Working Group III – Mitigation of Climate Change
TS
Technical Summary
A report accepted by Working Group III of the IPCC but not approved in detail.
*Note: this document of the Technical Summary differs in minimal formatting only from the version
made available on April 15, 2014.*
Note:
This document is the copy‐edited version of the final draft Report, dated 17 December 2013, of the
Working Group III contribution to the IPCC 5th Assessment Report "Climate Change 2014:
Mitigation of Climate Change" that was accepted but not approved in detail by the 12th Session of
Working Group III and the 39th Session of the IPCC on 12 April 2014 in Berlin, Germany. It consists
of the full scientific, technical and socio‐economic assessment undertaken by Working Group III.
The Report should be read in conjunction with the document entitled “Climate Change 2014:
Mitigation of Climate Change. Working Group III Contribution to the IPCC 5th Assessment Report ‐
Changes to the underlying Scientific/Technical Assessment” to ensure consistency with the approved
Summary for Policymakers (WGIII: 12th/Doc. 2a, Rev.2) and presented to the Panel at its 39th
Session. This document lists the changes necessary to ensure consistency between the full Report
and the Summary for Policymakers, which was approved line‐by‐line by Working Group III and
accepted by the Panel at the aforementioned Sessions.
Before publication, the Report (including text, figures and tables) will undergo final quality check as
well as any error correction as necessary, consistent with the IPCC Protocol for Addressing Possible
Errors. Publication of the Report is foreseen in September/October 2014.
Disclaimer:
The designations employed and the presentation of material on maps do not imply the expression of
any opinion whatsoever on the part of the Intergovernmental Panel on Climate Change concerning
the legal status of any country, territory, city or area or of its authorities, or concerning the
delimitation of its frontiers or boundaries.
Final Draft
Technical Summary
IPCC WGIII AR5
Title:
Technical Summary
Authors:
CLAs:
Ottmar Edenhofer, Ramon Pichs‐Madruga, Youba Sokona, Susanne Kadner,
Jan Minx, Steffen Brunner
LAs:
Shardul Agrawala, Giovanni Baiocchi, Igor Bashmakov, Gabriel Blanco, John
Broome, Thomas Bruckner, Mercedes Bustamante, Leon Clarke, Mariana
Conte Grand, Felix Creutzig, Xochitl Cruz‐Núñez, Shobhakar Dhakal, Navroz
K. Dubash, Patrick Eickemeier, Ellie Farahani, Manfred Fischedick, Marc
Fleurbaey, Reyer Gerlagh, Luis Gomez‐Echeverri, Shreekant Gupta, Sujata
Gupta, Jochen Harnisch, Kejun Jiang, Frank Jotzo, Sivan Kartha, Stephan
Klasen, Charles Kolstad, Volker Krey, Howard Kunreuther, Oswaldo Lucon,
Omar Masera, Yacob Mulugetta, Richard Norgaard, Anthony Patt, Nijavalli
H. Ravindranath, Keywan Riahi, Joyashree Roy, Ambuj Sagar, Roberto
Schaeffer, Steffen Schlömer, Karen Seto, Kristin Seyboth, Ralph Sims, Pete
Smith, Eswaran Somanathan, Robert Stavins, Christoph von Stechow,
Thomas Sterner, Taishi Sugiyama, Sangwon Suh, Kevin Urama, Diana Ürge‐
Vorsatz, Anthony Venables, David Victor, Elke Weber, Dadi Zhou, Ji Zou,
Timm Zwickel
Adolf Acquaye, Kornelis Blok, Gabriel Chan, Jan Fuglestvedt, Edgar Hertwich,
Elmar Kriegler, Oliver Lah, Sevastianos Mirasgedis, Carmenza Robledo Abad,
Claudia Sheinbaum, Steven Smith, Detlef van Vuuren
CAs:
REs:
Tomas Hernandez‐Tejeda, Roberta Quadrelli
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IPCC WGIII AR5
Technical Summary
Contents
TS.1 Introduction and framing ............................................................................................................ 3
TS.2 Trends in stocks and flows of greenhouse gases and their drivers .......................................... 10
TS.2.1 Greenhouse gas emission trends ...................................................................................... 10
TS.2.2 Greenhouse gas emission drivers ...................................................................................... 18
TS.3 Mitigation pathways and measures in the context of sustainable development .................... 21
TS.3.1 Mitigation pathways .......................................................................................................... 22
TS.3.1.1 Understanding mitigation pathways in the context of multiple objectives .............. 22
TS.3.1.2 Short‐ and long‐term requirements of mitigation pathways..................................... 23
TS.3.1.3 Costs, investments and burden sharing ..................................................................... 31
TS.3.1.4 Implications of transformation pathways for other objectives ................................. 35
TS.3.2 Sectoral and cross‐sectoral mitigation measures.............................................................. 39
TS.3.2.1 Cross‐sectoral mitigation pathways and measures ................................................... 39
TS.3.2.2 Energy supply ............................................................................................................. 46
TS.3.2.3 Transport .................................................................................................................... 51
TS.3.2.4 Buildings ..................................................................................................................... 58
TS.3.2.5 Industry ...................................................................................................................... 62
TS.3.2.6 Agriculture, forestry and other land‐uses (AFOLU).................................................... 70
TS.3.2.7 Human Settlements, Infrastructure, and Spatial Planning ........................................ 75
TS.4 Mitigation policies and institutions .......................................................................................... 81
TS.4.1 Policy design, behaviour and political economy ............................................................... 81
TS.4.2 Sectoral and national policies ............................................................................................ 83
TS.4.3 Development and regional cooperation ........................................................................... 90
TS.4.4 International cooperation ................................................................................................. 92
TS.4.5 Investment and finance ..................................................................................................... 96
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TS.1 Introduction and framing
‘Mitigation’, in the context of climate change, is a human intervention 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 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 atmosphere 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 threatened 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 concentrations nor fully
understand the harm it will impose on individuals, societies, and ecosystems. Article 2 requires that
societies balance a variety of considerations some rooted in the impacts of climate 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,
including socio‐economic development. Such issues are inherently value‐laden and involve different
actors who have varied interests and disparate decision‐making power.
This report examines the results of scientific research about mitigation, with a special attention on
how knowledge has evolved since the Fourth Assessment Report (AR4) published in 2007.
Throughout, the focus is on the implications of its findings for policy, without being prescriptive
about the particular policies that governments and other important participants 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 criteria, concepts, and methods for evaluating alternative policies.
1
Boxes throughout this summary provide background information on main research concepts and methods
that were used to generate insight.
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Box TS.1. Many disciplines aid decision making on climate change
Something is dangerous if it leads to a significant risk of considerable harm. Judging whether human
interference in the climate system is dangerous therefore divides into two tasks. One is to estimate
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 climate
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 wellbeing 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 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 mediate 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 report. The sciences of human and
social behaviour—among them psychology, political science, sociology, and non‐normative branches
of economics—investigate 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 conflicting 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 utility 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, jurisprudence, and economics. For example, some have
argued that countries owe restitution for the harms that result from their past emissions, and it has
been debated, on jurisprudential and other grounds, whether restitution is owed only for harms that
result from negligent or blameworthy emissions. [3.3, 4.6]
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The remainder of this summary offers the main findings of this report.2 This section continues with
providing a framing of important concepts 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, technology, 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 in
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. References in [square brackets] indicate chapters, sections, figures, tables, and boxes in the
underlying report where supporting evidence can be found.
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
emissions of any agent (individual, company, country) affect every other agent, an effective outcome
will not be achieved if individual agents advance their interests independently of others.
International 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 cooperation 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 individuals, 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’ [Box 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 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 wellbeing, 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
2
Throughout this summary, the validity of findings is expressed as a qualitative level of confidence and, when
possible, probabilistically with a quantified likelihood. Confidence in the validity of findings is based on the type,
amount, quality, and consistency of evidence (e.g., theory, data, models, expert judgment) and the degree of
agreement. Levels of evidence and agreement can be disclosed instead of aggregate confidence levels. Where
appropriate, findings are also formulated as statements of fact without using uncertainty qualifiers. For more
details, please refer to the Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent
Treatment of Uncertainties.
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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 interconnections between different levels of decision making and among different 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 interactions 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, 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 conundrums, 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 in each chapter highlighting the concerns of developing countries.
Economic evaluation can be useful for policy design and be given a foundation in ethics, provided
appropriate distributional weights are applied. While the limitations of economics are widely
documented [2.4, 3.5], economics nevertheless provides useful 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 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 [Box TS.2, 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, 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 inequality aversion [Box TS.10, 3.6.2].
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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 wellbeing 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 willing 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 may be appropriate between times.
Box TS.10, 3.6.2
Most climate policies intersect with other societal goals, either positively or negatively, creating
the possibility of ‘co‐benefits’ or ‘adverse side‐effects’. Since the publication of AR4 a substantial
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 requires examining the interaction between climate policies and pre‐existing other
policies [Box TS.11, 3.6.3, 6.3.6.5].
Mitigation efforts generate tradeoffs and synergies with other societal goals that can be evaluated
in a sustainable development 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‐
benefits. 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 wellbeing, in particular overcoming 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 formulate and implement economic and
social policies generally, they are critical to the adoption of effective climate policy.
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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 economic 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 considerations—long the domain of
psychology, behavioural economics, political economy, and other disciplines—need to be taken into
account in assessing climate policy [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 development pathways, which can have profound
impacts on emissions and mitigation policy. [1.4.2, 2.4, 3.8, 3.10, 4.3]
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 policy
makers—make choices that involve risk and uncertainty, they rely on deliberative as well intuitive
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 deliberative 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 experience. 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 decision making are prevalent in the
real world, risk management programmes 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]
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Effective climate policy involves building institutions and capacity for governance. While there is
strong evidence that a transition to a sustainable and equitable path is technically feasible, charting
an effective and viable course for climate change mitigation is not merely a technical exercise. It will
involve myriad and sequential decisions 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 considering uncertainties in possible
physical impacts as well as human and social responses. Climate change mitigation and adaption 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. Effective risk management strategies not only consider people’s values,
and their intuitive decision processes but utilize formal models and decision aids for systematically
addressing issues of risk and uncertainty [Box TS.3, 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]. A special challenge arises with the
growing evidence that climate change may result in extreme impacts whose trigger points and
outcomes are shrouded in high levels of uncertainty [Box TS.4, 2.5, Box 3.9]. A risk management
strategy for climate change will require integrating 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. One of the central challenges in developing a risk management strategy is to have it adaptive
to new information and different governing institutions [2.5].
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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 uncertainty in the climate system and
its implications for mitigation and adaptation policies. By assessing the chain of structural
uncertainties that affect the climate system, the resulting compound probability 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 CO2 (climate sensitivity). IPCC Working Group I (WGI) estimates may be
used to calibrate two possible distributions, 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 characterized 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 consequences. 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]
TS.2 Trends in stocks and flows of greenhouse gases and their drivers
This section summarizes historical GHG emission trends and their underlying drivers. As in most of
the underlying literature, all aggregate GHG emission estimates are converted to CO2eq based on
Global Warming Potentials with a 100‐year time horizon (GWP100) [Box TS.5]. The majority of
changes in GHG emission trends that are observed in this section are related to changes in drivers
such as economic 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. Since AR4 there is a growing
literature on uncertainties in global GHG emission data sets. This section tries to make these
uncertainties explicit and reports variation 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) GtCO2eq/yr in 2010. Current trends are at
the high end of levels that had been projected for the last decade. Emission growth has occurred
despite the presence of a wide array of multilateral institutions as well as national policies aimed at
mitigating emissions. From 2000 to 2010, GHG emissions grew on average 2.2% per year compared
to 1.3% per year over the entire period from 1970 to 2000 [Figure TS.1]. The global economic crisis
2007/2008 has temporarily reduced global emissions but not changed the longer‐term trend.
Whereas more recent data are not available for all gases, initial evidence suggests that growth in
global CO2 emissions from fossil fuel combustion has continued with emissions increasing by about
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3% between 2010 and 2011 and by about 1–2% between 2011 and 2012. [1.3, 5.2, 13.3, 15.2.2,
Figure 15.1]
CO2 remains the major anthropogenic GHG with 76% of total GHG emissions in 2010 weighed by
GWP100 (high confidence). Since AR4 the shares of the major groups of GHG emissions have
remained stable. The share of CO2 emission was 76% in 2010, CH4 contributed 16%, N2O about 6%
and the combined fluorinated‐gases3 (F‐gases) about 2% [Figure TS.1]. Using the most recent GWP100
values from the Fifth Assessment Report [WG1 8.6] global GHG emission totals would be slightly
higher (52 GtCO2eq/yr) and non‐CO2 emission shares would be 20% for CH4, 5% for N2O and 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 CO2 would decline to just over 50% of total anthropogenic GHG emissions and short‐lived
gases would rise in relative importance. The choice of emission metric and time horizon involves
explicit or implicit value judgements and depends on the purpose of the analysis [Box TS.5]. [1.2, 3.9,
5.2]
Figure TS.1. Total annual anthropogenic GHG emissions (GtCO2eq/yr) by groups of gases 19702010: CO2 from fossil fuel combustion and industrial processes; CO2 from Forestry and Other Land
Use (FOLU); methane (CH4); nitrous oxide (N2O); fluorinated gases3 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 anthropogenic GHG emissions uncertainties are derived from the individual gas
estimates as described in chapter 5 [5.2.3.6]. Emissions are converted into CO2-equivalents based on
Global Warming Potentials with a 100 year time horizon (GWP100) from the IPCC Second Assessment
Report. The emissions data from FOLU represents land-based CO2 emissions from forest and peat
fires and decay that approximate to net CO2 flux from the FOLU as described in chapter 11 of this
report. Average annual growth rate for the four decades are highlighted with the brackets. The
average annual growth rates from 1970 to 2000 is 1.3% per year. [Figure 1.3]
3
In this report data on fluorinated gases is taken from the EDGAR database (Annex A.II.9), which covers
substances included in the Kyoto Protocol.
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Over the last four decades total cumulative CO2 emissions have increased by a factor of 2 from
about 900 GtCO2 for the period 1750–1970 to about 2000 GtCO2 for 1750–2010 (high confidence).
In 1970 the cumulative CO2 emissions from fossil fuel combustion, cement production and flaring
since 1750 was 420±35 GtCO2; in 2010 that cumulative total had tripled to 1300 ±110 GtCO2 (Figure
TS.2). Cumulative CO2 emissions associated with Forestry and Other Land Use (FOLU)4 since 1750
increased from about 490±180 GtCO2 in 1970 to approximately 680±300 GtCO2 in 2010. [5.2]
Regional patterns of GHG emissions are shifting along with changes in the world economy (high
confidence). 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 GtCO2eq of this sectoral increase occurred in upper‐middle income countries,5
where the most rapid economic development and infrastructure expansion has taken place. GHG
emission growth in the other sectors has been more modest in absolute (0.3–1.1 Gt CO2eq) as well
as in relative terms (3%–11%). [1.3, 5.3, Figure 5.18]
Current GHG emission levels are dominated by contributions from the energy supply, AFOLU, and
industry sectors; industry and building gain considerably in importance if indirect emissions are
accounted for (robust evidence, high agreement). Of the 49(±4.5) GtCO2eq emissions in 2010, 35% of
GHG emissions were released in the energy supply sector, 24% in Agriculture, Forestry and Other
Land‐Use (AFOLU), 21% in industry, 14% in transport, and 6.4% in buildings. When indirect emissions
from electricity and heat production 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%, respectively (Figure
TS3). [1.3, 7.3, 8.2, 9.2, 10.3, 11.2]
4
FOLU (Forestry and Other Land Use) – also referred to as LULUCF (Land use, land‐use change, and forestry) –
is the subset of AFOLU emissions and removals of greenhouse gases related to direct human‐induced land use,
land‐use change and forestry activities excluding agricultural emissions (see Annex I).
5
When countries are assigned to income groups in this Technical Summary, the World Bank income
classification for 2013 is used. For details see Annex A.II.3.
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Figure TS.2. Historical anthropogenic CO2 emissions from fossil fuel combustion, flaring, cement, and
Forestry and Other Land Use (FOLU) in five major world regions: OECD-1990 (blue); Economies in
Transition (yellow); Asia (green); Latin America (red); Middle East and Africa (brown). Emissions are
reported in gigatonnnes of CO2 per year (Gt/yr). Left panels show regional CO2 emission trends
1750–2010 from: (a) the sum of all CO2 sources (c+e); (c) fossil fuel combustion, flaring, and cement;
and (e) FOLU. The right panels report regional contributions to cumulative CO2 emissions over
selected time periods from: (b) the sum of all CO2 sources (d+f); (d) fossil fuel combustion, flaring and
cement; and (f) FOLU. Error bars on (d) and (f) give an indication of the uncertainty range (90%
confidence interval). See Annex II.2 for regional definitions. [Figure 5.3]
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Figure TS.3. Allocation of GHG emissions across sectors and country income groups. Panel a: Share
(in %) of direct GHG emissions in 2010 across the sectors. Indirect CO2 emission shares from
electricity and heat production are attributed to sectors of final energy use. Panel b: Shares (in %) of
direct and indirect emissions in 2010 by major economic sectors with CO2 emissions from electricity
and heat production attributed to the sectors of final energy use. Lower panel: Total anthropogenic
GHG emissions in 1970, 1990 and 2010 by economic sectors and country income groups. GHG
emissions from international transportation are reported separately. The emissions data from
Agriculture, Forestry and Other Land Use (AFOLU) includes land-based CO2 emissions from forest
and peat fires and decay that approximate to net CO2 flux from the Forestry and Other Land Use
(FOLU) sub-sector as described in chapter 11 of this report. Emissions are converted into CO2-
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equivalents based on Global Warming Potentials with a 100 year time horizon (GWP100) from the
IPCC Second Assessment Report. 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. [Figure 1.3, Figure 1.6]
Per capita GHG emissions in 2010 are highly unequal (high confidence). In 2010, median per capita
GHG emissions (1.4 tCO2eq/cap/yr) for the group of low‐income countries are around nine times
lower than median per capita GHG emissions (13 tCO2eq/cap/yr) of high‐income countries (Figure
TS.4; for region definitions see Annex II.2.3). For low‐income countries, the largest part of emissions
come from AFOLU; for high‐income countries, emissions are dominated by sources related to energy
supply and industry. There are substantial variations in per capita GHG emissions within country
income groups with emissions 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 heterogeneous members than mean per capita emissions. Mean per
capita emissions are different from median mainly in low‐income countries as some low‐income
countries have higher per capita emissions due to larger CO2 emissions from land‐use change. [1.3,
5.2, 5.3]
Figure TS.4. Trends in GHG emissions by country income groups. Left panel: Total annual
anthropogenic GHG emissions from 1970 to 2010 (GtCO2eq/yr). Middle panel: Trends in annual per
capita mean and median GHG emissions from 1970 to 2010 (tCO2eq/cap/yr). Right panel: Distribution
of annual per capita GHG emissions in 2010 of countries within each income group (tCO2/cap/yr).
Mean values show the GHG emission levels weighed by population. Median values describe GHG
emission levels per capita of the country at the 50th percentile of the distribution within each income
group. Emissions are converted into CO2-equivalents based on Global Warming Potentials with a 100
year time horizon (GWP100) from the IPCC Second Assessment Report. Assignment of countries to
income groups is based on the World Bank income classification in 2013. For details see Annex II.2.3.
[Figure 1.4, Figure 1.8] [Figure 1.4, Figure 1.8]
A growing share of total anthropogenic CO2 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 traditional ‘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 CO2 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 CO2
emissions from the non‐Annex I group now exceed those of the Annex I group using territorial and
consumption accounting methods, but per‐capita emissions are still markedly higher in the Annex I
group. [1.3, 5.3]
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Regardless of the perspective taken, the largest share of anthropogenic CO2 emissions is emitted
by a small number of countries (high confidence). In 2010, 10 countries accounted for about 70% of
CO2 emissions from fossil fuel combustion and industrial processes. A similarly small number of
countries emit the largest share of consumption‐based CO2 emissions as well as cumulative CO2
emissions going back to 1750. [1.3]
The upward trend in global fossil fuel related CO2 emissions is robust across databases and despite
uncertainties (high confidence). Global CO2 emissions from fossil fuel combustion are known within
8% uncertainty. CO2 emissions related to FOLU have very large uncertainties attached in the order of
50%. Uncertainty for global emissions of CH4, N2O, and the F‐gases has been estimated as 20%, 60%,
and 20%. Combining these values yields an illustrative total global GHG uncertainty estimate of
order 10% (Figure TS.1). Uncertainties can increase at finer spatial scales and for specific sectors.
Attributing emissions to the country of final consumption increases uncertainties, but literature on
this topic is just emerging. GHG emission estimates in the AR4 were 5–10% higher than the
estimates reported here, but lie within the estimated uncertainty range. All uncertainties reported
here are reported for a 90% confidence interval. [5.2]
Figure TS.5. Total annual CO2 emissions (GtCO2/yr) from fossil fuel combustion for country income
groups attributed on the basis of territory (solid line) and final consumption (dotted line). The shaded
areas are the net CO2 trade balance (difference) between each of the four country income groups and
the rest of the world. Blue shading indicates that the country group is a net importer of embodied CO2
emissions, leading to consumption-based emission estimates that are higher than traditional territorial
emission estimates. Orange indicates the reverse situation – the country group is a net exporter of
embodied CO2 emissions. Assignment of countries to income groups is based on the World Bank
income classification in 2013. For details see Annex II.2.3. [Figure 1.5]
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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 important purposes, including apportioning
mitigation efforts among several gases and aggregating emissions of a variety of GHGs. However, it
turns out that there is no perfect 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
causes a stronger instantaneous radiative forcing compared to CO2, 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 resulting 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—GWP100), caused by emitting one
kilogram of the gas, relative to that of the reference gas CO2. This metric is used to transform the
effects of different emissions to a common scale (CO2‐equivalents).6 One strength of the GWP is that
it can be calculated in a relatively transparent and straightforward manner. However, there are also
some important limitations, including the requirement to use a specific time horizon, the focus on
cumulative 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 GWP100 is 28 while its Global
Temperature 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, GWP100 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, 6.2]
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 emissions in the different groups. [3.9.6; WGI 8.7]
6
In this summary, all quantities of GHG emissions are expressed in CO2‐equivalent (CO2eq) emissions that are
calculated based on GWP100. Unless otherwise stated, GWP values for different gases are taken from the
Second Assessment 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 GWP100 values from SAR to AR4 have
little impact on the optimal mitigation strategy at the global level. [6.3.2.5, A.II.9.1]
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TS.2.2 Greenhouse gas emission drivers
This section examines the factors that have, historically, been associated with changes in emission
levels. Typically, such analysis is based on a decomposition of total emissions into various
components such as growth in the economy (GDP/capita), growth in the population (capita), the
energy intensity needed per unit of economic output (energy/GDP) and the emission 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 CO2 from industry and energy, almost all this research focuses on CO2
from those sectors.
Growth in economic output and population are the two main drivers for worldwide increasing
GHG emissions, outpacing emission reductions from improvements in energy intensity (high
confidence). Worldwide population increased by 86% between 1970 and 2010, from 3.7 to 6.9 billion.
Over the same period, economic growth as measured through production and/or consumption has
also grown a comparable amount, although the exact measurement of global economic growth is
difficult because countries use different currencies and converting individual national economic
figures into global totals can be done in various ways. With rising population and economic output,
emissions of CO2 from fossil fuel combustion have risen as well. Over the last decade the importance
of economic growth as a driver of global emissions has risen sharply while population growth has
remained roughly steady. Due to technology, changes in the economic structure, the mix of energy
sources and changes in other inputs such as capital and labour, the energy intensity of economic
output has steadily declined worldwide, and that decline has had an offsetting effect on global
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 emissions over longer
periods of time. Decoupling remains largely atypical, especially when considering consumption‐
based emissions. [1.3, 5.3]
Figure TS.6. Decomposition of decadal absolute changes in total CO2 emissions from fossil fuel
combustion by Kaya factors: population (blue), GDP per capita (red), energy intensity of GDP (green)
and carbon intensity of energy (purple). Total decadal changes in CO2 emissions are indicated by a
black triangle. Changes are measured in gigatonnes of CO2 emissions per year (Gt/yr). [Figure 1.7]
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Between 2000 and 2010 increased use of coal relative to many 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 indicate that coal, and unconventional gas and oil resources
are large; therefore reducing the carbon intensity of energy may not be primarily 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 behaviour affect emissions through
productivity growth, energy‐ and carbon‐intensity and consumption patterns (medium confidence).
Technological innovation improves labour and resource productivity; it can support economic
growth both with increasing and with decreasing emissions. The direction and speed of technological
change also depends on policies. Technology is also central to the choices of infrastructure and
spatial organization, such as in cities, which can have long‐lasting effects on emissions. In addition, 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 emissions. [5.3, 5.5, 5.6, 12.3]
Without explicit efforts to reduce GHG emissions, the fundamental drivers of emissions growth
are expected to persist despite major improvements in energy supply and end‐use technologies
(high confidence). Atmospheric concentrations in baseline scenarios collected for this assessment
(scenarios without explicit additional efforts to constrain emissions) exceed 450 ppm CO2eq by 2030.
They reach CO2eq concentration levels from 750 to more than 1300 ppm CO2eq by 2100. The range
of 2100 concentrations corresponds roughly to the range of CO2eq concentrations in the
Representative Concentration Pathways RCP 6.0 and RCP 8.5 pathways7, with the majority of
scenarios falling below the latter. Based on calculations consistent with the scenario evidence
presented in this report, atmospheric CO2eq concentrations were about 400ppm CO2eq in 2010. This
represents full radiative forcing including greenhouse gases, halogenated gases, tropospheric ozone,
aerosols, and albedo change. The scenario literature does not systematically explore the full range of
uncertainty surrounding development pathways and possible evolution of key drivers such as
population, technology, and resources. Nonetheless, the scenarios strongly suggest that absent any
explicit mitigation efforts, cumulative CO2 emissions since 2010 suggest that will exceed 700 GtCO2
by 2030, 1,500 GtCO2 by 2050, and potentially well over 4,000 GtCO2 by 2100. [6.3.1]
7
For the Fifth Assessment Report of IPCC, the scientific community has defined a set of four new scenarios,
denoted Representative Concentration Pathways (RCPs, see Glossary). They are identified by their
approximate total radiative forcing in year 2100 relative to 1750: 2.6 W m‐2 for RCP2.6, 4.5 W m‐2 for RCP4.5,
6.0 W m‐2 for RCP6.0, and 8.5 W m‐2 for RCP8.5.
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Figure TS.7. Global baseline projection ranges for Kaya factors. Scenarios harmonized with respect
to a particular factor are depicted with individual lines. Other scenarios depicted as a range with
median emboldened; shading reflects interquartile range (darkest), 5th – 95th percentile range
(lighter), and full extremes (lightest), excluding one indicated outlier in population panel. 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]
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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 W/m2 (corresponding to
concentrations of 450, 650, 850, and 1370 ppm CO2eq), respectively, in 2100, covering the range of
anthropogenic climate forcing 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 Working Group I. [WGI
6.4, 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 emissions (‘mitigation
scenarios’), respectively, generally include socio‐economic projections in addition to emission,
concentration, and climate change information. Working Group III 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‐economic
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 assumptions 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 exception: 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 Working Group III
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 CO2eq in 2100. The
climate change projections of Working Group I based on RCPs, and the mitigation scenarios assessed
by Working Group III can be related to each other through the climate outcomes they imply. [6.2.1]
TS.3 Mitigation pathways and measures in the context of sustainable
development
This section assesses the literature on mitigation pathways and measures in the context of
sustainable development. Section TS 3.1 first examines the emissions characteristics and potential
temperature implications of mitigation pathways leading to a range of future atmospheric CO2eq
concentrations. It then explores the technological, economic, and institutional requirements of these
pathways along with their potential co‐benefits and adverse side‐effects. Section TS 3.2 then
examines options for managing emissions by sector and how mitigation strategies may interact
across sectors.
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TS.3.1 Mitigation pathways
TS.3.1.1 Understanding mitigation pathways in the context of multiple objectives
Society will need to both mitigate and adapt to climate 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, inherent difficulties associated with quantifying their
interdependencies have limited their representation in models used to generate mitigation scenarios
assessed in WGIII AR5 [Box TS.7]. [2.4.4.4, 6.3.3]
There is no single pathway to stabilize greenhouse gas concentrations at any level; instead, the
literature points to a wide range of mitigation pathways that might meet any concentration level
(high confidence). 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
CO2eq concentrations to a particular level, the degree to which concentrations temporarily exceed
(overshoot) 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 countries, 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, political, cultural and technological
features—enables and constrains the prospects for mitigation. [4.2, 6.3]
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 aggregate economic
costs and benefits have often been put forward as key decision‐making factors, they are far from the
only outcomes that matter. Mitigation pathways inherently involve a range of synergies and
tradeoffs connected with other policy objectives such as energy and food security, the distribution of
economic impacts, local air quality, other environmental factors associated with different
technological solutions, and economic competitiveness. Many of these fall under the umbrella of
sustainable development. In addition, requirements such as the rates of upscaling of energy
technologies or the rates of reductions in emissions may provide important insights into the degree
of challenge presented by meeting a particular long‐term goal. [4.5, 4.8, 6.3, 6.4, 6.6]
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Box TS.7. Scenarios from integrated models to help 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 representation 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, in this assessment only
integrated models with sufficient sectoral and geographic resolution to understand the evolution of
key processes such as energy systems or land systems have been included.
Scenarios from integrated models are invaluable to help understand how possible actions or choices
might lead to different future outcomes in these complex systems. They provide quantitative, 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, excluding the consideration of some behavioural factors.
Scenarios from these models capture only some of the dimensions of development pathways that
are relevant to mitigation options, often only minimally treating issues such as distributional impacts
of mitigation 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 independently from climate
impacts. Finally, and most fundamentally, integrated 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]
TS.3.1.2 Short‐ and long‐term requirements of mitigation pathways
Mitigation scenarios point to a range of technological and behavioral measures that would allow
the world’s societies to follow emissions pathways compatible with atmospheric concentration
levels between about 450 ppm CO2eq to more than 750 ppm CO2eq by 2100; this is comparable to
CO2eq concentrations between RCP 2.6 and RCP 6.0 (high confidence). As part of this assessment,
about 900 mitigation scenarios (out of more than 1200 total scenarios) have been collected from
integrated modelling research groups from around the world [Box TS.7]. These scenarios have been
constructed to reach a range of atmospheric CO2eq concentrations and cumulative GHG emissions
levels under very different assumptions about energy demands, international cooperation,
technology, the contributions of CO2 and other forcing agents, as well as the degree by which
concentrations peak and decline during the century (concentration overshoot) [Box TS.8]. No multi‐
model comparison study and only a limited number of individual studies have explored pathways to
atmospheric concentrations of below 430 ppm CO2eq by 2100 [Figure TS.8, left panel]. [6.3]
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Figure TS.8. Development of total GHG emission for different long-term concentration levels (left
panel) and for scenarios reaching 430–530 ppm CO2eq in 2100 with and without net negative CO2
emissions larger than 20 GtCO2/yr (right panel). Ranges are given for the 10–90th percentile of
scenarios. The grey bars to the right of the top panels indicate the full 2100 range (not only the 10th–
90th percentile) for baseline scenarios. [Figure 6.7]
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 with
Article 2 of the UNFCCC calling for the need to ‘stabilize’ concentrations of greenhouse gases.
Stabilization of concentrations is generally understood to mean that the CO2eq 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
emissions reductions to reach a long‐term concentration level and implies more flexibility for the
system to reach specific long‐term concentration levels with comparatively less mitigation in the
near term.
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. Transient
temperature response is less uncertain than the equilibrium response and correlates more strongly
with GHG emissions in the near and medium term. An additional reason this assessment focuses on
transient temperature is that the mitigation pathways assessed in 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 concentration pathway will limit either transient or equilibrium temperature change
below a specified level. It is only possible to express the temperature implications of particular
concentration pathways in probabilistic terms, and such estimates will be dependent on the source
of the probability distribution of different climate parameters. This report employs a distribution of
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climate parameters that result in temperature outcomes with dynamics similar to those from the
Earth System Models assessed in WGI. 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 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 maintain transient temperature below specific
levels. Providing the combination of information about the plausible range of temperature outcomes
as well as the likelihood of meeting different targets is of critical importance for policy making, since
it facilitates the assessment of different climate objectives from a risk management perspective.
[6.2]
Limiting peak atmospheric concentrations over the course of the century—not only reaching long‐
term concentration levels—is critical for limiting temperature change (high confidence). The
temperature response results presented in this assessment are based on climate simulations with
dynamics similar to those from the Earth System Models assessed in WGI. Scenarios that reach 2100
concentrations between 530 ppm and 580 ppm CO2eq while exceeding this range during the course
of the century are unlikely to limit transient temperature change to below 2°C over the course of the
century compared to pre‐industrial levels.8 The majority of scenarios reaching long‐term
concentrations between 430 to 480 ppm CO2eq in 2100 are likely to keep temperature change below
2°C over the course of the century relative to pre‐industrial levels and are associated with peak
concentrations below 530 ppm CO2eq [Table TS.1, Box TS.8]. Only a limited number of studies have
explored emissions pathways consistent with limiting long‐term temperature change to below 1.5°C
in 2100 relative to pre‐industrial times. In these scenarios, temperature peaks over the course of the
century and is brought back to 1.5°C with a likely chance at the end of the century. These scenarios
assume immediate introduction of climate policies as well as the rapid upscaling of the full portfolio
of mitigation technologies combined with low energy demand in order to bring concentration levels
below 430 ppm CO2eq in 2100. [6.3]
Many scenarios that reach atmospheric concentrations of 430 to 580 ppm CO2eq by 2100 are
based on concentration overshoot; concentrations peak during the century before descending
toward their 2100 levels (high confidence). Overshoot involves relatively less mitigation in the near
term, but it also involves more rapid and deeper emissions reductions in the long run. The vast
majority of scenarios reaching between 430 to 480 ppm CO2eq 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 530 to 580 ppm CO2eq by 2100 without overshoot. Many overshoot scenarios
rely on the deployment of carbon dioxide removal (CDR) technologies to remove CO2 from the
atmosphere (negative emissions) in the second half of the century; however, CDR technologies are
also valuable in non‐overshoot scenarios. The majority of scenarios with overshoot of greater than
0.4 W/m2 (>35–50 ppm CO2eq concentration) deploy CDR technologies to an extent that net global
CO2 emissions become negative. These scenarios are associated with lower flexibility with respect to
choices about the technology portfolio, since they rely on negative emissions from the deployment
of CDR technologies whose availability and scale is uncertain. A variety of CDR technologies have
been identified with diverse risk profiles. Long‐term mitigation scenarios in the literature have
focused on large‐scale afforestation and bioenergy coupled with CCS (BECCS) (Figure TS.8, right
panel). [6.3, 6.9]
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 AR5 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.
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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
1,2
shown . [Table 6.3]
CO2eq
Concentrations in
2100 (CO2eq)
Cumulative CO2 emission3
(GtCO2)
Subcategories
Category label
(concentration
range) 9
<
–
–
–
–
–
1
>
–
Total range 1,10
No overshoot of
RCP2.6
Total range
2100
ppm CO eq
–
–
‐47 to ‐19
–
‐134 to ‐50
–
‐38 to 24
‐11 to 17
–
–
18 to 54
52 to 95
‐7 to 72
–
–
RCP4.5
RCP6.0
RCP8.5
Temperature change (relative to 1850–1900)5,6
2100 Temperature
change (°C)7
Likelihood of staying below temperature level over the 21st century8
1.5°C
Only a limited number of individual model studies have explored levels below 430 ppm CO2eq
More unlikely
‐72 to ‐41
‐118 to ‐78
–
. – .
. – .
than likely
–
‐57 to ‐42
ppm CO eq
Total range
2050
–
Overshoot of
Total range
2011–2100
–
ppm CO eq
Total range
2011–2050
ppm CO eq
Overshoot of
No overshoot of
Relative
position of
the RCPs5
Change in CO2eq
emissions compared to
2010 in (%)4
–
–
–
–
–
–
‐107 to ‐73
‐55 to ‐25
‐114 to ‐90
‐16 to 7
‐183 to ‐86
‐81 to ‐59
. – .
. – .
. – .
. – .
. – .
. – .
. – .
. – .
. – .
. – .
‐54 to ‐21
. – .
. – .
74 to 178
. – .
. – .
. – .
Unlikely
. – .
2.0 °C
3.0 °C
4.0 °C
Likely
More likely than
not
About as likely
as not
Likely
Likely
More unlikely
than likely
Unlikely
Unlikely11
Unlikely11
More likely than
not
More unlikely
than likely
Unlikely
More unlikely
than likely
The 'total range' for the 430–480 ppm CO2eq scenarios corresponds to the range of the 10–90th percentile of the subcategory of these scenarios shown in table 6.3.
Baseline scenarios (see SPM.3) are categorized in the >1000 and 720–1000 ppm CO2eq categories. The latter category includes also 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 CO2eq category, this leads to an overall 2100 temperature range of 2.5–7.8°C (median: 3.7–4.8°C) for baseline scenarios across both concentration categories.
3
For comparison of the cumulative CO2 emissions estimates assessed here with those presented in WGI, an amount of 515 [445 to 585] GtC (1890 [1630 to 2150] GtCO2), was already emitted by 2011 since 1870 [Section WGI 12.5]. Note that
cumulative emissions are presented here for different periods of time (2011–2050 and 2011–2100) while cumulative emissions in WGI 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 emission estimates presented in this report). CO2eq emissions include the basket of Kyoto gases (CO2, CH4, N2O as well as F‐gases).
5
The assessment in WGIII involves a large number of scenarios published in the scientific literature and is thus not limited to the RCPs. To evaluate the greenhouse gas concentration and climate implications of these scenarios, the MAGICC model
was used in a probabilistic mode (see Annex II). For a comparison between MAGICC model results and the outcomes of the models used in WGI, see Section WGI 12.4.1.2 and 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 AR4 (Table 3.5, Chapter 3 WGIII). For the 2100 temperature estimates, the transient climate response (TCR) is the most
relevant system property. The assumed 90th percentile uncertainty range of the TCR for MAGICC is 1.2–2.6°C (median 1.8°C). This compares to the 90th percentile 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 IPCC AR5 WGI report (Box 12.2 in chapter 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 also 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 using MAGICC and the assessment in WGI of the uncertainty of the temperature projections not covered by climate models. The
statements are therefore consistent with the statements in WGI, 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 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.
2
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The CO2 equivalent concentration includes the forcing of all GHGs including halogenated gases and tropospheric ozone, aerosols and albedo change (calculated on the basis of the total forcing from a simple carbon cycle/climate model MAGICC).
The vast majority of scenarios in this category overshoot the category boundary of 480 ppm CO2eq concentrations.
11
For scenarios in this category no CMIP5 run (WGI AR5: 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 CO2eq 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 exceed the 2°C temperature level, while the former are mostly assessed to have an unlikely probability of exceeding this level.
10
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Reaching atmospheric concentrations levels of 430 to 530 ppm CO2eq by 2100 will require cuts in
GHG emissions and limits on cumulative CO2 emissions in both the medium‐ and long‐term (high
confidence). The majority of scenarios reaching 430 to 480 ppm CO2eq by 2100 are associated with
GHG emissions reductions of over 40% to 70% by 2050 compared to 2010. The majority of scenarios
that reach 480 to 530 ppm CO2eq in 2100 without exceeding this concentration at any point during
the century are associated with CO2eq emissions reductions of 40% to 55% by 2050 compared to
2010 [Figure TS.8, left panel]. In contrast, in some scenarios in which concentrations exceed 530
ppm CO2eq during the century before descending to concentrations below this level by 2100,
emissions rise to as high as 20% above 2010 levels in 2050, but these scenarios are characterized by
negative emissions of over 20 GtCO2 in the second half of the century [Figure TS.8, right panel].
Cumulative CO2 emissions between 2011 and 2100 are 630–1180 GtCO2 in scenarios reaching 430 to
480 ppm CO2eq in 2100; they are 960–1550 GtCO2 in scenarios reaching 480 ppm to 530 ppm CO2eq
in 2100. The variation in cumulative CO2 emissions across scenarios is due to differences in the
contribution of non‐CO2 greenhouse gases 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 430 to 530 ppm CO2eq by 2100, the majority
of mitigation relative to baseline emissions over the course of century will occur in the non‐OECD
countries (high confidence). In scenarios that attempt to cost‐effectively allocate emissions
reductions across countries and over time, the total CO2eq 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 outstrip those from the OECD countries,
but it also derives from higher carbon intensities in non‐OECD countries and different terms of trade
structures. In these scenarios, emissions peak earlier in the OECD countries than in the non‐OECD
countries. [6.3]
Reaching atmospheric concentrations levels of 430 to 650 ppm CO2eq by 2100 will require large‐
scale changes to global and national energy systems over the coming decades (high confidence).
Scenarios reaching atmospheric concentrations levels between 430 ppm and 530 ppm CO2eq by
2100 are characterized by a tripling to nearly a quadrupling of the share of low‐carbon energy supply
from renewables, nuclear energy, and fossil energy with carbon dioxide capture and storage (CCS) by
the year 2050 relative to 2010 (about 17%) [Figure TS.10, left panel]. The increase in total low‐
carbon energy supply is from three‐fold to seven‐fold over this same period. Many models cannot
reach 2100 concentration levels between 430 ppm and 480 ppm CO2eq if the full suite of low‐
carbon technologies is not available. Studies indicate a large potential for energy demand reductions,
but also indicate that demand reductions on their own would not be sufficient to bring about the
reductions needed to reach levels of 650 ppm CO2eq 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 alternative land transformations may be consistent with similar
concentration levels (medium confidence). Land use dynamics in mitigation are heavily influenced by
the production of bioenergy and the degree to which afforestation is deployed as a negative
emissions, or carbon dioxide removal (CDR) option. They are, in addition, influenced by forces
independent of mitigation such as agricultural productivity improvements and increased demand for
food. The range of land use transformations 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 through 2030 will increase the challenges of, and reduce the options for,
bringing atmospheric concentration levels to 530 ppm CO2eq or lower by the end of the century
(high confidence). The majority of scenarios leading to atmospheric concentration levels between
430 ppm CO2eq and 530 ppm CO2eq at the end of the 21st century are characterized by 2030
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emissions roughly between 30 GtCO2eq and 50 GtCO2eq. Scenarios with emissions above 55
GtCO2eq in 2030 are predominantly driven by delays in mitigation [Figure TS.9, left panel; Figure
TS.11]. These scenarios are characterized by substantially higher rates of emissions reductions from
2030 to 2050 (mean emission reductions of 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 (a quadrupling compared to
a doubling of the low‐carbon energy share) [Figure TS 10, right panel]; a larger reliance on CDR
technologies in the long term [Figure TS.8, right panel]; and higher transitional and long term
economic impacts [Figure TS 13, left panel]. Due to these increased challenges, many models with
2030 emissions in this range could not produce scenarios reaching atmospheric concentrations
levels in the range between 430 and 530 ppm CO2eq in 2100. [6.4, 7.11]
The Cancún Pledges for 2020 are higher than GHG emission levels from scenarios that reach
atmospheric concentrations levels between 430 and 530 ppm CO2eq by 2100 at lowest global costs.
The Cancun Pledges correspond to scenarios that explicitly delay mitigation through 2020 or
beyond relative to what would achieve lowest global cost (robust evidence, high agreement). The
Cancún Pledges are broadly consistent with scenarios reaching 550 ppm CO2eq to 650 ppm CO2eq by
2100 without delays in mitigation. Studies confirm that delaying mitigation through 2030 has
substantially larger influence on the subsequent challenges of mitigation than do delays through
2020 [Figure TS.11]. [6.4]
Figure TS.9 The implications of different 2030 GHG emissions levels for the pace of CO2 emissions
reductions to 2050 in mitigation scenarios reaching 430–530 ppm CO2eq concentrations by 2100. Left
panel shows the development of GHG emissions to 2030. Right panel denotes the corresponding
annual CO2 emissions reduction rates for the period 2030–2050. The scenarios are grouped
according to different emissions levels by 2030 (coloured in different shades of green). The range of
global GHG emissions in 2020 implied by the Cancún Pledges is based on an analysis of alternative
interpretations of national pledges (see Section 13.13.1.3 for details). The right panel compares the
median and interquartile range across scenarios from recent intermodelling comparisons with explicit
2030 interim goals with the range of scenarios in the WG III AR5 Scenario Database. Annual rates of
historical emissions change (sustained over a period of 20 years) are shown in grey. Note: Only
scenarios with default technology assumptions are shown. Scenarios with non-optimal timing of
mitigation due to exogenous carbon price trajectories are excluded. [Figure 6.32]
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Figure TS.10 The upscaling of low-carbon energy in scenarios meeting different 2100 CO2eq
concentration levels (left panel). The right panel shows the rate of upscaling subject to different 2030
GHG emissions levels in mitigation scenarios reaching 430–530 ppm CO2eq by 2100 (from model
intercomparisons with explicit 2030 interim goals). Bars show the interquartile range and error bands
the full range across the scenarios. Low-carbon technologies include renewables, nuclear energy and
fossil fuels and bioenergy with CCS. Note: Only scenarios with default technology assumptions are
shown. In addition, scenarios with non-optimal timing of mitigation due to exogenous carbon price
trajectories are excluded in the right panel. [Figure 7.16]
Figure TS.11 Near-term GHG emissions from mitigation scenarios reaching 430–530 ppm CO2eq
concentrations by 2100. 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%. Colours refer to scenario classification in terms of whether net CO2 emissions become
negative before 2100 and the timing of international participation (immediate vs. delay). 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 (see
Section 13.13.1.3 for details). Note: In the AR5 scenarios database, only four reported scenarios were
produced based on delayed mitigation without net negative emissions while still lying below 530 ppm
CO2eq 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]
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TS.3.1.3 Costs, investments and burden sharing
Globally comprehensive and harmonized mitigation actions would result in significant economic
benefits compared to fragmented approaches, but would require establishing effective
institutions (high confidence). Economic analysis of mitigation scenarios demonstrate that
coordinated and globally comprehensive mitigation 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 economy. Instruments with limited coverage of emissions reductions
among sectors and climate policy regimes with fragmented regional action increase aggregate
economic costs. These increased costs 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 confidence). Most scenario studies 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 430–480 ppm CO2eq by 2100 would entail global consumption losses of 1% to 4% in
2030, 2% to 6% in 2050, and 2% to 12% in 2100 relative to what would happen without mitigation
[Figure TS.12, Box TS.9, Box TS.10]. These consumption losses do not consider the benefits of
mitigation, including the reduction in climate impacts. To put these losses in context, studies assume
increases in consumption from four‐fold to over ten‐fold over the century without mitigation. Costs
for maintaining concentrations in the range of 530‐650 ppm CO2eq are estimated to be roughly one‐
third to two‐thirds lower than for associated 430‐530 ppm CO2eq 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]
Figure TS.12 Global carbon prices (left panel) and consumption losses (right panel) over time in
idealized implementation scenarios. Consumption losses are expressed as the percentage reduction
from consumption in the baseline. Box plots show range, 25 to 75 percentile (box) and median (bold
line) of scenario samples. The number of scenarios included in the boxplots is indicated at the bottom
of the panels. 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. Colours refer to
categories of long-term atmospheric CO2eq concentrations in 2100: 430-480 ppm CO2eq (light blue),
480-530 ppm CO2eq (dark blue), 530-580 ppm CO2eq (yellow), 580-650 ppm CO2eq (orange), 650720 ppm CO2eq (red). [Figure 6.21]
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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.
Consumption losses are often used as a metric because they emerge from many integrated models
and they directly impact welfare.
Mitigation costs need to be distinguished from emissions prices. Emissions prices measure the cost
of an additional unit of emissions 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 emission reductions are
induced).
In general, model‐based assessments 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, idealized implementation scenarios assume a uniform price on CO2 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]
Delays in mitigation 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 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 decision makers to undertake a more rapid,
deeper, and costlier future transformation from this infrastructure. Studies have found that 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 studies have
found that delayed mitigation through 2030 can substantially increase the mitigation costs of
meeting 2100 concentrations between 430 ppmv CO2eq and 530 ppmv CO2eq, particularly in
scenarios with emissions greater than 55 GtCO2eq in 2030. Many models could not reach 2100
concentrations levels of 430 to 530 ppm CO2eq from such emission levels in 2030 [Figure TS.13, left
panel]. [6.3]
The technological options available for mitigation greatly influence mitigation costs and the
challenges of reaching atmospheric concentration levels between 430 and 580 ppm CO2eq by 2100
(high confidence). Many models in recent model intercomparisons could not produce scenarios
reaching atmospheric concentrations between 430 and 480 ppm CO2eq 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
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mitigation costs and the challenges of reaching concentration levels in this range. For those models
that could produce such scenarios, pessimistic assumptions about these increased discounted global
mitigation costs of reaching concentration goals in the range of 430–480 ppm and 530–580 ppm
CO2eq by the end of the century significantly, with the effect being larger for more stringent
mitigation scenarios. The studies also showed that reducing energy demand could potentially
decrease mitigation costs significantly [Figure TS.13, right panel]. [6.3]
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 = unavailability of 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 = 20% limit on solar and wind electricity generation; Limited
Bioenergy = maximum of 100 EJ/yr bioenergy supply [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 mitigation (referred to here
as ‘mitigation gap’). The mitigation gap is defined as the difference in cumulative CO2 emissions
reductions until 2030 between the immediate and delayed 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 GtCO2, respectively, are found. [Figure 6.25]
Effort‐sharing frameworks can help to clarify discrepancies between the distribution of costs
based on mitigation potential and the distribution of responsibilities based on ethical principles,
and they can help reconcile those discrepancies through international financial transfers (medium
confidence). Studies find that in order to reach concentrations of 430 ppm to 580 ppm CO2eq in
2100 at lowest global cost, the majority of mitigation investments over the course of century will
occur in the non‐OECD countries. Studies estimate that the financial transfers to ameliorate this
asymmetry could be in the order of hundred billions of USD per year before mid‐century to bring
concentrations within the range of 430‐530 ppm CO2eq in 2100. Most studies assume efficient
mechanisms for international transfers, in which case economic theory and empirical research
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suggest that the choice of effort sharing allocations will not meaningfully affect the globally efficient
levels of regional abatement or aggregate global costs. The actual implementation of international
transfers can deviate from this assumption. [6.3, 13.4.2.4]
Geoengineering denotes two clusters of technologies that are quite distinct: carbon dioxide
removal (CDR) and solar radiation management (SRM). Mitigation scenarios assessed in AR5 do
not assume any geoengineering options beyond large scale CDR due to afforestation and
bioenergy coupled with CCS (BECCS). Carbon dioxide removal techniques include afforestation,
using biomass energy along with carbon capture and storage (BECCS), and enhancing uptake of CO2
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. Solar radiation management includes various
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. Solar radiation management has attracted considerable attention, mainly because of
the potential for rapid deployment in case of climate emergency. The suggestion that deployment
costs for individual technologies could potentially be low could result in new challenges for
international cooperation because nations may be tempted to prematurely deploy unilaterally
systems that are perceived to be inexpensive. Consequently, SRM technologies raise questions
about costs, risks, governance, 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. Solar
radiation management 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 precipitation. Non‐climate effects
could include possible depletion of stratospheric 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].
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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 generation of people should give less
weight to later people’s wellbeing 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 mitigation 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]
TS.3.1.4 Implications of transformation pathways for other objectives
Recent multi‐objective studies show that mitigation reduces the costs of reaching energy security
and/or air quality objectives (medium confidence). 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.3.3–3.7]. The impact of mitigation on the overall 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 [Figure TS.14, Box TS.11]. [3.6.3,
4.8, 6.6]
The majority of mitigation scenarios 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
supply (medium confidence). The majority of mitigation scenarios show improvements in terms of
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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]. [6.3.6, 6.6, 7.9, 8.7, 9.7, 10.8,
11.13.6, 12.8]
Mitigation policy may devalue endowments of fossil fuel exporting countries, but differences
between regions and fuels exist (medium confidence). There is uncertainty over how climate
policies would impact energy export revenues and volumes. The effect on coal exporters is expected
to be negative in the short‐ and long‐term as policies could reduce the benefits of using coal as an
energy source provided that no cost‐competitive CCS technologies are available. Gas exporters could
benefit in the medium term as coal is replaced by gas. The overall impact on oil is more uncertain.
Several studies suggest that mitigation policies reduce export revenues from oil. However, some
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. [6.3.6, 6.6, 14.4.2]
Fragmented mitigation policy can provide incentives for emission‐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 emissions to be 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]
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Figure TS.14 Co-benefits of mitigation for energy security and air quality in scenarios with stringent
climate policies reaching 430–530 ppm CO2eq concentrations in 2100). Upper panels show cobenefits 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 highest cost-effectiveness due to synergies (w+x+y>z). Policy costs are given as the increase in
total energy system costs relative to a no-policy baseline. Costs are indicative and do not represent
full uncertainty ranges. [Figure 6.33]
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Mitigation scenarios leading to atmospheric concentration levels between 430 and 530 ppm CO2eq
in 2100 are associated with significant co‐benefits for air quality, human health and ecosystem
outcomes. Associated welfare gains are expected to be particularly high where currently legislated
and planned air pollution controls are weak (high confidence). Stringent mitigation policies result in
co‐controls with major cuts in air pollutant emissions significantly below baseline scenarios (Figure
TS.14). Co‐benefits for health are particularly high in today’s developing world. The extent to which
air pollution policies, targeting for example black carbon, can mitigate climate change is uncertain
and subject to scientific debate. [WGIII 5.7, 6.3, 6.6, 7.9, 8.7, 9.7, 10.8, 11.7, 11.13.6, 12.8; WGII 11.9]
Potential adverse side‐effects of mitigation due to higher energy prices, for example, on improving
access of the poor to clean, reliable, and affordable energy services, can be avoided (medium
confidence). Whether mitigation scenarios will have adverse distributional effects and thus impede
achieving energy access objectives will depend on the climate policy design and the extent to which
complementary policies are in place to support the poor. About 1.3 billion people worldwide do not
have access to electricity and about 3 billion are dependent on traditional solid fuels for cooking and
heating with adverse effects on development, ecosystems and severe health implications. Scenario
studies show that the costs for achieving nearly universal access are between USD 72–95 billion per
year until 2030. The contribution of renewable energy‐to‐energy access can be substantial.
Achieving universal energy access reduces air pollutants emissions, such as sulfur dioxide (SO2),
nitrogen oxides (NOx), carbon monoxide (CO), and black carbon (BC), and yields large health benefits
but only negligibly higher GHG emissions from power generation. [4.3, 6.6, 7.9, 9.7, 11.13.6, 16.8]
The effect of mitigation on water use depends on technological choices and the portfolio of
mitigation measures (high confidence). While the switch from fossil energy to renewable energy like
solar photovoltaic (PV) or wind can help reducing water use of the energy system, deployment of
other renewables, such as some forms of hydropower, concentrated concentrated solar power, and
bioenergy may have adverse effects on water use. [6.6, 7.9, 9.7, 10.8, 11.7, 11.13.6]
Transformation pathways and sectoral studies show that the number of co‐benefits for energy end
use mitigation measures outweighs the number of the adverse side‐effects, whereas the evidence
suggests this is not the case for all supply‐side measures (high confidence). [Tables TS.3.2.2‐3.2.6;
Sections 4.8, 5.7, 6.6, 7.9, 8.7, 9.7, 10.8, 11.7, 11.13.6, 12.8]
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Box TS.11. Accounting for the co-benefits and adverse side-effects of mitigation
A government policy or a measure intended to achieve one objective (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‐benefits on social welfare are not
evaluated in most studies, and one reason is that the value of a co‐benefit depends on local
circumstances and can be positive, zero, or even negative. For example, the value of the extra tonne
of SO2 reduction that occurs with mitigation depends greatly on the stringency of existing SO2
control policies: in the case of weak existing SO2 policy, the value of SO2 reductions may be large, but
in the case of stringent existing SO2 policy it may be near zero. If SO2 policy is too stringent, the value
of the co‐benefit may be negative (assuming SO2 policy is not adjusted). While climate policy affects
non‐climate objectives [Tables TS.3.2.2–3.2.6] other policies also affect climate change outcomes.
[3.6.3, 4.8, 6.6, Annex I]
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, agricultural 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
and related health and ecosystem impacts, energy security, income distribution, efficiency of the
taxation system, labour supply and employment, urban sprawl, and the sustainability 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]
TS.3.2 Sectoral and cross‐sectoral mitigation measures
Anthropogenic greenhouse gas emissions result from a broad set of human activities, most notably
those associated with energy supply and consumption and with the use of land for food production
and other purposes. A large proportion of emissions arise in urban areas. Mitigation options can be
grouped into three broad sectors: 1) energy supply, 2) energy end‐use sectors including transport,
buildings, industry, and 3) agriculture, forestry, and other land use (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 themselves.
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 CO2
emissions in the land‐use sector (robust evidence, medium agreement). Energy supply sector
emissions are expected to continue to be the major source of GHG emissions in baseline scenarios.
As a result, significant increases in indirect emissions from electricity use in buildings and the
industry sector are expected. Deforestation decreases in most of the baseline scenarios, which leads
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to a decline in CO2 emissions from the land‐use sector. In some scenarios the land‐use sector
changes from an emission source to a net emission sink around 2050. (Figure TS.15)
Figure TS.15. Direct (left panel) and direct and indirect emissions (right panel) of CO2 and non-CO2
GHGs across sectors in baseline scenarios. Non CO2 GHGs are converted to CO2 equivalents using
100-year global warming potentials from the IPCC SAR (see Box TS.5). Note that in the case of
indirect emissions, only electricity emissions are allocated from energy supply to end-use sectors. 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 resolution and time horizon of models. [Figure 6.34]
Infrastructure developments and long‐lived products that lock societies into GHG intensive
emissions pathways may be difficult or very costly to change (robust evidence, high agreement).
This lock‐in risk is compounded by the lifetime of the infrastructure, by the difference in emissions
associated with alternatives, and the magnitude of the investment cost. As a result, land‐use
planning related lock‐in is the most difficult to eliminate, and thus avoiding options that lock high
emission patterns in permanently is an important part of mitigation strategies in regions with rapidly
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,
longer lifetimes of low‐emission products and infrastructure 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, 9.4, 12.3, 12.4]
Systemic and cross‐sectoral approaches to mitigation are expected to be more cost‐effective and
more effective in cutting emissions than sector‐by‐sector policies (medium confidence). Cost‐
effective mitigation policies need to employ a system perspective in order to account for inter‐
dependencies among different economic sectors and to maximize synergistic effects. Stabilizing
atmospheric CO2eq 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
limiting their deployment. This applies also to the adoption of new technologies, and structural and
behavioural change, in the energy end‐use sectors (robust evidence, high agreement) [7.9.4, 8.7,
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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 fuels, 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.2) [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‐CO2 emissions exist across all sectors, but most notably in agriculture, energy
supply, and industry.
Demand reductions in the energy end‐use sectors are a key mitigation 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 (Figure 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 number of
co‐benefits for energy end‐use measures outweighs the adverse side‐effects which is not the case
for all supply‐side measures (see Tables TS.3–7); 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]
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Figure TS.16. Influence of energy demand on the deployment of energy supply technologies in 2050
in mitigation scenarios reaching 430–530 ppm CO2eq 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 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]
Behaviour, lifestyle, and culture have a considerable influence on energy use and associated
emissions, and can have a high mitigation potential through complementing technological and
structural change (limited evidence, medium agreement). Emissions can be substantially lowered
through: changes in consumption patterns (e.g., mobility demand, energy use in households, choice
of longer‐lasting products); dietary change and reduction in food wastes; and change of lifestyle
(e.g., stabilizing/lowering consumption in some of the most developed countries, sharing economy
and other behavioural changes affecting activity) (Table TS.2). [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 decarbonization of energy supply is a key
requirement for stabilizing atmospheric CO2eq concentrations below 580ppm (robust evidence,
high agreement). In most long‐term mitigation scenarios not exceeding 580ppm CO2eq by 2100,
global energy supply is fully decarbonized at the end of the twenty‐first century with many scenarios
relying on a net removal of CO2 from the atmosphere. However, because existing 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 reducing indirect emissions from electricity
generation (Figure TS.15). In the long term, the reduction in electricity emissions is accompanied by
an increase in the share of electricity in end uses (e.g., for space and process heating, potentially for
some modes of transport). 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
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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 carbon dioxide removal technologies affects the size of the mitigation challenge
for the energy end‐use sectors (robust evidence, high agreement) [6.8, 7.11]. There are strong
interdependencies 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. However, 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 decarbonisation of the energy end‐use sectors in scenarios achieving low CO2eq
concentration levels (Figure TS.17). The availability of mature large‐scale energy generation or
carbon sequestration technologies in the AFOLU sector also provides flexibility for the development
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.
Figure TS.17. Direct emissions of CO2 and non-CO2 GHGs across sectors in mitigation scenarios that
reach around 450 (430-480) ppm CO2eq concentrations in 2100 with using 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 resolution
and time horizon of models. [Figures 6.35]
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 use zoning, transport‐oriented development, increasing
density, and co‐locating jobs and homes can contribute to mitigation across sectors by a) reducing
emissions from travel demand for both work and leisure, and enabling non‐motorized transport, b)
reducing floor space for housing, and hence c) 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 agriculture 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 adaptation and mitigation in concert (medium evidence, high
agreement). Particular mitigation actions can affect sectoral climate vulnerability, both by
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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 establishing more diversified cropping systems in agriculture, on
GHG emissions and radiative forcing [11.5.4, 9.5]. There is a growing evidence base for such
interdependencies in each sector, but there are substantial knowledge gaps that prevent the
generation of integrated results at the cross‐sectoral level.
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Table TS.2: Main sectoral mitigation measures categorized by key mitigation strategies (in bold) and associated sectoral indicators (highlighted in grey)
Agriculture, Forestry
and other Land use
Human
Settlements
Industry
Buildings
Transport
Energy
GHG emission intensity reduction
Energy intensity reduction by
improving technical efficiency
Emissions / secondary energy output
Energy input / energy output
Greater deployment of RES, nuclear energy, Extraction, transport, conversion of
fossil fuels; electricity, heat, fuel
and (BE)CCS; fuel switching within the
transmission, distribution, and storage;
group of fossil fuels; reduction of fugitive
(methane) emissions in the fossil fuel chain CHP (cogeneration, see Buildings)
Emissions / final energy
Final energy/transport service
Production and resource efficiency
improvement
Embodied energy / energy output
Energy embodied in manufacturing of
energy extraction, conversion,
transmission and distribution
technologies
Shares for each mode
Total distance per year
Fuel carbon intensity (CO2eq/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/p‐km, t‐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 LDVs to public
transit, cycling/walking, and from
aviation and 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)
Emissions / final energy
Fuel carbon intensity (CO2eq/MJ): Building
integrated RES; fuel switching to low‐
carbon fuels, e.g., electricity (see Energy)
Final energy / useful energy
Device efficiency: heating/ cooling
(high‐performance boilers, ventilation,
air‐conditioning, heat pumps), water
heating, cooking (advanced biomass
stoves), lighting, appliances
Embodied energy / operating energy
Building lifetime; component, equipment,
and appliance durability; low(er) energy
& emission material choice for
construction (see Industry)
Energy service demand
Behavioural change (e.g., thermostat
setting, appliance use); lifestyle change
(e.g., per capita dwelling size, adaptive
comfort)
Emissions / Final energy
Final energy / material production
Material input / product output
Useful energy / energy service
Systemic efficiency: integrated design
process; low/zero energy buildings;
building automation and controls;
urban planning; district
heating/cooling and CHP; smart
meters/grids; commissioning
Product demand / service demand
Emissions intensity: Process emissions
reductions; use of waste (e.g., MSP/ sewage
sludge in cement kilns) and CCS in industry;
HFC replacement and leak repair; fuel
switching among fossil fuels, to low‐carbon
electricity (see Energy) or biomass (see
AFOLU)
Emissions / Final energy
Energy efficiency/BAT: 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
Final energy / useful energy
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
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 Managed infrastructure supply; reduce
Compact urban form; increased
energy
primary materials input for infrastructure accessibility; mixed land use
Supply‐side improvements
Sequestration: Increasing the size of
existing carbon pools, and thereby
extracting carbon dioxide from the
atmosphere (e.g., afforestation,
reforestation, integrated systems,
carbon sequestration in soils)
Addressing integration needs
Activity indicator change
Final energy use
Demand from end‐use sectors for
different energy carriers (see Transport,
Buildings and Industry)
Service demand
Increasing accessibility: shorter travel
time, more transport mode options
Demand‐side measures
Emissions / area or unit product (conserved, restored)
Emission 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).
Structural and systems efficiency
improvement
Animal/crop product consumption per capita
Substitution: of biological products for fossil Demand‐side measures: Reducing losses and wastes of food,
fuels or energy‐intensive products, thereby changes in human diets towards less emission‐intensive
reducing CO2 emissions, e.g., biomass co‐
products, use of long‐lived wood products)
firing/CHP (see Energy), biofuels (see
Transport), biomass‐based stoves,
insulation products (see Buildings)
2
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TS.3.2.2 Energy supply
The energy supply sector is the largest contributor to global greenhouse gas emissions (robust
evidence, high agreement). Greenhouse gas emissions from the energy sector grew more rapidly
between 2001 and 2010 than in the previous decade; their growth accelerated from 1.7% per year
from 1991–2000 to 3.1% per year from 2001–2010. 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, conversion, storage,
transmission, and distribution processes that deliver final energy to the end‐use sectors (industry,
transport, and building, agriculture and forestry). [7.2, 7.3]
Direct CO2 emissions from the energy supply sector are projected to increase from 14.4 GtCO2/yr in
2010 to 24–33 GtCO2/yr in 2050 (25–75th percentile; full range 15–42 GtCO2/yr) in baseline
scenarios; most baseline scenarios assessed in AR5 show 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 intensity improvements that go well beyond the observed improvements over the
past 40 years. While direct GHG emissions from energy end‐use sectors tend to stabilize in the
second half of this century in baseline scenarios, the growth of the direct emissions from the energy
supply sector is projected to continue in the long‐term. [6.8, 7.11]
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
reductions 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.2). [7.5, 7.8.1, 7.11]
The stabilization of greenhouse gas concentrations at low levels requires a fundamental
transformation of the energy supply system, including the long‐term phase‐out of unabated fossil
fuel conversion technologies and their substitution by low‐GHG alternatives (robust evidence, high
agreement). Concentrations of CO2 in the atmosphere can only be stabilized if global (net) CO2
emissions peak and decline toward zero in the long term. Improving the energy efficiencies of fossil
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]
In integrated modelling studies, decarbonizing electricity generation is a key component of cost‐
effective mitigation strategies; in most scenarios, it happens more rapidly than the
decarbonization of the building, transport, and industry sectors (Figure TS.17) (medium evidence,
high agreement). In general, the rapid decarbonization of electricity generation is realized by a rapid
reduction of conventional coal power generation associated with a limited expansion of natural gas
without CCS over the near term [6.8, 7.11]. In the majority of mitigation scenarios reaching 430–480
ppm CO2eq concentrations by 2100, the share of low‐carbon energy in electricity supply increases
from the current share of around 30% to more than 80% by 2050. In the long run (2100), fossil
power generation without CCS is phased out almost entirely in mitigation scenarios (Figures TS.17
and TS.18).
Since AR4, renewable energy (RE) has become a fast growing category in energy supply, with many
RE technologies having advanced substantially in terms of performance and cost, and a growing
number of RE technologies has achieved technical and economic maturity (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. 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, including various modern and
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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 market shares are to be increased. Additional enabling
policies are needed to address their integration into future energy systems. (medium evidence,
medium agreement) (Figure TS.18) [7.5.3, 7.6.1, 7.8.2, 7.12, 11.13]
Figure TS.18. Share of low-carbon energy in total primary energy, electricity and liquid supply sectors
for the year 2050. Dashed horizontal lines show the low-carbon share for the year 2010. Low-carbon
energy includes nuclear, renewables, and fossil fuels with CCS. [Figure 7.14]
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.3). At the same time, however, some RE technologies can have technology and
location‐specific 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 penetrations 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 technology but its share in world power generation
has continued to decline (robust evidence, high agreement) (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]
Barriers to an increasing use of nuclear energy include concerns about operational safety and
(nuclear weapon) proliferation risks, unresolved waste management issues, as well as financial
and regulatory risks (robust evidence, high agreement) (Table TS.3). New fuel cycles and reactor
technologies addressing some of these issues are under development. Investigation of mitigation
scenarios not exceeding 580 ppm CO2eqhas 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 also constrained the
role of nuclear power expands. [6.3.6, 7.5.4, 7.8.2, 7.9, 7.11]
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Figure TS.19. Specific direct and lifecycle emissions (gCO2/kWh and gCO2eq/kWh, respectively) and
levelized cost of electricity (LCOE in USD2010/MWh) for various power-generating technologies (see
Annex III, Section A.III.2 for data and assumptions and Annex II, Section A.II.3.1 and Section A.II.9.3
for methodological issues). The upper left graph shows global averages of specific direct CO2
emissions (gCO2/kWh) of power generation in 2030 and 2050 for the set of 430–530 ppm scenarios
that are contained in the WG III AR5 Scenario Database (cf. Annex II, Section A.II.10). The global
average of specific direct CO2 emissions (gCO2/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.[Figure 7.7]
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Where natural gas is available and the fugitive emissions associated with its extraction and supply
are low, near‐term GHG emissions from energy supply can be reduced by replacing coal‐fired with
highly efficient natural gas combined cycle (NGCC) power plants or combined heat and power
(CHP) plants (robust evidence, high agreement). In mitigation scenarios reaching 430‐480 ppm
CO2eq concentrations by 2100, the contribution of natural gas power generation without CCS is
below current levels in 2050 and further declines in the second half of the century (medium
evidence, medium agreement). [7.5.1, 7.8, 7.9, 7.11, 7.12]
Carbon dioxide capture and storage (CCS) technologies could reduce the specific CO2eq lifecycle
emissions of fossil fuel power plants (medium evidence, medium agreement). Although CCS has not
yet been applied at scale to a large, commercial fossil‐fired power generation facility, all of the
components of integrated CCS systems exist and are in use in various parts of the fossil energy chain.
Carbon dioxide capture and storage power plants will only become competitive with their unabated
counterparts if the additional investment and operational costs faced by CCS plants are
compensated (e.g., by direct support or sufficiently high carbon prices). Beyond economic incentives,
well‐defined regulations concerning 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 CO2 storage, as well as risks related to transport and the required
upscaling of infrastructure (limited evidence, medium agreement) (Table TS.3). There is, however, a
growing body of literature on how to ensure the integrity of CO2 wells, on the potential
consequences of a CO2 pressure build‐up within a geologic formation (such as induced seismicity),
and on the potential human health and environmental impacts from CO2 that migrates out of the
primary injection zone. [7.5.5, 7.9, 7.11]
Combining bioenergy and carbon dioxide capture and storage (BECCS) could result in net removal
of CO2 from the atmosphere (limited evidence, medium agreement). Until 2050, bottom‐up studies
estimate the economic potential to be between 2–10 GtCO2 per year [11.13]. Some mitigation
scenarios show higher deployment of BECCS towards the end of the century. Technological
challenges and risks include those associated with the provision of the biomass feedstock, as well as
with the capture, transport, and long‐term storage of CO2. Currently, no large‐scale projects have
been financed. [6.9, 7.5.5, 7.9, 11.13]
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Table TS.3: 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 (see Table 7.3). 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.
Effect on additional objectives/concerns
Energy Supply
Economic
Social
Environmental
Other
For possible upstream effects of biomass supply for bioenergy, see Table TS.7.
↑ Energy security (reduced exposure to fuel
price volatility) (m/m)
Nuclear replacing coal
↑ Local employment impact (but uncertain net
effect) (l/m)
↑ Legacy cost of waste and abandoned reactors
(m/h)
RE (Wind, PV, CSP,
hydro, geothermal,
bioenergy) replacing
coal
Fossil CCS replacing
coal
↓
↑
↑
↑
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)
Methane leakage
prevention, capture or
treatment
Ecosystem impact via
Air pollution (m/h) and coal mining (l/h)
Nuclear accidents (m/m)
Proliferation risk
(m/m)
↓
↓
↑
↑
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)
Higher use of
critical metals for
PV and direct
drive wind
turbines (r/m)
↓ Water use (for wind and PV) (m/m)
Threat of displacement (for large hydro) (m/h)
↑ Water use (for bioenergy, CSP, geothermal, and
reservoir hydro) (m/h)
Health impact via
Risk of CO2 leakage (m/m)
Upstream supply‐chain activities (m/h)
↑ Ecosystem impact via upstream supply‐chain activities Long‐term
(m/m)
monitoring of
CO2 storage
↑ Water use (m/h)
(m/h)
↑ Safety concerns (CO2 storage and transport) (m/h)
BECCS replacing coal
↓
↑
↑ Safety and waste concerns (r/h)
↑ 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 reservoirs and regulated
rivers)(m/h)
↑
↑ Extra measures to match demand (for PV, wind
and some CSP) (r/h)
↑↑Preservation vs lock‐in of human and physical
capital in the fossil industry (m/m)
Health impact via
Air pollution and coal mining accidents (m/h)
Nuclear accidents and waste treatment, uranium
mining and milling (m/l)
See fossil CCS where applicable. For possible upstream effect of biomass supply, see Table TS.7.
↑ 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)
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TS.3.2.3 Transport
Since AR4, emissions in the transport sector have grown in spite of more efficient vehicles (road,
rail, watercraft, and aircraft) and policies being adopted (robust evidence, high agreement). Road
transport dominates overall emissions but aviation could play an increasingly important role in total
CO2 emissions in the future. [8.1, 8.3, 8.4]
Direct CO2 emissions from transport are projected to increase from 6.7 GtCO2/yr in 2010 to 9.3–12
GtCO2/yr in 2050 (25–75th percentile; full range 6.2–16 GtCO2/yr) in baseline scenarios; most of the
baseline scenarios assessed in AR5 foresee a significant increase (medium evidence/medium
agreement) (Figure TS.15). Without 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 CO2 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 mitigation
potential in the transport sector than in the AR4 (medium evidence, medium agreement). Transport
energy demand per capita in developing and emerging economies is far lower than in Organisation
for Economic Co‐operation and Development (OECD) countries but is expected to increase at a much
faster rate in the next decades due to rising incomes and the development of infrastructure.
Baseline scenarios thus show increases in transport energy demand from 2010 out to 2050 and
beyond. However, sectoral and integrated mitigation scenarios indicate that energy demand
reductions 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, 8.10, Figure 8.9.4]
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, hydrogen, and liquid
biofuels; right panel) in transport by 2030 and 2050 in mitigation scenarios from three different CO2eq
concentrations ranges shown in box plots (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]
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A combination of low‐carbon fuels, the uptake of improved vehicle and engine performance
technologies, behavioural change leading to avoided journeys and modal shifts, investments in
related infrastructure and changes in the built environment, 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 (CO2eq/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.2).
Other short‐term mitigation strategies include reducing black carbon, aviation contrails, and NOx
emissions. [8.4]
The required energy density of fuels makes the transport sector difficult to decarbonize, and
integrated and sectoral studies broadly agree that opportunities for fuel switching are low in the
short term but grow over time (medium evidence, medium agreement) (Figure TS.20, right panel).
Electric, hydrogen, and some biofuel technologies could help reduce the carbon intensity of fuels,
but their total mitigation potentials are very uncertain (medium evidence, medium agreement). In
particular, the mitigation potential of biofuels (particularly advanced ‘drop‐in’ fuels for aircraft and
other vehicles) will depend on technology advances and sustainable feedstocks (medium evidence,
medium agreement). Up to 2030, the majority of integrated studies expect a continued reliance on
liquid and gaseous fuels, supported by an increase in the use of biofuels. During the second‐half of
the century, many integrated studies also include substantial shares of electricity and/or hydrogen
to fuel electric and fuel‐cell light‐duty vehicles (LDVs).
Energy efficiency measures through improved vehicle and engine designs have the largest
potential for emission reductions in the short term (high confidence). Potential energy efficiency
and vehicle performance improvements range from 30–50% relative to 2010 depending on mode
and vehicle type (Figure TS.21, TS.22). Realizing this efficiency potential will depend on large
investments 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]
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Figure TS.21. Indicative emission intensity (tCO2/p-km) and levelized costs of conserved carbon
(LCCC in USD2010/tCO2 saved) of selected passenger transport technologies. Variations in emission
intensities stem from variation in vehicle efficiencies and occupancy rates. Estimated LCCC for
passenger road transport options are point estimates ±100 USD2010/tCO2 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 CO2 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,
Section A.III.3 for data and assumptions on emission intensities and cost calculations and Annex II,
Section A.II.3.1 for methodological issues on levelized cost metrics).
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 new infrastructure, linked with land use policies, could evolve to reduce GHG
intensity through more compact urban form, integrated transit, and urban planning oriented to
support cycling and walking. This could reduce GHG emissions by 20–50% compared to baseline.
Pricing strategies, when supported by public acceptance initiatives and public and non‐motorized
transport infrastructures, can reduce travel demand, increase the demand for more efficient vehicles
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(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.4). Business
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]
Figure TS.22. Indicative emission intensity (tCO2/t-km) and levelized costs of conserved carbon
(LCCC in USD2010/tCO2 saved) of selected freight transport technologies. Variations in emission
intensities largely stems 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 CO2 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,
Section A.III.3 for data and assumptions on emission intensities and cost calculations and Annex II,
Section A.II.3.1 for methodological issues on levelized cost metrics).
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Sectoral and integrated studies agree that substantial, sustained, and directed policy interventions
could limit transport emissions to be consistent with low concentration goals, but the societal
mitigation costs (USD/tCO2 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 potential is limited. The mitigation cost of electric vehicles is currently high, especially if using
grid electricity with a high emissions factor, but their LCCC are expected to decline by 2030. The
emissions intensity of aviation could decline by around 50% in 2030 but the LCCC, although
uncertain, are probably over USD 100/tCO2eq. 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]
Figure TS.23. Direct global CO2 emissions from all passenger and freight transport are indexed
relative to 2010 values for each scenario with integrated model studies grouped by CO2eq
concentration levels by 2100, and sectoral studies grouped by baseline and policy categories. Where
the data is sourced from the AR5 scenario database, a line denotes the median scenario and the
boxes in bold colours highlight the inter-quartile range. The specific observations from sectoral studies
are shown as dots (policy) and squares (baseline) with boxes to illustrate the data ranges. [Figure 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, institutional,
cultural, and legal barriers constrain transport technology uptake and behavioural change. They
include the high investment 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 emissions, and other measures 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 infrastructure around low‐carbon options, particularly in developing and
emerging countries where most future urban growth will occur (robust evidence, high agreement).
Possible transformation pathways vary with region and country due to differences in the dynamics
of motorization, age and type of vehicle fleets, existing infrastructure, and urban development
processes. In least developed countries, prioritizing access to pedestrians, integrating non‐motorized
and public transport services, and managing excessive road speed for both urban and rural travellers
can result in economic and social prosperity. In fast‐growing emerging economies, investments in
mass transit and other low‐carbon transport infrastructure can help avoid future lock‐in to carbon
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intensive modes. In OECD countries, advanced vehicle technologies could play a bigger role than
structural and behavioural changes since economic growth will be slower than for non‐OECD
countries. (limited evidence, medium agreement) [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
strategies associated with broader non‐climate policies at all government levels can usually target
several objectives simultaneously to give lower travel costs, improved 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 feasibility as well as having access to appropriate
and cost‐effective advanced technologies (Table TS.4). (medium evidence, high agreement) Since
rebound effects can reduce the CO2 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, mobility, productivity, safety, and health
(medium evidence, medium agreement). [8.4, 8.7, 8.10]
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Table TS.4: 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 (see Table 8.4). 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.
Effect on additional objectives/concerns
Transport
Economic
Social
Environmental
Other
For possible upstream effects of low‐carbon electricity, see Table TS.3. For possible upstream effects of biomass supply, see Table TS.7.
Reduction of fuel
carbon intensity: e.g.,
electricity, H2, CNG,
biofuels, and other
measures
Reduction of energy
intensity
↑ 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, H2: reducing most pollutants (r/h)
Diesel: potentially increasing pollution (l/m)
Noise (electrification and fuel cell LDVs) (l/m)
↓
↑
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
↓ Road safety (silent electric LDVs at low speed) (l/l)
↑ Energy security (reduced oil dependence and
exposure to oil price volatility) (m/m)
↑ Energy security (reduced oil dependence and
exposure to oil price volatility) (m/m)
Compact urban form +
improved transport
infrastructure
Modal shift
?
↓
↑
↓
↑ 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 via reduced urban air pollution (r/h)
↑ Road safety (via increased crash‐worthiness) (m/m)
↓
↑
↓
Health impact for non‐motorized modes via
Increased activity (r/h)
Potentially higher exposure to air pollution (r/h)
Noise (modal shift and travel reduction) (r/h)
↓ Ecosystem and biodiversity impact via reduced urban
air pollution (m/h)
↓
↓
Ecosystem impact via
Urban air pollution (r/h)
Land‐use competition (m/m)
↓
↑
Ecosystem impact via
Urban air pollution (r/h)
New/shorter shipping routes (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)
Journey reduction and
avoidance
↑ Energy security (reduced oil dependence and
exposure to oil price volatility) (r/h)
↓ Health impact (non‐motorized transport modes) (r/h)
↑ Productivity (reduced urban congestion, travel
times, walking) (r/h)
↓ Land‐use competition (transport infrastructure) (r/h)
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TS.3.2.4 Buildings
Greenhouse gas emissions from the building sector have more than doubled since 1970,
accounting for 19% of global GHG emissions in 2010, including indirect emissions from electricity
generation. The share rises to 25% if AFOLU emissions are excluded from the total. The building
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 evidence,
medium agreement) [9.2].
Direct and indirect CO2 emissions from buildings are projected to increase from 8.8 GtCO2/yr in
2010 to 13–17 GtCO2/yr in 2050 (25–75th percentile; full range 7.9–22 GtCO2/yr) in baseline
scenarios; most of the baseline scenarios assessed in 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. Without further policies, building sector final energy use may
grow from approximately 120 EJ/yr in 2010, to 270 EJ/yr in 2050 [9.9].
Significant lock‐in risks arise from the long lifespans of buildings 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 2005 building sector final energy use . [9.4]
Improvements in wealth, lifestyle, urbanization, and the provision of access to modern energy
services and adequate housing will drive the increases in building energy demand (robust evidence,
high agreement). The manner in which those without access to adequate housing (0.8 billion
people), modern energy carriers, and sufficient levels of energy services including clean cooking (3
billion people) and heating 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 opportunity from a
mitigation perspective. [9.2, 9.4, 9.9]
The recent proliferation of advanced technologies, know‐how, and policies in the building sector,
however, make it feasible that global total sector final energy use stabilizes or even declines by
mid‐century (robust evidence, medium agreement). Recent advances in technology, 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
m2 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). Reductions of heating/cooling energy use by 50–90% have
been achieved using best practices. Strong evidence shows that very low‐energy construction and
retrofits can be economically attractive. [9.3]
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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 savings 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 reductions 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 climate change. [6.8.4, 9.8.2, Figure 9.19]
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 2030 and
2050 in mitigation scenarios from three different CO2eq 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]
The history of energy efficiency programmes in buildings shows that 25–30% efficiency
improvements have been available at costs substantially lower than marginal energy supply
(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 standards since AR4,
including ambitious building codes and targets, voluntary 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 reductions in thermal energy use in buildings are
possible at costs lower than energy supply, with the most cost‐effective options including very high‐
performance new commercial buildings; the same holds for efficiency improvements in some
appliances and cooking equipment. [9.5, 9.6, 9.9]
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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. Energy use has been shown to vary by 3–5 fold for similar levels of energy service (low
evidence, high agreement). In developed countries, evidence indicates that behaviours informed by
awareness of energy and climate issues can reduce demand by up to 20% in the short term and up
to 50% by 2050 (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 suggests 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 buildings have considerable 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.5) [9.8]
Especially strong barriers in this sector hinder the market 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, principal/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 discount rates, and insufficient service levels are further barriers. Therefore, market forces
alone are not expected to achieve the necessary transformation without external stimuli. Policy
intervention addressing all levels of the building and appliance lifecycle and use, plus new business
and financial models are essential. [9.7]
A large portfolio of building‐specific energy efficiency policies 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 already been saving emissions at large negative costs. Among the most
environmentally and cost‐effective policies are regulatory instruments such as building and
appliance 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 demonstrated the feasibility of stabilising 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 need to be substantially strengthened and
extended to further jurisdictions, and to other building 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. [9.9]
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Table TS.5: Overview of potential co-benefits (green arrows) and adverse side-effects (orange arrows) of the main mitigation measures in the building
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 (see Table 9.7). 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.
Effect on additional objectives/concerns
Buildings
Economic
Social
Environmental
Other
For possible upstream effects of fuel switching and RES, see Table TS.3.
Fuel switching, RES
incorporation, green
roofs, and other
measures reducing
emissions intensity
Retrofits of existing
buildings (e.g., cool
roof, passive solar,
etc.)
Exemplary new
buildings
Efficient equipment
Behavioural changes
reducing energy
demand
↑ 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)
↑ Urban biodiversity (for green roofs) (m/m)
↑ Energy security (m/h)
↓ Fuel poverty (for retrofits, efficient equipment) (m/h)
↑ Employment impact (m/m)
↓ Energy access (higher cost for housing due to the
investments needed) (l/m)
↑ Productivity (for commercial buildings) (m/h)
↑ Lower need for energy subsidies (l/l)
↑ Asset values of buildings (l/m)
↑ Disaster resilience (l/m)
Health impact in residential buildings via
Outdoor air pollution (r/h)
Indoor air pollution (in DCs) (r/h)
Fuel poverty (r/h)
↓
↓
↓
↓
↑
↑ Thermal comfort (for retrofits and exemplary new
buildings) (m/h)
↑ Productive time for women and children
(for replaced traditional cookstoves) (m/h)
↑ Energy security (m/h)
↓ Ecosystem impact (less outdoor air pollution) (r/h)
↓
↓
↓
↓
↓
Health impact via
Outdoor air pollution (r/h)
Indoor air pollution (for efficient cookstoves) (r/h)
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)
↓ Health impact via less outdoor air pollution (r/h) &
improved indoor environmental conditions (m/h)
↑ Lower need for energy subsidies (l/l)
↓ Ecosystem impact (less outdoor air pollution) (r/h)
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Reduced Urban
Heat Island Effect
(UHI) (l/m)
Reduced UHI
(retrofits and
new exemplary
buildings) (l/m)
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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 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 individuals 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
information on opportunities to conserve energy; asymmetric information – 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 saving [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].
TS.3.2.5 Industry
Currently, in the industry sector direct and indirect 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
industry in global GDP, global industry and waste/wastewater GHG emissions grew from 10 GtCO2eq
in 1990, to 13 GtCO2eq in 2005 and to 16 GtCO2eq in 2010. [10.3]
Direct and indirect CO2 emissions from industry are projected to increase from 13 GtCO2/yr in 2010
to 20–24 GtCO2/yr in 2050 (25–75th percentile; full range 9.5–34 GtCO2/yr) in baseline scenarios;
most of the baseline scenarios assessed in 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 deployment of best available technologies, particularly in countries where these
are not in practice, and in non‐energy intensive industries, could reduce the energy intensity of the
sector by up to 25% (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 approximately up to 20% in energy intensity may potentially be realized
(low evidence, medium agreement). Barriers to implementing energy efficiency relate largely to the
initial investment costs and lack of information. Information programmes are the most prevalent
approach for promoting energy efficiency, followed by economic instruments, regulatory
approaches, and voluntary actions. [10.4]
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);
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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 [10.4, 10.7]. (limited evidence, high agreement)
(Table TS.2, Figure TS.25)
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 10.1]
While detailed industry sector studies tend to be more conservative than integrated studies, both
identify possible industrial final energy demand savings of around 30% by 2050 in mitigation
scenarios not exceeding 650ppm CO2eq by 2100 relative to baseline scenarios (medium evidence,
medium agreement) (Figure 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
heterogeneous 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 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. 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 dumpsites or landfills. About 47% of wastewater produced in the domestic and
manufacturing sectors is still untreated. The largest cost range is for reducing 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 the wastewater treatment but they are clustered among the higher cost
options (medium evidence, medium agreement). (Figure TS.29) [10.4, 10.14]
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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
CO2eq 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]
Waste policy and regulation have largely influenced material consumption, but few policies have
specifically pursued material efficiency or product service intensity (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 consumers to address
improvements in material or product service efficiency, or to reduce product demand. [10.9]
CO2 emissions dominate GHG emissions from industry, but there are also substantial mitigation
opportunities for non‐CO2 gases (robust evidence, high agreement). Key opportunities comprise, e.g.,
reduction of hydrofluorocarbon (HFC) emissions by leak repair, refrigerant recovery and recycling,
and proper disposal and replacement by alternative refrigerants (ammonia, HC, CO2). Nitrous oxide
(N2O) emissions from adipic and nitric acid production can be reduced through the implementation
of thermal destruction and secondary catalysts. The reduction of non‐CO2 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. [10.7]
Besides sector specific technologies, cross‐cutting technologies and measures applicable in both
large energy intensive industries and Small and Medium Enterprises (SMEs) can help to reduce
GHG emissions (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
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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 agreement). While options in cost ranges of 0–20 and 20–50
USD/tCO2eq and even below 0 USD/tCO2eq exist, achieving near‐zero emission intensity levels in the
industry sector would require the additional realization of long‐term step‐change options (e.g., CCS),
which are associated with higher levelized costs of conserved carbon (LCCC) in the range of 50–150
USD/tCO2eq. Similar cost estimates for implementing 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 specific 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 competitiveness through cost‐reductions, new business opportunities,
better environmental compliance, health benefits through better local air and water quality and
better work conditions, and reduced waste, all of which provide multiple indirect private and social
benefits (Table TS.6). [10.8]
There is no single policy that can address the full range of mitigation measures available for
industry and overcome associated 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]
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Figure TS.27. Indicative CO2 emission intensities for cement (top panel) and steel (bottom panel), as
well as indicative levelized cost of conserved carbon shown for various production
practices/technologies and for 450ppm CO2eq scenarios of a limited selection of integrated models
(for data and methodology, see Annex III). [Figures 10.7, 10.8]
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Figure TS.28. Global CO2eq emissions for chemicals production (top panel) and indicative CO2
emission intensities for paper production (bottom panel) as well as indicative levelized cost of
conserved carbon shown for various production practices/technologies and for 450ppm CO2eq
scenarios of a limited selection of integrated models (for data and methodology, see Annex III).
[Figures 10.9, 10.10]
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Figure TS.29. Indicative CO2 emission intensities for waste (top panel) and wastewater (bottom
panel) of various practices as well as indicative levelized cost of conserved carbon (for data and
methodology, see Annex III). [Figures 10.19 and 10.20]
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Table TS.6: 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 implementation practice, pace and scale (see Table 10.5). 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.
Effect on additional objectives/concerns
Industry
Economic
Social
Environmental
For possible upstream effects of low‐carbon energy supply (incl CCS), see Table TS.3.
For possible upstream effects of biomass supply, see Table TS.7.
CO2/non‐CO2 emission
intensity reduction
↑ Competitiveness and productivity (m/h)
↓ Health impact via reduced local air pollution and
better work conditions (PFC from aluminium) (m/m)
↓ Ecosystem impact via reduced local air pollution and
reduced water pollution (m/m)
↑ Water conservation (l/m)
↑ Energy security (lower energy intensity)(m/m) ↓ Health impact via reduced local pollution (l/m)
↓
↑ Employment impact (l/l)
↑ New business opportunities (m/m)
Energy efficiency
↓
improvements via new ↑ Competitiveness and productivity (m/h)
↑ Water availability and quality (l/l)
processes/technologies ↑ Technological spillovers in DCs (due to supply ↑ Safety, working conditions and job satisfaction (m/m)
Ecosystem impact via:
Fossil fuel extraction (l/l)
Local pollution and waste (m/m)
chain linkages) (l/l)
↓ National sales tax revenue (medium term) (l/l) ↓ Health impacts and safety concerns (l/m)
Material efficiency of
goods, recycling
↑ Employment impact (waste recycling) (l/l)
↑ New business opportunities (m/m)
↑ Competitiveness in manufacturing (l/l)
↓ Local conflicts (reduced resource extraction) (l/m)
↑ New infrastructure for industrial clusters (l/l)
Product demand
reductions
↓ National sales tax revenue (medium term) (l/l) ↓ Local conflicts (reduced inequity in consumption)(l/l)
↑ New diverse lifestyle concept (l/l)
1
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↓ 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)
↓ Post‐consumption waste (l/l)
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TS.3.2.6 Agriculture, forestry and other land‐uses (AFOLU)
Since AR4, emissions from the AFOLU sector have stabilized but the share of total anthropogenic
emissions has decreased (robust evidence, high agreement). The average annual total GHG flux from
the AFOLU sector was 10–12 GtCO2eq in 2000–2010, with global emissions of 5.0–5.8 GtCO2eq/yr
from agriculture on average and around 4.3–5.5 GtCO2eq/yr from forestry and other land uses. Non‐
CO2 emissions derive largely from agriculture, dominated by N2O emissions from agricultural soils
and methane emissions from livestock enteric fermentation, manure management, and emissions
from rice paddies, totalling 5.0–5.8 GtCO2eq/yr in 2010 (robust evidence, high agreement). Over
recent years, most estimates of forestry and other land use (FOLU) CO2 fluxes indicate a decline in
emissions, largely due to decreasing deforestation rates (limited evidence, medium agreement). The
absolute levels of emissions from deforestation 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 CO2 emissions from AFOLU are projected to decline over time with emissions
potentially less than half of what they are today by 2050, and the possibility of the terrestrial
system becoming a net sink before the end of century. However, there is significant uncertainty in
historical and well as projected baseline AFOLU emissions. (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 CO2
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 (limited evidence, medium agreement). There are few reported
projections of baseline global land‐related N2O and CH4 emissions and they indicate an increase over
time. Cumulatively, land CH4 emissions are projected to be 44–53% of total CH4 emissions through
2030, and 41–59% through 2100, and land N2O emissions 85–89% and 85–90%, respectively (limited
evidence, medium agreement). [11.9]
Opportunities for mitigation in the AFOLU sector include supply‐ and demand‐side mitigation
options (robust evidence, high agreement). Supply‐side measures involve reducing emissions arising
from land use change, in particular reducing deforestation, 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.2). 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 potential of the AFOLU sector, but there is limited evidence 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 emission
intensity, (i.e., the GHG emissions per unit of product), 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]
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Figure TS.30. GHG emissions intensities of selected major AFOLU commodities for decades 1960s–
2000s. i) Cattle meat, defined as GHG (enteric fermentation+ manure management of cattle, dairy
and non-dairy)/meat produced; ii) pig meat, defined as GHG (enteric fermentation+ manure
management of swine, market and breeding) /meat produced; iii) chicken meat, defined as GHG
(manure management of chickens)/meat produced; iv) milk, defined as GHG (enteric fermentation+
manure management of cattle, dairy)/milk produced; v) eggs, defined as GHG (manure management
of chickens, layers)/egg produced; vi) rice, defined as GHG (rice cultivation)/rice produced; vii) cereals,
defined as GHG (synthetic fertilizers)/cereals produced; viii) wood, defined as GHG (carbon loss from
harvest)/roundwood produced. [Figure 11.15]
Among supply‐side measures, the most cost‐effective forestry options are reducing deforestation
and forest management; in agriculture, low carbon prices (20 USD/tCO2eq) favour cropland and
grazing land management and high carbon prices (100 USD/tCO2eq) favour restoration 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 mitigation
potential in the AFOLU sector is estimated to be 7.18 to 10.6 (full range of all studies : 0.49–10.6)
GtCO2eq/yr at carbon prices up to 100 USD/ tCO2eq, about a third of which can be achieved at <20
USD/ tCO2eq (medium evidence, medium agreement). The range of global estimates at a given
carbon price partly reflects uncertainty surrounding AFOLU mitigation potentials in the literature
and the land use assumptions of the scenarios considered. The ranges of estimates also reflect
differences in the GHGs and options considered in the studies. A comparison of estimates 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 could have a significant impact on GHG emissions from
food production (0.76–8.55 GtCO2eq/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 behaviour 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]
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Figure TS.31. Estimates of economic mitigation potentials in the AFOLU sector published since AR4,
(AR4 estimates shown for comparison, denoted by black arrows), including 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/tCO2eq (actual range 1.64–21.45), up to ~50
USD/tCO2eq (actual range 31.39–50.00), and up to ~100 USD/tCO2eq (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 et al.
(2013) are the mean of the range. Not all studies consider the same measures or the same GHGs.
[11.6.2, Figure 11.14]
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
compete or work in synergy. The main barriers to mitigation are institutional (lack of tenure and
poor governance), accessibility to financing 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 agricultural and silvicultural efficiency, and can reduce
climate vulnerability 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] (limited
evidence, high agreement). Recent frameworks, such as those for assessing environmental or
ecosystem services, could provide tools for valuing the multiple synergies and trade‐offs that may
arise from mitigation actions (Table TS.7) (medium evidence, medium agreement). [11.7, 11.8]
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Table TS.7: 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 biophysic, institutional and socio-economic aspects) as well as on the scale of implementation (see Table 11.9 and 11.12). 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.
Effect on additional objectives/concerns
AFOLU
Economic
Social
Environmental
Institutional
Note: co‐benefits and adverse side‐effects depend on the development context and the scale of the intervention (size).
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 (m/m)
↓
technologies in agriculture
↑ * 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 (r/m)
systems and sustainable agriculture intensification
↓ * Food production (locally) due to large‐scale
monocultures of non‐food crops (r/l)
↑ Cultural habitats and recreational areas via (m/m)
(sustainable) forest management and conservation
↑ *Human health and animal welfare e.g., through less
pesticides, reduced burning practices, and practices
like agroforestry & 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)
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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)
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Policies governing practices in agriculture as well as forest conservation and management need to
account for the needs of both mitigation and adaptation (medium evidence, high agreement).
Economic incentives (e.g., special credit lines for low carbon agriculture, sustainable 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 deforestation, set‐
aside policies, air and water pollution control reducing nitrate load and N2O emissions) have been
effective in different cases. Investments in research, development, and diffusion (e.g., increase of
resource use‐efficiency (fertilizers), livestock improvement, better forestry management practices)
could result in synergies between adaptation 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 evidence, high agreement). [11.10, 15.11]
Reducing Emissions from Deforestation and Forest Degradation (REDD+)9 can be a very cost
effective policy option for mitigating climate change, if implemented in a sustainable manner
(limited 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 emissions
from the AFOLU sector, especially through reducing deforestation in tropical regions, with potential
economic, social and other environmental co‐benefits. To assure these co‐benefits, the
implementation of national REDD+ strategies would need to consider financing mechanisms to local
stakeholders, safeguards (such as land rights, conservation of biodiversity and other natural
resources), and the appropriate scale and institutional capacity for monitoring and verification.
[11.10]
Bioenergy deployment offers significant potential for climate change mitigation, but also carries
considerable risks (medium evidence, medium agreement). The IPCC’s Special Report on Renewable
Energy Sources and Climate Change Mitigation (SRREN) suggested potential bioenergy deployment
levels to be between 100–300 EJ. This assessment agrees on a technical bioenergy potential of
around 100 EJ (medium evidence, high agreement), and possibly 300 EJ and higher (limited evidence,
low agreement). Integrated models project between 15–245 EJ/yr deployment in 2050, excluding
traditional bioenergy. Achieving high deployment levels would require, amongst others, extensive
use of agricultural residues and second‐generation biofuels to mitigate adverse impacts on land use
and food production, and the co‐processing of biomass with coal or natural gas with CCS to produce
low net GHG‐emitting transportation fuels and/or electricity (medium evidence, high agreement).
The integration of crucial sectoral research (albedo effects, evaporation, counterfactual land carbon
sink assumptions) into transformation pathways research, and the exploration of risks of imperfect
policy settings (for example, in absence of a global CO2 price on land carbon) is subject of further
research. [11.9, 11.13.2, 11.13.4]
Small‐scale bioenergy systems aimed at meeting rural energy needs synergistically provide
mitigation and energy access benefits (robust evidence, high agreement). Decentralized deployment
of biomass for energy, in combination with improved cookstoves, biogas, and small‐scale biopower,
could improve livelihoods and health of around 2.6 billion people. Both mitigation potential and
sustainability hinge crucially on the protection of land carbon (high density carbon ecosystems),
careful fertilizer application, interaction with food markets, and good land and water management.
Sustainability and livelihood concerns might constrain beneficial deployment of dedicated biomass
plantations to lower values. [11.13.3, 11.13.5, 11.13.7]
9
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.
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Lifecycle assessments for bioenergy options demonstrate a plethora of pathways, site‐specific
conditions, and technologies that produce a wide range of climate‐relevant effects (high
confidence). Specifically, land‐use change emissions, nitrous oxide emissions from soil and fertilizers,
co‐products, process design and process fuel use, end‐use technology, and reference system can all
influence the total attributional lifecycle emissions of bioenergy use. The large variance for specific
pathways points to the importance of management decisions in reducing the lifecycle emissions of
bioenergy use. The total marginal global warming impact of bioenergy can only be evaluated in a
comprehensive setting that also addresses equilibrium effects, e.g., indirect land‐use change
emissions, actual fossil fuel substitution, and other effects. Structural uncertainty in modelling
decision‐making renders such evaluation exercises uncertain. Available data suggest a differentiation
between options that offer low lifecycle emissions under good land‐use management (e.g.,
sugarcane, Miscanthus, and fast‐growing tree species) and those that are unlikely to contribute to
climate change mitigation (e.g., corn and soybean), pending new insights from more comprehensive
consequential analyses. [8.7, 11.13.4]
Land‐demand and livelihoods are often affected by bioenergy deployment (high confidence). Land
demand for bioenergy depends on (1) the share of bioenergy derived from wastes and residues; (2)
the extent to which bioenergy production can be integrated with food and fibre production, and
conservation to minimize land‐use competition; (3) the extent to which bioenergy can be grown on
areas with little current production; and (4) the quantity of dedicated energy crops and their yields.
Considerations of tradeoffs with water, land, and biodiversity are crucial to avoid adverse effects.
The total impact on livelihood and distributional consequences depends on global market factors,
impacting income and income‐related food‐security, and site‐specific factors such as land tenure and
social dimensions. The effects of bioenergy deployment on livelihoods are often site‐specific and
have not yet been comprehensively evaluated [11.9, 11.13].
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. Today, more than half 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 CO2
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 CO2 emissions. Taking account of direct emissions only, the urban share of
emissions is 44% (Figure TS.32). [12.2, 12.3]
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Figure TS.32. Estimated shares of direct (Scope 1) and indirect urban CO2 emissions in total
emissions across world regions (GtCO2). Indirect emissions (Scope 2) allocate emissions from
thermal power plants to urban areas. [12.2.2, Figure 12.4]
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, locational factors, economic
structure, and market failures. Per capita final energy use and CO2 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). Following current trends of declining densities, urban areas are expected to triple
between 2000 and 2030. If the global population increases to 9.3 billion 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
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about 470 GtCO2 emissions. Currently, average per capita CO2 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]
Urban mitigation options vary across urbanization trajectories and are expected to be most
effective when policy instruments 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 refurbishing existing
systems and infrastructures. Key mitigation strategies include co‐locating high residential with high
employment densities, achieving high land use mixes, increasing accessibility 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 infrastructure inertia has not set in; however, the required governance, technical,
financial, and institutional capacities can be limited (high confidence). 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 the extent of urban mitigation is
highly uncertain (robust evidence, high agreement). Local governments and institutions possess
unique opportunities to engage in urban mitigation activities and local mitigation efforts have
expanded rapidly. However, little systematic reporting or evidence exists regarding the overall
extent to which cities are implementing mitigation policies, and even less regarding their GHG
impacts. 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]
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Figure TS.33. Four key aspects of urban form and structure (density, land use mix, connectivity, and
accessibility), their VKT elasticities, commonly used metrics, and stylized graphics. [Figure 12.14]
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Figure TS.34. Common mitigation measures in Climate Action Plans. [Figure 12.22]
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 implemented 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 bundling of instruments and
a high level of coordination across institutions can increase the likelihood of achieving emissions
reductions and avoiding unintended outcomes. [12.6, 12.7]
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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]
For designing and implementing climate policies effectively, institutional arrangements,
governance mechanisms, and financial resources should be aligned with the goals of reducing
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 planning 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
(medium evidence, high agreement). Co‐benefits of local climate change mitigation can include
public savings, air pollution and associated health benefits, and productivity increases in urban
centres, providing additional motivation for undertaking mitigation activities. [12.5, 12.6, 12.7, 12.8]
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TS.4 Mitigation policies and institutions
The previous section shows that since AR4 the scholarship on transformation pathways has begun to
consider in much more detail how a variety of real world considerations—such as institutional and
political constraints, uncertainty associated with climate change risks, the availability 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 section
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 conceptual issues, and then presents a summary of the main findings from 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 aggregating, ultimately ending at
the global level and cross‐cutting investment and finance issues.
TS.4.1 Policy design, behaviour and political economy
There are multiple criteria for evaluating policies. Policies are frequently 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 efficiency, the principle of maximizing net economic benefits. Economic
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
economies 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 political 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, political
systems. Criteria may be mutually reinforcing, but there may also be conflicts or tradeoffs among
them. Policies designed for maximum environmental effectiveness or economic performance may
fare less well on other criteria, for example. Such tradeoffs arise at multiple levels of governing
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
undermine 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 normative 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
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Government provision, for example of new technologies or in state enterprises
Voluntary actions, initiated by governments, firms, and 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 international
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 differences in how sectors and countries
are organized economically 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 presence of institutions capable of designing and implementing regulatory policies and the
willingness of respective publics to accept these policies. Many policies have effects, sometimes
unanticipated, across multiple jurisdictions—across cities, regions and countries—because the
economic effects of policies and the technological 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‐enhancing or welfare‐degrading. The
chances of welfare‐enhancing interactions are particularly high when policy instruments address
multiple different market failures – for example, a subsidy or other policy instrument 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 example, 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 international
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
acting in a coordinating role (medium confidence). In these cases, governments themselves seek to
advance voluntary approaches, especially when traditional forms of regulation are thought to be
inadequate or 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 factors associated with human and
institutional behaviour (very high confidence). One of the challenges in designing effective
instruments 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 agricultural 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 effective by taking these factors into account,
such as in the case of financing mechanisms for household investments in energy efficiency and
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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 interventions 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 confidence). 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 technology cost
and performance. Dedicated financial instruments exist to lower these risks for private actors – for
example, credit insurance, feed‐in tariffs, 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 policies and institutions to address climate
change since AR4 (Figure TS.35). Policies and strategies are in their early stages in many countries,
and there is inadequate evidence to assess whether and how they will result in appropriate
institutional and policy change, and therefore, their impact on future emissions. However, to date
these policies, taken together, have not yet achieved a substantial deviation in emissions from the
past trend. Theories of institutional change suggest they might play a role in shaping incentives,
political contexts, and policy paradigms in a way that encourages 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 CO2eq by 2100, which is far from a concentration with a
likely probability of maintaining temperature increases below 2°C this century. Mitigation scenarios
suggest 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 political economy factors, sectoral considerations, and the potential for realizing co‐
benefits. In many countries, mitigation policies have also been actively pursued at state and local
levels. [15.2, 15.5, 15.8]
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Figure TS.36. National Climate legislation and strategies in 2007 and 2012. 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]
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 confidence). 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
representatives of new industries and technologies can facilitate transitions to low emission
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 infrastructure
and is administratively easier to implement than many other alternatives such as cap and trade
[15.5]. The extent of institutional innovation required for policies can be a factor in instrument
choice, especially in developing countries.
Sector‐specific policies have been more widely used than economy‐wide, market‐based policies
(medium evidence, high agreement). Although economic theory suggests that market‐based,
economy‐wide policies are generally more cost‐effective than sectoral approaches, political
economy considerations often make those policies harder to achieve than sectoral 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 contributed 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
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investments 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.8 presents a range of sector specific policies that have been
implemented in practice. [15.1, 15.2, 15.5, 15.8, 15.9]
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Table TS.8: Sector policy instruments. The Table brings together evidence on policy instruments discussed in Chapters 7 to 12. [Table 15.1]
Policy
Instruments
Economic
Instruments –
Taxes
(Carbon taxes
may be
economy‐wide)
Economic
Instruments –
Tradable
Allowances
(May be
economy‐wide)
Energy [Section 7.12]
Transport [8.10]
Buildings [9.10]
Industry [10.11]
AFOLU [11.10]
‐ Carbon tax (e.g.,
applied to
electricity or fuels)
‐ 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
‐ Emission trading
‐ Emission credits
under CDM
‐ Tradable Green
Certificates
‐Fuel and vehicle
standards
‐ Tradable certificates
for energy efficiency
improvements (white
certificates)
‐ Emission trading
‐ Emission credit under CDM
‐ Tradable Green
Certificates
Economic
Instruments –
Subsidies
‐ Fossil fuel subsidy
removal
‐ Feed in tariffs for
renewable energy
‐ Biofuel subsidies
‐ Vehicle purchase
subsidies
‐ Feebates
‐ Subsidies (e.g., for energy
audits)
‐ Fiscal incentives (e.g., for
fuel switching)
Regulatory
Approaches
‐ Efficiency or
environmental
performance
standards
‐ Renewable
Portfolio standards
(RPS) for renewable
energy (RE)
‐ Fuel economy
performance
standards
‐ Fuel quality
standards
‐ GHG emission
performance
standards
‐ Regulatory
restrictions to
encourage modal
shifts (road to rail)
‐ Restriction on use of
‐ Subsidies or Tax
exemptions for
investment in efficient
buildings, retrofits
and products
‐ Subsidized loans
‐ Building codes and
standards
‐ Equipment and
appliance standards
‐ Mandates for energy
retailers to assist
customers invest in
energy efficiency
‐ Emission credits
under CDM (Adam)
‐ Compliance schemes
outside Kyoto
protocol (national
schemes)
‐ Voluntary carbon
markets
‐ Credit lines for low
carbon agriculture,
sustainable forestry.
‐ Energy efficiency
standards for equipment
‐ Energy management
systems (also voluntary)
‐ Voluntary agreements
(where bound by
regulation)
‐ Labelling and public
procurement regulations
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‐ 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
Human Settlements and
Infrastructure [12.5]
‐ Sprawl taxes, Impact fees,
exactions, split‐rate property
taxes, tax increment finance,
betterment taxes,
congestion charges
‐ Urban‐scale Cap‐and‐Trade
‐ Special Improvement or
Redevelopment Districts
‐ Mixed use zoning
‐ Development restrictions
‐ Affordable housing
mandates
‐ Site access controls
‐ Transfer development rights
‐ Design codes
‐ Building codes
‐ Street codes
‐ Design standards
Final Draft
Policy
Instruments
Technical Summary
Energy [Section 7.12]
Transport [8.10]
vehicles in certain
areas
‐ Environmental
capacity constraints
on airports
‐ Urban planning and
zoning restrictions
‐ Fuel labelling
‐ Vehicle efficiency
labelling
Information
Programmes
Government
Provision of
Public Goods or
Services
‐ Provision of district
heating and cooling
infrastructure
Voluntary Actions
‐ Voluntary
agreements
‐ Investment in transit
and human powered
transport
‐ Investment in
alternative fuel
infrastructure
‐ Low emission vehicle
procurement
IPCC WGIII AR5
Buildings [9.10]
Industry [10.11]
AFOLU [11.10]
Human Settlements and
Infrastructure [12.5]
‐ Energy audits
‐ Labelling programmes
‐ Energy advice
programmes
‐ Energy audits
‐ Benchmarking
‐ Brokerage for industrial
cooperation
‐
‐ Public procurement of
efficient buildings and
appliances
‐ Training and education
‐ Certification schemes
for sustainable forest
practices
‐ Information policies
to support REDD+
including monitoring,
reporting and
verification
Protection of national,
state, and local forests.
Investment in
improvement and
diffusion of innovative
technologies in
agriculture and forestry
‐ Labelling programmes
for efficient buildings
‐ Product eco‐labelling
‐ Voluntary agreements on
energy targets, adoption of
energy management
systems, or resource
efficiency
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Promotion of
sustainability by
developing standards
and educational
campaigns
‐Provision of utility
infrastructure such as
electricity distribution, district
heating/cooling and
wastewater connections, etc.
‐ Park improvements
‐ Trail improvements
‐ Urban rail
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Carbon taxes have been implemented in some countries and—alongside technology and other
policies—have contributed to decoupling of emissions from GDP (high confidence). Differentiation
by sector, which is quite common, reduces cost‐effectiveness 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 competitiveness 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 policies that raise
government revenue (e.g., auctioned emission allowances under a cap‐and‐trade system or emission
taxes) generally have lower social costs than approaches which 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 revenue – they are not necessarily designed for the purpose of mitigation (high
confidence). In Europe, where fuel taxes are highest, they have contributed to reductions in carbon
emissions from the transport sector of roughly 50% for this group of countries. The short‐run
response to higher fuel prices is often small, but long‐run price elasticities 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 GHGs are being established in a growing number of countries and
regions. Their environmental 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 feasibility 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 EU Emissions Trading System (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 emission reduction targets, import of credits from the Clean
Development Mechanism (CDM), and the interaction with other policy instruments, 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 emission permits temporarily,
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 subsidies
have no further impact on total emissions (although they may affect costs and possibly the viability
of more stringent future targets). If the cap is loose relative to other policies, it becomes ineffective.
This is an example of a negative interaction between policy instruments. 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 environmental 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 actually accrue
to the relatively wealthy, and utilized lump‐sum cash transfers or other mechanisms that are more
targeted to the poor. [15.5.3]
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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 include energy efficiency standards and
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 to firms and individuals [Box TS.12, 3.9.3,
15.5.5, 15.5.6]. Since AR4 there has been continued investigation into the ‘rebound’ effects 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 [Box TS.13, 3.9.5, 5.7.2, 15.5.4].
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 efficiency
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
improvement changes the relative prices of goods, as well as an income effect when an EE
improvement lowers production costs and creates greater wealth.
Rebound is sometimes confused with the concept of carbon leakage, 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 restrictions on such emissions [5.4.1,
14.4]. Energy efficiency rebound can occur regardless of the geographic scope of the adopted policy.
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].
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 challenges 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 energy such as FIT and RPS. These have promoted substantial diffusion and innovation of
new energy technologies such as wind turbines and photovoltaic panels, but have raised questions
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about their economic efficiency, and introduced challenges 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 confidence). 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 to be built into technology 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 transport); and in urban planning [12.5]. The provision of adequate infrastructure is important
for behavioural change [15.5.6].
Successful voluntary agreements on mitigation between governments and industries are
characterized by a strong institutional framework with capable industrial associations (medium
confidence). The strengths of voluntary agreements are speed and flexibility in phasing measures,
and facilitation of barrier removal activities 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 environmental impacts without a proper institutional
framework. [15.5.7]
TS.4.3 Development and regional cooperation
Regional cooperation offers substantial opportunities for mitigation 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 natural 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
development trajectories are the poorest developing regions where there are few lock‐in effects in
terms of modern energy systems and urbanization patterns. However, these regions also have the
lowest financial, technological, and institutional capacities to embark on such low‐carbon
development paths [Figure TS.36] and their cost of waiting 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]
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Figure TS.37. Economic and governance indicators affecting regional capacities to embrace
mitigation policies. 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]
Regional cooperation has, to date, only had a limited (positive) impact on mitigation (medium
evidence, high agreement). Nonetheless, regional cooperation could play an enhanced role in
promoting mitigation in the future, particularly if it explicitly incorporates mitigation objectives in
trade, infrastructure and energy policies and promotes direct mitigation action at the regional level.
[14.4.2, 14.5]
Most literature suggests that climate‐specific regional cooperation agreements in areas of policy
have not played an important role in addressing mitigation challenges to date (medium confidence).
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 directives 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, including economic instruments and
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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 cooperation could have significant implications
for mitigation, even if mitigation objectives are not a component (medium confidence). Regional
cooperation with non‐climate‐related objectives but possible 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 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 further mitigation potential in such arrangements. [14.4.2]
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‐faceted view
of climate policy (very high confidence). Two characteristics 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 emissions, mitigation opportunities, climate impacts,
and capacity for mitigation and adaptation [13.2.1.1]. Traditional policy‐making efforts focused on
international cooperation as a task centrally focused on the coordination of national policies that
would be adopted with the goal of mitigation. More recent policy developments suggest that there
is a more complicated set of relationships between national, regional, and global policy‐making,
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 highly decentralized
policy action is consistent with and can lead to global mitigation efforts that are effective, equitable,
and efficient [6.1.2.1, 13.13.1.3].
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]. The United Nations Framework Convention on
Climate Change (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,
13.12]. 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 international cooperation toward climate
goals. [13.3, 13.4, 13.5] Finally, there has been an emergence of new transnational climate related
institutions 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. 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) [Figure TS.37, 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‐sharing [13.4.2]. Existing and proposed
modes of international cooperation are assessed in Table TS.9. [13.13]
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The UNFCCC is currently the only international climate policy venue with broad legitimacy, due in
part to its virtually universal 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]
Legend: Loose coordination of policies: examples include transnational city networks or 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); See Figure 13.1 for the details of these
examples.
Figure TS.38. International cooperation over ends/means and degrees of centralized authority.
Examples in blue are existing agreements. Examples in pale pink are proposed structures for
agreements. The width of individual boxes indicates the range of possible degrees of centralization for
a particular 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]
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 climate 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‐benefits 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 commitments in forums of international cooperation (medium confidence). 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
regarding those outcomes [2.6.3].
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International cooperation can stimulate public and private investment and the adoption of
economic incentives and direct regulations that promote technological innovation (medium
confidence). Technology policy can help lower mitigation costs, thereby increasing incentives for
participation and compliance with international 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, international agreement on climate change,
policy linkages between and among existing and nascent international, regional, national, and sub‐
national climate policies offer potential climate benefits (medium confidence). 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 policy experiments. Yet integrating climate policies raises a number of concerns about
the performance of a system of linked legal rules and economic activities. [13.5.3] 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, 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 can be modified to be more
climate friendly; whether border adjustment 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, 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 chlorofluorocarbons (CFCs) and hydro
chlorofluorocarbons (HCFCs). Substitutes for those ozone‐depleting gases (such as HFCs, which are
not ozone‐depleting) may also be potent GHGs. Lessons learned from the Montreal Protocol, for
example, 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. [Table TS.9, 13.3.3, 13.3.4, 13.13.1.4,]
The Kyoto Protocol was the first binding step toward implementing the principles and goals
provided by the UNFCCC, but it has not been as successful as intended (medium evidence, low
agreement). While the parties of the Kyoto Protocol surpassed their collective emission reduction
target, the Protocol’s environmental effectiveness has been less than it could have been because of
incomplete participation and compliance of Annex I countries and crediting for emissions reductions
that would have occurred without the Protocol in economies in transition. Additionally, the design of
the Kyoto Protocol does not directly regulate the emissions of non‐Annex I countries, which have
grown rapidly over the past decade. [Table TS.9, 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 mechanisms,
created a market for emissions offsets from developing countries, generating credits equivalent to
over 1.3 billion tCO2eq as of July 2013. The CDM’s environmental effectiveness has been mixed due
to concerns about the limited additionality of projects, the invalid determination of some project
baselines, the possibility of emissions leakage, and recent price decreases. Its distributional impact
has been unequal due to the concentration of projects in a limited number of countries. The
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Protocol’s other flexible mechanisms, Joint Implementation and International Emissions Trading,
have been undertaken both by governments and private market participants, but have raised
concerns related to government sales of emission units. [Table TS.9, 13.7.2, 13.13.1,]
Recent UNFCCC negotiations have sought to include more ambitious commitments from countries
listed in Annex B of the Kyoto Protocol, mitigation commitments from a broader set of countries
than those covered under Annex B, and substantial new funding mechanisms. Voluntary pledges of
quantified, economy‐wide emission reductions targets by developed countries and voluntary
pledges to mitigation actions by many developing countries were formalized in the 2010 Cancún
Agreement. The distributional impact of the agreement will depend in part on sources of financing,
including the successful fulfilment by developed countries of their expressed joint commitment to
mobilize USD 100 billion per year by 2020 for climate action in developing countries. [Table TS.9,
13.5.1.1, 13.13.1.3, 16.2.1.1]
TableTS.9: 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
Existing
forms of
cooperation
[13.13.1]
Assessment Criteria
Environmental
Effectiveness
Aggregate
Economic
Performance
Distributional
Impacts
Institutional
Feasibility
UNFCCC
Aggregate GHG emissions
in Annex I countries
declined by 6 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, multigas approach,
sources and sinks,
and domestic policy
choice. Cost and
benefit estimates
depend on baseline,
discount rate,
participation,
leakage, co-benefits,
adverse side-effects,
and other factors.
Commitments
distinguish
between Annex I
(industrialized) and
non-Annex I
countries.
Principle of
“common but
differentiated
responsibility.”
Commitment to
“equitable and
appropriate
contributions by
each [party].”
Ratified (or
equivalent) by 195
countries and
regional
organizations.
Compliance depends
on national
communications.
The Kyoto Protocol
Aggregate emissions in
Annex I countries were
reduced by 8.5 to 13.6
percent below 1990 levels
by 2011, more than the
Protocol’s first commitment
period collective reduction
target of 5.2 percent.
Reductions occurred mainly
in EITs; emissions
increased in some others.
Incomplete participation in
in the first commitment
period (even lower in the
second)
Cost-effectiveness
improved by flexible
mechanisms (Joint
Implementation,
Clean Development
Mechanism,
International
Emissions Trading)
and domestic policy
choice. Cost and
benefit estimates
depend on baseline,
discount rate,
participation,
leakage, co-benefits,
adverse side-effects,
and other factors.
Commitments
distinguish
between
developed and
developing
countries, but
dichotomous
distinction
correlates only
partly (and
decreasingly) with
historical
emissions and with
changing
economic
circumstances.
Intertemporal
equity affected by
short term actions.
Ratified (or
equivalent) by 192
countries and
regional
organizations, but
took 7 years to enter
into force.
Compliance depends
on national
communications,
plus Kyoto Protocol
compliance system.
Later added
approaches to
enhance
measurement,
reporting, and
verification.
The Kyoto Mechanisms
About 1.4 billion tCO2eq
credits under the Clean
Development Mechanism
(CDM), 0.8 billion under
Joint Implementation (JI),
and 0.2 billion under
International Emissions
Trading (IET). Additionality
of CDM projects remains an
issue but regulatory reform
underway.
CDM mobilized low
cost options,
particularly industrial
gases, reducing
costs, except for
some project types.
Medium evidence
that technology is
transferred to nonAnnex I countries.
Limited direct
investment from
Annex I 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
international carbon
markets to date. Has
built institutional
capacity in
developing countries.
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Further Agreements under the
UNFCCC
Pledges to limit emissions
made by all major emitters
under Cancún 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. Costeffectiveness might
be improved by
market-based policy
instruments,
inclusion of forestry
sector, commitments
by more nations than
Annex I countries
(as envisioned in
Durban Platform).
Depends on
sources of
financing,
particularly for
actions of
developing
countries.
Cancún Conference
of the Parties
decision; 97
countries made
pledges of emission
reduction targets or
actions for 2020.
G8, G20,
Major
Economies
Forum (MEF)
G8 and MEF have
recommended emission
reduction by all major
emitters. G20 may spur
GHG reductions by phasing
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 oilexporting 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 percent of global
emissions. Opens
possibility for forumshopping, based on
issue preferences.
Montreal
Protocol on
OzoneDepleting
Substances
(ODS)
Spurred emission
reductions through ozonedepleting substances phase
outs approximately 5 times
the magnitude of the Kyoto
Protocol’s first commitment
period targets. Contribution
may be negated by highGWP substitutes, though
efforts to phase out
hydrofluorocarbons (HFCs)
are growing.
Cost-effectiveness
supported by multigas approach. Some
countries used
market-based
mechanisms to
implement
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 tCO2eq,
but inconsistencies in
certification remain.
Credit prices are
heterogeneous,
indicating market
inefficiencies.
[No literature
cited.]
Fragmented and
non-transparent
market.
Strong
multilateralism
Tradeoff between ambition
(deep) and participation
(broad).
More cost effective
with greater reliance
on market
mechanisms.
Multilateralism
facilitates
integrating
distributional
impacts into
negotiations and
may apply equitybased criteria as
outlined in Chapter
4
Depends on number
of parties; degree of
ambition
Harmonized
national
policies
Depends on net aggregate
change in ambition across
countries resulting from
harmonization.
More cost effective
with greater reliance
on market
mechanisms.
Depends on
specific national
policies
Depends on
similarity of national
policies; more
similarity may
support
harmonization but
domestic
circumstances may
vary. National
enforcement.
Decentralized
architectures,
coordinated
national
polices
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
similarity of national
policies. National
enforcement.
Agreements
outside the
UNFCCC
Proposed
forms of
cooperation
[13.13.2]
IPCC WGIII AR5
Proposed
architectures
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.
TS.4.5 Investment and finance
A transformation to a low‐carbon economy implies new patterns of investment. A limited number
of studies have examined the investment needs for different mitigation scenarios. Information is
largely limited to energy use. Mitigation scenarios that stabilize atmospheric CO2eq concentrations
in the range from 430 to 530 ppm CO2eq by 2100 (without overshoot) show substantial shifts in
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annual investment flows during the period 2010–2029 if compared to baseline scenarios [Figure
TS.38]: annual investment in the existing technologies associated with the energy supply sector (e.g.,
conventional fossil fuelled power plants and fossil fuel extraction) would decline by USD 30 (2 to
166) billion per year (roughly 20%) (limited evidence, medium agreement). Investment in low‐
emissions generation technologies (renewable, nuclear, and fossil fuels with CCS) would increase by
USD 147 (31 to 360) billion per year (roughly 100%) during the same period (limited evidence,
medium agreement) in combination with an increase by USD 336 (1 to 641) 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 technologies 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 USD 116 (‐8 to 369) billion (limited evidence, medium
agreement). Such spillover 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 investment of USD 21 to 35 billion per year (low confidence).
[16.2.2]
Figure TS.39. Change of average annual investment in mitigation scenarios (2010–2029). Investment
changes are calculated by a limited number of model studies and model comparisons for mitigation
scenarios that stabilize concentrations within the range of 430–530 ppm CO2eq 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]
Estimates of total climate finance range from USD 343 to 385 billion per year between 2010 and
2012 (limited evidence, medium agreement). 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 (limited evidence, high agreement). The figures reflect the total
financial flow for the underlying investments, not the incremental investment, i.e., the portion
attributed to the mitigation/adaptation cost increment [Box TS.14]. In general, quantitative data on
climate finance are limited, relate to different concepts, and are incomplete. [16.2.1.1]
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Depending on definitions and approaches, climate finance flows to developing countries are
estimated to range from USD 39 to 120 billion per year during the period 2009 to 2012 (medium
agreement, limited evidence). The range covers public and the more uncertain flows of private
funding for mitigation and adaptation. Public climate finance was USD 35 to 49 billion (2011/2012
USD) (medium confidence). Most public climate finance provided to developing countries flows
through bilateral and multilateral 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 USD 10 billion per year from 2005 to 2010 (medium confidence). Between 2010 and
2012, the ´fast start finance´ provided by some developed countries amounted to over USD 10 billion
per year (medium confidence). Figure TS.39 provides an overview of climate finance, outlining
sources and managers of capital, financial instruments, project owners, and projects. [16.2.1.1]
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]
Private climate finance is important and dependent on an enabling environment. The private
sector contribution to total climate finance is estimated at an average of USD 267 billion (74%) per
year in the period 2010 to 2011 and at USD 224 billion (62%) per year in the 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. A country’s broader context—including the efficiency of its institutions, security of
property rights, credibility of policies, and other factors—has a substantial 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 CO2 emissions (low confidence). This makes
them attractive for international 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 international 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 average rates of return for different investment
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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 systems), as well as renewable energy
premiums, feed‐in tariffs, portfolio standards, investment grants, soft loans and credit insurance can
move risk‐return profiles into the required direction. [16.4]. For some instruments, the presence 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 feed‐in tariffs, do not appear to
suffer from this problem [2.4.4].
Box TS.14. There is no agreed definition of ‘climate finance’
Total climate finance includes all financial flows whose expected effect is to reduce net greenhouse
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 international 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 countries 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 bilateral and multilateral institutions 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 climate investment is the extra capital required for the initial investment for a
mitigation or adaptation project in comparison to a reference project. Incremental investment for
mitigation and adaptation measures 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 incremental 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 policy is the reduction of aggregate consumption or gross domestic product induced by
the reallocation of investments and expenditures induced by climate policy. 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]
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