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2
Foundations for
Decision Making
Coordinating Lead Authors:
Roger N. Jones (Australia), Anand Patwardhan (India)
Lead Authors:
Stewart J. Cohen (Canada), Suraje Dessai (UK/Portugal), Annamaria Lammel (France),
Robert J. Lempert (USA), M. Monirul Qader Mirza (Bangladesh/Canada), Hans von Storch
(Germany)
Contributing Authors:
Werner Krauss (Germany), Johanna Wolf (Germany/Canada), Celeste Young (Australia)
Review Editors:
Rosina Bierbaum (USA), Nicholas King (South Africa)
Volunteer Chapter Scientist:
Pankaj Kumar (India/Japan)
This chapter should be cited as:
Jones
, R.N., A. Patwardhan, S.J. Cohen, S. Dessai, A. Lammel, R.J. Lempert, M.M.Q. Mirza, and H. von Storch,
2014: Foundations for decision making. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach,
M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy,
S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, pp. 195-228.
2
196
Executive Summary............................................................................................................................................................ 198
2.1. Introduction and Key Concepts .............................................................................................................................. 199
2.1.1. Decision-Making Approaches in this Report ..................................................................................................................................... 199
2.1.2. Iterative Risk Management ............................................................................................................................................................... 200
2.1.3. Decision Support ............................................................................................................................................................................... 202
2.2. Contexts for Decision Making ................................................................................................................................ 203
2.2.1. Social Context ................................................................................................................................................................................... 203
2.2.1.1.Cultural Values and Determinants ........................................................................................................................................ 203
2.2.1.2.Psychology ........................................................................................................................................................................... 204
2.2.1.3.Language and Meaning ....................................................................................................................................................... 204
2.2.1.4.Ethics ................................................................................................................................................................................... 205
2.2.2. Institutional Context ......................................................................................................................................................................... 206
2.2.2.1.Institutions ........................................................................................................................................................................... 206
2.2.2.2.Governance .......................................................................................................................................................................... 207
2.3. Methods, Tools, and Processes for Climate-related Decisions ................................................................................ 207
2.3.1. Treatment of Uncertainties ............................................................................................................................................................... 207
2.3.2. Scenarios .......................................................................................................................................................................................... 208
2.3.3. Evaluating Trade-offs and Multi-metric Valuation ............................................................................................................................. 208
2.3.4. Learning, Review, and Reframing ..................................................................................................................................................... 209
2.4. Support for Climate-related Decisions ................................................................................................................... 210
2.4.1. Climate Information and Services ..................................................................................................................................................... 210
Box 2-1. Managing Wicked Problems with Decision Support ..................................................................................................... 211
2.4.1.1.Climate Services: History and Concepts ................................................................................................................................ 211
2.4.1.2.Climate Services: Practices and Decision Support ................................................................................................................. 212
2.4.1.3.The Geo-political Dimension of Climate Services ................................................................................................................. 212
2.4.2. Assessing Impact, Adaptation, and Vulnerability on a Range of Scales ............................................................................................. 213
2.4.2.1.Assessing Impacts ................................................................................................................................................................ 213
2.4.2.2.Assessing Vulnerability, Risk, and Adaptive Capacity ........................................................................................................... 214
2.4.3. Climate-related Decisions in Practice ................................................................................................................................................ 214
2.5. Linking Adaptation with Mitigation and Sustainable Development ...................................................................... 216
2.5.1. Assessing Synergies and Trade-offs with Mitigation ......................................................................................................................... 216
2.5.2. Linkage with Sustainable Development: Resilience .......................................................................................................................... 216
2.5.3. Transformation: How Do We Make Decisions Involving Transformation? .......................................................................................... 217
References ......................................................................................................................................................................... 218
Table of Contents
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Frequently Asked Questions
2.1: What constitutes a good (climate) decision? .................................................................................................................................... 200
2.2: Which is the best method for climate change decision making/assessing adaptation? .................................................................... 210
2.3: Is climate change decision making different from other kinds of decision making? ......................................................................... 216
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Executive Summary
Decision support for impacts, adaptation, and vulnerability is expanding from science-driven linear methods to a wide range of
methods drawing from many disciplines (robust evidence, high agreement). This chapter introduces new material from disciplines
including behavioral science, ethics, and cultural and organizational theory, thus providing a broader perspective on climate change decision
making. Previous assessment methods and policy advice have been framed by the assumption that better science will lead to better decisions.
Extensive evidence from the decision sciences shows that while good scientific and technical information is necessary, it is not sufficient, and
decisions require context-appropriate decision-support processes and tools (robust evidence, high agreement). There now exists a sufficiently
rich set of available methods, tools, and processes to support effective climate impact, adaptation, and vulnerability (CIAV) decisions in a wide
range of contexts (medium evidence, medium agreement), although they may not always be appropriately combined or readily accessible to
decision makers. {2.1.1, 2.1.2, 2.1.3, 2.3}
Risk management provides a useful framework for most climate change decision making. Iterative risk management is most
suitable in situations characterized by large uncertainties, long time frames, the potential for learning over time, and the influence
of both climate as well as other socioeconomic and biophysical changes (robust evidence, high agreement).
Complex decision-making
contexts will ideally apply a broad definition of risk, address and manage relevant perceived risks, and assess the risks of a broad range of
plausible future outcomes and alternative risk management actions (robust evidence, medium agreement). The resulting challenge is for people
and organizations to apply CIAV decision-making processes in ways that address their specific aims. {2.1.2, 2.2.1, 2.3, 2.4.3}
Decision support is situated at the intersection of data provision, expert knowledge, and human decision making at a range of
scales from the individual to the organization and institution. Decision support is defined as a set of processes intended to create the
conditions for the production of decision-relevant information and its appropriate use. Such support is most effective when it is context-sensitive,
taking account of the diversity of different types of decisions, decision processes, and constituencies (robust evidence, high agreement).
Boundary organizations, including climate services, play an important role in climate change knowledge transfer and communication, including
translation, engagement, and knowledge exchange (medium evidence, high agreement). {2.1.3, 2.2.1, 2.2, 2.3, 2.4.1, 2.4.2, 2.4.3}
Scenarios are a key tool for addressing uncertainty (robust evidence, high agreement). They can be divided into those that explore
how futures may unfold under various drivers (problem exploration) and those that test how various interventions may play out
(solution exploration).
Historically, most scenarios used for CIAV assessments have been of the former type, though the latter are becoming
more prevalent (medium evidence, high agreement). The new RCP scenario process can address both problem and solution framing in ways
that previous IPCC scenarios have not been able to (limited evidence, medium agreement). {2.2.1.3, 2.3.2}
CIAV decision making involves ethical judgments expressed at a range of institutional scales; the resulting ethical judgements
are a key part of risk governance (robust evidence, medium agreement). Recognition of local and indigenous knowledge and diverse
stakeholder interests, values, and expectations is fundamental to building trust within decision-making processes (robust evidence, high
agreement). {2.2.1.1, 2.2.1.2, 2.2.1.3, 2.2.1.4, 2.4, 2.4.1}
Climate services aim to make knowledge about climate accessible to a wide range of decision makers. In doing so they have to
consider information supply, competing sources of knowledge, and user demand. Knowledge transfer is a negotiated process that takes a
variety of cultural values, orientations, and alternative forms of knowledge into account (medium evidence, high agreement). {2.4.1, 2.4.2}
Climate change response can be linked with sustainable development through actions that enhance resilience, the capacity to
change in order to maintain the same identity while also maintaining the capacity to adapt, learn, and transform.
Mainstreamed
adaptation, disaster risk management, and new types of governance and institutional arrangements are being studied for their potential to
support the goal of enhanced resilience (medium evidence, high agreement). {2.5.2}
Transformational adaptation may be required if incremental adaptation proves insufficient (medium evidence, high agreement). This
process may require changes in existing social structures, institutions, and values, which can be facilitated by iterative risk management and triple-
loop learning that considers a situation and its drivers, along with the underlying frames and values that provide the situation context. {2.1.2, 2.5.3}
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2.1. Introduction and Key Concepts
This chapter addresses the foundations of decision making with respect
to climate impact, adaptation, and vulnerability (CIAV). The Fourth
A
ssessment Report (AR4) summarized methods for assessing CIAV
(Carter et al., 2007), which we build on by surveying the broader
literature relevant for decision making.
Decision making under climate change has largely been modeled on
the scientific understanding of the cause-and-effect process whereby
increasing greenhouse gas emissions cause climate change, resulting
in changing impacts and risks, potentially increasing vulnerability to
those risks. The resulting decision-making guidance on impacts and
adaptation follows a rational-linear process that identifies potential
risks and then evaluates management responses (e.g., Carter et al.,
1994; Feenstra et al., 1998; Parry and Carter, 1998; Fisher et al., 2007).
This process has been challenged on the grounds that it does not
adequately address the diverse contexts within which climate decisions
are being made, often neglects existing decision-making processes, and
overlooks many cultural and behavioral aspects of decision making
(Smit and Wandel, 2006; Sarewitz and Pielke, 2007; Dovers, 2009; Beck,
2010). While more recent guidance on CIAV decision making typically
accounts for sectoral, regional, and socioeconomic characteristics
(Section 21.3), the broader decision-making literature is still not fully
reflected in current methods. This is despite an increasing emphasis
on the roles of societal impacts and responses to climate change in
decision-making methodologies (high confidence) (Sections 1.1, 1.2,
21.2.1).
The main considerations that inform the decision-making contexts
addressed here are knowledge generation and exchange, who makes
and implements decisions, and the issues being addressed and how
these can be addressed. These decisions occur within a broader social
and cultural environment. Knowledge generation and exchange
includes knowledge generation, development, brokering, exchange, and
application to practice. Decision makers include policymakers, managers,
planners, and practitioners, and range from individuals to organizations
and institutions (Table 21-1). Relevant issues include all areas affected
directly and indirectly by climate impacts or by responses to those
impacts, covering diverse aspects of society and the environment.
These issues include consideration of values, purpose, goals, available
resources, the time over which actions are expected to remain effective,
and the extent to which the objectives being pursued are regarded as
appropriate. The purpose of the decision in question, for example,
assessment, strategic planning, or implementation, will also define the
framework and tools needed to enable the process. This chapter neither
provides any standard template or instructions for decision making, nor
does it endorse particular decisions over others.
The remainder of this chapter is organized as follows. Section 2.1.2
addresses risk management, which provides an overall framework
suitable for CIAV decision making; Section 2.1.3 introduces decision
support; Section 2.2 discusses contexts for decision making; Section 2.3
discusses methods, tools, and processes; Section 2.4 discusses support
for and application of decision making; and Section 2.5 describes some
of the broader contexts influencing CIAV decision making.
2.1.1. Decision-Making Approaches in this Report
The overarching theme of the chapter and the AR5 report is managing
current and future climate risks (Sections 1.2.4, 16.2, 19.1), principally
through adaptation (Chapters 14 to 17), but also through resilience and
sustainable development informed by an understanding of both impacts
and vulnerability (Section 19.2). The International Standard ISO:31000
defines risk as the effect of uncertainty on objectives (ISO, 2009) and
the Working Group II AR5 Glossary defines risk as The potential for
consequences where something of human value (including humans
themselves) is at stake and where the outcome is uncertain (Rosa, 2003).
However, the Glossary also refers to a more operational definition for
assessing climate-related hazards: risk is often represented as
probability of occurrence of hazardous events or trends multiplied by the
consequences if these events occur. Risk can also refer to an uncertain
opportunity or benefit (see Section 2.2.1.3). This chapter takes a broader
perspective than the latter by including risks associated with taking
action (e.g., will this adaptation strategy be successful?) and the
broader socially constructed risks that surround “climate change” (e.g.,
fatalism, hope, opportunity, and despair).
Because all decisions on CIAV are affected by uncertainty and focus on
valued objectives, all can be considered as decisions involving risk
(e.g., Giddens, 2009) (high confidence). AR4 endorsed iterative risk
management as a suitable decision support framework for CIAV
assessment because it offers formalized methods for addressing
uncertainty, involving stakeholder participation, identifying potential
policy responses, and evaluating those responses (Carter et al., 2007;
IPCC, 2007b; Yohe et al., 2007). The literature shows significant
advances on all these topics since AR4 (Section 1.1.4), greatly expanding
methodologies for assessing impacts, adaptation, and vulnerability in
a risk context (Agrawala and Fankhauser, 2008; Hinkel, 2011; Jones and
Preston, 2011; Preston et al., 2011).
Many different risk methodologies, such as financial, natural disaster,
infrastructure, environmental health, and human health, are relevant for
CIAV decision making (very high confidence). Each methodology utilizes
a variety of different tools and methods. For example, the standard CIAV
methodology follows a top-down cause and effect pathway as outlined
previously. Others follow a bottom-up pathway, starting with a set of
decision-making goals that may be unrelated to climate and consider
how climate may affect those goals (see also Sections 15.2.1, 15.3.1).
Some methodologies such as vulnerability, resilience, and livelihood
assessments are often considered as being different from traditional
risk assessment, but may be seen as dealing with particular stages
within a longer term iterative risk management process. For example,
developing resilience can be seen as managing a range of potential
risks that are largely unpredictable; and sustainable development aims
to develop a social-ecological system robust to climate risks.
A major aim of decision making is to make good or better decisions. Good
and better decisions with respect to climate adaptation are frequently
mentioned in the literature but no universal criterion exists for a good
decision, including a good climate-related decision (Moser and Ekstrom,
2010). This is reflected in the numerous framings linked to adaptation
decision making, each having its advantages and disadvantages
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(Preston et al., 2013; see also Section 15.2.1). Extensive evidence from
the decision sciences shows that good scientific and technical information
alone is rarely sufficient to result in better decisions (Bell and Lederman,
2003; Jasanoff, 2010; Pidgeon and Fischhoff, 2011) (high confidence).
Aspects of decision making that distinguish climate change from most
other contexts are the long time scales involved, the pervasive impacts
and resulting risks, and the “deep” uncertainties attached to many of
those risks (Kandlikar et al., 2005; Ogden and Innes, 2009; Lempert and
McKay, 2011). These uncertainties include not only future climate but
also socioeconomic change and potential changes in norms and values
within and across generations.
2.1.2. Iterative Risk Management
Iterative risk management involves an ongoing process of assessment,
action, reassessment, and response (Kambhu et al., 2007; IRGC, 2010)
that will continue—in the case of many climate-related decisions—for
decades if not longer (National Research Council, 2011). This development
is consistent with an increasing focus on risk governance (Power, 2007;
Renn, 2008), the integration of climate risks with other areas of risk
management (Hellmuth et al., 2011; Measham et al., 2011), and a wide
range of approaches for structured decision making involving process
uncertainty (Ohlson et al., 2005; Wilson and McDaniels, 2007; Ogden
and Innes, 2009; Martin et al., 2011).
Two levels of interaction can be recognized within the iterative risk
management process: one internal and one external (Figure 2-1).
External factors are present through the entire process and shape the
process outcomes. The internal aspects describe the adaptation process
itself. The first major internal iteration (in yellow) reflects the interplay
with the analysis phase by addressing the interactions between evolving
risks and their feedbacks (not shown) and during the development and
choice of options. This process may also require a revision of criteria
and objectives. This phase ends with decisions on the favored options
being made. A further internal iteration covers the implementation of
actions and their monitoring and review (in orange). Throughout all
stages the process is reflexive, in order to enable changes in knowledge,
risks, or circumstances to be identified and responded to. At the end of
the implementation stage, all stages are evaluated and the process starts
again with the scoping phase. Iterations can be successive, on a set
timetable, triggered by specific criteria or informally by new information
informing risk or a change in the policy environment. An important
aspect of this process is to recognize emergent risks and respond to
them (Sections 19.2.3, 19.2.4, 19.2.5, 19.3).
Complexity is an important attribute for framing and implementing
decision-making processes (very high confidence). Simple, well-bounded
contexts involving cause and effect can be addressed by straightforward
linear methods. Complicated contexts require greater attention to process
but can generally be unravelled, providing an ultimate solution (Figure
2-2). However, when complex environments interact with conflicting
values they become associated with wicked problems. Wicked problems
are not well bounded, are framed differently by various groups and
individuals, harbor large scientific to existential uncertainties and have
unclear solutions and pathways to those solutions (Rittel and Webber,
Frequently Asked Questions
FAQ 2.1 | What constitutes a good (climate) decision?
No universal criterion exists for a good decision, including a good climate-related decision. Seemingly reasonable
decisions can turn out badly, and seemingly unreasonable decisions can turn out well. However, findings from
decision theory, risk governance, ethical reasoning, and related fields offer general principles that can help improve
the quality of decisions made.
Good decisions tend to emerge from processes in which people are explicit about their goals; consider a range of
alternative options for pursuing their goals; use the best available science to understand the potential consequences
of their actions; carefully consider the trade-offs; contemplate the decision from a wide range of views and
vantages, including those who are not represented but may be affected; and follow agreed-upon rules and norms
that enhance the legitimacy of the process for all those concerned. A good decision will be implementable within
constraints such as current systems and processes, resources, knowledge, and institutional frameworks. It will have
a given lifetime over which it is expected to be effective, and a process to track its effectiveness. It will have defined
and measurable criteria for success, in that monitoring and review is able to judge whether measures of success
are being met, or whether those measures, or the decision itself, need to be revisited.
A good climate decision requires information on climate, its impacts, potential risks, and vulnerability to be integrated
into an existing or proposed decision-making context. This may require a dialog between users and specialists to
jointly ascertain how a specific task can best be undertaken within a given context with the current state of scientific
knowledge. This dialog may be facilitated by individuals, often known as knowledge brokers or extension agents,
and boundary organizations, who bridge the gap between research and practice. Climate services are boundary
organizations that provide and facilitate knowledge about climate, climate change, and climate impacts for planning,
decision making, and general societal understanding of the climate system.
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1973; Australian Public Service Commission, 2007). Such “deep
uncertainty” cannot easily be quantified (Dupuy and Grinbaum, 2005;
Kandlikar et al., 2005). Another important attribute of complex
systems is reflexivity, where cause and effect feed back into each
other (see Glossary). For example, actions taken to manage a risk will
affect the outcomes, requiring iterative processes of decision making
(very high confidence). Under climate change, calculated risks will
also change with time as new knowledge becomes available (Ranger
et al., 2010).
In complex situations, sociocultural and cognitive-behavioral contexts
become central to decision making. This requires combining the scientific
understanding of risk with how risks are framed and perceived by
individuals, organizations, and institutions (Hansson, 2010). For that
reason, formal risk assessment is moving from a largely technocratic
exercise carried out by experts to a more participatory process of decision
support (Fiorino, 1990; Pereira and Quintana, 2002; Renn, 2008),
although this process is proceeding slowly (Christoplos et al., 2001;
Pereira and Quintana, 2002; Bradbury, 2006; Mercer et al., 2008).
Different traditional and modern epistemologies, or “ways of knowing”
exist for risk (Hansson, 2004; Althaus, 2005; Hansson, 2010),
vulnerability (Weichselgartner, 2001; O’Brien et al., 2007), and
adaptation assessments (Adger et al., 2009), affecting the way they are
framed by various disciplines and are also understood by the public
(Garvin, 2001; Adger, 2006; Burch and Robinson, 2007). These differences
have been identified as a source of widespread misunderstanding and
disagreement. They are also used to warn against a uniform epistemic
approach (Hulme, 2009; Beck, 2010), a critique that has been leveled
against previous IPCC assessments (e.g., Hulme and Mahony, 2010).
The following three types of risk have been identified as important
epistemological constructs (Thompson, 1986; Althaus, 2005; Jones,
2012):
1. Idealized risk: the conceptual framing of the problem at hand. For
example, dangerous anthropogenic interference with the climate
system is how climate change risk is idealized within the UNFCCC.
2. Calculated risk: the product of a model based on a mixture of
historical (observed) and theoretical information. Frequentist or
recurrent risks often utilize historical information whereas single-
event risks may be unprecedented, requiring a more theoretical
approach.
3. Perceived risk: the subjective judgment people make about an
idealized risk (see also Section 19.6.1.4).
These different types show risk to be partly an objective threat of harm
and partly a product of social and cultural experience (Kasperson et al.,
1988; Kasperson, 1992; Rosa, 2008). The aim of calculating risk is to be
as objective as possible, but the subjective nature of idealized and
perceived risk reflects the division between positivist (imposed norms)
and constructivist (derived norms) approaches to risk from the natural
and social sciences respectively (Demeritt, 2001; Hansson, 2010).
Idealized risk is important for framing and conceptualizing risk and will
often have formal and informal status in the assessment process,
contributing to both calculated and perceived risk. These types of risk
combine at the societal scale as socially constructed risk, described and
Identify risks,
vulnerabilities,
and objectives
Establish decision
making criteria
Scoping
Implementation
Review
& learn
Implement
decision
Monitor
Assess
risks
Identify
options
Evaluate
tradeoffs
Analysis
Knowledge
Context
Deliberative Process
People
Figure 2-1 | Iterative risk management framework depicting the assessment process, and indicating multiple feedbacks within the system and extending to the overall context
(adapted from Willows and Connell, 2003).
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Chapter 2 Foundations for Decision Making
2
assessed in a wide range of research literature such as psychology,
anthropology, geography, ethics, sociology, and political science (see
Sections 2.2.1.2, 19.6.1.4).
Acceptance of the science behind controversial risks is strongly influenced
by social and cultural values and beliefs (Leiserowitz, 2006; Kahan et
al., 2007; Brewer and Pease, 2008). Risk perceptions can be amplified
socially where events pertaining to hazards interact with psychological,
social, institutional, and cultural processes in ways that heighten or
attenuate individual and social perceptions of risk and shape risk
behavior (Kasperson et al., 1988; Renn et al., 1992; Pidgeon et al.,
2003; Rosa, 2003; Renn, 2011). The media have an important role in
propagating both calculated and perceived risk (Llasat et al., 2009),
sometimes to detrimental effect (Boykoff and Boykoff, 2007; Oreskes
and Conway, 2010; Woods et al., 2012).
Understanding how these perceptions resonate at an individual and
collective level can help overcome constraints to action (Renn, 2011).
Science is most suited to calculating risk in areas where it has predictive
skill and will provide better estimates than may be obtained through
more informal methods (Beck, 2000), but an assessment of what is at
risk generally needs to be accepted by stakeholders (Eiser et al., 2012).
Therefore, the science always sits within a broader social setting
(Jasanoff, 1996; Demeritt, 2001; Wynne, 2002; Demeritt, 2006), often
requiring a systems approach where science and policy are investigated
in tandem, rather than separately (Pahl-Wostl, 2007; Ison, 2010) (very
high confidence). These different types of risk give rise to complex
interactions between formal and informal knowledge that cannot be
bridged by better science or better predictions but require socially and
culturally mediated processes of engagement (high confidence).
2.1.3. Decision Support
The concept of decision support provides a useful framework for
understanding how risk-based concepts and information can help
enhance decision making (McNie, 2007; National Research Council Panel
on Design Issues for the NOAA Sectoral Applications Research Program
et al., 2007; Moser, 2009; Romsdahl and Pyke, 2009; Kandlikar et al.,
2011; Pidgeon and Fischhoff, 2011) The concept also helps situate
methods, tools, and processes intended to improve decision making
within appropriate institutional and cultural contexts.
Decision support is defined as “a set of processes intended to create
the conditions for the production of decision-relevant information and
for its appropriate use(National Research Council, 2009a, p. 33).
Information is decision-relevant if it yields deeper understanding of, or
is incorporated into making a choice that improves outcomes for decision
makers and stakeholder or precipitates action to manage known risks.
Effective decision support provides users with information they find useful
because they consider it credible, legitimate, actionable, and salient
(e.g., Jones et al., 1999; Cash et al., 2003; Mitchell, 2006; Reid et al.,
2007). Such criteria can be used to evaluate decision support and such
evaluations lead to common principles of effective decision support,
which have been summarized in National Research Council (2009b) as:
Begins with user’s needs, not scientific research priorities. Users
may not always know their needs in advance, so user needs are
often developed collaboratively and iteratively among users and
researchers.
Emphasizes processes over products. Though the information
products are important, they are likely to be ineffective if they are
not developed to support well-considered processes.
Methodology
Approach
Stakeholder strategy
Mental models
Values and outcomes
Monitoring
Linear, cause and effect
Analytic and technical
Communication
Common model
Widely accepted
Straightforward
Top down and/or bottom up,
iterative
Collaborative process with
technical input
Collaboration
Negotiated and shared
Negotiated over project by user
perspectives and calculated risk
With review and trigger points
Iterative and/or adaptive, ongoing and systemic
Process driven. Frame and model multiple
drivers and valued outcomes
Deliberation, creating shared understanding
and ownership
Contested initially and negotiated over project
Contested initially and negotiated over project
As real-time as possible, adaptive with
management feedback and trigger points
Calculated
risk
Perceived
risk
Simple risk Complicated risk Complex risk
Characteristics of decision making
Circle size increases with uncertainty
Figure 2-2 | Hierarchy of simple, complicated, and complex risks, showing how perceived risks multiply and become less connected with calculated risk with increasing
complexity. Also shown are major characteristics of assessment methods for each level of complexity.
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Incorporates systems that link users and producers of information.
These systems generally respect the differing cultures of decision
makers and scientists, but provide processes and institutions that
effectively link individuals from these differing communities.
Builds connections across disciplines and organizations, in order to
provide for the multidisciplinary character of the needed information
and the differing communities and organizations in which this
information resides.
Seeks institutional stability, either through stable institutions and/or
networks, which facilitates building the trust and familiarity needed
for effective links and connections among information users and
producers in many different organizations and communities.
Incorporates learning, so that all parties recognize the need for and
contribute to the implementation of decision support activities
structured for flexibility, adaptability, and learning from experience.
These principles can lead to different decision support processes
depending on the stage and context of the decision in question. For
instance, decision support for a large water management agency
operating an integrated system serving millions of people will have
different needs than a small town seeking to manage its groundwater
supplies. A community in the early stages of developing a response to
climate change may be more focused on raising awareness of the issue
among its constituents, while a community with a well-developed
understanding of its risks may be more focused on assessing trade-offs
and allocating resources.
2.2. Contexts for Decision Making
This section surveys aspects of decision making that relate to context
setting. Social context addresses cultural values, psychology, language,
and ethics (Section 2.2.1) and institutional context covers institutions
and governance (Section 2.2.2).
2.2.1. Social Context
Decision support for CIAV must recognize that diverse values, language
uses, ethics, and human psychological dimensions play a crucial role in
the way that people use and process information and take decisions
(Kahan and Braman, 2006; Leiserowitz, 2006). As illustrated in Figure
2-1, the context defines and frames the space in which decision-making
processes operate.
2.2.1.1. Cultural Values and Determinants
Cultural differences allocate values and guide socially mediated change.
Five value dimensions that show significant cross-national variations
are: power distance, individualism/collectivism, uncertainty avoidance,
long-/short-term orientation, and masculinity/femininity (Hofstede, 1980,
2001; Hofstede et al., 2010). Power distance and individualism/
collectivism both show a link to climate via latitude; the former relates
to willingness to conform to top-down directives, whereas the latter
relates to the potential efficacy of market-/community-based strategies.
Uncertainty avoidance and long-term orientation show considerable
v
ariation between countries (Hofstede et al., 2010), potentially producing
significant differences in risk perception and agency.
Environmental values have also been linked to cultural orientation.
Schultz et al. (2004) identified the association between self and nature in
people as being implicit—informing actions without specific awareness.
A strong association was linked to a more connected self and a weaker
association with a more egoistic self. Explicit environmental values can
substantially influence climate change-related decision-making processes
(Nilsson et al., 2004; Milfont and Gouveia, 2006; Soyez et al., 2009) and
public behavior toward policies (Stern and Dietz, 1994; Xiao and
Dunlap, 2007). Schaffrin (2011) concludes that geographical aspects,
vulnerability, and potential policy benefits associated with a given issue
can influence individual perceptions and willingness to act (De Groot
and Steg, 2007, 2008; Shwom et al., 2008; Milfont et al., 2010). Cultural
values can interrelate with specific physical situations of climate change
(Corraliza and Berenguer, 2000), or seasonal and meteorological factors
influencing peoples implicit connections with nature (Duffy and Verges,
2010). Religious and sacred values are also important (Goloubinoff,
1997; Katz et al., 2002; Lammel et al., 2008), informing the perception
of climate change and risk, as well as the actions to adapt (Crate and
Nuttal, 2009; see also Section 16.3.1.3). The role of protected values
(values that people will not trade off, or negotiate) can also be culturally
and spiritually significant (Baron and Spranca, 1997; Baron et al., 2009;
Hagerman et al., 2010). Adger et al. (2013) emphasize the importance
of cultural values in assessing risks and adaptation options, suggesting
they are at least as important as economic values in many cases, if not
more so. These aspects are important for framing and conceptualizing
CIAV decision making. Cultural and social barriers are described in
Section 16.3.2.7.
Two distinct ways of thinking—holistic and analytical thinking—reflect
the relationship between humans and nature and are cross-culturally
and even intra-culturally diverse (Gagnon Thompson and Barton, 1994;
Huber and Pedersen, 1997; Atran et al., 2005; Ignatow, 2006; Descola,
2010; Ingold, 2011). Holistic thinking is primarily gained through
experience and is dialectical, accepting contradictions and integrating
multiple perspectives. Characteristic of collectivist societies, the holistic
conceptual model considers that social obligations are reciprocal and
individuals take an active part in the community for the benefit of all
(Peng and Nisbett, 1999; Nisbett et al., 2001; Lammel and Kozakai,
2005; Nisbett and Miyamoto, 2005). Analytical thinking isolates the
object from its broader context, understanding its characteristics
through categorization, and predicting events based on intrinsic rules.
In the analytic conceptual model, individual interests take precedence
over the collective; the self is independent and communication comes
from separate fields. These differences influence the understanding of
complex systemic phenomena such as climate change (Lammel et al.,
2011, 2012, 2013) and decision-making practices (Badke-Schaub and
Strohschneider, 1998; Strohschneider and Güss, 1999; Güss et al., 2010).
The above models vary greatly across the cultural landscape, but neither
model alone is sufficient for decision making in complex situations (high
confidence). At a very basic level, egalitarian societies may respond
more to community based adaptation in contrast to more individualistic
societies that respond to market-based forces (medium confidence). In
small-scale societies, knowledge about climate risks are often integrated
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nto a holistic view of community and environment (e.g., Katz et al.,
2002; Strauss and Orlove, 2003; Lammel et al., 2008). Many studies
highlight the importance of integrating local, traditional knowledge with
scientific knowledge when assessing CIAV (Magistro and Roncoli, 2001;
Krupnik and Jolly, 2002; Vedwan, 2006; Nyong et al., 2007; Dube and
Sekhwela, 2008; Crate and Nuttal, 2009; Mercer et al., 2009; Roncoli et
al., 2009; Green and Raygorodetsky, 2010; Orlove et al., 2010; Crate,
2011; Nakashima et al., 2012; see also Sections 12.3, 12.3.1, 12.3.2,
12.3.3, 12.3.4, 14.4.5, 14.4.7, 15.3.2.7, 25.8.2, 28.2.6.1, 28.4.1). For
example, a case study in Labrador (Canada) demonstrated the need to
account for local material and symbolic values because they shape the
relationship to the land, underlie the way of life, influence the intangible
effects of climate change, and can lead to diverging views on
adaptations (Wolf et al., 2012). In Kiribati, the integration of local cultural
values attached to resources/assets is fundamental to adaptation
planning and water management; otherwise technology will not be
properly utilized (Kuruppu, 2009).
2.2.1.2. Psychology
Psychology plays a significant role in climate change decision making
(Gifford, 2008; Swim et al., 2010; Anderson, 2011). Important
psychological factors for decision making include perception,
representation, knowledge acquisition, memory, behavior, emotions,
and understanding of risk (Böhm and Pfister, 2000; Leiserowitz, 2006;
Lorenzoni et al., 2006; Oskamp and Schultz, 2006; Sterman and
Sweeney, 2007; Gifford, 2008; Kazdin, 2009; Sundblad et al., 2009;
Reser et al., 2011; Swim et al., 2011).
Psychological research contributes to understanding on both risk
perception and the process of adaptation. Several theories, such as
multi-attribute utility theory (Keeney, 1992), prospect theory (Kahneman
and Tversky, 1979; Hardman, 2009), and cumulative prospect theory
relate to decision making under uncertainty (Tversky and Kahneman,
1992), especially to risk perception and agency. Adaptation in complex
situations pits an unsure gain against an unsure loss, so creates an
asymmetry in preference that magnifies with time as gains/losses are
expected to accrue in future. Decisions focusing on values and
uncertainty are therefore subject to framing effects. Recent cognitive
approaches include the one-reason decision process that uses limited
data in a limited time period (Gigerenzer and Goldstein, 1996) or decision
by sampling theory that samples real-world data to account for the
cognitive biases observed in behavioral economics (Stewart et al., 2006;
Stewart and Simpson, 2008). Risk perception is further discussed in
Section 19.6.1.4.
Responses to new information can modify previous decisions, even
producing contradictory results (Grothmann and Patt, 2005; Marx et al.,
2007). Although knowledge about climate change is necessary (Milfont,
2012), understanding such knowledge can be difficult (Rajeev Gowda
et al., 1997; Boyes et al., 1999; Andersson and Wallin, 2000). Cognitive
obstacles in processing climate change information include psychological
distances with four theorized dimensions: temporal, geographical, social
distance, and uncertainty (Spence et al., 2012; see also Section 25.4.3).
Emotional factors also play an important role in climate change
perception, attitudes, decision making, and actions (Meijnders et al.,
2
002; Leiserowitz, 2006; Klöckner and Blöbaum, 2010; Fischer and Glenk
2011; Roeser, 2012) and even shape organizational decision making
(Wright and Nyberg, 2012). Other studies on attitudes and behaviors
relevant to climate change decision making, include place attachment
(Scannell and Gifford, 2013; see also Section 25.4.3), political affiliation
(Davidson and Haan, 2011), and perceived costs and benefits (Tobler et
al., 2012). Time is a critical component of action-based decision making
(Steel and König, 2006). As the benefits of many climate change actions
span multiple temporal scales, this can create a barrier to effective
motivation for decisions through a perceived lack of value associated
with long-term outcomes.
Protection Motivation Theory (Rogers, 1975; Maddux and Rogers, 1983),
which proposes that a higher personal perceived risk will lead to a
higher motivation to adapt, can be applied to climate change-related
problems (e.g., Grothmann and Reusswig, 2006; Cismaru et al., 2011).
The person-relative-to-event approach predicts human coping strategies
as a function of the magnitude of environmental threat (Mulilis and
Duval, 1995; Duval and Mulilis, 1999; Grothmann et al., 2013). People’s
responses to environmental hazards and disasters are represented in
the multistage Protective Action Decision Model (Lindell and Perry,
2012). This model helps decision makers to respond to long-term threat
and apply it in long-term risk management. Grothmann and Patt (2005)
developed and tested a socio-cognitive model of proactive private
adaptation to climate change showing that perceptions of adaptive
capacities were important as well as perceptions of risk. If a perceived
high risk is combined with a perceived low adaptive capacity (see
Section 2.4.2.2; Glossary), the response is fatalism, denial, and wishful
thinking.
Best-practice methods for incorporating and communicating information
about risk and uncertainties into decisions about climate change
(Climate Change Science Program, 2009; Pidgeon and Fischhoff, 2011)
suggests that effective communication of uncertainty requires products
and processes that (1) closes psychological distance, explaining why
this information is important to the recipient; (2) distinguishes between
and explains different types of uncertainty; (3) establishes self-agency,
explaining what the recipient can do with the information and ways to
make decisions under uncertainty (e.g., precautionary principle, iterative
risk-management); (4) recognizes that each person’s view of risks and
opportunities depends on their values; (5) recognizes that emotion is a
critical part of judgment; and (6) provides mental models that help
recipients to understand the connection between cause and effect.
Information providers also need to test their messages, as they may not
be communicating what they think they are.
2.2.1.3. Language and Meaning
Aspects of decision making concerned with language and meaning
include framing, communication, learning, knowledge exchange, dialog,
and discussion. Most IPCC-related literature on language and
communication deals with definitions, predictability, and incomplete
knowledge, with less emphasis given to other aspects of decision
support such as learning, ambiguity, contestedness, and complexity.
Three important areas assessed here are definitions, risk language and
communication, and narratives.
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ecision-making processes need to accommodate both specialist and
non-specialist meanings of the concepts they apply. Various disciplines
often have different definitions for the same terms or use different terms
for the same action or object, which is a major barrier for communication
and decision making (Adger, 2003; see also Chapter 21). For example,
adaptation is defined differently with respect to biological evolution,
climate change, and social adaptation. Budescu et al. (2012) found that
people prefer imprecise wording but precise numbers when appropriate.
Personal lexicons vary widely, leading to differing interpretations of
uncertainty terms (Morgan et al., 1990); in the IPCC’s case leading to
uncertainty ranges often being interpreted differently than intended
(Patt and Schrag, 2003; Patt and Dessai, 2005; Budescu et al., 2012).
Addressing both technical and everyday meanings of key terms can help
bridge the analytic and emotive aspects of cognition. For example,
words like danger, disaster, uncertainty, and catastrophe have technical
and emotive aspects (Britton, 1986; Carvalho and Burgess, 2005). Terms
where this issue is especially pertinent include adaptation, vulnerability,
risk, dangerous, catastrophe, resilience, and disaster. Other words have
definitional issues because they contain different epistemological frames;
sustainability and risk are key examples (Harding, 2006; Hamilton et al.,
2007). Many authors advocate that narrow definitions focused solely
on climate need to be expanded to suit the context in which they are
being used (Huq and Reid, 2004; O’Brien et al., 2007; Schipper, 2007).
This is a key role for risk communication, ensuring that different types
of knowledge are integrated within decision context and outlining the
different values—implicit and explicit—involved in the decision process
(e.g., Morgan, 2002; Lundgren and McMakin, 2013).
The language of risk has a crucial role in framing and belief. Section
2.1.2 described over-arching and climate-specific definitions but risk
enters into almost every aspect of social discourse, so is relevant to how
risk is framed and communicated (e.g., Hansson, 2004). Meanings of
risk range from its ordinary use in everyday language to power and
political discourse, health, emergency, disaster, and seeking benefits,
ranging from specific local meanings to broad-ranging concepts such
as the risk society (Beck and Ritter, 1992; Beck, 2000; Giddens, 2000).
Complex framings in the word risk (Fillmore and Atkins, 1992; Hamilton
et al., 2007) feature in general English as both a noun and a verb,
reflecting harm and chance with negative and positive senses (Fillmore
and Atkins, 1992). Problem analysis applies risk as a noun (at-risk),
whereas risk management applies risk as a verb (to-risk) (Jones, 2011).
For simple risks, this transition is straightforward because of agreement
around values and agency (Figure 2-2). In complex situations, risk as a
problem and as an opportunity can compete with each other, and if
socially amplified can lead to action paralysis (Renn, 2011). For example,
unfamiliar adaptation options that seem to be risky themselves will force
a comparison between the risk of maladaptation and future climate risks,
echoing the risk trap where problems and solutions come into conflict
(Beck, 2000). Fear-based dialogs in certain circumstances can cause
disengagement (O’Neill and Nicholson-Cole, 2009), by emphasizing risk
aversion. Young (2013) proposes framing adaptation as a solution to
overcome the limitations of framing through the problem, and links it to
innovation, which provides established pathways for the implementation
of actions, proposing a problem-solution framework linking decision
making to action. Framing decisions and modeling actions on positive risk-
seeking behavior can help people to address uncertainty as opportunity
(e.g., Keeney, 1992).
N
arratives are accounts of events with temporal or causal coherence
that may be goal directed (László and Ehmann, 2012) and play a key
role in communication, learning, and understanding. They operate at
the personal to societal scales, are key determinants of framing, and
have a strong role in creating social legitimacy. Narratives can also be
non-verbal: visualization, kinetic learning by doing, and other sensory
applications can be used to communicate science and art and to enable
learning through play (Perlovsky, 2009; Radford, 2009). Narratives of
climate change have evolved over time and invariably represent
uncertainty and risk (Hamblyn, 2009) being characterized as tools for
analysis, communication, and engagement (Cohen, 2011; Jones et al.,
2013; Westerhoff and Robinson, 2013) by:
Providing a social and environmental context to modelled futures
(Arnell et al., 2004; Kriegler et al., 2012; O’Neill et al., 2014), by
describing aspects of change that drive or shape those futures as
part of scenario construction (Cork et al., 2012).
Communicating knowledge and ideas to increase understanding
and increase agency framing it in ways so that actions can be
implemented (Juhola et al., 2011) or provide a broader socio-
ecological context to specific knowledge (Burley et al., 2012). These
narratives bridge the route between scientific knowledge and local
understandings of adaptation, often by working with multiple
actors in order to creatively explore and develop collaborative
potential solutions (Turner and Clifton, 2009; Paton and Fairbairn-
Dunlop, 2010; Tschakert and Dietrich, 2010).
Exploring responses at an individual/institutional level to an aspect
of adaptation, and communicating that experience with others
(Bravo, 2009; Cohen, 2011). For example, a community that believes
itself to be resilient and self-reliant is more likely to respond
proactively, contrasted to a community that believes itself to be
vulnerable (Farbotko and Lazrus, 2012). Bravo (2009) maintains that
narratives of catastrophic risk and vulnerability demotivate
indigenous peoples whereas narratives combining scientific
knowledge and active citizenship promote resilience (Section2.5.2).
2.2.1.4. Ethics
Climate ethics can be used to formalize objectives, values (Section
2.2.1.1), rights, and needs into decisions, decision-making processes,
and actions (see also Section 16.7). Principal ethical concerns include
intergenerational equity; distributional issues; the role of uncertainty in
allocating fairness or equity; economic and policy decisions; international
justice and law; voluntary and involuntary levels of risk; cross-cultural
relations; and human relationships with nature, technology, and the
sociocultural world. Climate change ethics have been developing over
the last 20 years (Jamieson, 1992, 1996; Gardiner, 2004; Gardiner et al.,
2010), resulting in a substantial literature (Garvey, 2008; Harris, 2010;
O’Brien et al., 2010; Arnold, 2011; Brown, 2012; Thompson and Bendik-
Keymer, 2012). Equity, inequity, and responsibility are fundamental
concepts in the UNFCCC (UN, 1992) and therefore are important
considerations in policy development for CIAV. Climate ethics examine
effective responsible and “moral” decision making and action, not only
by governments but also by individuals (Garvey, 2008).
An important discourse on equity is that industrialized countries have,
through their historical emissions, created a natural debt (Green and
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mith, 2002). Developing nations experience this debt through higher
impacts and greater vulnerability combined with limited adaptive
capacity. Regional inequity is also of concern (Green and Smith, 2002),
particularly indigenous or marginalized populations exposed to current
climate extremes, who may become more vulnerable under a changing
climate (Tsosie, 2007; see also Section 12.3.3). With respect to adaptation
assessment, cost-benefit or cost-effectiveness methods combined with
transfer of funds will not satisfy equity considerations (Broome, 2008;
see also Section 17.3.1.4) and modifications such as equity-weighting
(Kuik et al., 2008) and cost-benefit under uncertainty (Section 17.3.2.1),
have not been widely used. Adaptation measures need to be evaluated
by considering their equity implications (Section 17.3.1.4) especially
under uncertainty (Hansson, 2004).
Intergenerational issues are frequently treated as an economic problem,
with efforts to address them through an ethical framework proving to
be controversial (Nordhaus, 2007; Stern and Treasury of Great Britain,
2007; Stern, 2008). However, future harm may make the lives of future
generations difficult or impossible, dilemmas that involve ethical choices
(Broome, 2008), therefore discount rates matter (Section 17.4.4.4).
Some authors question whether the rights and interests of future people
should even be subject to a positive discount rate (Caney, 2009). Future
generations can neither defend themselves within current economic
frameworks (Gardiner, 2011) nor can these frameworks properly account
for the dangers, interdependency, and uncertainty under climate change
(Nelson, 2011), even though people’s values may change over time
(Section 16.7). The limits to adaptation raise questions of irreversible
loss and the loss of unique cultural values that cannot necessarily be
easily transferred (Section 16.7), contributing to key vulnerabilities and
informing ethical issues facing mitigation (see Section 19.7.1).
Environmental ethics considers the decisions humans may make
concerning a range of biotic impacts (Schalow, 2000; Minteer and Collins,
2010; Nanda, 2012; Thompson and Bendik-Keymer, 2012). Intervention
in natural systems through assisted colonization” or “managed
relocation” raises important ethical and policy questions (Minteer and
Collins, 2010; Section 4.4.2.4) that include the risk of unintended
consequences (Section 4.4.4). Various claims are made for a more
pragmatic ethics of ecological decision making (Minteer and Collins,
2010), consideration of moral duties toward species (Sandler, 2009),
and ethically explicit and defendable decision making (Minteer and
Collins, 2005a,b).
Cosmopolitan ethics and global justice can lead to successful adaptation
and sustainability (Caney, 2006; Harris, 2010) and support collective
decision making on public matters through voting procedures (Held,
2004). Ethics also concerns the conduct and application of research,
especially research involving stakeholders. Action-based and participatory
research requires that a range of ethical guidelines be followed, taking
consideration of the rights of stakeholders, respect for cultural and
practical knowledge, confidentiality, dissemination of results, and
development of intellectual property (Macaulay et al., 1999; Kindon et
al., 2007; Daniell et al., 2009; Pearce et al., 2009). Ethical agreements
and processes are an essential part of participatory research, whether
taking part as behavioral change processes promoting adaptation or
projects of collaborative discovery (high confidence). Although the climate
change ethics literature is rapidly developing, the related practice of
d
ecision making and implementation needs further development.
Ethical and equity issues are discussed in WGIII AR5 Chapter 3.
2.2.2. Institutional Context
2.2.2.1. Institutions
Institutions are rules and norms held in common by social actors that
guide, constrain, and shape human interaction (North, 1990; Glossary).
Institutions can be formal, such as laws and policies, or informal, such as
norms and conventions. Organizations—such as parliaments, regulatory
agencies, private firms, and community bodies—develop and act in
response to institutional frameworks and the incentives they frame
(Young et al., 2008). Institutions can guide, constrain, and shape human
interaction through direct control, through incentives, and through
processes of socialization (Glossary). Virtually all CIAV decisions will be
made by or influenced by institutions because they shape the choices
made by both individuals and organizations (Bedsworth and Hanak,
2012). Institutional linkages are important for adaptation in complex
and multi-layered social and biophysical systems such as coastal areas
(Section 5.5.3.2) and urban systems (Section 8.4.3.4), and are vital in
managing health (Section 11.6), human security (Sections 12.5.1,
12.6.2), and poverty (Section 13.1). Institutional development and
interconnectedness are vital in mediating vulnerability in social-
ecological systems to changing climate risks, especially extremes
(Chapters 5, 7 to 9, 11 to 13).
The role of institutions as actors in adaptation are discussed in Section
14.4, in planning and implementing adaptation in Section 15.5, and in
providing barriers and opportunities in Section 16.3. Their roles can be
very diverse. Local institutions usually play important roles in accessing
resources and in structuring individual and collective responses
(Agarwal, 2010; see also Section 14.4.2) but Madzwamuse (2010) found
that in Africa, state-level actors had significantly more influence on formal
adaptation policies than did civil society and local communities.
This suggests a need for greater integration and cooperation among
institutions of all levels (Section 15.5.1.2). Section 14.2.3 identifies four
institutional design issues: flexibility; potential for integration into
existing policy plans and programs; communication, coordination, and
cooperation; and the ability to engage with multiple stakeholders.
Institutions are instrumental in facilitating adaptive capacity, by utilizing
characteristics such as variety, learning capacity, room for autonomous
change, leadership, availability of resources, and fair governance (Gupta
et al., 2008). They play a key role in mediating the transformation of
coping capacity into adaptive capacity and in linking short and long-
term responses to climate change and variability (Berman et al., 2012).
Most developing countries have weaker institutions that are less capable
of managing extreme events, increasing vulnerability to disasters (Lateef,
2009; Biesbroek et al., 2013). Countries with strong functional institutions
are generally assumed to have a greater capacity to adapt to current
and future disasters. However, Hurricane Katrina of 2005 in the USA and
the European heat wave of 2003 demonstrate that strong institutions
and other determinants of adaptive capacity do not necessarily reduce
vulnerability if these attributes are not translated to actions (IPCC,
2007a; see also Box 2-1, Section 2.4.2.2).
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o facilitate adaptation under uncertainty, institutions need to be
flexible enough to accommodate adaptive management processes such
as evaluation, learning, and refinement (Agarwal, 2010; Gupta et al.,
2010; see also Section 14.2.3). Organizational learning can lead to
significant change in organizations’ purpose and function (Bartley,
2007), for example, where non-governmental organizations have moved
from advocacy to program delivery with local stakeholders (Ziervogel
and Zermoglio, 2009; Kolk and Pinkse, 2010; Worthington and Pipa,
2010).
Boundary organizations are increasingly being recognized as important
to CIAV decision support (Guston, 2001; Cash et al., 2003; McNie, 2007;
Vogel et al., 2007). A boundary organization is a bridging institution, social
arrangement, or network that acts as an intermediary between science
and policy (Glossary). Its functions include facilitating communication
between researchers and stakeholders, translating science and technical
information, and mediating between different views of how to interpret
that information. It will also recognize the importance of location-specific
contexts (Ruttan et al., 1994); provide a forum in which information can
be co-created by interested parties (Cash et al., 2003); and develop
boundary objects, such as scenarios, narratives, and model-based decision
support systems (White et al., 2010). Adaptive and inclusive management
practices are considered to be essential, particularly in addressing wicked
problems such as climate change (Batie, 2008). Boundary organizations
also link adaptation to other processes managing global change and
sustainable development.
Boundary organizations already contributing to regional CIAV assessments
include the Great Lakes Integrated Sciences and Assessments Center in
the USA (GLISA; http://www.glisa.umich.edu/); part of the Regional
Integrated Sciences and Assessments Program of the U.S. government
(RISA; Pulwarty et al., 2009); the UK Climate Impacts Program (UKCIP;
UK Climate Impacts Program, 2011); the Alliance for Global Water
Adaptation (AGWA; http://alliance4water.org); and institutions working
on water issues in the USA, Mexico, and Brazil (Kirchhoff et al., 2012;
Varady et al., 2012).
2.2.2.2. Governance
Effective climate change governance is important for both adaptation
and mitigation and is increasingly being seen as a key element of risk
management (high confidence) (Renn, 2008; Renn et al., 2011). Some
analysts propose that governance of adaptation requires knowledge of
anticipated regional and local impacts of climate change in a more
traditional planning approach (e.g., Meadowcroft, 2009), whereas
others propose governance consistent with sustainable development
and resilient systems (Adger, 2006; Nelson et al., 2007; Meuleman and
in ’t Veld, 2010). Quay (2010) proposes “anticipatory governance”—a
flexible decision framework based on robustness and learning (Sections
2.3.3, 2.3.4). Institutional decisions about climate adaptation are taking
place within a multi-level governance system (Rosenau, 2005; Kern and
Alber, 2008). Multi-level governance could be a barrier for successful
adaptation if there is insufficient coordination as it comprises different
regulatory, legal, and institutional systems (Section 16.3.1.4), but is
required to manage the “adaptation paradox” (local solutions to a global
problem), unclear ownership of risks and the adaptation bottleneck
l
inked to difficulties with implementation (Section 14.5.3). Lack of
horizontal and vertical integration between organizations and policies
leads to insufficient risk governance in complex social-ecological
systems such as coasts (Section 5.5.3.2) and urban areas (Section 8.4),
including in the management of compound risks (Section 19.3.2.4).
Legal and regulatory frameworks are important institutional components
of overall governance, but will be challenged by the pervasive nature
of climate risks (high confidence) (Craig, 2010; Ruhl, 2010a,b). Changes
proposed to manage these risks better under uncertainty include
integration between different areas of law, jurisdictions and scale,
changes to property rights, greater flexibility with respect to adaptive
management, and a focus on ecological processes rather than
preservation (Craig, 2010; Ruhl, 2010a; Abel et al., 2011; Macintosh et
al., 2013). Human security in this report is not seen just as an issue of
rights (Box 12-1), given that a minimum set of universal rights exists
(though not always exercised), but is instead assessed as being subject
to a wide range of forces. Internationally, sea level rise could alter the
maritime boundaries of many nations that may lead to new claims by
affected nations or loss of sovereignty (Barnett and Adger, 2003). New
shipping routes, such as the North West Passage, will be opened up by
losses in Arctic sea ice (Sections 6.4.1.6, 28.2.6). Many national and
international legal institutions and instruments need to be updated to
face climate-related challenges and decision implementation (medium
confidence) (Verschuuren, 2013).
2.3. Methods, Tools, and Processes
for Climate-related Decisions
This section deals with methods, tools, and processes that deal with
uncertainties (Section 2.3.1); describes scenarios (Section 2.3.2); covers
trade-offs and multi-metric valuation (Section 2.3.3); and reviews
learning and reframing (Section 2.3.4).
2.3.1. Treatment of Uncertainties
Most advice on uncertainty, including the latest guidance from the IPCC
(Mastrandrea et al., 2010; see also Section 1.1.2.2), deals with
uncertainty in scientific findings and to a lesser extent confidence.
Although this is important, uncertainty can invade all aspects of decision
making, especially in complex situations. Whether embodied in formal
analyses or in the training and habits of decision makers, applied
management is often needed because unaided human reasoning can
produce mismatches between actions and goals (Kahneman, 2011). A
useful high-level distinction is between ontological uncertainty—what
we know—and epistemological uncertainty—how different areas of
knowledge and “knowing” combine in decision making (van Asselt and
Rotmans, 2002; Walker et al., 2003). Two other areas of relevance are
ambiguity (Brugnach et al., 2008) and contestedness (Klinke and Renn,
2002; Dewulf et al., 2005), commonly encountered in wicked problems/
systemic risks (Renn and Klinke, 2004; Renn et al., 2011).
Much of this uncertainty can be managed through framing and decision
processes. For example, a predict-then-act framing is different to an
assess-risk-of-policy framing (SREX Section 6.3.1 and Figure 6.2; Lempert
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t al., 2004). In the former, also known as “top-down,” model or
impacts-first, science-first, or standard approach, climate or impact
uncertainty is described independently of other parts of the decision
problem. For instance, probabilistic climate projections (see Figure 21-4
or WGI AR5 Chapters 11 and 12; Murphy et al., 2009) are generated for
wide application, and thus are not tied to any specific choice. This follows
the cause and effect model described in Section 2.1. The basic structure
of IPCC Assessment Reports follows this pattern, with WGI laying out
what is known and uncertain about current and future changes to the
climate system. Working Groups II and III then describe impacts resulting
from and potential policy responses to those changes (Jones and Preston,
2011).
In contrast, the “assess-risk-of-policy” framing (Lempert et al., 2004;
UNDP, 2005; Carter et al., 2007; Dessai and Hulme, 2007) starts with the
decision-making context. This framing is also known as “context-first”
(Ranger et al., 2010); “decision scaling” (Brown et al., 2011); “bottom-
up”; vulnerability, tipping point (Kwadijk et al., 2010); critical threshold
(Jones, 2001); or policy-first approaches (SREX Section 6.3.1). In engaging
with decision makers, the “assess-risk-of-policy” approach often requires
information providers work closely with decision makers to understand
their plans and goals, before customizing the uncertainty description to
focus on those key factors. This can be very effective, but often needs
to be individually customized for each decision context (Lempert and
Kalra, 2011; Lempert, 2012) requiring collaboration between researchers
and users (see Box 2-1). A “predict-then-act” framing is appropriate
when uncertainties are shallow, but when uncertainties are deep, an
“assess-risk-of-policy” framing is more suitable (Dessai et al., 2009).
The largest focus on uncertainty in CIAV has been on estimating climate
impacts such as streamflow or agricultural yield changes and their
consequent risks. Since AR4, the treatment of these uncertainties has
advanced considerably. For example, multiple models of crop responses
to climate change have been compared to estimate inter-model
uncertainty (Asseng et al., 2013). Although many impact studies still
characterize uncertainty by using a few climate scenarios, there is a
growing literature that uses many climate realizations and also assesses
uncertainty in the impact model itself (Wilby and Harris, 2006; New et
al., 2007). Some studies propagate uncertainties to evaluate adaptation
options locally (Dessai and Hulme, 2007) by assessing the robustness
of a water company’s plan to climate change uncertainties or regionally
(Lobell et al., 2008) by identifying which regions are most in need of
adaptation to food security under a changing climate. Alternatively, the
critical threshold approach, where the likelihood of a given criterion can
be assessed as a function of climate change, is much less sensitive to
input uncertainties than assessments estimating the “most likely”
outcome (Jones, 2010). This is one of the mainstays of robustness
assessment discussed in Section 2.3.3.
2.3.2. Scenarios
A scenario is a story or image that describes a potential future, developed
to inform decision making under uncertainty (Section 1.1.3). A scenario
is not a prediction of what the future will be but rather a description of
how the future might unfold (Jäger et al., 2008). Scenario use in the
CIAV research area has expanded significantly beyond climate into
b
roader socioeconomic areas as it has become more mainstream (high
confidence) (Sections 1.1.3, 2.4.2.1). Climate change has also become a
core feature of many scenarios used in regional and global assessments
of environmental and socioeconomic change (Carpenter et al., 2005;
Raskin et al., 2005). Scenarios can be used at a number of stages within
an assessment process or can underpin an entire assessment. They serve
a variety of purposes, including informing decisions under uncertainty,
scoping and exploring poorly understood issues, and integrating
knowledge from diverse domains (Parson et al., 2007; Parson, 2008).
Scenarios also contribute to learning and discussion, facilitate knowledge
exchange, and can be expressed using a range of media. Local scale
visualization of impacts and adaptation measures, depicted on realistic
landscapes, is an emerging technology that is being tested to support
dialog on adaptation planning at the local scale (Schroth et al., 2011;
Sheppard, 2012). Although visual representations of scenario-based
impact assessments may be available for a location, scenario-based
adaptation assessments usually are not. Artistic depictions of potential
adaptation measures and outcomes are being negotiated and assessed
with local stakeholders in communities within Metro Vancouver, Canada
(Shaw et al., 2009; Burch et al., 2010; Sheppard et al., 2011).
Climate, socioeconomic, or other types of scenarios are widely used to
assess the impacts of climate change. Fewer studies report on the use of
scenarios as participatory tools to enable decision making on adaptation
(e.g., Harrison et al., 2013). However, the scenario literature emphasizes
the importance of process over product. The new generation of climate
and socioeconomic scenarios being developed from the Representative
Concentration Pathways (RCPs; 1.1.3.1) and Shared Socioeconomic
Pathways (SSPs; 1.1.3.2), which are storylines corresponding to the new
RCPs (Moss et al., 2010; Kriegler et al., 2012) have yet to be applied
within CIAV studies in any substantive way (van Ruijven et al., 2013;
Ebi et al., 2014).
By separating risks into simple and systemic or wicked-problem risks,
scenario needs for decision making can be better identified (medium
confidence). For simple risks, if probabilities cannot be easily calculated
then scenarios can be used to explore the problem, test for acceptable
or unacceptable levels of risk, and illustrate alternative solutions for
evaluation and testing. Wicked problems will need to be thoroughly scoped
to select the most suitable decision-making process, with scenarios playing
an important role. They may require separate applications of problem
(exploratory or descriptive) and solution-based (normative or positive)
scenarios or the development of reflexive scenarios, the latter being
updated with new knowledge over time that may re-examine values and
goals (van Notten, 2006; Wilkinson and Eidinow, 2008; Jones, 2012);
these categories can also be structured as top-down, bottom-up, and
interactive (Berkhout et al., 2013). Even if conditional probabilities can
be used to illustrate climate futures, scenarios are needed to explore the
solutions space involving strategic actions, options planning, and
governance using process and goal-oriented methods (high confidence).
2.3.3. Evaluating Trade-offs and Multi-metric Valuation
Decision makers bring diverse aims, interests, knowledge, and values
to CIAV decision making. With effective decision support, parties to a
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d
ecision can manage competing views by more clearly articulating their
goals; understanding how various options affect trade-offs between goals;
and making informed choices that participants regard as legitimate,
salient, and credible (high confidence) (Cash et al., 2003). The decision
theory, risk governance, and ethical reasoning literatures use two broad
sets of criteria for decision making: outcome-based criteria focus on
whether a decision is likely to meet specified goals; process-based
criteria compare alternative actions according to the process by which a
decision is arrived. In particular, decision process aims to help stakeholders
choose between the risks, costs, and obligations being proposed
(Morgan et al., 1990), including specified levels of risk tolerance. Such
choices around risk tolerance, including acceptable levels of risk, are
ethical choices (DesJardins, 2012; Nanda, 2012). Selection strategies
informing context and process are described in Section 14.3.5. Decision
criteria inform the discussions of adaptation options, planning, and
economics in Chapters 14 to 17 and WGIII AR5 Chapter 2.
Multi-attribute decision theory (Keeney and Raiffa, 1993), or multi-criteria
decision analysis (MCDA), provides the most general framework for
assessing outcomes-based criteria. MCDA concepts and tools organize
and display the implications of alternative decisions on differing objectives
(e.g., cost and environmental quality), order and test preferences among
trade-offs between potentially incommensurate objectives, and show
how alternative processes for choosing options can lead to different
decisions. Cost-benefit analysis under uncertainty, one key tool for
evaluating trade-offs, is described in Section 17.3.2.1. Simple MCDA
tools include scorecards that graphically display how alternative policy
choices affect different goals. For example, the “burning embers” diagram
displays how risks to various attributes (e.g., health of unique systems,
extreme weather events) depend on targets for a given global mean
temperature increase (Figure 19-5). More sophisticated MCDA tools can
optimize a portfolio of choices in a variety of ways; for example, one
recent method applies scenarios representing significant uncertainty to
optimize between four or more choices in order to identify robust
combinations and system vulnerabilities (Kasprzyk et al., 2013).
Successful use of MCDA in CIAV decisions include the U.S. Bureau of
Reclamation helping stakeholders with diverse interests and values to
consider 26 alternative performance measures for the Colorado River
system, to agree on potential climate-related risks, and to consider options
for reducing those risks. Trade-offs also occur where adaptation measures
produce negative impacts in other areas of value—for example, where
adaptation in agricultural and urban areas negatively affect ecosystems
(Section 4.3.3.3). Korteling et al. (2013) assess the robustness of
adaptation options for six criteria including risk of water shortage,
environmental impact, local self-sufficiency, cost, carbon footprint, and
social acceptability. Chapter 17 describes many criteria commonly used
in MCDA analyses.
Robustness is often nominated as the most appropriate criterion for
managing large decision uncertainty. It is a satisficing (sufficient rather
than optimal) criterion (Rosenhead, 1989) that seeks decisions likely
to perform well over a wide range of plausible climate futures,
socioeconomic trends, and other factors (Dessai and Hulme, 2007;
Groves et al., 2008; Wilby and Dessai, 2010; WUCA, 2010; Brown et al.,
2011; Lempert and Kalra, 2011). Robust decisions often perform better
than other methods if the future turns out differently than expected.
Testing for robustness can often illuminate trade-offs that help decision
m
akers achieve consensus even when they have different future
expectations. Robust choices often trade some optimality for being able
to manage unanticipated outcomes. Many forms of the precautionary
principle are consistent with robustness criteria (Lempert and Collins,
2007). Flexible and reversible options are often needed to manage
situations with significant potential for unanticipated outcomes and
differences in values and interests among decision makers (Gallopín,
2006; Hallegatte, 2009; see also Sections 2.3.4, 5.5.3.1). Flexibility is
signaled by reaching of specific management thresholds, critical control
points, or design states (Box 5-1). The literature disagrees on the
relationship between robustness and resilience (Folke, 2006). Chapter 20
describes resilience as a property of systems that might be affected by
decision makerschoices, while robustness is a property of the choices
made by those decision makers (SREX Chapter 1).
Process-based criteria focus on the credibility and legitimacy of a
decision process. Institutional (Section 2.2.2) and cultural and ethical
(Section 2.2.1) contexts will strongly influence the appropriateness and
importance of such criteria in a given situation (high confidence).
Process criteria provide institutional rules, and governance for decision
making in a wide range of circumstances (Dietz and Stern, 2008; Sen,
2009). For instance, many environmental laws require advanced notice
and periods of public comment before any regulations are issued. Water
rights can be made tradable, giving users extra flexibility during times
of water shortage or oversupply. Participants may regard any decision
that fails to respect such rights as illegitimate. In complex situations of
a collaborative nature, both outcome and process-related criteria will
be needed in a decision-making process (high confidence).
Stakeholder involvement is a central process for climate-related decision
making and since the AR4 has grown in importance, particularly for
adaptation decision making (e.g., Lebel et al., 2010), covering methods
(Debels et al., 2009; Gardner et al., 2009; Salter et al., 2010; André et al.,
2012) and reflecting concrete experiences with stakeholder involvement
in CIAV assessments and adaptation processes (de la Vega-Leinert et al.,
2008; Ebi and Semenza, 2008; Posthumus et al., 2008; Raadgever et al.,
2008; Tompkins et al., 2008a,b; Preston et al., 2009). Lebel et al. (2010)
differentiate six advantages of social learning and stakeholder involvement
for adaptation to climate change: (1) reduces informational uncertainty;
(2) reduces normative uncertainty; (3) helps to build consensus on
criteria for monitoring and evaluation; (4) can empower stakeholders
to influence adaptation and take appropriate actions themselves by
sharing knowledge and responsibility in participatory processes; (5) can
reduce conflicts and identify synergies between adaptation activities of
various stakeholders, thus improving overall chances of success; and
(6) can improve the likely fairness, social justice, and legitimacy of
adaptation decisions and actions by addressing the concerns of all
relevant stakeholders. Complex settings will require a detailed mapping
of stakeholder roles and responsibilities (André et al., 2012).
2.3.4. Learning, Review, and Reframing
Effective decision support processes generally include learning, where
learning and review become important to track decision progress
(National Research Council, 2009b; see also Box 2-1, Figure 2-1). This can
be achieved by developing an ongoing monitoring and review process
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during the scoping stage of a project or program. If circumstances
change so much that desired outcomes may not be achieved, then
reframing of the decision criteria, process, and goals may be required.
This iterative approach begins with the many participants to a decision
working together to define its objectives and other parameters, working
with experts to generate and interpret decision-relevant information,
then revisiting the objectives and choices based on that information
(Figure 2-1). Again, process is important. Pelling et al. (2008) found that
accounting for different personal values in both an official and informal
capacity could enhance social learning and therefore adaptive capacity.
Measuring progress on adaptation and adaptive capacity by tracking
impacts, vulnerability, and related adaptation metrics and process
indicators is discussed in Section 14.6. Such metrics are needed to
transfer wider learning on adaptation to new situations.
Learning and review can range from periodic reporting to adaptive
management. Adaptive man