effective-
ness that arises from the changes in production methods, consumption
patterns, lifestyle shifts, and technology development, but it may
increase political feasibility, or be preferred for reasons of competitive-
ness or distributional equity. In some countries, high carbon and fuel
taxes have been made politically feasible by refunding revenues or by
lowering other taxes in an environmental fiscal reform. Mitigation poli-
cies that raise government revenue (e. g., auctioned emission allow-
ances under a cap-and-trade system or emission taxes) generally have
lower social costs than approaches that do not, but this depends on
how the revenue is used [3.6.3]. [15.2, 15.5.2, 15.5.3]
Fuel taxes are an example of a sector-specific policy and are
often originally put in place for objectives such as reve-
nue — they are not necessarily designed for the purpose of miti-
gation (high confidence). In Europe, where fuel taxes are highest, they
have contributed to reductions in carbon emissions from the trans-
port sector of roughly 50 % for this group of countries. The short-run
response to higher fuel prices is often small, but long-run price elas-
ticities are quite high, or roughly – 0.6 to – 0.8. This means that in the
long run, 10 % higher fuel prices correlate with 7 % reduction in fuel
use and emissions. In the transport sector, taxes have the advantage of
being progressive or neutral in most countries and strongly progressive
in low-income countries. [15.5.2]
Cap-and-trade systems for GHG emissions are being established
in a growing number of countries and regions. Their environmen-
tal effect has so far been limited because caps have either been loose
or have not yet been binding (limited evidence, medium agreement).
There appears to have been a tradeoff between the political feasibil-
ity and environmental effectiveness of these programmes, as well as
between political feasibility and distributional equity in the allocation
of permits. Greater environmental effectiveness through a tighter cap
may be combined with a price ceiling that improves political feasibility.
[14.4.2, 15.5.3]
Different factors reduced the price of European Union Emissions
Trading System (EU ETS) allowances below anticipated levels,
thereby slowing investment in mitigation (high confidence). While
the European Union demonstrated that a cross-border cap-and-trade
system can work, the low price of EU ETS allowances in recent years
provided insufficient incentives for significant additional investment in
mitigation. The low price is related to unexpected depth and duration of
the economic recession, uncertainty about the long-term reduction tar-
gets for GHG emissions, import of credits from the Clean Development
Mechanism (CDM), and the interaction with other policy instruments,
9797
Technical Summary
TS
Table TS.9 | Sector policy instruments. The table brings together evidence on mitigation policy instruments discussed in Chapters 7 to 12. [Table 15.2]
Policy Instruments Energy [7.12] Transport [8.10] Buildings [9.10] Industry [10.11] AFOLU [11.10]
Human Settlements
and Infrastructure
Economic Instru-
ments — Taxes
(Carbon taxes may
be economy-wide)
•Carbon taxes •Fuel taxes
•Congestion charges,
vehicle registration
fees, road tolls
•Vehicle taxes
•Carbon and / or energy
taxes (either sectoral
or economy wide)
•Carbon tax or
energy tax
•Waste disposal
taxes or charges
•Fertilizer or Nitrogen
taxes to reduce
nitrous oxide
•Sprawl taxes, Impact
fees, exactions, split-
rate property taxes,
tax increment finance,
betterment taxes,
congestion charges
Economic Instru-
ments — Tradable
Allowances
(May be econ-
omy-wide)
•Emissions trading
(e. g., EU ETS)
•Emission credits
under CDM
•Tradable Green
Certificates
•Fuel and vehicle
standards
•Tradable certificates
for energy efficiency
improvements
(white certificates)
•Emissions trading
•Emission credit
under CDM
•Tradable Green
Certificates
•Emission credits under
the Kyoto Protocol’s
Clean Development
Mechanism (CDM)
•Compliance schemes
outside Kyoto protocol
(national schemes)
•Voluntary carbon
markets
•Urban-scale Cap
and Trade
Economic Instru-
ments — Subsidies
•Fossil fuel subsidy
removal
•Feed-in-tariffs for
renewable energy
•Capital subsidies
and insurance for 1st
generation Carbon
Dioxide Capture
and Storage (CCS)
•Biofuel subsidies
•Vehicle purchase
subsidies
•Feebates
•Subsidies or Tax
exemptions for
investment in efficient
buildings, retrofits
and products
•Subsidized loans
•Subsidies (e. g., for
energy audits)
•Fiscal incentives (e. g.,
for fuel switching)
•Credit lines for low
carbon agriculture,
sustainable forestry.
•Special Improvement
or Redevelopment
Districts
Regulatory
Approaches
•Efficiency or
environmental
performance standards
•Renewable Portfolio
standards for
renewable energy
•Equitable access
to electricity grid
•Legal status of long
term CO
2
storage
•Fuel economy
performance standards
•Fuel quality standards
•GHG emission
performance standards
•Regulatory restrictions
to encourage modal
shifts (road to rail)
•Restriction on
use of vehicles in
certain areas
•Environmental capacity
constraints on airports
•Urban planning and
zoning restrictions
•Building codes
and standards
•Equipment and
appliance standards
•Mandates for energy
retailers to assist
customers invest in
energy efficiency
•Energy efficiency
standards for
equipment
• Energy management
systems (also
voluntary)
•Voluntary agreements
(where bound
by regulation)
•Labelling and
public procurement
regulations
•National policies
to support REDD+
including monitoring,
reporting and
verification
•Forest law to reduce
deforestation
•Air and water pollution
control GHG precursors
•Land-use planning
and governance
•Mixed use zoning
•Development
restrictions
•Affordable housing
mandates
•Site access controls
•Transfer development
rights
•Design codes
•Building codes
•Street codes
•Design standards
Information
Programmes
•Fuel labelling
•Vehicle efficiency
labelling
•Energy audits
•Labelling programmes
•Energy advice
programmes
•Energy audits
•Benchmarking
•Brokerage for
industrial cooperation
•Certification schemes
for sustainable
forest practices
•Information policies
to support REDD+
including monitoring,
reporting and
verification
Government
Provision of Public
Goods or Services
•Research and
development
•Infrastructure
expansion (district
heating / cooling or
common carrier)
•Investment in
transit and human
powered transport
•Investment in
alternative fuel
infrastructure
•Low emission vehicle
procurement
•Public procurement
of efficient buildings
and appliances
•Training and education
•Brokerage for
industrial cooperation
•Protection of national,
state, and local forests.
•Investment in
improvement and
diffusion of innovative
technologies in
agriculture and forestry
•Provision of utility
infrastructure such as
electricity distribution,
district heating / cooling
and wastewater
connections, etc.
•Park improvements
•Trail improvements
•Urban rail
Voluntary Actions
•Labelling programmes
for efficient buildings
•Product eco-labelling
•Voluntary agreements
on energy targets or
adoption of energy
management systems,
or resource efficiency
•Promotion of
sustainability by
developing standards
and educational
campaigns
9898
TS
Technical Summary
Box TS.13 | The rebound effect can reduce energy savings from technological improvement
Technological improvements in energy efficiency (EE) have direct
effects on energy consumption and thus GHG emissions, but can
cause other changes in consumption, production, and prices that
will, in turn, affect GHG emissions. These changes are generally
called ‘rebound’ or ‘takeback’ because in most cases they reduce
the net energy or emissions reduction associated with the effi-
ciency improvement. The size of EE rebound is controversial, with
some research papers suggesting little or no rebound and others
concluding that it offsets most or all reductions from EE policies
[3.9.5, 5.7.2].
Total EE rebound can be broken down into three distinct parts:
substitution-effect, income-effect, and economy-wide effect
[3.9.5]. In end-use consumption, substitution-effect rebound, or
‘direct rebound’ assumes that a consumer will make more use
of a device if it becomes more energy efficient because it will be
cheaper to use. Income-effect rebound or ‘indirect rebound’, arises
if the improvement in EE makes the consumer wealthier and leads
her to consume additional products that require energy. Economy-
wide rebound refers to impacts beyond the behaviour of the entity
benefiting directly from the EE improvement, such as the impact of
EE on the price of energy.
Analogous rebound effects for EE improvements in production are
substitution towards an input with improved energy efficiency, and
substitution among products by consumers when an EE improve-
ment changes the relative prices of goods, as well as an income
effect when an EE improvement lowers production costs and cre-
ates greater wealth.
Rebound is sometimes confused with the concept of carbon leak-
age, which often describes the incentive for emissions-intensive
economic activity to migrate away from a region that restricts
GHGs (or other pollutants) towards areas with fewer or no restric-
tions on such emissions [5.4.1, 14.4]. Energy efficiency rebound
can occur regardless of the geographic scope of the adopted pol-
icy. As with leakage, however, the potential for significant rebound
illustrates the importance of considering the full equilibrium effects
of a mitigation policy [3.9.5, 15.5.4].
particularly related to the expansion of renewable energy as well as
regulation on energy efficiency. It has proven to be politically difficult
to address this problem by removing GHG emission permits temporar-
ily, tightening the cap, or providing a long-term mitigation goal. [14.4.2]
Adding a mitigation policy to another may not necessarily
enhance mitigation. For instance, if a cap-and-trade system has a
sufficiently stringent cap then other policies such as renewable sub-
sidies have no further impact on total GHG emissions (although they
may affect costs and possibly the viability of more stringent future tar-
gets). If the cap is loose relative to other policies, it becomes ineffec-
tive. This is an example of a negative interaction between policy instru-
ments. Since other policies cannot be ‘added on’ to a cap-and-trade
system, if it is to meet any particular target, a sufficiently low cap is
necessary. A carbon tax, on the other hand, can have an additive envi-
ronmental effect to policies such as subsidies to renewables. [15.7]
Reduction of subsidies to fossil energy can achieve significant
emission reductions at negative social cost (very high confidence).
Although political economy barriers are substantial, many countries have
reformed their tax and budget systems to reduce fuel subsidies that actu-
ally accrue to the relatively wealthy, and utilized lump-sum cash trans-
fers or other mechanisms that are more targeted to the poor. [15.5.3]
Direct regulatory approaches and information measures are
widely used, and are often environmentally effective, though
debate remains on the extent of their environmental impacts
and cost-effectiveness (medium confidence). Examples of regula-
tory approaches include energy efficiency standards; examples of
information programmes include labelling programmes that can help
consumers make better-informed decisions. While such approaches
often work at a net social benefit, the scientific literature is divided
on whether such policies are implemented with negative private costs
(see Box TS.12) to firms and individuals [3.9.3, 15.5.5, 15.5.6]. Since
AR4 there has been continued investigation into the ‘rebound’ effects
(see Box TS.13) that arise when higher efficiency leads to lower energy
costs and greater consumption. There is general agreement that such
rebound effects exist, but there is low agreement in the literature on
the magnitude [3.9.5, 5.7.2, 15.5.4].
There is a distinct role for technology policy as a complement to
other mitigation policies (high confidence). Properly implemented
technology policies reduce the cost of achieving a given environmental
target. Technology policy will be most effective when technology-push
policies (e. g., publicly funded R&D) and demand-pull policies (e. g.,
governmental procurement programmes or performance regulations)
are used in a complementary fashion. While technology-push and
demand-pull policies are necessary, they are unlikely to be sufficient
without complementary framework conditions. Managing social chal-
lenges of technology policy change may require innovations in policy
and institutional design, including building integrated policies that
make complementary use of market incentives, authority, and norms
(medium confidence). Since AR4, a large number of countries and sub-
national jurisdictions have introduced support policies for renewable
9999
Technical Summary
TS
energy such as feed-in tariffs and renewable portfolio standards. These
have promoted substantial diffusion and innovation of new energy
technologies such as wind turbines and photovoltaic panels, but have
raised questions about their economic efficiency, and introduced chal-
lenges for grid and market integration. [2.6.5, 7.12, 15.6.5]
Worldwide investment in research in support of mitigation is
small relative to overall public research spending (medium con-
fidence). The effectiveness of research support will be greatest if it is
increased slowly and steadily rather than dramatically or erratically. It is
important that data collection for program evaluation is built into tech-
nology policy programmes, because there is limited empirical evidence
on the relative effectiveness of different mechanisms for supporting the
invention, innovation and diffusion of new technologies. [15.6.2, 15.6.5]
Government planning and provision can facilitate shifts to less
energy- and GHG-intensive infrastructure and lifestyles (high
confidence). This applies particularly when there are indivisibilities in
the provision of infrastructure as in the energy sector [7.6] (e. g., for
electricity transmission and distribution or district heating networks);
in the transport sector [8.4] (e. g., for non-motorized or public trans-
port); and in urban planning [12.5]. The provision of adequate infra-
structure is important for behavioural change [15.5.6].
Successful voluntary agreements on mitigation between gov-
ernments and industries are characterized by a strong institu-
tional framework with capable industrial associations (medium
confidence). The strengths of voluntary agreements are speed and flex-
ibility in phasing measures, and facilitation of barrier removal activi-
ties for energy efficiency and low-emission technologies. Regulatory
threats, even though the threats are not always explicit, are also an
important factor for firms to be motivated. There are few environmen-
tal impacts without a proper institutional framework. [15.5.7]
TS.4.3 Development and regional cooperation
Regional cooperation offers substantial opportunities for mitiga-
tion due to geographic proximity, shared infrastructure and policy
frameworks, trade, and cross-border investment that would be
difficult for countries to implement in isolation (high confidence).
Examples of possible regional cooperation policies include regionally-
linked development of renewable energy power pools, networks of natu-
ral gas supply infrastructure, and coordinated policies on forestry. [14.1]
At the same time, there is a mismatch between opportunities
and capacities to undertake mitigation (medium confidence). The
regions with the greatest potential to leapfrog to low-carbon devel-
opment trajectories are the poorest developing regions where there
are few lock-in effects in terms of modern energy systems and urban-
ization patterns. However, these regions also have the lowest finan-
cial, technological, and institutional capacities to embark on such
low-carbon development paths (Figure TS.37) and their cost of wait-
ing is high due to unmet energy and development needs. Emerging
economies already have more lock-in effects but their rapid build-up of
modern energy systems and urban settlements still offers substantial
opportunities for low-carbon development. Their capacity to reorient
themselves to low-carbon development strategies is higher, but also
faces constraints in terms of finance, technology, and the high cost of
delaying the installation of new energy capacity. Lastly, industrialized
economies have the largest lock-in effects, but the highest capacities
to reorient their energy, transport, and urbanizations systems towards
low-carbon development. [14.1.3, 14.3.2]
Regional cooperation has, to date, only had a limited (positive)
impact on mitigation (medium evidence, high agreement). Nonethe-
less, regional cooperation could play an enhanced role in promoting
mitigation in the future, particularly if it explicitly incorporates miti-
gation objectives in trade, infrastructure and energy policies and pro-
motes direct mitigation action at the regional level. [14.4.2, 14.5]
Most literature suggests that climate-specific regional coopera-
tion agreements in areas of policy have not played an important
role in addressing mitigation challenges to date (medium confi-
dence). This is largely related to the low level of regional integration and
associated willingness to transfer sovereignty to supra-national regional
bodies to enforce binding agreements on mitigation. [14.4.2, 14.4.3]
Climate-specific regional cooperation using binding regulation-
based approaches in areas of deep integration, such as EU direc-
tives on energy efficiency, renewable energy, and biofuels, have
had some impact on mitigation objectives (medium confidence).
Nonetheless, theoretical models and past experience suggest that
there is substantial potential to increase the role of climate-specific
regional cooperation agreements and associated instruments, includ-
ing economic instruments and regulatory instruments. In this context it
is important to consider carbon leakage of such regional initiatives and
ways to address it. [14.4.2, 14.4.1]
In addition, non-climate-related modes of regional coopera-
tion could have significant implications for mitigation, even if
mitigation objectives are not a component (medium confidence).
Regional cooperation with non-climate-related objectives but pos-
sible mitigation implications, such as trade agreements, cooperation
on technology, and cooperation on infrastructure and energy, has to
date also had negligible impacts on mitigation. Modest impacts have
been found on the level of GHG emissions of members of regional
preferential trade areas if these agreements are accompanied with
environmental agreements. Creating synergies between adaptation
and mitigation can increase the cost-effectiveness of climate change
actions. Linking electricity and gas grids at the regional level has
also had a modest impact on mitigation as it facilitated greater use
of low-carbon and renewable technologies; there is substantial fur-
ther mitigation potential in such arrangements. [14.4.2]
100100
TS
Technical Summary
TS.4.4 International cooperation
Climate change mitigation is a global commons problem that
requires international cooperation, but since AR4, scholarship
has emerged that emphasizes a more complex and multi-fac-
eted view of climate policy (very high confidence). Two character-
istics of climate change necessitate international cooperation: climate
change is a global commons problem, and it is characterized by a high
degree of heterogeneity in the origins of GHG emissions, mitigation
opportunities, climate impacts, and capacity for mitigation and adapta-
tion [13.2.1.1]. Policymaking efforts to date have primarily focused on
international cooperation as a task centrally focused on the coordina-
tion of national policies that would be adopted with the goal of miti-
gation. More recent policy developments suggest that there is a more
complicated set of relationships between national, regional, and global
policymaking, based on a multiplicity of goals, a recognition of policy
co-benefits, and barriers to technological innovation and diffusion [1.2,
6.6, 15.2]. A major challenge is assessing whether decentralized policy
action is consistent with and can lead to total mitigation efforts that
are effective, equitable, and efficient [6.1.2.1, 13.13].
Figure TS.37 | Economic and governance indicators affecting regional capacities to embrace mitigation policies. Regions include EAS (East Asia), EIT (Economies in Transition), LAM
(Latin America and Caribbean), MNA (Middle East and North Africa), NAM (North America), POECD (Pacific Organisation for Economic Co-operation and Development (OECD)-1990
members), PAS (South East Asia and Pacific), SAS (South Asia), SSA (sub-Saharan Africa), WEU (Western Europe), LDC (least-developed countries). Statistics refer to the year 2010
or the most recent year available. Note: The lending interest rate refers to the average interest rate charged by banks to private sector clients for short- to medium-term financing
needs. The governance index is a composite measure of governance indicators compiled from various sources, rescaled to a scale of 0 to 1, with 0 representing weakest governance
and 1 representing strongest governance. [Figure 14.2]
0,0 0,2 0,4 0,6 0,8 1,0
Adjusted Net Savings, Including Particulate Emission Damage [% of GNI]High-Technology Exports [% of Manufactured Exports]
Lending Interest Rate [%] Governance index [0: Weak, 1: Strong]
0 10 20 30 40 50 60 70
0 10 20 30 40 50 60
-50 -40 -30 -20 -10 0 10 20 30 40
LAM
EAS
SSA
MNA
SAS
EIT
NAM
WEU
POECD
PAS
LDC
LAM
NAM
EAS
WEU
POECD
SSA
MNA
SAS
EIT
PAS
LDC
LAM
EAS
SSA
MNA
SAS
EIT
NAM
WEU
POECD
PAS
LDC
LAM
NAM
EAS
WEU
POECD
SSA
MNA
SAS
EIT
PAS
LDC
Min
75
th
Percentile
Max
Median
25
th
Percentile
c) d)
a) b)
101101
Technical Summary
TS
International cooperation on climate change has become more
institutionally diverse over the past decade (very high confidence).
Perceptions of fairness can facilitate cooperation by increasing the
legitimacy of an agreement [3.10, 13.2.2.4]. UNFCCC remains a primary
international forum for climate negotiations, but other institutions have
emerged at multiple scales, namely: global, regional, national, and local
[13.3.1, 13.4.1.4, 13.5]. This institutional diversity arises in part from
the growing inclusion of climate change issues in other policy arenas
(e. g., sustainable development, international trade, and human rights).
These and other linkages create opportunities, potential co-benefits, or
harms that have not yet been thoroughly examined. Issue linkage also
creates the possibility for countries to experiment with different forums
of cooperation (‘forum shopping’), which may increase negotiation
costs and potentially distract from or dilute the performance of interna-
tional cooperation toward climate goals. [13.3, 13.4, 13.5] Finally, there
has been an emergence of new transnational climate-related institu-
tions not centred on sovereign states (e. g., public-private partnerships,
private sector governance initiatives, transnational NGO programmes,
and city level initiatives) [13.3.1, 13.12].
Existing and proposed international climate agreements vary
in the degree to which their authority is centralized. As illus-
trated in Figure TS.38, the range of centralized formalization spans
strong multilateral agreements (such as the Kyoto Protocol targets),
harmonized national policies (such as the Copenhagen / Cancún
pledges), and decentralized but coordinated national policies (such
as planned linkages of national and sub-national emissions trading
schemes) [13.4.1, 13.4.3]. Four other design elements of international
agreements have particular relevance: legal bindingness, goals and
targets, flexible mechanisms, and equitable methods for effort-shar-
Figure TS.38 | Alternative forms of international cooperation. The figure represents a compilation of existing and possible forms of international cooperation, based upon a survey
of published research, but is not intended to be exhaustive of existing or potential policy architectures, nor is it intended to be prescriptive. Examples in orange are existing agree-
ments. Examples in blue are structures for agreements proposed in the literature. The width of individual boxes indicates the range of possible degrees of centralization for a particu-
lar agreement. The degree of centralization indicates the authority an agreement confers on an international institution, not the process of negotiating the agreement. [Figure 13.2]
UNFCCC Objective
Other IO GHG Regulation
Linked Cap-and-Trade Systems
and Harmonized Carbon Taxes
Multilateral Clubs
Green Climate
Fund
Bilateral Financial/
Technology Transfers
International Cooperation
for Supporting Adaptation Planning
Kyoto
Targets
Kyoto Flexibility Mechanisms
Loose Coordination of Policies
Offset Certification Systems
UNFCCC/Kyoto/Copenhagen MRV Rules
R&D Technology Cooperation
Regional ETS
Pledge and Review
Copenhagen/
Cancún Pledges
Centralized AuthorityDecentralized Authority
Cooperation
over Means
Cooperation
over Ends
Loose coordination of policies: examples include transnational city networks and Nationally Appropriate Mitigation Actions (NAMAs);
R&D technology cooperation: examples include the Major Economies Forum on Energy and Climate (MEF), Global Methane Initiative (GMI),
or Renewable Energy and Energy Efficiency Partnership (REEEP); Other international organization (IO) GHG regulation:
examples include the Montreal Protocol, International Civil Aviation Organization (ICAO), International Maritime Organization (IMO).
102102
TS
Technical Summary
Table TS.10 | Summary of performance assessments of existing and proposed forms of cooperation. Forms of cooperation are evaluated along the four evaluation criteria described
in Sections 3.7.1 and 13.2.2. [Table 13.3]
Mode of International
Cooperation
Assessment Criteria
Environmental
Effectiveness
Aggregate Economic
Performance
Distributional Impacts Institutional Feasibility
Existing
Cooperation
[13.13.1]
UNFCCC Aggregate GHG emis-
sions in AnnexI countries
declined by 6.0 to 9.2 %
below 1990 levels by 2000,
a larger reduction than the
apparent ‘aim’ of returning
to 1990 levels by 2000.
Authorized joint fulfilment
of commitments, multi-gas
approach, sources and sinks,
and domestic policy choice.
Cost and benefit estimates
depend on baseline, discount
rate, participation, leak-
age, co-benefits, adverse
effects, and other factors.
Commitments distinguish
between AnnexI (indus-
trialized) and non-AnnexI
countries. Principle of
‘common but differentiated
responsibility.’ Commitment
to ‘equitable and appropriate
contributions by each [party].’
Ratified (or equivalent) by 195
countries and regional organi-
zations. Compliance depends
on national communications.
The Kyoto Protocol (KP)
Aggregate emissions in AnnexI
countries were reduced by 8.5
to 13.6 % below 1990 levels by
2011, more than the first com-
mitment period (CP1) collective
reduction target of 5.2 %. Reduc-
tions occurred mainly in EITs;
emissions; increased in some
others. Incomplete participation
in CP1 (even lower in CP2).
Cost-effectiveness improved
by flexible mechanisms (Joint
Implementation (JI), CDM,
International Emissions
Trading (IET)) and domestic
policy choice. Cost and benefit
estimates depend on baseline,
discount rate, participation,
leakage, co-benefits, adverse
effects, and other factors.
Commitments distinguish
between developed and
developing countries, but
dichotomous distinction
correlates only partly (and
decreasingly) with historical
emissions trends and with
changing economic circum-
stances. Intertemporal equity
affected by short-term actions.
Ratified (or equivalent) by
192 countries and regional
organizations, but took 7 years
to enter into force. Compli-
ance depends on national
communications, plus KP
compliance system. Later
added approaches to enhance
measurement, reporting,
and verification (MRV).
The Kyoto Mechanisms About 1.4 billion tCO
2
eq
credits under the CDM, 0.8
billion under JI, and 0.2 bil-
lion under IET (through July
2013). Additionality of CDM
projects remains an issue but
regulatory reform underway.
CDM mobilized low cost
options, particularly indus-
trial gases, reducing costs.
Underperformance of some
project types. Some evidence
that technology is transferred
to non-AnnexI countries.
Limited direct investment from
AnnexI countries. Domestic
investment dominates, leading
to concentration of CDM
projects in few countries.
Limited contributions to local
sustainable development.
Helped enable political
feasibility of Kyoto Protocol.
Has multi-layered governance.
Largest carbon markets to date.
Has built institutional capacity
in developing countries.
Further Agreements
under the UNFCCC
Pledges to limit emissions made
by all major emitters under
Cancun Agreements. Unlikely
sufficient to limit temperature
change to 2 °C. Depends on
treatment of measures beyond
current pledges for mitigation
and finance. Durban Platform
calls for new agreement
by 2015, to take effect in
2020, engaging all parties.
Efficiency not assessed.
Cost-effectiveness might be
improved by market-based
policy instruments, inclusion of
forestry sector, commitments
by more nations than AnnexI
countries (as envisioned
in Durban Platform).
Depends on sources of financ-
ing, particularly for actions
of developing countries.
Cancún Conference of the
Parties (COP) decision; 97
countries made pledges of
emission reduction targets
or actions for 2020.
Agreements
outside the
UNFCCC
G8, G20,
Major
Economies
Forum on
Energy and
Climate (MEF)
G8 and MEF have recom-
mended emission reduction by
all major emitters. G20 may
spur GHG reductions by phas-
ing out of fossil fuel subsidies.
Action by all major emitters
may reduce leakage and
improve cost-effectiveness, if
implemented using flexible
mechanisms. Potential efficiency
gains through subsidy removal.
Too early to assess economic
performance empirically.
Has not mobilized climate
finance. Removing fuel
subsidies would be progressive
but have negative effects
on oil-exporting countries
and on those with very low
incomes unless other help
for the poorest is provided.
Lower participation of countries
than UNFCCC, yet covers 70 %
of global emissions. Opens
possibility for forum-shopping,
based on issue preferences.
Montreal
Protocol on
Ozone-
Depleting
Substances
(ODS)
Spurred emission reductions
through ODS phaseouts
approximately 5 times the
magnitude of Kyoto CP1
targets. Contribution may
be negated by high-GWP
substitutes, though efforts to
phase out HFCs are growing.
Cost-effectiveness supported
by multi-gas approach. Some
countries used market-based
mechanisms to imple-
ment domestically.
Later compliance period for
phaseouts by developing
countries. Montreal Protocol
Fund provided finance to
developing countries.
Universal participation.
but the timing of required
actions vary for developed
and developing countries
Voluntary
Carbon
Market
Covers 0.13 billion tCO
2
eq, but
certification remains an issue
Credit prices are het-
erogeneous, indicating
market inefficiencies
[No literature cited.] Fragmented and non-
transparent market.
⇒
103103
Technical Summary
TS
ing [13.4.2]. Existing and proposed modes of international coopera-
tion are assessed in Table TS.10. [13.13]
The UNFCCC is currently the only international climate policy
venue with broad legitimacy, due in part to its virtually univer-
sal membership (high confidence). The UNFCCC continues to evolve
institutions and systems for governance of climate change. [13.2.2.4,
13.3.1, 13.4.1.4, 13.5]
Incentives for international cooperation can interact with other
policies (medium confidence). Interactions between proposed and
existing policies, which may be counterproductive, inconsequential, or
beneficial, are difficult to predict, and have been understudied in the
literature [13.2, 13.13, 15.7.4]. The game-theoretic literature on cli-
mate change agreements finds that self-enforcing agreements engage
and maintain participation and compliance. Self-enforcement can be
derived from national benefits due to direct climate benefits, co-bene-
fits of mitigation on other national objectives, technology transfer, and
climate finance. [13.3.2]
Decreasing uncertainty concerning the costs and benefits of
mitigation can reduce the willingness of states to make com-
mitments in forums of international cooperation (medium con-
fidence). In some cases, the reduction of uncertainty concerning the
costs and benefits of mitigation can make international agreements
less effective by creating a disincentive for states to participate [13.3.3,
2.6.4.1]. A second dimension of uncertainty, that concerning whether
the policies states implement will in fact achieve desired outcomes,
can lessen the willingness of states to agree to commitments regard-
ing those outcomes [2.6.3].
International cooperation can stimulate public and private
investment and the adoption of economic incentives and direct
regulations that promote technological innovation (medium con-
fidence). Technology policy can help lower mitigation costs, thereby
increasing incentives for participation and compliance with interna-
tional cooperative efforts, particularly in the long run. Equity issues can
be affected by domestic intellectual property rights regimes, which can
alter the rate of both technology transfer and the development of new
technologies. [13.3, 13.9]
In the absence of — or as a complement to — a binding, interna-
tional agreement on climate change, policy linkages between
and among existing and nascent international, regional,
national, and sub-national climate policies offer potential cli-
mate change mitigation and adaptation benefits (medium confi-
dence). Direct and indirect linkages between and among sub-national,
national, and regional carbon markets are being pursued to improve
market efficiency. Linkage between carbon markets can be stimulated
by competition between and among public and private governance
regimes, accountability measures, and the desire to learn from pol-
icy experiments. Yet integrating climate policies raises a number of
concerns about the performance of a system of linked legal rules and
economic activities. [13.3.1, 13.5.3, 13.13.2.3] Prominent examples
of linkages are among national and regional climate initiatives (e. g.,
planned linkage between the EU ETS and the Australian Emission
Trading Scheme, international offsets planned for recognition by a
number of jurisdictions), and national and regional climate initiatives
with the Kyoto Protocol (e. g., the EU ETS is linked to international
carbon markets through the project-based Kyoto Mechanisms) [13.6,
13.7, Figure 13.4, 14.4.2].
International trade can promote or discourage international
cooperation on climate change (high confidence). Developing
constructive relationships between international trade and climate
agreements involves considering how existing trade policies and rules
Mode of International
Cooperation
Assessment Criteria
Environmental
Effectiveness
Aggregate Economic
Performance
Distributional Impacts Institutional Feasibility
Proposed
Cooperation
[13.13.2]
Proposed
architectures
Strong mul-
tilateralism
Tradeoff between ambi-
tion (deep) and par-
ticipation (broad).
More cost-effectivewith greater
reliance on market mechanisms.
Multilateralism facilitates
integrating distributional
impacts into negotiations
and may apply equity-based
criteria as outlined in Ch. 4
Depends on number of
parties; degree of ambition
Harmonized
national
policies
Depends on net aggre-
gate change in ambition
across countries resulting
from harmonization.
More cost-effectivewith greater
reliance on market mechanisms.
Depends on specific
national policies
Depends on similarity of
national policies; more similar
may support harmonization but
domestic circumstances may
vary. National enforcement.
Decentralized
architectures,
coordinated
national
policies
Effectiveness depends on
quality of standards and
credits across countries
Often (though not necessarily)
refers to linkage of national
cap-and-trade systems, in
which case cost effective.
Depends on specific
national policies
Depends on similar-
ity of national policies.
National enforcement.
Effort (burden) sharing
arrangements
Refer to Sections 4.6.2 for discussion of the principles on which effort (burden) sharing arrangements may be based, and Section 6.3.6.6
for quantitative evaluation.
104104
TS
Technical Summary
can be modified to be more climate-friendly; whether border adjust-
ment measures or other trade measures can be effective in meeting
the goals of international climate policy, including participation in and
compliance with climate agreements; or whether the UNFCCC, World
Trade Organization (WTO), a hybrid of the two, or a new institution is
the best forum for a trade-and-climate architecture. [13.8]
The Montreal Protocol, aimed at protecting the stratospheric
ozone layer, achieved reductions in global GHG emissions (very
high confidence). The Montreal Protocol set limits on emissions of
ozone-depleting gases that are also potent GHGs, such as chlorofluo-
rocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs). Substitutes
for those ozone-depleting gases (such as hydrofluorocarbons (HFCs),
which are not ozone-depleting) may also be potent GHGs. Lessons
learned from the Montreal Protocol, for example about the effect of
financial and technological transfers on broadening participation in
an international environmental agreement, could be of value to the
design of future international climate change agreements (see Table
TS.10). [13.3.3, 13.3.4, 13.13.1.4]
The Kyoto Protocol was the first binding step toward imple-
menting the principles and goals provided by the UNFCCC, but
it has had limited effects on global GHG emissions because
some countries did not ratify the Protocol, some Parties did not
meet their commitments, and its commitments applied to only a
portion of the global economy (medium evidence, low agreement).
The Parties collectively surpassed their collective emission reduction
target in the first commitment period, but the Protocol credited emis-
sions reductions that would have occurred even in its absence. The
Kyoto Protocol does not directly influence the emissions of non-Annex
I countries, which have grown rapidly over the past decade. [5.2,
13.13.1.1]
The flexible mechanisms under the Protocol have cost-saving
potential, but their environmental effectiveness is less clear
(medium confidence). The CDM, one of the Protocol’s flexible mecha-
nisms, created a market for GHG emissions offsets from developing
countries, generating credits equivalent to nearly 1.4 GtCO
2
eq as of
October 2013. The CDM’s environmental effectiveness has been mixed
due to concerns about the limited additionality of projects, the valid-
ity of baselines, the possibility of emissions leakage, and recent credit
price decreases. Its distributional impact has been unequal due to the
concentration of projects in a limited number of countries. The Proto-
col’s other flexible mechanisms, Joint Implementation (JI) and Inter-
national Emissions Trading (IET), have been undertaken both by gov-
ernments and private market participants, but have raised concerns
related to government sales of emission units. (Table TS.10) [13.7.2,
13.13.1.2, 14.3.7.1]
Recent UNFCCC negotiations have sought to include more ambi-
tious contributions from the countries with commitments under
the Kyoto Protocol, mitigation contributions from a broader
set of countries, and new finance and technology mechanisms.
Under the 2010 Cancún Agreement, developed countries formalized
voluntary pledges of quantified, economy-wide GHG emission reduc-
tion targets and some developing countries formalized voluntary
pledges to mitigation actions. The distributional impact of the agree-
ment will depend in part on the magnitude and sources of financ-
ing, although the scientific literature on this point is limited, because
financing mechanisms are evolving more rapidly than respective scien-
tific assessments (limited evidence, low agreement). Under the 2011
Durban Platform for Enhanced Action, delegates agreed to craft a
future legal regime that would be ‘applicable to all Parties […] under
the Convention’ and would include substantial new financial support
and technology arrangements to benefit developing countries, but the
delegates did not specify means for achieving those ends. [13.5.1.1,
13.13.1.3, 16.2.1]
TS.4.5 Investment and finance
A transformation to a low-carbon economy implies new pat-
terns of investment. A limited number of studies have examined
the investment needs for different mitigation scenarios. Information
is largely limited to energy use with global total annual investment
in the energy sector at about 1200 billion USD. Mitigation scenarios
that reach atmospheric CO
2
eq concentrations in the range from 430 to
530 ppm CO
2
eq by 2100 (without overshoot) show substantial shifts
in annual investment flows during the period 2010 – 2029 if compared
to baseline scenarios (Figure TS.39): annual investment in the exist-
ing technologies associated with the energy supply sector (e. g., con-
ventional fossil fuelled power plants and fossil fuel extraction) would
decline by 30 (2 to 166) billion USD per year (median: – 20 % compared
to 2010) (limited evidence, medium agreement). Investment in low-
emissions generation technologies (renewables, nuclear, and power
plants with CCS) would increase by 147 (31 to 360) billion USD per year
(median: +100 % compared to 2010) during the same period (limited
evidence, medium agreement) in combination with an increase by 336
(1 to 641) billion USD in energy efficiency investments in the building,
transport and industry sectors (limited evidence, medium agreement).
Higher energy efficiency and the shift to low-emission generation tech-
nologies contribute to a reduction in the demand for fossil fuels, thus
causing a decline in investment in fossil fuel extraction, transformation
and transportation. Scenarios suggest that average annual reduction
of investment in fossil fuel extraction in 2010 – 2029 would be 116 (– 8
to 369) billion USD (limited evidence, medium agreement). Such spill-
over effects could yield adverse effects on the revenues of countries
that export fossil fuels. Mitigation scenarios also reduce deforestation
against current deforestation trends by 50 % reduction with an invest-
ment of 21 to 35 billion USD per year (low confidence). [16.2.2]
Estimates of total climate finance range from 343 to 385 billion
USD per year between 2010 and 2012 (medium confidence). The
range is based on 2010, 2011, and 2012 data. Climate finance was
almost evenly invested in developed and developing countries. Around
95 % of the total was invested in mitigation (medium confidence). The
105105
Technical Summary
TS
figures reflect the total financial flow for the underlying investments,
not the incremental investment, i. e., the portion attributed to the miti-
gation / adaptation cost increment (see Box TS.14). In general, quantita-
tive data on climate finance are limited, relate to different concepts,
and are incomplete. [16.2.1.1]
Depending on definitions and approaches, climate finance flows
to developing countries are estimated to range from 39 to 120
billion USD per year during the period 2009 to 2012 (medium
confidence). The range covers public and private flows for mitiga-
tion and adaptation. Public climate finance was 35 to 49 billion USD
(2011 / 2012 USD) (medium confidence). Most public climate finance
provided to developing countries flows through bilateral and multilat-
eral institutions usually as concessional loans and grants. Under the
UNFCCC, climate finance is funding provided to developing countries
by Annex II Parties and averaged nearly 10 billion USD per year from
2005 to 2010 (medium confidence). Between 2010 and 2012, the ´fast
start finance´ provided by some developed countries amounted to over
10 billion USD per year (medium confidence). Estimates of interna-
tional private climate finance flowing to developing countries range
from 10 to 72 billion USD (2009 / 2010 USD) per year, including foreign
direct investment as equity and loans in the range of 10 to 37 billion
USD (2010 USD and 2008 USD) per year over the period of 2008 – 2011
(medium confidence). Figure TS.40 provides an overview of climate
finance, outlining sources and managers of capital, financial instru-
ments, project owners, and projects. [16.2.1.1]
Within appropriate enabling environments, the private sec-
tor, along with the public sector, can play an important role in
financing mitigation. The private sector contribution to total climate
finance is estimated at an average of 267 billion USD (74 %) per year in
the period 2010 to 2011 and at 224 billion USD (62 %) per year in the
Figure TS.39 | Change of average annual investment flows in mitigation scenarios (2010 – 2029). Investment changes are calculated by a limited number of model studies and
model comparisons for mitigation scenarios that reach concentrations within the range of 430 – 530 ppm CO
2
eq by 2100 compared to respective average baseline investments. The
vertical bars indicate the range between minimum and maximum estimate of investment changes; the horizontal bar indicates the median of model results. Proximity to this median
value does not imply higher likelihood because of the different degree of aggregation of model results, low number of studies available and different assumptions in the different
studies considered. The numbers in the bottom row show the total number of studies assessed. [Figure 16.3]
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
Power Plants
with CCS
Renewables Energy Efficiency
Across Sectors
Extraction of
Fossil Fuels
Fossil Fuel
Power Plants
without CCS
NuclearTotal Electricity
Generation
# of Studies: 43444444 3545
4545455 44
Changes in Annual Investment Flows 2010-2029 [USD
2010
Billion /yr]
Max
Median
Mean
Min
Non-OECD
WorldOECD
106106
TS
Technical Summary
Figure TS.40 | Types of climate finance flows. ‘Capital’ includes all relevant financial flows. The size of the boxes is not related to the magnitude of the financial flow. [Figure 16.1]
Source of Capital
Carbon Taxes
and Auction of
Allowances
General Tax
Revenue
International
Levies
Funds from
Capital Markets
Corporate
Cash Flow
Household
Income
Manager of Capital
Governments
National,
Bilateral and
Multilateral
Financial
Institutions
Commercial
Financial
Institutions
Corporate
Actors and
Institutional
Investors
(Private and
Public)
Households
Financial Instrument
Grants
Project Debt
(Market Based/
Concessional)
Project Level
Equity
Balance Sheet
Financing
Credit
Enhancement /
Risk
Management
Project Owner/Sponsor
Governments,
Corporations,
and Households
(Developed and
Developing
Countries)
Project
Adaptation
Mitigation
(incl. REDD)
Box TS.14 | There are no agreed definitions of ´climate investment´ and ‘climate finance’
‘Total climate finance’ includes all financial flows whose expected
effect is to reduce net GHG emissions and / or to enhance resilience
to the impacts of climate variability and the projected climate
change. This covers private and public funds, domestic and inter-
national flows, expenditures for mitigation and adaptation, and
adaptation to current climate variability as well as future climate
change. It covers the full value of the financial flow rather than
the share associated with the climate change benefit. The share
associated with the climate change benefit is the incremental cost.
The ‘total climate finance flowing to developing countries’ is the
amount of the total climate finance invested in developing coun-
tries that comes from developed countries. This covers private and
public funds for mitigation and adaptation. ‘Public climate finance
provided to developing countries’ is the finance provided by devel-
oped countries´ governments and bilateral institutions as well as
multilateral institutions for mitigation and adaptation activities in
developing countries. ‘Private climate finance flowing to develop-
ing countries’ is finance and investment by private actors in / from
developed countries for mitigation and adaptation activities in
developing countries. Under the UNFCCC, climate finance is not
well-defined. Annex II Parties provide and mobilize funding for
climate-related activities in developing countries.
The ‘incremental investment’ is the extra capital required for the
initial investment for a mitigation or adaptation project in compar-
ison to a reference project. Incremental investment for mitigation
and adaptation projects is not regularly estimated and reported,
but estimates are available from models. The ‘incremental cost’
reflects the cost of capital of the incremental investment and the
change of operating and maintenance costs for a mitigation or
adaptation project in comparison to a reference project. It can be
calculated as the difference of the net present values of the two
projects. Many mitigation measures have higher investment costs
and lower operating and maintenance costs than the measures
displaced so incremental cost tends to be lower than the incre-
mental investment. Values depend on the incremental investment
as well as projected operating costs, including fossil fuel prices,
and the discount rate. The ‘macroeconomic cost of mitigation pol-
icy’ is the reduction of aggregate consumption or GDP induced
by the reallocation of investments and expenditures induced by
climate policy (see Box TS.9). These costs do not account for the
benefit of reducing anthropogenic climate change and should
thus be assessed against the economic benefit of avoided climate
change impacts. [16.1]
107107
Technical Summary
TS
period 2011 to 2012 (limited evidence, medium agreement) [16.2.1]. In
a range of countries, a large share of private sector climate investment
relies on low-interest and long-term loans as well as risk guarantees
provided by public sector institutions to cover the incremental costs
and risks of many mitigation investments. The quality of a country’s
enabling environment — including the effectiveness of its institutions,
regulations and guidelines regarding the private sector, security of
property rights, credibility of policies, and other factors — has a sub-
stantial impact on whether private firms invest in new technologies
and infrastructure [16.3]. By the end of 2012, the 20 largest emitting
developed and developing countries with lower risk country grades
for private sector investments produced 70 % of global energy related
CO
2
emissions (low confidence). This makes them attractive for inter-
national private sector investment in low-carbon technologies. In many
other countries, including most least-developed countries, low-carbon
investment will often have to rely mainly on domestic sources or inter-
national public finance. [16.4.2]
A main barrier to the deployment of low-carbon technologies
is a low risk-adjusted rate of return on investment vis-à-vis
high-carbon alternatives (high confidence). Public policies and
support instruments can address this either by altering the aver-
age rates of return for different investment options, or by creating
mechanisms to lessen the risks that private investors face [15.12,
16.3]. Carbon pricing mechanisms (carbon taxes, cap-and-trade sys-
tems), as well as renewable energy premiums, FITs, RPSs, investment
grants, soft loans and credit insurance can move risk-return profiles
into the required direction [16.4]. For some instruments, the pres-
ence of substantial uncertainty about their future levels (e. g., the
future size of a carbon tax relative to differences in investment and
operating costs) can lead to a lessening of the effectiveness and / or
efficiency of the instrument. Instruments that create a fixed or
immediate incentive to invest in low-emission technologies, such as
investment grants, soft loans, or FITs, do not appear to suffer from
this problem. [2.6.5]