151
2
Integrated Risk and
Uncertainty Assessment
of Climate Change
Response Policies
Coordinating Lead Authors:
Howard Kunreuther (USA), Shreekant Gupta (India)
Lead Authors:
Valentina Bosetti (Italy), Roger Cooke (USA), Varun Dutt (India), Minh Ha-Duong (France),
Hermann Held (Germany), Juan Llanes-Regueiro (Cuba), Anthony Patt (Austria / Switzerland),
Ekundayo Shittu (Nigeria / USA), Elke Weber (USA)
Contributing Authors:
Hannes Böttcher (Austria / Germany), Heidi Cullen (USA), Sheila Jasanoff (USA)
Review Editors:
Ismail Elgizouli (Sudan), Joanne Linnerooth-Bayer (Austria / USA)
Chapter Science Assistants:
Siri-Lena Chrobog (Germany), Carol Heller (USA)
This chapter should be cited as:
Kunreuther H., S. Gupta, V. Bosetti, R. Cooke, V. Dutt, M. Ha-Duong, H. Held, J. Llanes-Regueiro, A. Patt, E. Shittu, and E.
Weber, 2014: Integrated Risk and Uncertainty Assessment of Climate Change Response Policies. In: Climate Change 2014:
Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum,
S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cam-
bridge University Press, Cambridge, United Kingdom and New York, NY, USA.
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Chapter 2
Contents
Executive Summary � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 154
2�1 Introduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 155
2�2 Metrics of uncertainty and risk � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 157
2�3 Risk and uncertainty in climate change � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 157
2�3�1 Uncertainties that matter for climate policy choices
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 157
2�3�2 What is new on risk and uncertainty in AR5
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 159
2�4 Risk perception and responses to risk and uncertainty � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 160
2�4�1 Considerations for design of climate change risk reduction policies
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 160
2�4�2 Intuitive and deliberative judgment and choice
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 160
2�4�3 Consequences of intuitive decision making
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 161
2.4.3.1 Importance of the status quo
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
2.4.3.2 Focus on the short term and the here-and-now
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
2.4.3.3 Aversion to risk, uncertainty, and ambiguity
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
2�4�4 Learning
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 164
2�4�5 Linkages between different levels of decision making
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 165
2�4�6 Perceptions of climate change risk and uncertainty
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 166
2�5 Tools and decision aids for analysing uncertainty and risk � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 168
2�5�1 Expected utility theory
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 168
2.5.1.1 Elements of the theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
2.5.1.2 How can expected utility improve decision making?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
2�5�2 Decision analysis
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 169
2.5.2.1 Elements of the theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
2.5.2.2 How can decision analysis improve
decision making? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
2�5�3 Cost-benefit analysis
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 170
2.5.3.1 Elements of the theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
2.5.3.2 How can CBA improve decision making?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
2.5.3.3 Advantages and limitations of CBA
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
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2�5�4 Cost-effectiveness analysis � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 171
2.5.4.1 Elements of the theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
2.5.4.2 How can CEA improve decision making?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
2.5.4.3 Advantages and limitations of CEA over CBA
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
2�5�5 The precautionary principle and robust decision making
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 172
2.5.5.1 Elements of the theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
2�5�6 Adaptive management
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 173
2�5�7 Uncertainty analysis techniques
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 173
2.5.7.1 Structured expert judgment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
2.5.7.2 Scenario analysis and ensembles
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
2�6 Managing uncertainty, risk and learning � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 177
2�6�1 Guidelines for developing policies
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 177
2�6�2 Uncertainty and the science/policy interface
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 178
2�6�3 Optimal or efficient stabilization pathways (social planner perspective)
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 178
2.6.3.1 Analyses predominantly addressing climate or damage response uncertainty
. . . . . . . . . . . . . . . . . . . . . . . 178
2.6.3.2 Analyses predominantly addressing policy response uncertainty
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
2�6�4 International negotiations and agreements
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 181
2.6.4.1 Treaty formation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
2.6.4.2 Strength and form of national commitments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
2.6.4.3 Design of measurement, verification regimes, and treaty compliance
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
2�6�5 Choice and design of policy instruments
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 183
2.6.5.1 Instruments creating market penalties for GHG emissions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
2.6.5.2 Instruments promoting technological RDD&D
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
2.6.5.3 Energy efficiency and behavioural change
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
2.6.5.4 Adaptation and vulnerability reduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
2�6�6 Public support and opposition to climate policy
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 187
2.6.6.1 Popular support for climate policy
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
2.6.6.2 Local support and opposition to infrastructure projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
2�7 Gaps in knowledge and data � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 189
2�8 Frequently Asked Questions � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 189
References � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 192
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Chapter 2
Executive Summary
The scientific understanding of climate change and the impact it
has on different levels of decision-making and policy options has
increased since the publication of the Intergovernmental Panel on
Climate Change (IPCC) Fourth Assessment Report (AR4). In addi-
tion, there is a growing recognition that decision makers often rely
on intuitive thinking processes rather than undertaking a systematic
analysis of options in a deliberative fashion. It is appropriate that
climate change risk management strategies take into account both
forms of thinking when considering policy choices where there is risk
and uncertainty.
Consideration of risk perception and decision processes can
improve risk communication, leading to more effective poli-
cies for dealing with climate change� By understanding the sys-
tematic biases that individuals utilize in dealing with climate change
problems, one can more effectively communicate the nature of the
climate change risk. An understanding of the simplified decision
rules employed by decision makers in making choices may be helpful
in designing policies that encourage the adoption of mitigation and
adaptation measures. [Section 2.4]
Decision processes often include both deliberative and intuitive
thinking� When making mitigation and adaptation choices, decision
makers sometimes calculate the costs and benefits of their alterna-
tives (deliberative thinking). They are also likely to utilize emotion- and
rule-based responses that are conditioned by personal past experience,
social context, and cultural factors (intuitive thinking). [2.4.2]
Laypersons tend to judge risks differently than experts� Layper-
sons’ perceptions of climate change risks and uncertainties are often
influenced by past experience, as well as by emotional processes that
characterize intuitive thinking. This may lead them to overestimate or
underestimate the risk. Experts engage in more deliberative thinking
than laypersons by utilizing scientific data to estimate the likelihood
and consequences of climate change. [2.4.6]
Cost-benefit analysis (CBA) and cost-effectiveness analysis
(CEA) can enable decision makers to examine costs and ben-
efits, but these methodologies also have their limitations� Both
approaches highlight the importance of considering the likelihood of
events over time and the importance of focusing on long-term hori-
zons when evaluating climate change mitigation and adaptation poli-
cies. CBA enables governments and other collective decision-making
units to compare the social costs and benefits of different alternatives.
However, CBA cannot deal well with infinite (negative) expected utili-
ties arising from low probability catastrophic events often referred to
as ‘fat tails’. CEA can generate cost estimates for stabilizing green-
house gas (GHG) concentrations without having to take into account
the uncertainties associated with cost estimates for climate change
impacts. A limitation of CEA is that it takes the long-term stabilization
as a given without considering the economic efficiency of the target
level. [2.5.3, 2.5.4]
Formalized expert judgment and elicitation processes improve
the characterization of uncertainty for designing climate
change strategies (high confidence). Experts can quantify uncer-
tainty through formal elicitation processes. Their judgments can char-
acterize the uncertainties associated with a risk but not reduce them.
The expert judgment process highlights the importance of undertaking
more detailed analyses to design prudent climate policies. [2.5.6]
Individuals and organizations that link science with policy grap-
ple with several different forms of uncertainty� These uncertain-
ties include absence of prior agreement on framing of problems and
ways to scientifically investigate them (paradigmatic uncertainty), lack
of information or knowledge for characterizing phenomena (epistemic
uncertainty), and incomplete or conflicting scientific findings (transla-
tional uncertainty). [2.6.2]
The social benefit from investments in mitigation tends to
increase when uncertainty in the factors relating GHG emissions
to climate change impacts are considered (medium confidence).
If one sets a global mean temperature (GMT) target, then normative
analyses that include uncertainty on the climate response to elevated
GHG concentration, suggest that investments in mitigation measures
should be accelerated. Under the assumption of nonlinear impacts of
a GMT rise, inclusion of uncertainty along the causal chain from emis-
sions to impacts suggests enhancing mitigation. [2.6.3]
The desirability of climate policies and instruments are affected
by decision makers’ responses to key uncertainties� At the
national level, uncertainties in market behaviour and future regulatory
actions have been shown to impact the performance of policy instru-
ments designed to influence investment patterns. Both modelling and
empirical studies have shown that uncertainty as to future regulatory
and market conditions adversely affects the performance of emission
allowance trading markets [2.6.5.1]. Other studies have shown that
subsidy programmes (e. g., feed-in tariffs, tax credits) are relatively
immune to market uncertainties, but that uncertainties with respect to
the duration and level of the subsidy program can have adverse effects
[2.6.5.2]. In both cases, the adverse effects of uncertainty include less
investment in low-carbon infrastructure, increasing consumer prices,
and reducing the pressure for technological development.
Decision makers in developing countries often face a particu-
lar set of challenges associated with implementing mitigation
policies under risk and uncertainty (medium confidence). Manag-
ing risk and uncertainty in the context of climate policy is of particular
importance to developing countries that are resource constrained and
face other pressing development goals. In addition, institutional capac-
ity in these countries may be less developed compared to advanced
economies. Therefore, decision makers in these countries (governments
and economic agents such as firms, farmers, households, to name a
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Chapter 2
few) have less room for ‘error’ (uncertain outcomes and / or wrong or
poorly implemented policies). The same applies to national, regional
and local governments in developed countries who can ill afford to
waste scarce resources through policy errors. [Box 2.1]
2.1 Introduction
This framing chapter considers ways in which risk and uncertainty can
affect the process and outcome of strategic choices in responding to
the threat of climate change.
‘Uncertainty’ denotes a cognitive state of incomplete knowledge that
results from a lack of information and / or from disagreement about
what is known or even knowable. It has many sources ranging from
quantifiable errors in the data to ambiguously defined concepts or ter-
minology to uncertain projections of human behaviour. The Guidance
Note for Lead Authors of the IPCC Fifth Assessment Report on Consis-
tent Treatment of Uncertainties (Mastrandrea etal., 2010) summarizes
alternative ways of representing uncertainty. Probability density func-
tions and parameter intervals are among the most common tools for
characterizing uncertainty.
‘Risk’ refers to the potential for adverse effects on lives, livelihoods,
health status, economic, social and cultural assets, services (includ-
ing environmental), and infrastructure due to uncertain states of the
world. To the extent that there is a detailed understanding of the char-
acteristics of a specific event, experts will normally be in agreement
regarding estimates of the likelihood of its occurrence and its resulting
consequences. Risk can also be subjective in the sense that the likeli-
hood and outcomes are based on the knowledge or perception that a
person has about a given situation. There may also be risks associated
with the outcomes of different climate policies, such as the harm aris-
ing from a change in regulations.
There is a growing recognition that today’s policy choices are highly
sensitive to uncertainties and risk associated with the climate system
and the actions of other decision makers. The choice of climate policies
can thus be viewed as an exercise in risk management (Kunreuther
etal., 2013a). Figure 2.1 suggests a risk management framework that
serves as the structure of the chapter.
After defining risk and uncertainty and their relevant metrics (Section
2.2), we consider how choices with respect to climate change policy
options are sensitive to risk and uncertainty (Section 2.3). A taxon-
omy depicts the levels of decision making ranging from international
agreements to actions undertaken by individuals in relation to climate
change policy options under conditions of risk and uncertainty that
range from long-term global temperature targets to lifestyle choices.
The goals and values of the different stakeholders given their immedi-
ate and long-term agendas will also influence the relative attractive-
ness of different climate change policies in the face of risk and uncer-
tainty.
Sections 2.4, 2.5 and 2.6 characterize descriptive and normative
theories of decision-making and models of choice for dealing with
risk and uncertainty and their implications for prescriptive analysis.
Descriptive refers to theories of actual behaviour, based on experi-
mental evidence and field studies that characterize the perception
of risk and decision processes. Normative in the context of this chap-
ter refers to theories of choice under risk and uncertainty based on
abstract models and axioms that serve as benchmarks as to how
decision makers should ideally make their choices. Prescriptive refers
to ways of improving the decision process and making final choices
(Kleindorfer etal., 1993).
A large empirical literature has revealed that individuals, small groups
and organizations often do not make decisions in the analytic or ratio-
nal way envisioned by normative models of choice in the economics
and management science literature. People frequently perceive risk
in ways that differ from expert judgments, posing challenges for risk
communication and response. There is a tendency to focus on short
time horizons, utilize simple heuristics in choosing between alterna-
tives, and selectively attend to subsets of goals and objectives.
To illustrate, the voting public in some countries may have a wait-
and-see attitude toward climate change, leading their governments to
postpone mitigation measures designed to meet specified climate tar-
gets (Sterman, 2008; Dutt and Gonzalez, 2011). A coastal village may
decide not to undertake measures for reducing future flood risks due
to sea level rise (SLR), because their perceived likelihood that SLR will
cause problems to their village is below the community council’s level
of concern.
Section 2.4 provides empirical evidence on behavioural responses to
risk and uncertainty by examining the types of biases that influence
individuals’ perception of the likelihood of an event (e. g., availability,
learning from personal experience), the role that emotional, social, and
cultural factors play in influencing the perception of climate change
risks and strategies for encouraging decision makers to undertake
cost-effective measures to mitigate and adapt to the impacts of cli-
mate change.
A wide range of decision tools have been developed for evaluating
alternative options and making choices in a systematic manner even
when probabilities are difficult to characterize and / or outcomes are
uncertain. The relevance of these tools for making more informed
decisions depends on how the problem is formulated and framed, the
nature of the institutional arrangements, and the interactions between
stakeholders (Hammond etal., 1999; Schoemaker and Russo, 2001).
Governments debating the merits of a carbon tax may turn to cost-
benefit analysis or cost-effectiveness analysis to justify their positions.
They may need to take into account that firms who utilize formal
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approaches, such as decision analysis, may not reduce their emissions
if they feel that they are unlikely to be penalized because the carbon
tax will not be well enforced. Households and individuals may find the
expected utility model or decision analysis to be useful tools for evalu-
ating the costs and benefits of adopting energy efficient measures
given the trajectory of future energy prices.
Section 2.5 delineates formal methodologies and decision aids for ana-
lysing risk and uncertainty when individuals, households, firms, com-
munities and nations are making choices that impact their own well-
being and those of others. These tools encompass variants of expected
utility theory, decision analysis, cost-benefit analyses or cost-effective-
ness analyses that are implemented in integrated assessment models
(IAMs). Decision aids include adaptive management, robust decision
making and uncertainty analysis techniques such as structured expert
judgment and scenario analysis. The chapter highlights the importance
of selecting different methodologies for addressing different problems.
Developing robust policy response strategies and instruments should
take into account how the relevant stakeholders perceive risk and their
behavioural responses to uncertain information and data (descriptive
analysis). The policy design process also needs to consider the meth-
odologies and decision aids for systematically addressing issues of
risk and uncertainty (normative analysis) that suggest strategies for
improving outcomes at the individual and societal level (prescriptive
analysis).
Section 2.6 examines how the outcomes of particular options, in terms
of their efficiency or equity, are sensitive to risks and uncertainties and
affect policy choices. After examining the role of uncertainty in the sci-
ence / policy interface, it examines the role of integrated assessment
models (IAMs) from the perspective of the social planner operating
at a global level and the structuring of international negotiations and
paths to reach agreement. Integrated assessment models combined
with an understanding of the negotiation process for reaching inter-
national agreements may prove useful to delegates for justifying the
positions of their country at a global climate conference. The section
also examines the role that uncertainty plays in the performance of dif-
ferent technologies now and in the future as well as how lifestyle deci-
sions such as investing in energy efficient measures can be improved.
Figure 2�1 | A risk management framework. Numbers in brackets refer to sections where more information on these topics can be found.
Managing Uncertainty, Risk and Learning
(Prescriptive Analysis)
[Section 2.6]
Risk Perception and Responses
to Risk and Uncertainty
(Descriptive Analysis)
[Section 2.4]
Tools and Decisions Aids for
Analysing Uncertainty and Risk
(Normative Analysis)
[Section 2.5]
Impact of Risk and Uncertainty on
Climate Change Policy Choices
[Sections 2.2 and 2.3]
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The section concludes by examining the roles that risk and uncertainty
play in support of or opposition to climate policies.
The way climate change is managed will have an impact on policy
choices as shown by the feedback loop in Figure 2.1, suggesting that
the risk management process for addressing climate change is itera-
tive. The nature of this feedback can be illustrated by the following
examples. Individuals may be willing to invest in solar panels if they
are able to spread the upfront cost over time through a long-term
loan. Firms may be willing to promote new energy technologies that
provide social benefits with respect to climate change if they are
given a grant to assist them in their efforts. National governments
are more likely to implement carbon markets or international trea-
ties if they perceive the short-term benefits of these measures to be
greater than the perceived costs. Education and learning can play key
roles in how climate change is managed through a reconsideration
of policies for managing the risks and uncertainties associated with
climate change.
2.2 Metrics of uncertainty
and risk
The IPCC strives for a treatment of risk and uncertainty that is consis-
tent across all three Working Groups based the Guidance Note (GN)
for Lead Authors of the IPCC Fifth Assessment Report on Consistent
Treatment of Uncertainties (Mastrandrea et al., 2010). This section
summarizes key aspects of the GN that frames the discussion in this
chapter.
The GN indicates that author teams should evaluate the associated
evidence and agreement with respect to specific findings that involve
risk and uncertainty. The amount of evidence available can range from
small to large, and can vary in quality and consistency. The GN recom-
mends reporting the degree of certainty and / or uncertainty of a given
topic as a measure of the consensus or agreement across the scien-
tific community. Confidence expresses the extent to which the IPCC
authors do in fact support a key finding. If confidence is sufficiently
high, the GN suggests specifying the key finding in terms of probabil-
ity. The evaluation of evidence and degree of agreement of any key
finding is labelled a traceable account in the GN.
The GN also recommends taking a risk-management perspective by
stating that “sound decision making that anticipates, prepares for,
and responds to climate change depends on information about the
full range of possible consequences and associated probabilities.”
The GN also notes that, “low-probability outcomes can have signifi-
cant impacts, particularly when characterized by large magnitude, long
persistence, broad prevalence, and / or irreversibility.” For this reason,
the GN encourages the presentation of information on the extremes
of the probability distributions of key variables, reporting quantitative
estimates when possible and supplying qualitative assessments and
evaluations when appropriate.
2.3 Risk and uncertainty
in climate change
Since the publication of AR4, political scientists have documented the
many choices of climate policy and the range of interested parties con-
cerned with them (Moser, 2007; Andonova etal., 2009; Bulkeley, 2010;
Betsill and Hoffmann, 2011; Cabré, 2011; Hoffmann, 2011; Meckling,
2011; Victor, 2011).
There continues to be a concern about global targets for mean surface
temperature and GHG concentrations that are discussed in Chapter 6
of this report. This choice is normally made at the global level with
some regions, countries, and sub-national political regions setting their
own targets consistent with what they believe the global ones should
be. Policymakers at all levels of decision making face a second-order
set of choices as to how to achieve the desired targets. Choices in this
vein that are assessed in Chapters 7 – 12 of this report, include tran-
sition pathways for various drivers of emissions, such as fossil fuels
within the energy system, energy efficiency and energy-intensive
behavioural patterns, issues associated with land-use and spatial plan-
ning, and / or the emissions of non- CO
2
greenhouse gases.
The drivers influencing climate change policy options are discussed in
more detail in Chapters 13 – 16 of this report. These options include
information provision, economic instruments (taxes, subsidies, fines),
direct regulations and standards, and public investments. At the same
time, individuals, groups and firms decide what actions to take on their
own. These choices, some of which may be in response to governmen-
tal policy, include investments, lifestyle and behaviour.
Decisions for mitigating climate change are complemented by climate
adaptation options and reflect existing environmental trends and driv-
ers. The policy options are likely to be evaluated with a set of crite-
ria that include economic impacts and costs, equity and distributional
considerations, sustainable development, risks to individuals and soci-
ety and co-benefits. Many of these issues are discussed in Chapters 3
and 4.
2�3�1 Uncertainties that matter for climate
policy choices
The range and number of interested parties who are involved in cli-
mate policy choices have increased significantly in recent years. There
has been a widening of the governance forums within which climate
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Chapter 2
policies and international agreements are negotiated at the global
level (Victor, 2011), across multiple networks within national gov-
ernments (Andonova etal., 2009; Hoffmann, 2011), and at the local,
regional and / or interest group level (Moser, 2007; Bulkeley, 2010). At
the same time, the number of different policy instruments under active
discussion has increased, from an initial focus on cap-and-trade and
carbon tax instruments (Betsill and Hoffmann, 2011; Hoffmann, 2011),
to feed-in tariffs or quotas for renewable energy (Wiser etal., 2005;
Mendonça, 2007), investments in research and development (Sagar
and van der Zwaan, 2006; De Coninck etal., 2008; Grubler and Riahi,
2010), and reform of intellectual property laws (Dechezleprêtre etal.,
2011; Percival and Miller, 2011).
Choices are sensitive to the degree of uncertainty with respect to a
set of parameters that are often of specific importance to particular
climate policy decisions. Here, and as shown in Figure 2.2, we group
these uncertainties into five broad classes, consistent with the
approach taken in Patt and Weber (2014):
• Climate responses to greenhouse gas (GHG) emissions, and their
associated impacts. The large number of key uncertainties with
respect to the climate system are discussed in Working GroupI
(WGI). There are even greater uncertainties with respect to the
impacts of changes in the climate system on humans and the eco-
logical system as well as their costs to society. These impacts are
assessed in WGII.
• Stocks and flows of carbon and other GHGs. The large uncertain-
ties with respect to both historical and current GHG sources and
sinks from energy use, industry, and land-use changes are assessed
in Chapter 5. Knowledge gaps make it especially difficult to esti-
mate how the flows of greenhouse gases will evolve in the future
under conditions of elevated atmospheric CO
2
concentrations and
their impact on climatic and ecological processes.
• Technological systems. The deployment of technologies is likely to
be the main driver of GHG emissions and a major driver of climate
vulnerability. Future deployment of new technologies will depend
on how their price, availability, and reliability evolve over time as a
result of technological learning. There are uncertainties as to how
fast the learning will take place, what policies can accelerate learn-
ing and the effects of accelerated learning on deployment rates of
new technologies. Technological deployment also depends on the
degree of public acceptance, which in turn is typically sensitive to
perceptions of health and safety risks.
• Market behaviour and regulatory actions. Public policies can create
incentives for private sector actors to alter their investment behav-
iour, often in the presence of other overlapping regulations. The
extent to which firms change their behaviour in response to the
policy, however, often depends on their expectations about other
highly uncertain market factors, such as fossil fuel prices. There are
also uncertainties concerning the macro-economic effects of the
aggregated behavioural changes. An additional factor influencing
the importance of any proposed or existing policy-driven incen-
tive is the likelihood with which regulations will be enacted and
enforced over the lifetime of firms’ investment cycles.
• Individual and firm perceptions. The choices undertaken by key
decision makers with respect to mitigation and adaptation mea-
sures are impacted by their perceptions of risk and uncertainties,
as well as their perceptions of the relevant costs and expected
benefits over time. Their decisions may also be influenced by the
actions undertaken by others.
Section 2.6 assesses the effects of uncertainties of these different
parameters on a wide range of policy choices, drawing from both
empirical studies and the modelling literature. The following three
examples illustrate how uncertainties in one or more of the above fac-
tors can influence choices between alternative options.
Example 1: Designing a regional emissions trading system (ETS). Over
the past decade, a number of political jurisdictions have designed and
implemented ETSs, with the European ETS being the one most stud-
ied. In designing the European system, policymakers took as their
starting point pre-defined emissions reduction targets. It was unclear
whether these targets would be met, due to uncertainties with respect
to national baseline emissions. The stocks and flows of greenhouse
gas emissions were partly determined by the uncertainty of the perfor-
mance of the technological systems that were deployed. Uncertainties
in market behaviour could also influence target prices and the number
of emissions permits allocated to different countries (Betsill and Hoff-
mann, 2011).
Example 2: Supporting scientific research into solar radiation manage-
ment (SRM). SRM may help avert potentially catastrophic temperature
increases, but may have other negative impacts with respect to global
and regional climatic conditions (Rasch etal., 2008). Research could
reduce the uncertainties as to these other consequences (Robock etal.,
2010). The decision to invest in specific research activities requires an
assessment as to what impact SRM will have on avoiding catastrophic
temperature increases. Temperature change will be sensitive to the
stocks and flows of greenhouse gases (GHG) and therefore to the
responses by key decision makers to the impacts of GHG emissions. The
decision to invest in specific research activities is likely to be influenced
by the perceived uncertainty in the actions undertaken by individuals
and firms (Blackstock and Long, 2010).
Example 3: Renting an apartment in the city versus buying a house
in the suburbs. When families and households face this choice, it is
likely to be driven by factors other than climate change concerns. The
decision, however, can have major consequences on CO
2
emissions as
well as on the impacts of climate change on future disasters such as
damage from flooding due to sea level rise. Hence, governments may
seek to influence these decisions as part of their portfolio of climate
change policies through measures such as land-use regulations or the
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Chapter 2
pricing of local transportation options. The final choice is thus likely to
be sensitive to uncertainties in market behaviour as well as actions
undertaken by individuals and firms.
To add structure and clarity to the many uncertainties that different
actors face for different types of problems, we introduce a taxonomy
shown in Figure 2.2 that focuses on levels of decision making (the
rows) that range from international organizations to individuals and
households, and climate policy options (the columns) that include
long-term targets, transition pathways, policy instruments, resource
allocation and lifestyle options. The circles that overlay the cells in Fig-
ure 2.2 highlight the principal uncertainties relevant to decision-mak-
ing levels and climate policy choices that appear prominently in the
literature associated with particular policies. These are reviewed in
Section 2.6 of this chapter and in many of the following chapters of
WGIII. The literature appraises the effects of a wide range of uncertain-
ties, which we group according to the five types described above.
2�3�2 What is new on risk and uncertainty in
AR5
Chapter 2 in WGIII AR4 on risk and uncertainty, which also served as a
framing chapter, illuminated the relationship of risk and uncertainty to
decision making and reviewed the literature on catastrophic or abrupt
climate change and its irreversible nature. It examined three pillars for
dealing with uncertainties: precaution, risk hedging, and crisis preven-
tion and management. The report also summarized the debate in the
economic literature about the limits of cost-benefit analysis in situa-
tions of uncertainty.
Since the publication of AR4, a growing number of studies have con-
sidered additional sources of risk and uncertainties, such as regulatory
and technological risks, and examined the role they play in influenc-
ing climate policy. There is also growing awareness that risks in the
extremes or tail of the distribution make it problematic to rely on his-
torical averages. As the number of political jurisdictions implement-
ing climate policies has increased, there are now empirical findings to
supplement earlier model-based studies on the effects of such risks. At
the local level, adaptation studies using scenario-based methods have
been developed (ECLACS, 2011).
This chapter extends previous reports in four ways. First, rather than
focusing solely at the global level, this chapter expands climate-related
decisions to other levels of decision making as shown in Figure 2.2.
Second, compared to AR4, where judgment and choice were primar-
ily framed in rational-economic terms, this chapter reviews the psy-
chological and behavioural literature on perceptions and responses to
risk and uncertainty. Third, the chapter considers the pros and cons of
alternative methodologies and decision aids from the point of view of
practitioners. Finally, the chapter expands the scope of the challenges
associated with developing risk management strategies in relation to
Figure 2�2 | Taxonomy of levels of decision making and climate policy choices. Circles show type and extent of uncertainty sources as they are covered by the literature. Numbers in
brackets refer to sections where more information on these uncertainty sources can be found.
International
Agreement
National
Government
Local or Regional
Government or
Interest Group
Industry
or Firm
Household or
Individual
Long-Term
Targets
Transition
Pathway
Policy
Instrument
Resource
Allocation
Lifestyle and
Behavior
Climate Responses
and Associated Impacts
[Section 2.6.3.1]
Technological Systems
[2.6.3.2]
[2.6.4]
[2.6.5]
Stocks and Flows
of Carbon and GHGs
[2.6.4.3]
Market Behavior &
Regulatory Actions
[2.6.5]
Individual and
Firm Perceptions
[2.6.5.3]
[2.6.6]
Climate Policy Choices
Scale of
Action
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Chapter 2
AR4 that requires reviewing a much larger body of published research.
To illustrate this point, the chapter references more than 50 publica-
tions on decision making under uncertainty with respect to integrated
assessment models (IAMs), the first time such a detailed examination
of this literature has been undertaken.
2.4 Risk perception and
responses to risk
and uncertainty
2�4�1 Considerations for design of climate
change risk reduction policies
When stakeholders are given information about mitigation and adap-
tation measures to reduce climate change risks, they make the fol-
lowing judgments and choices: How serious is the risk? Is any action
required? Which options are ruled out because the costs seem prohibi-
tive? Which option offers the greatest net expected benefits?
In designing such measures and in deciding how to present them to
stakeholders, one needs to recognize both the strengths and limita-
tions of decision makers at the different levels delineated in Figure 2.2.
Decision makers often have insufficient or imperfect knowledge about
climate risks, a deficit that can and needs to be addressed by better
data and public education. However, cognitive and motivational bar-
riers are equally or more important in this regard (Weber and Stern,
2011).
Normative models of choice described in Section 2.5 indicate how
decisions under risk and uncertainty should be made to achieve effi-
ciency and consistency, but these approaches do not characterize how
choices are actually made. Since decision makers have limitations in
their ability to process information and are boundedly rational (Simon,
1955), they often use simple heuristics and rules of thumb (Payne etal.,
1988). Their choices are guided not only by external reality (objective
outcomes and their likelihood) but also by the decision makers’ inter-
nal states (e. g., needs and goals) and their mental representation of
outcomes and likelihood, often shaped by previous experience. In other
words, a descriptive model of choice needs to consider cognitive and
motivational biases and decision rules as well as factors that are con-
sidered when engaging in deliberative thinking. Another complicating
factor is that when groups or organizations make decisions, there is the
potential for disagreement and conflict among individuals that may
require interpersonal and organizational facilitation by a third party.
Mitigation and adaptation decisions are shaped also by existing eco-
nomic and political institutional arrangements. Policy and market tools
for addressing climate change, such as insurance, may not be feasible
in developing countries that have no history of this type of protection;
however, this option may be viewed as desirable in a country with an
active insurance sector (see Box 2.1). Another important determinant
of decisions is the status quo, because there is a tendency to give more
weight to the negative impacts of undertaking change than the equiv-
alent positive impacts (Johnson etal., 2007). For example, proposing
a carbon tax to reduce GHG emissions may elicit much more concern
from affected stakeholders as to how this measure will impact on
their current activities than the expected climate change benefits from
reducing carbon emissions. Choices are also affected by cultural differ-
ences in values and needs (Maslow, 1954), in beliefs about the exis-
tence and causes of climate change (Leiserowitz etal., 2008), and in
the role of informal social networks for cushioning catastrophic losses
(Weber and Hsee, 1998). By considering actual judgment and choice
processes, policymakers can more accurately characterize the effective-
ness and acceptability of alternative mitigation policies and new tech-
nologies. Descriptive models also provide insights into ways of framing
mitigation or adaptation options so as to increase the likelihood that
desirable climate policy choices are adopted. Descriptive models, with
their broader assumptions about goals and processes, also allow for
the design of behavioural interventions that capitalize on motivations
such as equity and fairness.
2�4�2 Intuitive and deliberative judgment and
choice
The characterization of judgment and choice that distinguishes intui-
tive processes from deliberative processes builds on a large body of
cognitive psychology and behavioural decision research that can
be traced to William James (1878) in psychology and to Friedrich
Nietzsche (2008) and Martin Heidegger (1962) in philosophy. A recent
summary has been provided by Kahneman (2003; 2011) as detailed in
Table 2.1:
Table 2�1 | Intuitive and deliberative process characteristics.
Intuitive Thinking (System 1)
Operates automatically and quickly, with little or no effort and no voluntary control.
Uses simple and concrete associations, including emotional reactions or simple rules of
conduct that have been acquired by personal experience with events and their consequences.
Deliberative Thinking (System 2)
Initiates and executes effortful and intentional abstract cognitive
operations when these are seen as needed.
These cognitive operations include simple or complex computations or formal logic.
Even though the operations of these two types of processes do not
map cleanly onto distinct brain regions, and the two systems often
operate cooperatively and in parallel (Weber and Johnson, 2009), the
distinction between Systems 1 and 2 helps to clarify the tension in the
human mind between the automatic and largely involuntary processes
of intuitive decisions, versus the effortful and more deliberate pro-
cesses of analytic decisions (Kahneman, 2011).
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Many of the simplified decision rules that characterize human judg-
ment and choice under uncertainty utilize intuitive (System 1) pro-
cesses. Simplification is achieved by utilizing the experiences, expec-
tations, beliefs, and goals of the interested parties involved in the
decision. Such shortcuts require much less time and effort than a
more detailed analysis of the tradeoffs between options and often
leads to reasonable outcomes. If one takes into account the con-
straints on time and attention and processing capacity of decision
makers, these decisions may be the best we can do for many choices
under uncertainty (Simon, 1955). Intuitive processes are utilized not
only by the general public, but also by technical experts such as insur-
ers and regulators (Kunreuther etal., 2013c) and by groups and orga-
nizations (Cyert and March, 1963; Cohen etal., 1972; Barreto and
Patient, 2013).
Intuitive processes work well when decision makers have copious
data on the outcomes of different decisions and recent experience is
a meaningful guide for the future, as would be the case in station-
ary environments (Feltovich etal., 2006). These processes do not work
well, however, for low-probability high-consequence events for which
the decision maker has limited or no past experience (Weber, 2011).
In such situations, reliance on intuitive processes for making decisions
will most likely lead to maintaining the status quo and focusing on the
recent past. This suggests that intuitive decisions may be problematic
in dealing with climate change risks such as increased flooding and
storm surge due to sea level rise, or a surge in fossil fuel prices as
a result of an unexpected political conflict. These are risks for which
there is limited or no personal experience or historical data and con-
siderable disagreement and uncertainty among experts with respect to
their risk assessments (Taleb, 2007).
The formal models and tools that characterize deliberative (System 2)
thinking require stakeholders to make choices in a more abstract and
systematic manner. A deliberative process focuses on potential short-
and long-term consequences and their likelihoods, and evenly evalu-
ates the options under consideration, not favouring the status quo. For
the low-probability high-consequence situations for which decision
makers have limited experience with outcomes, alternative decision
frameworks that do not depend on precise specification of probabili-
ties should be considered in designing risk management strategies for
climate change (Charlesworth and Okereke, 2010; Kunreuther etal.,
2013a).
The remainder of this section is organized as follows. Section 2.4.3
describes some important consequences of the intuitive processes uti-
lized by individuals, groups, and organizations in making decisions.
The predicted effectiveness of economic or technological climate
change mitigation solutions typically presuppose rational delibera-
tive thinking and evaluation without considering how perceptions
and reactions to climate risks impose on these policy options. Sec-
tion 2.4.4 discusses biases and heuristics that suggest that individu-
als learn in ways that differ significantly from deliberative Bayesian
updating. Section 2.4.5 addresses how behaviour is affected by social
amplification of risk and considers the different levels of decision
making in Figure 2.2 by discussing the role of social norms, social
comparisons, and social networks in the choice process. Section 2.4.6
characterizes the general public’s perceptions of climate change risks
and uncertainty and their implications for communicating relevant
information.
Empirical evidence for the biases associated with climate change
response decisions triggered by intuitive processes exists mostly at
the level of the individual. As discussed in Sections 2.5 and 2.6, intui-
tive judgment and choice processes at other levels of decision making,
such as those specified in Figure 2.2, need to be acknowledged and
understood.
2�4�3 Consequences of intuitive decision
making
The behaviour of individuals are captured by descriptive models of
choice such as prospect theory (Kahneman and Tversky, 1979) for
decisions under risk and uncertainty and the beta-delta model (Laib-
son, 1997) for characterizing how future costs and benefits are evalu-
ated. While individual variation exists, the patterns of responding to
potential outcomes over time and the probabilities of their occur-
rence have an empirical foundation based on controlled experiments
and well-designed field studies examining the behaviour of technical
experts and the general public (Loewenstein and Elster, 1992; Cam-
erer, 2000).
2�4�3�1 Importance of the status quo
The tendency to maintain the current situation is a broadly observed
phenomenon in climate change response contexts (e. g., inertia in
switching to a non-carbon economy or in switching to cost-effective
energy efficient products) (Swim etal., 2011). Sticking with the current
state of affairs is the easy option, favoured by emotional responses in
situations of uncertainty (“better the devil you know than the devil
you don’t”), by many proverbs or rules (“when in doubt, do nothing”),
and observed biases in the accumulation of arguments for different
choice options (Weber etal., 2007). Overriding the status quo requires
commitment to change and effort (Fleming etal., 2010).
Loss aversion and reference points
Loss aversion is an important property that distinguishes prospect the-
ory (Tversky and Kahneman, 1992) from expected utility theory (von
Neumann and Morgenstern, 1944) by introducing a reference-depen-
dent valuation of outcomes, with a steeper slope for perceived losses
than for perceived gains. In other words, people experience more pain
from a loss than they get pleasure from an equivalent gain. The status
quo is often the relevant reference point that distinguishes outcomes
perceived as losses from those perceived as gains. Given loss aversion,
the potential negative consequences of moving away from the current
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state of affairs are weighted much more heavily than the potential
gains, often leading the decision maker not to take action. This behav-
iour is referred to as the status quo bias (Samuelson and Zeckhauser,
1988).
Loss aversion explains a broad range of decisions in controlled labora-
tory experiments and real world choices that deviate from the predic-
tions of rational models like expected utility theory (Camerer, 2000).
Letson etal. (2009) show that adapting to seasonal and inter-annual
climate variability in the Argentine Pampas by allocating land to dif-
ferent crops depends not only on existing institutional arrangements
(e. g., whether the farmer is renting the land or owns it), but also on
individual differences in farmers’ degree of loss aversion and risk
aversion. Greene etal. (2009) show that loss aversion combined with
uncertainty about future cost savings can explain why consumers fre-
quently appear to be unwilling to invest in energy-efficient technology
such as a more expensive but more fuel-efficient car that has posi-
tive expected utility. Weber and Johnson (2009) distinguish between
perceptions of risk, attitudes towards risk, and loss aversion that have
different determinants, but are characterized by a single ‘risk attitude’
parameter in expected utility models. Distinguishing and measuring
these psychologically distinct components of individual differences in
risk taking (e. g., by using prospect theory and adaptive ways of elicit-
ing its model parameters; Toubia etal., 2013) provides better targeted
entry points for policy interventions.
Loss aversion influences the choices of experienced decision makers
in high-stakes risky choice contexts, including professional financial
markets traders (Haigh and List, 2005) and professional golfers (Pope
and Schweitzer, 2011). Yet, other contexts fail to elicit loss aversion,
as evidenced by the failure of much of the global general public to be
alarmed by the prospect of climate change (Weber, 2006). In this and
other contexts, loss aversion does not arise because decision makers
are not emotionally involved (Loewenstein etal., 2001).
Use of framing and default options for the design of decision
aids and interventions
Descriptive models not only help explain behaviours that deviate from
the predictions of normative models of choice but also provide entry
points for the design of decision aids and interventions collectively
referred to as choice architecture, indicating that people’s choices
depend in part on the ways that possible outcomes of different
options are framed and presented (Thaler and Sunstein, 2008). Pros-
pect theory suggests that changing decision makers’ reference points
can impact on how they evaluate outcomes of different options and
hence their final choice. Patt and Zeckhauser (2000) show, for exam-
ple, how information about the status quo and other choice options
can be presented differently to create an action bias with respect to
addressing the climate change problem. More generally, choice archi-
tecture often involves changing the description of choice options and
the context of a decision to overcome the pitfalls of intuitive (System
1) processes without requiring decision makers to switch to effortful
(System 2) thinking (Thaler and Sunstein, 2008).
One important choice architecture tool comes in the form of behav-
ioural defaults, that is, recommended options that will be implemented
if no active decision is made (Johnson and Goldstein, 2013). Default
options serve as a reference point so that decision makers normally
stick with this option due to loss aversion (Johnson etal., 2007; Weber
etal., 2007). ‘Green’ energy defaults have been found to be very effec-
tive in lab studies involving choices between different lighting tech-
nologies (Dinner etal., 2011), suggesting that environmentally friendly
and cost-effective energy efficient technology will find greater deploy-
ment if it were to show up as the default option in building codes and
other regulatory contexts. Green defaults are desirable policy options
because they guide decision makers towards individual and social
welfare maximizing options without reducing choice autonomy. In a
field study, German utility customers adopted green energy defaults, a
passive choice that persisted over time and was not changed by price
feedback (Pichert and Katsikopoulos, 2008). Moser (2010) provides
other ways to frame climate change information and response options
in ways consistent with the communication goal and characteristics of
the audience.
2�4�3�2 Focus on the short term and the here-and-now
Finite attention and processing capacity imply that unaided intuitive
choices are restricted in their scope. This makes individuals susceptible
to different types of myopia or short-sightedness with respect to their
decisions on whether to invest in measures they would consider cost-
effective if they engaged in deliberative thinking (Weber and Johnson,
2009; Kunreuther etal., 2013b).
Present bias and quasi-hyperbolic time discounting
Normative models suggest that future costs and benefits should be
evaluated using an exponential discount function, that is, a constant
discount rate per time period (i. e., exponentially), where the discount
rate should reflect the decision maker’s opportunity cost of money (for
more details see Section 3.6.2). In reality, people discount future costs
or benefits much more sharply and at a non-constant rate (i. e., hyper-
bolically), so that delaying an immediate receipt of a benefit is viewed
much more negatively than if a similar delay occurs at a future point in
time (Loewenstein and Elster, 1992). Laibson (1997) characterized this
pattern by a quasi-hyperbolic discount function, with two parameters:
(1) present bias, i. e., a discount applied to all non-immediate outcomes
regardless how far into the future they occur, and (2) a rational dis-
counting parameter. The model retains much of the analytical tracta-
bility of exponential discounting, while capturing the key qualitative
feature of hyperbolic discounting.
Failure to invest in protective measures
In the management of climate-related natural hazards such as flood-
ing, an extensive empirical literature reveals that adoption rates of
protective measures by the general public are much lower than if indi-
viduals had engaged in deliberative thinking by making relevant trad-
eoffs between expected costs and benefits. Thus, few people living in
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flood prone areas in the United States voluntarily purchase flood insur-
ance, even when it is offered at highly subsidized premiums under the
National Flood Insurance Program (NFIP) (Kunreuther etal., 1978). In
the context of climate change mitigation, many efficient responses like
investments in household energy efficiency are not adopted because
decision makers focus unduly on the upfront costs of these measures
(due to hyperbolic discounting amplified by loss aversion) and weight
the future benefits of these investments less than predicted by norma-
tive models (see Sections 2.6.4.3 and 3.10). The failure of consumers
to buy fuel-efficient cars because of their higher upfront costs (Section
8.3.5) is another example of this behaviour.
At a country or community level, the upfront costs of mitigating CO
2
emissions or of building seawalls to reduce the effects of sea level rise
loom large due to loss aversion, while the uncertain and future ben-
efits of such actions are more heavily discounted than predicted by
normative models. Such accounting of present and future costs and
benefits on the part of consumers and policymakers might make it dif-
ficult for them to justify these investments today and arrive at long-
term sustainable decisions (Weber, 2013).
Focus on short-term goals
Krantz and Kunreuther (2007) emphasize the importance of goals
and plans as a basis for making decisions. In the context of climate
change, protective or mitigating actions often require sacrificing
short-term goals that are highly weighted in people’s choices in order
to meet more abstract, distant goals that are typically given very low
weight. A strong focus on short-term goals (e. g., immediate survival)
may have been helpful as humans evolved, but may have negative
consequences in the current environment where risks and challenges
are more complex and solutions to problems such as climate change
require a focus on long time horizons. Weber etal. (2007) succeeded
in drastically reducing people’s discounting of future rewards by
prompting them to first generate arguments for deferring consump-
tion, contrary to their natural inclination to focus initially on rationales
for immediate consumption. To deal with uncertainty about future
objective circumstances as well as subjective evaluations, one can
adopt multiple points of view (Jones and Preston, 2011) or multiple
frames of reference (De Boer et al., 2010); a generalization of the
IPCC’s scenario approach to an uncertain climate future is discussed
in Chapter 6.
Mental accounting as a protection against short-term focus
People often mentally set up separate ‘accounts’ for different classes
of expenditures and do not treat money as fungible between these
accounts (Thaler, 1999). Mental accounts for different expenditures
serve as effective budgeting and self-control devices for decision mak-
ers with limited processing capacity and self-control. A focus on short-
term needs and goals can easily deplete financial resources, leaving not
enough for long(er)-term goals. Placing a limit on short-term spending
prevents this from happening. But such a heuristic also has a down-
side by unduly limiting people’s willingness to invest in climate change
mitigation or adaptation measures (e. g., flood proofing or solar pan-
els) that exceed their allocated budget for this account, regardless of
future benefits. Such constraints (real or mental) often lead to the use
of lexicographic (rather than compensatory) choice processes, where
option sets are created or eliminated sequentially, based on a series of
criteria of decreasing importance (Payne etal., 1988).
Mental accounting at a nonfinancial level may also be responsible for
rebound effects of a more psychological nature, in addition to the eco-
nomically based rebound effects discussed in Section 8.3.5. Rebound
effects describe the increase in energy usage that sometimes fol-
lows improvements in household, vehicle, or appliance efficiency. For
example, households who weatherize their homes tend to increase
their thermostat settings during the winter afterwards, resulting in a
decrease in energy savings relative to what is technologically achiev-
able (Hirst etal., 1985). While rebound effects on average equal only
10 – 30 % of the achievable savings, and therefore do not cancel out
the benefits of efficiency upgrades (Ehrhardt-Martinez and Laitner,
2010), they are significant and may result from fixed mental accounts
that people have for environmentally responsible behaviour. Having
fulfilled their self-imposed quota by a particular action allows decision
makers to move on to other goals, a behaviour also sometimes referred
to as the single-action bias (Weber, 2006).
2�4�3�3 Aversion to risk, uncertainty, and ambiguity
Most people are averse to risk and to uncertainty and ambiguity when
making choices. More familiar options tend to be seen as less risky, all
other things being equal, and thus more likely to be selected (Figner
and Weber, 2011).
Certainty effect or uncertainty aversion
Prospect theory formalizes a regularity related to people’s perceptions
of certain versus probabilistic prospects. People overweight outcomes
they consider certain, relative to outcomes that are merely proba-
ble — a phenomenon labelled the certainty effect (Kahneman and Tver-
sky, 1979). This frequently observed behaviour can explain why the
certain upfront costs of adaptation or mitigation actions are viewed as
unattractive when compared to the uncertain future benefits of under-
taking such actions (Kunreuther etal., 2013b).
Ambiguity aversion
Given the high degree of uncertainty or ambiguity in most forecasts
of future climate change impacts and the effects of different mitiga-
tion or adaptation strategies, it is important to consider not only deci-
sion makers’ risk attitudes, but also attitudes towards ambiguous out-
comes. The Ellsberg paradox (Ellsberg, 1961) revealed that, in addition
to being risk averse, most decision makers are also ambiguity averse,
that is, they prefer choice options with well-specified probabilities
over options where the probabilities are uncertain. Heath and Tversky
(1991) demonstrated, however, that ambiguity aversion is not present
when decision makers believe they have expertise in the domain of
choice. For example, in contrast to the many members of the general
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public who consider themselves to be experts in sports or the stock
market, relatively few people believe themselves to be highly compe-
tent in environmentally relevant technical domains such as the trad-
eoffs between hybrid electric versus conventional gasoline engines in
cars, so they are likely to be ambiguity averse. Farmers who feel less
competent with respect to their understanding of new technology are
more ambiguity averse and less likely to adopt farming innovations (in
Peru; Engle-Warnick and Laszlo, 2006; and in the USA; Barham et al.,
2014). With respect to the likelihood of extreme events, such as natural
disasters, insurers feel they do not have special expertise in estimating
the likelihood of these events so they also tend to be ambiguity averse
and set premiums that are considerably higher than if they had more
certainty with respect to the likelihood of their occurrence (Kunreuther
etal., 1993; Cabantous etal., 2011).
2�4�4 Learning
The ability to change expectations and behaviour in response to new
information is an important survival skill, especially in uncertain and
non-stationary environments. Bayesian updating characterizes learning
when one engages in deliberative thinking. Individuals who engage
in intuitive thinking are also highly responsive to new and especially
recent feedback and information, but treat the data differently than
that implied by Bayesian updating (Weber etal., 2004).
Availability bias and the role of salience
People’s intuitive assessment of the likelihood of an uncertain event
is often based on the ease with which instances of its occurrence can
be brought to mind, a mechanism called availability by Tversky and
Kahneman (1973). Sunstein (2006) discusses the use of the availabil-
ity heuristics in response to climate change risks and how it differs
among groups, cultures, and nations. Availability is strongly influenced
by recent personal experience and can lead to an underestimation of
low-probability events (e. g., typhoons, floods, or droughts) before they
occur, and their overestimation after an extreme event has occurred.
The resulting availability bias can explain why individuals first pur-
chase insurance after a disaster has occurred and cancel their policies
several years later, as observed for earthquake (Kunreuther et al., 1978)
and flood insurance (Michel-Kerjan et al., 2012). It is likely that most
of these individuals had not suffered any losses during this period
and considered the insurance to be a poor investment. It is difficult
to convince insured individuals that the best return on their policy is
no return at all. They should celebrate not having suffered a loss (Kun-
reuther etal., 2013c).
Linear thinking
A majority of people perceive climate in a linear fashion that reflects
two common biases (Sterman and Sweeney, 2007; Cronin etal., 2009;
Dutt and Gonzalez, 2011). First, people often rely on the correlation
heuristic, which means that people wrongly infer that an accumulation
(CO
2
concentration) follows the same path as the inflow (CO
2
emis-
sions). This implies that cutting emissions will quickly reduce the con-
centration and damages from climate change (Sterman and Sweeney,
2007). According to Dutt (2011) people who rely on this heuristic likely
demonstrate wait-and-see behaviour on policies that mitigate climate
change because they significantly underestimate the delay between
reductions in CO
2
emissions and in the CO
2
concentration. Sterman and
Sweeny (2007) show that people‘s wait-and-see behaviour on mitiga-
tion policies is also related to a second bias whereby people incorrectly
infer that atmospheric CO
2
concentration can be stabilized even when
emissions exceeds absorption.
Linear thinking also leads people to draw incorrect conclusions from
nonlinear metrics, like the miles-per-gallon (mpg) ratings of vehicles’
gasoline consumption in North America (Larrick and Soll, 2008). When
given a choice between upgrading to a 15-mpg car from a 12-mpg car,
or to a 50-mpg car from a 29-mpg car, most people choose the latter
option. However, for 100 miles driven under both options, it is easily
shown that the first upgrade option saves more fuel (1.6 gallons for
every 100 miles driven) than the second upgrade option (1.4 gallons
for every 100 miles driven).
Effects of personal experience
Learning from personal experience is well predicted by reinforcement
learning models (Weber etal., 2004). Such models describe and predict
why the general public is less concerned about low-probability high-
impact climate risks than climate scientists would suggest is warranted
by the evidence (Gonzalez and Dutt, 2011). These learning models also
capture the volatility of the public’s concern about climate change
over time, for example in reaction to the personal experience of local
weather abnormalities (an abnormal cold spell or heat wave) that have
been shown to influence belief in climate change (Li etal., 2011).
Most people do not differentiate very carefully between weather, cli-
mate (average weather over time), and climate variability (variations
in weather over time). People confound climate and weather in part
because they have personal experience with weather and weather
abnormalities but little experience with climate change, an abstract
statistical concept. They thus utilize weather events in making judg-
ments about climate change (Whitmarsh, 2008). This confusion has
been observed in countries as diverse as the United States (Bostrom
et al., 1994; Cullen, 2010) and Ethiopia (BBC World Service Trust,
2009).
Personal experience can differ between individuals as a function of
their location, history, and / or socio-economic circumstances (Figner
and Weber, 2011). Greater familiarity with climate risks, unless accom-
panied by alarming negative consequences, could actually lead to a
reduction rather than an increase in the perceptions of its riskiness
(Kloeckner, 2011). On the other hand, people’s experience can make
climate a more salient issue. For example, changes in the timing and
extent of freezing and melting (and associated effects on sea ice, flora,
and fauna) have been experienced since the 1990s in the American
and Canadian Arctic and especially indigenous communities (Laidler,
2006), leading to increased concern with climate change because tra-
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ditional prediction mechanisms no longer can explain these phenom-
ena (Turner and Clifton, 2009).
People’s expectations of change (or stability) in climate variables also
affect their ability to detect trends in probabilistic environments. For
instance, farmers in Illinois were asked to recall growing season tem-
perature or precipitation statistics for seven preceding years. Farmers
who believed that their region was affected by climate change recalled
precipitation and temperature trends consistent with this expectation,
whereas farmers who believed in a constant climate, recalled precipita-
tions and temperatures consistent with that belief (Weber, 1997). Rec-
ognizing that beliefs shape perception and memory provides insight
into why climate change expectations and concerns vary between seg-
ments of the US population with different political ideologies (Leise-
rowitz etal., 2008).
The evidence is mixed when we examine whether individuals learn
from past experience with respect to investing in adaptation or miti-
gation measures that are likely to be cost-effective. Even after the
devastating 2004 and 2005 hurricane seasons in the United States, a
large number of residents in high-risk areas had still not invested in
relatively inexpensive loss-reduction measures, nor had they under-
taken emergency preparedness measures (Goodnough, 2006). Surveys
conducted in Alaska and Florida, regions where residents have been
exposed more regularly to physical evidence of climate change, show
greater concern and willingness to take action (ACI, 2004; Leiserowitz
and Broad, 2008; Mozumder etal., 2011).
A recent study assessed perceptions and beliefs about climate change of
a representative sample of the Britain public (some of whom had expe-
rienced recent flooding in their local area). It also asked whether they
would reduce personal energy use to reduce greenhouse gas emission
(Spence etal., 2011). Concern about climate change and willingness to
take action was greater in the group of residents who had experienced
recent flooding. Even though the flooding was only a single and local
data point, this group also reported less uncertainty about whether cli-
mate change was really happening than those who did not experience
flooding recently, illustrating the strong influence of personal experi-
ence. Other studies fail to find a direct effect of personal experience with
flooding generating concern about climate risks (Whitmarsh, 2008).
Some researchers find that personal experience with ill health from air
pollution affects perceptions of and behavioural responses to climate
risks (Bord etal., 2000; Whitmarsh, 2008), with the negative effects
from air pollution creating stronger pro-environmental values. Myers
etal. (2012) looked at the role of experiential learning versus moti-
vated reasoning among highly engaged individuals and those less
engaged in the issue of climate change. Low-engaged individuals
were more likely to be influenced by their perceived personal experi-
ence of climate change than by their prior beliefs, while those highly
engaged in the issue (on both sides of the climate issue) were more
likely to interpret their perceived personal experience in a manner that
strengthens their pre-existing beliefs.
Indigenous climate change knowledge contributions from Africa
(Orlove et al., 2010), the Arctic (Gearheard et al., 2009), Australia
(Green et al., 2010), or the Pacific Islands (Lefale, 2010), derive from
accumulated and transmitted experience and focus mostly on pre-
dicting seasonal or interannual climate variability. Indigenous knowl-
edge can supplement scientific knowledge in geographic areas with
a paucity of data (Green and Raygorodetsky, 2010) and can guide
knowledge generation that reduces uncertainty in areas that matter
for human responses (ACI, 2004). Traditional ecological knowledge is
embedded in value-institutions and belief systems related to historical
modes of experimentation and is transferred from generation to gen-
eration (Pierotti, 2011).
Underweighting of probabilities and threshold models of
choice
The probability weighting function of prospect theory indicates that
low probabilities tend to be overweighted relative to their objective
probability unless they are perceived as being so low that they are
ignored because they are below the decision maker’s threshold level
of concern. Prior to a disaster, people often perceive the likelihood of
catastrophic events occurring as below their threshold level of con-
cern, a form of intuitive thinking in the sense that one doesn’t have
to reflect on the consequences of a catastrophic event (Camerer and
Kunreuther, 1989). The need to take steps today to deal with future cli-
mate change presents a challenge to individuals who are myopic. They
are likely to deal with this challenge by using a threshold model that
does not require any action for risks below this level. The problem is
compounded by the inability of individuals to distinguish between low
likelihoods that differ by one or even two orders of magnitude (e. g.,
between 1 in 100 and 1 in 10,000) (Kunreuther etal., 2001).
2�4�5 Linkages between different levels of
decision making
Social amplification of risk
Hazards interact with psychological, social, institutional, and cultural
processes in ways that may amplify or attenuate public responses to
the risk or risk event by generating emotional responses and other
biases associated with intuitive thinking. Amplification may occur
when scientists, news media, cultural groups, interpersonal networks,
and other forms of communication provide risk information. The ampli-
fied risk leads to behavioural responses, which, in turn, may result in
secondary impacts such as the stigmatization of a place that has expe-
rienced an adverse event (Kasperson etal., 1988; Flynn etal., 2001).
The general public’s overall concern about climate change is influ-
enced, in part, by the amount of media coverage the issue receives as
well as the personal and collective experience of extreme weather in a
given place (Leiserowitz etal., 2012; Brulle etal., 2012).
Social norms and social comparisons
Individuals’ choices are often influenced by other people’s behaviour,
especially under conditions of uncertainty. Adherence to formal rules
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(e. g., standard operating procedures or best practices in organizations)
or informal rules of conduct is an important way in which we intui-
tively decide between different courses of action (Weber and Linde-
mann, 2007). “When in doubt, copy what the majority is doing” is not
a bad rule to follow in many situations, as choices adopted by oth-
ers are assumed to be beneficial and safe (Weber, 2013). In fact, such
social imitation can lead to social norms. Section 3.10.2 describes the
effects of social norms in greater detail. Goldstein etal. (2008) demon-
strate the effectiveness of providing descriptive norms (“this is what
most people do”) versus injunctive norms (“this is what you should
be doing”) to reduce energy use in US hotels. The application of social
norms to encourage investment in energy efficient products and tech-
nology is discussed in Section 2.6.5.3.
Social comparisons are another effective way to evaluate and learn
about the quality of obtained outcomes (Weber, 2004). It helps, for
example, to compare one’s own energy consumption to that of neigh-
bours in similar-sized apartments or houses to see how effective
efforts at energy conservation have been. Such non-price interventions
can substantially change consumer behaviour, with effects equivalent
to that of a short-run electricity price increase of 11 % to 20 % (Alcott,
2011). Social comparisons, imitation, and norms may be necessary to
bring about lifestyle changes that are identified in Chapter 9 as reduc-
ing GHG emissions from the current levels (Sanquist etal., 2012).
Social learning and cultural transmission
Section 9.3.10 suggests that indigenous building practices in many
parts of the world provide important lessons for affordable low-
energy housing design and that developed countries can learn from
traditional building practices, transmitted over generations, the social-
scale equivalent of ‘intuitive’ processing and learning at the individual
level.
Risk protection by formal (e� g�, insurance) and informal
institutions (e� g�, social networks)
Depending on their cultural and institutional context, people can pro-
tect themselves against worst-case and / or potentially catastrophic
economic outcomes either by purchasing insurance (Kunreuther etal.,
2013c) or by developing social networks that will help bail them out or
assist them in the recovery process (Weber and Hsee, 1998). Individual-
ist cultures favour formal insurance contracts, whereas collectivist soci-
eties make more use of informal mutual insurance via social networks.
This distinction between risk protection by either formal or informal
means exists at the individual level and also at the firm level, e. g., the
chaebols in Korea or the keiretsus in Japan (Gilson and Roe, 1993).
Impact of uncertainty on coordination and competition
Adaptation and especially mitigation responses require coordination
and cooperation between individuals, groups, or countries for many
of the choices associated with climate change. The possible outcomes
often can be viewed as a game between players who are concerned
with their own payoffs but who may still be mindful of social goals and
objectives. In this sense they can be viewed in the context of a pris-
oners’ dilemma (PD) or social dilemma. Recent experimental research
on two-person PD games reveals that individuals are more likely to
be cooperative when payoffs are deterministic than when the out-
comes are probabilistic. A key factor explaining this difference is that
in a deterministic PD game, the losses of both persons will always be
greater when they both do not cooperate than when they do. When
outcomes are probabilistic there is some chance that the losses will be
smaller when both parties do not cooperate than when they do, even
though the expected losses to both players will be greater if they both
decide not to cooperate than if they both cooperate (Kunreuther etal.,
2009).
In a related set of experiments, Gong etal. (2009) found that groups
are less cooperative than individuals in a two-person deterministic PD
game; however, in a stochastic PD game, where defection increased
uncertainty for both players, groups became more cooperative than
they were in a deterministic PD game and more cooperative than indi-
viduals in the stochastic PD game. These findings have relevance to
behaviour with respect to climate change where future outcomes of
specific policies are uncertain. Consider decisions made by groups of
individuals, such as when delegations from countries are negotiating
at the Conference of Parties (COP) to make commitments for reduc-
ing GHG emissions where the impacts on climate change are uncer-
tain. These findings suggest that there is likely to be more cooperation
between governmental delegations than if each country was repre-
sented by a single decision maker.
Cooperation also plays a crucial role in international climate agree-
ments. There is a growing body of experimental literature that looks at
individuals’ cooperation when there is uncertainty associated with oth-
ers adopting climate change mitigation measures. Tavoni etal. (2011)
found that communication across individuals improves the likelihood
of cooperation. Milinski etal. (2008) observed that the higher the risky
losses associated with the failure to cooperate in the provision of a
public good, the higher the likelihood of cooperation. If the target for
reducing CO
2
is uncertain, Barrett and Dannenberg (2012) show in an
experimental setting that cooperation is less likely than if the target is
well specified.
2�4�6 Perceptions of climate change risk and
uncertainty
Empirical social science research shows that the perceptions of climate
change risks and uncertainties depend not only on external reality but
also on the observers’ internal states, needs, and the cognitive and
emotional processes that characterize intuitive thinking. Psychological
research has documented the prevalence of affective processes in the
intuitive assessment of risk, depicting them as essentially effort-free
inputs that orient and motivate adaptive behaviour, especially under
conditions of uncertainty that are informed and shaped by personal
experience over time (Finucane etal., 2000; Loewenstein etal., 2001;
Peters etal., 2006).
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Two important psychological risk dimensions have been shown to
influence people’s intuitive perceptions of health and safety risks
across numerous studies in multiple countries (Slovic, 1987). The first
factor, ‘dread risk’, captures emotional reactions to hazards like nuclear
reactor accidents, or nerve gas accidents, that is, things that make peo-
ple anxious because of a perceived lack of control over exposure to
the risks and because consequences may be catastrophic. The second
factor, ‘unknown risk‘, refers to the degree to which a risk (e. g., DNA
technology) is perceived as new, with unforeseeable consequences
and with exposures not easily detectable.
Perceptions of the risks associated with a given event or hazard are
also strongly influenced by personal experience and can therefore dif-
fer between individuals as a function of their location, history, and / or
socio-economic circumstances (see Box 2.1) (Figner and Weber, 2011).
Whereas personal exposure to adverse consequences increases fear
and perceptions of risk, familiarity with a risk can lower perceptions
of its riskiness unless it is accompanied by alarming negative conse-
quences (Kloeckner, 2011). Seeing climate change only as a simple
and gradual change from current to future average temperatures and
precipitation may make it seem controllable — the non-immediacy
of the danger seems to provide time to plan and execute protective
responses (Weber, 2006). These factors suggest that laypersons differ
in their perception of climate risks more than experts who engage in
deliberative thinking and estimate the likelihood and consequences of
climate change utilizing scientific data.
Impact of uncertainties in communicating risk
If the uncertainties associated with climate change and its future
impact on the physical and social system are not communicated accu-
rately, the general public may misperceive them (Corner and Hahn,
2009). Krosnick etal. (2006) found that perceptions of the seriousness
of global warming as a national issue in the United States depended on
the degree of certainty of respondents as to whether global warming is
Box 2�1 | Challenges facing developing countries
One of the key findings on developing countries is that non-state
actors such as tribes, clans, castes, or guilds may be of substantial
influence on how climate policy choices are made and diffused
rather than having the locus of decision making at the level of the
individual or governmental unit. For instance, a farming tribe / caste
may address the climate risks and uncertainties faced by their com-
munity and opt for a system of crop rotation to retain soil fertil-
ity or shift cultivation to preserve the nutritious state of farmlands.
Research in developing countries in Africa has shown that people
may understand probabilistic information better when it is pre-
sented in a group where members have a chance to discuss it (Patt
et al., 2005; Roncoli, 2006). This underscores why the risks and
uncertainty associated with climate change has shifted governmen-
tal responsibility to non-state actors (Rayner, 2007).
In this context, methodologies and decision aids used in individual-
centred western societies for making choices that rely on uncertain
probabilities and uncertain outcomes may not apply to develop-
ing countries. Furthermore, methodologies, such as expected utility
theory, assume an individual decision maker whereas in develop-
ing countries, decisions are often made by clans or tribes. In addi-
tion, tools such as cost-benefit analysis, cost-effectiveness analysis
and robust decision making may not always be relevant for devel-
oping countries since decisions are often based on social norms,
traditions, and customs
The adverse effects of climate change on food, water, security,
and incidences of temperature-influenced diseases (Shah and Lele,
2011), are further fuelled by a general lack of awareness about
climate change in developing countries (UNDP, 2007); conse-
quently, policymakers in these countries support a wait-and-see
attitude toward climate change (Dutt, 2011). Resource allocation
and investment constraints may also lead policy-makers to post-
pone policy decisions to deal with climate change, as is the case
with respect to integration of future energy systems in small island
states (UNFCCC, 2007). The delay may prevent opportunities for
learning and increase future vulnerabilities. It may also lock in
countries into infrastructure and technologies that may be difficult
to alter.
The tension between short- and long-term priorities in low income
countries is often accentuated by uncertainties in political culture
and regulatory policies (Rayner, 1993). This may lead to policies
that are flawed in design and / or implementation or those that
have unintended negative consequences. For example, subsidies
for clean fuels such as liquefied petroleum gas (LPG) in a country
like India often do not reach their intended beneficiaries (the poor),
and at the same time add a large burden to the exchequer (Gov-
ernment of India, Ministry of Finance, 2012; IISD, 2012).
Other institutional and governance factors impede effective cli-
mate change risk management in developing countries. These
include lack of experience with insurance (Patt etal., 2010), dearth
of data, and analytical capacity. A more transparent and effec-
tive civil service would also be helpful, for instance in stimulating
investments in renewable energy generation capacities (Komen-
dantova et al., 2012). Financial constraints suggest the impor-
tance of international assistance and private sector contribution to
implement adaptation and mitigation strategies for dealing with
climate change in developing countries.
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occurring and will have negative consequences coupled with their belief
that humans are causing the problem and have the ability to solve it.
Accurately communicating the degree of uncertainty in both climate
risks and policy responses is therefore a critically important challenge
for climate scientists and policymakers (Pidgeon and Fischhoff, 2011).
Roser-Renouf etal. (2011), building upon the work of Krosnick etal.
(2006), apply social cognitive theory to develop a model of climate
advocacy to increase the attention given to climate change in the spirit
of social amplification of risk. They found that campaigns looking to
increase the number of citizens contacting elected officials to advocate
climate policy action should focus on increasing the belief that global
warming is real, human-caused, a serious risk, and solvable. These four
key elements, coupled with the understanding that there is strong sci-
entific agreement on global warming (Ding etal., 2011), are likely to
build issue involvement and support for action to reduce the impacts
of climate change.
The significant time lags within the climate system and a focus on
short-term outcomes lead many people to believe global warming
will have only moderately negative impacts. This view is reinforced
because adverse consequences are currently experienced only in some
regions of the world or are not easily attributed to climate change. For
example, despite the fact that “climate change currently contributes to
the global burden of disease and premature deaths” (IPCC, 2007) rela-
tively few people make the connection between climate change and
human health risks.
One challenge is how to facilitate correct inferences about the role of
climate change as a function of extreme event frequency and sever-
ity. Many parts of the world have seen increases in the frequency and
magnitude of heat waves and heavy precipitation events (IPCC, 2012).
In the United States, a large majority of Americans believe that climate
change exacerbated extreme weather events (Leiserowitz etal., 2012).
That said, the perception that the impact of climate change is neither
immediate nor local persists (Leiserowitz etal., 2008), leading many
to think it rational to advocate a wait-and-see approach to emissions
reductions (Sterman, 2008; Dutt and Gonzalez, 2013).
Differences in education and numeracy
Individual and group differences in education and training and the
resulting different cognitive and affective processes have additional
implications for risk communication. It may help to supplement the
use of words to characterize the likelihood of an outcome recom-
mended by the current IPCC Guidance Note (GN) with numeric prob-
ability ranges (Budescu etal., 2009). Patt and Dessai (2005) show that
in the IPCC Third Assessment Report (TAR), words that characterized
numerical probabilities were interpreted by decision makers in incon-
sistent and often context-specific ways, a phenomenon with a long his-
tory in cognitive psychology (Wallsten etal., 1986; Weber and Hilton,
1990). These context-specific interpretations of probability words are
deeply rooted, as evidenced by the fact that the likelihood of using
the intended interpretation of TAR probability words did not differ with
level of expertise (attendees of a UN COP conference versus students)
or as a function of whether respondents had read the TAR instructions
that specify how the probability words characterized numerical prob-
abilities (Patt and Dessai, 2005).
Numeracy, the ability to reason with numbers and other mathemati-
cal concepts, is a particularly important individual and group differ-
ence in this context as it has implications for the presentation of likeli-
hood information using either numbers (for example, 90 %) or words
(for example, “very likely” or “likely”) or different graphs or diagrams
(Peters etal., 2006; Mastrandrea etal., 2011). Using personal experi-
ence with climate variables has been shown to be effective in com-
municating the impact of probabilities (e. g., of below-, about-, and
above-normal rainfall in an El Ni
~
no year) to decision makers with low
levels of numeracy, for example subsistence farmers in Zimbabwe (Patt
etal., 2005).
2.5 Tools and decision
aids for analysing
uncertainty and risk
This section examines how more formal approaches can assist deci-
sion makers in engaging in more deliberative thinking with respect to
climate change policies when faced with the risks and uncertainties
characterized in Section 2.3.
2�5�1 Expected utility theory
Expected utility [E(U)] theory (Ramsey, 1926; von Neumann and Mor-
genstern, 1944; Savage, 1954); remains the standard approach for pro-
viding normative guidelines against which other theories of individual
decision making under risk and uncertainty are benchmarked. Accord-
ing to the E(U) model, the solution to a decision problem under uncer-
tainty is reached by the following four steps:
1. Define a set of possible decision alternatives.
2. Quantify uncertainties on possible states of the world.
3. Value possible outcomes of the decision alternatives as utilities.
4. Choose the alternative with the highest expected utility.
This section clarifies the applicability of expected utility theory to the
climate change problem, highlighting its potentials and limitations.
2�5�1�1 Elements of the theory
E(U) theory is based on a set of axioms that are claimed to have nor-
mative rather than descriptive validity. Based on these axioms, a per-
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son’s subjective probability and utility function can be determined by
observing preferences in structured choice situations. These axioms
have been debated, strengthened, and relaxed by economists, psy-
chologists, and other social scientists over the years. The axioms have
been challenged by controlled laboratory experiments and field stud-
ies discussed in Section 2.4 but they remain the basis for parsing deci-
sion problems and recommending options that maximize expected
utility.
2�5�1�2 How can expected utility improve decision
making?
E(U) theory provides guidelines for individual choice, such as a farmer
deciding what crops to plant or an entrepreneur deciding whether to
invest in wind technology. These decision makers would apply E(U) the-
ory by following the four steps above. The perceptions and responses
to risk and uncertainty discussed in Section 2.5 provide a rationale
for undertaking deliberative thinking before making final choices.
More specifically, a structured approach, such as the E(U) model, can
reduce the impact of probabilistic biases and simplified decision rules
that characterize intuitive thinking. At the same time, the limitations
of E(U) must be clearly understood, as the procedures for determining
an optimal choice do not capture the full range of information about
outcomes and their risks and uncertainties.
Subjective versus objective probability
In the standard E(U) model, each individual has his / her own subjec-
tive probability estimates. When there is uncertainty on the scientific
evidence, experts’ probability estimates may diverge from each other,
sometimes significantly. With respect to climate change, observed rela-
tive frequencies are always preferred when suitable sets of observa-
tions are accessible. When these data are not available, one may want
to utilize structured expert judgment for quantifying uncertainty (see
Section 2.5.7).
Individual versus social choice
In applying E(U) theory to problems of social choice, a number of
issues arise. Condorcet’s voting paradox shows that groups of ratio-
nal individuals deciding by majority rule do not exhibit rational prefer-
ences. Using a social utility or social welfare function to determine an
optimal course of action for society requires some method of mea-
suring society’s preferences. In the absence of these data the social
choice problem is not a simple exercise of maximizing expected utility.
In this case, a plurality of approaches involving different aggregations
of individual utilities and probabilities may best aid decision makers.
The basis and use of the social welfare function are discussed in Sec-
tion 3.4.6.
Normative versus descriptive
As noted above, the rationality axioms of E(U) are claimed to have
normative as opposed to descriptive validity. The paradoxes of Allais
(1953) and Ellsberg (1961) reveal choice behaviour incompatible
with E(U); whether this requires modifications of the normative the-
ory is a subject of debate. McCrimmon (1968) found that business
executives willingly corrected violations of the axioms when they
were made aware of them. Other authors (Kahneman and Tversky,
1979; Schmeidler, 1989; Quiggin, 1993; Wakker, 2010) account for
such paradoxical choice behaviour by transforming the probabilities
of outcomes into decision weight probabilities that play the role of
likelihood in computing optimal choices but do not obey the laws
of probability. However, Wakker (2010, p.350) notes that decision
weighting fails to describe some empirically observed behavioural
patterns.
2�5�2 Decision analysis
2�5�2�1 Elements of the theory
Decision analysis is a formal approach for choosing between alterna-
tives under conditions of risk and uncertainty. The foundations of deci-
sion analysis are provided by the axioms of expected utility theory. The
methodology for choosing between alternatives consists of the follow-
ing elements that are described in more detail in Keeney (1993):
1. Structure the decision problem by generating alternatives and
specifying values and objectives or criteria that are important to
the decision maker.
2. Assess the possible impacts of different alternatives by determin-
ing the set of possible consequences and the probability of each
occurring.
3. Determine preferences of the relevant decision maker by develop-
ing an objective function that considers attitudes toward risk and
aggregates the weighted objectives.
4. Evaluate and compare alternatives by computing the expected util-
ity associated with each alternative. The alternative with the high-
est expected utility is the most preferred one.
To illustrate the application of decision analysis, consider a homeowner
that is considering whether to invest in energy efficient technology as
part of their lifestyle options as depicted in Figure 2.2:
1. The person focuses on two alternatives: (A1) Maintain the status
quo, and (A2) Invest in solar panels, and has two objectives: (O1)
Minimize cost, and (O2) Assist in reducing global warming.
2. The homeowner would then determine the impacts of A1 and A2
on the objectives O1 and O2 given the risks and uncertainties
associated with the impact of climate change on energy usage as
well as the price of energy.
3. The homeowner would then consider his or her attitude toward
risks and then combine O1 and O2 into a multiattribute utility
function.
4. The homeowner would then compare the expected utility of A1
and A2, choosing the one that had the highest expected utility.
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2�5�2�2 How can decision analysis improve
decision
making?
Decision analysis enables one to undertake sensitivity analyses with
respect to the uncertainties associated with the various consequences
and to different value structures. Suppose alternative A1 had the high-
est expected utility. The homeowner could determine when the deci-
sion to invest in solar panels would be preferred to maintaining the
status quo by asking questions such as:
• What would the minimum annual savings in energy expenses have
to be over the next 10 years to justify investing in solar panels?
• What is the fewest number of years one would have to reside in
the house to justify investing in solar panels?
• What impact will different levels of global warming have on the
expected costs of energy over the next 10 years for the home-
owner to want to invest in solar panels?
• How will changing the relative weights placed on minimizing cost
(O1) and assisting in reducing global warming (O2) affect the
expected utility of A1 and A2?
2�5�3 Cost-benefit analysis
2�5�3�1 Elements of the theory
Cost-benefit analysis (CBA) compares the costs and benefits of differ-
ent alternatives with the broad purpose of facilitating more efficient
allocation of society’s resources. When applied to government deci-
sions, CBA can indicate the alternative that has the highest social net
present value based on a discount rate, normally constant over time,
that converts future benefits and costs to their present values (Board-
man et al., 2005; see also the extensive discussion in Section 3.6).
Social, rather than private, costs and benefits are compared, including
those affecting future generations (Brent, 2006). In this regard, bene-
fits across individuals are assumed to be additive. Distributional issues
may be addressed by putting different weights on specific groups to
reflect their relative importance. Under conditions of risk and uncer-
tainty, one determines expected costs and benefits by weighting out-
comes by their likelihoods of occurrence. In this sense, the analysis is
similar to expected utility theory and decision analysis discussed in
Sections 2.5.1 and 2.5.2.
CBA can be extremely useful when dealing with well-defined problems
that involve a limited number of actors who make choices among dif-
ferent mitigation or adaptation options. For example, a region could
examine the benefits and costs over the next fifty years of building
levees to reduce the likelihood and consequences of flooding given
projected sea level rise due to climate change.
CBA can also provide a framework for defining a range of global
long-term targets on which to base negotiations across countries
(see for example Stern, 2007). However, CBA faces major challenges
when defining the optimal level of global mitigation actions for the
following three reasons: (1) the need to determine and aggregate
individual welfare, (2) the presence of distributional and intertempo-
ral issues, and (3) the difficulty in assigning probabilities to uncertain
climate change impacts. The limits of CBA in the context of climate
change are discussed at length in Sections 3.6 and 3.9. The discus-
sion that follows focuses on challenges posed by risk and uncer-
tainty.
2�5�3�2 How can CBA improve decision making?
Cost-benefit analysis assumes that the decision maker(s) will even-
tually choose between well-specified alternatives. To illustrate this
point, consider a region that is considering measures that coastal vil-
lages in hazard-prone areas can undertake to reduce future flood risks
that are expected to increase in part due to sea level rise. The different
options range from building a levee (at the community level) to pro-
viding low interest loans to encourage residents and businesses in the
community to invest in adaptation measures to reduce future damage
to their property (at the level of an individual or household).
Some heuristics and resulting biases discussed in the context of
expected utility theory also apply to cost-benefit analysis under uncer-
tainty. For example, the key decision maker, the mayor, may utilize a
threshold model of choice by assuming that the region will not be
subject to flooding because there have been no floods or hurricanes
during the past 25 years. By relying solely on intuitive processes there
would be no way to correct this behaviour until the next disaster
occurred, at which time the mayor would belatedly want to protect the
community. The mayor and his advisors may also focus on short-time
horizons, and hence do not wish to incur the high upfront costs associ-
ated with building flood protection measures such as dams or levees.
They are unconvinced that that such an investment will bring signifi-
cant enough benefits over the first few years when these city officials
are likely to be held accountable for the expenditures associated with
a decision to go forward on the project.
Cost-benefit analysis can highlight the importance of considering
the likelihood of events over time and the need to discount impacts
exponentially rather than hyperbolically, so that future time periods
are given more weight in the decision process. In addition, CBA can
highlight the tradeoffs between efficient resource allocation and distri-
butional issues as a function of the relative weights assigned to differ-
ent stakeholders (e. g., low income and well-to-do households in flood
prone areas).
2�5�3�3 Advantages and limitations of CBA
The main advantage of CBA in the context of climate change is that it is
internally coherent and based on the axioms of expected utility theory.
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As the prices used to aggregate costs and benefits are the outcomes
of market activity, CBA is, at least in principle, a tool reflecting people’s
preferences. Although this is one of the main arguments in favour of
CBA (Tol, 2003), this line of reasoning can also be the basis for rec-
ommending that this approach not be employed for making choices if
market prices are unavailable. Indeed, many impacts associated with
climate change are not valued in any market and are therefore hard to
measure in monetary terms. Omitting these impacts distorts the cost-
benefit relationship.
Several ethical and methodological critiques have been put forward
with respect to the application of CBA to climate policy (Charlesworth
and Okereke, 2010; Caney, 2011). For example, the uncertainty sur-
rounding the potential impacts of climate change, including possible
irreversible and catastrophic effects on ecosystems, and their asym-
metric distribution around the planet, suggests CBA may be inappro-
priate for assessing optimal responses to climate change in these cir-
cumstances.
A strong and recurrent argument against CBA (Azar and Lindgren,
2003; Tol, 2003; Weitzman, 2009, 2011) relates to its failure in dealing
with infinite (negative) expected utilities arising from low-probability
catastrophic events often referred to as ‘fat tails’. In these situations,
CBA is unable to produce meaningful results, and thus more robust
techniques are required. The debate concerning whether fat tails are
indeed relevant to the problem at hand is still unsettled (see for exam-
ple Pindyck, 2011). Box 3.9 in Chapter 3 addresses the fat tail problem
and suggests the
importance of understanding the impacts associated
with low probability, high impact climate change scenarios in evaluat-
ing alternative mitigation strategies.
One way to address the fat tail problem would be to focus on the
potential catastrophic consequences of low-probability, high-impact
events in developing GHG emissions targets and to specify a thresh-
old probability and a threshold loss. One can then remove events from
consideration that are below these critical values in determining what
mitigation and / or adaptation to adopt as part of a risk management
strategy for dealing with climate change (Kunreuther et al., 2013c).
Insurers and reinsurers specify these thresholds and use them to deter-
mine the amount of coverage that they are willing to offer against
a particular risk. They then diversify their portfolio of policies so the
annual probability of a major loss is below a pre-specified thresh-
old level of concern (e. g., 1 in 1000) (Kunreuther etal., 2013c). This
approach is in the spirit of a classic paper by Roy (1952) on safety-
first behaviour and can be interpreted as an application of probabilis-
tic cost-effectiveness analysis (i. e., chance constrained programming)
discussed in the next section. It was applied in a somewhat different
manner to environmental policy by Ciriacy-Wantrup (1971) who con-
tended that “a safe minimum standard is frequently a valid and rel-
evant criterion for conservation policy.”
One could also view uncertainty or risk associated with different
options as one of the many criteria on which alternatives should be
evaluated. Multi-criteria analysis (MCA) is sometimes proposed to
overcome some of the limitations of CBA (see more on its basic fea-
tures in Chapter 3 and for applications in Chapter 6). MCA implies that
the different criteria or attributes should not be aggregated by convert-
ing all of them into monetary units. MCA techniques commonly apply
numerical analysis in two stages:
• Scoring: for each option and criterion, the expected consequences
of each option are assigned a numerical score on a strength of
preference scale. More (less) preferred options score higher (lower)
on the scale. In practice, scales often extend from 0 to 100, where
0 is assigned to a real or hypothetical least preferred option, and
100 is assigned to a real or hypothetical most preferred option. All
options considered in the MCA would then fall between 0 and 100.
• Weighting: numerical weights are assigned to define their relative
performance on a chosen scale that will often range from 0 (no
importance) to 1 (highest importance) (Dodgson etal., 2009).
2�5�4 Cost-effectiveness analysis
2�5�4�1 Elements of the theory
Cost-effectiveness analysis (CEA) is a tool based on constrained optimi-
zation for comparing policies designed to meet a pre-specified target.
The target can be defined through CBA, by applying a specific guide-
line such as the precautionary principle (see Section 2.5.5), or by speci-
fying a threshold level of concern or environmental standard in the
spirit of the safety-first models discussed above. The target could be
chosen without the need to formally specify impacts and their respec-
tive probabilities. It could also be based on an ethical principle such as
minimizing the worst outcome, in the spirit of a Rawlsian fair agree-
ment, or as a result of political and societal negotiation processes.
Cost-effectiveness analysis does not evaluate benefits in monetary
terms. Rather, it attempts to find the least-cost option that achieves a
desired quantifiable outcome. In one sense CEA can be seen as a spe-
cial case of CBA in that the technique replaces the criterion of choos-
ing a climate policy based on expected costs and benefits with the
objective of selecting the option that minimizes the cost of meeting
an exogenous target (e. g., equilibrium temperature, concentration, or
emission trajectory).
Like CBA, CEA can be generalized to include uncertainty. One solution
concept requires the externally set target to be specified with certainty.
The option chosen is the one that minimizes expected costs. Since
temperature targets cannot be met with certainty (den Elzen and van
Vuuren, 2007; Held etal., 2009), a variation of this solution concept
requires that the likelihood that an exogenous target (e. g., equilib-
rium temperature) will be exceeded is below a pre-defined threshold
probability. This solution procedure, equivalent to chance constrained
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programming (CCP) (Charnes and Cooper, 1959), enables one to use
stochastic programming to examine the impacts of uncertainty with
respect to the cost of meeting a pre-specified target. Chance con-
strained programming is a conceptually valid decision-analytic frame-
work for examining the likelihood of attaining climate targets when
the probability distributions characterizing the decision maker’s state
of knowledge is held constant over time (Held etal., 2009).
2�5�4�2 How can CEA improve decision making?
To illustrate how CEA can be useful, consider a national government
that wants to set a target for reducing greenhouse gas (GHG) emis-
sions in preparation for a meeting of delegates from different countries
at the Conference of Parties (COP). It knows there is uncertainty as to
whether specific policy measures will achieve the desired objectives.
The uncertainties may be related to the outcomes of the forthcoming
negotiation process at the COP and / or to the uncertain impacts of
proposed technological innovations in reducing GHG emissions. Cost-
effectiveness analysis could enable the government to assess alterna-
tive mitigation strategies (or energy investment policies) for reduc-
ing GHG emissions in the face of these uncertainties by specifying a
threshold probability that aggregate GHG emissions will not be greater
than a pre-specified target level.
2�5�4�3 Advantages and limitations of CEA over CBA
Cost-effectiveness analysis has an advantage over CBA in tackling
the climate problem in that it does not require formalized knowledge
about global warming impact functions (Pindyck, 2013). The focus of
CEA is on more tangible elements, such as energy alternatives, where
scientific understanding is more established (Stern, 2007). Still, CEA
does require scientific input on potential risks associated with climate
change. National and international political processes specify tempera-
ture targets and threshold probabilities that incorporate the prefer-
ences of different actors guided by data from the scientific community.
The corresponding drawback of CEA is that the choice of the target is
specified without considering its impact on economic efficiency. Once
costs to society are assessed and a range of temperature targets is
considered, one can assess people’s preferences by considering the
potential benefits and costs associated with different targets. However,
if costs of a desirable action turn out to be regarded as too high, then
CEA may not provide sufficient information to support taking action
now. In this case additional knowledge on the mitigation benefit side
would be required.
An important application of CEA in the context of climate change is
evaluating alternative transition pathways that do not violate a pre-
defined temperature target. Since a specific temperature target can-
not be attained with certainty, formulating probabilistic targets as a
CCP problem is an appropriate solution technique to use. However,
introducing anticipated future learning so that probability distribu-
tions change over time can lead to infeasible solutions (Eisner etal.,
1971). Since this is a problem with respect to specifying temperature
targets, Schmidt etal. (2011) proposed an approach that that com-
bines CEA and CBA. The properties of this hybrid model (labelled ‘cost
risk analysis’) require further investigation. At this time, CEA through
the use of CCP represents an informative concept for deriving miti-
gation costs for the case where there is no learning over time. With
learning, society would be no worse off than the proposed CEA solu-
tion.
2�5�5 The precautionary principle and robust
decision making
2�5�5�1 Elements of the theory
In the 1970s and 1980s, the precautionary principle was proposed for
dealing with serious uncertain risks to the natural environment and
to public health (Vlek, 2010). In its strongest form the precautionary
principle implies that if an action or policy is suspected of having a risk
that causes harm to the public or to the environment, precautionary
measures should be taken even if some cause and effect relationships
are not established. The burden of proof that the activity is not harmful
falls on the proponent of the activity rather than on the public. A con-
sensus statement to this effect was issued at the Wingspread Confer-
ence on the Precautionary Principle on 26 January 1998.
The precautionary principle allows policymakers to ban products or
substances in situations where there is the possibility of their caus-
ing harm and / or where extensive scientific knowledge on their risks is
lacking. These actions can be relaxed only if further scientific findings
emerge that provide sound evidence that no harm will result. An influ-
ential statement of the precautionary principle with respect to climate
change is principle 15 of the 1992 Rio Declaration on Environment and
Development: “where there are threats of serious or irreversible dam-
age, lack of full scientific certainty shall not be used as a reason for
postponing cost-effective measures to prevent environmental degra-
dation.”
Robust decision making (RDM) is a particular set of methods devel-
oped over the last decade to address the precautionary principle in a
systematic manner. RDM uses ranges or, more formally, sets of plau-
sible probability distributions to describe uncertainty and to evaluate
how well different policies perform with respect to different outcomes
arising from these probability distributions. RDM provides decision
makers with tradeoff curves that allow them to debate how much
expected performance they are willing to sacrifice in order to improve
outcomes in worst case scenarios. RDM thus captures the spirit of the
precautionary principle in a way that illuminates the risks and benefits
of different policies. Lempert etal. (2006) and Hall etal. (2012) review
the application of robust approaches to decision making with respect
to mitigating or adapting to climate change.
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The tolerable windows approach can also be regarded as a ‘robust
method’. Temperature targets are specified and the bundle of decision
paths compatible with the targets is characterized. Mathe matically, the
tolerable windows approach incorporates the features of CEA or CCP
without optimization. The selection of the relevant targets and the paths
to achieving it are left to those making the decision. (See Bruckner and
Zickfeld (2008) for an introduction and an overview to peer-reviewed
literature on the tolerable windows approach.)
2�5�6 Adaptive management
Adaptive management is an approach to governance that that grew
out of the field of conservation ecology in the 1970s and incorporates
mechanisms for reducing uncertainty over time (Holling, 1978; Walters
and Hilborn, 1978). Paraphrasing the IPCC Special Report on Extreme
Events (SREX) (IPCC, 2012), adaptive management represents struc-
tured processes for improving decision making and policy over time,
by incorporating lessons learned. From the theoretical literature, two
strands of adaptive management have been developed for improving
decision making under uncertainty: passive and active.
Passive adaptive management (PAM) involves carefully designing
monitoring systems, at the relevant spatial scales, so as to be able to
track the performance of policy interventions and improve them over
time in response to what has been learned. Active adaptive manage-
ment (AAM) extends PAM by designing the interventions themselves
as controlled experiments, so as to generate new knowledge. For
example, if a number of political jurisdictions were seeking to imple-
ment support mechanisms for technology deployment, in an AAM
approach they would deliberately design separate mechanisms that
are likely to differ across jurisdictions. By introducing such variance
into the management regime, however, one would collectively learn
more about how industry and investors respond to a range of interven-
tions. All jurisdictions could then use this knowledge in a later round
of policymaking, reflecting the public goods character of institutional
knowledge.
With respect to the application of PAM, Nilsson (2005) reports on a
case study of Sweden, in which policymakers engaged in repetitive ex
post analyses of national climate policy, and then responded to the les-
sons learned by modifying their goals and strategies. There are many
documented cases of PAM applications in the area of climate change
adaptation (Lawler etal., 2008; Berkes etal., 2000; Berkes and Jolly,
2001; Joyce etal., 2009; Armitage, 2011). The information gathering
and reporting requirements of the UNFCCC are also in the spirit of
PAM with respect to policy design, as are the diversity of approaches
implemented for renewable energy support across the states and prov-
inces of North America and the countries in Europe. The combination of
the variance in action with data gathered about the consequences of
these actions by government agencies has allowed for robust analysis
on the relative effectiveness of different instruments (Blok, 2006; Men-
donça, 2007; Butler and Neuhoff, 2008).
Individuals relying on intuitive thinking are unlikely to undertake
experimentation that leads to new knowledge, as discussed in Section
2.4.3.1. In theory, adaptive management ought to correct this problem
by making the goal of learning through experimentation an explicit
policy goal. Lee (1993) illustrates this point by presenting a paradig-
matic case of AAM designed to increase salmon stocks in the Columbia
River watershed in the western United States and Canada. In this case,
there was the opportunity to introduce a number of different manage-
ment regimes on the individual river tributaries, and to reduce uncer-
tainty about salmon population dynamics. As Lee (1993) documented,
policymakers on the Columbia River were ultimately not able to carry
through with AAM: local constituencies, valuing their own immediate
interests over long-term learning in the entire region, played a crucial
role in blocking it. One could imagine such political and institutional
issues hindering the application of AAM at a global scale with respect
to climate change policies.
To date, there are no cases in the literature specifically documenting
climate change policies explicitly incorporating AAM. However, there
are a number of examples where policy interventions implicitly fol-
low AAM principles. One of these is promotion of energy research and
development (R&D). In this case the government invests in a large
number of potential new technologies, with the expectation that some
technologies will not prove practical, while others will be successful
and be supported by funding in the form of incentives such as subsi-
dies (Fischer and Newell, 2008).
2�5�7 Uncertainty analysis techniques
Uncertainty analysis consists of both qualitative and quantitative
methodologies (see Box 2.2 for more details). A Qualitative Uncer-
tainty Analysis (QLUA) helps improve the choice process of decision
makers by providing data in a form that individuals can easily under-
stand. QLUA normally does not require complex calculations so that it
can be useful in helping to overcome judgmental biases that character-
ize intuitive thinking. QLUA assembles arguments and evidence and
provides a verbal assessment of plausibility, frequently incorporated in
a Weight of Evidence (WoE) narrative.
A Quantitative Uncertainty Analysis (QNUA) assigns a joint distribu-
tion to uncertain parameters of a specific model used to characterize
different phenomena. Quantitative Uncertainty Analysis was pioneered
in the nuclear sector in 1975 to determine the risks associated with
nuclear power plants (Rasmussen, 1975). The development of QNUA
and its prospects for applications to climate change are reviewed by
Cooke (2012).
2�5�7�1 Structured expert judgment
Structured expert judgment designates methods in which experts
quantify their uncertainties to build probabilistic input for complex
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decision problems (Morgan and Henrion, 1990; Cooke, 1991; O’Hagan
et al., 2006). A wide variety of activities fall under the heading of
expert judgment that includes blue ribbon panels, Delphi surveys, and
decision conferencing.
Elements
Structured expert judgment such as science-based uncertainty quan-
tification was pioneered in the Rasmussen Report on risks of nuclear
power plants (Rasmussen, 1975). The methodology was further elabo-
rated in successive studies and involves protocols for expert selection
and training, elicitation procedures and performance-based combi-
nations that are described in more detail in Goossens etal. (2000).
In large studies, multiple expert panels provide inputs to computer
models with no practical alternative for combining expert judg-
ments except to use equal weighting. Hora (2004) has shown that
equal weight combinations of statistically accurate (‘well calibrated’)
experts loses statistical accuracy. Combinations based on experts’
statistical accuracy have consistently given more accurate and infor-
mative results (see for example Cooke and Goossens, 2008; Aspinall,
2010).
How can this tool improve decision making under uncertainty?
Structured expert judgment can provide insights into the nature of
the uncertainties associated with a specific risk and the importance of
undertaking more detailed analyses to design meaningful strategies
and policies for dealing with climate change in the spirit of deliberative
thinking. In addition to climate change (Morgan and Keith, 1995; Zick-
feld etal., 2010), structured expert judgment has migrated into many
fields such as volcanology (Aspinall, 1996, 2010), dam/dyke safety
(Aspinall, 2010), seismicity (Klügel, 2008), civil aviation (Ale et al.,
2009), ecology (Martin et al., 2012; Rothlisberger etal., 2012), toxi-
cology (Tyshenko etal., 2011), security (Ryan etal., 2012), and epidemi-
ology (Tuomisto etal., 2008).
The general conclusions emerging from experience with structured
expert judgments to date are: (1) formalizing the expert judgment pro-
cess and adhering to a strict protocol adds substantial value to under-
standing the importance of characterizing uncertainty; (2) experts
differ greatly in their ability to provide statistically accurate and infor-
mative quantifications of uncertainty; and (3) if expert judgments must
be combined to support complex decision problems, the combination
Box 2�2 | Quantifying uncertainty
Natural language is not adequate for propagating and com-
municating uncertainty. To illustrate, consider the U. S. National
Research Council 2010 report Advancing the Science of Climate
Change (America’s Climate Choices: Panel on Advancing the Sci-
ence of Climate Change; National Research Council, 2010). Using
the AR4 calibrated uncertainty language, the NRC is highly confi-
dent that (1) the Earth is warming and that (2) most of the recent
warming is due to human activities.
What does the second statement mean? Does it mean the NRC is
highly confident that the Earth is warming and the recent warm-
ing is anthropogenic or that, given the Earth is warming, are they
highly confident humans cause this warming? The latter seems
most natural, as the warming is asserted in the first statement. In
that case the ‘high confidence’ applies to a conditional statement.
The probability of both statements being true is the probability
of the condition (Earth is warming) multiplied by the probability
of this warming being caused by humans, given that warming is
taking place. If both statements enjoy high confidence, then in
the calibrated language of AR4 where high confidence implies a
probability of 0.8, the statement that both are true would only be
“more likely than not” (0.8 x 0.8 = 0.64).
Qualitative uncertainty analysis easily leads the unwary to errone-
ous conclusions. Interval analysis is a semi-qualitative method in
which ranges are assigned to uncertain variables without distribu-
tions and can mask the complexities of propagation, as attested
by the following statement in an early handbook on risk analysis:
“The simplest quantitative measure of variability in a parameter or
a measurable quantity is given by an assessed range of the values
the parameter or quantity can take. This measure may be adequate
for certain purposes (e. g., as input to a sensitivity analysis), but in
general it is not a complete representation of the analyst’s knowl-
edge or state of confidence and generally will lead to an unreal-
istic range of results if such measures are propagated through an
analysis”, (U. S. NRC, 1983, Chapter12, p.12).
The sum of 10 independent variables each ranging between
zero and ten, can assume any value between zero and 100. The
upper (lower) bound can be attained only if ALL variables take
their maximal (minimal) values, whereas values near 50 can
arise through many combinations. Simply stating the interval
[0, 100] conceals the fact that very high (low) values are much
more exceptional than central values. These same concepts are
widely represented throughout the uncertainty analysis literature.
According to Morgan and Henrion (1990): “Uncertainty analysis
is the computation of the total uncertainty induced in the out-
put by quantified uncertainty in the inputs and models […] Fail-
ure to engage in systematic sensitivity and uncertainty analysis
leaves both analysts and users unable to judge the adequacy of
the analysis and the conclusions reached”, (Morgan and Henrion,
1990, p.39).
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method should be subjected to the following quality controls: statisti-
cal accuracy and informativeness (Aspinall, 2010).
As attested by a number of governmental guidelines, structured expert
judgment is increasingly accepted as quality science that is applicable
when other methods are unavailable (U. S. Environmental Protection
Agency, 2005). Some expert surveys of economists concerned with
climate change examine damages (Nordhaus, 1994) and appropri-
ate discount rates (Weitzman, 2001). Structured expert judgments of
climate scientists were recently used to quantify uncertainty in the
ice sheet contribution to sea level rise, revealing that experts’ uncer-
tainty regarding the 2100 contribution to sea level rise from ice sheets
increased between 2010 and 2012 (Bamber and Aspinall, 2013).
Damages or benefits to ecosystems from invasions of non-indigenous
species are difficult to quantify and monetize on the basis of histori-
cal data. However ecologists, biologists and conservation economists
have substantial knowledge regarding the possible impacts of inva-
sive species. Recent studies applied structured expert judgment with
a performance-based combination and validation to quantify the costs
and benefits of the invasive species introduced since 1959 into the U. S.
Great Lakes by opening the St. Lawrence Seaway (Rothlisberger etal.,
2009, 2012). Lessons from studies such as these reveal that experts
may have applicable knowledge that can be captured in a structured
elicitation when historical data have large uncertainties associated
with them.
Advantages and limitations of structured expert judgment
Expert judgment studies do not reduce uncertainty; they merely quan-
tify it. If the uncertainties are large, as indeed they often are, then deci-
sion makers cannot expect science to relieve them of the burden of
deciding under conditions of ambiguity. Since its inception, structured
expert judgment has been met with scepticism in some quarters; it is,
after all, just opinions and not hard facts. Its steady growth and widen-
ing acceptance over 35 years correlates with the growth of complex
decision support models. The use of structured expert judgment must
never justify a diminution of effort in collecting hard data.
2�5�7�2 Scenario analysis and ensembles
Scenario analysis develops a set of possible futures based on extrapo-
lating current trends and varying key parameters, without sampling in
a systematic manner from an uncertainty distribution. Utilizing suffi-
ciently long time horizons ensures that structural changes in the sys-
tem are considered. The futurist Herman Kahn and colleagues at the
RAND Corporation are usually credited with inventing scenario analy-
sis (Kahn and Wiener, 1967). In the climate change arena, scenarios are
currently presented as different emission pathways or Representative
Concentration Pathways (RCPs). Predicting the effects of such path-
ways involves modelling the Earth’s response to changes in GHG con-
centrations from natural and anthropogenic sources. Different climate
models will yield different projections for the same emissions scenario.
Model Intercomparison studies generate sets of projections termed
‘ensembles’ (van Vuuren etal., 2011).
Elements of the theory
Currently, RCPs are carefully constructed on the bases of plausible
storylines while insuring (1) they are based on a representative set of
peer-reviewed scientific publications by independent groups, (2) they
provide climate and atmospheric models as inputs, (3) they are harmo-
nized to agree on a common base year, and (4) they extend to the year
2100. The four RCP scenarios, shown in Figure 2.3 relative to the range
of baseline scenarios in the literature, roughly span the entire scenario
literature, which includes control scenarios reaching 430 ppm CO
2
eq
or lower by 2100. The scenarios underlying the RCPs were originally
developed by four independent integrated assessment models, each
with their own carbon cycle. To provide the climate community with
four harmonized scenarios, they were run through the same carbon
cycle / climate model (Meinshausen etal., 2011). Note that a represen-
tative set is not a random sample from the scenarios as they do not
represent independent samples from some underlying uncertainty dis-
tribution over unknown parameters.
Ensembles of model runs generated by different models, called multi-
model ensembles or super-ensembles, convey the scatter of the climate
response and natural internal climate variability around reference sce-
narios as sampled by a set of models, but cannot be interpreted proba-
bilistically without an assessment of model biases, model interdepen-
dence, and how the ensemble was constructed (see WGI AR5 Section
12.2; Knutti et al., 2010). In many cases the assessed uncertainty is
larger than the raw model spread, as illustrated in Figure 2.4. The
shaded areas (+ / - 1 standard deviation) around the time series do not
imply that 68 % are certain to fall in the shaded areas, but the model-
ers’ assessed uncertainty (likely ranges, vertical bars on the right) are
larger. These larger ranges reflect uncertainty in the carbon cycle and
the full range of climate sensitivity (WGI AR4 Section 10.5.4.6 and Box
10.3; Knutti etal., 2008) but do not reflect other possible sources of
uncertainty (e. g., ice sheet dynamics, permafrost, or changes in future
solar and volcanic forcings). Moreover, many of these models have
common ancestors and share parameterizations or code (Knutti etal.,
2013) creating dependences between different model runs. Probability
statements on global surface warming require estimating the models’
bias and interdependence (see WGI AR5 Sections 12.2 and 12.4.1.2).
WGI AR5 assigns likelihood statements (calibrated language) to global
temperature ranges for the RCP scenarios (WGI AR5 Table SPM.2) but
does not provide probability density functions (PDFs), as there is no
established formal method to generate PDFs based on results from dif-
ferent published studies.
Advantages and limitation of scenario and ensemble analyses
Scenario and ensemble analyses are an essential step in scoping the
range of effects of human actions and climate change. If the scenarios
span the range of possible outcomes, they may be seen as providing
support for uncertainty distributions in a formal uncertainty analysis. If
specific assumptions are imposed when generating the scenarios, then
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Chapter 2
Figure 2�3 | Total radiative forcing (left panel) and cumulative carbon emissions since 1751 (right panel) in baseline scenario literature compared to RCP scenarios. Forcing was estimated
ex-post from models with full coverage using the median output from the MAGICC results. Secondary axis in the left panel expresses forcing in CO
2
eq concentrations. Scenarios are depicted
as ranges with median emboldened; shading reflects interquartile range (darkest), 5th – 95th percentile range (lighter), and full extremes (lightest). Source: Figure 6.6 from WGIII AR5.
0
1
2
3
4
5
6
7
8
9
10
Total Radiative Forcing [W/m
2
]
1600
1200
900
700
550
450
0
1
2
3
2010 2030 2050 2070 2090 2010 2030 2050 2070 2090
Cumulative Carbon Emissions [TtC]
0.55 TtC (1751-2010)
CO
2
-Equivalent Concentration
[ppm CO
2
eq]
Percentile0-100
th
5-95
th
25-75
th
RCP 8.5
RCP 6.0
RCP 4.5
RCP 2.6
Figure 2�4 | Solid lines are multi-model global averages of surface warming (relative to 1980 – 1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th century
simulations. Shading denotes the ± 1 standard deviation range of individual model annual averages. The orange line is for the experiment where concentrations were held constant
at year 2000 values. The grey bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the six families of emissions scenarios discussed
in the IPCC’s Fourth Assessment Report (AR4). The assessment of the best estimate and likely ranges in the grey bars includes the Atmosphere-Ocean General Circulation Models
(AOGCMs) in the left part of the figure, as well as results from a hierarchy of independent models and observational constraints. Based on: Figure SPM.5 from WGI AR5.
1900 2000 2100
Global Surface Warming [°C]
6.0
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
B1
A1B
A2
B1
B2
A1T
A2
A1B
20th Century
Year 2000 Constant
Concentrations
A1FI
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the support is conditional on these assumptions (see Section 6.2.3).
The advantage of scenario / ensemble analyses is that they can be per-
formed without quantifying the uncertainty of the underlying unknown
parameters. On the downside, it is easy to read more into these analy-
ses than is justified. Analysts often forget that scenarios are illustra-
tive possible futures along a continuum. They tend to use one of those
scenarios in a deterministic fashion without recognizing that they have
a low probability of occurrence and are only one of many possible out-
comes. The use of probabilistic language in describing the swaths of
scenarios (such as standard deviations in Figure 2.4) may also encour-
age the misunderstandings that these represent science-based ranges
of confidence.
The study of representative scenarios based on probabilistic fore-
casts have been shown to facilitate strategic planning by professional
groups such as military commanders, oil company managers, and poli-
cymakers (Schoemaker, 1995; Bradfield etal., 2005). Recent work on
ice sheet modelling (Little etal., 2013) points in this direction. Using
modelling assumptions and prior distributions on model coefficients,
Monte Carlo simulations are used to produce probabilistic predictions.
Expert informed modelling is methodologically intermediate between
structured expert judgment (Bamber and Aspinall, 2013) and non-
probabilistic scenario sweeps. Structured expert judgment leaves the
modelling assumptions to the experts who quantify their uncertainty
on future observables.
2.6 Managing uncertainty,
risk and learning
2�6�1 Guidelines for developing policies
This section assesses how the risks and uncertainties associated with
climate change can affect choices with respect to policy responses,
strategies, and instruments. At the time of the AR4, there was some
modelling-based literature on how uncertainties affected policy design,
but very few empirical studies. In the intervening years, international
negotiations failed to establish clear national emissions reductions
targets, but established a set of normative principles, such as limit-
ing global warming to 2 °C. These are now reflected in international,
national, and subnational planning processes and have affected the
risks and uncertainties that matter for new climate policy develop-
ment. Greater attention and effort has been given to finding syner-
gies between climate policy and other policy objectives, so that it is
now important to consider multiple benefits of a single policy instru-
ment. For example, efforts to protect tropical rainforests (McDermott
etal., 2011), rural livelihoods (Lawlor etal., 2010), biodiversity (Jin-
nah, 2011), public health (Stevenson, 2010), fisheries (Axelrod, 2011),
arable land (Conliffe, 2011), energy security (Battaglini etal., 2009),
and job creation (Barry etal., 2008) have been framed as issues that
should be considered when evaluating climate policies.
The treatment here complements the examination of policies and
instruments in later chapters of this report, such as Chapter 6 (which
assesses the results of IAMs) and Chapters 13 – 15 (which assess policy
instruments at a range of scales). Those later chapters provide greater
details on the overall tradeoffs to be made in designing policies. The
focus here is on the special effects of various uncertainties and risks on
those tradeoffs.
• Section 2.6.2 discusses how institutions that link science with pol-
icy grapple with several different forms of uncertainty so that they
meet both scientific and political standards of accountability.
• Section 2.6.3 presents the results of integrated assessment models
(IAMs) that address the choice of a climate change temperature
target or the optimal transition pathway to achieve a particular
target. IAMs normally focus on a social planner operating at the
global level.
• Section 2.6.4 summarizes the findings from modelling and empiri-
cal studies that examine the processes and architecture of interna-
tional treaties.
• Section 2.6.5 presents the results of modelling studies and the
few empirical analyses that examine the choice of particular policy
instruments at the sovereign state level for reducing GHG emis-
sions. It also examines how the adoption of energy efficiency prod-
ucts and technologies can be promoted at the firm and household
levels. Special attention is given to how uncertainties affect the
performance and effectiveness of these policy instruments.
• Section 2.6.6 discusses empirical studies of people’s support or
opposition with respect to changes in investment patterns and
livelihood or lifestyles that climate policies will bring about. These
studies show people’s sensitivity to the impact that climate change
will have on their personal health or safety and their perceptions
of the health and safety risks associated with the new technolo-
gies addressing the climate change problem.
Linking intuitive thinking and deliberative thinking processes for deal-
ing with uncertainties associated with climate change and climate
policy should increase the likelihood that instruments and robust poli-
cies will be implemented. In this sense, the concepts presented in this
section should be viewed as a starting point for integrating descriptive
models with normative models of choice for developing risk manage-
ment strategies.
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2�6�2 Uncertainty and the science/policy
interface
Science/policy interfaces are defined as social processes which encom-
pass relationships between scientists and other actors in the policy
process, and which allow for exchanges, co-evolution, and joint con-
struction of knowledge with the aim of enriching decision making (Van
den Hove, 2007). Analysts have called attention to several different
forms of uncertainty affecting the science/policy relationship that can
be summarized as follows:
• Paradigmatic uncertainty results from the absence of prior agree-
ment on the framing of problems, on methods for scientifically
investigating them, and on how to combine knowledge from
disparate research traditions. Such uncertainties are especially
common in cross-disciplinary, application-oriented research and
assessment for meeting policy objectives (Gibbons, 1994; Nowotny
etal., 2001).
• Epistemic uncertainty results from lack of information or knowl-
edge for characterizing phenomena. Stirling (2007) further dis-
tinguishes between uncertainty (insufficient knowledge to assess
probabilities), ambiguity (insufficient knowledge about possible
outcomes), and ignorance (insufficient knowledge of likely out-
comes and their probabilities). Others have noted that producing
more knowledge may exacerbate uncertainty, especially when
actors disagree about how to frame a problem for scientific inves-
tigation (Beck, 1992; Gross, 2010).
• Translational uncertainty results from scientific findings that are
incomplete or conflicting, so that they can be invoked to support
divergent policy positions (Sarewitz, 2010). In such circumstances,
protracted controversy often occurs, as each side challenges the
methodological foundations of the other’s claims in a process
called ‘experimenters’ regress’ (Collins, 1985).
Institutions that link science to policy must grapple with all of the
above forms of uncertainty, often simultaneously. Because their
work cuts across conventional lines between science and politics,
these institutions have been called ‘boundary organizations’ (Gus-
ton, 2001) and their function has been termed ‘hybrid management’
(Miller, 2001). Straddling multiple worlds, science-policy institutions
are required to meet both scientific and political standards of account-
ability. Whereas achieving scientific consensus frequently calls for
bounding and closing down disagreements, achieving political legiti-
macy requires opening up areas of conflict in order to give voice to
divergent perspectives.
The task of resolving conflicts in policy-relevant science is generally
entrusted to multidisciplinary expert bodies. These organizations are
best suited to addressing the paradigmatic uncertainties that arise
when problems are novel or when synthesis is required across fields
with different standards of good scientific practice. Bridging epistemic
and translational uncertainties, however, imposes added demands. For
expert advisory bodies to be viewed as legitimate they must represent
all relevant viewpoints in a politically acceptable manner (Jasanoff,
1990; 2005a). What counts as acceptable varies to some degree across
national decision-making cultures. Each culture may place different
weights on experts’ personal integrity, the reliability of their disciplin-
ary judgments, and their ability to forge agreement across competing
values (Jasanoff, 2005b, pp. 209 – 224).
To achieve legitimacy, institutions charged with linking science to policy
must also open themselves up to public input at one or more stages in
their deliberations. This process of “extended peer review” (Funtowicz
and Ravetz, 1992) is regarded as necessary, though insufficient, for the
production of “socially robust knowledge”, that is, knowledge that can
withstand public scrutiny and scepticism (Gibbons, 1994). Procedures
that are sufficient to produce public trust in one political context may
not work in others because national political cultures are character-
ized by different “civic epistemologies”, i. e., culturally specific modes
of generating and publicly testing policy-relevant knowledge (Jasanoff,
2005a).
International and global scientific assessment bodies confront addi-
tional problems of legitimacy because they operate outside long-
established national decision-making cultures and are accountable to
publics subscribing to different civic epistemologies (Jasanoff, 2010).
The temptation for such bodies has been to seek refuge in the linear
model in the hope that the strength of their internal scientific consen-
sus will be sufficient to win wide political buy-in. The recent research
on linking science to policy suggests otherwise.
2�6�3 Optimal or efficient stabilization
pathways (social planner
perspective)
Integrated assessment models (IAMs) vary widely in their underly-
ing structure and decision-making processes. IAMs designed for
cost-benefit analysis typically simulate the choices of an idealized
‘social planner’, who by definition is someone who makes decisions
on behalf of society, in order to achieve the highest social welfare by
weighting the benefits and cost of mitigation measures. In contrast,
many IAMs designed for cost-effectiveness analysis (CEA) specify the
social planner’s objective as identifying the transformation pathway
that achieves a pre-defined climate goal at the lowest discounted
aggregated costs to society. In both cases, the analyses do not con-
sider distributional effects of policies on different income groups, but
instead focus on the effect on total macroeconomic costs. Hence,
with these types of IAMs, negotiators that are part of the political
process are able to rank the relative desirability of alternative poli-
cies to the extent that they share the definition of social welfare
embedded in the model (e. g., discounted aggregate cost minimi-
zation), and believe that those implementing the policy will do so
cooperatively.
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Chapter 6 describes in more detail important structural characteristics
of a set of IAMs used to generate transformation pathways. The mod-
elling analyses highlighted in Chapter 6 utilize the scenario approach
to represent uncertainty. In this section we instead focus on IAM
results where uncertainty is an integral part of the decision-analytic
framework.
Climate policy assessment should be considered in the light of uncer-
tainties associated with climate or damage response functions, the
costs of mitigation technology and the uncertainty in climate change
policy instruments. A key question these analyses address is how
uncertainty with respect to the above factors alters the optimal social
planner’s short-term reactions to climate change. A subset also asks
whether adjusting behaviour to uncertainty and designing more flex-
ible policies and technology solutions would induce a significant wel-
fare gain.
Table 2.2 provides an overview of the existing literature on IAMs that
examine mitigation actions. The rows classify the literature on the
basis of the type of uncertainty: upstream, associated with emission
baseline drivers, such as economic and population growth; down-
stream continuous, associated with climate feedbacks and damages;
downstream strongly nonlinear, associated with the possibility of
thresholds and irreversibilities; policy responses, associated with the
uncertain adoption of policy tools; and multiple sources, when more
than one of the sources above are considered simultaneously. The
three columns categorize the literature according to the ways intro-
ducing uncertainty influence the findings. The theoretical economic
literature shows that the effect of including uncertainty in decision
making on near-term mitigation is ambiguous (for an overview see
e. g., Lange and Treich, 2008; De Zeeuw and Zemel, 2012). However,
for most studies that assume downstream strongly nonlinear uncer-
tainties under a social welfare maximization or downstream uncer-
tainties in combination with a temperature target, including uncer-
tainty in the analysis leads to an optimal or efficient level of
mitigation that is greater and / or accelerated than under conditions of
certainty.
The literature on IAMs incorporating uncertainty uses either Monte
Carlo simulations or fully stochastic programming techniques. Monte
Carlo studies provide insights regarding the order-of-magnitude effect
of multiple model parameter uncertainties for model output (Nordhaus
and Popp, 1997; Tol, 1999; Webster etal., 2002; Hope, 2008, p.200;
Ackerman etal., 2010; Dietz, 2011; Pycroft etal., 2011). In this sense
they can be interpreted as a preparatory step towards a full-fledged
decision analysis under uncertainty.
Table 2�2 | Overview of literature on integrated assessment models examining mitigation actions. (cea) indicates: analysis based on a probabilistic generalization of CEA. Papers
that appear several times report different scenarios or assumptions. The few studies highlighted by “*” use non-probabilistic decision criteria under uncertainty (e. g., minimax regret
or maximin).
1
Type of Uncertainty Considered
Effect on Mitigation Action
Accelerates / Increases Mitigation Action Delays/Decreases Mitigation Action Ambiguous Effect
Upstream (emission drivers) Reilly etal., 1987; Webster etal., 2002; O’Neill
and Sanderson, 2008; Rozenberg etal., 2010
O’Neill and Sanderson, 2008
Downstream (climate
and damages) — mildly
nonlinear damages
Chichilnisky and Heal, 1993; Peck and Teisberg, 1994;
Ha-Duong and Treich, 2004; Syri etal., 2008; Athanassoglou
and Xepapadeas, 2011; Kaufman, 2012; Ackerman etal., 2013
Kolstad, 1994, 1996a; Baranzini etal., 2003 Clarke and Reed, 1994; Kolstad, 1996b;
Tsur and Zemel, 1996; Gollier etal., 2000;
Fisher and Narain, 2003; Ha-Duong and
Treich, 2004; Baker etal., 2006; Lange and
Treich, 2008; Lorenz etal., 2012b; Ulph
and Ulph, 1997; Ackerman etal., 2013
Downstream (climate and
damages) — strongly nonlinear
event or temperature target
Ha-Duong, 1998; Gjerde etal., 1999; O’Neill and Oppenheimer,
2002; Baranzini etal., 2003; Dumas and Ha-Duong, 2005;
Syri etal., 2008(cea); Johansson etal., 2008(cea); Hope,
2008; Webster, 2008; Tsur and Zemel, 2009; Schmidt etal.,
2011(cea); Funke and Paetz, 2011; Iverson and Perrings,
2012*; Lorenz etal., 2012b; de Zeeuw and Zemel, 2012
Peck and Teisberg, 1995 Gollier and Treich, 2003
Uncertainty on Policy Response Ha-Duong etal., 1997; Blanford, 2009; Bosetti and Tavoni,
2009; Bosetti etal., 2009; Durand-Lasserve etal., 2010(cea)
Baudry, 2000; Baker and Shittu, 2006(cea)
2
Farzin and Kort, 2000(cea)
Multiple Sources of Uncertainty Nordhaus and Popp, 1997; Grubb, 1997; Pizer, 1999; Tol,
1999; Obersteiner etal., 2001; Yohe etal., 2004; Keller etal.,
2004; Baker and Shittu, 2008; Baker and Adu-Bonnah, 2008;
Bahn etal., 2008; Held etal., 2009; Hope, 2009; Labriet etal.,
2012(cea), 2010; Hof etal., 2010* ; Funke and Paetz, 2011*
Scott etal., 1999 Manne and Richels, 1991; Baker and Shittu,
2008(4); Baker and Adu-Bonnah, 2008
3
Notes:
1
In some studies the ‘baseline case’ is a decision analysis based on a reduced form of uncertainty.
2
The impact on R&D investments depend on technology; the most common result is, however, that uncertainty decreases the optimal level of R&D investments.
3
In the sense of: increasing damage uncertainty would lead to higher investments in less risky programmes, but the effect depends on the type of technology.
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Table 2.2 also characterizes the effect of the inclusion of uncertainty
on early-period mitigation efforts. A decision analysis is generally com-
pared to a baseline-case represented by a deterministic study utilizing
average values of uncertain parameters. (In some studies, the baseline
case is a decision analysis based on a reduced form of uncertainty.)
It should be noted that, although IAMs mimic decision makers who
utilize deliberative processes, in reality social planners might resort to
intuitive thinking to simplify their decision processes, leading to biases
and inferior choices. To date there is no research that considers such
behaviour by decision makers and how it affects the projections of
IAMs. We discuss the need for such studies in Section 2.7 on gaps in
knowledge and data.
2�6�3�1 Analyses predominantly addressing climate or
damage response uncertainty
Although studies differ in their approaches, the case against acceler-
ated or increased mitigation action is the possibility that irreversible
sunk cost investments in abatement options outweigh the irreversible
effects of climate change. This has been an infrequent finding, with the
exception of those studies that have not included catastrophic / thresh-
old damage and give no consideration to the non-climate related
benefits of these investments, such as enhancing energy security or
local pollution benefits. Indeed, the one set of papers that finds a need
for increased or accelerated mitigation action is ambiguous when
the social welfare optimum is examined under downstream continu-
ous / mildly nonlinear damages uncertainty. Lorenz etal. (2012a) show
that this is due primarily to the fact that damage nonlinearities are
often compensated by other nonlinearities such as a concave (i. e., sub-
linear) concentration-temperature relation.
Studies that cluster in the first column (accelerated or increased
mitigation action) assumed strongly non-linear damage functions or
temperature targets (3rd row). Cost-effectiveness analysis has been
applied to reflect targets when the models have been generalized to
include uncertainty. In this regard, Held etal. (2009), utilizing chance
constrained programming (CCP) (see Section 2.5.4.1), examine uncer-
tainty in climate and technology response properties. As their reference
case they calculated the mitigation effort needed to achieve a 2 °C
temperature target, assuming average values for all uncertain param-
eters. Given uncertainty, however, it is clear that any given mitigation
effort will exceed the target with some probability; for the reference
case this is approximately 50 %. As the required probability for meet-
ing the target increases, a greater level of mitigation effort is required.
(An analogous argument holds for tipping-point derived targets. See
McInerney and Keller, 2008). If the required probability is 66.6 % rather
than 50 %, investments in mitigation technologies need to occur in
earlier decades.
The effects on investment in mitigation also depend on whether uncer-
tainty is expected to be reduced. Is a reduction of uncertainty on cli-
mate sensitivity and related climate response properties realistic? In
an early paper, Kelly and Kolstad (1999) evaluated the amount of time
needed to significantly reduce uncertainty about the parameters influ-
encing climate sensitivity by observing global warming. They found the
required time to be 90 to 160 years. Leach (2007) conducted a simi-
lar analysis that allowed two rather than one independent sources of
downstream uncertainty. In that case, the time required to resolve the
climate sensitivity parameters is likely to be even longer. These kind of
studies assumed that our basic understanding of atmospheric chem-
istry and physics would remain unchanged over time. If one were to
relax this constraint, then one could imagine that learning would prog-
ress more rapidly.
Another set of papers examines the ‘anticipation effect’, namely what
it means if we believe we will learn in the future, rather than that our
knowledge will remain constant. Lange and Treich (2008) showed that
the sign and magnitude of mitigation depend on the particular numeri-
cal model and type of uncertainty when introducing the anticipation
effect. Using CBA, for example, Lorenz etal. (2012b), Peck and Teisberg
(1993), Webster etal. (2008), and Yohe and Wallace (1996) showed
the anticipation effect to be negligible when assuming continuous and
only weakly non-linear damages. However, Lorenz (2012b) showed
slightly less immediate mitigation (compared to no-learning) if one
anticipates learning within a given, narrow, time window with respect
to threshold-type impacts. Such a mild reduction of early mitigation
in response to anticipation was also reported in Keller etal. (2004) in
accordance with Ulph and Ulph (1997).
When CEA is used to represent temperature targets in combination
with climate response uncertainty, it is difficult to evaluate learning
effects (see the discussion in Section 2.5.4.3). One way to allow for
numerical solutions in this case is to assume an upper limit on the dis-
tribution of climate sensitivity to examine the effect of learning in the
presence of a climate target. Under this assumption, more mitigation is
called for (Bahn etal., 2008; Syri etal., 2008; Fouquet and Johansson,
2008; Webster, 2008).
A further set of papers considers the impossibility of specifying a pre-
cise probability density function for characterizing climate sensitivity
as suggested by many climate scientists. This implies that these prob-
abilities are difficult to estimate and decisions have to be made under
conditions of ambiguity. Funke and Paetz (2011) account for model
structure uncertainty by employing a robust control approach based
on a maximin principle. When considering uncertainty on the ecologi-
cal side of the balance, they conclude that model uncertainty implies
a need for more aggressive near-term emissions reductions. Athanas-
soglou and Xepapadeas (2011) extend this approach to include adap-
tation. Iverson and Perrings (2012) apply combinations of maximin
and / or minimax decision criteria, examining the effects of widening
the range of climate sensitivity. Hof etal. (2010), contrast a CBA with
a minimax regret approach and find that the minimax regret approach
leads to more stringent and robust climate targets for relatively
low discount rates if both high climate sensitivity and high damage
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estimates are assumed. What remains unresearched is the possibil-
ity of using non-probabilistic methods to evaluate the effects of an
unbounded, or ‘fat-tails’, distribution for climate responses and cli-
mate impacts.
Finally, a potentially path-breaking development in economics is the
effort of Ackerman etal. (2013), Crost and Traeger (2013), and Kaufman
(2012) to disentangle risk aversion (a static effect) from consumption
smoothing (an intertemporal effect) (for a conceptual discussion see
Ha-Duong and Treich, 2004) in an Integrated Assessment Model. Com-
pared to the results of a standard discounted expected utility model
that relates risk aversion to consumption smoothing, Ackerman (2013)
as well as Crost and Traeger (2013) find optimal mitigation to be twice
as great. Since these are the first papers on this topic, it is too early to
tell whether their results represent a robust result that captures soci-
ety’s risk preferences.
2�6�3�2 Analyses predominantly addressing policy
response uncertainty
There are two strands of research in the area of policy response uncer-
tainty. The first has focused on examining how the extent and timing of
mitigation investments are affected by the uncertainty on the effective-
ness of Research, Development, and Demonstration (RD&D) and / or the
future cost of technologies for reducing the impact of climate change.
An example of this would be optimal investment in energy technolo-
gies that a social planner should undertake, knowing that there might
be a nuclear power ban in the near future. Another strand of research
looks at how uncertainty concerning future climate policy instruments
in combination with climate and / or damage uncertainty affects a miti-
gation strategy. An example would be the optimal technological mix in
the power sector to hedge future climate regulatory uncertainty.
With respect to the first strand, the main challenge is to quantify
uncertainty related to the future costs and / or availability of mitigation
technologies. Indeed, there does not appear to be a single stochastic
process that underlies all (RD&D) programmes’ effectiveness or inno-
vation processes. Thus elicitation of expert judgment on the probabi-
listic improvements in technology performance and cost becomes a
crucial input for numerical analysis. A literature is emerging that uses
expert elicitation to investigate the uncertain effects of RD&D invest-
ments on the prospect of success of mitigation technologies (see for
example Baker etal., 2008; Curtright etal., 2008; Chan etal., 2010;
Baker and Keisler, 2011). In future years, this new body of research
will allow the emergence of a literature studying the probabilistic
relationship between R&D and the future cost of energy technologies
in IAMs.
The few existing papers reported in Table 2.2 under the Policy Response
uncertainty column (see Blanford, 2009; Bosetti and Tavoni, 2009)
point to increased investments in energy RD&D and in early deploy-
ment of carbon-free energy technologies in response to uncertainty.
An interesting analysis has been performed in Goeschl and Perino
(2009), where the potential for technological ‘boomerangs’ is consid-
ered. Indeed, while studies cited above consider an innovation failure
an R&D project that does not deliver a clean technology at a competi-
tive cost, Goeschl and Perino (2009) define R&D failure when it brings
about a new, environmentally harmful, technology. Under such char-
acterization they find that short-term R&D investments are negatively
affected.
Turning to the second strand of literature reported in the Policy
Response or in the Multiple Uncertainty columns of Table 2.2 (see Ha-
Duong et al., 1997; Baker and Shittu, 2006; Durand-Lasserve et al.,
2010), most analyses imply increased mitigation in the short term
when there is uncertainty about future climate policy due to the asym-
metry of future states of nature. In the event of the realization of the
‘no climate policy’ state, investment in carbon-free capital has low or
zero value. Conversely, if a ‘stringent climate policy’ state of nature is
realized, it will be necessary to rapidly ramp up mitigation to reduce
the amount of carbon in the atmosphere. This cost is consistently
higher, thus implying higher mitigation prior to the realization of the
uncertain policy state.
2�6�4 International negotiations and
agreements
Social planner studies, as reviewed in the previous sub-sections, con-
sider the appropriate magnitude and pace of aggregate global emis-
sions reduction. These issues have been the subject of negotiations
about long-term strategic issues at the international level along with
the structuring of national commitments and the design of mecha-
nisms for compliance, monitoring, and enforcement.
2�6�4�1 Treaty formation
A vast literature looks at international treaties in general and how they
might be affected by uncertainties. Cooper (1989) examined two cen-
turies of international agreements that aimed to control the spread of
communicable diseases and concludes that it is only when uncertainty
is largely resolved that countries will enter into agreements. Young
(1994), on the other hand, suggests that it may be easier to enter into
agreements when parties are uncertain over their individual net bene-
fits from an agreement than when that uncertainty has been resolved.
Coalition theory predicts that for international negotiations related to
a global externality such as climate change, stable coalitions will gen-
erally be small and / or ineffective (Barrett, 1994). Recently, De Canio
and Fremstad (2013) show how the recognition of the seriousness of
a climate catastrophe on the part of leading governments — which
increases the incentives for reaching an agreement — could transform
a prisoner’s dilemma game into a coordination game leading to an
increased likelihood of reaching an international agreement to limit
emissions.
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Relatively little research has been undertaken on how uncertainty
affects the stability of multilateral environmental agreements (MEAs)
and when uncertainty and learning has the potential to unravel agree-
ments. Kolstad (2007), using a game theoretic model, looks specifically
at environmental agreements. He finds that systematic uncertainty
decreases the size of the largest stable coalition of an MEA. Kolstad
and Ulph (2011) show that partial or complete learning has a nega-
tive impact on the formation of an MEA because as outcomes become
more certain, some countries also learn the MEA will reduce their own
welfare benefits, which deters them from joining the coalition. Baker
(2005), using a model of the impacts of uncertainty and learning in a
non-cooperative game, shows that the level of correlation of damages
across countries is a crucial determinant of outcome.
Barrett (2013) has investigated the role of catastrophic, low probabil-
ity events on the likelihood of cooperation with respect to a global
climate agreement. By comparing a cooperative agreement with the
Nash equilibrium it is possible to assess a country’s incentives for par-
ticipating in such an agreement. Looking at stratospheric ozone as an
analogy for climate, Heal and Kunreuther (2013) observed that the
signing of the Montreal Protocol by the United States led many other
countries to follow suit. The authors in turn suggest how it could be
applied to foster an international treaty on greenhouse gas emissions
by tipping a non-cooperative game from an inefficient to an efficient
equilibrium.
Several analyses, including Victor (2011) and Hafner-Burton et al.
(2012), contend that the likelihood of a successful comprehensive
international agreement for climate change is low because of the sen-
sitivity of negotiations to uncertain factors, such as the precise align-
ment and actions of participants. Keohane and Victor (2011), in turn,
suggest that the chances of a positive outcome would be higher in
the case of numerous, more limited agreements. Developing countries
have been unlikely to agree to binding targets in the context of inter-
national agreements due in part to the interests of developed coun-
tries dominating the negotiation process. For the situation to change,
the developing countries would have to enhance their negotiating
power in international climate change discussions by highlighting their
concerns (Rayner and Malone, 2001).
The above analyses all assume that the agents are deliberative think-
ers, each of whom has the same information on the likelihood and con-
sequences of climate change. Section 2.7 indicates the need for future
research that examines the impact of intuitive thinking on behaviour
on international negotiations and processes for improving the chances
of reaching an agreement on treaties.
2�6�4�2 Strength and form of national commitments
Buys etal. (2009) construct a model to predict national level support
for a strong global treaty based on both the climatic and economic
risks that parties to the treaty face domestically; however Buys etal.
do not test the model empirically. Their model distinguishes between
vulnerabilities to climate impacts and climate policy restrictions with
respect to carbon emissions and implies that countries would be most
supportive of strong national commitments when they are highly vul-
nerable to climate impacts and their emitting sectors are not greatly
affected by stringent policy measures.
Victor (2011) analyzes the structure of the commitments themselves,
or what Hafner-Burton etal. (2012) call rational design choices. Victor
suggests that while policymakers have considerable control over the
carbon intensity of their economies, they have much less control over
the underlying economic growth of their country. As a result, there is
greater uncertainty on the magnitude of emissions reductions, which
depends on both factors, than on the reductions in carbon intensity.
Victor suggests that this could account for the reluctance by many
countries to make binding commitments with respect to emissions
reductions. Consistent with this reasoning, Thompson (2010) examined
negotiations within the UNFCCC and found that greater uncertainty
with respect to national emissions was associated with a decrease in
support for a national commitment to a global treaty.
Webster etal. (2010) examined whether uncertainty with respect to
national emissions increases the potential for individual countries to
hedge by joining an international trade agreement. They found that
hedging had a minor impact compared to the other effects of interna-
tional trade, namely burden sharing and wealth transfer. These find-
ings may have relevance for structuring a carbon market to reduce
emissions by taking advantage of disparities in marginal abatement
costs across different countries. In theory, the right to trade emission
permits or credits could lessen the uncertainties associated with any
given country’s compliance costs compared to the case where no trad-
ing were possible. Under a trading scheme, if a country discovered its
own compliance costs to be exceptionally high, for example, it could
purchase credits on the market.
2�6�4�3 Design of measurement, verification regimes,
and treaty compliance
A particularly important issue in climate treaty formation and com-
pliance is uncertainty with respect to actual emissions from industry
and land use. Measurement, reporting, and verification (MRV) regimes
have the potential to set incentives for participation in a treaty and
still be stringent, robust, and credible with respect to compliance.
The effects of strategies for managing GHG emissions are uncertain
because the magnitude of the emissions of carbon dioxide and other
GHG gases, such as methane, often cannot be detected given the
error bounds associated with the measurement process. This is espe-
cially the case in the agriculture, forestry, and other land-use (AFOLU)
sectors.
In the near term, an MRV regime that met the highest standards
could require stock and flow data for carbon and other GHGs. These
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data are currently available only in wealthy countries, thus preclud-
ing developing countries from participating (Oliveira etal., 2007). By
contrast, there are design options for MRV regimes that are less accu-
rate, but which still provide data on the drivers of emissions so that
the developing countries could be part of the system. By being more
inclusive, these options could be a more effective way to actually
reduce aggregate emissions, at least in the near term (Bucki etal.,
2012). In the longer term, robust and harmonized estimation of GHG
flows — emissions and their removal — in agriculture and forestry
requires investment in monitoring and reporting capacity, especially
in developing countries (Böttcher etal., 2009; Romijn etal., 2012).
Reflecting this need for an evolving MRV regime to match data avail-
ability, the 2006 Guidelines for National Greenhouse Gas Invento-
ries, prepared by an IPCC working group, suggested three hierarchi-
cal tiers of data for emission and carbon stock change factors with
increasing levels of data requirements and analytical complexity. Tier
1 uses IPCC default values of high uncertainty; Tier 2 uses country-
specific data; and Tier 3 uses higher spatial resolution, models, and
inventories. In 2008, only Brazil, India and Mexico had the capacity
to use Tier 2 and no developing country was able to use tier 3 (Hard-
castle and Baird, 2008). Romijn etal. (2012) focused on 52 tropical
countries and found that four of them had a very small capacity gap
regarding the monitoring of their forests through inventories, while
the remaining 48 had limited or no ability to undertake this monitor-
ing process.
In order to overcome the gaps and uncertainties associated with
lower tier approaches, different principles can be applied to form
pools (Böttcher etal., 2008). For example, a higher level of aggrega-
tion by including soil, litter and harvested products in addition to a
biomass pool as part of the MRV regime decreases relative uncer-
tainty: the losses in one pool (e. g., biomass) are likely to be offset by
gains in other pools (e. g., harvested products) (Böttcher etal., 2008).
Researchers have suggested that the exclusion of a pool (e. g., soil)
in an MRV regime should be allowed only if there is adequate docu-
mentation that the exclusion provides a more conservative estimate of
emissions (Grassi etal., 2008). They also suggest that an international
framework needs to create incentives for investments. In this respect,
overcoming initialization costs and unequal access to monitoring
technologies would be crucial for implementation of an integrated
monitoring system, and fostering international cooperation (Böttcher
etal., 2009).
2�6�5 Choice and design of policy
instruments
Whether motivated primarily by a binding multilateral climate treaty
or by some other set of factors, there is a growing set of policy instru-
ments that countries have implemented or are considering to deal with
climate change. Typically, these instruments will influence the deci-
sions of firms and private individuals, so that policymakers try to antici-
pate how these agents will react to them.
Some policy instruments operate by mandating particular kinds of
behaviour, such as the installation of pollution control technology or
limits on emissions from particular sources. There is an extensive litera-
ture in political science demonstrating that the effects of these instru-
ments are fairly predictable (Shapiro and McGarity, 1991) and are
insensitive to market or regulatory uncertainties, simply because they
prescribe particular technologies or practices which must be strictly
adhered to. There is a literature in economics, however, suggesting that
their very inflexibility makes them inefficient (Malueg, 1990; Jaffe and
Stavins, 1995).
In the presence of substantial technological uncertainty, no matter
what policy instrument is employed, interventions that shift invest-
ment behaviour from currently low cost to currently high cost tech-
nologies run the risk of increasing short-term costs and energy security
concerns for consumers (Del Rio and Gual, 2007; Frondel etal., 2008,
2010). In some cases, long-term costs may be higher or lower, depend-
ing on how different technologies evolve over time (Williges et al.,
2010; Reichenbach and Requate, 2012). This section is structured by
considering two broad classes of interventions for targeting the energy
supply: interventions that focus on emissions, by placing a market price
or tax on CO
2
or other greenhouse gases; and interventions that pro-
mote Research, Development, Deployment, and Diffusion (RDD&D) of
particular technologies. In both types of interventions, policy choices
can be sensitive to uncertainties in technology costs, markets, and the
state of regulation in other jurisdictions and over time. In the case
of technology-oriented policy, choices are also sensitive to the risks
that particular technologies present. We then describe instruments for
reducing energy demand by focusing on lifestyle choice and energy
efficient products and technologies. Finally, we briefly contrast the
effects of uncertainties in the realm of climate change adaptation with
climate change mitigation, recognizing that more detail on adaptation
can be found in the WGII AR5.
2�6�5�1 Instruments creating market penalties for GHG
emissions
Market-based instruments increase the cost of energy derived from
fossil fuels, potentially leading firms involved in the production and
conversion of energy to invest in low carbon technologies. Consider-
able research prior to AR4 identified the differences between two such
instruments — carbon taxes and cap-and-trade regimes — with respect
to uncertainty. Since AR4, research has examined the effects of regula-
tory risk and market uncertainty on one instrument or the other by
addressing the following question: how is the mitigation investment
decision affected by uncertainty with respect to whether and to what
extent a market instrument and well-enforced regulations will be in
place in the future?
Much of this research has focused on uncertainty with respect to car-
bon prices under a cap-and-trade system. A number of factors influence
the relationship between the size of the cap and the market price that
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includes fossil fuel prices, consumer demand for energy, and economic
growth more generally. Each of these factors can lead to volatility in car-
bon market prices (Alberola etal., 2008; Carraro etal., 2009; Chevallier,
2009). Vasa and Michaelowa (2011) assessed the impact of policy uncer-
tainty on carbon markets and found that the possibility of easily creating
and destroying carbon markets leads to extreme short-term rent-seeking
behaviour and high volatility in market prices. Experience so far with the
most developed carbon market — the European Emissions Trading Sys-
tem (ETS) — reveals high volatility marked by not-infrequent decreases
of the price of carbon to very low values (Feng etal., 2011).
Numerous modelling studies have shown that regulatory uncertainty
reduces the effectiveness of market-based instruments. More specifi-
cally, a current or expected carbon price induces a decrease in invest-
ment into lower carbon infrastructure and hence less technological
learning, when there is uncertainty as to future market conditions, com-
pared to the case where future conditions are known (Yang etal., 2008;
Fuss etal., 2009; Oda and Akimoto, 2011). In order to compensate and
maintain a prescribed level of change in the presence of uncertainty, car-
bon prices would need to be higher. Estimates of the additional macro-
economic costs range from 16 – 37 % (Blyth et al., 2007) to as much
as 50 % (Reinelt and Keith, 2007), depending on the particular type of
investment under consideration. The precise instrument design details
can affect investment behaviour. Patiño-Echeverri etal. (2007, 2009), for
example, found that less frequent but larger regulatory policy changes
had less of a negative interactive effect with uncertainty, while Zhao
(2003) found a greater impact of uncertainty on the performance of a
carbon tax than on a cap-and-trade system. Fan etal. (2010) added to
this analysis by examining the sensitivity of these results to increasing
risk aversion, under two alternative carbon market designs: one in which
carbon allowances were auctioned by the government to firms, and a
second in which existing firms received free allowances due to a grand-
fathering rule.
Under an auctioned system for carbon allowances, increasing risk
aversion leads to greater investments in low carbon technologies. In
contrast, under a grandfathered market design, increasing risk aver-
sion combined with uncertainty pushes investment behaviour closer to
what it would be in the absence of the carbon market: more invest-
ment in coal. The intuition behind this finding is that the grandfathered
scheme would create a situation of windfall profits (since the freely
allocated permits have a value to the firms receiving them), and risk-
averse investors would be more influenced by the other, less desirable
state of the world, the absence of carbon markets. Fan etal., (2012)
replicated these results using a broader range of technological choices
than in their earlier paper. Whereas these latter two papers used a
game-theoretic model, Fuss etal., (2012) employed a real options the-
ory model to arrive at qualitatively the same conclusions.
One option for reducing carbon price volatility is to set a cap or floor
for that price to stabilize investment expectations (Jacoby and Eller-
man, 2004; Philibert, 2009). Wood and Jotzo (2011) found that setting
a price floor increased the effectiveness of the carbon price in stimulat-
ing investments in low carbon technologies, given a particular expec-
tation of macroeconomic drivers (e. g., economic growth and fossil fuel
prices that influence the degree to which a carbon cap is a constraint
on emissions). Szolgayova etal., (2008), using a real options model to
examined the value of waiting for information, found the cap stabi-
lized expectations. In the process, the cap lessened the effectiveness
of an expected carbon price at altering investment behaviour, as many
investments in low carbon technologies are undertaken only because
of the possibility of very high carbon prices in the future. In another
study assuming rational actor behaviour, Burtraw etal. (2010) found
that a symmetric safety valve that sets both a floor and a ceiling price
outperforms a single-sided safety valve in terms of both emissions
reduction and economic efficiency. Murray etal. (2009) suggested that
a reserve allowance for permits outperforms a simple safety valve in
this regard.
Empirical research on the influence of uncertainty on carbon market per-
formance has been constrained by the small number of functioning mar-
kets, thus making it difficult to infer the effects of differences in market
design. The few studies to date suggest that the details of market design
can influence the perception of uncertainty, and in turn the performance
of the market. More specifically, investment behaviour into the Clean
Development Mechanism (CDM) has been influenced by uncertainties
in terms of what types of projects are eligible (Castro and Michaelowa,
2011), as well as the actual number of Certified Emissions Reductions
(CERs) that can be acquired from a given project (Richardson, 2008).
Looking at the European Union’s Emission Trading System (ETS),
researchers have observed that expected carbon prices do affect invest-
ment behaviour, but primarily for investments with very short amortiza-
tion periods. High uncertainty with respect to the longer-term market
price of carbon has limited the ETS from having an impact on longer-term
investments such as R&D or new power plant construction (Hoffmann,
2007). Blyth and Bunn (2011) found that uncertainty for post-2012
targets was a major driver of ETS prices, with an effect of suppress-
ing those prices. The literature suggests that prices have not been high
enough to drive renewable energy investment in the absence of feed-in
tariffs (Blanco and Rodrigues, 2008). Barbose etal. (2008) examined a
region — the western United States — where no ETS was functioning but
many believed that it would, and found that most utilities did consider
the possibility of carbon prices in the range of USD 4 to USD 22 a ton. At
the same time, the researchers could not determine whether this projec-
tion of carbon prices would have an actual effect on utilities’ decisions,
were an actual ETS in place, because they were unable to document the
analysis underlying the utilities’ investment decisions.
2�6�5�2 Instruments promoting technological RDD&D
Several researchers suggest that future pathways for RDD&D will be
the determining factor for emissions reductions (Prins and Rayner,
2007; Lilliestam etal., 2012). Policy instruments can provide an incen-
tive for firms not only to alter their investment portfolio towards low
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carbon technologies, but also to devote resources towards innovation
(Baker etal., 2008). Because instruments differ in terms of how they
influence behaviour, such as whether or not they create an immediate
incentive or one that accrues over the lifetime of the investment, their
relative effectiveness can be sensitive to relevant market uncertainties.
The literature reviewed in the previous section reveals that in the pres-
ence of substantial regulatory uncertainty, market-based instruments do
a poor job of promoting RDD&D. This has given rise to policy proposals
to supplement a pure-market system with another instrument — such
as a cap, floor, or escape valve — to reduce price volatility and stabilize
expectations. By contrast, combining a market-based instrument with
specific technology support can lead to greater volatility in the carbon
price, even when there is very little uncertainty about which technolo-
gies will be assisted in the coming years (Blyth etal., 2009).
Several empirical studies with a focus on risk and uncertainty have
compared the effectiveness of market instruments with other instru-
ments such as feed-in tariffs or renewable quota systems, in stimu-
lating low carbon investments and R&D. Butler and Neuhoff (2008)
compared the feed-in tariff in Germany with the quota system in the
United Kingdom, and found the German system outperformed the UK
system on two dimensions: stimulating overall investment quantity,
and reducing costs to consumers. The primary driver was the effective-
ness of the feed-in tariff in reducing risks associated with future reve-
nues from the project investment, therefore making it possible to lower
the cost of project financing. Other researchers replicate this finding
using other case studies (Mitchell etal., 2006; Fouquet and Johansson,
2008). Lüthi and Wüstenhagen (2012) surveyed investors with access
to a number of markets, and found that they steered their new projects
to those markets with feed-in tariff systems, as it was more likely than
other policy instruments to reduce their risks. Lüthi (2010) compared
policy effectiveness across a number of jurisdictions with feed-in tar-
iffs, and found that above a certain level of return, risk-related factors
did more to influence investment than return-related factors.
Looking at the early stages in the technology development process,
Bürer and Wüstenhagen (2009) surveyed ‘green’ tech venture capital-
ists in the United States and Europe using a stated preference approach
to identify which policy instrument or instruments would reduce the
perceived risks of investment in a particular technology. They identi-
fied a strong preference in both continents, but particularly Europe,
for feed-in tariffs over cap-and-trade and renewable quota systems,
because of the lower risks to return on investment associated with the
former policy instrument. Moreover, venture capital investors typically
look for short- to medium-term returns on their investment, for which
the presence of feed-in tariffs has the greatest positive effect.
Held etal. (2006) identified patterns of success across a wide variety of
policy instruments to stimulate investment in renewable energy tech-
nologies in Europe. They found that long-term regulatory consistency
was vital for new technology development. Other studies have shown
that regulatory inconsistency with respect to subsidy programs — such
as feed-in tariffs in Spain or tax credits in the United States — can lead to
temporarily overheated markets, pushing up investment costs and con-
sumer prices, and reducing the pressure for technological development
(Del Rio and Gual, 2007; Sáenz de Miera etal., 2008; Barradale, 2010).
In contrast to the large literature looking at the overall effects of
uncertainty, there have only been a few empirical papers documenting
the particular risks that concern investors the most. Leary and Este-
ban (2009) found regulatory uncertainty — particularly with respect to
issues of siting — to concern investors in wave- and tide-based energy
projects. Komendantova et al. (2012) examined perceptions among
European investors in solar projects in North Africa, and found con-
cerns about regulatory change and corruption were much greater than
concerns about terrorism and technology risks. The same researchers
modelled the sensitivity of required state subsidies for project develop-
ment in response to these risks, and found the subsidies required to
stimulate a given level of solar investment rose by a factor of three,
suggesting large benefits from stemming corruption and stabilizing
regulations (Komendantova etal., 2011). Meijer etal. (2007) examined
the perceived risks for biogas project developers in the Netherlands,
and found technological, resource, and political uncertainty to be their
most important concerns. These studies are useful by documenting
policymakers’ concerns so they can address these issues in the future.
Table 2.3 synthesizes the modelling and empirical results on renewable
quota systems and feed-in tariffs, as well as with results for cap-and-
trade systems from the previous sub-section. The table highlights the
Table 2�3 | Uncertainties affecting the effectiveness of alternative policy instruments.
Instrument Uncertainty Investor fears
Effect on low carbon
technology
Allowance trading market
Technological systems Other low carbon technologies will prove more cost-effective Dampened investment
Market behaviour Growth in energy demand will decline Dampened investment
Market behaviour Fossil fuel prices will fall Dampened investment
Regulatory actions Governments will increase the number of allowances Dampened investment
Renewable quotas
Technological systems Other low carbon technologies will prove more cost-effective Dampened investment
Market behaviour Supply for renewable energy will rise faster than the quota Dampened investment
Subsidies and feed-in tariffs Regulatory actions Subsidy for this particulartechnology will decline Overheated market
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effects of three of the classes of uncertainties identified earlier in this
chapter, namely with respect to technological systems, market behav-
iour, and the future regulatory actions of governments.
2�6�5�3 Energy efficiency and behavioural change
As pointed out in Section 2.6.5.2 and earlier sections, one way to
mitigate climate risk is to encourage RD&D with respect to provid-
ing energy from renewable sources, such as wind and solar, as well
as to promote low energy use products. For firms to undertake these
investments, there needs to be some guarantee that a market for their
products will exist. Currently consumers are reluctant to adopt energy
efficient measures, such as compact fluorescent bulbs, energy efficient
refrigerators, boilers and cooling systems, as well as new technologies
such as solar installations and wind power. This can be attributed to
the uncertainties associated with future energy prices and consump-
tion of energy coupled with misperceptions of the products’ benefits
and an unwillingness to incur the upfront costs of these measures as
discussed in Section 2.4.3.2.
Gardner and Stern (2008) identified a list of energy efficient measures
that could reduce North American consumers’ energy consumption by
almost 30 % but found that individuals were not willing to invest in
them because they have misconceptions about the measures’ effec-
tiveness. Other studies show that the general public has a poor under-
standing of energy consumption associated with familiar activities
(Sterman and Sweeney, 2007). A national online survey of 505 partici-
pants by Attari etal. (2010) revealed that most respondents felt that
measures such as turning off the lights or driving less were much more
effective as energy efficient improvements than experts’ viewed them
to be.
There are both behavioural and economic factors described in Sec-
tion 2.4.3.2 that can explain the reluctance of households to incur
the upfront costs of these energy efficient measures. Due to a focus
on short-term horizons, individuals may underestimate the savings in
energy costs from investing in energy efficient measures. In addition
they are likely to discount the future hyperbolically so that the upfront
cost is perceived to be greater than expected discounted reduction
in energy costs (Dietz etal., 2013; Kunreuther etal., 2013b). Coupled
with these descriptive models or choices that are triggered by intui-
tive thinking, households may have severe budget constraints that
discourage them from investing in these energy efficient measures.
If they intend to move in several years and feel that the investment
in the energy efficient measure will not be adequately reflected in an
increase in their property value, then it is inappropriate for them not to
invest in these measures if they undertake deliberative thinking.
To encourage households to invest in energy efficient measures, mes-
sages that communicate information on energy use and savings from
undertaking these investments need to be conveyed (Abrahamse etal.,
2005). Recent research has indicated the importance of highlighting
indirect and direct benefits (e. g., being ‘green’, energy independence,
saving money) in people’s adoption of energy efficiency measures
to address the broad range and heterogeneity in people’s goals and
values that contribute to the subjective utility of different courses of
action (Jakob, 2006). One also needs to recognize the importance of
political identity considerations when choosing the nature of these
messages, as different constituencies have different associations to
options that mitigate climate change and labels that convey potential
benefits from adopting energy efficient measures (Hardisty etal., 2010;
Gromet etal., 2013).
The advent of the ‘smart’ grid in Western countries, with its ‘smart’
metering of household energy consumption and the development of
‘smart’ appliances will make it feasible to provide appliance-specific
feedback about energy use and energy savings to a significant number
of consumers within a few years. A field study involving more than
1,500 households in Linz, Austria revealed that feedback on electric-
ity consumption corresponded with electricity savings of 4.5 % for the
average household in this pilot group (Schleich etal., 2013).
To deal with budget constraints, the upfront costs of these measures
need to be spread over time so the measures are viewed as economi-
cally viable and attractive. The Property Assessed Clean Energy (PACE)
programme in the United States is designed to address the budget con-
straint problem. Participants in this programme receive financing for
improvements that is repaid through an assessment on their property
taxes for up to 20 years. Financing spreads the cost of energy improve-
ments over the expected life of measures such as weather sealing,
energy efficient boilers and cooling systems, and solar installations
and allows for the repayment obligation to transfer automatically to
the next property owner if the property is sold. The program addresses
two important barriers to increased adoption of energy efficiency and
small-scale renewable energy: high upfront costs and fear that project
costs will not be recovered prior to a future sale of the property (Kun-
reuther and Michel-Kerjan, 2011).
Social norms that encourage greater use of energy efficient technology
at the household level can also encourage manufacturers to invest in
the R&D for developing new energy efficient technologies and public
sector actions such as well-enforced standards of energy efficiency as
part of building sale requirements, (Dietz etal., 2013).
2�6�5�4 Adaptation and vulnerability reduction
Compared to mitigation measures, investments in adaptation appear
to be more sensitive to uncertainties in the local impacts associated
with the damage costs of climate change. This is not surprising for
two reasons. First, while both mitigation and adaptation may result in
lower local damage costs associated with climate impacts, the benefits
of adaptation flow directly and locally from the actions taken (Prato,
2008). Mitigation measures in one region or country, by contrast,
deliver benefits that are global; however, they are contingent on the
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actions of people in other places and in the future, rendering their local
benefits more uncertain. One cannot simply equate marginal local
damage costs with marginal mitigation costs, and hence the impor-
tance of uncertainty with respect to the local damage costs is dimin-
ished (Webster etal., 2003).
Second, politically negotiated mitigation targets, such as the 2 °C
threshold appear to have been determined by what is feasible and
affordable in terms of the pace of technological diffusion, rather than
by an optimization of mitigation costs and benefits (Hasselmann
etal., 2003; Baker etal., 2008; Hasselmann and Barker, 2008). Hence,
mitigation actions taken to achieve a temperature target would not
be changed if the damage costs (local or global) were found to be
somewhat higher or lower. This implies that mitigation measures will
be insensitive to uncertainty of these costs associated with climate
change. Adaptation decisions, in contrast, face fewer political and
technical constraints, and hence can more closely track what is needed
in order to minimize local expected costs and hence will be more sensi-
tive to the uncertainties surrounding future damage costs from climate
change (Patt etal., 2007, 2009).
There are two situations where decisions on adaptation policies and
actions may be largely insensitive to uncertainties about the poten-
tial impacts of climate change on future damage. The first is where
adaptation is constrained by the availability of finance, such as inter-
national development assistance. Studies by the World Bank, OECD,
and other international organizations have estimated the financing
needs for adaptation in developing countries to be far larger than
funds currently available (Agrawala and Fankhauser, 2008; World
Bank, 2010; Patt etal., 2010). In this case, adaptation actions are
determined by decisions with respect to the allocation of available
funds in competing regions rather than the local impacts of climate
change on future damage (Klein etal., 2007; Hulme et al., 2011).
Funding decisions and political constraints at the national level can
also constrain adaptation so that choices no longer are sensitive to
uncertainties with respect to local impacts (Dessai and Hulme, 2004,
2007).
The other situation is where adaptation is severely constrained by cul-
tural norms and / or a lack of local knowledge and analytic skill as to
what actions can be taken (Brooks etal., 2005; Füssel and Klein, 2006;
O’Brien, 2009; Jones and Boyd, 2011). In this case, adaptive capacity
could be improved through investments in education, development of
local financial institutions and property rights systems, women’s rights,
and other broad-based forms of poverty alleviation. There is a grow-
ing literature to suggest that such policies bring substantial benefits in
the face of climate change that are relatively insensitive to the precise
nature and extent of local climate impacts (Folke etal., 2002; World
Bank, 2010; Polasky etal., 2011). These policies are designed to reduce
these countries’ vulnerability to a wide range of potential risks rather
than focusing on the impacts of climate change (Thornton etal., 2008;
Eakin and Patt, 2011).
2�6�6 Public support and opposition to climate
policy
In this section, we review what is known about public support or
opposition to climate policy, climate-related infrastructure, and cli-
mate science. In all three cases, a critical issue is the role that percep-
tions of risks and uncertainties play in shaping support or opposition.
Hence, the material presented here complements the discussion of
perceptions of climate change risks and uncertainties (see Section
2.4.6). Policy discussions on particular technologies often revolve
around the health and safety risks associated with technology
options, transition pathways, and systems such as nuclear energy
(Pidgeon etal., 2008; Whitfield etal., 2009), coal combustion (Car-
michael etal., 2009; Hill etal., 2009), and underground carbon stor-
age (Itaoka etal., 2009; Shackley etal., 2009). There are also risks to
national energy security that have given rise to political discussions
advocating the substitution of domestically produced renewable
energy for imported fossil fuels (Eaves and Eaves, 2007; Lilliestam
and Ellenbeck, 2011).
2�6�6�1 Popular support for climate policy
There is substantial empirical evidence that people’s support or oppo-
sition to proposed climate policy measures is determined primarily by
emotional factors and their past experience rather than explicit cal-
culations as to whether the personal benefits outweigh the personal
costs. A national survey in the United States found that people’s sup-
port for climate policy also depended on cultural factors, with region-
ally differentiated worldviews playing an important role (Leiserowitz,
2006), as did a cross-national comparison of Britain and the United
States (Lorenzoni and Pidgeon, 2006), and studies comparing develop-
ing with developed countries (Vignola etal., 2012).
One of the major determinants of popular support for climate policy
is whether people have an underlying belief that climate change is
dangerous. This concern can be influenced by both cultural factors and
the methods of communication (Smith, 2005; Pidgeon and Fischhoff,
2011). Leiserowitz (2005) found a great deal of heterogeneity linked
to cultural effects with respect to the perception of climate change
in the United States. The use of language used to describe climate
change — such as the distinction between ‘climate change’ and ‘global
warming’ — play a role in influencing perceptions of risk, as well as
considerations of immediate and local impacts (Lorenzoni etal., 2006).
The portrayal of uncertainties and disagreements with respect to cli-
mate impacts was found to have a weak effect on whether people per-
ceived the impacts as serious, but a strong effect on whether they felt
that the impacts deserved policy intervention (Patt, 2007). Studies in
China (Wang etal., 2012) and Austria (Damm etal., 2013) found that
people’s acceptance of climate-related policies was related to their
underlying perceptions of risk but also to their beliefs about govern-
ment responsibility.
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An important question related to climate change communication is
whether the popular reporting of climate change through disaster sce-
narios has the effect of energizing people to support aggressive policy
intervention, or to become dismissive of the problem. A study examin-
ing responses to fictionalized disaster scenarios found them to have
differential effects on perceptions and support for policy. They reduced
people’s expectation of the local impacts, while increasing their sup-
port for global intervention (Lowe etal., 2006). Other studies found
interactive effects: those with a low awareness of climate change
became concerned about being exposed to disaster scenarios, while
those with a high awareness of climate change were dismissive of the
possible impacts (Schiermeier, 2004).
Finally, the extent to which people believe it is possible to actually
influence the future appears to be a major determinant of their support
for both individual and collective actions to respond to climate change.
In the case of local climate adaptation, psychological variables associ-
ated with self-empowerment were found to have played a much larger
role in influencing individual behaviour than variables associated with
economic and financial ability (Grothmann and Patt, 2005; Grothmann
and Reusswig, 2006). With respect to mitigation policy, perceptions
concerning the barriers to effective mitigation and beliefs that it was
possible to respond to climate change were found to be important
determinants of popular support (Lorenzoni etal., 2007).
2�6�6�2 Local support and opposition to infrastructure
projects
The issue of local support or opposition to infrastructure projects in
implementing climate policy is related to the role that perceived tech-
nological risks play in the process. This has been especially important
with respect to nuclear energy, but is of increasing concern for carbon
storage and renewable energy projects, and has become a major issue
when considering expansion of low carbon energy technologies (Ellis
etal., 2007; Van Alphen etal., 2007; Zoellner etal., 2008).
In the case of renewable energy technologies, a number of factors
appear to influence the level of public support or opposition, factors
that align well with a behavioural model in which emotional responses
are highly contextual. One such factor is the relationship between proj-
ect developers and local residents. Musall and Kuik (2011) compared
two wind projects, where residents feared negative visual impacts. They
found that their fear diminished, and public support for the projects
increased when there was co-ownership of the development by the local
community. A second factor is the degree of transparency surrounding
project development. Dowd et al. (2011) investigated perceived risks
associated with geothermal projects in Australia. Using a survey instru-
ment, they found that early, transparent communication of geothermal
technology and risks tended to increase levels of public support.
A third such factor is the perception of economic costs and benefits
that go hand-in-hand with the perceived environmental risks. Zoellner
etal. (2008) examined public acceptance of three renewable technolo-
gies (grid-connected PV, biomass, and wind) and found that perceived
economic risks associated with higher energy prices were the largest
predictor of acceptance. Concerns over local environmental impacts,
including visual impacts, were of concern where the perceived eco-
nomic risks were high. Breukers and Wolsink (2007) also found that
that the visual impact of wind turbines was the dominant factor in
explaining opposition against wind farms. Their study suggests that
public animosity towards a wind farm is partly reinforced by the plan-
ning procedure itself, such as when stakeholders perceive that norms
of procedural justice are not being followed.
Many studies have assessed the risks and examined local support for
carbon dioxide capture and storage (CCS). According to Ha-Duong
etal. (1997), the health and safety risks associated with carbon dioxide
capture and transportation technologies differ across causal pathways
but are similar in magnitude to technologies currently supported by
the fossil-fuel industry. Using natural analogues, Roberts etal. (2011)
concluded that the health risks of natural CO
2
seepage in Italy was
significantly lower than many socially accepted risks. For example, it
were three orders of magnitude lower than the probability of being
struck by lightning.
Despite these risk assessments, there is mixed evidence of public
acceptance of CO
2
storage. For example, a storage research project was
authorized in Lacq, France, but another was halted in Barendreich, The
Netherlands due to public opposition. On the other hand, Van Alphen
etal. (2007) evaluated the concerns with CCS among important stake-
holders, including government, industry, and NGO representatives and
found support if the facility could be shown to have a low probability
of leakage and was viewed as a temporary measure.
Wallquist et al. (2012) used conjoint analysis to interpret a Swiss
survey on the acceptability of CCS and found that concerns over
local risks and impacts dominated the fears of the long-term climate
impacts of leakage. The local concerns were less severe, and the public
acceptance higher, for CCS projects combined with biomass combus-
tion, suggesting that positive feelings about removing CO
2
from the
atmosphere, rather than simply preventing its emission into the atmo-
sphere, influences perceptions of local risks. Terwel etal. (2011) found
that support for CCS varied as a function of the stakeholders promot-
ing and opposing it, in a manner similar to the debate on renewable
energy. Hence, there was greater support of CCS when its promoters
were perceived to be acting in the public interest rather than purely for
profit. Those opposing CCS were less likely to succeed when they were
perceived to be acting to protect their own economic interests, such as
property values, rather than focusing on environmental quality and the
public good.
In the period between the publication of AR4 and the accident at
the Fukushima power plant in Japan in March 2011, the riskiness of
nuclear power as a climate mitigation option has received increasing
attention. Socolow and Glaser (2009) highlight the urgency of taking
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steps to reduce these risks, primarily by ensuring that nuclear fuels
and waste materials are not used for weapons production. A number
of papers examine the public’s perceived risks of nuclear power. In the
United States, Whitfield etal. (2009) found risk perceptions to be fairly
stable over time, with those people expressing confidence in ‘tradi-
tional values’ perceiving nuclear power to be less risky than others.
In the United Kingdom, Pidgeon etal. (2008) found a willingness to
accept the risks of nuclear power when it was framed as a means of
reducing the risks of climate change, but that this willingness largely
dissipated when nuclear power was suggested as an alternative to
renewable energy for accomplishing this same objective.
2.7 Gaps in knowledge
and data
The interface between science and policy is affected by epistemic
uncertainty or uncertainty due to lack of information or knowledge for
characterizing phenomena. Below we characterize suggested areas for
future research that may enable us to reduce epistemic uncertainty.
Perceptions and responses to risk and uncertainty:
• Examine cross-cultural differences in human perception and reac-
tion to climate change and response options.
• Understand the rebound effect induced by adopting mitiga-
tion measures for reducing the impact of climate change (e. g.,
increased driving when switching to a more fuel efficient car).
• Consider the design of long-term mitigation and adaptation strat-
egies coupled with short-term economic incentives to overcome
myopic behaviour (e. g., loans for investing in energy efficient tech-
nologies so yearly payments are lower than the reduction in the
annual energy bill).
• Encourage deliberative thinking in the design of policies to over-
come biases such as a preference for the current state of affairs or
business-as-usual.
• Understand judgment and choice processes of key decision makers
in firms and policymakers, especially in a climate change response
context.
• Use descriptive models and empirical studies to design strategies
for climate change negotiations and implementation of treaties.
Tools and decision aids for improving choices related to climate
change:
• Characterize the likelihood of extreme events and examine their
impact on the design of climate change policies.
• Study how robust decision making can be used in designing cli-
mate policy options when there is uncertainty with respect to the
likelihood of climate change and its impacts.
• Examine how integrated assessment models can quantify the
value of new climate observing systems.
• Empirically study how decision makers could employ intuitive
and deliberative thinking to improve decisions and climate policy
choices.
• Study the effectiveness of experiential methods like simulations,
games, and movies in improving public understanding and percep-
tion of climate change processes.
• Consider the role of structured expert judgment in characterizing
the nature of uncertainties associated with climate change and the
design of mitigation and adaptation policies for addressing this risk.
Managing uncertainty risk and learning:
• Exploit the effectiveness of social norms in promoting mitigation
and adaptation.
• Quantify the environmental and societal risks associated with new
technologies.
• Consider the special challenges faced by developing countries in
dealing with risk and uncertainty with respect to climate change
policies.
• Measure investor rankings of different risks associated with new
technologies.
• Examine impact of government policy on mitigation decisions by
firms and households.
• Determine what risks and uncertainties matter the most in devel-
oping policy instruments for dealing with climate change.
• Examine the risks to energy systems, energy markets, and the secu-
rity of energy supply stemming from mitigation policies.
• Integrate analysis of the effects of interrelated policy decisions,
such as how much to mitigate, what policy instruments to use for
promoting climate change mitigation, and adaptation investment
under conditions of risk and uncertainty.
2.8 Frequently Asked
Questions
FAQ 2�1 When is uncertainty a reason to wait
and learn rather than acting now in
relation to climate policy and risk
management strategies? [Section 2�6�3]
Faced with uncertainty, policymakers may have a reason to wait and
learn before taking a particular action rather than taking the action
now. Waiting and learning is desirable when external events are likely
to generate new information of sufficient importance as to suggest
that the planned action would be unwise. Uncertainty may not be a
reason to delay when the action itself generates new information and
knowledge.
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Uncertainty may also be a reason to avoid actions that are irreversible
and / or have lock-in effects, such as making long-term investments in
fossil-fuel based energy systems when climate outcomes are uncertain.
This behaviour would reflect the precautionary principle for not under-
taking some measures or activities.
While the above criteria are fairly easy to understand, their applica-
tion can be complicated because a number of uncertainties relevant
to a given decision may reinforce each other or may partially cancel
each other out (e. g., optimistic estimates of technological change may
offset pessimistic estimates of climate damages). Different interested
parties may reach different conclusions as to whether external infor-
mation is likely or not to be of sufficient importance as to render the
original action / inaction regrettable.
A large number of studies examine the act-now-or-wait-and-see ques-
tion in the context of climate change mitigation. So far, most of these
analyses have used integrated assessment models (IAMs). At the
national level, these studies examine policy strategies and instruments
to achieve mitigation targets; at the firm or individual level the studies
examine whether one should invest in a particular technology.
A truly integrated analysis of the effects of multiple types of uncer-
tainty on interrelated policy decisions, such as how much to mitigate,
with what policy instruments, promoting what investments, has yet to
be conducted. The probabilistic information needed to support such an
analysis is currently not available.
FAQ 2�2 How can behavioural responses and
tools for improving decision making
impact on climate change policy?
[
Section 2�4]
The choice of climate change policies can benefit from examining the
perceptions and responses of relevant stakeholders. Empirical evidence
indicates decision makers such as firms and households tend to place
undue weight on short-run outcomes. Thus, high upfront costs make
them reluctant to invest in mitigation or adaptation measures. Consis-
tent with the theory of loss aversion, investment costs and their associ-
ated risks have been shown to be of greater importance in decisions
to fund projects that mitigate climate change than focusing on the
expected returns associated with the investment.
Policy instruments (e. g., long-term loans) that acknowledge these
behavioural biases and spread upfront costs over time so that they
yield net benefits in the short-run have been shown to perform quite
well. In this context, policies that make investments relatively risk free,
such as feed-in tariffs, are more likely to stimulate new technology
than those that focus on increasing the expected price such as cap-
and-trade systems.
Human responses to climate change risks and uncertainties can also
indicate a failure to put adequate weight on worst-case scenarios.
Consideration of the full range of behavioural responses to informa-
tion will enable policymakers to more effectively communicate cli-
mate change risks to stakeholders and to design decision aids and
climate change policies that are more likely to be accepted and imple-
mented.
FAQ 2�3 How does the presence of uncertainty
affect the choice of policy instruments?
[Section 2�6�5]
Many climate policy instruments are designed to provide decision
makers at different levels (e. g., households, firms, industry asso-
ciations, guilds) with positive incentives (e. g., subsidies) or penalties
(e. g., fines) to incentivize them to take mitigation actions. The impact
of these incentives on the behaviour of the relevant decision makers
depends on the form and timing of these policy instruments.
Instruments such as carbon taxes that are designed to increase the
cost of burning fossil fuels rely on decision makers to develop expec-
tations about future trajectories of fuel prices and other economic
conditions. As uncertainty in these conditions increases, the respon-
siveness of economic agents decreases. On the other hand, invest-
ment subsidies and technology standards provide immediate incen-
tives to change behaviour, and are less sensitive to long-term market
uncertainty. Feed-in tariffs allow investors to lock in a given return on
investment, and so may be effective even when market uncertainty is
high.
FAQ 2�4 What are the uncertainties and risks
that are of particular importance to
climate policy in developing countries?
[Box 2�1]
Developing countries are often more sensitive to climate risks, such as
drought or coastal flooding, because of their greater economic reliance
on climate-sensitive primary activities, and because of inadequate
infrastructure, finance, and other enablers of successful adaptation and
mitigation. Since AR4, research on relevant risks and uncertainties in
developing countries has progressed substantially, offering results in
two main areas.
Studies have demonstrated how uncertainties often place low carbon
energy sources at an economic disadvantage, especially in developing
countries. The performance and reliability of new technologies may
be less certain in developing countries than in industrialized coun-
tries because they could be unsuited to the local context and needs.
Other reasons for uncertain performance and reliability could be due
191191
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
to poor manufacturing, a lack of adequate testing in hot or dusty envi-
ronments, or limited local capacity to maintain and repair equipment.
Moreover, a number of factors associated with economic, political,
and regulatory uncertainty result in much higher real interest rates in
developing countries than in the developed world. This creates a disin-
centive to invest in technologies with high upfront but lower operating
costs, such as renewable energy, compared to fossil-fuel based energy
infrastructure.
Given the economic disadvantage of low carbon energy sources,
important risk tradeoffs often need to be considered. On the one hand,
low-carbon technologies can reduce risks to health, safety, and the
environment, such as when people replace the burning of biomass for
cooking with modern and efficient cooking stoves. But on the other
hand, low-carbon modern energy is often more expensive than its
higher-carbon alternatives. There are however, some opportunities for
win-win outcomes on economic and risk grounds, such as in the case
of off-grid solar power.
192192
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
References
Abrahamse W�, L� Steg, C� Vlek, and T� Rothengatter (2005)� A review of inter-
vention studies aimed at household energy conservation. Journal of Environ-
mental Psychology 25, 273 – 291.
ACI (2004)� Impacts of a Warming Arctic — Arctic Climate Impact Assessment.
Impacts of a Warming Arctic — Arctic Climate Impact Assessment, by Arctic
Climate Impact Assessment, Pp. 144. ISBN 0521617782. Cambridge, UK: Cam-
bridge University Press, December 2004.
Ackerman F�, E� A� Stanton, and R� Bueno (2010)� Fat tails, exponents, extreme
uncertainty: Simulating catastrophe in DICE. Ecological Economics 69,
1657 – 1665.
Ackerman F�, E� A� Stanton, and R� Bueno (2013)� Epstein – Zin Utility in DICE:
Is Risk Aversion Irrelevant to Climate Policy? Environmental and Resource
Economics 56, 73 – 84. doi: 10.1007 / s10640 - 013 - 9645-z, ISSN: 0924-6460,
1573 – 1502.
Agrawala S�, and S� Fankhauser (2008)� Economic Aspects of Adaptation to Cli-
mate Change: Costs, Benefits and Policy Instruments. OECD Publishing, 139 pp.
ISBN: 9789264046030.
Alberola E�, J� Chevallier, and B� Cheze (2008)� Price drivers and structural
breaks in European carbon prices 2005 – 2007. Energy Policy 36, 787 – 797. doi:
10.1016 / j.enpol.2007.10.029.
Alcott H� (2011)� Social norms and energy conservation. Journal of Public Econom-
ics 95, 1082 – 1095.
Ale B� J� M�, L� J� Bellamy, R� Van der Boom, J� Cooper, R� M� Cooke, L� H� J� Goos-
sens, A� R� Hale, D� Kurowicka, O� Morales, A� L� C� Roelen, and others (2009)�
Further development of a Causal model for Air Transport Safety (CATS): Building
the mathematical heart. Reliability Engineering & System Safety 94, 1433 – 1441.
Allais M� (1953)� Le comportement de l’homme rationel devant le risque. Econo-
metrica 21, 503 – 546.
Van Alphen K�, Q� Van Voorst Tot Voorst, M� P� Hekkert, and R� E� H� Smits
(2007)� Societal acceptance of carbon capture and storage technologies.
Energy Policy 35, 4368 – 4380.
America’s Climate Choices: Panel on Advancing the Science of Climate
Change; National Research Council (2010)� Advancing the Science of Cli-
mate Change. The National Academies Press, Washington, D. C., 528 pp. ISBN:
0309145880.
Andonova L�, M� Betsill, and H� Bulkeley (2009)� Transnational climate gover-
nance. Global Environmental Politics 9, 52 – 73. doi: 10.1162 / glep.2009.9.2.52.
Armitage D� (2011)� Co-management and the co-production of knowledge: Learn-
ing to adapt in Canada’s Arctic. Symposium on Social Theory and the Environ-
ment in the New World (dis)Order, 21(3), 995 – 1004.
Aspinall W� P� (1996)� Structured elicitation of expert judgment for probabilistic
hazard and risk assessment in volcanic eruptions. In: Mader, H. M., Coles, S. G.,
Connor, C. B. & Connor, L. J. (eds.) Statistics in Volcanology. Special Publications
of IAVCEI. Geological Society, London, pp. 15 – 30.
Aspinall W� P� (2010)� A route to more tractable expert advice. Nature 463,
294 – 295. doi: 10.1038 / 463294a, ISSN: 0028-0836.
Athanassoglou S�, and A� Xepapadeas (2011)� Pollution control with uncertain
stock dynamics: When, and how, to be precautious. Journal of Environmental
Economics and Management 63, 304 – 320.
Attari S� Z�, M� L� DeKay, C� I� Davidson, and W� B� de Bruin (2010)� Public per-
ceptions of energy consumption and savings. Proceedings of the National Acad-
emy of Sciences 107, 16054 – 16059.
Axelrod M� (2011)� Climate Change and Global Fisheries Management: Linking
Issues to Protect Ecosystems or to Save Political Interests? Global Environmen-
tal Politics 11, 64 – 84. doi: 10.1162 / GLEP_a_00069.
Azar C�, and K� Lindgren (2003)� Catastrophic Events and Stochastic Cost-
benefit Analysis of Climate Change. Climatic Change 56, 245 – 255. doi:
10.1023 / A:1021743622080, ISSN: 0165-0009.
Bahn O�, A� Haurie, and R� Malhamé (2008)� A stochastic control model for opti-
mal timing of climate policies. Automatica 44, 1545 – 1558. doi: 10.1016 / j.auto-
matica.2008.03.004, ISSN: 0005-1098.
Baker E� (2005)� Uncertainty and learning in a strategic environment: Global cli-
mate change. Resource and Energy Economics 27, 19 – 40.
Baker E�, and K� Adu-Bonnah (2008)� Investment in risky R&D programs in the
face of climate uncertainty. Energy Economics 30, 465 – 486. doi: 10.1016 / j.
eneco.2006.10.003, ISSN: 0140-9883.
Baker E�, L� Clarke, and E� Shittu (2008)� Technical change and the marginal cost
of abatement. Energy Economics 30, 2799 – 2816.
Baker E�, L� Clarke, and J� Weyant (2006)� Optimal Technology R&D in the Face of
Climate Uncertainty. Climatic Change 78, 157 – 179. doi: 10.1007 / s10584 - 006 -
9092 - 8, ISSN: 0165-0009.
Baker E�, and J� M� Keisler (2011)� Cellulosic biofuels: Expert views on prospects
for advancement. Energy 36, 595 – 605.
Baker E�, and E� Shittu (2006)� Profit-maximizing R&D in response to a random
carbon tax. Resource and Energy Economics 28, 160 – 180. doi: 10.1016 / j.rese-
neeco.2005.08.002, ISSN: 0928-7655.
Baker E�, and E� Shittu (2008)� Uncertainty and endogenous technical change in
climate policy models. Energy Economics 30, 2817 – 2828.
Bamber J�, and W� Aspinall (2013)� An expert judgement assessment of future sea
level rise from the ice sheets. Nature Climate Change 3, 424 – 427. Available at:
http: / / cat.inist.fr / ?aModele=afficheN&cpsidt=27221118.
Baranzini A�, M� Chesney, and J� Morisset (2003)� The impact of possible climate
catastrophes on global warming policy. Energy Policy 31, 691 – 701. doi: 10.101
6 / S0301 - 4215(02)00101 - 5, ISSN: 0301-4215.
Barbose G�, R� Wiser, A� Phadke, and C� Goldman (2008)� Managing carbon reg-
ulatory risk in utility resource planning: Current practices in the Western United
States. Energy Policy 36, 3300 – 3311. doi: 10.1016 / j.enpol.2008.04.023, ISSN:
0301-4215.
Barham B�, J� P� Chavas, D� Fitz, V� Rios Salas, and L� Schechter (2014)� The Roles
of Risk and Ambiguity in Technology Adoption. Journal of Economic Behavior &
Organization 97, 204 – 218.
Barradale M� J� (2010)� Impact of public policy uncertainty on renewable energy
investment: Wind power and the production tax credit. Energy Policy 38,
7698 – 7709. Available at: http: / / www. sciencedirect. com / science / article / pii /
S0301421510006361.
Barreto I�, and D� Patient (2013)� Toward a theory of intraorganizational atten-
tion based on desirability and feasibility factors. Strategic Management Journal,
34(6), 687 – 703.
Barrett S� (1994)� Self-enforcing international environmental agreements. Oxford
Economic Papers 46, 878 – 894.
Barrett S� (2013)� Climate treaties and approaching catastrophes. Journal of Envi-
ronmental Economics and Management 66, 235 – 250.
193193
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Barrett S�, and A� Dannenberg (2012)� Climate negotiations under scien-
tific uncertainty. Proceedings of the National Academy of Sciences 109,
17372 – 17376. doi: 10.1073 / pnas.1208417109, ISSN: 0027-8424, 1091 – 6490.
Barry J�, G� Ellis, and C� Robinson (2008)� Cool Rationalities and Hot Air: A Rhe-
torical Approach to Understanding Debates on Renewable Energy. Global Envi-
ronmental Politics 8, 67 – 98. doi: 10.1162 / glep.2008.8.2.67, ISSN: 1526-3800.
Battaglini A�, J� Lilliestam, A� Haas, and A� Patt (2009)� Development of
SuperSmart Grids for a more efficient utilisation of electricity from renewable
sources. Journal of Cleaner Production 17, 911 – 918.
Baudry M� (2000)� Joint management of emission abatement and technological
innovation for stock externalities. Environmental and Resource Economics 16,
161 – 183. doi: 10.1023 / A:1008363207732, ISSN: 0924-6460.
BBC World Service Trust (2009)� Research Briefing Ethiopia. BBC World Ser-
vice Trust, London, UK. Available at: http: / / r4d.dfid.gov.uk / PDF / Outputs /
MediaBroad / climatebrief-ethopia_web.pdf.
Beck U� (1992)� Risk Society: Towards a New Modernity. Sage Publications Ltd, 272
pp. ISBN: 0803983468.
Berkes F�, J� Colding, and C� Folke (2000)� Rediscovery of traditional ecological
knowledge as adaptive management. Ecological Applications 10, 1251 – 1262.
Berkes F�, and D� Jolly (2001)� Adapting to climate change: social-ecological resil-
ience in a Canadian western Arctic community. Conservation Ecology 5(2), 18.
Betsill M�, and M� J� Hoffmann (2011)� The contours of “cap and trade”: the
evolution of emissions trading systems for greenhouse gases. Review of Policy
Research 28, 83 – 106.
Blackstock J� J�, and J� C� Long (2010)� The politics of geoengineering. Science 327,
527 – 527. Available at: http: / / www. sciencemag. org / content / 327 / 5965 / 527.
short.
Blanco M� I�, and G� Rodrigues (2008)� Can the future EU ETS support wind
energy investments? Energy Policy 36.4, 1509 – 1520.
Blanford G� J� (2009)� R&D investment strategy for climate change. Energy Eco-
nomics 31, Supplement 1, S27 – S36. doi: 10.1016 / j.eneco.2008.03.010, ISSN:
0140-9883.
Blok K� (2006)� Special issue: Renewable energy policies in the European Union.
Energy Policy 34, 251 – 375.
Blyth W�, R� Bradley, D� Bunn, C� Clarke, T� Wilson, and M� Yang (2007)� Invest-
ment risks under uncertain climate change policy. Energy Policy 35, 5766 – 5773.
doi: 16 / j.enpol.2007.05.030, ISSN: 0301-4215.
Blyth W�, and D� Bunn (2011)� Coevolution of policy, market and technical price
risks in the EU ETS. Energy Policy 39, 4578 – 4593.
Blyth W�, D� Bunn, J� Kettunen, and T� Wilson (2009)� Policy interactions, risk
and price formation in carbon markets. Energy Policy 37, 5192 – 5207. doi:
10.1016 / j.enpol.2009.07.042, ISSN: 0301-4215.
Boardman A� E�, D� H� Greenberg, A� R� Vining, and D� L� Weimer (2005)� Cost
Benefit Analysis: Concepts and Practice. Prentice Hall, ISBN: 0131435833.
De Boer J�, J� A� Wardekker, and J� P� Van Der Sluijs (2010)� Frame-based guide
to situated decision-making on climate change. Global Environmental Change
20, 502 – 510.
Bord R� J�, R� E� O’Connor, and A� Fisher (2000)� In what sense does the public
need to understand global climate change? Public Understanding of Science 9,
205 – 218. Available at: http: / / pus.sagepub.com / content / 9 / 3 / 205.short.
Bosetti V�, C� Carraro, A� Sgobbi, and M� Tavoni (2009)� Delayed action and
uncertain stabilisation targets. How much will the delay cost? Climatic Change
96, 299 – 312. doi: 10.1007 / s10584 - 009 - 9630 - 2, ISSN: 0165-0009.
Bosetti V�, and M� Tavoni (2009)� Uncertain R&D, backstop technology and GHGs
stabilization. Energy Economics 31, S18 – S26.
Bostrom A�, M� G� Morgan, B� Fischhoff, and D� Read (1994)� What do peo-
ple know about global climate change? 1. Mental models. Risk Analysis 14,
959 – 970. doi: 10.1111 / j.1539 - 6924.1994.tb00065.x, ISSN: 1539-6924.
Böttcher H�, K� Eisbrenner, S� Fritz, G� Kindermann, F� Kraxner, I� McCallum,
and M� Obersteiner (2009)� An assessment of monitoring requirements and
costs of “Reduced Emissions from Deforestation and Degradation.” Carbon
Balance and Management 4, 7.
Böttcher H�, A� Freibauer, M� Obersteiner, and E� D� Schulze (2008)� Uncertainty
analysis of climate change mitigation options in the forestry sector using a
generic carbon budget model. Ecological Modelling 213, 45 – 62.
Bradfield R�, G� Wright, G� Burt, G� Cairns, and K� Van Der Heijden (2005)� The
origins and evolution of scenario techniques in long range business planning.
Futures 37, 795 – 812. Available at: http: / / www. sciencedirect. com / science /
article / pii / S0016328705000042.
Brent R� J� (2006)� Applied Cost-Benefit Analysis. Edward Elgar Publishing.
Breukers S�, and M� Wolsink (2007)� Wind power implementation in chang-
ing institutional landscapes: An international comparison. Energy Policy 35,
2737 – 2750. ISSN: 0301-4215.
Brooks N�, W� N� Adger, and P� M� Kelly (2005)� The determinants of vulnerability
and adaptive capacity at the national level and the implications for adaptation.
Global Environmental Change Part A 15, 151 – 163.
Bruckner T�, and K� Zickfeld (2008)� Inverse Integrated Assessment of Climate
Change: the Guard-rail Approach. International Conference on Policy Modeling
(EcoMod2008).
Brulle R� J�, J� Carmichael, and J� C� Jenkins (2012)� Shifting public opinion on
climate change: an empirical assessment of factors influencing concern over
climate change in the U. S., 2002 – 2010. Climatic Change 114, 169 – 188. doi:
10.1007 / s10584 - 012 - 0403-y, ISSN: 0165-0009, 1573 – 1480.
Bucki M�, D� Cuypers, P� Mayaux, F� Achard, C� Estreguil, and G� Grassi (2012)�
Assessing REDD+ performance of countries with low monitoring capacities: the
matrix approach. Environmental Research Letters 7, 014031. doi: 10.1088 / 174
8 - 9326 / 7 / 1 / 014031, ISSN: 1748-9326.
Budescu D� V�, S� Broomell, and H�-H� Por (2009)� Improving communication of
uncertainty in the reports of the Intergovernmental Panel on Climate Change.
Psychological Science 20, 299 – 308. doi: 10.1111 / j.1467 - 9280.2009.02284.x.
Bulkeley H� (2010)� Cities and the governing of climate change. Annual Review of
Environment and Resources 35, 229 – 253.
Bürer M� J�, and R� Wüstenhagen (2009)� Which renewable energy policy is
a venture capitalist’s best friend? Empirical evidence from a survey of inter-
national cleantech investors. Energy Policy 37, 4997 – 5006. doi: 10.1016 / j.
enpol.2009.06.071, ISSN: 0301-4215.
Burtraw D�, K� Palmer, and D� Kahn (2010)� A symmetric safety valve. Energy
Policy 38, 4921 – 4932. doi: 10.1016 / j.enpol.2010.03.068, ISSN: 0301-4215.
Butler L�, and K� Neuhoff (2008)� Comparison of feed-in tariff, quota and auc-
tion mechanisms to support wind power development. Renewable Energy 33,
1854 – 1867. doi: 10.1016 / j.renene.2007.10.008, ISSN: 0960-1481.
Buys P�, U� Deichmann, C� Meisner, T� Ton-That, and D� Wheeler (2009)� Country
stakes in climate change negotiations: two dimensions of vulnerability. Climate
Policy 9, 288 – 305.
194194
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Cabantous L�, D� Hilton, H� Kunreuther, and E� Michel-Kerjan (2011)� Is impre-
cise knowledge better than conflicting expertise? Evidence from insurers’ deci-
sions in the United States. Journal of Risk and Uncertainty 42, 211 – 232. Avail-
able at: http: / / link.springer.com / article / 10.1007 / s11166 - 011 - 9117 - 1.
Cabré M� M� (2011)� Issue-linkages to climate change measured through NGO
participation in the UNFCCC. Global Environmental Politics 11, 10 – 22. doi:
10.1162 / GLEP_a_00066, ISSN: 1526-3800.
Camerer C� F� (2000)� Prospect Theory in the wild. In: Choice, Values, and Frames. D.
Kahneman, A. Tversky, eds. Cambridge University Press, New York.
Camerer C� F�, and H� Kunreuther (1989)� Decision processes for low probabil-
ity events: Policy implications. Journal of Policy Analysis and Management 8,
565 – 592. doi: 10.2307 / 3325045, ISSN: 1520-6688.
Caney S� (2011)� Climate Change, Energy Rights and Equality. Climate Change. In:
The Ethics of Global Climate Change. D. Arnold, (ed.), Cambridge University
Press, pp. 77 – 103.
De Canio S� J�, and A� Fremstad (2013)� Game theory and climate diplomacy.
Ecological Economics 85, 177 – 187. Available at: http: / / www. sciencedirect.
com / science / article / pii / S0921800911001698.
Carmichael G� R�, B� Adhikary, S� Kulkarni, A� D’Allura, Y� Tang, D� Streets, Q�
Zhang, T� C� Bond, V� Ramanathan, A� Jamroensan, and P� Marrapu (2009)�
Asian aerosols: Current and year 2030 distributions and implications to human
health and regional climate change. Environ. Sci. Technol. 43, 5811 – 5817. doi:
10.1021 / es8036803, ISSN: 0013-936X.
Carraro C�, V� Bosetti, E� De Cian, R� Duval, E� Massetti, and M� Tavoni (2009)�
The incentives to participate in and the stability of international climate coali-
tions: a game theoretic approach using the WITCH Model. Working Papers.
Castro P�, and A� Michaelowa (2011)� Would preferential access measures be suf-
ficient to overcome current barriers to CDM projects in least developed coun-
tries? Climate and Development 3, 123 – 142.
Chan G� L�, L� Diaz Anadon, M� Chan, and A� Lee (2010)� Expert Elicitation of
Cost, Performance, and RD&D Budgets for Coal Power with CCS. Working Paper,
Energy Technology Innovation Policy Research Group, Belfer Center for Science
and International Affairs, Harvard Kennedy School.
Charlesworth M�, and C� Okereke (2010)� Policy responses to rapid climate
change: An epistemological critique of dominant approaches. Global Envi-
ronmental Change 20, 121 – 129. Available at: http: / / www. sciencedirect.
com / science / article / pii / S0959378009000727.
Charnes A�, and W� W� Cooper (1959)� Chance-Constrained Programming. Man-
agement Science 6, 73 – 79. doi: 10.1287 / mnsc.6.1.73.
Chevallier J� (2009)� Carbon futures and macroeconomic risk factors: A view from
the EU ETS. Energy Economics 31, 614 – 625. doi: 10.1016 / j.eneco.2009.02.008,
ISSN: 0140-9883.
Chichilnisky G�, and G� Heal (1993)� Global environmental risks. The Journal of
Economic Perspectives 7, 65 – 86. ISSN: 0895-3309.
Ciriacy-Wantrup S� V� (1971)� The economics of environmental policy. Land Eco-
nomics 47, 36 – 45.
Clarke H� R�, and W� J� Reed (1994)� Consumption / pollution tradeoffs in an envi-
ronment vulnerable to pollution-related catastrophic collapse. Journal of Eco-
nomic Dynamics and Control 18, 991 – 1010. doi: 10.1016 / 0165 - 1889(94)900
42 - 6, ISSN: 0165-1889.
Cohen M� D�, J� G� March, and J� P� Olsen (1972)� A garbage can model of
organizational choice. Administrative Science Quarterly 17, 1 – 25. doi:
10.2307 / 2392088, ISSN: 0001-8392.
Collins H� M� (1985)� Changing Order: Replication and Induction in Scientific Prac-
tice. Sage Publications, 208 pp. ISBN: 9780803997578.
De Coninck H�, C� Fischer, R� G� Newell, and T� Ueno (2008)� International
technology-oriented agreements to address climate change. Energy Policy 36,
335 – 356.
Conliffe A� (2011)� Combating ineffectiveness: climate change bandwagoning and
the UN Convention to Combat Desertification. Global Environmental Politics
11, 44 – 63.
Cooke R� M� (1991)� Experts in Uncertainty: Opinion and Subjective Probability in
Science. Oxford University Press, USA, 321 pp.
Cooke R� M� (2012)� Model uncertainty in economic impacts of climate change:
Bernoulli versus Lotka Volterra dynamics. Integrated Environmental Assessment
and Management, n / a – n / a. doi: 10.1002 / ieam.1316, ISSN: 1551-3793.
Cooke R� M�, and L� L� H� J� Goossens (2008)� TU Delft expert judgment data base.
Reliability Engineering & System Safety 93, 657 – 674.
Cooper R� N� (1989)� International cooperation in public health as a prologue to
macroeconomic cooperation. In: Can Nations Agree? R. N. Cooper, etal. (eds.).
Brookings Institution, Washington, DC, pp. 178 – 254.
Corner A�, and U� Hahn (2009)� Evaluating science arguments: Evidence, uncer-
tainty, and argument strength. Journal of Experimental Psychology: Applied 15,
199. Available at: http: / / psycnet.apa.org / journals / xap / 15 / 3 / 199 / .
Cronin M�, C� Gonzalez, and J� Sterman (2009)� Why don’t well-educated adults
understand accumulation? A challenge to researchers, educators and citizens.
Organizational Behavior and Human Decision Processes 108, 116 – 130.
Crost B�, and C� P� Traeger (2013)� Optimal climate policy: Uncertainty vs Monte
Carlo. Economics Letters 120, 552 – 558. Available at: http: / / www. sciencedirect.
com / science / article / pii / S0165176513002565.
Cullen H� (2010)� The Weather of the Future: Heat Waves, Extreme Storms, and
Other Scenes from a Climate-Changed Planet. HarperCollins, 358 pp. ISBN:
9780061726880.
Curtright A� E�, M� G� Morgan, and D� W� Keith (2008)� Expert assessments of
future photovoltaic technologies. Environmental Science and Technology 42,
9031 – 9038.
Cyert R�, and J� March (1963)� A Behavioral Theory of the Firm. Prentice Hall,
Englewood Cliffs, 364 pp.
Damm A�, K� Eberhard, J� Sendzimir, and A� Patt (2013)� Perception of landslides
risk and responsibility: a case study in eastern Styria, Austria. Natural Hazards,
1 – 19. doi: 10.1007 / s11069 - 013 - 0694-y, ISSN: 0921-030X.
Dechezleprêtre A�, M� Glachant, I� Haščič, N� Johnstone, and Y� Ménière
(2011)� Invention and transfer of climate change – mitigation technologies: A
global analysis. Review of Environmental Economics and Policy 5, 109 – 130.
Dessai S�, and M� Hulme (2004)� Does climate adaptation policy need probabili-
ties. Climate Policy 4, 107 – 128.
Dessai S�, and M� Hulme (2007)� Assessing the robustness of adaptation decisions
to climate change uncertainties: A case study on water resources management
in the East of England. Global Environmental Change 17, 59 – 72. doi: 16 / j.
gloenvcha.2006.11.005, ISSN: 0959-3780.
Dietz S� (2011)� High impact, low probability? An empirical analysis of risk in the
economics of climate change. Climate Change 108 (3), 519 – 541.
Dietz T�, P� Stern, and E� U� Weber (2013)� Reducing carbon-based energy con-
sumption through changes in household behavior. Daedalus, 142.1: 78 – 79.
195195
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Ding D�, E� W� Maibach, X� Zhao, C� Roser-Renouf, and A� Leiserowitz (2011)�
Support for climate policy and societal action are linked to perceptions about sci-
entific agreement. Nature Climate Change 1, 462 – 466. Available at: http: / / www.
nature. com / nclimate / journal / vaop / ncurrent / full / nclimate1295.html.
Dinner I�, E� J� Johnson, D� G� Goldstein, and K� Liu (2011)� Partitioning default
effects: Why people choose not to choose. Journal of Experimental Psychology:
Applied 17, 332. Available at: http: / / psycnet.apa.org / journals / xap / 17 / 4 / 332 / .
Dodgson J� S�, M� Spackman, A� Pearman, and L� D� Phillips (2009)� Multi-Cri-
teria Analysis: A Manual. Department for Communities and Local Government:
London, Available at: http: / / eprints.lse.ac.uk / 12761 / 1 / Multi-criteria_Analysis.
pdf.
Dowd A� M�, N� Boughen, P� Ashworth, and S� Carr-Cornish (2011)� Geother-
mal technology in Australia: Investigating social acceptance. Energy Policy 39,
6301 – 6307.
Dumas P�, and M� Ha-Duong (2005)� An Abrupt Stochastic Damage Function
to Analyze Climate Policy Benefits. Advances in Global Change Research. In:
The Coupling of Climate and Economic Dynamics. A. Haurie, L. Viguier, (eds.),
Springer Netherlands, pp. 97 – 111. ISBN: 978 - 1-4020 - 3425 - 1.
Durand-Lasserve O�, A� Pierru, and Y� Smeers (2010)� Uncertain long-run
emissions targets, CO
2
price and global energy transition: A general equi-
librium approach. Energy Policy 38, 5108 – 5122. Available at: http: / / www.
sciencedirect. com / science / article / pii / S0301421510003150.
Dutt V� (2011)� Why do we want to defer actions on climate change? A psychologi-
cal perspective. Carnegie Mellon University. Available at: http: / / www. hss. cmu.
edu / departments / sds / ddmlab / papers / GonzalezDutt2011_psychreview.pdf.
Dutt V�, and C� Gonzalez (2011)� Human control of climate change. Cli-
matic Change 111, 497 – 518. Available at: http: / / www. springerlink.
com / index / 835l601768q73873.pdf.
Dutt V�, and C� Gonzalez (2013)� Climate Risk Communication: Effects of cost,
timing, and probability of climate consequences in decisions from description
and experience. In: Psychology of Policy Making. Nova Science Publishers,
Hauppauge, New York.
Eakin H� C�, and A� Patt (2011)� Are adaptation studies effective, and what can
enhance their practical impact? Wiley Interdisciplinary Reviews: Climate
Change 2, 141 – 153.
Eaves J�, and S� Eaves (2007)� Renewable corn-ethanol and energy security. Energy
Policy 35, 5958 – 5963. doi: 10.1016 / j.enpol.2007.06.026, ISSN: 0301-4215.
ECLACS (2011)� The Economics of Climate Change in the Caribbean. Summary
Report 2011. UNECLAC / POS.
Ehrhardt-Martinez K�, and J� A� Laitner (2010)� Rebound, technology and people:
Mitigating the rebound effect with energy-resource management and people-
centered initiatives. Proceedings of the 2010 ACEEE Summer Study on Energy
Efficiency in Buildings, 76 – 91. Available at: http: / / rste040vlmp01.blackmesh.
com / files / proceedings / 2010 / data / papers / 2142.pdf.
Eisner M� J�, R� S� Kaplan, and J� V� Soden (1971)� Admissible Decision Rules for
the E-Model of Chance-Constrained Programming. Management Science 17,
337 – 353. ISSN: 0025-1909.
Ellis G�, J� Barry, and C� Robinson (2007)� Many ways to say no, different ways
to say yes: Applying Q-Methodology to understand public acceptance of wind
farm proposals. Journal of Environmental Planning and Management 50,
517 – 551. ISSN: 0964-0568.
Ellsberg D� (1961)� Risk, Ambiguity, and the Savage Axioms. The Quarterly Journal
of Economics 75, 643 – 669. doi: 10.2307 / 1884324, ISSN: 0033-5533.
Den Elzen M�, and D� van Vuuren (2007)� Peaking profiles for achieving
long-term temperature targets with more likelihood at lower costs. Pro-
ceedings of the National Academy of Sciences 104, 17931 – 17936. doi:
10.1073 / pnas.0701598104.
Engle-Warnick J�, and S� Laszlo (2006)� Learning-by-Doing in an Ambiguous Envi-
ronment. CIRANO Working Papers2006s-29, CIRANO.
Fan L�, B� F� Hobbs, and C� S� Norman (2010)� Risk aversion and CO
2
regulatory
uncertainty in power generation investment: Policy and modeling implica-
tions. Journal of Environmental Economics and Management 60, 193 – 208. doi:
10.1016 / j.jeem.2010.08.001, ISSN: 0095-0696.
Fan L�, C� S� Norman, and A� Patt (2012)� Electricity capacity investment under risk
aversion: A case study of coal, gas, and concentrated solar power. Energy Eco-
nomics 34, 54 – 61. doi: 10.1016 / j.eneco.2011.10.010, ISSN: 0140-9883.
Farzin Y� H�, and P� M� Kort (2000)� Pollution Abatement Investment When Environ-
mental Regulation Is Uncertain. Journal of Public Economic Theory 2, 183 – 212.
Available at: http: / / ideas.repec.org / a / bla / jpbect / v2y2000i2p183 - 212.html.
Feltovich P� J�, M� J� Prietula, and K� A� Ericsson (2006)� Studies of expertise
from psychological perspectives. In: The Cambridge Handbook of Expertise
and Expert Performance. K. A. Ericsson, N. Charness, P. J. Feltovich, R. R. Hoff-
man, (eds.), Cambridge University Press, Available at: http: / / psycnet.apa.org /
psycinfo / 2006 - 10094 - 004.
Feng Z� H�, L� L� Zou, and Y� M� Wei (2011)� Carbon price volatility: Evidence from
EU ETS. Applied Energy 88, 590 – 598.
Figner B�, and E� U� Weber (2011)� Who takes risks when and why? Current Direc-
tions in Psychological Science 20, 211 – 216.
Finucane M� L�, A� Alhakami, P� Slovic, and S� M� Johnson (2000)� The affect heu-
ristic in judgments of risks and benefits. Journal of Behavioral Decision Making
13, 1 – 17.
Fischer C�, and R� G� Newell (2008)� Environmental and technology policies for
climate mitigation. Journal of Environmental Economics and Management
55, 142 – 162. Available at: http: / / www. sciencedirect. com / science / article / pii /
S0095069607001064.
Fisher A� C�, and U� Narain (2003)� Global warming, endogenous risk, and
irreversibility. Environmental and Resource Economics 25, 395 – 416. doi:
10.1023 / A:1025056530035, ISSN: 0924-6460.
Fleming S� M�, C� L� Thomas, and R� J� Dolan (2010)� Overcoming status quo bias
in the human brain. Proceedings of the National Academy of Sciences 107,
6005 – 6009. Available at: http: / / www. pnas. org / content / 107 / 13 / 6005.short.
Flynn J�, P� Slovic, and H� Kunreuther (2001)� Risk Media and Stigma: Understand-
ing Public Challenges to Modern Science and Technology. Earthscan, London UK.
Folke C�, S� Carpenter, T� Elmqvist, L� Gunderson, C� S� Holling, and B� Walker
(2002)� Resilience and sustainable development: building adaptive capacity in
a world of transformations. AMBIO: A Journal of the Human Environment 31,
437 – 440.
Fouquet D�, and T� B� Johansson (2008)� European renewable energy policy
at crossroads — Focus on electricity support mechanisms. Energy Policy 36,
4079 – 4092. doi: 10.1016 / j.enpol.2008.06.023, ISSN: 0301-4215.
Frondel M�, N� Ritter, and C� M� Schmidt (2008)� Germany’s solar cell promotion:
Dark clouds on the horizon. Energy Policy Energy Policy 36, 4198 – 4204. ISSN:
0301-4215.
Frondel M�, N� Ritter, C� M� Schmidt, and C� Vance (2010)� Economic impacts
from the promotion of renewable energy technologies: The German experience.
Energy Policy 38, 4048 – 4056. ISSN: 0301-4215.
196196
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Funke M�, and M� Paetz (2011)� Environmental policy under model uncertainty: a
robust optimal control approach. Climatic Change 107, 225 – 239. doi: 10.1007 /
s10584 - 010 - 9943 - 1, ISSN: 0165-0009.
Funtowicz S� O�, and J� R� Ravetz (1992)� Three Types of Risk Assessment and the
Emergence of Post Normal Science. In: Social Theories of Risk. S. Krimsky, D.
Golding, (eds.),Westport, pp. 251 – 273.
Fuss S�, D� Johansson, J� Szolgayova, and M� Obersteiner (2009)� Impact of
climate policy uncertainty on the adoption of electricity generating technolo-
gies. Energy Policy 37, 733 – 743. doi: 10.1016 / j.enpol.2008.10.022, ISSN: 0301-
4215.
Fuss S�, J� Szolgayová, N� Khabarov, and M� Obersteiner (2012)� Renewables
and climate change mitigation: Irreversible energy investment under uncer-
tainty and portfolio effects. Energy Policy 40, 59 – 68. ISSN: 0301-4215.
Füssel H� M�, and R� J�� Klein (2006)� Climate change vulnerability assessments: an
evolution of conceptual thinking. Climatic Change 75, 301 – 329.
Gardner G� T�, and P� C� Stern (2008)� The short list: The most effective actions US
households can take to curb climate change. Environment: Science and Policy
for Sustainable Development 50, 12 – 25.
Gearheard S�, M� Pocernich, R� Stewart, J� Sanguya, and H� P� Huntington
(2009)� Linking Inuit knowledge and meteorological station observations
to understand changing wind patterns at Clyde River, Nunavut. Climatic
Change 100, 267 – 294. Available at: http: / / www. springerlink. com / index /
337542v57l5m32k5.pdf.
Gibbons M� (1994)� The New Production of Knowledge: The Dynamics of Science
and Research in Contemporary Societies. SAGE, 196 pp. ISBN: 9780803977945.
Gilson R� J�, and M� J� Roe (1993)� Understanding the Japanese keiretsu: Overlaps
between corporate governance and industrial organization. Yale Law Journal,
871 – 906. Available at: http: / / www. jstor. org / stable / 10.2307 / 796835.
Gjerde J�, S� Grepperud, and S� Kverndokk (1999)� Optimal climate policy under
the possibility of a catastrophe. Resource and Energy Economics 21, 289 – 317.
doi: 10.1016 / S0928 - 7655(99)00006 - 8, ISSN: 0928-7655.
Goeschl T�, and G� Perino (2009)� On backstops and boomerangs: Envi-
ronmental R&D under technological uncertainty. Energy Economics 31,
800 – 809. Available at: http: / / www. sciencedirect. com / science / article / pii /
S014098830900036X.
Goldstein N� J�, R� B� Cialdini, and V� Griskevicius (2008)� A room with a view-
point: Using social norms to motivate environmental conservation in hotels.
Journal of Consumer Research 35, 472 – 482. Available at: http: / / www. jstor.
org / stable / 10.1086 / 586910.
Gollier C�, B� Jullien, and N� Treich (2000)� Scientific progress and irreversibility:
an economic interpretation of the “Precautionary Principle.” Journal of Public
Economics 75, 229 – 253. doi: 10.1016 / S0047 - 2727(99)00052 - 3, ISSN: 0047-
2727.
Gollier C�, and N� Treich (2003)� Decision-making under scientific uncertainty: the
economics of the precautionary principle. Journal of Risk and Uncertainty 27,
77 – 103.
Gong M�, J� Baron, and H� Kunreuther (2009)� Group cooperation under uncer-
tainty. Journal of Risk and Uncertainty 39, 251 – 270.
Gonzalez C�, and V� Dutt (2011)� Instance-based learning: Integrating sampling
and repeated decisions from experience. Psychological Review 118, 523 – 551.
Available at: http: / / psycnet.apa.org / journals / rev / 118 / 4 / 523 / .
Goodnough A� (2006)� As hurricane season looms, state aim to scare. The New
York Times. Available at: http: / / www. nytimes. com / 2006 / 05 / 31 / us / 31prepare.
html?pagewanted=print&_r=0.
Goossens L� H� J�, F� T� Harper, B� C� P� Kraan, and H� Métivier (2000)� Expert
judgement for a probabilistic accident consequence uncertainty analysis. Radia-
tion Protection Dosimetry 90, 295 – 301. Available at: http: / / rpd.oxfordjournals.
org / content / 90 / 3 / 295.abstract.
Government of India, Ministry of Finance (2012)� Economic Survey 2012 – 13.
Grassi G�, S� Monni, S� Federici, F� Achard, and D� Mollicone (2008)� Applying
the conservativeness principle to REDD to deal with the uncertainties of the
estimates. Environmental Research Letters 3, 035005.
Green D�, J� Billy, and A� Tapim (2010)� Indigenous Australians’ knowledge of
weather and climate. Climatic Change 100, 337 – 354. Available at: http: / / www.
springerlink. com / index / u04024q252848663.pdf.
Green D�, and G� Raygorodetsky (2010)� Indigenous knowledge of a changing
climate. Climatic Change 100, 239 – 242. Available at: http: / / www. springerlink.
com / index / 27kg6682148j0670.pdf.
Greene D� L�, J� German, and M� A� Delucchi (2009)� Fuel economy: The case
for market failure. In: Reducing climate impacts in the transportation sec-
tor. Springer, pp. 181 – 205. Available at: http: / / link.springer.com / chapter /
10.1007 / 978 - 1-4020 - 6979 - 6_11 / fulltext.html.
Gromet D� M�, H� Kunreuther, and R� P� Larrick (2013)� Political ideology
affects energy-efficiency attitudes and choices. Proceedings of the National
Academy of Sciences 110, 9314 – 9319. Available at: http: / / www. pnas. org /
content / 110 / 23 / 9314.short.
Gross M� (2010)� Ignorance and Surprise: Science, Society, and Ecological Design.
MIT Press, 255 pp. ISBN: 9780262013482.
Grothmann T�, and A� Patt (2005)� Adaptive capacity and human cognition: the
process of individual adaptation to climate change. Global Environmental
Change Part A 15, 199 – 213.
Grothmann T�, and F� Reusswig (2006)� People at risk of flooding: Why some
residents take precautionary action while others do not. Natural Hazards 38,
101 – 120.
Grubb M� (1997)� Technologies, energy systems and the timing of CO
2
emissions
abatement: An overview of economic issues. Energy Policy 25, 159 – 172. doi:
10.1016 / S0301 - 4215(96)00106 - 1, ISSN: 0301-4215.
Grubler A�, and K� Riahi (2010)� Do governments have the right mix in their
energy R&D portfolios? Carbon 1, 79 – 87.
Guston D� H� (2001)� Boundary Organizations in Environmental Policy and Science.
Sage Publications, 133 pp.
Ha-Duong M� (1998)� Quasi-option value and climate policy choices. Energy Eco-
nomics 20, 599 – 620. doi: 10.1016 / S0140 - 9883(98)00011 - 5, ISSN: 0140-9883.
Ha-Duong M�, M� J� Grubb, and J� C� Hourcade (1997)� Influence of socioeco-
nomic inertia and uncertainty on optimal CO
2
-emission abatement. Nature 389,
270 – 273.
Ha-Duong M�, and N� Treich (2004)� Risk aversion, intergenerational equity and
climate change. Environmental and Resource Economics 28, 195 – 207. doi: 10.1
023 / B:EARE.0000029915.04325.25, ISSN: 0924-6460.
Hafner-Burton E� M�, D� G� Victor, and Y� Lupu (2012)� Political science research
on International Law. American Journal of International Law 106, 47 – 97.
Haigh M� S�, and J� A� List (2005)� Do professional traders exhibit myopic loss aver-
sion? An experimental analysis. The Journal of Finance 60, 523 – 534. Available
at: http: / / onlinelibrary.wiley.com / doi / 10.1111 / j.1540 - 6261.2005.00737.x / full.
197197
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Hall J� W�, R� J� Lempert, K� Keller, A� Hackbarth, C� Mijere, and D� J� McInerney
(2012)� Robust climate policies under uncertainty: A comparison of robust deci-
sion making and info-gap methods. Risk Analysis 32, 1657 – 72. Available at:
http: / / onlinelibrary.wiley.com / doi / 10.1111 / j.1539 - 6924.2012.01802.x / full.
Hammond J� S�, R� L� Keeney, and H� Raiffa (1999)� Smart Choices: A Practical
Guide to Making Better Decisions. Harvard Business Press, 244 pp.
Hardcastle P� D�, and D� Baird (2008)� Capability and Cost Assessment of the
Major Forest Nations to Measure and Monitor Their Forest Carbon. LTS Inter-
national, Penicuick, UK.
Hardisty D� J�, E� J� Johnson, and E� U� Weber (2010)� A dirty word or a dirty world?
Attribute framing, political affiliation, and query theory. Psychological Science
21, 86 – 92. Available at: http: / / pss.sagepub.com / content / 21 / 1 / 86.short.
Hasselmann K�, and T� Barker (2008)� The Stern Review and the IPCC Fourth
Assessment Report: Omplications for interaction between policymakers and cli-
mate experts. An editorial essay. Climatic Change 89, 219 – 229.
Hasselmann K�, M� Latif, G� Hooss, C� Azar, O� Edenhofer, C� C� Jaeger, O� M�
Johannessen, C� Kemfert, M� Welp, and A� Wokaun (2003)� The challenge of
long-term climate change. Science 302, 1923 – 1925.
Heal G� M�, and H� Kunreuther (2012)� Tipping Climate Negotiations. In Common
Sense and Climate Change: Essays in Honor of Thomas Schelling, edited by
Robert Hahn and Alistair Ulph, Oxford University Press.
Heath C�, and A� Tversky (1991)� Preference and belief: Ambiguity and compe-
tence in choice under uncertainty. Journal of Risk and Uncertainty 4, 5 – 28. doi:
10.1007 / BF00057884, ISSN: 0895-5646, 1573 – 0476.
Heidegger M� (1962)� Being and Time (J. Macquarrie and E. Robinson, Trans.).
Harper & Row, New York, NY.
Held H�, E� Kriegler, K� Lessmann, and O� Edenhofer (2009)� Efficient climate
policies under technology and climate uncertainty. Energy Economics 31,
S50 – S61.
Held H�, M� Ragwitz, and R� Haas (2006)� On the success of policy strategies for
the promotion of electricity from renewable energy sources in the EU. Energy &
Environment 17, 849 – 868.
Hill J�, S� Polasky, E� Nelson, D� Tilman, H� Huo, L� Ludwig, J� Neumann, H�
Zheng, and D� Bonta (2009)� Climate change and health costs of air emis-
sions from biofuels and gasoline. Proceedings of the National Academy of Sci-
ences 106, 2077 – 2082. doi: 10.1073 / pnas.0812835106.
Hirst E�, D� White, and R� Goeltz (1985)� Indoor temperature changes in retro-
fit homes. Energy 10, 861 – 870. Available at: http: / / www. sciencedirect. com /
science / article / pii / 0360544285901197.
Hof A� F�, D� P� van Vuuren, and M� G� J� den Elzen (2010)� A quantitative minimax
regret approach to climate change: Does discounting still matter? Ecological
Economics 70, 43 – 51. doi: 16 / j.ecolecon.2010.03.023, ISSN: 0921-8009.
Hoffmann V� H� (2007)� EU ETS and investment decisions: The case of the german
electricity industry. European Management Journal 25, 464 – 474.
Hoffmann M� J� (2011)� Climate Governance at the Crossroads. Experimenting with
a Global Response after Kyoto. Oxford University Press.
Holling C� S� (Ed.) (1978)� Adaptive Environmental Assessment and Management.
Wiley & Sons.
Hope C� W� (2008)� Optimal carbon emissions and the social cost of carbon over
time under uncertainty. Integrated Assessment 8, 107 – 122. ISSN: 1389-5176.
Hope C� W� (2009)� How deep should the deep cuts be? Optimal CO
2
emissions over
time under uncertainty. Climate Policy 9, 3 – 8. doi: 10.3763 / cpol.2008.0583a,
ISSN: 1469-3062.
Hora S� C� (2004)� Probability judgments for continuous quantities: Linear combina-
tions and calibration. Management Science 50, 597 – 604.
Van den Hove S� (2007)� A rationale for science – policy interfaces. Futures 39,
807 – 826. Available at: http: / / www. sciencedirect. com / science / article / pii /
S0016328706002060.
Hulme M�, S� J� O’Neill, and S� Dessai (2011)� Is weather event attribution neces-
sary for adaptation funding? Science 334, 764 – 765.
IISD (2012)� Fossil-Fuel Subsidy Reform in India: Cash Transfers for PDS Kerosene
and Domestic LPG. International Institute for Sustainable Development, Avail-
able at: http: / / www. iisd. org / gsi / sites / default / files / ffs_india_teri_rev.pdf.
IPCC (2007)� Summary for Policymakers. In: Climate Change 2007: The Physi-
cal Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change [S. Solomon, D.
Qin, M. Manning, M. Marquis, K. Averyt, M. M. B. Tignor, H. L. Miller, Z. Chen
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 18 pp. Available at: http: / / www. ipcc. ch / pdf / assessment-report /
ar4 / wg1 / ar4-wg1-spm.pdf.
IPCC (2012)� Managing the Risks of Extreme Events and Disasters to Advance
Climate Change Adaption. A Special Report of Working Groups I and II of the
Intergovernmental Panel on Climate Change [Field, C. B., V. Barros, T. F. Stocker,
D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K.
Allen, M. Tignor, and P. M. Midgley (eds.)] Cambridge University Press, Cam-
bridge, UK, and New York, NY, USA, 582 pp.
Itaoka K�, Y� Okuda, A� Saito, and M� Akai (2009)� Influential information and
factors for social acceptance of CCS: The 2nd round survey of public opinion
in Japan. Energy Procedia 1, 4803 – 4810. doi: 10.1016 / j.egypro.2009.02.307,
ISSN: 1876-6102.
Iverson T�, and C� Perrings (2012)� Precaution and proportionality in the man-
agement of global environmental change. Global Environmental Change 22,
161 – 177. Available at: http: / / www. sciencedirect. com / science / article / pii /
S0959378011001440.
Jacoby H� D�, and A� D� Ellerman (2004)� The safety valve and climate policy.
Energy Policy 32, 481 – 491. doi: 10.1016 / S0301 - 4215(03)00150 - 2, ISSN: 0301-
4215.
Jaffe A�, and R� Stavins (1995)� Dynamic incentives of environmental regulations:
The effects of alternative policy instruments on technology diffusion. Journal of
Environmental Economics and Management 29, 43 – 63.
Jakob M� (2006)� Marginal costs and co-benefits of energy efficiency investments:
The case of the Swiss residential sector. Energy Policy 34, 172 – 187. Available
at: http: / / www. sciencedirect. com / science / article / pii / S030142150400271X.
James W� (1878)� Remarks on Spencer’s Definition of Mind as Correspondence. The
Journal of Speculative Philosophy XII, 1 – 18. Available at: http: / / www. jstor.
org / stable / 25666067?seq=1.
Jasanoff S� (1990)� The Fifth Branch: Science Advisers As Policymakers. Harvard
University Press, Cambridge, 322 pp. ISBN: 9780674300620.
Jasanoff S� (2005a)� Designs on Nature: Science And Democracy in Europe
And the United States. Princeton University Press, New York, 392 pp. ISBN:
9780691130422.
Jasanoff S� (2005b)� Judgment under Siege: The Three-Body Problem of Expert
Legitimacy. In: Democratization of Expertise?: Exploring Novel Forms of Scien-
tific Advice in Political Decision-Making (S. Maasen and peter weingart, Eds.).
Springer Science & Business, 256 pp. ISBN: 9781402037535.
198198
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Jasanoff S� (2010)� Testing time for climate science. Science 328, 695 – 696. doi:
10.1126 / science.1189420, ISSN: 0036-8075, 1095 – 9203.
Jinnah S� (2011)� Marketing linkages: Secretariat governance of the climate-biodi-
versity interface. Global Environmental Politics 11, 23 – 43.
Johansson D� J� A�, U� M� Persson, and C� Azar (2008)� Uncertainty and learning:
implications for the trade-off between short-lived and long-lived greenhouse
gases. Climatic Change 88, 293 – 308. Available at: http: / / www. springerlink.
com / index / K2052K4611313K26.pdf.
Johnson E� J�, and D� G� Goldstein (2013)� Decisions By Default. In: Behavioral
Foundations of Policy. E. Shafir, (ed.), Princeton University Press, Princeton, NJ.
Johnson E� J�, G� Häubl, and A� Keinan (2007)� Aspects of endowment: a query
theory of value construction. Journal of Experimental Psychology: Learning,
Memory, and Cognition 33, 461. Available at: http: / / psycnet.apa.org / journals /
xlm / 33 / 3 / 461 / .
Jones L�, and E� Boyd (2011)� Exploring social barriers to adaptation: Insights from
Western Nepal. Global Environmental Change 21, 1262 – 1274.
Jones R� N�, and B� L� Preston (2011)� Adaptation and risk management. Wiley
Interdisciplinary Reviews: Climate Change 2, 296 – 308.
Joyce L� A�, G� M� Blate, S� G� McNulty, C� I� Millar, R� P� Neilson, R� P� Neilson,
and D� L� Peterson (2009)� Managing for multiple resources under climate
change: National forests. Environmental Management 44, 1022 – 1032. ISSN:
0364-152X.
Kahn H�, and A� J� Wiener (1967)� The Year 2000: A Framework for Speculation on
the Next Thirty-Three Years. New York.
Kahnemann D� (2003)� A psychological perspective on economics. The American
Economic Review 93, 162 – 168.
Kahneman D� (2011)� Thinking, Fast and Slow. Macmillan, 511 pp. ISBN:
9780374275631.
Kahneman D�, and A� Tversky (1979)� Prospect theory: An analysis of decision
under risk. Econometrica 47, 263 – 291. doi: 10.2307 / 1914185, ISSN: 0012-9682.
Kasperson R� E�, O� Renn, P� Slovic, H� S� Brown, J� Emel, R� Goble, J� X� Kasper-
son, and S� Ratick (1988)� The social amplification of risk: A conceptual
framework. Risk Analysis 8, 177 – 187.
Kaufman N� (2012)� The bias of integrated assessment models that ignore climate
catastrophes. Climatic Change 110, 575 – 595. Available at: http: / / link.springer.
com / article / 10.1007 / s10584 - 011 - 0140 - 7.
Keeney R� L� (1993)� Decision analysis: An overview. Operations Research 30,
803 – 838.
Keller K�, B� M� Bolker, and D� F� Bradford (2004)� Uncertain climate thresholds
and optimal economic growth. Journal of Environmental Economics and Man-
agement 48, 723 – 741. doi: 10.1016 / j.jeem.2003.10.003, ISSN: 0095-0696.
Kelly D� L�, and C� D� Kolstad (1999)� Bayesian learning, growth, and pollu-
tion. Journal of Economic Dynamics and Control 23, 491 – 518. Available at:
http: / / www. sciencedirect. com / science / article / pii / S0165188998000347.
Keohane R�, and D� G� Victor (2011)� The Regime of Climate Change. Perspectives
on Politics 9, 7 – 23.
Klein R� J��, S� E�� Eriksen, L� O� Naess, A� Hammill, T� M� Tanner, C� Robledo,
and K� L� O’Brien (2007)� Portfolio screening to support the mainstreaming
of adaptation to climate change into development assistance. Climatic Change
84, 23 – 44.
Kleindorfer P� R�, H� C� Kunreuther, and P� Schoemaker (1993)� Decision Sci-
ences: An Integrative Perspective. Cambridge University Press, Available at:
http: / / books.google.com / books?hl=en&lr=&id=lN4fXDap37MC&oi=fnd&p
g=PR7&dq=Decision+Sciences,+An+Integrated+Perspective+(with+Paul+
Kleindorfer+and+Paul+Schoemaker).+Cambridge+University+Press.+(1993).&
ots=ipLgqDwpfM&sig=sjUix58NuM3Xpqu0d-r35wRCZyY.
Kloeckner C� A� (2011)� Towards a psychology of climate change. Climate Change
Management. In: The Economic, Social and Political Elements of Climate
Change. pp. 153 – 173. Available at: http: / / www. springerlink. com / index /
K17H385232021072.pdf.
Klügel J� U� (2008)� Seismic hazard analysis — Quo vadis? Earth-Science Reviews
88, 1 – 32.
Knutti R�, G� Abramowitz, M� Collins, V� Eyring, P� Glecker, B� Hewitson, and L�
Mearns (2010)� Good Practice Guidance Paper on Assessing and Combining
Multi Model Climate Projections. In: Meeting Report of the Intergovernmental
Panel on Climate Change Expert Meeting on Assessing and Combining Multi
Model Climate Projections [T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, P. M.
Midgley, (eds.)]. IPCC Working Group I Technical Support Unit, University of
Bern, Bern, Switzerland.
Knutti R�, M� R� Allen, P� Friedlingstein, J� M� Gregory, G� C� Hegerl, G� Meehl,
M� Meinshausen, J� M� Murphy, G�-K� Plattner, S� C� B� Raper, T� F� Stocker,
P� A� Stott, H� Teng, and T� M� L� Wigley (2008)� A review of uncertainties in
global temperature projections over the twenty-first century. Journal of Climate
21, 2651 – 2663.
Knutti R�, D� Masson, and A� Gettelman (2013)� Climate model genealogy:
Generation CMIP5 and how we got there. Geophysical Research Letters 40,
1194 – 1199. doi: 10.1002 / grl.50256.
Kolstad C� D� (1994)� George Bush versus Al Gore: Irreversibilities in greenhouse
gas accumulation and emission control investment. Energy Policy 22, 771 – 778.
doi: 10.1016 / 0301 - 4215(94)90053 - 1, ISSN: 0301-4215.
Kolstad C� D� (1996a)� Fundamental irreversibilities in stock externalities. Journal
of Public Economics 60, 221 – 233. Available at: http: / / www. sciencedirect.
com / science / article / pii / 0047272795015213.
Kolstad C� D� (1996b)� Learning and stock effects in environmental regula-
tion: the case of greenhouse gas emissions. Journal of Environmental
Economics and Management 31, 1 – 18. Available at: http: / / are.berkeley.
edu / courses / ARE263 / fall2008 / paper / Learning / KolstadLearning%20JEEM.pdf.
Kolstad C� D� (2007)� Systematic uncertainty in self-enforcing international environ-
mental agreements. Journal of Environmental Economics and Management 53,
68 – 79. Available at: http: / / ideas.repec.org / a / eee / jeeman / v53y2007i1p68-79.
html.
Kolstad C� D�, and A� Ulph (2011)� Uncertainty, learning and heterogeneity in
international environmental agreements. Environmental and Resource Econom-
ics 50, 389 – 403.
Komendantova N�, A� Patt, L� Barras, and A� Battaglini (2012)� Percep-
tion of risks in renewable energy projects: The case of concentrated solar
power in North Africa. Energy Policy 40, 103 – 109. Available at: http: / / www.
sciencedirect. com / science / article / pii / S0301421509009458.
Komendantova N�, A� Patt, and K� Williges (2011)� Solar power investment in
North Africa: Reducing perceived risks. Renewable and Sustainable Energy
Reviews.
Krantz D� H�, and H� C� Kunreuther (2007)� Goals and plans in decision making.
Judgment and Decision Making 2, 137 – 168.
199199
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Krosnick J� A�, A� L� Holbrook, L� Lowe, and P� S� Visser (2006)� The origins
and consequences of democratic citizens’ policy agendas: A study of popu-
lar concern about global warming. Climatic Change 77, 7 – 43. Available at:
http: / / www. springerlink. com / index / WU4K81W185X1V576.pdf.
Kunreuther H�, R� Ginsberg, L� Miller, P� Sagi, P� Slovic, B� Borkin, and N� Katz
(1978)� Disaster Insurance Protection: Public Policy Lessons. Wiley and Sons,
New York, 400 pp.
Kunreuther H�, G� Heal, M� Allen, O� Edenhofer, C� B� Field, and G� Yohe
(2013a)� Risk management and climate change. Nature Climate Change 3,
447 – 450. Available at: http: / / www. nber. org / papers / w18607.
Kunreuther H�, R� Hogarth, and J� Meszaros (1993)� Insurer ambiguity and mar-
ket failure. Journal of Risk and Uncertainty 7, 71 – 87.
Kunreuther H�, R� Meyer, and E� Michel-Kerjan (2013b)� Overcoming Decision
Biases to Reduce Losses from Natural Catastrophes. In: The Behavioral Foun-
dations of Policy, E. Shafir (ed.). Princeton University Press, New Jersey, USA,
pp.398 – 425. ISBN: 9780691137568.
Kunreuther H�, and E� Michel-Kerjan (2011)� People get ready: Disaster prepared-
ness. Issues in Science and Technology XXVIII, 1 – 7. Available at: http: / / www.
issues. org / 28.1 / kunreuther.html.
Kunreuther H�, N� Novemsky, and D� Kahneman (2001)� Making low probabilities
useful. Journal of Risk and Uncertainty 23, 103 – 20. Available at: http: / / econ-
papers.repec.org / article / kapjrisku / v_3a23_3ay_3a2001_3ai_3a2_3ap_
3a103 - 20.htm.
Kunreuther H�, M� Pauly, and S� McMorrow (2013c)� Insurance and Behavioral
Economics: Improving Decisions in the Most Misunderstood Industry. New York:
Cambridge University Press.
Kunreuther H�, G� Silvasi, E� T� Bradlow, and D� Small (2009)� Bayesian analysis
of deterministic and stochastic prisoner’s dilemma games. Judgment and Deci-
sion Making 4, 363 – 384.
Labriet M�, A� Kanudia, and R� Loulou (2012)� Climate mitigation under an
uncertain technology future: a TIAM-WORLD analysis. Energy Economics 34,
S366 – S377. Available at: http: / / www. sciencedirect. com / science / article / pii /
S0140988312000461.
Labriet M�, R� Loulou, and A� Kanudia (2010)� Modeling uncertainty in a large
scale integrated energy-climate model. International Series in Operations
Research & Management Science. In: Uncertainty and Environmental Decision
Making. Springer, pp. 51 – 77. Available at: http: / / link.springer.com / chapter /
10.1007 / 978 - 1-4419 - 1129 - 2_2.
Laibson D� (1997)� Golden eggs and hyperbolic discounting. The Quarterly Journal
of Economics 112, 443 – 478.
Laidler G� J� (2006)� Inuit and scientific perspectives on the relationship between sea
ice and climate change: the ideal complement? Climatic Change 78, 407 – 444.
Available at: http: / / www. springerlink. com / index / M72813263P4U2380.pdf.
Lange A�, and N� Treich (2008)� Uncertainty, learning and ambiguity in economic
models on climate policy: some classical results and new directions. Climatic
Change 89, 7 – 21.
Larrick R� P�, and J� B� Soll (2008)� The MPG illusion. Science 320, 1593 – 1594.
Lawler J� J�, T� H� Tear, C� Pyke, R� M� Shaw, P� Gonzales, P� Kareiva, L� Hansen,
L� Hannah, K� Klausmeyer, A� Aldous, C� Bienz, and S� Pearsall (2008)�
Resource management in a changing and uncertain climate. Frontiers in Ecol-
ogy and the Environment 8, 35 – 43.
Lawlor K�, E� Weinthal, and L� Olander (2010)� Institutions and policies to protect
rural livelihoods in REDD+ regimes. Global Environmental Politics 10, 1 – 11.
Leach A� J� (2007)� The climate change learning curve. Journal of Economic Dynam-
ics and Control 31, 1728 – 1752. Available at: http: / / www. sciencedirect.
com / science / article / pii / S0165188906001266.
Leary D�, and M� Esteban (2009)� Climate Change and renewable energy from the
ocean and tides: Calming the sea of regulatory uncertainty. The International
Journal of Marine and Coastal Law 24, 617 – 651. doi: 10.1163 / 092735209X1
2499043518269.
Lee K� N� (1993)� Compass and Gyroscope: Integrating Science and Politics for the
Environment. Island Press. Washington, DC, USA, 255 pp.
Lefale P� (2010)� Ua ‘afa le Aso Stormy weather today: traditional ecological knowl-
edge of weather and climate. The Samoa experience. Climatic Change 100,
317 – 335. doi: 10.1007 / s10584 - 009 - 9722-z.
Leiserowitz A� (2005)� American risk perceptions: Is climate change dangerous?
Risk Analysis 25, 1433 – 1442.
Leiserowitz A� (2006)� Climate change risk perception and policy preferences: The
role of affect, imagery, and values. Climatic Change 77, 45 – 72.
Leiserowitz A�, and K� Broad (2008)� Florida: Public Opinion on Climate Change.
A Yale University / University of Miami / Columbia University Poll. New Haven, CT:
Yale Project on Climate Change.
Leiserowitz A�, E� Maibach, and C� Roser-Renouf (2008)� Global Warming’s“ Six
America”: An Audience Segmentation. Yale School of Forestry and Environmen-
tal Studies and George Mason University Center for Climate Change Commu-
nication.
Leiserowitz A� and E� Maibach, C� Roser-Renouf, and J� Hmielowski (2012)�
Extreme, weather, climate and preparedness in the American mind. Yale Project
on Climate Change Communication.
Lempert R� J�, D� G� Groves, S� W� Popper, and S� C� Bankes (2006)� A General,
Analytic Method for Generating Robust Strategies and Narrative Scenarios.
Management Science 52, 514 – 528. doi:10.1287 / mnsc.1050.0472.
Letson D�, C� E� Laciana, F� E� Bert, E� U� Weber, R� W� Katz, X� I� Gonzalez, and
G� P� Podestá (2009)� Value of perfect ENSO phase predictions for agriculture:
evaluating the impact of land tenure and decision objectives. Climatic Change
97, 145 – 170.
Li Y�, E� J� Johnson, and L� Zaval (2011)� Local warming. Psychological Science 22,
454 – 459. doi: 10.1177 / 0956797611400913.
Lilliestam J�, A� Battaglini, C� Finlay, D� Fürstenwerth, A� Patt, G� Schellekens,
and P� Schmidt (2012)� An alternative to a global climate deal may be unfold-
ing before our eyes. Climate and Development 4, 1 – 4.
Lilliestam J�, and S� Ellenbeck (2011)� Energy security and renewable electricity
trade — Will Desertec make Europe vulnerable to the “energy weapon”? Energy
Policy 39, 3380 – 3391. doi: 10.1016 / j.enpol.2011.03.035, ISSN: 0301-4215.
Little C� M�, N� M� Urban, and M� Oppenheimer (2013)� Probabilistic framework
for assessing the ice sheet contribution to sea level change. Proceedings of the
National Academy of Sciences 110, 3264 – 3269. Available at: http: / / www. pnas.
org / content / 110 / 9 / 3264.short.
Loewenstein G�, and J� Elster (1992)� Choice Over Time. Russell Sage Foundation,
434 pp. ISBN: 9780871545589.
Loewenstein G� F�, E� U� Weber, C� K� Hsee, and N� Welch (2001)� Risk as feelings.
Psychological Bulletin 127, 267.
Lorenz A�, E� Kriegler, H� Held, and M� G� Schmidt (2012a)� How to measure the
importance of climate risk for determining optimal global abatement policies?
Climate Change Economics 3, 01 – 28. Available at: http: / / www. worldscientific.
com / doi / pdf / 10.1142 / S2010007812500042.
200200
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Lorenz A�, M� Schmidt, E� Kriegler, and H� Held (2012b)� Anticipating Climate
Threshold Damages. Environmental Modeling and Assessment 17, 163 – 175.
doi: 10.1007 / s10666 - 011 - 9282 - 2, ISSN: 1420-2026.
Lorenzoni I�, A� Leiserowitz, M� D�� Doria, W� Poortinga, and N� F� Pidgeon
(2006)� Cross-national comparisons of image associations with “global warm-
ing” and “climate change” among laypeople in the United States of America
and Great Britain. Journal of Risk Research 9, 265 – 281.
Lorenzoni I�, S� Nicholson-Cole, and L� Whitmarsh (2007)� Barriers perceived to
engaging with climate change among the UK public and their policy implica-
tions. Global Environmental Change 17, 445 – 459.
Lorenzoni I�, and N� F� Pidgeon (2006)� Public views on climate change: European
and USA perspectives. Climatic Change 77, 73 – 95.
Lowe T�, K� Brown, S� Dessai, M� de França Doria, K� Haynes, and K� Vincent
(2006)� Does tomorrow ever come? Disaster narrative and public perceptions
of climate change. Public Understanding of Science 15, 435 – 457.
Lüthi S� (2010)� Effective deployment of photovoltaics in the Mediterranean coun-
tries: Balancing policy risk and return. Solar Energy 84, 1059 – 1071.
Lüthi S�, and R� Wüstenhagen (2012)� The price of policy risk — Empirical insights
from choice experiments with European photovoltaic project developers.
Energy Economics 34, 1001 – 1011.
Malueg D� A� (1990)� Welfare consequences of emission credit trading programs.
Journal of Environmental Economics and Management 18, 66 – 77. ISSN: 0095-
0696.
Manne A� S�, and R� G� Richels (1991)� Buying greenhouse insurance. Energy Policy
19, 543 – 552. doi: 10.1016 / 0301 - 4215(91)90034-L, ISSN: 0301-4215.
Martin T� G�, M� A� Burgmann, F� Fidler, P� M� Kuhnert, S� Low-Choy, M� McBride,
and K� Mengersen (2012)� Eliciting expert knowledge in conservation science.
Conservation Biology 26, 29 – 38.
Maslow A� H� (1954)� Motivation and Personality. Harper (New York), Available at:
http: / / www. getcited. org / pub / 101181741.
Mastrandrea M�, C� Field, T� Stocker, O� Edenhofer, K� Ebi, D� Frame, H� Held, E�
Kriegler, K� Mach, P� Matschoss, and others (2010)� Guidance note for lead
authors of the IPCC Fifth Assessment Report on consistent treatment of uncer-
tainties. Intergovernmental Panel on Climate Change, Geneva, 5.
Mastrandrea M� D�, K� J� Mach, G�-K� Plattner, O� Edenhofer, T� F� Stocker, C� B�
Field, K� L� Ebi, and P� R� Matschoss (2011)� The IPCC AR5 guidance note on
consistent treatment of uncertainties: a common approach across the work-
ing groups. Climatic Change 108, 675 – 691. doi: 10.1007 / s10584 - 011 - 0178 - 6,
ISSN: 0165-0009, 1573 – 1480.
McCrimmon K� R� (1968)� Descriptive and normative implications of the decision-
theory postulates. In: Risk and Uncertainty. MacMillan, London, pp. 3 – 24.
McDermott C� L�, K� Levin, and B� Cashore (2011)� Building the forest-climate
bandwagon: REDD+ and the logic of problem amelioration. Global Environ-
mental Politics 11, 85 – 103.
McInerney D�, and K� Keller (2008)� Economically optimal risk reduction strate-
gies in the face of uncertain climate thresholds. Climatic Change 91, 29 – 41.
doi: 10.1007 / s10584 - 006 - 9137-z, ISSN: 0165-0009, 1573 – 1480.
Meckling J� (2011)� The globalization of carbon trading: transnational business
coalitions in climate politics. Global Environmental Politics 11, 26 – 50.
Meijer I� S� M�, M� P� Hekkert, and J� F� M� Koppenjan (2007)� The influence of
perceived uncertainty on entrepreneurial action in emerging renewable energy
technology; biomass gasification projects in the Netherlands. Energy Policy 35,
5836 – 5854. doi: 10.1016 / j.enpol.2007.07.009, ISSN: 0301-4215.
Meinshausen M�, S� J� Smith, K� Calvin, J� S� Daniel, M� L� T� Kainuma, J-F�
Lamarque, K� Matsumoto, S� A� Montzka, S� C� B� Raper, K� Riahi, A� Thom-
son, G� J� M� Velders, and D� P� P van Vuuren (2011)� The RCP greenhouse gas
concentrations and their extensions from 1765 to 2300. Climatic Change 109,
213 – 241. ISSN: 0165-0009.
Mendonça M� (2007)� Feed-in Tariffs: Accelerating the Deployment of Renewable
Energy. Earthscan, 173 pp. ISBN: 9781844074662.
Michel-Kerjan E�, S� Lemoyne de Forges, and H� Kunreuther (2012)� Policy
tenure under the U. S. National Flood Insurance Program (NFIP). Risk Analysis
32(4), 644 – 658. doi: 10.1111 / j.1539 - 6924.2011.01671.x, ISSN: 1539-6924.
Milinski M�, R� D� Sommerfeld, H� J� Krambeck, F� A� Reed, and J� Marotzke
(2008)� The collective-risk social dilemma and the prevention of simulated dan-
gerous climate change. Proceedings of the National Academy of Sciences 105,
2291.
Miller C� (2001)� Hybrid management: boundary organizations, science policy, and
environmental governance in the climate regime. Science, Technology & Human
Values 26, 478 – 500. Available at: http: / / sth.sagepub.com / content / 26 / 4 / 478.
short.
Mitchell C�, D� Bauknecht, and P� M� Connor (2006)� Effectiveness through risk
reduction: a comparison of the renewable obligation in England and Wales and
the feed-in system in Germany. Energy Policy 34, 297 – 305. doi: 10.1016 / j.
enpol.2004.08.004, ISSN: 0301-4215.
Morgan M� G�, and M� Henrion (1990)� Uncertainty: A Guide to Dealing with
Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University
Press, 354 pp. ISBN: 9780521427449 paperback, 0 – 521 – 36542 – 2 hardback.
Morgan M� G�, and D� W� Keith (1995)� Subjective judgements by climate experts.
Environmental Science & Technology 29, 468 – 476.
Moser S� C� (2007)� In the long shadows of inaction: The quiet building of a cli-
mate protection movement in the United States. Global Environmental Politics
7, 124 – 144.
Moser S� C� (2010)� Communicating climate change: history, challenges, process
and future directions. Wiley Interdisciplinary Reviews: Climate Change 1,
31 – 53. Available at: http: / / onlinelibrary.wiley.com / doi / 10.1002 / wcc.11 / full.
Mozumder P�, E� Flugman, and T� Randhir (2011)� Adaptation behavior in the
face of global climate change: Survey responses from experts and decision
makers serving the Florida Keys. Ocean & Coastal Management 54, 37 – 44.
Murray B� C�, R� G� Newell, and W� A� Pizer (2009)� Balancing cost and emissions
certainty: An allowance reserve for cap-and-trade. Review of Environmental
Economics and Policy 3, 84 – 103.
Musall F� D�, and O� Kuik (2011)� Local acceptance of renewable energy — A case
study from southeast Germany. Energy Policy 39(6), 3252 – 3260.
Myers T� A�, M� C� Nisbet, E� W� Maibach, and A� A� Leiserowitz (2012)� A
public health frame arouses hopeful emotions about climate change. Cli-
matic Change 113, 1105 – 1112. Available at: http: / / www. springerlink.
com / index / B0072M7777772K7R.pdf.
Von Neumann J�, and O� Morgenstern (1944)� Theory of Games and Economic
Behavior. Princeton University Press, Princeton, NJ.
Nietzsche F� (2008)� The Birth of Tragedy. Oxford University Press, 224 pp.
Nilsson M� (2005)� The role of assessments and institutions for policy learning: a study
on Swedish climate and nuclear policy formation. Policy Sciences 38, 225 – 249.
Available at: http: / / link.springer.com / article / 10.1007 / s11077 - 006 - 9006 - 7.
201201
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Nordhaus W� D� (1994)� Expert opinion on climatic change. American Scientist 82,
45 – 51.
Nordhaus W� D�, and D� Popp (1997)� What is the value of scientific knowledge?
An application to global warming using the PRICE model. The Energy Journal,
1 – 45. Available at: http: / / www. jstor. org / stable / 10.2307 / 41322716.
Nowotny H�, P� Scott, and M� Gibbons (2001)� Re-Thinking Science: Knowledge
and the Public in an Age of Uncertainty. Polity, 292 pp. ISBN: 9780745626086.
O’Brien K� (2009)� Do values subjectively define the limits to climate change
adaptation? In: Adapting to Climate Change. W. Adger, I. Lorenzoni, K. O’Brien,
(eds.), Cambridge University Press., Cambridge, UK, pp. 164 – 180.
O’Hagan A�, E� Caitlin, C� E� Buck, A� Daneshkhah, J� R� Eiser, P� H� Garthwaite,
D� J� Jenkinson, J� E� Oakley, and T� Rakow (2006)� Uncertainty Judgements:
Eliciting Experts’ Probabilities. John Wiley & Sons Inc, ISBN: 0 - 470 - 02999 - 4.
O’Neill B� C�, and M� Oppenheimer (2002)� Dangerous climate impacts and the
Kyoto Protocol. Science 296, 1971 – 1972. Available at: http: / / www. sciencemag.
org / content / 296 / 5575 / 1971.short.
O’Neill B�, and W� Sanderson (2008)� Population, uncertainty, and learning in cli-
mate change decision analysis. Climatic Change 89, 87 – 123. doi: 10.1007 / s105
84 - 008 - 9419 - 8, ISSN: 0165-0009.
Obersteiner M�, C� Azar, P� Kauppi, K� Möllersten, J� Moreira, S� Nilsson,
P� Read, K� Riahi, B� Schlamadinger, and Y� Yamagata (2001)� Managing
climate risk. Science 294, 786 – 787. Available at: http: / / webarchive.iiasa.
ac.at / Admin / PUB / Documents / IR-01 - 051.pdf.
Oda J�, and K� Akimoto (2011)� An analysis of CCS investment under uncer-
tainty. Energy Procedia 4, 1997 – 2004. Available at: http: / / www. sciencedirect.
com / science / article / pii / S1876610211002785.
Oliveira P� J��, G� P� Asner, D� E� Knapp, A� Almeyda, R� Galván-Gildemeister, S�
Keene, R� F� Raybin, and R� C� Smith (2007)� Land-use allocation protects the
Peruvian Amazon. Science 317, 1233 – 1236.
Orlove B�, C� Roncoli, and A� Majugu (2009)� Indigenous climate knowledge in
Southern Uganda: the multiple components of a dynamic regional system. Cli-
matic Change 100(2), 243 – 265. doi: 10.1007 / s10584 - 009 - 9586 - 2.
Patiño-Echeverri D�, P� Fischbeck, and E� Kriegler (2009)� Economic and envi-
ronmental costs of regulatory uncertainty for coal-fired power plants. Environ-
mental Science and Technology 43, 578 – 584. doi: 10.1021 / es800094h, ISSN:
0013-936X.
Patiño-Echeverri D�, B� Morel, J� Apt, and C� Chen (2007)� Should a coal-fired
power plant be replaced or retrofitted? Environmental Science and Technology
41, 7980 – 7986. doi: 10.1021 / es0711009, ISSN: 0013-936X.
Patt A� (2007)� Assessing model-based and conflict-based uncertainty. Global Envi-
ronmental Change 17, 37 – 46.
Patt A�, and S� Dessai (2005)� Communicating uncertainty: lessons learned and
suggestions for climate change assessment. Comptes Rendus Geoscience
337, 425 – 441. Available at: http: / / www. sciencedirect. com / science / article / pii /
S1631071304002822.
Patt A�, R� J�� Klein, and A� de la Vega-Leinert (2005)� Taking the uncertainty
in climate-change vulnerability assessment seriously. Comptes Rendus Geosci-
ences 337, 411 – 424.
Patt A�, L� Ogallo, and M� Hellmuth (2007)� Learning from 10 years of climate
outlook forums in Africa. Science 318, 49 – 50. doi: 10.1126 / science.1147909,
ISSN: 0036-8075, 1095 – 9203.
Patt A�, M� Tadross, P� Nussbaumer, K� Asante, M� Metzger, J� Rafael, A�
Goujon, and G� Brundrit (2010)� Estimating least-developed countries’
vulnerability to climate-related extreme events over the next 50 years.
Proceedings of the National Academy of Sciences 107, 1333 – 1337. doi:
10.1073 / pnas.0910253107, ISSN: 0027-8424, 1091 – 6490.
Patt A�, D� P� van Vuuren, F� Berkhout, A� Aaheim, A� F� Hof, M� Isaac, and R�
Mechler (2009)� Adaptation in integrated assessment modeling: where do we
stand? Climatic Change 99, 383 – 402. doi: 10.1007 / s10584 - 009 - 9687-y, ISSN:
0165-0009, 1573 – 1480.
Patt A�, and E� U� Weber (2014)� Perceptions and communications strategies
for the many uncertainty relevant for climate policy. Wiley Interdisciplinary
Reviews: Climate Change 5(2), 219 – 232.
Patt A�, and R� Zeckhauser (2000)� Action bias and environmental decisions. Jour-
nal of Risk and Uncertainty 21, 45 – 72.
Payne J� W�, J� R� Bettman, and E� J� Johnson (1988)� Adaptive strategy selection
in decision making. Journal of Experimental Psychology: Learning, Memory, and
Cognition 14, 534.
Peck S� C�, and T� J� Teisberg (1993)� Global warming uncertainties and the value
of information: an analysis using CETA. Resource and Energy Economics 15,
71 – 97.
Peck S� C�, and T� J� Teisberg (1994)� Optimal carbon emissions trajectories when
damages depend on the rate or level of global warming. Climatic Change 28,
289 – 314. doi: 10.1007 / BF01104138, ISSN: 0165-0009.
Peck S� C�, and T� J� Teisberg (1995)� Optimal CO
2
control policy with sto-
chastic losses from temperature rise. Climatic Change 31, 19 – 34. doi:
10.1007 / BF01092979, ISSN: 0165-0009.
Percival R� V�, and A� Miller (2011)� Resolving conflicts between green technology
transfer and intellectual property law. University of Maryland School of Law
Working Paper No. 2011 – 27.
Peters E�, P� Slovic, J� H� Hibbard, and M� Tusler (2006)� Why worry? Worry, risk
perceptions, and willingness to act to reduce medical errors. Health Psychol-
ogy; Health Psychology 25, 144. Available at: http: / / psycnet.apa.org / journals /
hea / 25 / 2 / 144 / .
Philibert C� (2009)� Assessing the value of price caps and floors. Climate Policy 9,
612 – 633.
Pichert D�, and K� V� Katsikopoulos (2008)� Green defaults: Information presen-
tation and pro-environmental behaviour. Journal of Environmental Psychology
28, 63 – 73.
Pidgeon N�, and B� Fischhoff (2011)� The role of social and decision sciences in com-
municating uncertain climate risks. Nature Climate Change 1, 35 – 41. Available at:
http: / / www. nature. com / nclimate / journal / v1 / n1 / full / nclimate1080.html?WT.
ec_id=NCLIMATE-201104.
Pidgeon N�, I� Lorenzoni, and W� Poortinga (2008)� Climate change or nuclear
power — No thanks! A quantitative study of public perceptions and risk fram-
ing in Britain. Global Environmental Change 18, 69 – 85. doi: 10.1016 / j.gloenv-
cha.2007.09.005, ISSN: 09593780.
Pierotti R� (2011)� The World According to Is’a: Combining Empiricism and Spiritual
Understanding in Indigenous Ways of Knowing. Wiley-Blackwell Press.
Pindyck R� S� (2011)� Fat tails, thin tails, and climate change policy. Review of Envi-
ronmental Economics and Policy 5, 258 – 274.
Pindyck R� S� (2013)� Climate Change Policy: What Do the Models Tell Us?
National Bureau of Economic Research, Available at: http: / / www. nber.
org / papers / w19244.
202202
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Pizer W� A� (1999)� The optimal choice of climate change policy in the presence of
uncertainty. Resource and Energy Economics 21, 255 – 287. doi: 10.1016 / S092
8 - 7655(99)00005 - 6.
Polasky S�, S� R� Carpenter, C� Folke, and B� Keeler (2011)� Decision-making
under great uncertainty: Environmental management in an era of global change.
Trends in Ecology & Evolution 26, 398 – 404. doi: 10.1016 / j.tree.2011.04.007,
ISSN: 01695347.
Pope D� G�, and M� E� Schweitzer (2011)� Is Tiger Woods loss averse? Persistent
bias in the face of experience, competition, and high stakes. The American
Economic Review 101, 129 – 157. Available at: http: / / www. ingentaconnect.
com / content / aea / aer / 2011 / 00000101 / 00000001 / art00009.
Prato T� (2008)� Accounting for risk and uncertainty in determining preferred strate-
gies for adapting to future climate change. Mitigation and Adaptation Strate-
gies for Global Change 13, 47 – 60.
Prins G�, and S� Rayner (2007)� Time to ditch Kyoto. Nature 449, 973 – 975.
Pycroft J�, L� Vergano, C� Hope, D� Paci, and J� C� Ciscar (2011)� A tale of tails:
Uncertainty and the social cost of carbon dioxide. Economics: The Open-Access,
Open-Assessment E-Journal. doi: 10.5018 / economics-ejournal.ja.2011 - 22,
ISSN: 1864-6042.
Quiggin J� (1993)� Generalized Expected Utility Theory: The Rank-Dependent
Model. Springer.
Ramsey F� P� (1926)� Truth and probability. In: The Foundations of Mathematics and
Other Logical Essays. Kegan, Paul, Trench, Trubner & Co, London, pp. 156 – 198.
Rasch P� J�, S� Tilmes, R� P� Turco, A� Robock, L� Oman, C�-C� (Jack) Chen, G� L�
Stenchikov, and R� R� Garcia (2008)� An overview of geoengineering of
climate using stratospheric sulphate aerosols. Philosophical Transactions of
the Royal Society A: Mathematical, Physical and Engineering Sciences 366,
4007 – 4037. doi: 10.1098 / rsta.2008.0131.
Rasmussen N� (1975)� Reactor Safety Study. U. S. Nuclear Regulatory Commission,
Washington, DC,
Rayner S� (1993)� Introduction. Global Environmental Change 3, 7 – 11. doi: 10.101
6 / 0959 - 3780(93)90011 - 9, ISSN: 0959-3780.
Rayner S� (2007)� The rise of risk and the decline of politics. Environmental Haz-
ards 7, 165 – 172. Available at: http: / / www. sciencedirect. com / science / article /
pii / S1747789107000142.
Rayner S�, and E� L� Malone (2001)� Climate change, poverty, and intragenera-
tional equity:the national level. International Journal of Global Environmental
Issues 1, 175 – 202. Available at: http: / / inderscience.metapress.com / index / vhd-
j0r6pbgr09fqe.pdf.
Reichenbach J�, and T� Requate (2012)� Subsidies for renewable energies in the
presence of learning effects and market power. Resource and Energy Economics
34, 236 – 254. ISSN: 0928-7655.
Reilly J� M�, J� A� Edmonds, R� H� Gardner, and A� L� Brenkert (1987)� Uncertainty
analysis of the IEA / ORAU CO
2
emissions model. The Energy Journal 8, 1 – 29.
ISSN: 0195-6574.
Reinelt P� S�, and D� W� Keith (2007)� Carbon capture retrofits and the cost of regu-
latory uncertainty. The Energy Journal 28, 101.
Richardson J� (2008)� Needful things. Trading Carbon 2, 30 – 32.
Del Rio P�, and M�� Gual (2007)� An integrated assessment of the feed-in tariff
system in Spain. Energy Policy 35, 994 – 1012. ISSN: 0301-4215.
Roberts J� J�, R� A� Wood, and R� S� Haszeldine (2011)� Assessing the health risks
of natural CO
2
seeps in Italy. Proceedings of the National Academy of Sciences
108, 16545 – 16548. doi: 10.1073 / pnas.1018590108.
Robock A�, M� Bunzl, B� Kravitz, and G� L� Stenchikov (2010)� A test for geoengi-
neering? Science 327, 530 – 531. doi: 10.1126 / science.1186237.
Romijn E�, M� Herold, L� Kooistra, D� Murdiyarso, and L� Verchot (2012)�
Assessing capacities of non-AnnexI countries for national forest monitoring
in the context of REDD+. Environmental Science & Policy 19 – 20, 33 – 48. doi:
10.1016 / j.envsci.2012.01.005, ISSN: 1462-9011.
Roncoli C� (2006)� Ethnographic and participatory approaches to research on farm-
ers’ responses to climate predictions. Climate Research 33, 81. Available at:
http: / / www. int-res. com / abstracts / cr / v33 / n1 / p81 - 99 / .
Roser-Renouf C�, E� W� Maibach, A� Leiserowitz, and X� Zhao (2011)� The gen-
esis of climate change activism: From key beliefs to political advocacy. In Press.
Rothlisberger J� D�, D� C� Finnoff, R� M� Cooke, and D� M� Lodge (2012)� Ship-
borne nonindigenous species diminish great lakes ecosystem services. Ecosys-
tems 15, 1 – 15.
Rothlisberger J� D�, D� M� Lodge, R� M� Cooke, and D� C� Finnoff (2009)� Future
declines of the binational Laurentian Great Lakes fisheries: the importance of
environmental and cultural change. Frontiers in Ecology and the Environment
8, 239 – 244.
Roy A� D� (1952)� Safety first and the holding of assets. Econometrica: Journal of the
Econometric Society 20, 431 – 449.
Rozenberg J�, S� Hallegatte, A� Vogt-Schilb, O� Sassi, C� Guivarch, H� Waisman,
and J�-C� Hourcade (2010)� Climate policies as a hedge against the uncer-
tainty on future oil supply. Climatic Change 101, 663 – 668. doi: 10.1007 / s1058
4 - 010 - 9868 - 8, ISSN: 0165-0009.
Ryan J� J� C� H�, T� A� Mazzuchi, D� J� Ryan, J� Lopez de la Cruz, and R� M� Cooke
(2012)� Quantifying information security risks using expert judgment elici-
tation. Computers & Operations Research 39, 774 – 784. doi: 10.1016 / j.
cor.2010.11.013, ISSN: 0305-0548.
Sáenz de Miera G�, P� del Río González, and I� Vizcaíno (2008)� Analysing
the impact of renewable electricity support schemes on power prices: The
case of wind electricity in Spain. Energy Policy 36, 3345 – 3359. Available at:
http: / / www. sciencedirect. com / science / article / pii / S0301421508001882.
Sagar A� D�, and B� van der Zwaan (2006)� Technological innovation in the energy
sector: R&D, deployment, and learning-by-doing. Energy Policy 34, 2601 – 2608.
doi: 10.1016 / j.enpol.2005.04.012, ISSN: 0301-4215.
Samuelson W�, and R� Zeckhauser (1988)� Status quo bias in decision making.
Journal of Risk and Uncertainty 1, 7 – 59.
Sanquist T� F�, H� Orr, B� Shui, and A� C� Bittner (2012)� Lifestyle factors in US
residential electricity consumption. Energy Policy 42, 354 – 364. Available at:
http: / / www. sciencedirect. com / science / article / pii / S0301421511009906.
Sarewitz D� (2010)� Curing climate backlash. Nature 464, 28. doi: 10.1038 /
464028a, ISSN: 1476-4687.
Savage L� J� (1954)� The Foundations of Statistics. Courier Dover Publications, 356
pp. ISBN: 9780486623498.
Schiermeier Q� (2004)� Disaster movie highlights transatlantic divide. Nature 431,
4 – 4.
Schleich J�, M� Klobasa, S� Gölz, and M� Brunner (2013)� Effects of feedback
on residential electricity demand — Findings from a field trial in Austria. Energy
Policy 61, 1097 – 1106. Available at: http: / / www. sciencedirect. com / science /
article / pii / S0301421513003443.
Schmeidler D� (1989)� Subjective probability and expected utility without additiv-
ity. Econometrica: Journal of the Econometric Society 57, 571 – 587.
203203
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Schmidt M� G� W�, A� Lorenz, H� Held, and E� Kriegler (2011)� Climate targets
under uncertainty: challenges and remedies. Climatic Change 104, 783 – 791.
Available at: http: / / www. springerlink. com / index / 607010683813VU36.pdf.
Schoemaker P� (1995)� Scenario planning: A tool for strategic thinking. Sloan Man-
agement Review 4, 25 – 40.
Schoemaker P� J� H�, and J� E� Russo (2001)� Winning Decisions: Getting It Right
the First Time. Crown Business, 352 pp. Available at: http: / / books.google.
com / books?hl=en&lr=&id=iHG_2i4Z0wYC&oi=fnd&pg=PR7&dq=Russo,+J.+
Edward+%26+Paul+Schoemaker.+Winning+Decisions:+Getting+it+Right+the
+First+Time&ots=th9D5OnH3f&sig=ULqGHHsV4kDFyfOMZUdIfskMT14.
Scott M� J�, R� D� Sands, J� Edmonds, A� M� Liebetrau, and D� W� Engel (1999)�
Uncertainty in integrated assessment models: modeling with MiniCAM 1.0.
Energy Policy 27, 855 – 879. doi: 10.1016 / S0301 - 4215(99)00057 - 9, ISSN: 0301-
4215.
Shackley S�, D� Reiner, P� Upham, H� de Coninck, G� Sigurthorsson, and J�
Anderson (2009)� The acceptability of CO
2
capture and storage (CCS) in
Europe: An assessment of the key determining factors: Part 2. The social accept-
ability of CCS and the wider impacts and repercussions of its implementation.
International Journal of Greenhouse Gas Control 3, 344 – 356. doi: 10.1016 / j.
ijggc.2008.09.004, ISSN: 1750-5836.
Shah T�, and U� Lele (2011)� Climate Change, Food and Water Security in South
Asia: Critical Issues and Cooperative Strategies in an Age of Increased Risk and
Uncertainty. Synthesis of Workshop Discussions. A Global Water Partnership
(GWP) and International Water Management Institute (IWMI) Workshop. Stock-
holm, Sweden, 1 – 47 pp.
Shapiro S� A�, and T� O� McGarity (1991)� Not So Paradoxical: The Rationale for Tech-
nology-Based Regulation. Duke LJ, 729. Available at: http: / / heinonlinebackup.
com / hol-cgi-bin / get_pdf.cgi?handle=hein.journals / duklr1991§ion=30.
Simon H� A� (1955)� A Behavioral Model of Rational Choice. The Quarterly Journal
of Economics 69, 99 – 118. doi: 10.2307 / 1884852, ISSN: 0033-5533.
Slovic P� (1987)� Perception of risk. Science 236, 280 – 285. doi: 10.1126 /
science.3563507.
Smith J� (2005)� Dangerous news: Media decision making about climate change
risk. Risk Analysis 25, 1471 – 1482.
Socolow R� H�, and A� Glaser (2009)� Balancing risks: nuclear energy & climate
change. Daedalus 138, 31 – 44. doi: 10.1162 / daed.2009.138.4.31, ISSN: 0011-
5266.
Spence A�, W� Poortinga, C� Butler, and N� F� Pidgeon (2011)� Perceptions of cli-
mate change and willingness to save energy related to flood experience. Nature
Climate Change 1, 46 – 49. doi: 10.1038 / nclimate1059, ISSN: 1758-678X.
Sterman J� D� (2008)� Risk communication on climate: Mental models and Mass
Balance. Science 322, 532 – 533. doi: 10.1126 / science.1162574, ISSN: 0036-
8075, 1095 – 9203.
Sterman J� D�, and L� B� Sweeney (2007)� Understanding public complacency
about climate change: Adults’ mental models of climate change violate conser-
vation of matter. Climatic Change 80, 213 – 238. doi: 10.1007 / s10584 - 006 - 910
7 - 5, ISSN: 0165-0009, 1573 – 1480.
Stern N� H� (2007)� The Economics of Climate Change: The Stern Review. Cam-
bridge University Press, 713 pp. ISBN: 9780521700801.
Stevenson M� A� (2010)� Framing anthropogenic environmental change in
public health terms. Global Environmental Politics 10, 152 – 157. doi:
10.1162 / glep.2010.10.1.152, ISSN: 1526-3800.
Stirling A� (2007)� Risk, precaution and science: towards a more constructive
policy debate. Talking point on the precautionary principle. EMBO Reports 8,
309 – 315. doi: 10.1038 / sj.embor.7400953, ISSN: 1469-221X.
Sunstein C� R� (2006)� The availability heuristic, intuitive cost-benefit analysis, and
climate change. Climatic Change 77, 195 – 210. doi: 10.1007 / s10584 - 006 - 9073-
y, ISSN: 0165-0009, 1573 – 1480.
Swim J� K�, P� C� Stern, T� J� Doherty, S� Clayton, J� P� Reser, E� U� Weber, R� Gif-
ford, and G� S� Howard (2011)� Psychology’s contributions to understanding
and addressing global climate change. American Psychologist 66, 241. Avail-
able at: http: / / psycnet.apa.org / journals / amp / 66 / 4 / 241 / .
Syri S�, A� Lehtilä, T� Ekholm, I� Savolainen, H� Holttinen, and E� Peltola
(2008)� Global energy and emissions scenarios for effective climate change
mitigation — Deterministic and stochastic scenarios with the TIAM model.
International Journal of Greenhouse Gas Control 2, 274 – 285. Available at:
http: / / www. sciencedirect. com / science / article / pii / S1750583608000029.
Szolgayova J�, S� Fuss, and M� Obersteiner (2008)� Assessing the effects of CO
2
price caps on electricity investments — A real options analysis. Energy Policy 36,
3974 – 3981. doi: 10.1016 / j.enpol.2008.07.006, ISSN: 0301-4215.
Taleb N� N� (2007)� The Black Swan: The Impact of the Highly Improbable. Random
House Publishing Group, 481 pp. ISBN: 9781588365835.
Tavoni A�, A� Dannenberg, G� Kallis, and A� Löschel (2011)� Inequality, com-
munication, and the avoidance of disastrous climate change in a pub-
lic goods game. Proceedings of the National Academy of Sciences. doi:
10.1073 / pnas.1102493108.
Terwel B� W�, F� Harinck, N� Ellemers, and D� D� Daamen (2011)� Going beyond
the properties of CO
2
capture and storage (CCS) technology: How trust
in stakeholders affects public acceptance of CCS. International Journal of
Greenhouse Gas Control 5, 181 – 188. Available at: http: / / www. sciencedirect.
com / science / article / pii / S1750583610001507.
Thaler R� H� (1999)� Mental accounting matters. Journal of Behavioral Decision
Making 12, 183 – 206. doi: 10.1002 / (SICI)1099 - 0771(199909)12:3<183::AID-
BDM318>3.0.CO;2-F, ISSN: 1099-0771.
Thaler R� H�, and C� R� Sunstein (2008)� Nudge: Improving Decisions About Health,
Wealth, and Happiness. Yale Univ Press, Available at: http: / / books.google.
com / books?hl=en&lr=&id=dSJQn8egXvUC&oi=fnd&pg=PA17&dq=thaler-
+sunstein+nudge&ots=0cJNMCGnRt&sig=W0TkD9mBUvN68xjgj5sFwpaW-NU.
Thompson A� (2010)� Rational design in motion: Uncertainty and flexibility in
the global climate regime. European Journal of International Relations 16,
269 – 296.
Thornton P� K�, P� G� Jones, T� Owiyo, R� L� Kruska, M� Herrero, V� Orindi, S� Bhad-
wal, P� Kristjanson, A� Notenbaert, N� Bekele, and others (2008)� Climate
change and poverty in Africa: Mapping hotspots of vulnerability. African Jour-
nal of Agriculture and Resource Economics 2, 24 – 44.
Tol R� S� J� (1999)� Safe policies in an uncertain climate: an application of FUND.
Global Environmental Change, Part A: Human and Policy Dimensions 9,
221 – 232.
Tol R� S� J� (2003)� Is the uncertainty about climate change too large for expected
cost-benefit analysis? Climatic Change 56, 265 – 289.
Toubia O�, E� Johnson, T� Evgeniou, and P� Delquié (2013)� Dynamic experi-
ments for estimating preferences: An adaptive method of eliciting time and risk
parameters. Management Science 59, 613 – 640. Available at: http: / / mansci.
journal.informs.org / content / 59 / 3 / 613.short.
204204
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Tsur Y�, and A� Zemel (1996)� Accounting for global warming risks: Resource man-
agement under event uncertainty. Journal of Economic Dynamics and Control
20, 1289 – 1305. doi: 10.1016 / 0165 - 1889(95)00900 - 0, ISSN: 0165-1889.
Tsur Y�, and A� Zemel (2009)� Endogenous discounting and climate policy. Environ-
mental and Resource Economics 44, 507 – 520. doi: 10.1007 / s10640 - 009 - 9298
- 0, ISSN: 0924-6460.
Tuomisto J� T�, A� Wilson, J� S� Evans, and M� Tainio (2008)� Uncertainty in mortal-
ity response to airborne fine particulate matter: Combining European air pollu-
tion experts. Reliability Engineering & System Safety 93, 732 – 744.
Turner N� J�, and H� Clifton (2009)� “It’s so different today”: Climate change and
indigenous lifeways in British Columbia, Canada. Global Environmental Change
19, 180 – 190. Available at: http: / / www. sciencedirect. com / science / article /
pii / S0959378009000223.
Tversky A�, and D� Kahneman (1973)� Availability: A heuristic for judging fre-
quency and probability. Cognitive Psychology 5, 207 – 232. doi: 10.1016 / 0010
- 0285(73)90033 - 9, ISSN: 0010-0285.
Tversky A�, and D� Kahneman (1992)� Advances in prospect theory: Cumulative
representation of uncertainty. Journal of Risk and Uncertainty 5, 297 – 323. doi:
10.1007 / BF00122574, ISSN: 0895-5646, 1573 – 0476.
Tyshenko M� G�, S� ElSaadany, T� Oraby, S� Darshan, W� Aspinall, R� M� Cooke, A�
Catford, and D� Krewski (2011)� Expert elicitation for the judgment of prion
disease risk uncertainties. Journal of Toxicology and Environmental Health, Part
A 74, 261 – 285.
Ulph A�, and D� Ulph (1997)� Global warming, irreversibility and learning. The Eco-
nomic Journal 107, 636 – 650.
UNDP (2007)� Human Development Report 2007 / 2008: Fighting Climate Change:
Human Solidarity in a Divided World. United Nations Development Programme,
New York, 384 pp. Available at: http: / / hdr.undp.org / en / media / HDR_20072008_
EN_Complete.pdf.
UNFCCC (2007)� Vulnerability and Adaptation To Climate Change In Small
Island Developing States. UNFCC, 38 pp. Available at: http: / / unfccc.int / files /
adaptation / adverse_effects_and_response_measures_art_48 / application / pdf /
200702_sids_adaptation_bg.pdf.
U� S� Environmental Protection Agency (2005)� Guidelines for Carcino-
gen Risk Assessment. Washington, DC, Available at: http: / / cfpub.epa.
gov / ncea / cfm / recordisplay.cfm?deid=116283.
U� S� NRC (1983)� PRA Procedures Guide. A Guide to the Performance of Probabilis-
tic Risk Assessments for Nuclear Power Plants. Final Report. NUREG / CR-2300.
U. S. NRC (U. S. Nuclear Regulatory Commission). Available at: Available from:
http: / / www. nrc. gov / reading-rm / doc-ollections / nuregs / contract / cr2300 / vol2 /
cr2300v2-a.pdf.p.12 - 12).
Vasa A�, and A� Michaelowa (2011)� Uncertainty in Climate Policy — Impacts on
Market Mechanisms. In: Climate Change and Policy. G. Gramelsberger, J. Feich-
ter, (eds.), Springer Berlin Heidelberg, pp. 127 – 144. ISBN: 978 - 3-642 - 17699 - 9,
978 – 3-642 – 17700 – 2.
Victor D� G� (2011)� Global Warming Gridlock: Creating More Effective Strategies
for Protecting the Planet. Cambridge University Press, Cambridge, UK, 392 pp.
ISBN: 9780521865012 0521865018.
Vignola R�, S� Klinsky, J� Tam, and T� McDaniels (2012)� Public perception, knowl-
edge and policy support for mitigation and adaption to Climate Change in
Costa Rica: Comparisons with North American and European studies. Mitiga-
tion and Adaptation Strategies for Global Change 18, 1 – 21. doi: 10.1007 / s110
27 - 012 - 9364 - 8, ISSN: 1381-2386.
Vlek C� (2010)� Judicious management of uncertain risks: I. Developments
and criticisms of risk analysis and precautionary reasoning. Journal of Risk
Research 13, 517 – 543. Available at: http: / / www. tandfonline. com / doi / abs /
10.1080 / 13669871003629887.
Van Vuuren D� P�, J� Edmonds, M� Kainuma, K� Riahi, A� Thomson, K� Hibbard,
G� C� Hurtt, T� Kram, V� Krey, J� F� Lamarque, T� Masui, M� Meinshausen, N�
Nakicenovic, S� J� Smith, and S� K� Rose (2011)� The representative concen-
tration pathways: An overview. Climatic Change 109, 5 – 31.
Wakker P� P� (2010)� Prospect Theory: For Risk and Ambiguity. Cambridge Univer-
sity Press, Cambridge; New York, 606 pp. ISBN: 9780521765015 0521765013
9780521748681 0521748682.
Wallquist L�, S� L� Seigo, V� H�� Visschers, and M� Siegrist (2012)� Public accep-
tance of CCS system elements: A conjoint measurement. International Journal
of Greenhouse Gas Control 6, 77 – 83.
Wallsten T� S�, D� V� Budescu, A� Rapoport, R� Zwick, and B� Forsyth (1986)�
Measuring the vague meanings of probability terms. Journal of Experimental
Psychology: General 115, 348. Available at: http: / / psycnet.apa.org / journals /
xge / 115 / 4 / 348 / .
Walters C� J�, and R� Hilborn (1978)� Ecological optimization and adaptive man-
agement. Annual Review of Ecology and Systematics 9, 157 – 188.
Wang M�, C� Liao, S� Yang, W� Zhao, M� Liu, and P� Shi (2012)� Are People Willing
to Buy Natural Disaster Insurance in China? Risk Awareness, Insurance Accep-
tance, and Willingness to Pay. Risk Analysis 32, 1717 – 1740. doi: 10.1111 / j.153
9 - 6924.2012.01797.x, ISSN: 1539-6924.
Weber E� U� (1997)� Perception and expectation of climate change: Precondition
for economic and technological adaptation. In: M. Bazerman, D. Messick, A.
Tenbrunsel, & K. Wade-Benzoni (eds.), Psychological Perspectives to Environ-
mental and Ethical Issues in Management. Jossey-Bass, San Francisco, CA, pp.
314 – 341.
Weber E� U� (2006)� Experience-based and description-based perceptions of long-
term risk: Why global warming does not scare us (yet). Climatic Change 77,
103 – 120. doi: 10.1007 / s10584 - 006 - 9060 - 3, ISSN: 0165-0009, 1573 – 1480.
Weber E� U� (2011)� Climate change hits home. Nature Climate Change 1, 25 – 26.
doi: doi:10.1038 / nclimate1070.
Weber E� U� (2013)� Doing the right thing willingly: Behavioral decision theory and
environmental policy. In: The Behavioral Foundations of Policy. E. Shafir, (ed.),
Princeton University Press., Princeton, NJ, pp. pp. 380 – 397.
Weber E� U�, and D� J� Hilton (1990)� Contextual effects in the interpretations of
probability words: Perceived base rate and severity of events. Journal of Experi-
mental Psychology: Human Perception and Performance 16, 781. Available at:
http: / / psycnet.apa.org / journals / xhp / 16 / 4 / 781 / .
Weber E� U�, and C� Hsee (1998)� Cross-cultural differences in risk perception, but
cross-cultural similarities in attitudes towards perceived risk. Management Sci-
ence, 1205 – 1217.
Weber E� U�, and E� J� Johnson (2009)� Decisions under uncertainty: Psychological,
economic, and neuroeconomic explanations of risk preference. In: Neuroeco-
nomics: Decision Making and the Brain. P. Glimcher, C. F. Camerer, E. Fehr, R.
Poldrack, (eds.), Elsevier, New York, pp. 127 – 144.
Weber E� U�, E� J� Johnson, K� F� Milch, H� Chang, J� C� Brodscholl, and D� G� Gold-
stein (2007)� Asymmetric discounting in intertemporal choice. Psychological
Science 18, 516 – 523. doi: 10.1111 / j.1467 - 9280.2007.01932.x.
205205
Integrated Risk and Uncertainty Assessment of Climate Change Response Policies
2
Chapter 2
Weber E� U�, and P� G� Lindemann (2007)� From intuition to analysis: Making deci-
sions with our head, our heart, or by the book. In: Intuition in Judgment and
Decision Making. H. Plessner, C. Betsch, T. Betsch, (eds.), Lawrence Erlbaum,
Mahwah, NJ, pp. 191 – 208.
Weber E� U�, S� Shafir, and A�-R� Blais (2004)� Predicting risk sensitivity in humans
and lower animals: Risk as variance or coefficient of variation. Psychological
Review 111, 430 – 445. doi: 10.1037 / 0033 - 295X.111.2.430, ISSN: 1939-1471
(ELECTRONIC); 0033 – 295X (PRINT).
Weber E� U�, and P� C� Stern (2011)� Public understanding of climate change in the
United States. American Psychologist 66, 315 – 328. doi: 10.1037 / a0023253,
ISSN: 1935-990X, 0003 – 066X.
Webster M� D� (2008)� Uncertainty and the IPCC. An editorial comment. Climatic
Change 92, 37 – 40. doi: 10.1007 / s10584 - 008 - 9533 - 7, ISSN: 0165-0009.
Webster M� D�, M� Babiker, M� Mayer, J� M� Reilly, J� Harnisch, R� Hyman, M� C�
Sarofim, and C� Wang (2002)� Uncertainty in emissions projections for cli-
mate models. Atmospheric Environment 36, 3659 – 3670. doi: 10.1016 / S1352 - 2
310(02)00245 - 5, ISSN: 1352-2310.
Webster M� D�, C� Forest, J� Reilly, M� Babiker, D� Kicklighter, M� Mayer, R�
Prinn, M� Sarofim, A� Sokolov, P� Stone, and C� Wang (2003)� Uncertainty
analysis of climate change and policy response. Climatic Change 61, 295 – 320.
doi: 10.1023 / B:CLIM.0000004564.09961.9f, ISSN: 0165-0009.
Webster M� D�, L� Jakobovits, and J� Norton (2008)� Learning about climate
change and implications for near-term policy. Climatic Change 89(1 – 2), 67 – 85.
Webster M� D�, S� Paltsev, and J� Reilly (2010)� The hedge value of interna-
tional emissions trading under uncertainty. Energy Policy 38, 1787 – 1796. doi:
10.1016 / j.enpol.2009.11.054, ISSN: 0301-4215.
Weitzman M� L� (2001)� Gamma discounting. American Economic Review 91,
260 – 271.
Weitzman M� L� (2009)� On modeling and interpreting the economics of cata-
strophic climate change. The Review of Economics and Statistics 91, 1 – 19.
Weitzman M� L� (2011)� Fat-tailed uncertainty in the economics of catastrophic cli-
mate change. Review of Environmental Economics and Policy 5, 275 – 292.
Whitfield S� C�, E� A� Rosa, A� Dan, and T� Dietz (2009)� The Future of nuclear
power: Value orientations and risk perception. Risk Analysis 29, 425 – 437. doi:
10.1111 / j.1539 - 6924.2008.01155.x, ISSN: 1539-6924.
Whitmarsh L� (2008)� Are flood victims more concerned about climate change than
other people? The role of direct experience in risk perception and behavioural
response. Journal of Risk Research 11, 351 – 374. Available at: http: / / www.
tandfonline. com / doi / abs / 10.1080 / 13669870701552235.
Williges K�, J� Lilliestam, and A� Patt (2010)� Making concentrated solar power
competitive with coal: The costs of a European feed-in tariff. Energy Policy 38,
3089 – 3097.
Wiser R�, K� Porter, and R� Grace (2005)� Evaluating experience with renewables
portfolio standards in the United States. Mitigation and Adaptation Strate-
gies for Global Change 10, 237 – 263. doi: 10.1007 / s11027 - 005 - 6573 - 4, ISSN:
1381-2386, 1573 – 1596.
Wood P� J�, and F� Jotzo (2011)� Price floors for emissions trading. Energy Policy 39,
1746 – 1753. doi: 10.1016 / j.enpol.2011.01.004, ISSN: 0301-4215.
World Bank (2010)� Economics of Adaptation to Climate Change: Social Synthesis
Report. World Bank. The International Bank for Reconstruction and Develop-
ment.
Yang M�, W� Blyth, R� Bradley, D� Bunn, C� Clarke, and T� Wilson (2008)� Evaluat-
ing the power investment options with uncertainty in climate policy. Energy
Economics 30, 1933 – 1950. doi: 10.1016 / j.eneco.2007.06.004, ISSN: 0140-
9883.
Yohe G�, N� Andronova, and M� Schlesinger (2004)� To hedge or not against
an uncertain climate future? Science 306, 416 – 417. doi: 10.1126 / science.
1101170.
Yohe G�, and R� Wallace (1996)� Near term mitigation policy for global change
under uncertainty: Minimizing the expected cost of meeting unknown concen-
tration thresholds. Environmental Modeling & Assessment 1, 47 – 57. Available
at: http: / / link.springer.com / article / 10.1007 / BF01874846.
Young O� R� (1994)� International Governance: Protecting the Environment in a
Stateless Society. Cornell University Press, 244 pp. ISBN: 9780801481765.
De Zeeuw A�, and A� Zemel (2012)� Regime shifts and uncertainty in pollu-
tion control. Journal of Economic Dynamics and Control 36, 939 – 950. doi:
10.1016 / j.jedc.2012.01.006, ISSN: 0165-1889.
Zhao J� (2003)� Irreversible abatement investment under cost uncertainties: trad-
able emission permits and emissions charges. Journal of Public Economics 87,
2765 – 2789.
Zickfeld K�, M� G� Morgan, D� J� Frame, and D� W� Keith (2010)� Expert judg-
ments about transient climate response to alternative future trajectories
of radiative forcing. Proceedings of the National Academy of Sciences. doi:
10.1073 / pnas.0908906107.
Zoellner J�, P� Schweizer-Ries, and C� Wemheuer (2008)� Public acceptance of
renewable energies: Results from case studies in Germany. Energy Policy 36,
4136 – 4141.
