FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1
2 Chapter 17: Decision Making Options for Managing Risk
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4 Coordinating Lead Authors: Mark New (South Africa), Diana Reckien (Netherlands), David Viner (United
5 Kingdom)
6
7 Lead Authors: Carolina Adler (Switzerland/Chile/Australia), So-Min Cheong (Republic of Korea), Cecilia
8 Conde (Mexico), Andrew Constable (Australia), Erin Coughlan de Perez (USA), Annamaria Lammel
9 (France), Reinhard Mechler (Austria), Ben Orlove (USA), William Solecki (USA)
10
11 Contributing Authors: Rachel Bezner Kerr (Canada), Sukaina Bharwani (United Kingdom), Robbert
12 Biesbroek (Netherlands), Laurens Bouwer (The Netherlands), Lily Burge (United Kingdom), Massimo
13 Cattino (Italy), Isabelle Cojocaru-Durand (Canada), Mauricio Dom韓guez Aguilar (Mexico), Hannah Farkas
14 (USA), Simon French (United Kingdom), Adugna Gemeda (Ethiopia), Michael Gerrard (USA), Elisabeth
15 Anne Gilmore (USA), Nicoletta Giulivi (Italy/Guatemala), Maron Greenleaf (USA), Marjolijn Haasnoot
16 (The Netherlands), Ralph Hamman (Germany), Kirstin Holsman (USA), Christian Huggel (Switzerland),
17 Margot Hurlbert (Canada), Kripa Jagannathan (India/USA), Catalina Jaime (UK/Colombia), Sirkku Juhola
18 (Finland), Zoe Klobus (USA), Carola Kloeck (Germany/France), Bettina Koelle (South Africa/Germany),
19 Robert Kopp (USA), Carolien Kraan (The Netherlands), Judy Lawrence (New Zealand), Timo Leiter
20 (Germany/United Kingdom), Robert Lempert (USA), Debora Ley (Mexico), Megan Lukas-Sithole (South
21 Africa), Katharine Mach (USA), Alexandre Magnan (France), Kathleen Miller (USA), Lionel Mok
22 (Canada), Veruska Muccione (Italy), Rupa Mukerji (India), Baysa Naran (Mongolia), (Camille Parmesan
23 (USA), Lei Pei (China), Lavinia Perumal (South Africa), Madeleine Rawlins (United Kingdom), Neha Rai
24 (United Kingdom), Britta Rennkamp (South Africa/Germany), Alexandra Rinaldi (USA), Olivia Rumble
25 (South Africa), Liane Schalatek (USA), Emma Lisa Freia Schipper (Sweden/USA), Pasang Yangjee Sherpa
26 (USA/Nepal), Sabrina Shih (USA), Roopam Shukla (India/Germany), Rachael Shwom (USA), Chandni
27 Singh (India), M. Cristina Tirado-von der Pahlen (USA/Spain), Cathy Vaughn (USA), Maria Alejandra
28 Velez (Colombia), Ivo Wallimar-Helmer (Switzerland), Charlene Watson (United Kingdom), Romain
29 Weikmans (Belgium), Andrew Jordan Wilson (USA), Katy Wilson (United Kingdom), Mark Workman
30 (United Kingdom)
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32 Review Editors: Richard Klein (Germany/The Netherlands), Zinta Zommers (Latvia/Sierra Leone)
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34 Chapter Scientists: Megan Lukas-Sithole (South Africa), Massimo Cattino (Italy), Lauren Arendse (South
35 Africa), Vita Karoblyte (United Kingdom), Leah Jones (USA)
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37 Date of Final Draft: 1 October 2021
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39 Note: TSU Compiled Version
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41
42 Table of Contents
43
44 Executive Summary..........................................................................................................................................3
45 17.1 Objectives and Framing of the Chapter .................................................................................................7
46 17.1.1 Introduction.....................................................................................................................................7
47 17.1.2 Objectives and Key Terms...............................................................................................................9
48 Box 17.1: How is Success in Adaptation Characterised in Chapter 17? ...................................................10
49 17.1.3 Outline of the Chapter...................................................................................................................11
50 17.2 Risk Management and Adaptation Options.........................................................................................12
51 17.2.1 Adaptation Options for Climate Risk Management ......................................................................13
52 17.2.2 Combining Adaptation Options: Portfolios of Risk Management and Risk Governance.............19
53 Box 17.2: Climate Risk Management in Conflict-affected Areas...............................................................23
54 Cross-Chapter Box LOSS: Loss and Damage .............................................................................................31
55 17.3 Decision-making Processes of Risk Management and Adaptation ....................................................34
56 17.3.1 Decision-analytic Methods and Approaches ................................................................................36
57 Cross Chapter Box DEEP: Effective adaptation and decision-making under deep uncertainties .........51
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1 17.3.2 Integration Across Portfolios of Adaptation Responses ...............................................................55
2 17.4 Enabling and Catalysing Conditions for Adaptation and Risk Management ..................................55
3 17.4.1 Introduction...................................................................................................................................55
4 17.4.2 Enabling Condition 1: Governance ..............................................................................................56
5 17.4.3 Enabling Condition 2: Finance.....................................................................................................61
6 Cross-Chapter Box FINANCE: Finance for Adaptation and Resilience ..................................................62
7 17.4.4 Enabling Condition 3: Knowledge and Capacity .........................................................................70
8 17.4.5 Enabling Condition 4: Catalysing Conditions..............................................................................76
9 Box 17.3: Climate Risk Decision-Making in Settlements: From Incrementalism to Transformational
10 Adaptation ...............................................................................................................................................82
11 17.5 Adaptation Success and Maladaptation, Monitoring, Evaluation and Learning.............................83
12 17.5.1 Adaptation Success and Maladaptation........................................................................................83
13 17.5.2 Adaptation Monitoring, Evaluation & Learning ..........................................................................91
14 Cross-Chapter Box PROGRESS: Approaches and Challenges to Assess Adaptation Progress at the
15 Global Level ............................................................................................................................................96
16 BOX 17.4: The Rio Markers Methodology to Track Climate Finance ...................................................100
17 17.6 Managing and Adapting to Climate Risks for Climate Resilient Development .............................101
18 17.6.1 Need for Integrated Risk Management .......................................................................................101
19 17.6.2 Strategies for Managing a Portfolio of Climate Risks................................................................102
20 17.6.3 Mainstreaming Climate Risk Management in Support of Climate Resilient Development ........102
21 FAQ17.1: Which guidelines, instruments and resources are available for decision-makers to recognize
22 climate risks and decide on the best course of action?......................................................................103
23 FAQ17.2: What financing options are available to support adaptation and climate resilience?..........104
24 FAQ17.3: Why is adaptation planning along a spectrum from incremental to transformational
25 adaptation important in a warming world?.......................................................................................106
26 FAQ17.4: Given the existing state of adaptation, and the remaining risks that are not being managed,
27 who bears the burden of these residual risks around the world? ....................................................107
28 FAQ17.5: How do we know whether adaptation is successful? ...............................................................107
29 References......................................................................................................................................................110
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1 Executive Summary
2
3 Introduction and Framing
4
5 Chapter 17 assesses the options, processes and enabling conditions for climate risk management, a key
6 component of climate resilient development. While Chapter 16 assesses the risks that society and ecosystems
7 face, and residual risks after adaptation, this chapter focuses on the "how" of climate risk management and
8 adaptation. It covers: the adaptation and risk management options that are available; the governance and
9 applicability of options in different contexts; residual risk and Loss & Damage; the methods and tools that
10 can be drawn on to support climate risk management planning and implementation; enabling conditions and
11 drivers for adaptation; the role of monitoring and evaluation for integrated risk management and tracking
12 progress, success and the risk of maladaptation; and finally, integration of risk management across sectors,
13 jurisdictions and time horizons, under dynamic conditions of environmental and societal change.
14
15 Adaptation options for managing a wide range of climate risks have been proposed, planned, or
16 implemented across all sectors and regions, with prospects for wide-ranging benefits to nearly all
17 people and ecosystems (high confidence1) {17.2.1}. Many options are widely applicable and could be
18 scaled up to reduce vulnerability or exposure for the majority of the world's population and the ecosystems
19 they depend on (high confidence). These include nature restoration (high confidence), changing diets and
20 reducing food waste (high confidence), infrastructure retrofitting (high confidence), building codes (medium
21 confidence), disaster early warning (high confidence), and cooperative governance (medium confidence). The
22 portfolio of adaptation options that could be successfully implemented varies across locations, with resource-
23 limited and conflict-affected contexts bearing large amounts of residual risk (high confidence) {17.2,
24 17.5.1}.
25
26 The majority of climate risk management and adaptation currently being planned and implemented is
27 incremental (high confidence). Transformational adaptation will become increasingly necessary at
28 higher global warming levels (medium confidence) but can be associated with significant and
29 inequitable trade-offs (medium confidence). Adaptations with some of the highest transformative potential
30 include migration (high confidence), spatial planning (medium confidence), governance cooperation (medium
31 confidence), universal access to healthcare (medium confidence) and changing food systems (medium
32 confidence). Options that tend to modify existing systems incrementally include early warning systems (high
33 confidence), insurance (medium confidence), and improved water use efficiency (high confidence) {17.2,
34 17.5.1}.
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36 Governance, especially when inclusive and context-sensitive, is an important enabling condition for
37 climate risk management and adaptation (very high confidence). The use of formal and informal
38 governance approaches, often in polycentric arrangements of public, private and community actors, is
39 being increasingly recognised as important across many decision-making settings (high confidence)
40 {17.3.2; 17.4.2}. Public governance leadership has the largest role for social safety nets, spatial planning, and
41 building codes (high confidence) {17.2.1}. Private sector governance is important for insurance and for
42 minimizing the stressors that can negatively impact ecosystems and their functions especially in the absence
43 of public regulations or enforcement (medium confidence) {17.2.1}. Communities and individuals play the
44 largest role in governance of adaptations to farming and fishery practices and ecosystem-based adaptations
45 (medium confidence) {17.2.1}. Informal or individual-led decision-making is more common in food security
46 and livelihood related adaptations, such as changes to diets, livelihood diversification and seasonal migration
47 (high confidence) {17.2.1}. People who have experienced climate shocks are more likely to take on informal
48 adaptation measures, and in places where people are more exposed to extreme events, autonomous
49 adaptation is more common (high confidence) {17.2.1}.
50
1
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust;
and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very
low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of
agreement are correlated with increasing confidence.
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1 National and international legal and policy frameworks and instruments support the planning and
2 implementation of adaptation and climate risk management across scales, especially when combined
3 with guidelines for action (medium confidence) {17.4.2}. Nationally Determined Contributions (NDCs)
4 have been drivers of national adaptation planning, with cascading effects on sectors and sub-national action,
5 especially in developing countries (high confidence) {17.4.2}. Nearly all developing countries (particularly
6 SIDS) that included an adaptation component in their NDCs consider adaptation the most urgent aspect of
7 their national climate change response (high confidence) {17.4.2}. A steady increase in national and sub-
8 national laws, policies, along with regulations that mandate reporting and risk disclosure have promoted
9 adaptation response across public agencies, private firms and community organizations (high confidence)
10 {17.4.2}. Greater adaptation is present where national climate laws and policies require adaptation action
11 from lower levels of government and include guidelines on how to do so (medium confidence) {17.4.2}.
12
13 Recognition of the critical role of financing for adaptation and resilience as an important enabler for
14 climate risk management has strengthened (high confidence). Yet, since AR5, the gap between the
15 estimated costs of adaptation and the documented (tracked) finance allocated to adaptation has
16 widened (high confidence). Estimated global and regional costs of adaptation vary widely due to differences
17 in assumptions, methods, and data; the majority of more recent estimates are higher than the figures
18 presented in AR5 (high confidence). Although the estimated cost of adaptation is higher for developed
19 countries (medium confidence), for developing countries they are much higher as a proportion of national
20 income, making the self-financing of adaptation more difficult (high confidence). A high proportion of
21 developing country NDC adaptation contributions are conditional on external financial support, underscoring
22 the crucial role of international finance to achieve adaptation efforts commensurate with climate risks (high
23 confidence) {17.4.2; Cross-Chapter Box FINANCE in this Chapter}. Developed country climate finance
24 leveraged for developing countries for mitigation and adaptation has fallen short of the 100 USD billion per
25 year Copenhagen commitment for 2020 (very high confidence) {Cross-Chapter Box FINANCE in this
26 Chapter}. Substantial opportunities exist for improving access to climate finance, as well as its impact and
27 effectiveness {17.4.2; Cross-Chapter Box FINANCE in this Chapter}.
28
29 Private sector financing for adaptation has been increasingly promoted as a response to realized
30 adaptation finance needs (high confidence). However, private sector financing of adaptation has been
31 limited, especially in developing countries (high confidence). Tracked private sector finance for climate
32 change action has grown substantially since 2015, but the proportion directed towards adaptation has
33 remained small (high confidence) {Cross-Chapter Box FINANCE in this Chapter}; in 2018 these
34 contributions were 0.05% of total climate finance and 1% of adaptation finance. A key challenge for private
35 sector financing of adaptation is demonstrating financial return on investment, as many benefits of
36 adaptation arise as avoided damages or public goods, rather than direct revenue streams (medium
37 confidence). Leveraging private finance in developing countries is often more difficult because of risk
38 (perceived and real) to investors, reducing the pool of potential investors and/or raising the cost (interest) of
39 investment (medium confidence) {17.4.3.; Cross-Chapter Box Finance in this Chapter}.
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41 Information and knowledge on climate risk and adaptation options, derived from different knowledge
42 systems, can support risk management and adaptation decisions (high confidence) {17.4.4}. Processes,
43 such as co-production, that link scientific, Indigenous, local, practitioner and other forms of knowledge can
44 make climate risk management processes and outcomes more effective and sustainable (high confidence)
45 {17.3.2; 17.4.4}.
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47 Climate services that provide reliable, relevant, and usable climate information for the short or long
48 term are increasingly being produced and used in climate risk management (high confidence) {17.4.4}.
49 In many regions and sectors, the utility of climate services is strengthened by sustained engagement between
50 stakeholders and experts and by co-production (medium confidence) {17.4.4; Cross-Chapter Box Climate
51 Services WGI Chapter 12}. Significant gaps remain in the evaluation of climate services, and some studies
52 indicate that climate services often do not reach the most vulnerable and more isolated people, maintaining
53 or exacerbating inequality.
54
55 Catalyzing conditions and windows of opportunity can drive shifts in motivation and adaptation
56 effort, stimulating more rapid uptake of existing and new adaptation options (medium confidence)
57 {17.4.5}. Decision-makers can take advantage of windows of opportunity to promote rapid and
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1 effective responses in reactive and proactive cases {17.4.5}. Disaster events or shocks such as wildfires,
2 tropical cyclones, heatwaves or coral bleaching have catalyzing characteristics (high confidence) {17.4.5.2}.
3 Additional types of catalyzing conditions include climate litigation and the presence of individuals and
4 organizations that act as policy and decision innovators, including government and business innovators in
5 cities (medium confidence) {17.4.5.3}, stimulating action within and beyond their immediate contexts
6 (medium confidence). Litigation on failure of government and business to adapt is becoming more frequent
7 and is expected to increase as climate impact attribution science matures further (high confidence) {Cross-
8 Chapter Box LOSS in this Chapter; 17.4.5.3}.
9
10 Urgency can stimulate prompt climate risk management (high confidence). A moderate level of urgency
11 contributes to enhanced climate action, while both high and low levels of urgency can impede response (high
12 confidence) {17.4.5.1}. Well-designed communication strategies can move decision makers from low to
13 moderate levels of urgency, stimulating action. As conditions approach a crisis state, however, urgency can
14 weaken decision-making rather than support it (medium confidence) {17.4.5.1}.
15
16 Decision support tools and decision-analytic methods are available and are being applied for
17 managing climate risks in varied contexts, including where deep uncertainty is present (high
18 confidence). These tools and methods have been shown to support deliberative processes where
19 stakeholders jointly consider factors such as the rate and magnitude of change and their uncertainties,
20 associated impacts and timescales of adaptation needed along multiple pathways and scenarios of
21 future risks (high confidence) {17.3.2; Cross-Chapter Box DEEP in this Chapter}. However,
22 comparative evidence on the relative utility of different analytical methods in their use by decision makers
23 for managing climate risks is an important gap (medium confidence). Nevertheless, robust decision-making,
24 using pathway analyses to determine `no regrets' options amongst trade-offs, has been shown to be a useful
25 starting point under deep uncertainty (medium confidence). Methods for analysing options differ across geo-
26 political scales, with modeling studies being a particularly prominent method across scales from community
27 and urban to regional and national (high confidence) {17.3.1; 17.6, Cross-Chapter Box DEEP in this
28 Chapter}.
29
30 Successful adaptation and maladaptation form the opposite poles of a continuum (medium confidence).
31 The evaluation of an adaptation option and its location on this continuum are context-specific and
32 vary across time, place and evaluation perspectives (high confidence) {17.5.2}. Despite knowledge gaps,
33 adaptation options can be assessed according to several criteria, such as benefits to humans, benefits to
34 ecosystem services, benefits to equity (marginalized ethnic groups, gender, low-income populations),
35 transformational potential, and contribution to greenhouse gas emission reduction (medium confidence)
36 {17.5.1}. These factors can aid evaluation of co-benefits and trade-offs within and between adaptation
37 responses (high confidence) facilitating successful adaptation and reducing the likelihood of maladaptation
38 (medium confidence) {17.5.1}.
39
40 Adaptation options across a range of climate risk settings (Representative Key Risks) have potential
41 for some degree of maladaptation alongside varied potential for success (very high confidence) {17.5.2}.
42 Maladaptation can result from unaccounted trade-offs with low-income groups and the transformational
43 potential of adaptation (medium confidence) {17.5.2}. Success is greatest when adaptation enhances gender
44 equity (medium confidence) {17.5.2} and supports ecosystem function and services (medium confidence)
45 {17.5.2}. Among adaptation options, coastal infrastructure is an example that has particularly high risk for
46 maladaptation through trade-offs for natural system functioning and human vulnerability over time.
47 Examples of options with high potential for successful adaptation are nature restoration (medium confidence)
48 {17.5.2}, social safety nets (medium confidence) {17.5.2} and adaptations relating to changes of diets and
49 reducing food waste (medium confidence) {17.5.2}.
50
51 Monitoring and evaluation (M&E) are key for iterative climate risk management, in particular
52 tracking adaptation progress and learning about adaptation success and maladaptation (high
53 confidence). M&E application has increased since AR5 at the local, project and national level, but is
54 still at an early stage in most countries (high confidence) and underutilized as a way to assess
55 adaptation outcomes at longer timeframes (high confidence) {17.5.2}. About one-third of countries have
56 undertaken steps to develop national adaptation M&E systems, but fewer than half of these are reporting on
57 implementation (medium confidence) {17.5.2}. M&E, as well as tracking global progress on adaptation, are
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1 confronted with a number of challenges (high confidence), such as a comparability in what counts as
2 adaptation and limited availability of data across scales {17.5.2; Cross-Chapter Box PROGRESS in this
3 Chapter}. The relative strength and weaknesses of different approaches and their applicability have not been
4 systematically assessed, but the diversity of approaches being used could provide a more comprehensive
5 assessment of global adaptation progress (Cross-Chapter Box PROGRESS in this Chapter).
6
7 Understanding of residual impacts and risks in vulnerable regions and implications for Loss &
8 Damage (L&D) has become increasingly relevant as the limits to adaptation are projected to be
9 reached in natural and human systems (high confidence) {17.2.2.5; Cross-Chapter Box LOSS in this
10 Chapter}. The international L&D policy debate has seen heightened attention, with some coalescence
11 around key issues, including risk management, limits to adaptation, existential risk, finance and support,
12 including liability, compensation and litigation. Advisory groups have been set up with participation of
13 policy and experts from research, civil society and practice to inform debate. Yet, the policy space and
14 concrete remit for L&D has remained vague, which renders policy formulation complex (high confidence)
15 {17.2; Cross-Chapter Box LOSS in this Chapter}.
16
17 Effective management of climate risks is dependent on systematically integrating adaptations across
18 interacting climate risks, ensuring that measures of success include factors important to climate
19 resilient development, and accounting for the dynamic nature of climate risks over time (very high
20 confidence) {17.6}. Across the Working Group II report are examples of how managing adaptations to
21 reduce climate risks can negatively or positively affect sustainable development, thereby impacting the
22 potential for climate resilient development. Climate risks can emerge at different rates and time horizons,
23 and the interactions between risks vary from region to region (very high confidence) {17.6}. The need to
24 manage these risks in an integrated manner is demonstrated by the diverse and interacting impacts of climate
25 risks on ecosystems, cities, health, and poverty and livelihoods, such as in the Water-Energy-Food nexus
26 (high confidence) {17.6}. Expertise and resources for integrated risk management varies between the
27 developed and developing countries (high confidence) {17.6}. Integrated pathways for managing climate
28 risks will be most suitable when `low regrets' anticipatory options are established jointly across sectors in a
29 timely manner, path dependencies are avoided in order to not limit future options for climate resilient
30 development, and maladaptations across sectors are avoided (high confidence) {17.6}. National Adaptation
31 Plans have potential to integrate participatory, iterative processes to monitor, review, and update adaptations
32 as knowledge, experience and resources become available {Cross-Chapter Box DEEP in this Chapter; 17.6}.
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1 17.1 Objectives and Framing of the Chapter
2
3 17.1.1 Introduction
4
5 Addressing the impacts and risks associated with observed and projected climate change (see Chapter 16) is
6 fundamentally and intricately tied to the decision-making options available to manage those risks. Climate
7 risk decision-making focuses on the processes needed to identify and characterise those risks, generate plans,
8 policies to reduce the likelihood and/or magnitude of adverse potential consequences, based on assessed or
9 perceived risks (derived from the definition of risk and risk management in Chapter 1). This chapter presents
10 an assessment of the evidence on climate risk decision-making as a set of processes that involve a range of
11 actors in different contexts resulting in diverse outcomes. The climate risk decision-makers and their actions
12 are the central focus of the assessment. The chapter is an assessment of the evidence of the decision-making
13 options that are available in practice, and functions as a central pivot point between the identification of key
14 climate risks (Chapter 16) and the means to integrate and leverage action on climate risk decision-making
15 into the broader requirements of climate resilient development pathways (Chapter 18). This section
16 introduces the main entry points on decision-making that have framed this assessment (Sections 17.1.1.1 to
17 17.1.1.5), as well as the key terms used to frame this assessment and its organisation in this chapter (Section
18 17.1.2).
19
20 A central framing point is the connection between climate risk decision-making and adaptation. Adaptation
21 for human systems in this report is introduced in Chapter 1 and defined in the Glossary as `the process of
22 adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial
23 opportunities'. In natural systems, adaptation is the process of adjustment to actual climate and its effects;
24 human intervention may facilitate adjustment to expected climate and its effects (see AR6 Glossary). In this
25 chapter, we consider adaptations that may be implemented by people, whether they be to support human,
26 managed, or natural systems, and the processes and factors that underpin adaptation in these diverse settings.
27 Different types of adaptation have been distinguished in Chapter 1, including anticipatory versus reactive,
28 autonomous versus planned, and incremental versus transformational (IPCC WGII glossaries; Chapters 16-
29 18). These dichotomies and interactions are assessed here. Implementation of adaptation through iterative
30 risk management decision-making emphasizes that anticipating and responding to climate change does not
31 consist of a single set of judgments at a single point in time, but rather an on-going cycle of assessment,
32 action, reassessment, learn, and response' (Chapter 1).
33
34 17.1.1.1 Decision-Making for Managing Climate Risks in AR6
35
36 The UN 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), as
37 well the UNFCCC Paris Climate Agreement, the UN Sendai Framework Disaster Risk Reduction, and the
38 UN Habitat New Urban Agenda helped push climate risk management and adaptation forward from the
39 global to the national level, from the planning stage into implementation and provides benchmarks for
40 adaptation progress. To assess adaptation progress (17.5), the interplay between top-down (institutional) and
41 bottom-up (individual/social/community) processes, multi-scale interaction (local, regional, national, and
42 international), iterative risk management, differing forms of knowledge, and equity are especially crucial
43 (particularly Sections 17.2, 17.4). Parallel to these advances is an understanding and assessment of
44 appropriate decision support tools, methods, and evaluation metrics (Section 17.3).
45
46 Since AR5, significant advances have been made in regard to the understanding of the drivers of decision-
47 making and contexts in which climate risk decision-making takes place. Climate risk decision-making
48 generally, and adaptation specifically, has been a focus within the IPCC special reports in the sixth
49 assessment cycle. An overall goal of climate risk management is to eliminate or reduce the risk to levels that
50 are to a level that is socio-politically and economically acceptable. Risk management to an acceptable
51 level may not be feasible because of limits or barriers to adaptation. Future potential risks are a more
52 complex matter given the need to define time scales and spatial extent, and uncertainties. In the Special
53 Report on the impacts of global warming of 1.5C [SR1.5] (IPCC, 2018a), the risks associated with climate-
54 related impacts were found to be higher under emission scenarios above 1.5癈, raising awareness for the
55 need to limit the impacts of warming through the acceleration of climate mitigation and both incremental and
56 transformational adaptation (IPCC, 2018a).
57
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1 The AR6 SRCCL (IPCC, 2019b) added the dimensions of pace, intensity, and scale of climate impacts and
2 adaptation or mitigation responses and adverse consequences. Relevant land-based adverse consequences
3 include those on lives, livelihoods, health and wellbeing, economic, social, and cultural assets and
4 investments, infrastructure, services (including ecosystem services), ecosystems and species.
5
6 While a generic understanding of the decision-making process has emerged from the literature, the chapter
7 assesses how these components and their dimensions interact across a range of temporal (short, long-term as
8 defined in SROCC), scalar (household to global), institutional/governance (formal, informal, bottom up, top
9 down), and magnitude (micro adaptation - small scale and macro adaptation - large scale) (Section 17.2).
10 The IPCC SRCCL placed emphasis on acknowledging co-benefits and trade-offs to avoid barriers to
11 implementation, with particular attention to land use decisions. It states that this coordination can be
12 supported by building networks of decision-makers across scales and sectors, including local stakeholders
13 from vulnerable groups, and by adopting and implementing policies in a flexible and iterative manner (IPCC,
14 2019b).
15
16 17.1.1.2 Approaches to Assess and Synthesise Options for Managing Risk
17
18 This chapter utilizes several points of departure to assess climate risk management that emerge from AR5
19 and AR6, specifically. SR Climate Change and Land, especially Chapter 7 and throughout SROCC.
20 These works provide foundational assessment of evidence on decision-making systems that connect
21 different spatial and temporal scales and diverse cultural contexts in which climate risk management takes
22 place, the varying interactions of decision-makers and their stakeholder groups, and the barriers and enablers
23 to decision making, including governance, finance, and knowledge (Section 17.4).
24
25 Another significant advance is that instead of cataloguing decision-making strategies, the literature has now
26 evolved to the point where adaptation progress, effectiveness and efficiency can be more meaningfully
27 assessed through increased monitoring and evaluation capacity. Although the ability to measure success and
28 effectiveness is not fully developed and hampered by lack of data, agreed methods and terms, and time to
29 fully evaluate adaptation actions (see Sections 17.3.3 and 17.5, Cross-Chapter Box PROGRESS in this
30 Chapter).). The ambition to describe effectiveness and success illustrates further maturation of the literature
31 on climate risk decision-making as a system process. Overall, the process of climate risk decision-making
32 remains dynamic, and the chapter attempts to assess a variety of proactive management approaches being
33 developed and tested to address adverse, diverse and complex risks in a wide range of developing and
34 developed country contexts (see Figure 17.1). The chapter provides a synthesis of how these new approaches
35 are reflected in the sectoral and regional chapters and cross-chapter papers of this report (Chapters 2-15;
36 CCPs 1-7). Specifically, the goal is to provide a line of sight between the sectoral and regional chapters and
37 cross-chapter papers' decision-making assessment to sections in this chapter. This synthesis also helps to
38 present the varying and context-driven character of adaptation strategies now in practice and being
39 considered.
40
41 17.1.1.3 Key Risks Considered in the Assessment of Climate Risk Decision-making
42
43 In AR6 (Chapter 16 and Cross-Chapter Papers), over 100 key risks have been identified across regions and
44 sectors, which have the potential to manifest into severe impacts that are relevant to the interpretation of
45 United Nations Framework Convention on Climate Change (UNFCCC) Article 2, specifically on the
46 objective to avoid dangerous anthropogenic interference with the climate system. These risks are likely2 to
47 become more severe under higher warming scenarios and social-ecological conditions that yield high
48 exposure and vulnerability to the associated climate-related hazards. In this report, these key risks have been
49 grouped into categories represented by eight overarching risks (called Representative Key Risks, RKRs)
50 relating to: 1) coastal socio-ecological systems; 2) terrestrial and ocean ecosystems; 3) critical physical
51 infrastructure, networks, and services; 4) living standards; 5) human health; 6) food security; 7) water
2
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result:
Virtually certain 99�100% probability, Very likely 90�100%, Likely 66�100%, About as likely as not 33�66%,
Unlikely 0�33%, Very unlikely 0�10%, and Exceptionally unlikely 0�1%. Additional terms (Extremely likely: 95�
100%, More likely than not >50�100%, and Extremely unlikely 0�5%) may also be used when appropriate. Assessed
likelihood is typeset in italics, e.g., very likely). This Report also uses the term `likely range' to indicate that the assessed
likelihood of an outcome lies within the 17-83% probability range.
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1 security; and 8) peace and human mobility (see Chapter 16). Decision-making options for managing these
2 risks, such as selecting the relevant adaptation options to implement, require an assessment of the local
3 context in which these impacts are likely to be experienced, as well as the local to global collective
4 implications of those actions (see Sections 17.2 and 17.5).
5
6 17.1.2 Objectives and Key Terms
7
8 17.1.2.1 Drivers
9
10 AR5 provides a broad overview of drivers as the determinants of climate decision-making by individuals and
11 organizations, including social, institutional, and regulatory contexts, cultural values and norms, economic
12 resources and constraints, and the availability of information and of tools to process it. This chapter expands
13 the discussion of the contexts for decision-making in a number of ways (see Section 17.4), including an
14 examination of informal as well as formal decisions, an attention to emerging actors, particularly social
15 movements, and consideration of several dimensions of governance. It expands the treatment of decision
16 processes, with particular attention to framing and to the integration of multiple time frames (Sections 17.3
17 and 17.6).
18
19 Since AR5, there has been an increasing ambition for adaptation, signalled by growing attention to the
20 adaptation gaps and deficits, which call for extensive and intensive levels of action (Chen et al., 2016;
21 UNEP, 2017; Tompkins et al., 2018; Valente and Veloso-Gomes, 2020; UNEP, 2021a), as well as increased
22 attention to co-benefits between climate risk reduction and other benefits, such as equity and biodiversity
23 conservation (Colloff et al., 2017, Section 17.5.1; Smith et al., 2020). Climate risk decision-making as an
24 object of study has emerged in a more central location within the literature as adaptation moves from
25 planning into the realm of practice. The broad sense of urgency (summarized in Wilson and Orlove, 2019;
26 Wilson and Orlove, 2021), show growth of the term "urgency" in both scholarly publications and the popular
27 press since 2014, building on earlier increases starting around 2005, and a dramatic spike of the terms
28 "climate crisis" and "climate emergency." Paralleling this call for more extensive and rapid action is the
29 emergence of the term "transformational" adaptation and decision-making. Transformational adaptation
30 (defined and deeply examined in Chapter 1, Chapter 16, and Section 17.2) highlights efforts that involve
31 large-scale, systemic change (Wilson et al., 2020) and involves "adapting to climate change resulting in
32 significant changes in structure or function that go beyond adjusting existing practices including approaches
33 that enable new ways of decision-making on adaptation" (IPCC, 2018a). The complex relationship between
34 incremental adaptation and transformational adaptation is presented and reviewed in 17.2. Furthermore, the
35 literature since the AR5 report has moved beyond the question of limits and barriers to adaptation as relevant
36 aspects for decision-making to additionally assessing drivers of change, with increasing focus devoted to
37 more nuanced and differentiated contexts for action.
38
39 17.1.2.2 Enabling Conditions
40
41 AR5 extensively assessed the conditions of adaptation with a focus on the role of governance, finance,
42 knowledge, and capacity. AR6 extends this examination of adaptation and the decision-making process
43 around it by focusing on enablers. Adaptation enablers are defined as those conditions or properties that
44 specifically promote or advance the adaptation process (see Chapter 1). Enablers are positively associated
45 with likelihood that adaptation planning occurs, and strategies will be put into practice. Three broad enabling
46 conditions are presented in the chapter (Section 17.4): governance (legislation, regulation, institutions,
47 litigation), finance (needs, sources, intermediaries, instruments flows, and equity) and knowledge (capacities,
48 climate services, big data, indigenous/local knowledge, co-production, boundary organizations). As an
49 extension of enabling conditions, the chapter also examines catalysing conditions for adaptation (Section
50 17.4.5). Catalysing conditions motivate and accelerate the process of decision-making leading to more
51 frequent and potentially substantial adaptations. The chapter recognises that the relative influence of
52 enabling conditions and catalysing conditions are set within the human dimensions of climate change
53 including vulnerability, inequality, poverty, and the achievement/non-achievement of SDGs (see Figure 8.1).
54
55 17.1.2.3 Mechanisms for Decision-making
56
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1 The mechanisms and conditions for decision-making provide the basis for the chapter. AR5 provided a
2 detailed chapter on the support of climate decision-making. Chapter 2 of AR5 concluded, with high
3 confidence, that risk management provides a useful framework for most climate change decision making,
4 and that iterative risk management is most suitable in situations characterised by large uncertainties, long
5 time frames, the potential for learning over time, and the influence of both climate as well as other
6 socioeconomic and biophysical changes. Furthermore, decision support is situated at the intersection of data
7 provision, expert knowledge, and human decision-making at a range of scales from the individual to the
8 organization and institution.
9
10 The climate risk management decision-making process follows a set of general considerations. The detail of
11 each decision is often highly context specific. Climate risk decision-making is bound to the question of how
12 and under what circumstance it is appropriate to alter, reduce or transfer and retain risk. Different types of
13 risk (e.g., gradual compared with catastrophic) and conditions of risk (e.g., known versus uncertain) are
14 associated with different types of responses (e.g., incremental versus transformational). As the risk decision
15 process precedes, individuals and organizations will formally or informally utilize any number of
16 mechanisms to guide, aid, or facilitate the decision-making process. Decision-making can then take place in
17 a linear set of steps or through a complex iterative process involving reflexive and recursive steps.
18
19 17.1.2.4 Costs and Non-Monetised Loss, Benefits, Synergies, and Trade-Off
20
21 AR5 provided an extensive discussion of the costs to human and natural systems associated with climate
22 risks. It recognized the challenges which long time frames, uncertainty and the differing values held by
23 stakeholders create for the monetisation of losses. The AR6 SROCC built on the discussion of cultural
24 values--typically also difficult to monetise--through a consideration of cultural ecosystem services and
25 cultural forms of valuation, with cases from high mountain areas and polar regions (Hock et al., 2019;
26 Meredith et al., 2019; IPCC, 2019c). AR6 expands this discussion of multiple forms of valuation in several
27 ways. It considers regulation and litigation as mechanisms for promoting the consideration of both
28 monetisable and non-monetisable losses in decision-making (Cross-Chapter Box LOSS in this Chapter).
29 AR5 treated the issues of equity and justice primarily with regard to mitigation, especially in WGIII AR5
30 Chapter 3; these issues in the adaptation sphere are considered extensively in this chapter in areas such as
31 finance, governance, success of adaptation, maladaptation, and monitoring and evaluation. The discussions
32 of maladaptation and success of adaptation (Section 17.5) consider questions of synergies and trade-offs
33 across values and goals, while the consideration of decision processes and tools shows opportunities to use
34 co-benefits to promote effective decision-making, including approaches to decision-making under conditions
35 of deep uncertainty (Section 17.3; Cross-Chapter Box DEEP in this Chapter). Successful adaptation across
36 the report (as specified in Ch1) is associated with conditions when co-benefits are high and (negative) trade-
37 offs are low.
38
39 17.1.2.5 Monitoring and Evaluation
40
41 This chapter assesses the evidence of monitoring and evaluation (M&E) (see AR6 Glossary) and their
42 approaches as part of the adaptation process at the national, local, and project level as well as in global
43 assessments (17.5.2; Cross-Chapter Box PROGRESS in this Chapter). M&E can serve multiple functions,
44 e.g., to: 1) facilitate an understanding on whether and how interventions work in achieving intended
45 objectives; 2) inform ongoing and future implementation, and 3) provide information that helps to
46 substantiate upward and downward accountability (Preston et al., 2009; UNFCCC, 2010b; Pringle, 2011;
47 Spearman and McGray, 2011) (see BOX 17.1 for more discussion). This chapter also addresses the relevance
48 of iterative learning as part of the design of M&E processes, as a means by which actors and institutions
49 engaged in M&E acquire new insights on how these processes work (or not) to achieve set objectives.
50
51
52 [START BOX 17.1 HERE]
53
54 Box 17.1: How is Success in Adaptation Characterised in Chapter 17?
55
56 Whether an adaptation is considered successful is context specific. It depends on who evaluates adaptation
57 and at what time as well as on the ability to compare the outcome of adaptation with a hypothetical situation
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1 without adaptation and without other parallel changes, such as development interventions (Singh et al., 2021;
2 Dilling et al., 2019a). The ability to compare the risk situation post and prior adaptation is complicated
3 through the long time-horizons at which adaptation outcomes often become apparent (see Cross-Chapter Box
4 ADAPT in Chapter 1; Section 17.5.1; Dilling et al., 2019a).
5
6 However, a wealth of information has recently become available on how success and effectiveness of
7 adaptation could be assessed, defined, or investigated in certain settings (Patt and Schr鰐er, 2008; Morecroft
8 Michael et al., 2019; Tubi and Williams, 2021) or across a larger set of adaptations (Hegger et al., 2012;
9 Eriksen et al., 2015; Gajjar et al., 2019a; Owen, 2020; Singh et al., 2021). Accordingly, successful adaptation
10 is understood as effective adaptation, in that it reduces climate impacts, vulnerabilities and risk, and
11 additionally balances synergies and trade-offs across diverse objectives, perspectives, expectations, and
12 values (Eriksen et al., 2015; Juhola et al., 2016; Gajjar et al., 2019a; Owen, 2020; Singh et al., 2021). Across
13 this report, four factors are identified as enabling conditions of successful adaptation, which include a focus
14 on recognitional, procedural, and distributional justice as well as flexible and strong institutions that seek
15 policy integration and account for long-term goals.
16
17 To operationalizable `success' in this chapter, it is characterised by the degree to which an adaptation
18 response benefits (1) human systems (number of people); (2) ecosystems or ecosystem services; (3)
19 marginalized ethnic groups, (4) women and girls, (5) and low-income populations, and can be characterised
20 as (6) transformational adaptation, and (7) contributing to greenhouse gases emission reductions (Section
21 17.5.1). Overarching to these factors are uncertainty and potential path-dependency of decisions that may
22 result in lock-in and maladaptation in the long-term, and recognition that what is successful in the near-term
23 is not necessarily successful in the long-term.
24
25 Success in adaptation is antithetical to maladaptation. Maladaptation refers to current or potential future
26 negative consequences, including failed or partially successful adaptation (or risk reduction), but also trade-
27 offs or side-effects of adaptation (see Glossary). Thus, success of adaptation and maladaptation form the
28 ends of a continuum that represents the balancing of synergies and trade-offs across regions, populations, or
29 sectors (Singh et al., 2016; Magnan et al., 2020; Schipper, 2020). Every adaptation action may be placed
30 along such a continuum reflecting the empirical evidence of adaptation practices and their assessment
31 (Section 17.5).
32
33 [END BOX 17.1 HERE]
34
35
36 17.1.3 Outline of the Chapter
37
38 The chapter is organised around the broad narrative of climate risk decision-making and management
39 (Figure 17.1), building from the assessment of risks within RKRs (Chapter 16) and options available to
40 address these risks and within a broader context of climate resilient development pathways (Chapter 18).
41 Decision-making is considered to be a reflexive and recursive process where different evidentiary threads
42 and information inputs become relevant to the understanding and assessment of factors underlying specific
43 decisions. Additionally, this is also a discursive process, whereby actors and institutions' interpretations of
44 climate risks are also key to these deliberations.
45
46
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1
2 Figure 17.1: Schematic representation of the climate risk management decision-making process as introduced in
3 Chapter 1 (Figure 1.6) and the key elements of this Chapter that address additional aspects of this process. In Chapter
4 17, climate risk management (middle box) is framed as the iterative response (i.e., what society could do and how it
5 could be done) to the climate risks described in Chapter 16, with outcomes (ideally reduced risk) that can support (or
6 perhaps hinder) climate resilient development, Chapter 18. Decision makers from diverse contexts sit at the centre of
7 the climate risk decision making process, and interact with and drive these processes as they play out. The main
8 sections of Chapter 17 (bottom panel of boxes) address a wide range of issues (keywords in bottom panel) that manifest
9 at one or more stages of climate risk management processes, illustrated by icons for section numbers and Cross-Chapter
10 Boxes in the interactive risk management process.
11
12
13 Decision-making processes of risk management and adaptation are varied and numerous. Section 17.2
14 assesses the risk management and adaptation options already in practice. Section 17.3 assesses decision-
15 support methods and tools available for application and the effectiveness of these in supporting climate
16 decision-making across degrees of uncertainties and levels of governance and expected reach (scale) across
17 populations from households to international cooperation. Closely interlinked across the decision-making
18 process, are the enabling and catalysing conditions for decisions on adaptation and risk management (section
19 17.4). Section 17.5 synthesizes evidence on maladaptation and adaptation successes, and assesses the current
20 knowledge on M&E of adaptation, including financial accounting, to support learning on those, respectively.
21 Here, M&E is considered distinct from the tracking of financial flows related to adaptation, given that
22 financial accounting does not necessarily provide information on the implementation of adaptation measures
23 and their results (see also Section 17.2.1.2). Finally, in Section 17.6, decision-making, climate risk
24 responses, and their relevance for climate resilient development are presented, where evidence on their
25 respective contributions to facilitate actions in the adaptation solution space within a broader context for
26 development is shown (Chapter 18). Throughout the decision-making process, crucial feedback loops are
27 present that define the results of specific actions and recursive nature of climate risk management and
28 adaptation.
29
30
31 17.2 Risk Management and Adaptation Options
32
33 There has been substantial progress in risk management and adaptation responses around the world, as
34 demonstrated in the sectoral and regional chapters of this report and illustrated in Chapter 16. This section
35 presents an overview of different options available to manage risk, explaining how they are currently
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1 governed and the extent to which they can be applied around the world. This section contains an assessment
2 of the ways in which different options are being combined to create adaptation portfolios, and describes how
3 incremental and transformational change is starting to be considered. Based on the human dimension of
4 climate change, as described in Chapter 8, vulnerability, inequality, and poverty influence these portfolios of
5 adaptation and transformational change. Particularly for change where residual risks remain that may lead to
6 exceeding the limits of adaptation, increasingly transformational adaptation and policy innovation will be
7 important.17.2.1 assesses options for climate risk management from around the world that reduce, manage,
8 or retain climate-related risks and assesses their contribution to reducing vulnerability and exposure, how
9 they are governed, and the benefits to humans and ecosystems. 17.2.2 presents portfolios of risk management
10 including the design principles and observed variations across the globe, before it discusses the need and
11 potential for transformational adaptation to complement incremental adaptation, for which we present
12 evidence across the report for selected adaptation options and some key risks. The Cross-Chapter Box LOSS
13 in this Chapter synthesises recent literature and assesses key strands of the international dialogue policy on
14 Loss & Damage, concerned with options that help to deal with residual impacts and risks in vulnerable
15 countries.
16
17 17.2.1 Adaptation Options for Climate Risk Management
18
19 This section assesses options for climate risk management (CRM) across common risk settings that have
20 been grouped into Representative Key Risks (RKRs). These risk management and adaptation actions target
21 the components of risk: hazards, vulnerabilities, and exposure associated with sudden or slow-onset events
22 (see Chapter 1 for more details on the definition of risk).
23
24 For each of the RKRs, three commonly discussed adaptation options are identified across the regional,
25 sectoral, and cross-chapters papers of this report. These 24 options have been selected to cover a
26 representative variety of strategies to adapt to climate change, while a particular adaptation option can be
27 relevant to many of the RKRs. For example, the adaptations listed under the RKR of "Food security" are also
28 related to the RKR on "Human health" (Ebi and Prats, 2015). See SM17.1 for more details. The list is not
29 comprehensive of all possible adaptations listed in the regional and sectoral chapters. For example, this does
30 not include adaptations by institutions who might become unable to cope with increasing pace and
31 magnitude of extreme events (see Chapter 11).
32
33 17.2.1.1 Adaptation Options and Their Contribution to Reduce Vulnerability and Exposure
34
35 Table 17.1 provides examples of each of these 24 adaptation options from across AR6 WGII. Detailed
36 information about sectors and regions where these adaptations are being discussed can be found in the
37 indicated chapters. Note that this list is curated to ensure a diversity of options, therefore most of the options
38 will apply to more than one RKR.
39
40
41
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1 Table 17.1: Selected adaptation options per RKR, with examples of how each option can reduce vulnerability or exposure, or support risk financing. Many of the adaptation options
2 are relevant to multiple RKRs, and have been selected to be representative of the wide variety of adaptation options implemented or suggested around the world.
RKR Adaptation option Examples from regional and sectoral chapters and cross-chapter papers
Risk to coastal Coastal accommodation Raising of dwellings, raising of coastal roads (15.5.2), amphibious building designs (CCP2), improved drainage (11.3.5.3)
socio-ecological Coastal infrastructure Seawalls, beach and shore nourishment (3.6, 15.5.1), breakwater structures (15.5.1), dikes, revetments, groynes, or tidal
systems Strategic coastal retreat barriers. (6.3.4.8)., land reclamation (15.5.2)
Retreating from coastal areas (3.6, Cross-Chapter Box SLR in Chapter 3, 6.3.5.1, CCP2), relocation/resettlement (CCP2)
Restore/create natural areas Marine protected areas (FAQ 3.5), active restoration of coral reefs (3.6.2.3.2), ridge-to-reef management (CCP1), restoring
dunes (CCP4), planting salinity-tolerant trees (4.5.2.1)
Risk to terrestrial Increasing forest cover (CCP7), detect and manage forest pests (11.3.4.3)
and ocean Reduce ecosystem stress Reduce pollution and eutrophication (3.3.3), reduce anthropogenic pressures on the Great Barrier Reef (Box 11.2),
sustainable fisheries harvest (3.6.2), increasing connectivity between natural areas (2.6.2)
ecosystems
Marine habitats to protect against storm surge (3.6), agroecology (5.14.1.1), coastal and marine vegetation and reefs
Ecosystem-based adaptation (6.3.3.4), vegetation corridors, greenspace, wetlands (FAQ 6.3), mangrove habitat restoration (8.5.2.2, 9.8.5.1), restoring
coasts, rivers, wetlands to reduce flood risk (2.6.3, CCP1), wrban green space to reduce temperatures (2.6.3)
Risks associated Infrastructure retrofitting Air conditioning (6.3.4), using thermosiphons for permafrost degradation (10.4.6.4.1), increasing rooftop albedo (for
with critical Building codes reflectivity) (11.3.5.3), shading (13.A.4)
physical Spatially redirect development Drainage systems (4.5.2.1), architectural and urban design regulations (6.3.4.2), infrastructure standards initiatives (CCP6),
infrastructure, Chile's Sustainable Housing Construction Code (12.5.5.3)
networks, and
services Zoning/land use planning (6.3.2.1), spatial development planning to regulate coastal development (CCP2)
Risk to living Insurance Agricultural insurance and micro-credit (4.5.2.1, 10.4.5.5), index-based insurance, market and price insurance (5.14.1.3),
standards and Diversification of livelihoods flood insurance (10.5.3.2), collective insurance schemes (12.5.7.5)
equity Social safety nets
Combining income-generating activities within fisheries sector (3.6.2.2)
Community level adaptation by Pangnirtung Inuit through diversification to stabilize income and food resources (CCP6)
Food for work programmes (4.5.2.1), school feeding programmes (7.4.2.1.3), social protection programmes, such as
unemployment compensation (10.5.6)
Availability of health Safe drinking water infrastructure (4.5.2.1), temperature-controlled low-income housing (11.3.6.3), Health care clinics (6.4
infrastructure case study), place-specific mental health infrastructure and "nature therapy" (14.4.6.8)
Risk to human Access to health care Access to healthcare services (11.3.6.3), Access to Health, Nutrition Services and Healthy Environments (water and
health sanitation) (7.6), enhanced access to culturally-appropriate mental health resources; "Telemedicine" (information
technologies and telecommunications for health and public health service delivery) (12.6.1.5)
Disaster early warning Early warning of marine heatwaves (3.6.2.3.3) early warning for pests (5.12.5), Heat Action Plans (HAP) (7.4.2.1.2), raising
public awareness through campaigns (FAQ13.3)
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Risk to food Farm/fishery improvements Changing fishing gear or vessel power (3.6.2.2.3), change crop variety or timing (4.5.2.1, CCP5, 8.5), close productivity
security gaps (5.12.5), biotechnology (5.12.5), irrigation schemes (9.12.5.3), integrated crop/livestock systems (5.10.1), relocating
Food storage/distribution livestock linked to improved pasture management (13.5.2)
improvements
Improve transportation infrastructure and trade networks, shortened supply chains (5.12.5, 9.12.5.3), improved food storage
(5.12.5, 7.4.2), local food production/chains (Cross-Chapter Box COVID in Chapter 7)
Behaviour change in diets and Reduce food loss and waste (5.12.5), shifts to more plant-based diets (7.4.5.2), creating demand for organically sourced food
food waste (10.5.3.2)
Water capture/storage Farm ponds and revival of water bodies (4.5.2.1), rain gardens, bioswales or retention ponds (6.3.3.6), water storage tanks
(10.5.3.2), multi-purpose water reservoirs and dams (CCP5)
Risk to water Efficient water use/demand Precision/drip irrigation (4.5.2.1), Managed Aquifer Recharge (MAR) (9.4), cooperative policies across multiple sectors
security (CCP4), changing water consumption patterns (CCP4)
Efficient water Constructing irrigation infrastructure (4.5.2.1), inter-basin transfers (6.3.3.6), water reuse (13.A.3), slum/water upgrading
supply/distribution (6.4.3)
Seasonal/temporary mobility Fishing fleet mobility to follow species distribution (3.6.2.2.2), mobility for seasonal employment and remittances (4.5.2.1,
Cross-Chapter Box MIGRATE in Chapter 7), legal/illegal labour migration (CCP3), pastoralist seasonal migrations (Cross-
Chapter Box MIGRATE in Chapter 7)
Risk to peace and Transboundary fishing agreements (3.6.4.1), ocean governance (3.6.2.2), collective water management (4.5.2.1), indigenous
water-sharing systems (4.5.2.1), enforcing the land rights of indigenous populations (CCP7), adaptive co-management in
migration Cooperative governance Arctic fisheries (CCP6), international compact on migration (Cross-Chapter Box MIGRATE in Chapter 7), policies for
adaptive governance (8.5)
Permanent migration Resettlement of flood-prone communities (4.5.2.1), rural-urban migration (6.1 case study), internal migration (Box 10.2),
international migration and remittances (8.6.3, 14.4.7.3)
1
2
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1 Of this list of adaptation options, many focus on reducing vulnerability to climate change (high confidence),
2 as vulnerability is one of the components of risk (see Chapter 1 and Chapter 8). Vulnerability is the
3 propensity or predisposition to be adversely affected, including sensitivity or susceptibility to harm and lack
4 of capacity to cope and adapt (see Chapter 1 for more details). In the world's threatened ecosystems,
5 reducing vulnerability often means reducing other non-climate negative pressures on ecosystems, such as
6 pesticide use or fishery overexploitation (see Chapter 3.3).
7
8 Vulnerability reduction is also a major focus in human systems, and this includes development of
9 investments that help people adapt to climate change. Examples include irrigation or diversifying crops.
10 Building infrastructure resilient to climate-related risks is another example; many of the structural and
11 physical adaptation options can reduce sensitivity to disasters, such as elevating houses or doing beach
12 nourishment in coastal areas (see Section 15.5 in Chapter 15). Extreme events often catalyse investment in
13 adaptation to reduce vulnerability for the future (Kreibich et al., 2017; Slav韐ov� et al., 2021).
14
15 Next to vulnerability reduction, a large number of adaptation options focus on reducing exposure to climate
16 change (high confidence). Selecting low-risk locations is the most basic example of reducing exposure; for
17 example, private companies are relocating factories to reduce flood-related disruptions to their supply chain
18 (Neise and Revilla Diez, 2019) and species are autonomously adjusting their ranges to a changing climate
19 (see Section 2.4). Land use planning or investing in resilient infrastructure can avoid exposure in rapidly
20 urbanizing areas, however, the design and enforcement of these regulations can negatively impact
21 marginalized people (Anguelovski et al., 2016).
22
23 Managed retreat is an example of exposure reduction that, while often controversial, is increasingly being
24 considered and implemented (CCP 2.2.2, Section 15.3.4; Cross-Chapter Box LOSS in this Chapter; Siders et
25 al., 2019). Examples include the US Hazard Mitigation Grant Programme, which, among other activities, has
26 helped people resettle outside of flood zones, and a "no-build zone" established in the Philippines after
27 Typhoon Haiyan (Hino et al., 2017). However, relocation is not always an option; immobility is sometimes
28 involuntary, e.g., in the case of "trapped" populations in Zambia (Nawrotzki and DeWaard, 2018; Section
29 8.2.1.3).
30
31 However, adaptation efforts can have negative impacts on ecosystems and vulnerable groups (high
32 confidence); see Table 17.2 and Section 17.5 for further information on maladaptation. While "hard"
33 structural investments have been popular to reduce exposure to climate extremes, barrier-type measures
34 provide protection only up to a certain limit, and are designed to fail in more extreme events. Given the risk
35 of catastrophe from a climate extreme overcoming a physical barrier, policy advancements in recent years
36 encourage any investment in structural measures to be complemented by "softer" vulnerability reduction
37 measures, such as accommodating building construction (Wesselink, 2016).
38
39 When it comes to "softer" vulnerability reduction initiatives, these were traditionally seen as "no regrets"
40 options for adaptation. However, subsequent studies have cautioned that notion as vulnerability is a dynamic
41 quality, and can be co-created while development or adaptation efforts are being implemented (Schipper and
42 Pelling, 2006; Tempels and Hartmann, 2014; Dilling et al., 2015). Some scholars have suggested the
43 application of a "do no harm" principle to climate change adaptation efforts (Mayer, 2016).
44
45 17.2.1.2 Governance of Adaptation Options
46
47 For each adaptation option identified for the RKRs (Table 17.1), this section presents an assessment of how
48 decisions are made and how the adaptations are being governed. The following section then covers benefits
49 to humans and ecosystems, and potential for maladaptation is covered in section 17.5. See SM17.1 for
50 more information on the assessment methods and underlying citations.
51
52 The following analysis of adaptation options provides a synthesized overview of adaptation globally, but
53 does not prescribe how important each adaptation should be in specific locations. Chapter 16 finds that the
54 "scope" and "speed" of adaptation is limited in many areas.
55
56 When it comes to decision-making, most of these 24 adaptations rely strongly on formal decision-making
57 (high confidence), which follows the procedures of a group of people rather than ad-hoc individual action.
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1 Formal decisions play a particularly strong role in the adaptations identified for infrastructure, early warning
2 systems, and water systems (Kolen and Helsloot, 2014; Calvello et al., 2015; Zhao et al., 2017; Belc醟ov� et
3 al., 2019; Teo et al., 2019).
4
5 In contrast, informal or individual-led decision-making is more common in several food security-related and
6 livelihood related adaptations, such as changes to diets, livelihood diversification and seasonal migration
7 (high confidence) (Li et al., 2017; Radel et al., 2018; Robinson et al., 2020). People who have experienced
8 climate shocks are more likely to take individual decisions to implement adaptation measures, and in
9 countries where people are more exposed to extreme events, autonomous adaptation is more common
10 (Koerth et al., 2017; Aerts et al., 2018b; van Valkengoed and Steg, 2019).
11
12
13
14 Figure 17.2: Governance of 24 major risk management options, grouped by relevance to the representative key risks.
15 Each option depicts the relative governance roles, between communities/individuals, private sector, and public sector.
16 The intensity of the colour refers to the level of confidence in the assessment.
17
18
19 All adaptation options can occur under a range of governance arrangements (high confidence), with cases of
20 either private, public, or community governance typically playing the dominant role, as depicted in Figure
21 17.2. Public governance is the most frequent governance type for most adaptations considered. This is
22 particularly true for social safety nets and spatial planning, where governments are often required to lead
23 adaptation efforts (high confidence) (Mesquita and Bursztyn, 2016; Hssaisoune et al., 2020; Wang et al.,
24 2021). While government actors do the day-to-day management of these systems, civil society and
25 international organizations also play a role in shaping agendas and priorities of government actors (Nagle
26 Alverio et al., 2021).
27
28 The private sector plays a large role in governance of insurance, minimizing ecosystem stressors, and
29 livelihood diversification (medium confidence) (Allen et al., 2018; Mimet et al., 2020; Alam et al., 2020a).
30 While having a key role in shaping and implementing many other adaptations, the private sector is not often
31 the governing entity.
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1
2 There are a number of adaptation options that tend to be governed by communities and individuals, including
3 adaptations to farming and fishery practices and ecosystem-based adaptations (high confidence) (Reid, 2016;
4 Basupi et al., 2019; Giffin et al., 2020; Karlsson and Mclean, 2020). In rapidly urbanizing areas of Asia and
5 Africa, individual or community-led adaptation is the norm in informal settlements that have poor
6 governance structures. Residents of Mathare slum in Nairobi have established methods to pool risks, such as
7 pooling labour to police looting during flood events and developing community health centres in churches
8 (Thorn et al., 2015). This is in addition to risk reduction measures such as building structures to withstand
9 rising water levels (Thorn et al., 2015). Residents in Bangkok have built walls around settlements, dug
10 informal drainage channels to vacant lots, and filled areas of land (Limthongsakul et al., 2017). In these
11 cases, individual-lead adaptation can have negative side-effects, such as the building of flood defences in
12 affluent communities increasing the flood impacts in less affluent regions of a city (Limthongsakul et al.,
13 2017).
14
15 17.2.1.3 Benefit to Humans and Ecosystems
16
17 While some of the 24 adaptation options are specific to certain risk contexts (e.g. coastal areas, agricultural
18 production), others are more widely applicable (e.g. early warning systems, health care systems,
19 creation/restoration of natural areas). Figure 17.3 depicts which of these are most context-specific, e.g.
20 benefitting less than 1 billion people. This is contrasted with the extent to which each adaptation option is
21 beneficial to ecosystem services. Many of the more generalizable adaptations have also been shown to have
22 benefits to ecosystem services, such as nature restoration and changes to diets/food waste (medium
23 confidence). While health care systems and the establishment of health-related infrastructure can be widely
24 used as adaptation options, their design and application to-date have not generally benefited ecosystems or
25 ecosystem services (medium evidence, low agreement).
26
27
28 Table 17.2: Breadth of applicability of each adaptation option benefiting humans, i.e number of people (x axis),
29 estimated by the degree to which each adaptation can be applied across multiple contexts. The benefit of each
30 adaptation option for ecosystems and ecosystem services (y axis). See Annex A for literature underpinning each
31 assessment. This figure uses the 24 representative adaptation options from Table 17.1 and Figure 17.2. Reduce water
32 demand**
33
34
35
36 As a general method related to adaptive management, "early warnings" are the most frequently discussed
37 adaptation option to deal with a changing climate across all key risks, sectors, and regions. Early warning
38 systems are an adaptation that can benefit more than 5 billion people (high confidence). Examples range
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1 from short-term disaster early warning systems to revision of sea level rise plans based on monitoring. For
2 example, the humanitarian community is investing in forecast-based financing systems to prepare for
3 extreme events (Coughlan de Perez et al., 2015; MacLeod et al., 2021). Forecasts are also used to manage
4 hydropower dams (Ahmad and Hossain, 2020), to trigger interventions before public health emergencies
5 (Chapter 7.4.2) and to alert fishermen of algal blooms in the world's oceans (Chapter 3.6.2.3.3). Table 17.3
6 provides examples of adaptations using early warning systems that have been used to address each of the key
7 risks.
8
9 In addition to immediate investments that reduce vulnerability and exposure, monitoring and early warning
10 systems allow people to take additional actions when there is an imminent event on the horizon (e.g.
11 temporary evacuation during extreme events rather than permanent migration). This allows for ongoing
12 adaptive decision making (Alessa et al., 2016; Ebi et al., 2016; Barnard et al., 2017; Haasnoot et al., 2018).
13 However, these systems are only cost-effective for forecastable and actionable hazards, and require effective
14 institutional governance (Wilkinson et al., 2018; IPCC, 2019c).
15
16
17 Table 17.3: Examples of adaptation investments and early warning system options for adaptive management for each of
18 the key risks in Chapter 16.
Key risk Adaptive Early Warning Systems-based measures
Risk to coastal socio-ecological systems Storm surge early warnings (15.5.7)
Early warnings of water-borne disease (Ch 3.6.2.3.3)
Risk to terrestrial and ocean ecosystems Fishery marine heatwave warnings and mobile fishing
equipment (Ch 3.6.2.3, 13)
Forecast of shifts and regime changes in ecosystems (Pace et
al., 2015; Bauch et al., 2016; Burthe et al., 2016).
Risks associated with critical physical Early warning for infrastructure and services (Ch 13.2.2.1,
infrastructure, networks, and services 10.4.6.4.1)
Risk to living standards and equity Adaptive social protection systems (Schwan and Yu, 2018;
Ulrichs et al., 2019; Daron et al., 2021).
Risk to human health Heat health early warning systems (Ch 7.4.2.1.2)
Health and disease monitoring and outbreak prediction (Ch
7.4.2.1.1, Ch 12.5.6)
Risk to food security Forecasting rainfall and droughts for seed selection (Ch
10.5.2.2.3),
Food price early warnings (Ch 7.4.2.1.3)
Risk to water security Early warnings for flood and drought (Ch 4.4.1, 10.5.2.2.3,
15.5.7)
Risk to peace and migration Transboundary flood early warnings (Tuncok, 2015).
19
20
21 17.2.2 Combining Adaptation Options: Portfolios of Risk Management and Risk Governance
22
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1 While the above assessments underlying Figures 17.2 and 17.3 isolate specific risk management options for
2 specific risks, several adaptation measures are present in any given location, affecting the overall risk of a
3 particular place. Policymakers are charged to evaluate risk comprehensively, deciding on a variety of
4 measures that are effective, feasible, and aligned with other policy goals for a specific place, or
5 implementing a new activity because of how it complements the existing package of risk management
6 activities (Girard et al., 2015).
7
8 17.2.2.1 From Risk Prevention to Risk Financing and Risk Retention
9
10 Portfolios of adaptation options generally include actions to reduce vulnerability and exposure,
11 complemented by risk financing mechanisms that help people avoid the impacts of loss events, particularly
12 very rare ones. There is also explicit or implicit risk retention, where further risk management is not
13 desirable, cost-effective or feasible (Mechler and Deubelli, 2021). Risk financing can include a variety of
14 instruments, with insurance as the most widely known. Formal insurance uptake is less in developing and
15 emerging economies than in wealthier countries (Ali et al., 2020). To overcome some of the barriers to
16 insurance uptake, index insurance has been offered for agriculture and livestock in many developing
17 economies, with varying levels of success (Chantarat et al., 2013; Isakson, 2015; Dewi et al., 2018). In recent
18 years, regional disaster insurance pools for sovereign states have been established, such as the Caribbean
19 Catastrophe Risk Insurance Facility (CCRIF) (Iyahen and Syroka, 2018). Insurance can encourage the
20 quantitative evaluation of climate-related risks and adaptation limits, and it can incentivize risk reduction by
21 charging lower premiums for less risky situations (Sch鋐er et al., 2019).
22
23 While insurance is increasingly accepted as an adaptation option (Linnerooth-Bayer and Hochrainer-Stigler,
24 2015), positive outcomes are not guaranteed (high confidence). First, there are concerns as to whether this
25 will shift responsibility to the most vulnerable people to pay premiums (Surminski et al., 2016). There is also
26 high risk for insurance to cause maladaptation (M黮ler et al., 2017); for example, Annan and Schlenker
27 (2015) showed that insured crops were less well adapted to heat stress. To avoid this, people simultaneously
28 invest in insurance and adaptations that reduce vulnerability/exposure (medium confidence) (Surminski et al.,
29 2016; Highfield and Brody, 2017; Sch鋐er et al., 2019; Reguero et al., 2020).
30
31
32
33 Figure 17.3: A graphical representation of layered risk management. Risks can be reduced or managed by risk finance
34 (insurance and other means), but some residual risk remains, particularly for high impact unavoided and unavoidable
35 risk, which is retained implicitly or explicitly. Where incremental and in situ adaptation is not effective in managing
36 risks, transformational adaptation supports systemic change. Risk management occurs in national systems and regional
37 insurance systems have stimulated regional collaboration. Particularly for high impact risks and impacts in specific
38 events, international assistance is required. Policy domains on Disaster Risk Reduction (DRR) and CCA (Climate
39 adaptation) as well as Loss&Damage overlap in their governance of risk management. Figure building on Mechler et al.
40 (2014); Cummins and Mahul (2009); Lal et al. (2012); Mechler and Deubelli (2021).
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1
2
3 The combination of interventions that reduce risk and risk finance for residual risk (often through insurance
4 for sudden-onset events, or social protection for risks including linked to slow-onset processes) will reduce
5 collective risk to a certain level. For very extreme and potentially catastrophic events, it is often impossible
6 (or financially infeasible) to fully reduce vulnerability and exposure, and people, communities and countries
7 therefore retain requiring the ex-post management of unavoided and unavoidable residual impacts in case of
8 events.
9
10 Ex-post risk management relies on national assistance, social safety nets (Ch. 7.4.2.1.3; B閚� et al., 2012;
11 Elmi and Minja, 2019), and support from social networks as well as lending from international institutions
12 (high confidence) (Hochrainer-Stigler et al., 2014). Even in places where normalized losses have stabilized
13 in recent years with investments in adaptation, effective planning to manage losses remains necessary
14 (Jongman, 2018). Resilient recovery can support adaptation goals in periods of loss and damage (Slav韐ov�
15 et al., 2021).
16
17 To coordinate between a suite of applicable risk management interventions, the concept of risk layering has
18 been discussed and used in (financial) risk governance for disaster risk management (Mechler et al., 2006;
19 Cummins and Mahul, 2009; Clarke and Mahul, 2011) and climate risk management (Lal et al., 2012;
20 Mechler et al., 2014; Herron et al., 2015; Sch鋐er et al., 2016; Mechler and Deubelli, 2021). Incremental risk
21 prevention and preparedness as well as risk financing occur within national systems. Over the years, regional
22 cooperation, such as through the regional sovereign insurance pools in the Caribbean, Pacific, Africa, but
23 also transboundary risk management elsewhere have become more important (medium confidence) (see
24 Martinez-Diaz et al., 2019). Also, with risks increasingly experienced as severe and existential (Boyd et al.,
25 2017), global governance and solidarity have been invoked (see, Linnerooth-Bayer et al., 2019; Pill, 2021),
26 largely as part of the policy discourse on Loss & Damage (Mechler et al., 2019) with further momentum
27 provided by discussions on the global goal of adaptation and recognition of climate risk as transboundary
28 (Benzie and Persson, 2019; Cross-Chapter Box INTERREG in Chapter 16). Transformational risk
29 management has emerged where incremental and in situ adaptation is not effective in managing risks, such
30 as for managed or strategic retreat for communities facing severe coastal and riverine flooding (Siders et al.,
31 2019). Transformation has not been well documented including as to its governance (see 17.2.2.5).
32
33 17.2.2.2 Global Variation in Portfolios of Risk Management
34
35 While many studies assess adaptation trends by geographical region or by sector, the amount of residual risk
36 varies across countries with different income and governance structures. Vulnerability, poverty, and
37 inequality, which constitute the human dimensions of climate change, affect how these portfolios of
38 adaptation options are structured around the world (see Chapter 8). Figure 17.4 depicts several illustrative
39 "typologies" of how risk is addressed. While no country or location fits any one typology, this illustrates a
40 range of risk portfolios found in different contexts.
41
42
43
44 Figure 17.4: Several illustrative typologies for how risk has been managed. The first is "extensive protection", in which
45 the bulk of investments are made in reducing exposure, through protection up to limits (e.g. flood levees) and including
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1 retreat. The second category is "Moderate investment focused on adaptive capacity", in which the bulk of investment is
2 made in reducing vulnerability (e.g. improved housing). The third category is "Little adaptation investment", in which
3 there is little investment in either reducing vulnerability or exposure, and the bulk of risk is residual, borne by the
4 population.
5
6
7 Extensive protection category
8 The first category in this typology, that of "extensive protection", requires substantial financial investment
9 (Figure 17.4). In higher-income contexts, this is often more feasible than in contexts with limited resources,
10 and adaptation investments are more likely to include structural measures to reduce exposure, complemented
11 by vulnerability-reducing measures and insurance protections (medium confidence). While this typology is
12 not universally representative of high-income areas (within or between countries), expensive exposure-
13 reduction measures tend to be easier to implement in high-income countries. For example, flood protection is
14 largest in countries with larger amounts of public spending and least amounts of corruption (Scussolini et al.,
15 2016). It is seen as more economically efficient to invest in expensive protection measures in wealthy
16 regions, under different scenarios of sea level rise and river flooding, although these calculations have equity
17 and justice implications (Peduzzi, 2017; Lincke and Hinkel, 2018). After flood events happen in regions with
18 high levels of protection, damages are comparatively limited, and people tend to continue living in close
19 proximity to the protected river (Mard et al., 2018). In contrast, flood displacement is higher in low-income
20 countries (Kakinuma et al., 2020).
21
22 Risk financing, especially insurance, is also common in higher-income countries with well-developed
23 insurance markets and higher levels of insurance penetration than in lower income countries, illustrated by
24 the green bar in Figure 17.4 (high confidence) (Linnerooth-Bayer et al., 2019). Of climate-related disasters,
25 floods and storms cause the largest amount of reported economic losses, however, at least 40% of these
26 losses are uninsured, even in the regions with high insurance penetration (Baur et al., 2018). Government
27 involvement in insurance schemes is associated with higher penetration rates of the general population
28 (Paleari, 2019). While some, predominantly high income countries can make use of disaster contingency
29 funds or dedicated budget items, these do not exist or are not well endowed to adequately support relief,
30 recovery and reconstruction (Linnerooth-Bayer and Hochrainer-Stigler, 2015). To help stabilize public
31 finance in regions with little market-based insurance coverage and fiscal response mechanisms, regional
32 public insurance pools have been set up with donor assistance, e.g., in the Caribbean, Africa and the Pacific
33 for flood and droughts (Sch鋐er et al., 2016; Surminski et al., 2016; Linnerooth-Bayer et al., 2019).
34
35 Moderate investment focused on adaptive capacity
36 In contrast to the "extensive protection" scenario, many regions of the world bear greater resemblance to the
37 second typology in Figure 17.4 "moderate investment focused on adaptive capacity" (medium confidence).
38 These contexts see greater adaptation funding invested in capacity building activities to reduce vulnerability,
39 rather than structural or ecosystem-based protection measures to reduce exposure (Biagini et al., 2014).
40 Because of limited international and domestic finance for large structural investments to reduce exposure,
41 the most prevalent adaptation choices in low-income contexts are household-level vulnerability-reducing
42 measures (Koerth et al., 2017).
43
44 Lack of access to finance can be one of the reasons countries engage more readily in adaptive capacity-
45 building activities. Countries that rank highly on the Corruption Perceptions Index engage less in
46 technological solutions for risk management (Berrang-Ford et al., 2014). In addition, countries with higher
47 levels of corruption receive less adaptation aid (Betzold and Mohamed, 2017; Weiler et al., 2018). Countries
48 are more likely to receive adaptation aid if they import goods from a donor country, or are a former colony
49 of that donor (Betzold and Mohamed, 2017; Weiler et al., 2018). In countries with poor governance and
50 limited aid flows, remittances make up a substantial portion of finance available to the local population for
51 risk management (Samuwai and Hills, 2018).
52
53 Risk financing does play a large role in the "moderate investment" category; there are a variety of
54 instruments in use globally. Many countries in the Global South have created national policies and a number
55 of regional catastrophe risk insurance pools, subsidized by international assistance, which make pay-outs to
56 the national government of affected nations when an extreme event happens and have helped to build risk
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1 awareness (Clarke et al., 2015; Thirawat et al., 2017). Beyond this, residual risk is often borne directly by
2 affected people (Andrianarimanana, 2015).
3
4 Little adaptation investment typology
5 In the third typology, there are limited resources for adaptation, and populations bear large amounts of
6 residual risk (depicted by the orange bar in the third typology in Figure 17.4, "little adaptation investment").
7 Small Island Developing States can often find themselves in this situation, because small populations, small
8 economies, lack of economies of scale, subsistence livelihoods, and other challenges mean risk reduction and
9 risk financing are both costly (see Chapter 15).
10
11 Another example of this third typology are people living in conflict-affected areas. These populations are
12 highly vulnerable to the impacts of climate change (Basher, 2006; OCHA, 2011; IPCC, 2012; Zommers and
13 Singh, 2014; Marktanner et al., 2015; Walch, 2018; Eckstein et al., 2019; Peters et al., 2019). In conflict-
14 affected areas similar to the third category of "little adaptation investment", a combination of high
15 vulnerability and relatively few supports for adaptation means that there is a large amount of "residual risk",
16 in which residents cope with the impacts of extreme events on a regular basis (high confidence). For
17 example, deaths from "natural" disasters are 40% higher in areas that are undergoing armed conflict
18 (Marktanner et al., 2015) (see Box 17.2).
19
20
21 [START BOX 17.2 HERE]
22
23 Box 17.2: Climate Risk Management in Conflict-affected Areas
24
25 Consequences of conflict that exacerbate vulnerability to climate change include: displacement, loss of
26 access to employment leading to illegal livelihoods, gender-based violence, lack of land tenure, low literacy,
27 poor access to social and health services, destruction, looting and theft of key assets, such as houses, food
28 stocks and livestock, among others (Jaspars and Maxwell, 2009; Chandra et al., 2017; Anguita Olmedo and
29 Gonz醠ez G髆ez del Mi駉, 2019). Such impacts perpetuate cycles of poverty (World Bank, 2013), making
30 conflict-affected populations more susceptible to suffer from climate related events (Basher, 2006; Coughlan
31 de Perez et al., 2019). For example, in Mindanao, Philippines, poverty is closely linked to long-standing
32 armed conflicts; both climate change and conflict have significantly increased smallholder vulnerability,
33 resulting in loss of livelihoods, financial assets, agricultural yield and the worsening of debt problems
34 (Chandra et al., 2017). In Colombia, displacement induced by conflict has pushed the population to live in
35 high-risk areas such as steep slopes susceptible to landslides and river banks exposed to flooding (Albuja and
36 Adarve, 2011). This conflict-induced vulnerability, with little adaptation activity, has in turn resulted in
37 climate-related disasters (Kuipers, 2019; Siddiqi et al., 2019).
38
39 Conflict can also limit the effectiveness of adaptation measures that do exist; a study across Africa, the
40 Caribbean, and Asia concluded that poor governance can limit the effectiveness of early warning systems in
41 these regions (Lumbroso et al., 2016). Poor state services have health consequences and can limit social
42 support networks (Peters, 2018). States are unable (even if they are willing) to assist or protect citizens in
43 disasters. Non-governmental stakeholders play a large role in these contexts, but questions of long-term
44 implications and accountability remain unaddressed (Peters, 2018).
45
46 Climate risk management and adaptation in conflict-affected contexts is challenging, first, given the complex
47 and dynamic nature of vulnerability (Hilhorst, 2003; Frerks et al., 2004) and second given factors such as
48 weak or nonexistence disaster risk governance, restricted access, human rights violations, power dynamics
49 between parties in conflict, and environmental degradation, among others (Kloos et al., 2013; Marktanner et
50 al., 2015; ICRC, 2016; Quinn et al., 2017; Field and Kelman, 2018; Siddiqi, 2018). Climate can also be a
51 contributing factor to conflict (Mach et al., 2019). There is little peer-reviewed documentation available on
52 adaptation in climate-affected contexts, and what exists is narrowly focused on agriculture at the expense of
53 other sectors, such as cities, infrastructure, and humanitarian operations (Sitati et al., Accepted).
54
55 To address risks to livelihoods, conflict-sensitive livelihood programming has used vouchers to meet
56 immediate needs, legal support to resolve land disputes, and disaster preparedness planning to identify safe
57 places for displacement (Jaspars and Maxwell, 2009). For example, cooperation in the Philippines between
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1 Moro Islamic Liberation Front and United Nations agencies included training of farmers in disaster risk
2 reduction, drought management, and production of improved crop varieties to support a transition away from
3 subsistence farming (Walch, 2018). In Mali, negotiations on fertilizer access and safe transport to
4 agricultural lands were brokered by the International Committee of the Red Cross, and in Afghanistan,
5 conflict-sensitive approaches have promoted ecosystem-based adaptation to support reforestation (Walch,
6 2018; Mena and Hilhorst, 2020). Despite several examples of conflict-sensitive adaptation practices, little is
7 known about the effectiveness of such efforts in reducing climate risks in these complex contexts (see
8 Section 17.5 for further discussion of "effectiveness").
9
10 [END BOX 17.2 HERE]
11
12
13 17.2.2.3 Adaptation Beyond Risk: Exploiting Opportunities
14
15 Several studies and many government planning documents reference how people can benefit from a changed
16 climate, beyond reducing risks. For example, several regions are expecting an increase in visitors to eco-
17 tourism sites or national parks with a changing climate (Fisichelli NA, 2015; Lwasa, 2015). In Europe,
18 several national adaptation plans include planning for potential benefits of a changing climate, including
19 reduced winter mortality and improved conditions for hydropower (Biesbroek et al., 2010). Recognizing the
20 need for economic diversification, people working in certain industries, such as coastal management,
21 perceive climate change as a factor increasing the need for their services (Fatori et al., 2017). Northern
22 countries are taking advantage of ice-free waters for shipping routes in the Arctic (Eguiluz et al., 2016; Melia
23 et al., 2016; IPCC, 2019e-a). In Africa, opportunistic adaptation has been observed by smallholder farmers,
24 who plant crops that are better suited for a changing climate (Lalou et al., 2019). Similar agricultural
25 adaptation in Pakistan has been associated with improved food security and reduced poverty (Ali and
26 Erenstein, 2017; Rahman et al., 2020). In each of these cases documenting benefits, there are also potential
27 negative impacts on other populations or ecosystems, such as ecosystem impacts from increased Arctic
28 shipping (Ng et al., 2018).
29
30 While adaptation is rarely focused on taking advantage of opportunities presented by a changed climate,
31 there are numerous co-benefits of adaptation opportunities, from health to reduced emissions to ecosystem
32 services (high confidence) (Watts et al., 2015; Geneletti and Zardo, 2016; Spencer et al., 2016). There is also
33 literature proposing that the actual process of adaptation planning can enable people to take advantage of
34 opportunities, including, e.g., opportunities for larger policy and governance reform (Coleman and Sandhu,
35 1965; Ernst and Preston, 2017; Brown et al., 2017a).
36
37 17.2.2.4 The Spectrum from Incremental to Transformational Adaptation [Or Maybe Measures] in Risk
38 Management Portfolios
39
40 Chapter 1.4.5 noted that transformational adaptation is increasingly being considered necessary to allow a
41 system to extend beyond its (soft) limits as incremental adaptation cannot guarantee to avoid intolerable
42 risks. Chapter 16.4 presents evidence on RKRs where a need for transformational adaptation and climate risk
43 management has been identified in order to further reduce climate risks and avoid breaching adaptation
44 limits. The following section identifies how the 24 adaptation options representative of the RKRs may
45 support incremental and transformational risk management/adaptation that can lead to small, medium, and
46 large systemic change, often as part of portfolios of options. This subsection further discusses the role of
47 transformational adaptation vis a vis incremental adaptation by reviewing evidence across chapters (see also
48 Box 17.3). The Cross Chapter Box on Loss and Damage further expands on the international debate
49 regarding the role of decision-making on incremental and transformational adaptation for dealing with
50 residual risks to address soft as well as hard adaptation limits (see Cross-Chapter Box LOSS in this Chapter).
51
52 As the literature distinguishes active transformation to shape future risks from passive and unintended
53 transformation (Lonsdale et al., 2015; Chapter 1), the section queries how to inspire actors to consider how
54 to develop or implement transformational adaptation to complement incremental adaptation/risk
55 management when and where appropriate.
56
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1 In contrast to a broadening literature on conceptualization and policy proposal, there has been little evidence
2 reported in the literature of transformational adaptation and risk management at scale of implementation
3 (high confidence) (Klein et al., 2017; Ajibade and Egge, 2019; T郻ara et al., 2019; Mechler and Deubelli,
4 2021). Deubelli and Venkateswaran (2021) review evidence on largely NGO -implemented community-level
5 adaptation for floods, heat and drought across the globe. They suggest that transformational adaptation
6 success, while multi-facetted and challenging, depends on the availability of appropriate enabling
7 environments including experiential and niche learning, alignment of transformational change objectives
8 with strategic (government or other actor's) priorities, strong bottom-up governance grounded in local
9 contexts, phased long-term program support and appropriate financing.
10
11 In order to distinguish incremental from transformational adaptation, Lonsdale et al. (2015), building on
12 Mustelin and Handmer (2013), identify criteria related to framing, learning and decision-making, space and
13 time, power, and type of change management. T郻ara et al. (2019), additionally, discuss transformation in
14 light of informing climate pathways, strategies and solutions. Broadly considering these criteria, they
15 identify twelve dimensions with additional discussion of change with regard to systems and dynamics,
16 options and solutions, agency, and the consideration of equity (see also Chapters 1, 6, 18 for more
17 discussion). In particular, the following key aspects for understanding the spectrum from incremental to
18 transformational adaptation are of relevance: change - within or across the system; agency-single or
19 heterogenous, a role for visioning and normative futures, the type of learning required (from first order,
20 business-as-usual, to second order), as well as how equity and distributional issues are explicit.
21
22 Applying these key aspects to the list of 24 adaptation options from Table 17.1, certain options are assessed
23 to be more transformational, often requiring large system changes that go beyond addressing individual risks.
24 Adaptations that are more transformative offer potential to lead to systemic change. Less transformative
25 adaptations allow people to address specific climate-related risks while maintaining existing systems (See
26 SM17.1 for more details; see also Box 17.3).
27
28 For example, several adaptations related to the RKR on risks to peace and migration, namely permanent
29 migration, and cooperative governance, require moderate to high levels of transformation (high confidence).
30 Some behavioural adaptations, such as changing diets and reducing food waste, can also require large
31 transformations in land use and food culture (medium confidence). Spatial planning, including urban zoning,
32 also tends to be more transformative (medium confidence).
33
34 On the other end of the spectrum, disaster early warning systems tend to be incremental rather than
35 transformational (high confidence), because they enable people to maintain/protect existing systems. Several
36 other adaptations allow people to maintain livelihoods and systems in the face of changing risks. For
37 example, improvements in agricultural and fishing practices can be done with moderate transformation to
38 systems (medium confidence). Similarly, insurance tends to require less transformation, as it can allow
39 people to maintain existing systems while being more resilient to climate-related shocks (medium
40 confidence).
41
42 None of the 24 adaptation options are consistently beneficial for vulnerable and marginalized groups (high
43 confidence). For each adaptation, there are examples of how it has been implemented in a way that benefits
44 poor, low-income, ethnic groups and/ or females, and other examples of implementation in different contexts
45 that have worsened the risks for those groups specifically. For example, while the goal of cooperative
46 governance can be to support the marginalized, these same marginalised groups are usually excluded from
47 participating in the design of the solutions, and many articles criticise governance results as protecting only
48 the interests of the wealthier and more powerful parties in the negotiations, especially in governance of
49 migration (Groutsis et al., 2015; Pijnenburg et al., 2018). This reinforces the need for context-specific
50 planning to ensure marginalized groups will benefit from an adaptation plan. See Table 17.4 for examples of
51 how each adaptation option can have or not have equity benefits.
52
53
54 Table 17.4: The 24 adaptation options from Table 17.1 grouped and coloured by their potential for transformation. (See
55 Appendix A for assessment methodology.) Adaptations in red tend to require small amounts of transformation,
56 adaptations in orange tend to require middling levels of transformation, and adaptations in yellow tend to require large
57 levels of transformation, or systemic change. Each option is paired with examples of how that adaptation can be done in
58 a way that does not benefit, or worsens the situation for marginalized groups, as well as an example in which that
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1 adaptation has benefitted those groups. Examples of equity focus on benefits to poor, low-income, ethnic groups, or
2 females.
3 * low confidence, ** medium confidence, *** high confidence
Example of the adaptation excluding or
worsening the situation for marginalized Example of the adaptation benefitting
Adaptation groups marginalized groups
Less transformation (small systemic change)
Index-based insurance policies in Mongolia The availability of capital after disaster
were accessible primarily to wealthy herders events can avoid a poverty trap from
Insurance** (Taylor, 2016b). disasters (Alam et al., 2020a).
Accommodation strategies in Jakarta have led The mosaic restoration project provided
to a false sense of security in an impoverished training for women to support local
Coastal and vulnerable neighbourhood (Esteban et al., accommodation of climate changes on Yap
accommodation***
2017). (Krishnapillai, 2018).
Early warning People of higher socio-economic status tend Famine and drought early warning systems
systems*** to receive warnings, while marginalized have helped avoid starvation among the
groups can be left out (Baudoin et al., 2016). world's most vulnerable people (Funk et al.,
2019).
Small farmers were unable to access supports Retrofits for water use efficiency were made
to implement drip irrigation in Morocco, and available free of charge to low-income
uptake was greater among wealthy farmers communities in the US (Lee and Tansel,
Water use/demand*** (Jobbins et al., 2015). 2013).
Construction of hard barriers increased flood Successful coastal embankments can help
risk for several low-income communities in people avoid poverty traps in Bangladesh by
Coastal hard protection** Bangladesh (Adnan et al., 2020). reducing exposure to flood events
(Borgomeo et al., 2017).
Moderate transformation (medium systemic change)
Infrastructure Low-income people often do not own their Energy policy could promote solar
retrofitting** homes, and there are few incentives for infrastructure in Nigeria, which can offer
landlords to upgrade (Tardy and Lee, 2019). electrification in underserved regions
(Ohunakin et al., 2014).
Building codes in Nepal and Bangladesh Slum upgrading projects in Latin America
often fail to increase resilience because many reduced the vulnerability of informal
buildings are built informally (Ahmed et al., settlements by improving built infrastructure
Building codes*** 2019). (Nez Collado and Wang, 2020).
Farm/fishery practice** Many agriculture improvement strategies Improved crop varieties have supported the
create higher workloads for women and do income of low-income farmers in Zambia
not directly enfranchise them, as seen in (Khonje et al., 2015).
Uganda, Ghana, and Bangladesh (Jost et al.,
2015).
Diversification of Diversifying livelihoods can increase A study on diversity of income sources in
livelihoods*
women's workloads, in a review of semi-arid Ghana indicated that diversification can
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regions across Africa and Asia (Rao et al., make people less vulnerable to extreme
2020). events (Baffoe and Matsuda, 2017).
Social safety nets** Social protection systems in Bangladesh focus Adaptive social protection can help poor
on specific groups in rural areas, and they people avoid the impact of extreme events
often fail to reach urban poor and other very by scaling up support at critical moments
disadvantaged people (Coirolo et al., 2013). (Bowen et al., 2020).
Infrastructure for The development of sanitary water Improvements to water and sanitation
health*** infrastructure in Germany had less benefit in infrastructure that avoid people fetching
areas with higher income inequality water is associated with improvements to
(Gallardo-Albarr醤, 2020). women's health (Geere and Hunter, 2020).
Increasing/improving livestock markets can Investments in large produce storage houses
Food favour high-income livestock producers has supported indigenous livelihoods in the
storage/distribution**
(Gautier et al., 2016). face of climate change (Mugambiwa, 2018).
Restoration/creation of Urban greening programmes in the US Afforestation reduced landslide risk for
natural areas** avoided minority neighbourhoods or caused informal settlements in Brazil (Sandholz et
displacement of people of colour al., 2018).
(Anguelovski et al., 2016; Watkins et al.,
2016).
Minimizing ecosystem Fish quota reduction had negative economic South Africa's Working for Water
stressors* impacts when done quickly (Barbeaux et al., programme employed poor people to control
2020). invasive species (van Wilgen and
Wannenburgh, 2016).
Ecosystem-based Payments to indigenous groups in return for Integrated water resource management is
adaptation** protecting conservation land can be less than proposed in the Caribbean as a way to
their original livelihoods and disadvantage maintain ecosystem services while
those not receiving the payments, such as improving economic welfare (Mycoo,
women (Bedelian and Ogutu, 2017). 2017).
Water tariffs during the Cape Town drought City Water Forums in Nepal have focused
Water negatively impacted poor households on equitable water allocation as an
supply/distribution**
(Millington and Scheba, 2021). adaptation (Pandey and Bajracharya, 2017).
Seasonal/temporary Women tend to have greater restrictions on Indigenous communities in Guatemala use
mobility** mobility than men (Lama, 2018). temporary migration to manage rainfall
variability (Ruano and Milan, 2014).
Most transformation (largest systemic changes needed)
Spatial planning in American cities has often
resulted in less green space in ethnic minority While difficult, strategic approaches to
neighbourhoods (Connolly and Anguelovski, urban planning can promote inclusive
Spatial planning** 2021) development (Chu et al., 2017).
Diets/food waste* Low-income groups have less opportunity to Changing dietary intake during heatwaves
diversify diets if certain foods become more (e.g. eating cooler foods) is seen as a low-
expensive or difficult to obtain (Reynolds et cost adaptation accessible to low-income
al., 2019). people in the UK (Porter et al., 2014).
Health care systems** Facilities in poor communities are often Universal health coverage can be highly
poorly sited and can lack capacity to support beneficial to poor people (Atun et al., 2015),
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people during climate-related extreme events when needed for climate-related health
(Codjoe et al., 2020). outcomes.
Many indignous populations have been
negatively affected by loss of their land when Improving water harvesting supports
displaced for dam construction (Siciliano and marginalized populations in dryland areas
Water capture/storage** Urban, 2017). (Bobadoye et al., 2016).
Cooperative International cooperation among national International cooperation has the potential to
governance** governments regarding migration can remove barriers to adaptation in informal
encourage human rights abuses and increase settlements in developing countries by
migration (Crawley and Skleparis, 2018). sharing knowledge and expectations
(Oberlack and Eisenack, 2014).
Permanent migration from small island Migration supported by social protection
nations can entail a loss of identity for systems can be sustainable for poor
Permanent migration*** indigenous groups (Bordner et al., 2020). populations (Schwan and Yu, 2018).
Strategic coastal Muslim people faced tensions with host In several cases of post-disaster relocation,
retreat*** communities when relocated in India, and community members initiated the retreat
faced difficulties in terms of fishing access and there were broader benefits to society
and land size (Mortreux et al., 2018). (Hino et al., 2017).
1
2
3 17.2.2.5 Incremental and Transformational Adaptation for Managing Risk in the Context of Adaptation
4 Limits
5
6 With evidence on soft and hard limits being experienced in natural and human systems including in
7 terrestrial, aquatic and marine ecosystems, coastal and island systems, agriculture, health systems, urban
8 spaces and tourism (Table 16.5, 16.4.2, medium confidence) transformation is also being considered to
9 expand the adaptation space beyond soft limits and before hard limits are being reached. As a key area of
10 advancement since AR5, this section assesses the relationship of residual risks, limits and incremental as
11 well transformational adaptation integrating the assessment of limits in 16.4 with ch.17 adaptation and risk
12 management assessment along a spectrum of adaptation change. 17.2.2.5 thus contributes to understanding
13 in which systems and regions transformational adaptation is increasingly required and considered once
14 incremental adjustments are exhausted in the context of soft and hard limits.
15
16 Assessing risk and limits requires in-depth analysis of the adaptability of human and natural systems under
17 different warming and risk levels, also considering socio-economic exposure and vulnerability drivers,
18 informed by perspectives on what breaching limits means, especially if significant change and losses and
19 damages may occur (see 16.4, 8.4). Assessments differ between natural systems (where adaptation potential
20 is often very limited; Klein et al., 2014) and human systems where incremental and transformational
21 adaptation can help to extend soft limits so that hard limits are not met or to buy time until hard limits are
22 reached with higher levels of warming.
23
24 The assessment synthesises global and regional evidence across regional and thematic report chapters along
25 a continuum from observed to projected impacts and risks, the spectrum of incremental and transformational
26 adaptation, and finally any evidence on soft and hard limits. We present regional evidence for two types of
27 salient natural and human systems and Representative Key Risks: RKR-B (risk to terrestrial and ocean
28 ecosystems), where we assess risks from marine heatwaves to coral reefs; and RKR- E (risk of heat on
29 human health as a human system). Both RKRs and systems are facing substantial (residual) risk,
30 characterised by adaptation limits and share heatwaves as the hazard, for which climate change has been
31 considered the major driver of increasing intensity and frequency (high confidence)(IPCC, 2021). The
32 assessment synthesises evidence on transformation as reported in the chapters as well as categorizes
33 identified adaptation options along an adaptation spectrum according to the criteria discussed in 17.2.2.4,
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1 specifically whether adaptation leads to systems' change or only change within a system, is driven by multi-
2 scale agency and considers equity impacts specifically.
3
4 Figure 17.5 organises global and regional findings for observed and projected health risks from heat (RKR-
5 E) from chapters across the report and organizes options according to findings on the potential for
6 transformational change as presented in17.2 and table 17.4. The discussion shows that heat has become a
7 significant health risk globally, incurring severe mortality and morbidity in all world regions with annual
8 heat related deaths estimated around ~300 000 with millions affected (high confidence) (9.3.1). Evidence
9 shows that adaptation and risk management, can be effective in reducing (relative) risks in developed
10 countries, with inconclusive evidence in low-middle incomes states (9.2.4.1, 13.7.3, 13.6). In absolute terms,
11 risk in terms of heat-related mortality and morbidity is projected to increase under medium and high heating
12 scenarios in many regions, even with implemented adaptation. By 2050 (compared to 1961-1991 and for a
13 mid-range emissions scenario), an excess of 94,000 deaths per year is projected globally as attributable to
14 climate change (9.3.1).
15
16 Planned and implemented adaptation interventions in all regions have remained largely incremental, while
17 uptake is being intensified in some regions; options have included air conditioning (as autonomously
18 deployed), public cooling spaces, heat action plans that incorporate early warning and response and heat-
19 adapted building design (9.9.5, 11.3.6, 12.5.6.1.1,13.11.3, 13.11.3, 15.6.2).
20
21 Given increasing risks projected and already reported soft and hard limits, transformation is being considered
22 as a complement potentially leading to systemic and transformational change. Adaptation, if upgraded to also
23 consider transformational interventions, will thus help to reduce heat risks (medium-high confidence, limited
24 evidence) albeit with reduced effectiveness at higher levels of warming, particularly in regions (Africa, Asia)
25 where lethal heat waves are projected to occur almost annually towards later in the 21st (medium confidence)
26 (9.1, 10.4.7).
27
28 This may involve urban redesign using nature-based solutions (such as green roofs and infrastructure) as
29 well as rescheduling of outdoor labour or cross-sectorial coordination. Integrated approaches across
30 interdependent systems (e.g. ecosystem- based approaches and climate-sensitive urban design) are being
31 proposed. Also, it may mean bolstering social safety nets and health systems that better attend to heat
32 impacts by providing universal coverage. Societal and political transformations to reduce climate change
33 risks for vulnerable groups are considered particularly relevant in some regions (9.4.2.1.2, 9.9.5, 10.4.6.4.3,
34 12.5.3.2, 13.6.2.1, 14.6). Yet, across all regions there is limited evidence on proposed transformational
35 adaptation and very little evidence regarding implementation (high confidence).
36
37 As a consequence, studies project soft limits to be further reached as increased mortality and morbidity will
38 add stress to health systems, and labour productivity will be severely hampered impacting economic systems
39 (medium to high confidence) at medium to higher levels of global warming (7.2.4.1, 9.10, 10.4.4.4, 11.9.1,
40 13.6.2.3, 13.7.2, 13.7.4, 13.10.2.1, 13.8, 15.3.4.9).
41
42 Hard limits may be breached in some regions where critical heat tolerance thresholds are projected to be
43 surpassed at medium to higher levels of global warming, such as physiological survivability thresholds,
44 which, e.g., may render urban outdoor labour in Asia, Africa and North America infeasible (10.4.6.3.2, 14.8,
45 Box 9.1).
46
47
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1
R贩� KROPIn-rbEcosr:jeeeRmrcivtseeeknddttoiramilshkapusdamactpastnahtieoanltchomfropmlemheenatted N� oHrigthh America have increased E穟7r0op0e00 and 54 000 deaths during 2003 and 2010 A� sSihaort-term effects of high temperatures on
temperatures
mortality and morbidity (**** ) with heatwaves, adaptation actions have reduced heat- daily mortality and morbidity reported in
impacts varying by age, gender, related mortality in parts of Southern Europe (***) several cities throughout Asia.
location, and socioeconomic � Risk of heat mortality and morbidity to more than � More frequent hot days and intense heat-
by conditions (**** ) triple at 3癈 compared to 1.5癈 with projected waves will increase heat-related risks and
贩� Transformational adaptation � Warming projected to increase heat- 90'000 deaths in 2100 (***) deaths in Asia (**)
Soft limit (to incremental adaptation) related mortality (**** ) and � Air cooling, heat warning and response systems, � Urban technological solutions (e.g. smart
Hard limit morbidity (**) building interventions, but largely incremental cities, early warning systems);and
� Air conditioning&cooling stations adaptation (***) behavioural adaptation growing from
� Transformational, long-term � Increasing use and plans for NBS in urban spaces; initial stages but unevenly distributed
adaptation action to increase large scale system transformations needed due to across large and small cities (**)
resilience such as through redesign of adaptation limits in Southern Europe (**) involving � Transformational adaptation largely
Global urban space (***) strong behavioural change combined with large lacking, some incipient in larger cities, incl.
� Available (incremental) adaptation portfolios of preventive and planning options NBS
� Heat is a significant health risk due to options unable to protect human � Above 3癈 limits to the adaptation potential of � Heat stress likely to approach critical
widespread urbanization, demographic
changes and increase in hot weather (***) health under high-emission scenarios people and existing health systems, particularly in health thresholds in West and South Asia
(***) Southern and Eastern Europe and with health under medium warming scenario, and in
323,000 estimated heat-related deaths and 13 � Hard limits to adaptation may be systems under pressure(***) some other regions such as East Asia under
million heat-related DALYs in 2019
� Temperature-related mortality expected to reached for rural and urban outdoor high warming (***)
labor towards end of century (**)
increase under medium and high heating
scenarios even with adaptation. By 2050
(compared to 1961-1991) an excess of 94,000 A� uHsetarat-lraesliaated deaths have increased with a
deaths per year attributable to climate change
projected due to heat for medium warming.
� Implementation of heat warning systems has third attributable to climate change in
Australia (***)
reduced relative mortality risk in developed � Increase in heat-related mortality and
countries (***), unclear trends in low-middle
income countries. Multi-sectoral integrated morbidity for people and wildlife in Australia
(***)
approach beneficial incl. heat early warning � Urban cooling, education to reduce heat
and response systems targeting vulnerable
groups (***) stress, heatwave early-warning systems,
Longer term building standards that improve
� urban planning and design, insulation/cooling. Current levels of
including Nature based solutions (NBS) to
reduce urban heat island effects. Improved adaptation largely incremental and reactive
inconsistent with rising risks (*** )
basic protection for outdoor work incl. work 贩 NBS and well-resourced primary health care
rescheduling to cooler times of the day (***) Fundamental limits include thermal threshold,
� Some regions with heat stress conditions A� fCrliicmaate variability impacting the health of some individuals and communities are already
approaching upper limits of labour tens of millions of Africans through exposure
productivity (***) to extreme heat. Heat extremes (hot days reaching their psycho-social adaptation limits
Thresholds of survivability (***)
� approached (***)
and hot nights) increased in frequency since
WG I Detection and attribution statement 1980 (***)
贩 Hot extremes (including heatwaves) have � Increasing temperatures will cause tens of Small Islands
thousands of additional deaths under
become more frequent and more intense moderate and high global warming scenarios,
across most land regions since the 1950s
(*****) particularly in North, West and Central Africa � Disproportionate health risks associated with
Human-induced (***)
� climate change is the main � Cooling stations, limited evidence of pro- changes in temperature. Heatwaves cause injuries
driver of these changes (***) and deaths
� Every additional 0.5癈 of global warming C贩 eHSniegtanratiflsictaarnendstsSinaocuhrteehaaslAtehms cienorniicncaeternns(i*ty*,*) active climate change adaptation in African � Heat-related mortality and risks of occupational heat
cities, (***)
causes clearly discernible increases in the � Urgent need for improved societal and stress in small island states projected to increase with
intensity and frequency of heatwaves (*** ) higher temperatures. Higher temperatures also can
frequency and duration of heatwaves political transformations to reduce climate affect productivity of outdoor workers.
(***), strong increases in heat-related change risks for vulnerable groups (**). � Limited evidence reported. early warning
mortality in urban areas Deployment considered necessary of NBS and
Focus on early warning and response systems; integrating climate services into
� with demonstrated health, ecological, health decision-making systems; public uptake and
surveillance systems for heat waves; economic and social co-benefits.
Confidence: political, institutional, and financial � Morbidity and mortality will escalate with � buy in; improving health data collection systems
* low barriers limit feasibility to date (***) further global warming, placing additional � No evidence if transformational adaptation.
** medium � NBS proposed to be combined with strain on health and economic systems (***) Reduced habitability of small islands through a
*** high Under high warming scenarios annual compounding of key risks including from heat-related
**** very high community engagement and � health stress for warming of 1.5 � degrees (***)
***** virtually certain integration of diverse knowledge to exceedance of deadly heat thresholds in
foster transformational adaptation North, West and Central Africa (***)
� No limits for health risk discussed
2
3 Figure 17.5: Understanding the spectrum of incremental to transformational planned adaptation for managing climate
4 related heat risk to health including associated soft and hard adaptation limits (RKR-E). Evidence from regional and
5 thematic chapters. The figure from the WG I Atlas shows the change in extreme hot days (above 35oC) across regions
6 for a medium-term scenario and medium global warming relative to 1850-1900. See table SI 17.2.2.5 RKR E.
7
8
9 Marine heatwaves have affected tropical coral reefs, which are analysed as part of RKR-B (see SM17.4).
10 Coral reefs across the tropic have recently seen massive bleaching events (such as for the Great Barrier reefs)
11 (very high confidence) Risks are projected to be further exacerbated by increases in intensity, frequency and
12 duration of marine heatwaves (high confidence) as well as impacts from extreme events such as tropical
13 cyclones (low to medium confidence) (3.4.2).
14
15 Although there is some evidence of autonomous natural thermal adaptation, as indicated by the presence of
16 stress tolerant symbionts adapted to higher thermal thresholds observed in the Persian/Arabian Gulf. Yet,
17 there is low confidence (with limited evidence, low agreement) that enhanced thermal tolerance can be
18 maintained over time (Ch.3 Box 5) as the adaptability in natural system is considered very limited and risk
19 are driven by water temperature. Evidence suggests that already at further warming of 1.5C coral reefs are
20 put at large risk (very high confidence) (3.4.2.1).
21
22 Planned adaptation can help to buy some limited time including through recovery and restoration efforts that
23 target resistant coral populations and interventions to culture heat-tolerant algal symbionts as well as by
24 setting up marine protected areas. Under higher warming levels, transformation has been proposed as
25 possibly complementing available management approaches with high-risk interventions, including enhanced
26 corals and reef shading, which may help to sustain some coral reef systems beyond 1.5癈 of global warming.
27 Modelling has shown, however, that the effectiveness of such high-risk interventions declines beyond 2癈 of
28 global warming (Figure 3.23, 3.4.2.1) (medium confidence).
29
30 Already for limited warming beyond 1.5癈 for mid-century with increasing intensity and frequency of
31 marine heatwaves hard limits are projected to become manifest in terms of widespread decline and loss of
32 structural integrity (very high confidence) (3.4.2.1), including for the two largest such systems, the Great
33 Barrier Reef and the Mesoamerican coral reef (11.3.2, Box 11.2, Table 11.14, 12.4).
34
35 In terms of planned adaptation options that would provide benefits to populations, evidence suggest these are
36 very limited, uncertain and bring along substantial risks to people, culture and ecosystems (3.5.2. Cross-
37 Chapter Box SLR). Concurrent with the loss of coral reefs important ecosystem services, including to
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1 fishery, tourism and coastal protection would be lost. Transformational adaptation, while requiring to make
2 difficult choices, is being discussed to help overcome soft limits through livelihood diversification for
3 alternative income sources, assisted migration and planned relocation of communities dependent on the
4 services provided by the reef ecosystem (medium confidence) (3.5.2).
5
6
7 [START CROSS-CHAPTER BOX LOSS HERE]
8
9 Cross-Chapter Box LOSS: Loss and Damage
10
11 Authors: Reinhard Mechler (17), Adelle Thomas (16), Christian Huggel [MR1] (12), Emily Boyd (8),
12 Veruska Muccione (13), Ivo Wallimann-Helmer (CA), Laurens Bouwer (CA), Sirkku Juhola (CA), Chandni
13 Singh (10), Carolina Adler (17), Kris Ebi (7), Patricia Pinho (8), Rawshan Ara Begum (1), Adugna Woyessa
14 (9), Johanna Nalau (15), Katja Frieler (16), Richard Jones (WG I), Riyanti Djalante (8), Rosa Perez (18),
15 Tabea Lissner (4), Anita Wreford (11), Mark Pelling (6), Francois Gemenne (8), Nick Simpson (9), Doreen
16 Stabinsky (WG III)
17
18 An intensifying dialogue
19
20 This Cross-Chapter Box offers an assessment of the growing literature on Loss & Damage. Capitalised letter
21 `Loss and Damage' (L&D) has been used to refer to political negotiation under the UNFCCC. Research has
22 used lowercase `losses and damages' for residual effects from (observed) impacts and (projected) risks (see
23 Glossary).
24
25 Dialogue around L&D issues started with a proposal for insurance and compensation by the Alliance of
26 Small Island States (AOSIS) (INC, 1991) and has intensified over recent years with suggestions made to
27 consider complements to adaptation in order to manage residual impacts and risks `beyond adaptation' in
28 vulnerable developing countries (1.4.5). L&D was formally recognized in 2013 at COP19 through the
29 Warsaw International Mechanism on Loss and Damage (UNFCCC, 2013), governed by an Executive
30 Committee (ExCom), to advance knowledge, foster dialogue as well as enhance action and support. Article 8
31 of the Paris Agreement provided a permanent legal basis for the WIM (UN, 2015).
32
33 IPCC's first assessment of L&D in 2018 found residual risks to rise with further global warming leading to
34 soft and hard adaptation limits in some natural and human systems (e.g., coral reefs, human health, coastal
35 livelihoods (Roy et al., 2018). Sections 8.4.5.6, 16.4 and 17.2 corroborate these findings concluding that,
36 depending on mitigation and adaptation pathways residual risks in key systems in many regions will create
37 potential for negative impacts beyond adaptation limits (medium confidence). The assessment in 2018 also
38 noted that there is "not one definition of L&D." This ambiguity has persisted and a policy space for L&D has
39 not clearly been delimited (high confidence). There is, however, coalescence in dialogue among academia,
40 civil society and policy around a distinct set of themes as identified by stakeholder surveys as well as
41 literature, methods and evidence reviews (Vanhala and Hestbaek, 2016; Boyd et al., 2017; Mechler et al.,
42 2018; Calliari, 2019; McNamara and Jackson, 2019): risk management, limits to adaptation, existential risk,
43 finance and support including liability, compensation and litigation (8.3, 16.4; medium confidence; Figure
44 Cross-Chapter Box LOSS.1). Various advisory groups have been set up with participation of policy and
45 experts from research, civil society and practice to help inform the implementation of WIM workplans (UN,
46 2015; UN, 2019).
47
48
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1
2 Figure Box Cross-Chapter Box LOSS.1: Charting out the L&D discursive and policy space. The figure shows key
3 discursive strands relevant for L&D including their interrelationships with and distinction from adaptation. The figure
4 also identifies expert groups set up under the WIM and showcases the scale of responses discussed, a focus on ex ante
5 risk management and ex post attention to losses and damages as well as contributions by climate change and other
6 stresses for the themes. Adapted from Boyd et al. (2017) and building on Vanhala and Hestbaek (2016), Mechler et al.
7 (2018), McNamara and Jackson (2019), and Calliari (2019).
8
9
10 Risk management
11
12 An increasing body of research has focussed on the role of climate risk management (8.3; 16.4 and 17.2;
13 high confidence) (Birkmann and Welle, 2015; Gall, 2015; van der Geest and Warner, 2015; Mechler and
14 Schinko, 2016; Boyd et al., 2017; IPCC, 2018b; IPCC, 2019b; Boda et al., 2020; Broberg and Romera,
15 2020). A technical advisory group on comprehensive risk management (TEG CRM) advises the WIM
16 ExCom while other expert groups focus on slow-onset events and non-economic L&D (UNFCCC, 2019a).
17
18 There is evidence that, without strong risk management and adaptation, losses and damages will continue to
19 affect the poorest vulnerable populations potentially creating poverty traps (high confidence) (8.3; 8.4.5.6
20 and Table 8.7; 17.2; Serdeczny, 2019; Tschakert et al., 2019; Thomas et al., 2020). Research has started to
21 develop global inventories on losses and damages including on intangible effects (Tschakert et al., 2019;
22 Otto et al., 2020) and engaged with the practice community for data collection. Practice has provided
23 guidance to report on losses and damages in countries' (I)NDCs (WWF & Practical Action, 2020). Yet,
24 systematic risk assessments of climate-related losses and damages including adaptation limits (see, e.g. Leal
25 Filho and Nalau, 2018; Robinson, 2018) have remained scarce (16.4; high confidence). Thus many
26 vulnerable countries lack comprehensive data at scale of risk management including on economic (e.g. loss
27 of livelihood assets and infrastructure), and non-economic losses and damages (e.g. culture, health,
28 biodiversity) thus hampering effective risk management (Thomas and Benjamin, 2018; Martyr-Koller et al.,
29 2021; Singh et al. 2021). van den Homberg and McQuistan (2019) propose a losses and damages inventory
30 also to be used to monitor how technologies may shape risks as well as adaptation limits. While early
31 warning and other risk reduction options as well as risk retention considerations are being discussed, L&D
32 dialogue has strongly focussed on risk finance for residual risks, particularly through the donor-supported
33 provision of public insurance systems (Linnerooth-Bayer et al., 2019; Sch鋐er et al., 2019; Broberg and
34 Romera, 2020; Nordlander et al., 2020).
35
36 Transformation
37
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1 The role of transformation in risk management for overcoming any soft limits to adaptation is seeing
2 emerging attention (medium confidence, limited evidence), and the TEG CRM has also been tasked to
3 consider transformation. Relocation and retreat of assets and communities, where in situ adaptation is
4 considered impossible, is increasingly being debated in research and practice, including in terms of finance
5 and L&D implications (8.4.4; Boston et al., 2021; Desai et al., 2021; Mach and Siders, 2021; van der Geest
6 and van den Berg, 2021; Zickgraf, 2021). Livelihood transformation occurs where current livelihoods
7 become unfeasible in the face of multiple climatic and non-climatic stressors (8.3.4.1) requiring change
8 within sectors (such as switching from cropping to livestock rearing (Escarcha et al., 2020) or across sectors,
9 when farming households relocate to offer labour elsewhere (9.1; Rasel et al., 2013). Biermann and Boas
10 (2017) suggest revamping global governance systems to effectively address the protection and voluntary
11 resettlement of those displaced by climate variability and change. A WIM taskforce on displacement is
12 tasked to further advise on human mobility, including migration, displacement and planned relocation
13 (UNFCCC, 2019a).
14
15 The existential dimension
16
17 There has been less and often implicit discussion on the existential dimension of climate-related risk as
18 pertaining to L&D (medium confidence).McNamara and Jackson (2019) infer an existential dimension from
19 notions of inevitability and irreversibility associated with migration and relocation of communities
20 (Eckersley, 2015; Mayer, 2017; McNamara et al., 2018), socio-cultural impacts linked to glacial retreat (Jurt
21 et al., 2015), as well as adverse psychological and intersubjective effects (Herington, 2017; Adams et al.,
22 2021). Many SIDS in their NDCs refer to sea level rise in particular posing existential threats, and call for
23 enhanced international support for L&D (Thomas and Benjamin, 2017).
24
25 Finance and support
26
27 International support and finance, including compensation for losses and damages, have been in the spotlight
28 from the beginning of the dialogue (high confidence), starting with AOSIS' proposal (INC, 1991). Recent
29 work has focussed on finance sources, such as solidarity-based donor and other support for experienced
30 losses and damages and climate-induced displacement as well as questions of compensation and litigation
31 (Roberts et al., 2017; Gewirtzman et al., 2018; Mechler and Deubelli, 2021; Robinson et al., 2021). A
32 selection of finance options has also been explored such as donor-supported insurance systems with built-in
33 risk reduction provisions (Gewirtzman et al., 2018) as well as roles for social protection (Aleksandrova and
34 Costella, 2021). International policy and donors have provided technical assistance for insurance-related
35 options such as (Insuresilience Global Partnership, 2018).
36
37 As national and donor-related funding for impacts and risk management remains limited (Sch鋐er and
38 K黱zel, 2019; 17.2; Serdeczny, 2019) even at current global warming, many highly exposed developing
39 countries remain financially constrained in their capacity to attend to residual impacts and risk management
40 needs (Linnerooth-Bayer and Hochrainer-Stigler, 2015; Roberts et al., 2017; UNEP, 2021a) (high
41 confidence). Discussion on options for the risk retention layer `beyond adaptation" are likely to see further
42 attention as the dialogue proceeds.
43
44 Although there is no explicit mandate regarding L&D, about a quarter of the Green Climate Fund's approved
45 projects explicitly refer to L&D while 16% of projects have thematic links to L&D across their main project
46 activities (Kempa et al., 2021). Any estimate of L&D finance needs and spending, however, remains highly
47 speculative, as long as its exact remit including in relation to adaptation has not been clarified politically
48 (medium evidence, high agreement) (Markandya and Gonz醠ez-Eguino, 2019).
49
50 Liability and compensation, implying legally defined reimbursement of losses and damages attributable to
51 climate change, remain contentious in L&D dialogue (high confidence). In half of the academic and grey
52 literature surveyed by McNamara and Jackson (2019), compensation is mentioned. Studies have laid out
53 responsibility principles, such as historical responsibility based on the polluter pays principle, beneficiary
54 pays, as well as ability to pay. Discussions on compensation are closely linked to justice and equity
55 scholarship which has studied compensatory, distributive and procedural equity considerations for burden
56 sharing (Roser et al., 2015; Wallimann-Helmer, 2015; Huggel et al., 2016; Boran, 2017; Page and Heyward,
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1 2017; Roberts et al., 2017; Shockley and Hourdequin, 2017; Wallimann-Helmer et al., 2019; Garcia-Portela,
2 2020).
3
4 Litigation and liability are linked and a growing research body has examined the role of litigation and
5 international law for the L&D context finding that litigation risks for governments and business may increase
6 as the science, particularly on attribution matures further (Mayer, 2016; Banda and Fulton, 2017; WGI
7 CWGB Attribution, 8.2.1.2); Marjanac and Patton, 2018; James et al., 2019; Simlinger and Mayer, 2019;
8 Wewerinke-Singh and Salili, 2019; Toussaint and Martinez Blanco, 2020) (high agreement, medium
9 evidence).
10
11 Outlook
12
13 The WIM has been reviewed twice as to its delivery on its key functions. As an outcome of the second
14 review in 2019, an expert group on Action and Support has been set up to further discuss issues pertaining to
15 finance, technology and capacity-building and a Santiago Network for Technical Assistance will be
16 established to consider providing technical support directly to developing countries (UNFCCC, 2019b).
17 Overall, the L&D dialogue under the WIM supported by an increasing body of research has made important
18 advances with regard to the two functions of knowledge generation and coordination; however, less so on
19 action and support (medium confidence) (Calliari et al., 2020). Resolution on the last item will need
20 additional attention as, despite the coalescence of themes, the L&D dialogue continues to proceed across
21 interlinked yet contested discussion strands.
22
23 [END CROSS-CHAPTER BOX LOSS HERE]
24
25
26 17.3 Decision-making Processes of Risk Management and Adaptation
27
28 AR5 (Chambwera et al., 2014; Jones et al., 2014; Klein et al., 2014; Kunreuther et al., 2014; Mimura et al.,
29 2014) represented a significant step forward in focusing attention on how decision-making may facilitate
30 effective and robust responses to climate risks remaining after mitigation measures have been taken,
31 following recognition of these needs in AR4, including the diverse contexts that face decision-makers (Klein
32 et al., 2007).
33
34 AR5 (Jones et al., 2014; Kunreuther et al., 2014) recognised that the decision-making procedures are as
35 important to consider in managing risks as are the options for responding to climate change, mostly because
36 the procedures can themselves constrain the choices of actions, which could, in turn, lead to constrained
37 pathways which are undesirable. It emphasised the importance of iterative risk management because risk
38 and adaptation are dynamic. It also identified that (i) risk assessments, decision-support tools, early warning
39 systems, accounting for uncertainty and delivering no-regret options by examining trade-offs are important,
40 (ii) integration across different governance portfolios is needed due to potential conflict of different actions
41 between portfolios, and (iii) planning, implementation and decision-making, including the use of methods,
42 are dependent on local context.
43
44 Since AR5, the IPCC special reports have provided assessed the value of integrated assessment processes for
45 assessing trade-offs and synergies (IPCC, 2018a), adaptive management and governance, the roles of formal
46 and informal decision making (IPCC, 2019b), and the importance of developing policy and governance
47 options for risk management, including managing disasters, enhancing resilience, addressing decision-
48 relevant uncertainties, and being prepared for abrupt change and extreme events (IPCC, 2019c)
49
50 Chapter 16 has shown that climate risks vary greatly from small to large, local to regional, uncertain to
51 deeply uncertain. The plethora of risks means there are many types of decisions, and many forms of
52 analyses and processes that may be drawn on. Decisions can differ according to whether they are strategic,
53 tactical or operational; whether there are one or many decision makers, from a domestic setting to national
54 governments; the level of uncertainty present; the time available to take the decision; and many more factors
55 (Chapter 1; Section 17.1).
56
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1 The pathway to a decision may not be linear, depending on when and in what detail the decision-making or
2 consultative group may need to be understanding the climate risk and its real world context (sense-making,
3 modelling), has sufficient background to analyse and explore options for ameliorating the risk (analysis,
4 exploration), or is ready for interpreting the analyses and deciding on the requirements and strategies for
5 implementing a chosen strategy (interpretation-implementation) (high confidence) (Figure 17.6; French et
6 al., 2020). The development of decision-support tools for climate risk management (Palutikof et al., 2019a;
7 Palutikof et al., 2019b) and more generally (Papathanasiou et al., 2016) along with archives of experiences
8 from practitioners (Watkiss and Hunt, 2013; Section 17.5; Bowyer et al., 2014; French, 2020a) means that
9 some aspects of the decision-making process can be circumvented or at least streamlined as that experience
10 is re-used (high confidence).
11
12 No single approach to decision making best suits an individual climate risk across any adaptation context
13 (Richards et al., 2013), although there is now a greater awareness of the methods and approaches that are
14 available and their requirements for best practice (Hurlbert et al., 2019) (high confidence). This section aims,
15 firstly, to assess the factors that people responsible for organising and facilitating decision-making may wish
16 to consider in choosing the methods and approach for them to make decisions in their context. It also
17 assesses existing experience in analysing the utility of methods for climate risk decision-making. The
18 second part then assesses progress in integrating decision-making across a portfolio of risks.
19
20 Processes and methods to facilitate decision-making, from problem recognition to implementing a solution,
21 have evolved in many contexts, disciplines and applications over the last century (high confidence). As a
22 result, decision-making terminology has a vast number of synonyms that are not compiled here. For clarity,
23 the term `decision-analytic methods' refers to procedures or tools that may be used by decision-makers to
24 help develop, analyse and contrast alternative actions/adaptations; `approaches' refers to processes that may
25 be undertaken by decision-makers to facilitate the development of proposed actions/adaptations; `decision-
26 support tools' refers to software or procedures that facilitate the use of knowledge and data (Papathanasiou et
27 al., 2016).
28
29
30
31
32 Figure 17.6: Relationships between different processes of decision-making to manage climate-related risks in the real
33 world, noting that, when appropriate, some aspects may only require experience to be re-used. 1. Formulation of risks
34 of concern and accompanying policies and objectives for managing those risks, forming prescriptive models for the
35 decision maker. 2. Knowledge, understanding and observations of the real world are used to assess past and current
36 impacts and future risks using descriptive models, based on the perspectives and prescriptive models arising from (1).
37 If not well formulated from other experience, processes in (1) and (2) interact to make sense of the world and what
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1 needs to be done. In iterative management, (1) and (2) also form the basis for monitoring, reviewing, and evaluating
2 effectiveness of adaptations. 3. Use of decision-support and decision-analytic tools to appraise costs and benefits of
3 different options for ameliorating future risks. The double-headed arrow indicates where two-way interactions occur
4 between different activities (likely to be iterative, feedback and non-linear processes) � modelling and assessments are
5 repeated and revised in tandem with the planning and evaluation of options, based on interactions with the policy-
6 makers and stakeholders. 4. The decision-maker, which may be a group of people, interacts with the evaluation of
7 options (two-way interaction) and interprets the efficacy of the options and the implications for the real world,
8 ultimately choosing one or more actions to satisfy the policy objectives to manage the risks. 5. Implementation of the
9 actions in the real world, which may be once-only actions or instigation of a feedback management system that enables
10 ongoing adjustments to meet objectives.
11
12
13 17.3.1 Decision-analytic Methods and Approaches
14
15 Different classes of decision-analytic methods have been variously presented in IPCC reports since AR4 but
16 without a summary assessment of their capacity to deal with different contexts of the decision maker.
17 `Communities-of-practice' are developing tool-boxes to support analysing and making of decisions generally
18 (French, 2020a). These communities of decision analysts can act like broad-based statisticians to advise on
19 matching methods to the climate risk and its context, before individual decision specialists are consulted.
20 Some scientific literature is presenting guides for choosing different methods, tools and approaches (Shi et
21 al., 2019). This sub-subsection provides a summary guide for policy analysts and decision-makers to help
22 identify the classes of decision-analytic methods that may be suitable for their context for managing climate
23 risks. It focuses on decision-analytic methods, noting that decision-support tools will underpin many of these
24 methods by organising information (Bourne et al., 2016; Papathanasiou et al., 2016; Ceccato et al., 2018;
25 Ha遝 and Kind, 2018) or support modelling (Papathanasiou et al., 2016; Kwakkel, 2017; Gardiner et al.,
26 2018), sometimes with a particular decision-analytic process in mind (Hadka et al., 2015; Torresan et al.,
27 2016; Tonmoy et al., 2018).
28
29 17.3.1.1 Factors to Consider in Selecting Methods to Facilitate Decision-Making
30
31 The choice of methods and approaches to decision-making for climate risks (next section) will depend on (i)
32 the cognitive needs of the deliberations, otherwise considered to be the phase in developing a decision, (ii)
33 the types of models and modelling available to facilitate the deliberations, (iii) the degree of uncertainty
34 surrounding the choices, and the (iv) context of a choice (high confidence) (Richards et al., 2013; Jones et
35 al., 2014; Shi et al., 2019; French, 2021).
36
37 17.3.1.1.1 Cognitive phases of decision making
38 The decision process often involves overlapping and iterative development of the components leading
39 toward a decision, resulting in the blurring of stages but involving different phases of cognitive activity
40 (Figure 17.6; Holtzman, 1989; French, 2015; French, 2020a). Framing the problem (Orlove et al., 2020), by
41 modelling its relationships with the human and natural systems and eliciting objectives, values and scope of
42 the problem from stakeholders, is a precursor to analyses of options but may be returned to whenever a phase
43 of `sense-making and modelling' is required (high confidence) (Ackermann, 2012; Keeney, 2012; Slotte and
44 H鋗鋖鋓nen, 2014; Abbas and Howard, 2015; Marttunen et al., 2017; Korhonen and Wallenius, 2020;
45 French, 2021).
46
47 The cognitive phase of 'analysing and exploring' uses models and existing data and/or knowledge services
48 as available to explore the relevance/efficacy of adaptations to ameliorate risk or to meet other adaptation
49 objectives, as well as possible flow-on effects of those actions (Section 17.3.1.4). Sensitivity and robustness
50 analyses can be useful if conditions are favourable to supplement the decision analysis, setting bounds on
51 some of the residual uncertainty (high confidence) (Borgonovo and Plischke, 2016; Ferretti et al., 2016).
52 Validation of models and verification of data (Tittensor et al., 2018) are becoming highlighted as important
53 steps in this phase or in the sense-making phase, particularly in their capacity to understand and test
54 decision-makers and stakeholders' perceptions (medium confidence). Randomisation methods, Bayesian
55 methods, interval methods, MCDA, DMDU and economic and financial approaches (e.g., Real Options
56 Analysis) are tools of choice in this phase (high confidence) (Table 17.5) (Abbas and Howard, 2015;
57 Bendoly and Clark, 2016; Borgonovo and Plischke, 2016; Iooss and Saltelli, 2017; Korhonen and Wallenius,
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1 2020; Saltelli et al., 2020). Decision-support tools in the provision of data and/or modelling methods are
2 regularly used in this and the sense-making phase (high confidence) (17.3.1.2).
3
4 The phase of interpreting the analyses to make decisions on climate adaptation followed by implementation
5 are the least described in the literature (Figure 17.7). Decision process management tools and methods for
6 communicating choices, outcomes and implementation are expected to be used to provide support in this
7 phase, particularly for understanding whether the advice is fit-for-purpose, and the efficacy of choices are
8 clear (low confidence) (Spetzler et al., 2016).
9
10 17.3.1.1.2 Types and capacity of models to support decision making
11 `Descriptive models' of socio-biophysical systems and their responses to different drivers (Argyris and
12 French, 2017; French and Argyris, 2018; Saltelli et al., 2020) and `prescriptive models', which capture the
13 beliefs, values and objectives of decision-makers and stakeholders (Parnell et al., 2013; Keisler et al., 2014;
14 French and Argyris, 2018), provide the foundations of sense making (high confidence) and thereby
15 influencing the options and choices available in the phase of analysis and exploration (medium confidence)
16 (Gorddard et al., 2016).
17
18 Socio-biophysical models may be qualitative network models, statistical models or dynamic mathematical
19 models (Melbourne-Thomas et al., 2017). Qualitative network modelling can help assess the nature and
20 consequences of the interactions, as well as facilitating understanding of possible structures to be used in
21 dynamic models for assessing long term adaptation options (Reckien et al., 2013; Reckien, 2014; Reckien
22 and Luedeke, 2014; Symstad et al., 2017). These approaches help articulate the direct and indirect effects of
23 fixed, long-term engineering or structural adaptations. Dynamic stochastic modelling (Fulton and Link,
24 2014; Ianelli et al., 2016) has been used to assess short to medium term interactions of more dynamic and
25 variable sectors, such as those with annual adjustments and management of water, agriculture, land and
26 marine uses (Holsman et al., 2019; Hollowed et al., 2020; Bahri et al., 2021). On a longer timeframe,
27 scenarios are used to test long term interactions but often with less variability and chance (Giupponi et al.,
28 2013; Adam et al., 2014; Rosenzweig et al., 2017).
29
30 Many sensitivity analyses based on scenarios, including procedures to randomise across model uncertainty,
31 relate to descriptive dynamic mathematical models with the user of the models characterised as an objective
32 observer (Borgonovo and Plischke, 2016; Ferretti et al., 2016; Symstad et al., 2017; French, 2020a).
33 Bayesian approaches enable these descriptive analyses to take account of the subjective choices in model
34 construction and implementation (Abbas and Howard, 2015; Sperotto et al., 2017; J鋑er et al., 2018;
35 Sperotto et al., 2019; French, 2020a). Organising descriptive analyses and deciding on a suitable option
36 across a diversity of opinions amongst stakeholders use prescriptive processes, which can be supported with
37 prescriptive modelling tools (Williamson and Goldstein, 2012; Gelman et al., 2013; Abbas and Howard,
38 2015; Dias et al., 2018; Phan et al., 2019; Hanea et al., 2021). These approaches are subjective, in that they
39 are constrained or directed by the particular views and emphases of the decision-making group (Gorddard et
40 al., 2016). Not all tools are appropriate for all these activities.
41
42 Decision-makers will be better able to choose decision-analytic methods when they have an understanding of
43 the types, scale and breadth of uncertainties around the climate risk (high confidence) (Symstad et al., 2017).
44 The Cynefin framework (Snowden, 2002; French, 2013) is a policy-driven framework that broadly
45 categorises the decision context of uncertainty within which decision makers and policy analysts may find
46 themselves (medium confidence) (Hurlbert et al., 2019; Helmrich and Chester, 2020). As Cynefin has helped
47 frame previous IPCC presentations on contexts of uncertainty (Hurlbert et al., 2019) and has a community of
48 practice to consult on its use (French, 2020a), it is used here, also because it considers the uncertainty in
49 knowledge around cause and effect in general terms, rather than specifically focussing on uncertainty in
50 formal models. Helmrich and Chester (2020) show how Cynefin can be used to frame climate adaptation
51 decision making in the infrastructure sector.
52
53 The Cynefin contexts relate to how well the system is understood for knowing precisely the outcomes of
54 actions that may be taken - range from known, knowable, complex to chaotic. If a context is known or
55 knowable, then it will be possible to build sophisticated models and make sound predictions. If the context is
56 complex and chaotic the outcomes of actions will be less predictable, no matter how complex the models
57 may be, although more complex dynamic models may be useful to test `what if' scenarios in these cases
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1 (Marchau et al., 2019). Under complex and chaotic circumstances an ensemble of models and approaches
2 may be needed to help categorise a satisfactory `solution space' across the broad knowledge of relationships
3 and dependencies, but will need to have iterative processes to update and refine adaptations as knowledge
4 improves (Marchau et al., 2019).
5
6 17.3.1.1.3 Uncertainty and attitudes to risk
7 Uncertainty does not just relate to what might happen given climate drivers or adaptations, but also about
8 how much one values potential consequences (Butler et al., 2016; Beven et al., 2018a; Cross-Chapter Boc
9 DEEP; Beven et al., 2018b; French, 2020a) (high confidence); the balance between how particular decision
10 analyses address uncertainties relating to the external world (descriptive models) and those relating to the
11 values driving the decision making (prescriptive models) is important (Butler et al., 2016). Some analyses
12 partially ignore uncertainties relating to the former in order to focus on conflicts in the values held by
13 different stakeholders and help structure debate (Korhonen and Wallenius, 2020; French, 2020a), while
14 others build very sophisticated models of the external world to predict potential consequences, but in doing
15 so lose transparency and risk becoming untrustworthy black boxes to many stakeholders (low confidence)
16 (Peterson and Thompson, 2020).
17
18 Much of the readily-available literature on how uncertainties affect decision-making relates to the
19 uncertainty in the biophysical models, with a recognition that the choice of tools will be influenced by the
20 types of uncertainty to be addressed (Le Cozannet et al., 2017; Symstad et al., 2017; Beven et al., 2018a;
21 Beven et al., 2018b; Durbach and Stewart, 2020b; French, 2020a). While terminology varies amongst
22 disciplines, three types of uncertainty are important in understanding assessments of the future from
23 descriptive models � epistemic (uncertainty in model construction relating to the lack of knowledge about
24 the system being represented), analytic (the degree to which a model fits observations, and its accuracy), and
25 stochastic (the natural variability or randomness in the system). The probability of an event arising in the
26 future is determined from all three uncertainties, noting that stochastic uncertainty is a property of the system
27 rather than a limitation of research (Le Cozannet et al., 2017; Beven et al., 2018a; Beven et al., 2018b).
28
29 Uncertainty in what constitutes a risk of concern is increasingly identified as important to consider when
30 managing risk (Chapter 16; Butler et al., 2016; Prober et al., 2017; French et al., 2020; Reis and Shortridge,
31 2020). The uncertainty here arises from what is an acceptable risk. Acceptability relates to the value or
32 importance of the consequence, which may include moral and ethical uncertainties (Prober et al., 2017), as
33 well as how ambiguous the understanding of the consequence may be between different groups (Beven et al.,
34 2018a; Beven et al., 2018b). The development of strategies to ameliorate risk will benefit from considering
35 these two uncertainties in specifying the risk to be managed (Prober et al., 2017; French et al., 2020) because
36 they can help set boundaries on a required likelihood of success, rather than simply casting stakeholders or
37 decision-makers as risk averse or risk tolerant, and can help identify and accept pathways of success
38 (Gregory et al., 2012). This can be important when decisions need to be made well in advance of the actions
39 needing to take effect, such as for many climate risks (Chapter 1; Chapter 16; Section 17.2.3; Cross-Chapter
40 Box DEEP in this Chapter).
41
42 Elicitation methods help reduce these uncertainties (high confidence) (Butler et al., 2016; Prober et al., 2017;
43 Symstad et al., 2017; Beven et al., 2018b). In addition, informal decision processes can assist in developing
44 consensus in approaches and outcomes (Orlove et al., 2020).
45
46 17.3.1.2 Decision Analytic Methods Used in Decision-Making and Climate Risk Management
47
48 Entities making decisions (countries, regions, organisations and individuals) select methods that best suit
49 them in their context (F黱fgeld et al., 2018; Shi et al., 2019; French, 2020a) (high confidence).
50 Classes of tools (Watkiss and Hunt, 2013; French, 2020a) include Bayesian methods, Interval methods,
51 decision making under deep uncertainty (DMDU; see Cross-Chapter Box DEEP in this Chapter), cost-
52 benefit analyses, multicriteria decision analysis, elicitation and general decision support tools (Table 17.5). A
53 summary guide for policy analysts and decision-makers is presented in Table 17.5 to help identify the classes
54 of decision-analytic methods that may be suitable for their context for managing climate risks. The table
55 summarises how well the methods address the Cynefin context, phase of decision making, the types of
56 uncertainties that exist through the decision-making process and the resources required. As terminology may
57 vary between disciplines and research groups, suitable references to better explain the methods within the
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1 class are provided. Also, there may be overlap between the classes as individual methods are often paired
2 with other methods to address specific requirements and approaches (Buurman and Babovic, 2016; Haasnoot
3 et al., 2019). In that respect, these methods are referred to in the next section discussing advances in the
4 different approaches to managing climate risks.
5
6 Case studies in Table 17.5 describe the utility of classes of decision-analytic tools to facilitate decisions
7 about climate adaptations (SM 17.2). These case studies are presented in Figure 17.7 according to the type of
8 decision-making body and mapped according to their contribution to a decision outcome relative to the
9 geopolitical scale of the actions being assessed. The effectiveness of these methods and tools in Table 17.5 in
10 the context of climate change adaptation (Box 17.1) has yet to be evaluated.
11
12
13 Table 17.5: Characteristics of the main approaches to decision analysis with respect to their Cynefin context, the
14 manner in which they can be used to address different uncertainties, where they may be used in different cognitive
15 phases of the decision-making process, the resources required, and some case studies for further exploring how they
16 might be used. Numbers in square brackets after references in Case Studies refer to the references plotted in Figure
17 17.7.
Bayesian Methods (Keeney and Raiffa, 1993; Smith, 2010; Gelman et al., 2013; Reilly and Clemen, 2013; Abbas
and Howard, 2015; Sperotto et al., 2017; Marchau et al., 2019)
A structured approach to assembling information around the consequences of choices, either by modelling, analysis of
multiple scenarios or by structuring deliberation; Underpinned by a theoretical base, coherent assumptions and
powerful computational methods; Can use both observational data and expert knowledge, weighting them
appropriately; Same approaches as in Artificial Intelligence algorithms. Biases (information, stakeholders, decision-
makers) can be made explicit. Traditionally, Bayesian methods computationally identify an `optimal' decision, based
on maximising the expected utility across a number of specified requirements, represented as functions.
Examples include the general application of decision network models (Richards et al., 2013; Sperotto et al., 2017),
the use of decision network analyses based on elicitation to choose adaptations to coastal management in a lagoonal
area in Italy (Catenacci and Giupponi, 2013) and coastal community in UK (J鋑er et al., 2018); combination of
economic models and decision models to assess research and development priorities (Baker and Solak, 2011);
combining outputs from models, observations and opinions in a decision framework for assessing climate impacts on
water nutrient loads in Italy (Sperotto et al., 2019) and a general review for water resource management (Phan et al.,
2019); combining results from different dynamic models to assess human mortality from ozone in the USA (Alexeeff
et al., 2016), assessing adaptive capacity of surf lifesaving in Australia (Richards et al., 2016), and assessing urban
flood risks in Denmark (舠tr鰉 et al., 2014).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Implementing
Modelling Exploring
Construction of Bayesian updating Use of graphical Bayesian decision (Alexeeff et al.,
2016) [1], (舠tr鰉
hierarchical models, and expected utility models (decision trees, analytic models can be et al., 2014) [2],
(Baker and Solak,
belief nets (Sperotto analysis, belief nets and applied with increasing 2011) [3],
(Catenacci and
et al., 2017; Phan et supplemented by influence diagrams) complexity and Giupponi, 2013)
[4], (J鋑er et al.,
al., 2019), decision robustness and and sensitivity plots sophistication to any 2018) [5], (Phan et
al., 2019) [6],
trees (Keeney and sensitivity analyses can help make given problem. (Richards et al.,
2013 )[7],
Raiffa, 1993) and (Rios Insua, 1999; transparent and explain Coherence between (Richards et al.,
2016) [8],
influence diagrams Rios Insua and reasoning for strategy different levels of (Sperotto et al.,
2017) [9],
(Keeney and Raiffa, Ruggeri, 2000; to stakeholders and sophistication can be (Sperotto et al.,
2019) [10]
1993; Reilly and French et al., 2009; implementers (Bendoly maintained. Thus, the
Clemen, 2013), Smith, 2010; Reilly and Clark, 2016) and resources can be tailored
supplemented by and Clemen, 2013; provide for auditable to the time and support
many soft elicitation Abbas and Howard, building of consensus. available for the
techniques help build 2015). analysis. The most
models for sophisticated analyses
quantitative analysis are computationally
(Gelman, 2003; demanding.
Bendoly and Clark,
2016)
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
Uncertainties Ambiguity Cynefin context Knowable Complex Chaotic
Value Known
Stochastic, (Prescriptive
Epistemic, Modelling)
Analytical
(Descriptive
Modelling)
All can be Uncertainties Any stochastic Epistemic More exploratory Formal
uncertainties
modelled resolved or reduced uncertainties updated via analysis (Gelman, modelling
Bayesian
probabilistically, by discussion, then modelled statistics/machine 2003) to impossible.
learning, then
perhaps values modelled by probabilistically; remaining understand Much
stochastic
supplemented by multi-attribute otherwise, uncertainties behaviours with exploratory
modelled
sensitivity values and utilities deterministic probabilistically. less complex work to identify
Full Bayesian
analysis (Rios (Keeney, 1992; modelling with decision modelling Bayesian potential causes
possible (French et
Insua, 1999; Keeney and Raiffa, sensitivity al., 2009; Smith, modelling and effects.
2010; Abbas and
Rios Insua and 1993; Gregory et analysis. Value Howard, 2015). support by Little if any
Ruggeri, 2000; al., 2012). Residual functions tend to sensitivity and complex
Iooss and uncertainties be used more than robustness analysis.
Saltelli, 2017). explored via utility functions studies (Rios
Deep sensitivity analysis. (Keeney and Insua, 1999;
uncertainties can Raiffa, 1993; French, 2003).
be investigated Goodwin and Scenario focused
via scenarios Wright, 2014). decision analysis
(French, 2020a). to cope with deep
uncertainties
(French, 2020a).
Careful
deliberations to
construct values
and utilities.
(Keeney and
Raiffa, 1993;
Gregory et al.,
2012).
Decision-making under deep uncertainty (DMDU) (Hallegatte et al., 2012; Weaver et al., 2013;
Marchau et al., 2019; Workman et al., 2021)
Deep uncertainty relates to circumstances in which data are too sparse, experts in too much disagreement or time is
too short to model the uncertainty. As such, DMDU methods are focused on working in the Cynefin Complex Space
context. Approaches emphasise robustness ("no regrets" options) and the use of scenarios, and often link well with
scenario-focused robust Bayesian studies (Cross-Chapter Box DEEP in this Chapter). DMDU studies draw in
many other approaches to decision analysis, using them to identify robust rather than optimal strategies, as in Robust
Decision Making (RDM). DMDU analyses can help decision makers to think contingently and build a more wide-
ranging recognition of the risks. They often integrate with other classes of tools.
Examples include RDM for hydro-power design using down-scaled climate data in sub-Saharan Africa (Taner et al.,
2017), RDM for water management in California, USA (Lempert and Groves, 2010), the Colorado River, USA, and
for international climate investment strategies (Groves et al., 2019), use of decision-scaling (Brown et al., 2019),
comparison of RDM and Info-gap methods (Hall et al., 2012) and review of using climate modelling in RDM
(Weaver et al., 2013).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
Some of the simpler Many Bayesian or DMDU with its Some of the simpler (Brown et al.,
DMDU tools MCDA tools can be emphasis on robustness models do not require 2019) [11], (Groves
complement soft used here but with encourages substantial resources, et al., 2019) [12],
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
elicitation tools and DMDU's additional contingency planning but the application of (Hall et al., 2012)
can help to identify emphasis on in implementation with parallel sophisticated [13], (Lempert and
relevant scenarios robustness and the careful monitoring to analyses in several Groves, 2010),
and help formulate exploration of identify emerging scenarios can be [14], (Taner et al.,
problems. several/many risks. computationally 2017) [15],
scenarios. demanding. Also, the (Weaver et al.,
emphasis on discussion 2013) [16]
of robustness can be
demanding on the time
of problem-owners,
experts and
stakeholders.
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
(Descriptive (Prescriptive
Modelling) Modelling)
Methods are Some DMDU Deep uncertainty is Deep uncertainty is The complex and Deep
designed for
deep epistemic methods draw on absent but the absent but the chaotic spaces are uncertainties
uncertainties.
Some can deal MCDA methods principles and principles of home to deep are rife in the
with stochastic
uncertainties. and thus consider processes of decision making uncertainties. chaotic
Analytical
uncertainties ambiguity and decision making may be used. DMDU tools and contexts.
seldom
accounted for. value uncertainties. may be used. more particularly DMDU
In any case, processes are emphases on
DMDU methods relevant here. The robustness and
support wide emphasis on possible
deliberation with robustness is very scenarios can
stakeholders. relevant. The stimulate
tools themselves creative
are relatively discussions of
simply structured ill understood
but are effective at issues.
stimulating
discussion.
Decision Process Management (Raz and Micheal, 2001; Dalkir, 2005; Burstein and W. Holsapple,
2008; Jashapara, 2011; Bonczek et al., 2014; Sauter, 2014; Holsapple et al., 2019)
A range of tools and techniques to help manage the decision-making process and support risk management and the
implementation of the chosen strategy. Some tools organise data and analyses, often being built on a geographic
information system, known as decision support tools. Others manage processes, organising workflows. Some have
inevitably expanded in function to support decision-making itself, even though their primary focus might be on, say,
implementation and monitoring risks. Such tools are closely related to knowledge management systems; knowledge
management processes and decision process management differ more in terminology than in substance.
Examples include tools for agriculture (Biehl et al., 2017), evaluating and comparing CMIP climate models (Parding
et al., 2020), development of action cycles (Park et al., 2012), and decision support systems across a range of sectors
and decision-group applications (Papathanasiou et al., 2016).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
Process, project, Tools help structure Project management Decision process (Biehl et al.,
tools plan management tools 2017)[17],
knowledge elicitation decision-making implementation and can reduce resources (Papathanasiou et
and risk management processes and ensure
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
tools help identify how timely involvement of risk management tools needed in the al., 2016), [18],
identify what to decision-making (Parding et al.,
to structure decision- problem owners, monitor during process. However, 2020) [19], (Park
implementation. this assumes that the et al., 2012) [20]
making processes. stakeholders, and Knowledge tools are already
management tools installed on local
Decision process tools experts. Knowledge maintain audit trail and information systems
track reasoning for and that the analysis
can capture details for management tools can choices made during team is experienced
implementation in using them.
implementation and capture details for Otherwise, resource
is needed to
document process for implementation and understand and train
in the use of the
audit trail. document process for tools.
audit trail.
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
(Descriptive (Prescriptive
Modelling) Modelling)
Not designed to Not usually Simple project Project Project Project
address addressed, since management tools management and
uncertainties ambiguities and may be sufficient risk management management and management
involved in the value uncertainties here. tools apply easily
decision itself, will be addressed in here. risk management and risk
but may handle the decision making
project risks in itself, but may use tools may be management
the decision those values in risk
process, management of used but tools may be
especially implementation.
implementation. attention needs used but
to be paid to attention
risks that are needs to be
complex in paid to risks
nature with little that are
knowledge of complex in
precise nature with
relationships little
between cause knowledge of
and effects. precise
relationships
between cause
and effects.
Economic and Financial Methods (Howell et al., 2001; Pearce et al., 2006; Boardman et al., 2017;
Atkinson et al., 2018a; Hurlbert et al., 2019)
Stem from economic theory and accounting practices: e.g. cost-benefit analysis, which seeks to price out all aspects of
the consequence of a strategy, portfolio analysis, or real options theory, which seeks to value financial investments
allowing for their risks and the contingent buying and selling. Such methods are perceived as objective when dealing
with tangibles, but are more controversial in their valuing of intangibles. Since these methods model uncertainties
with probabilities and then work with expectations, they share much in common with Bayesian methods. However,
many applications of cost-benefit analysis omit any detailed treatment of uncertainty.
Examples examine the economic costs and benefits of adaptation pathways for storm water infrastructure in
Singapore (Manocha and Babovic, 2017), and a coastal mega city, Los Angeles in the USA (de Ruig et al., 2019)
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
In themselves, these These tools focus Since CBA methods do Cost benefit analysis (de Ruig et al.,
methods do not for complex projects is 2019) [21],
support sense-making mainly on analysis not emphasise the a major undertaking (Manocha and
and modelling, with much data Babovic, 2017)
though discussions of and evaluating the analysis of uncertainties collection needed to [22]
how to value impacts, value outcomes. Real
both tangible and costs and benefits of and risks, they are less options also require
intangible can be data on risks and
catalytic in various options. suited for use in uncertainties. Both
understanding the may have high
issues. They are not designed developing and computational needs.
to be used communicating an
interactively so are implementation plan.
more often deployed Real options with their
and communicated via emphasis on
reports than contingency are much
interactive workshops. more suited (Fischhoff,
2015).
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
(Descriptive (Prescriptive
Modelling) Modelling)
Cost-benefit These methods Although CBA and The methods may The recognition Formal
methods usually reduce all value many financial be applied to of the need to modelling
deal with and preference methods work in evaluate complex treat deep impossible.
uncertainty via information to theory, the projects but CBA uncertainties Much
expectations financial complexity makes tends to 'average using real options exploratory
with little equivalents. The it seldom worth the out' rather than has been work to
attention to key issue is to find effort. analyse uncertainty. investigated identify
probability a market in which (Hallegatte et al., potential
distributions; all outcomes may 2012; Buurman causes and
real options be valued and Babovic, effects. Little
methods tend to financially. 2016). if any complex
treat uncertainty Modern CBA analysis.
in much more methods use much
sophisticated more subtle
ways. Both techniques for this
methods, when than those applied
applied fully in the last century
have many (Bedford et al.,
points of contact 2005; Saarikoski et
with Bayesian al., 2016).
methods (Neely
and de
Neufville, 2001;
Bedford et al.,
2005)
Interval Methods (Shafer, 1976; Pedrycz et al., 2011)
Because of concerns that the statistical accuracy of some data is unknown, and that decision-makers and experts
cannot make numerical judgements accurately, analyses have been suggested which work with ranges of values in
categories (intervals) as their inputs. While avoiding accuracy issues, weakening the arithmetic may result in other
foundational assumptions not being met, including some basic principles of rationality. Different types of uncertainty
can often be confused, and the analyses can contradict basic probability theory. Interval models of semantics and
imprecision can be useful in exploring ambiguity and value uncertainty, though modelling rather than resolving such
uncertainties does not necessary help in decision-making. Some interval methods can be thought of more as
sensitivity techniques applied to other decision analytic approaches. Typical approaches here relate to the fuzzy or
possibility theory, and evidential reasoning.
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
Examples include using fuzzy methods to. assessing climate adaptations in ports in China (Yang et al., 2018), water
supply vulnerability in South Korea (Kim and Chung, 2013) and resilience of the Nile River delta (Batisha, 2015);
and evidential reasoning in an environmental impact assessment for flood mitigation in Manila Philippines (Gilbuena
et al., 2013).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Implementing
Modelling Exploring
The emphasis on If there is substantial The emphasis on Many methods are rather (Batisha, 2015) [23],
simple in application and (Gilbuena et al., 2013)
modelling data available, then linguistic require only moderate [24], (Kim and Chung,
resources, but they may 2013) [25], (Yang et al.,
ambiguity may even the simplest of uncertainty may in face issues in scaling up 2018) [26]
to major complex
help structure a these methods can some cases it may problems.
model initially, produce useful mask some of the
but the lack of results. But with issues (French,
structures to small quantities of 1995).
model and data, their data
explore complex analysis may be too
interdependencies inefficient.
may inhibit the Evidential reasoning
ability to build a MCDA can be
valid insightful on the
representation of preference side.
the issues.
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
(Descriptive (Prescriptive
Modelling) Modelling)
There are issues of Some Methods can be Since the methods The recognition of the The ability to
operational
definition of methods can applied here often capture rather need to treat deep deal with
quantities in some
methodologies. be simplistic without major than explore and uncertainties using real ambiguity may
Some simpler
interval methods with issue, possibly resolve ambiguity options has been be helpful in
have no concept of
conditionality so quantities not because the and value investigated poorly
cannot model
learning effectively, being simple, uncertainties, they (Hallegatte et al., understood
but there are some
very sophisticated operationally repetitive can hide issues. 2012; Buurman and situations, but
theories of evidence
that can. Interval defined. The nature of the Also, the lack, in Babovic, 2016). the emphasis
methods can also
provide sensitivity evidential problem allows some cases, of on capturing
analyses for
Bayesian and reasoning access to much operational ambiguity may
MCDA methods
(Shafer, 1976; Rios approach to data and the definitions may ultimately
Insua, 1990).
MCDA possibility of mean that some slow the
allows tuning the quantification is building of
exploration methods to the dubious. Evidential understanding.
of the application. reasoning methods
relative can help analyse
weights on conflicting
different objectives (French,
criteria or 1995; Xu, 2012).
between
levels in
criteria (Xu,
2012; Zhang
et al., 2017).
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
Multi-Criteria Decision Analysis (MCDA): Full ranking and optimal seeking (Bell et al.,
2001; Belton and Stewart, 2002; Bouyssou et al., 2006; Zopounidis and Pardalos, 2010; Tzeng and Huang,
2011; Velasquez and Hester, 2013; Kumar et al., 2017)
Covers many approaches: indeed, Bayesian, DMDU and interval methods are sometimes considered MCDA. Some
MCDA seek an optimal or best strategy; others form partial rankings, eliminating weak strategies but not
discriminating fully between the better ones. Many MCDA methods eschew dealing with uncertainties and focus on
modelling and exploring conflicting objectives and balancing these. MCDA techniques are especially useful in
working with senior decision-makers in setting policy and broad objectives, and in processes of stakeholder
engagement.
Examples include ranking adaptation and mitigation priorities at a national level in the Netherlands (de Bruin et al.,
2009), Lithuania (Streimikiene and Balezentis, 2013) and Bangladesh (Haque, 2016), in the forestry sector in
Nicaragua (Guill閚 Bola駉s et al., 2018); and in emissions trading in the European Union (Konidari and Mavrakis,
2007).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
There is growing Emphasis is Use of graphical The more exploratory (de Bruin et al.,
experience in usually on models and sensitivity methods can be quite light 2009) [27], (Guill閚
combining soft analysing and plots can help explain in terms of computational Bola駉s et al., 2018)
elicitation with tools exploring, reasoning for strategy resource, but require [28], (Haque, 2016)
to formulate resolving to stakeholders and interactions with decision [29], (Konidari and
problems (Marttunen conflicting implementers makers and stakeholders in Mavrakis, 2007)
et al., 2017). Many objectives. (Bendoly and Clark, workshops. Methods with [30], (Streimikiene
MCDA tools MCDA Methods 2016). use complex stochastic and Balezentis, 2013)
naturally encourage come into their mathematical [31]
discussion and own at this stage programming can be
deliberation on of the process. computationally
developing Sensitivity tools demanding and require
appropriate value and intuitive substantial data.
structures. However, graphical displays
exploration and exist for many of
formulation of the methods
stochastic and (Gunawan and
epistemological Azarm, 2005;
uncertainties is less Boardman et al.,
developed (Durbach 2017).
and Stewart, 2020a).
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
(Descriptive (Prescriptive
Modelling) Modelling)
These methods Many methods Usually in the If the objective Methods can Formal
tend to focus on here use multi- known context, the function is not well explore modelling
balancing and attribute value objective function understood, then conflicting impossible.
resolving functions and is well understood; these methods can objectives, but Much
conflicting focus on using but in cases where be useful and can be seldom are able to exploratory
objectives and weights to it is not, extended to address deep work to
include little or no explore different interactive multi- stochastic epistemic identify
analysis of emphases on objective programming, but uncertainties, potential
stochastic and conflicting programming can epistemic unless combined causes and
epistemic objectives. One offer a way uncertainties are not with scenarios effects. Little
uncertainties. very popular forward (Klamroth really addressed (Stewart et al., if any complex
Interactive method is AHP et al., 2018). 2013; Marchau et analysis.
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
methods that use (Saaty, 1980) (Gutjahr and al., 2019;
Pichler, 2016). Durbach and
complex objective though this has Stewart, 2020a).
functions do need issues in scaling
to consider up to evaluate
convergence more than a
criteria for handful of
analytic policies.
uncertainties.
Multi-Criteria Decision Analysis (MCDA): Partial ranking (Roy, 1996; Bell et al., 2001; Belton
and Stewart, 2002; Bouyssou et al., 2006; Behzadian et al., 2010; Zopounidis and Pardalos, 2010; Tzeng
and Huang, 2011; Bouyssou and others, 2012; De Smet and Lidouh, 2012; Velasquez and Hester, 2013;
Figueira et al., 2016; Govindan and Jepsen, 2016)
Examples include developing criteria for assessing climate protection strategies and applying these to
retrofitting a school to manage climate risks in Germany (Markl-Hummel and Geldermann, 2014);
evaluating outranking approaches for managing heat stress in a large city in Australia (El-Zein and
Tonmoy, 2015); using MCDA to manage the interactions of climate change with tourism in Greece
(Michailidou et al., 2016); and identifying priorities to manage droughts and floods in agriculture in
Bangladesh (Xenarios and Polatidis, 2015).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
Graphical representations ELECTRE and The analysis of If an outranking (El-Zein and
dominance can algorithm is essentially Tonmoy, 2015)
of partial orders are useful PROMETHEE provide a sound combinatorial in its [32], (Markl-
footing for building approach, then for Hummel and
in model formulation, and implementations of risk registers to aid complex problems there Geldermann,
implementation. may be computational 2014) [33],
the emphasis on exploring outranking Understanding the problems. Some of the (Michailidou et
kernel of consensus methods may require al., 2016) [34],
what can be said approaches have can also aid less interaction with (Xenarios and
communication. decision-makers and Polatidis, 2015)
objectively about many tools for stakeholders if they can
deduce many partial [35]
dominance relations can exploring partial relations from objective
data.
build a kernel of relations and
consensus between analysing
decision-makers and agreements and the
stakeholders. reasoning behind
these.
Uncertainties Cynefin context
Stochastic, Epistemic, Ambiguity Known Knowable Complex Chaotic
Analytical Value
(Descriptive
Modelling) (Prescriptive
Modelling)
Modelling of all Partial ranking or Usually in the Since epistemic Outranking methods Formal
forms of uncertainty outranking known context, modelling
including epistemic methods seek, the objective uncertainties are may be combined impossible.
uncertainty is not the first of all, to function is well Much
primary objective of identify understood; but not fully with scenarios to exploratory
these methods. dominance when it is not, work to
Stochastic uncertainty between options outranking addressed, these explore and analyse identify
may be included as and preference methods can potential
probability relations that can identify a partial methods can only decisions under deep causes and
distributions but there be agreed ranking without effects.
is no formalism for somewhat need too many help in relation to uncertainty (Hyde et Little if any
conflicting al., 2003; Durbach,
objectives, but 2014).
robustness to
uncertainties will
need addressing
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
learning to address objectively. Thus, interactions with (Hyde et al., complex
2003). analysis.
epistemic first they problem-owners.
uncertainties (Hyde et eliminate
al., 2003; Behzadian suboptimal
et al., 2010; Gerv醩io alternatives
and Sim鮡s da Silva, before seeking a
2012). fuller ranking.
Ambiguity and
value uncertainty
may also be
quantified
(Behzadian et al.,
2010; Figueira et
al., 2016;
Govindan and
Jepsen, 2016).
Soft Elicitation (Rosenhead and Mingers, 2001; Shaw et al., 2006; Shaw et al., 2007; Ackermann, 2012;
Bendoly and Clark, 2016)
Also known as problem structuring, it is the process of asking problem owners, experts and stakeholders for the
knowledge, perceptions, beliefs, uncertainties and values that a model needs to embody before being populated with
numbers. Methods here help in problem formulation, structuring understanding: e.g. cognitive maps, soft operational
research diagrams, soft systems, prompts such as PESTLE and other qualitative tools (Prober et al., 2017; Symstad et
al., 2017). The output of soft elicitation can lead to the building of sophisticated quantitative models (Symstad et al.,
2017); and can also structure communications and deliberations with stakeholders. Exploratory data analysis and
visual analytics are also relevant. Soft elicitation has enormous advantages in setting the frame for communication
between all parties (Prober et al., 2017); there are many cases in which the clarity brought by framing the issues well
has obviated the need for formal quantitative analysis.
Examples include Adaptation Pathway planning and elicitation on managing a national park in the USA (Symstad et
al., 2017), poverty alleviation in a province in Indonesia (Butler et al., 2016), woodland landscapes in Australia
(Prober et al., 2017), as well as general considerations for contested adaptations (Bosomworth et al., 2017).
Cognitive Phase Resources required Case Studies
Sense-making and Analysing and Interpreting and
Modelling Exploring Implementing
Soft elicitation tools Soft elicitation is not The results of soft Physical resources (Bosomworth et
provide much support relevant to quantitative elicitation provide the requirements are al., 2017) [36],
to sense-making, analysis and evaluation dimensions for relatively slight: (Butler et al.,
formulating problems per se, but can support communication by sometimes post-its 2016) [37],
and identifying the exploration of identifying the issues and a white board can (Prober et al.,
relevant issues to be residuals to understand that are important to be sufficient, though 2017) [38],
addressed (Shaw et al., the quality of the stakeholders and modern visual (Symstad et al.,
2006; Shaw et al., models and detect building understanding analytics can require 2017) [39]
2007; Ackermann, further factors to be in those implementing substantial computing
2012). addressed. the policies. resource. However,
the demands on the
time of problem-
owners, stakeholders
and experts can be
significant
Uncertainties Cynefin context
Stochastic, Ambiguity Known Knowable Complex Chaotic
Epistemic, Value
Analytical
Do Not Cite, Quote or Distribute 17-47 Total pages: 156
FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
(Descriptive (Prescriptive
Modelling) Modelling)
Soft elicitation There are tools to Usually problems Problems falling Many soft Soft
falling into known
tools are catalyse deliberations contexts are well- into knowable space elicitation tools elicitation
understood and
available to and help problem- there is little need are usually well were developed tools and
to elicit or
elicit problem- owners and structure models structured and for complex processes can
to perform
owners' and stakeholders clarify analyses. problem owners' contexts: 'wicked' be used to
experts' their meanings and values are also well problems with catalyse
perceptions of contextualise their understood. deep creative
these values to the specific However, there may uncertainties: thinking
uncertainties issues being be a need to explore e.g., soft systems, about poorly
and, more considered (Keeney, error structures in cognitive maps understood
particularly, 1992). preparation to and similar tools contexts.
dependences estimate parameters to elicit
and in the models perceptions of
independences (Gelman, 2003; relationships
between them. Steed et al., 2013; between entities
Exploratory data Fekete and Primet, and problem-
analysis is also 2016). owners' and
relevant (Steed stakeholder's
et al., 2013; values (Keeney,
Bendoly and 1992; Rosenhead
Clark, 2016). and Mingers,
2001)
1
2
3
4 Figure 17.7: Decision-analytic tools used across different geo-political scales and how they contributed to decision
5 outcomes. Points comprise the type of decision-making body (C = Community; G = Government; B =
6 Business/Industry; F = Finance; N = NGO; A = All categories) coupled with the reference number in square brackets,
7 which correspond to numbered references in the case studies of Table 17.5. Colours of the points correspond to the
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1 class of decision-analytic tool as presented in Table 17.3: Bayesian (red), DMDU (Decision Making under Deep
2 Uncertainty) (brown), Decision Process Management (dark blue), Economic and Financial Methods (purple), Interval
3 Methods (light blue), MCDA � full ranking (light green) or partial ranking (dark green), Soft Elicitation (Black).
4
5
6 Many published studies on the utility of decision-analytic methods in managing climate risks are theoretical
7 and therefore it is difficult to find studies on p the value of analytic methods for underpinning final decisions
8 on climate risk adaptation. Bayesian, Deep Uncertainty and elicitation methods and tools to support decision
9 making were the most easily located classes of methods to be used in different contexts (Figure 17.6) while
10 the other classes were more oriented towards government processes. This result highlights a key gap at
11 present in the need to have real world experiences published and mapped for their utility for different tasks,
12 thereby creating a resource for policy-makers to identify suitable tools, such as in emerging communities-of-
13 practice of decision practitioners (Watkiss and Hunt, 2013; Street et al., 2019; French, 2020a).
14
15 17.3.1.3 Approaches to Support Decision-making
16
17 The common approaches presented here are not undertaken in isolation and are often combined throughout,
18 or applied at different stages of, a decision process, as illustrated in Figure 17.6.
19
20 17.3.1.3.1 Role of informal processes
21 Informal decision-making pervades decision-making in all contexts (high confidence) (Orlove et al., 2020);
22 decisions relating to climate change are affected not only by rational processes but also by many informal,
23 often behavioural responses to the situation, some of which may not require formal processes. Informal
24 processes were officially studied in only a few of the publications contributing to Figure 17.7, but all of the
25 studies have hints to informal decision-making that pervades all levels of governance. Although there are not
26 many concrete studies, citing roles of study participants can lead to a perception of a disconnect between the
27 process and the outcome that resulted (see Section 17.5.1 for enablers of success).
28
29 Generally, while governance requirements may define the processes of formal deliberations and decision-
30 making, informal deliberations will carry on in parallel, supported by social media, and these informal
31 deliberations may be used to affect the outcome of the formal processes. Stakeholders may feel excluded
32 from the formal deliberations either by governance structures or because they do not agree with their
33 representatives. Conflicting value systems may cause some stakeholders to feel side-lined, particularly if
34 some of the key decision-makers are perceived holding different personal views and interests or to have
35 engaged in political horse-trading, which connect independent decisions. There may be emotional responses,
36 driven by poor comprehension of risk and probabilistic information, and potential for group biases or
37 insularity of participants (Engler et al., 2019). Well-designed decision processes recognise the informal and
38 seek to gain information from it without introducing bias (medium confidence) (French and Argyris, 2018).
39
40 17.3.1.3.2 Stakeholder engagement
41 Stakeholder engagement has become increasingly part of climate-relevant decision processes (Orlove et al.,
42 2020). The degree of stakeholder engagement ranges from instructive, consultative to cooperative that are
43 equivalent to information exchange, influence, and partners in decision-making (Sen, 2000; Cattino and
44 Reckien, in press). Since the AR5, climate change adaptation and resilience literature has seen an increase in
45 participatory approaches that deepen engagement and overcome challenges, as well as making some
46 assessments of their effectiveness (Newton Mann et al., 2017; Wamsler, 2017; Esteve et al., 2018), including
47 structured interactions among different types of stakeholders, the use of place-based boundary organizations
48 to strengthen the interactions and heighten the awareness of the institutional context. A higher degree of
49 public participation can lead to more transformational adaptation as well as to higher ambition for local
50 mitigation (medium confidence) (17.4.4.2; Cattino and Reckien, in press). Challenges to stakeholder
51 participation are access to state-of-the-art science, capacity to recognize and respond to non-reliable or false
52 climate science information, and the removal of cognitive and other biases (high confidence) (Gorddard et
53 al., 2016; Engler et al., 2019; Fulton, 2021).
54
55 Participatory and elicitation approaches, where the concerns and involvement of a broader range of interest
56 groups and stakeholders are taken into account, can improve the effectiveness of decision-making (medium
57 confidence) (Gregory et al., 2012; Cvitanovic et al., 2019). Participatory planning includes a variety of co-
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1 generative strategies and approaches (e.g., qualitative scenario or adaptation pathway development) through
2 which goals and objectives, knowledge, and strategy implementation and evaluation can be decided
3 collaboratively between practitioners, policymaking, local interests and groups, and scientists (Butler et al.,
4 2016; Prober et al., 2017; Symstad et al., 2017). Specifically, for climate change adaptation, these decision-
5 making strategies can incorporate expert, indigenous and local knowledge (high confidence) (Cross-Chapter
6 Box INDIG; Gustafson et al., 2016). The challenge will be to bring together these different actors, as
7 stakeholders tend to act within rather than among systems and procedures, and it is important that platforms
8 are developed to integrate data effectively (Rizzo et al., 2020). Furthermore, reflexive and iterative risk
9 management may further ensure acceptance by participating groups.
10
11 Bayesian Methods are increasingly used in advancing approaches for decision-making and support in climate
12 adaptation (Sperotto et al., 2017), by being able to include stakeholder and decision-maker perceptions and
13 biases (Dias et al., 2018; Engler et al., 2019; Phan et al., 2019; Fulton, 2021) in a transparent modelling
14 environment, thereby facilitating consensus and impartiality (medium confidence) (Catenacci and Giupponi,
15 2013; Gelman and Hennig, 2017). Increasing computational efficiency means that these methods can enable
16 different approaches to be addressed and different descriptive and prescriptive models to be included within
17 a single probabilistic environment, which also can be updated in iterative processes (high confidence) (Table
18 17.5; Sperotto et al., 2017; Phan et al., 2019).
19
20 17.3.1.3.3 Scenario analyses
21 Scenarios are described in SR1.5 (IPCC, 2018a) and SRCCL (IPCC, 2019b) as a description of how the
22 future may develop based on a coherent and internally consistent set of assumptions about key driving forces
23 (e.g. rate of technological change, prices) and relationships. Scenarios are neither predictions nor forecasts
24 but are used to provide narratives and trajectories equipped with alternate outcomes. SR1.5 and the SRCCL
25 describe a range of scenarios methods and how scenarios are used to guide risk management decision
26 making. Scenario analysis includes a range of potential future conditions from low end, mid-range, to high-
27 end projections. Scenarios can also include a temporal component from short term, medium term and long
28 term, as defined in the SROCC (IPCC, 2019c).
29
30 Scenarios and pathways, combined with elicitation methods, are becoming widely used to assess adaptation
31 and resilience strategies (high confidence) (Butler et al., 2016; Prober et al., 2017; Symstad et al., 2017;
32 Lawrence et al., 2019; Phan et al., 2019; Sperotto et al., 2019; Haasnoot et al., 2020a). They can support the
33 consideration of a wide range of alternative possible futures (Catenacci and Giupponi, 2013; J鋑er et al.,
34 2018), enabling identification of potential path dependencies caused by adaptation options (high confidence)
35 (Pretorius, 2017; Haasnoot et al., 2020a). They can also increase the willingness of stakeholders to consider
36 costly actions, by placing them within broader sequences of action (limited evidence) (Barnett et al., 2014).
37 The development, consideration and understanding of scenarios can be enhanced by using visualisation tools
38 to better display storylines, enabling the discussion of alternative futures by participants in decision-making
39 processes (limited evidence) (Winters et al., 2016).
40
41 17.3.1.3.4 Evaluating trade-offs, robust decision making, and deep uncertainty
42 Trade-offs are pervasive in decision-making for climate change adaptation, including between adaptation
43 and mitigation, economic/social and environmental cost including distributional/equity considerations,
44 affordability and risk reduction, short and long-term consequences, and spatial variations (Borgomeo et al.,
45 2016; Hudson et al., 2016; Gil et al., 2018; Landauer et al., 2019).
46
47 Trade-offs are often directly compared in cost-benefit analyses which require rigorous estimation of the
48 monetized costs and benefits, where monetization is feasible and values uncontested (such as for
49 infrastructure) (high confidence) (de Ruig et al., 2019; Table 17.5). Other tools can be employed, such as
50 cost-effectiveness analysis and multi-criteria analysis in order to draw stakeholders into the process (Posner,
51 2004; Matheny, 2007; Mechler and Schinko, 2016). Stakeholder participation in measuring costs and
52 benefits and in the modelling can aid the process (Doukas and Nikas, 2020).
53
54 Logic trees include a range of decision protocols and multi-criteria rules, either based on quantitative or
55 qualitative categories (Roncoli et al., 2016), often termed multi-criteria analyses. The concept of the logic
56 tree has been increasingly applied in climate risk decision-making contexts (Nikas et al., 2018).
57
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1 Since the AR5, robust decision-making methods are increasingly used to account for deep uncertainty in
2 many climate related risks (high confidence) (Marchau et al., 2019; Table 17.5), particularly when decisions
3 need to be made well in advance of when the adaptations need to be implemented (Cross-Chapter Box.5 in
4 SROCC Chapter 1; Cross-Chapter Box DEEP in this Chapter). Reducing risk and building resilience under
5 the context of these types of wicked problems require asking "what if" questions about the future, remain
6 flexible in the face of uncertainty, and seek out policies that provide good outcomes no matter what the
7 future climate might bring (high confidence) (17.6; e.g. Larson et al., 2015; Bhave et al., 2016; Bhave et al.,
8 2018). In these cases, trade-offs can be assessed and options can be prioritized through iterative decision-
9 making processes, such as multi-criteria decision-making, robust decision-making, and dynamic adaptation
10 pathway planning (high confidence) (Table 17.5; Kwakkel et al., 2014; Kwakkel et al., 2016; Shortridge et
11 al., 2016; Lawrence and Haasnoot, 2017; Haasnoot et al., 2019; Lempert, 2019; Roelich and Giesekam,
12 2019; Haasnoot et al., 2020a). They can address limitations of data-intensive robust decision-making in
13 developing countries (Daron, 2015), can use proxy data to enable the use of robust decisions in data scarce
14 contexts (Shortridge and Guikema, 2016; Ahmad et al., 2019), incorporate multiple-objectives into robust
15 decision making (Singh et al., 2015), and pathway development supplemented by real options analysis
16 (Buurman and Babovic, 2016; Smet, 2017; Haasnoot et al., 2019; Lawrence et al., 2019). Often, there are
17 close synergies between the application of these methods and using scenario analyses (Workman et al.,
18 2021).
19
20
21 [START CROSS CHAPTER BOX DEEP HERE]
22
23 Cross Chapter Box DEEP: Effective adaptation and decision-making under deep uncertainties
24
25 Authors: Carolina Adler, Robert Lempert, Andrew Constable, Marjolijn Haasnoot, Judy Lawrence,
26 Katharine J. Mach, Simon French, Robert Kopp, Camille Parmesan, Mauricio Dom韓guez Aguilar, Elisabeth
27 A. Gilmore, Rachel Bezner Kerr, Adugna Gemeda, Cristina Tirado-von der Pahlen, Debora Ley, Rupa
28 Mukerji.
29
30 Decision relevant uncertainties for managing climate risk
31
32 Adaptation decision-making can benefit from assessments that support planning for both `what is most
33 likely' as well as for stress-testing adaptation options over a range of scenarios (Sections 11.7 and 17.3;
34 Cross-Chapter Box.5 in SROCC Chapter 1). This Cross-Chapter Box summarises how deep uncertainties
35 (Section 1.2; IPCC, 2019a) can be assessed in decision-making and addressed practically for adaptation.
36 The concept of deep uncertainty has evolved in IPCC assessments, expanding beyond a focus on reducing
37 uncertainty, to also considering a range of tools and approaches that guide robust and timely decisions to
38 address climate risks. Deep uncertainty is defined as circumstances where experts or stakeholders do not
39 know or cannot agree on one or more of the following: (1) appropriate conceptual models that describe
40 relationships among drivers in a system; (2) the probability distributions used to represent uncertainty about
41 variables and parameters; and/or (3) how to weigh and value desirable alternative outcomes (Cross-Chapter
42 Box.5 in Chapter 1; Lempert et al., 2003; IPCC, 2019a; IPCC, 2019c).
43
44 Decisions by individuals, households, the private sector, governments, and public-private partnerships are
45 generally made with partial or uncertain information. This is also the case for adaptation and development
46 decisions where there is often deep uncertainty about the impacts and the societal conditions, preferences and
47 priorities, and responses over time. Under such conditions, decision-makers employ decision processes and
48 scientific information differently from situations where most decision-relevant information is available,
49 uncontested, and confidently characterized with single joint probability distribution. Assuming scientific
50 information is certain, when it is not, is a barrier to effective communication of risks and to successful
51 decisions under uncertainty, increasing the potential for failure and regret of investments, lost opportunities,
52 and transfers of costs to future generations (Sarewitz and Byerly, 2000; Marchau et al., 2019; Sections 11.7
53 and 17.6).
54
55 Addressing deep uncertainty is contextual as it depends on the decision options available, outcomes at stake,
56 and the available scientific information (Box 1.1. in Marchau et al., 2019). The IPCC uncertainty guidance
57 note (Mastrandrea et al., 2010) addresses only the latter (see also Mastrandrea and Mach, 2011; Section
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1 1.3.4). Deep uncertainty is generally more salient when policy-relevant statements have low confidence or
2 lack relevant data or information, or in cases where significant uncertainty contributes to disagreements and
3 disputes (Sriver et al., 2018). Recent work has also included moral uncertainty (MacAskill et al., 2020) by
4 evaluating the outcomes of alternative strategies with analyses organized around different perspectives on
5 the appropriate principles of justice (Ciullo et al., 2020; Section 17.3; Jafino et al., 2021; Lempert and
6 Turner, 2021).
7
8 To better communicate deep uncertainty, WGI AR6 complements projections of likely global mean sea-level
9 change, driven by processes in which there is at least medium confidence, with projections that incorporate
10 ice-sheet processes in which there is low confidence (Section 9.6.3 in Fox-Kemper et al., 2021). The latter
11 are accompanied by storylines to highlight the physical processes that would generate extreme outcomes
12 (Box 9.4 in Fox-Kemper et al., 2021). These low-confidence projections and storylines are useful because
13 the likelihood of high-end (> 1.5 m) global mean sea level (GMSL) rise in the 21st century is difficult to
14 determine but important to consider in coastal settings (e.g., CCP2; Cross-Chapter Box SLR in Chapter 3).
15 High-end GMSL rise by 2100 could be caused by earlier-than-projected disintegration of marine ice shelves,
16 the abrupt, widespread onset of Marine Ice Sheet Instability and Marine Ice Cliff Instability around
17 Antarctica, or faster-than-projected changes in the surface mass balance and dynamical ice loss from
18 Greenland (Box TS.4 in Arias et al., 2021; Box 9.4 in Fox-Kemper et al., 2021). In a low-likelihood, high-
19 impact storyline and a high CO2 emissions scenario, such processes could in combination contribute more
20 than one additional meter of sea level rise by 2100 (Box TS.4 in Arias et al., 2021; Section 9.6.3 and Box 9.4
21 in Fox-Kemper et al., 2021). Other hazards assessed in WGI AR6 that address similar aspects that are
22 relevant for decision-making under deep uncertainty, include drought (Section 8.4.1.6 in Douville et al.,
23 2021; Section 11.6.5 in Seneviratne et al., 2021), flood (Section 8.4.1.5 in Douville et al., 2021); (Section
24 11.5.5 in Seneviratne et al., 2021), wildfire weather (days) (Section 11.8.3 and Box 11.2 in Seneviratne et al.,
25 2021), among others.
26
27 Approaches and information requirements for managing deep uncertainty
28
29 Many approaches are available for evaluating robust decisions under conditions of deep uncertainty
30 (Sections 17.3 and 11.7; Box 11.5 in Chapter 11). The majority use multiple scenarios to stress-test
31 adaptation options and explore how alternative adaptation pathways might evolve under a range of different
32 conditions (Swanson). Approaches differ in terms of their focus, types of strategies best addressed, and data
33 and other resources required (Marchau et al., 2019).
34
35 "Low regret" options are one simple and common approach to deep uncertainty (Sections 17.3 and 17.6)
36 expected to perform well over a wide range of scenarios and represent one example of robust strategies.
37 However, such options will generally be insufficient for adaptive responses to adapt over long timeframes
38 and to avoid lock-in of investments (Section 11.7; Box 11.5 in Chapter 11).
39
40 "Adaptation pathways" provide another approach for addressing deep uncertainty and staging decisions over
41 time (Haasnoot et al., 2013), by linking the choice of near-term adaptation actions with predetermined future
42 thresholds. Observation of such thresholds trigger subsequent actions in the planning or implementation
43 stages of adaptation strategies. Adaptation pathways can begin with low-regret, near-term actions that aim to
44 create and preserve future options to adjust if and when necessary. Alternative pathways can be explored and
45 evaluated to design an adaptive plan with short-term actions and long-term options.
46
47 Climate resilient development (CRD), and the pathways to it, can also involve decision making under deep
48 uncertainty. Literature assessed in sectoral and regional chapters of this report present several examples of
49 potential risks to achieving development goals under climate change, at global as well as national and local
50 levels (high confidence) (Chapter 18). Achieving CRD depends on negotiation, contestation, and
51 reconciliation of trade-offs among diverse actors, who in turn value preferred outcomes differently with
52 respect to associated climate risks and uncertainties, hence the prospect for deep uncertainty to manifest
53 (Section 18.5). Deep uncertainty also characterizes the development process itself, given that fundamental
54 changes and disruptions are part of the transformational changes required to shift towards CRDPs.
55 The "keeping options open" approach, plans by using a series of sequential decisions and actions in the near-
56 term to avoid closing off potentially promising future options (Rosenhead, 2001; Section 2.6), or by using
57 real options, take near-term actions that create currently unavailable options in the future (Kwakkel, 2020).
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1 Deep uncertainty approaches use a wide range of storylines as scenarios to test low regret options and to
2 provide information relevant for potential thresholds for use in adaptation pathways (Haasnoot et al., 2013;
3 Box 11.4; Box 11.6; Sections 11.7; 17.3).
4
5 Deep uncertainty approaches enhance the value of monitoring to detect signals of change in a timely manner
6 (medium confidence). Actionable warning can come from climate signals, and socio-economic
7 indicators/signposts, including drivers of change, vulnerability, and impacts, best suited for timely, reliable
8 and convincing signals for decision making that anticipate future changes and the need for adaptation or the
9 potential to seize opportunities (Hermans et al., 2017; Haasnoot et al., 2018; Stephens et al., 2018;
10 Oppenheimer et al., 2019). For early warning signals to be decision-relevant, they need to have institutional
11 connectivity to enable action (Haasnoot et al., 2018; Sections 1.4; 11.4; 11.7; Table 11.18) (medium
12 confidence).
13
14 Examples and case studies from across the WGII report
15
16 There are diverse examples of the practical application of deep uncertainty methods across different climate
17 change hazards in many regions of the world. For instance, low-regret options have been used to address the
18 impacts and risks of landslides and debris flows in mountains (Section CCP5.2.6). Their frequency and
19 magnitude are already widely experienced (Section CCP5.2.6) and projected to increase (Section
20 CCP5.3.2.1). However, managing these associated risks also requires joint consideration of projected
21 vulnerabilities and exposure of people and infrastructure, including the multiple and dynamic non-climate
22 related factors that are relevant for how the impacts manifest in context, such as population growth and land
23 use planning (CCP5.2.6). Here, context-specific deliberative processes are used that include scenarios to
24 guide and specify preventive measures with higher effectiveness than protective (infrastructure) measures
25 could achieve alone. Low-regret adaptation involves raising awareness and accounting for long planning
26 horizons to address the uncertainties associated with such risks, for instance in mountain regions, including
27 education (Sections CCP5.4.1; CCP5.2.6), with co-benefits such as addressing changes in water availability
28 for supply and demand (CCP5.4.1).
29
30 Adaptation pathways have been used to address SLR and changes in extreme rainfall through flood risk and
31 management (Cross-Chapter Box SLR in Chapter 3; CCP2; Sections 13.2, 11.3 and 11.7): for example,
32 adaptive plans in the Netherlands (Van Alphen, 2016; Bloemen et al., 2019), climate resilient development
33 in Bangladesh (Hossain et al., 2018; Zevenbergen et al., 2018), adaptive spatial pathways for infrastructure
34 retreat and for flood risk management in New Zealand (Lawrence et al., 2019a; Kool et al., 2020) and
35 adaptive strategies such as in the cities of London (Ranger et al., 2013; Hall et al., 2019), New York
36 (Rosenzweig and Solecki, 2014), and Los Angeles (Aerts et al., 2018a). This approach is mainstreamed into
37 guidance documents such as the Climate Risk Informed Decision Analysis (CRIDA) (Mendoza et al., 2018),
38 national guidance and policy briefs to address coastal hazards and sea-level rise planning in New Zealand
39 (Lawrence et al., 2018; Lawrence et al., 2019b), planning for sea-level rise in California (OCP, 2018), and
40 synthesis documents by the government of Canada on marine coasts (Lemmen et al., 2016). Furthermore,
41 examples from the United Kingdom, New Zealand and The Netherlands point to the development of
42 monitoring plans to detect signals for climate adaptation (Stephens et al., 2017; Haasnoot et al., 2018;
43 Bloemen et al., 2019).
44
45 Climate smart planning, with a focus on keeping options open, can play a role in reducing species extinction
46 rates (Sections 2.5; 2.6). When and where and for whom particular irreversible impacts will occur is deeply
47 uncertain, for example the extinction of a species. Even at the lowest emissions scenarios, some local species
48 will become extinct, but estimates of extinction risk are highly uncertain, typically varying by factors of 2-3
49 even for one species (Section 2.5) (medium confidence). Risks of species' extinctions are lowered by
50 reducing emissions but keeping options open for as long as possible and avoiding irreversible actions are key
51 to developing a climate-resilient adaptive pathway so that real-time climate-driven changes can inform
52 actions. Nature-based solutions (NBS) are emerging as key players for mitigation. With smart planning, NBS
53 offer approaches that not only provide substantial mitigation, but also considerable adaptation benefit to
54 biodiversity, and human health and well-being. Done poorly, such projects can result in large negative
55 impacts on humans and nature. An NBS climate-sensitive decision framework leading to "win-win"
56 solutions for mitigation and adaptation is shown in Figure 1 Cross-Chapter Box NATURAL in Chapter 2
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1 (see also Sections 2.4.2.5, 2.5, 2.6, 5.4.4.4, and 5.14.1; Cross-Chapter Box ILLNES in Chapter 2; Cross-
2 Chapter Box COVID in Chapter 7).
3
4 In view of these multiple and diverse examples, it is evident that the application of deep uncertainty methods
5 is enabling decisions to be made in a timely manner that avoid foreseeable and undesirable outcomes and
6 take opportunities as they arise (high confidence).
7
8 Prospects for adaptation decision-making
9
10 Deep uncertainty is increasingly salient for decision-making as recognition of climate-related risks and
11 related uncertainties has increased (high confidence). These risks can compound and cascade to become new
12 risks, increasing the breadth, frequency and severity of climate change impacts and the consequently
13 increasing scale and scope of adaptation (high confidence) (Cross-Chapter Box Extremes in Chapter 2;
14 Sections 1.3.1.2, 2.3, 2.5, 2.6, 11.5, 11.7, and CCP5.3.1). Waiting until uncertainties are resolved (if they
15 ever can) may leave little or no time to adapt. The lead-time for planning and implementation of adaptation
16 can take decades (Haasnoot et al., 2020b; Cross-Chapter Box SLR in Chapter 3) and socio-economic
17 developments can lock-in undesirable pathways where underlying vulnerabilities and exposure, such as
18 poverty, conflict, and their associated displacement of people, remain unaddressed (Sections 5.13.4;
19 16.5.2.3.8; Cross-Chapter Box Migrate in Chapter 7).
20
21 Overall, there is growing evidence that effective implementation of strategies developed for deeply uncertain
22 problems require adequate mandates and funding frameworks, preparedness and disaster response plans, and
23 monitoring and evaluation of the strategy outcomes, against how the future unfolds (medium confidence).
24 Collaborative and adaptive governance arrangements, and education and awareness raising, promote learning
25 environments for community engagement, and are essential for the effective implementation of robust
26 adaptation plans (medium confidence) (Sections 5.14.1; 17.3 and 11.7).
27
28 [END CROSS CHAPTER BOX DEEP HERE]
29
30
31 17.3.1.3.5 Adaptive feedback management
32 Iterative decision making requires that the implementation of adaptations are reviewed to determine whether
33 the adaptation effectively achieved the objectives, and whether adjustments or additional actions were
34 required (17.5). Adaptive feedback management is an approach to managing dynamic climate risks by
35 designing a field monitoring program to provide data to an assessment procedure which in turn advises on
36 what adjustments need to be made to a `control action', all of which are part of the adaptation to be
37 implemented (Hurlbert et al., 2019; Figure 17.6). Adaptive feedback management is more able to account for
38 the dynamic nature of risk and the future emergence of unforeseen risks because of the active design of how
39 to adjust the management approach (Dickey-Collas, 2014).
40
41 Adaptive feedback management is important for managing climate risks that fall within the Cynefin context
42 of chaos, relying on observations and indicators to learn about the system and to trigger actions (medium
43 confidence) (Helmrich and Chester, 2020). It has been a valued approach for managing wildfish fisheries in
44 many oceans (high confidence) (Fulton et al., 2019; Hollowed et al., 2020; Bahri et al., 2021), and is
45 important for responding to the challenges of climate change (high confidence) (Holsman et al., 2019;
46 Hollowed et al., 2020; Bahri et al., 2021).
47
48 While the benefits of investment in data and assessments can outweigh the costs of implementation (low
49 confidence) (Fulton et al., 2019), the implementation may take time when resources are limited, particularly
50 in developing nations, where low-cost approaches will be needed for deciding on pathways for adaptation
51 (Bhave et al., 2016; Shortridge et al., 2016).
52
53 Iterative decision making and adaptive feedback management meet when the feedback management
54 procedure is reviewed in total for its effectiveness in one of the review and adjustment iterations. At present,
55 a common approach for assessing different adaptation options and their interaction is by using, e.g.,
56 scenarios in dynamic models (Adam et al., 2014; Girard et al., 2015). An emerging field in adapting fisheries
57 to climate change is to embed the decision-making system in the scenario models in order to assess the
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1 capability of feedback management (decision-making, monitoring and capacity for adjustment of the options
2 over time) to achieve satisfactory trade-offs amongst the objectives of the different stakeholders (medium
3 confidence) (Melbourne-Thomas et al., 2017; Holsman et al., 2019; Hollowed et al., 2020). This method can
4 enable prospective evaluation of future whole-of-management scenarios described in this chapter.
5
6 17.3.2 Integration Across Portfolios of Adaptation Responses
7
8 In recent years, methods for simultaneously considering multiple societal and sectoral objectives, climate
9 risks and adaptation options have been emerging, often termed `integrated' approaches (Hadka et al., 2015;
10 Garner et al., 2016; Rosenzweig et al., 2017; Giupponi and Gain, 2017a; Stelzenmuller et al., 2018; Marchau
11 et al., 2019). Different decision-making approaches can be complementary (Kwakkel et al., 2016) and
12 multiple approaches will be needed to manage risks across sectors, in space and over short to long time
13 scales (see Section 17.6).
14
15 Higher level integration was first presented in SREX (Burton et al., 2012; Lal et al., 2012; O'Brien et al.,
16 2012) and includes concepts of planning, coordination and mainstreaming (Lal et al., 2012), consideration of
17 cross-scale dynamics and nested vulnerabilities (Klein et al., 2014), as well as decision-making across
18 governments and sectors (Denton et al., 2014; Mimura et al., 2014).
19
20 Since AR5, recognition of the importance of using integrated adaptation to improve climate risk
21 management across the nexus between many sectors and across regions has increased (high confidence)
22 (Harrison et al., 2016; Challinor et al., 2018). This was highlighted in the Special Report on Climate Change
23 and Land (Hurlbert et al., 2019); advanced planning and integration of adaptation responses are needed over
24 many levels (medium confidence) (G鰌fert et al., 2019; Section 17.6; Woodruff and Regan, 2019). The
25 complexity of managing this nexus may be compounded by the potential for antagonistic or synergistic
26 effects among and between climate impacts, and changes arising from local sectoral activities and
27 independent adaptation responses to those risks (high confidence) (Crain et al., 2008; Piggott et al., 2015;
28 Adger et al., 2018; Brown et al., 2018; Stelzenmuller et al., 2018; Simpson et al., 2021), such as the cross-
29 sectoral demands for freshwater (Xue et al., 2015; Azhoni et al., 2018). Integrated adaptation will also help
30 facilitate management of new and emerging risks, help identify when response plans may need to be changed
31 in light of the dynamics of risk over time, and help identify solutions that are less likely to constrain future
32 options for adapting to future needs (Wise et al., 2016).
33
34 Implicit to managing cross-sectoral interactions, including the nexus concept, is that the interlinkages
35 between multiple sectors are systemic, and therefore solutions to challenges arising from any one sector can
36 only be satisfactorily addressed by considering the connections to other sectors at the same time (Wichelns,
37 2017). Challenges for integrated adaptation include: (1) to sufficiently capture the complexities between the
38 nexus dimensions (Weitz et al., 2017); (2) to adequately consider the time, costs and challenges of
39 coordination and cooperation (Wichelns, 2017); (3) to consider the political economy in which progress
40 toward more integrated solutions could take place, not only account for technological requirements(Leck and
41 Roberts, 2015); (4) to obtain sufficient temporal or spatial data to capture the interactions between natural
42 and social processes (Shannak et al., 2018); (5) to connect these considerations to decision-making and
43 policy processes in order to gain insights into the conditions for collaboration and coordination across
44 sectors, including external dynamics and political and cognitive factors determining change (Weitz et al.,
45 2017); and (6) to develop a coherent framework against which to assess results and observations (Crain et
46 al., 2008; Wichelns, 2017).
47
48
49 17.4 Enabling and Catalysing Conditions for Adaptation and Risk Management
50
51 17.4.1 Introduction
52
53 The WGII AR5 identified - with high confidence - a range of factors that could enable or limit planning and
54 implementation of adaptation options and potentially their effectiveness (Klein et al., 2014; Mimura et al.,
55 2014; Noble et al., 2014). These included governance, finance, knowledge and capacity as enabling factors,
56 as well as cultural, social, political and economic differences that influence individual and collective
57 willingness and capability to act. The AR6 SRs (specifically, de Coninck et al., 2018; Roy et al., 2018;
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1 Collins et al., 2019; Hurlbert et al., 2019) reinforced the AR5 findings, further noting that the transitions
2 needed for climate resilient development would need to be supported by radical shifts in governance,
3 knowledge development, technology application, finance and economics, and social norms.
4
5 This section builds on the AR5 and AR6 SRs by reviewing new evidence on three key enablers identified in
6 the AR5: governance, finance and knowledge. The focus is on assessing new evidence on (i) understanding
7 of these enabling conditions, (ii) how they have changed on the ground, and (iii) whether these conditions
8 have enabled progress on adaptation and risk management. The section also addresses an emerging related
9 topic, the role of catalysing conditions and actors in accelerating action on climate change adaptation, such
10 as litigation on failure to adapt, understandings of urgency, and the aftermath of extreme weather events.
11 While enabling conditions are necessary for action, they are not by their presence enough; catalyzing
12 conditions emerge when game-changing circumstances become present, such as when a high-profile extreme
13 weather event occurs or when a champion drives change in an organisation.
14
15 17.4.2 Enabling Condition 1: Governance
16
17 Governance is an inclusive concept of the range of means for deciding, managing, implementing, and
18 monitoring climate change responses. It can involve the contributions of various levels of government
19 (global, international, regional, sub-national and local) along with those from the private sector, of
20 nongovernmental organisations, and of civil society. The importance of supportive governance
21 arrangements is reiterated widely across regional and sectoral chapters in this report, in multiple different
22 contexts (very high confidence).
23
24 17.4.2.1 Legal, Policy and Regulatory Instruments
25
26 17.4.2.1.1 Climate legislation
27 Legal systems play an important governance role in facilitating responses to climate change across all levels
28 of society (high confidence) (Ruhl, 2010; McDonald and Styles, 2014; Mehling, 2015). Laws can facilitate
29 climate action in multiple ways, including through: (i) mandating and guiding the behaviour of governance
30 structures and actors, (ii) fostering coordination between different levels of government, (iii) enforcing
31 climate responses, (iv) its symbolic value as well as (iv) aligning scientific evidence and societal norms
32 (Mehling, 2015; Scotford et al., 2017). Laws also can embed climate change planning within the
33 administrative structure of a state rendering policy less vulnerable to revocation (Scotford et al., 2017).
34 Extensive revision to laws has occurred in the last decade: a survey of 164 countries showed that over 1200
35 climate-related national laws and policies have been published with approximately 44% being acts of
36 parliament (Nachmany et al., 2017).
37
38 National climate change laws are important for transposing ratified international commitments into domestic
39 regimes, such as the Paris Agreement and the Convention on Biodiversity, as well as voluntary agreements
40 such as the Sendai Framework for Disaster Risk Reduction. In turn, the enactment of domestic laws can
41 yield useful experiences and foster engagements that positively influence and support the development of
42 international commitments (Townshend and Matthews, 2013; Mehling, 2015). Strong and consistent
43 regulatory frameworks also support the flow of climate finance to developing countries that have such
44 frameworks (Nachmany et al., 2017). The successful implementation of national and sub-national climate
45 change and related policies and strategies are often contingent upon the underlying legislative framework
46 empowering, mandating or guiding their review, implementation and enforcement (Averchenkova and
47 Matikainen, 2017; Scotford et al., 2017) (medium confidence).
48
49 Existing legal systems also pose potential barriers to adaptation, as described in Chapter 9 (Africa) and
50 Chapter 8 (Poverty, Livelihoods and Sustainable Development). Laws may reinforce governance
51 arrangements and regulations state that do not support responses to climate change, and exacerbate existing
52 vulnerabilities and inequalities (Craig, 2010; Arnold and Gunderson, 2013; Wenta et al., 2019). In such cases
53 laws may require review and revision or replacement, and at the same be written in ways that foster adaptive
54 management (Craig, 2010; Ruhl, 2010; Cosens et al., 2017).
55
56 Even though there is no agreed definition of or typology for climate change laws (Mehling, 2015), studies
57 have tended to classify climate change laws as being `framework' or `sectoral' (see Table 17.6 for
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1 examples). Framework laws offer a comprehensive, unifying basis for climate change policy, addressing
2 multiple aspects or areas of climate change mitigation or adaptation (or both) in a holistic and overarching
3 manner (Townshend et al., 2011; Fankhauser et al., 2014; Nachmany et al., 2015; Clare et al., 2017b); they
4 are powerful levers for setting national and sub-national agendas, creating climate change institutional
5 structures, enabling policy implementation, and driving the passage of additional sectoral legislation and
6 regulations (Clare et al., 2017b). Prior to 2010, national framework laws tended to have a mitigation focus
7 while more recent laws or amendments thereto have an increased adaptation focus (Rumble, 2019b). No
8 evidence indicates whether general or specific framework laws yield better outcomes; however, reviews of
9 more recent examples of framework laws in Africa suggest a trend towards more specificity in the required
10 content of adaptation strategies and duties (Rumble, 2019b).
11
12 A sectoral approach to climate change legislation grafts climate-related provisions into existing laws, such as
13 environmental impact assessment, flood insurance and infrastructure planning, collectively creating an
14 aggregated legal landscape (Townshend et al., 2011; Gerrard and Fischer, 2012; Nachmany et al., 2015;
15 Scotford et al., 2017; Rumble, 2019a). This approach is particularly relevant to adaptation challenges which
16 intersect with numerous bodies of law that are dedicated to other societal concerns (Gerrard and Fischer,
17 2012). However, integrating such considerations can be challenging in certain areas of law, particularly those
18 relating to property rights, water rights and endangered species protection (Gerrard and Fischer, 2012). The
19 incorporation of adaptive management principles (including monitoring, periodic evaluation, and response
20 modification) within existing laws can enhance their enabling role and foster greater resilience (Godden,
21 2012; Arnold and Gunderson, 2013; McDonald and Styles, 2014).
22
23 The legal regime for adaptation is too embryonic for assessment of good practice design and content,
24 although similarities can be seen in the framework laws and draft bills across several countries. Some studies
25 highlight the importance of domestic `whole of legal system' analysis prior to developing of modifying law.
26 This can identify the range of existing legislative instruments that can directly intersect with climate change,
27 along with related contextual factors such as national circumstances, governance frameworks, and political
28 and economic realities as well as national administrative culture (Scotford et al., 2017). This helps any new
29 climate change laws to be absorbed into, and harmonise with, the established legal system of each country
30 (Scotford et al., 2017). Efforts are underway to assist countries in such assessments and the identification of
31 areas for legislative reform, for example through the Commonwealth and UN Environment's Law and
32 Climate Change Toolkit. Similarly, databases such as the Grantham Research Institute on Climate Change
33 and the Environment and the Sabin Center on Climate Change Law are expanding the knowledge base of
34 national climate legislation developments.
35
36
37 Table 17.6: Selected examples of framework and sectoral law approaches adopted by different nations that represent a
38 variety of regional contexts.
Example Legal Description References
Approach
United Kingdom Framework Provides for development of climate change impact (Averchenkova
Climate Change Act reports and programmes for adaptation. Dedicated et al., 2021)
2008 institutional structure with advisory body, adaptation
planning provision, reporting/information obligations,
climate change mainstreaming, climate change trusts, or
financial arrangements.
Kenya Climate Change Framework Modelled on the United Kingdom Climate Change Act. (Rumble,
Act 2016 Provides for development of climate change impact 2019b)
reports and programmes for adaptation. Dedicated
institutional structure with advisory body, adaptation
planning provision, reporting/information obligations,
climate change mainstreaming, climate change trusts, or
financial arrangements.
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Mexican General Law Framework Imposes positive duties upon government to implement (Averchenkova
on Climate Change "adaptation actions" - conservation, sustainable use and and Guzman
2012 rehabilitation of beaches and coasts; water programmes Luna, 2018)
for watersheds; the establishment of protected areas and
biological corridors; the development of risk atlases;
human settlement and urban development programmes;
and prevention programs targeting diseases exacerbated
by climate change. Includes development of economic
instruments including fiscal incentives, credits, bonds,
civil liability insurance, market-based instruments.
New Zealand Sectoral Incorporates adaptive management principles by (Godden, 2012)
Exclusive Economic regulating the issuance of marine consents with
Zone and Continental conditions allowing change based on ecological change
Shelf (Environmental and indicators.
Effects) Act 2012
Seychelles Sectoral Provides for the establishment of a dedicated trust fund (Etongo et al.,
Conservation and for conservation measures and climate change 2021)
Climate Adaptation adaptation measures.
Trust of Seychelles Act
18 of 2015
Commonwealth of Sectoral Promotes disaster recovery and resilience building. (Government of
Dominica Climate Establishes the Dominica Climate Resilience Policy the
Resilience Act 16 of Board and sets out its functions and duties. Requires the Commonwealth
2018 development of a Climate Resilience and Recovery of Dominica,
Plan. 2018)
Swedish National Sectoral Amends Sweden's Planning and Building Act (2010: (Government of
Strategy for Climate 900) by requiring Municipalities to assess the risk of Sweden, 2017)
Change Adaptation damage to the built environment from climate risks well
(Government as how such risks may change in the future; requires
Proposition detailed plans for measures to address land permeability
2017/18:163) when issuing a land permit; adopts the Swedish National
Climate Strategy into law.
Argentinian Glaciers Sectoral Provides for minimum budgets to protect the national (Warner et al.,
Preservation Law N glacial water sources that supply the Mendoza oasis. 2019)
32.016 (2010) Establishes that all of Argentina's glaciers and its
periglacial environment are to be protected, irrespective
of size.
Netherlands Delta Act Sectoral Protects the Netherlands from risks such as sea level rise (Van Alphen,
on Water Safety and and extreme rainfall. Establishes a Delta Programme to 2016)
Fresh Water Supply secure fresh water supply and address climate risks/sea
level rise; a Delta Fund to operate the Programme and a
Commissioner.
1
2
3 17.4.2.1.2 Climate change policies, strategies and plans
4 Climate change policies and plans are important in the translation of national commitments and legal
5 requirements into specific on the ground strategies and guidelines, which enable actions across multiple
6 spheres and scales of government and non-government institutions and actors.
7
8 Substantial developments in adaptation policy have occurred since AR5 (high confidence). Perhaps the most
9 significant is the Nationally Determined Contributions (NDCs) required under the Paris Agreement, where
10 184 out of 197 parties to the UNFCCC have already submitted their first plans (UNDP and UNFCCC, 2019).
11 The NDCs have allowed countries to articulate their priorities and ambition with respect to climate action
12 and it has been suggested that these can in turn lead to cascading policies (and laws) that drive and enable
13 adaptation and climate risk management. Analysis of the first NDCs submitted in the lead up to and after the
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1 Paris Agreement showed that adaptation priorities were more often articulated by developing countries and
2 least developed countries, while developed countries and emerging economies focused mostly on mitigation
3 (Pauw et al., 2019). As of 2019, over 90 developing nations are at various stages of preparing National
4 Adaptation Plans and 112 nations have indicated their intention to revise their NDCs for the 2020 update
5 (UNDP and UNFCCC, 2019).
6
7 Several other international agreements including the Sendai Framework for Disaster Risk Reduction and the
8 UN Agenda 2030 Sustainable Development Goals have had significant impacts on the adaptation and risk-
9 management decision-making processes. For example, the Sendai Framework articulates the need for
10 improved understanding of disaster risk in all its dimensions of exposure, vulnerability and hazard
11 characteristics; accountability for disaster risk management; preparedness to "Build Back Better";
12 recognition of stakeholders and their roles; mobilization of risk-sensitive investment to avoid the creation of
13 new risk resilience of health infrastructure, cultural heritage and workplaces; strengthening of international
14 cooperation and partnership, and risk-informed donor policies and programs, including financial support and
15 loans from international financial institutions.
16
17 Specific adaptation policies have been formulated at national, regional/state and local levels across 68
18 countries and 136 coastal cities (Olazabal et al., 2019a). At the national level, the quantity and complexity of
19 adaptation policies have increased since AR5, with most policies coming into force since 2009 (Nachmany
20 and Setzer, 2018). Adaptation is addressed in the executive climate policies of at least 170 countries
21 (Nachmany et al., 2019a). Documented sub-national adaptation policies are more prevalent in developed
22 countries and emerging economies, as compared with low- and middle-income ones (Olazabal et al., 2019b).
23 For example, by 2017 26% of large and medium-sized European cities had an adaptation plan or a joint
24 adaptation-mitigation plan in place (Reckien et al., 2018a).
25
26 Adaptation policies often comprise multiple goals and instruments, which develop over time, especially
27 where jurisdiction over policy issues is shared among agencies or levels of government (R韔 and Howlett,
28 2013). The increase in the number and complexity of policy instruments across geared towards adaptation
29 raises questions of coherence and alignment between the selected policy mixes and their effectiveness
30 (England et al., 2018; Ranabhat et al., 2018; Lesnikowski et al., 2019).
31
32 Evaluation of national adaptation plans (NAPs) has only recently been undertaken. Woodruff and Regan
33 (2019) compared national adaptation plans from 38 countries and concluded that most were strong in
34 identifying vulnerabilities and identifying potential adaptation options but were weaker in articulating
35 implementation pathways and monitoring of progress; plans written by multi-agency teams were nearly
36 always of higher quality. Garschagen et al. (2021) showed that while most NAPs consider future changes in
37 climate hazard, many do not consider how vulnerability and exposure might change, concluding that this
38 limits the potential effectiveness of the plans. Morgan et al. (2019) showed that NAPs that are consistent
39 with the Paris Agreement can enable development pathways that promote synergies between environmental,
40 social, and economic goals.
41
42 17.4.2.1.3 Impact of legal and policy instruments
43 Commitment to act, and guidance on how to do so, from international and national governance levels can
44 drive national and sub-national adaptation (Reckien et al., 2013; Heidrich et al., 2016; Reckien et al., 2018a).
45 For example, more local plans have been developed in European countries where it is obligatory for local
46 municipalities to develop climate change plans (Reckien et al., 2018a). Local government have also drawn
47 on non-binding national climate frameworks, as well as international frameworks (such as European law) or
48 international networks (such as Global Covenant of Mayors for Climate and Energy) to guide their actions
49 (Reckien et al., 2013; De Gregorio Hurtado et al., 2015; Reckien et al., 2015; Heidrich et al., 2016; Reckien
50 et al., 2018a).
51
52 However, a national framework is not always sufficient to trigger climate change action on the lower level,
53 in particular when the national guiding document fails to clearly formulate how it should be used and
54 "translated down" to lower governance levels (De Gregorio Hurtado et al., 2015). Guidance on how to apply
55 a national framework at lower governance levels can assist in their uptake.
56
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1 In the case of climate change legislation, research on the impact of adaptation laws is limited, save for a few
2 studies (Averchenkova and Matikainen, 2017), because many framework laws, particularly those with more
3 of an adaptation focus, have only been published recently (Rumble, 2019b). Reviews of the implementation
4 of the risk assessment and adaptation components of the UK's Climate Change Act 2008 suggest that they
5 had a weaker implementation record compared to mitigation provisions (Fankhauser et al., 2018), potentially
6 because implementation of adaptation is more complex as compared to mitigation as shown for the local
7 level (Reckien et al., 2019). However, the UK Act is considered to have made action on climate change more
8 predictable, more structured and more evidence-based (Averchenkova et al., 2021).
9
10 There are numerous examples of regulatory and project-based innovations by local governments. Their
11 impact, however, is uneven, with much depending on the implementation capacity of local governments and
12 other socio-institutional barriers, including those relating to mandate and joint project implementation, cross-
13 departmental working, planning cycles, concerns relating to legal liability and compensation, political
14 appetite and cost (Godden, 2012; Taylor, 2016a). Notwithstanding implementation challenges, evidence is
15 emerging that overarching framework laws play a foundational and distinctive role in supporting effective
16 climate governance, including adaptation governance (Fankhauser et al., 2018) and are drivers of subsequent
17 activity (Townshend et al., 2011; Fankhauser et al., 2014; Clare et al., 2017b), especially when formulated
18 with clear guidance for all related actors, including lower level of governance (De Gregorio Hurtado et al.,
19 2015). This may explain the rapid increase in both local and national climate change laws, now with an
20 increased emphasis on regulatory provisions to increase resilience and reduce vulnerability.
21
22 17.4.2.1.4 Regulations and standards
23 The presence and articulation of regulations and standards that address climate risk, such as building codes
24 and land use zoning are key enabling factors for effective decision-making (Kim et al., 2020). Regulations
25 and standards provide a framework for common understanding of when and under what conditions action
26 should be taken specifically in relation to the construction and maintenance of the built environment,
27 infrastructure and environmental and social practice (Grynning et al., 2020). Regulations and standards for
28 climate action emerge primarily from two settings. First, as an addition or augmentation to existing
29 regulations and standards that emerged initially to address existing potential climate extremes and stresses
30 (e.g. size of culverts in response to maximum rainfall and runoff conditions). And second, new regulations
31 and standards that were developed in direct response to new or emergent climate risks (e.g. regulations in
32 response to new presence of mean monthly high tide flooding) (Qiao et al., 2018). Commonly agreed upon
33 social norms and conventions also can be described as regulatory and providing a set of standards.
34
35 The regional and sectoral chapters of this report provide significant evidence of how regulations and
36 standards enhance or hinder opportunities for climate risk management and adaptation. Relevant regulations
37 and standards are especially evident in the oceans and coastal domains (Chapter 3 and CCP2, in cities and
38 infrastructure (Chapter 6), and the water (Chapter 4) and food sectors (Chapter 5). Europe and North and
39 South America (Chapters 12, 13 and 14) have the most frequent documented occurrences of examples of
40 regulations and standards. Regulations and standards focused on building codes to protect against extreme
41 event and loss, water regulations and agreements to protect water supply and lessen drought impacts, and
42 health codes to limit heat exposure are the most frequent examples of such practices. Deficiencies of
43 regulations and standards have been noted with respect to their capacity to manage species migrating from
44 climate change, and to provide opportunities for transformative adaptation. The evidence from the sectors
45 and chapters illustrate that more comprehensive regulations and standards lead to positive adaptation
46 outcomes.
47
48 17.4.2.1.5 Environmental and social governance
49 Environmental and social governance refers to voluntary or non-legally required actions taken by
50 participating parties to achieve a commonly defined goal (Bodin, 2017; DeCaro et al., 2017; Partzsch, 2020).
51 While not explicitly described in the sectoral and regional chapters of this report, the maintenance and
52 exercise of environmental and social governance decision-making strategies do enable adaptation practice
53 and have become especially important when formal legal and policy regimes are not yet present. As formal
54 regulation promotes clear and common understanding of climate risks and mechanisms to develop context
55 specific appropriate solutions, voluntary code-making and self-regulation can forestall the need for legal
56 action or can function as precursors to the formulation and implementation of legislation, laws, and
57 regulations.
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1
2 Social and environmental governance long has been presented within climate risk decision-making, although
3 more typically in the domain of climate mitigation (Wright and Nyberg, 2016; Vandenbergh and Gilligan,
4 2017). Corporate climate decision-making emphasizes the importance of profit motives in shaping decisions
5 however reputational factors as appropriate environmental stewards also can be important when linked to
6 sensitivity of other stakeholders such as investors, lenders, customers, and employees (Vandenbergh and
7 Gilligan, 2017). Pulver (2011) notes that climate issues influence corporate decision-making more strongly
8 in organizations that are networked with other organizations that also consider these issues and through
9 direct experience with climate-related events and associated organizational learning.
10
11 Since AR5, more case studies of social and environmental governance within the domain of climate
12 adaptation have become evident, especially within the context of adaptive management experimentation
13 (Vella et al., 2016; Beunen and Patterson, 2019; Bl黨dorn and Deflorian, 2019). Environmental and social
14 governance strategies for climate adaptation are diverse and reflect context specific conditions of the
15 decision-making process including the role of the state, the individual and private interests,
16 formality/informality, social responsibility, sources of financing, and transparency. Environmental and social
17 governance enables the testing and definition of implementation solutions, enhancing the opportunities for
18 defining successful adaptation (Surminski, 2013). Several models and approaches to adaptive governance to
19 promote adaptation and resilience in response to extreme weather events have been observed. These include
20 polycentric and multi-layered institutions, participation and collaboration, self-organization and networks,
21 and learning and innovation (Djalante et al., 2011).
22
23 The effectiveness of social and environmental governance varies by sector. For example, in the private
24 business sector, Arag騨-Correa et al. (2019) assess the effects of mandatory and voluntary regulatory
25 pressure on firms' environmental strategies. In summary, they find that analyses of the effects of voluntary
26 pressure demonstrate that by themselves they are unlikely to bring about significant improvement in
27 environmental outcomes. Professional organisations, however, have made progress in addressing sectoral
28 standards relative to the adaptation process. This includes the development of new industry guidelines,
29 codes, standards, specifications, in addition to the implementation of infrastructure inventories that
30 incorporate evaluation of vulnerabilities and identification of priority at-risk areas (Chapter 14). Voluntary
31 pressures by themselves are not likely to result in positive outcomes and instead should be coupled with
32 mandatory regulatory pressure to achieve the environmental response desired (Bianco, 2020).
33
34 Since AR5, another key development in environmental and social governance has been the establishment of
35 the Task Force on Climate-related Financial Disclosures (TCFD), which aimed to develop guidelines for
36 companies to voluntarily report the financial implications of two broad categories of climate risk: the
37 transition risks of shifting to a lower-carbon economy and the physical risks of climate change itself (TCFD,
38 2017). As of 2019, ~1,340 companies with a market capitalization of USD12.6 trillion and financial
39 institutions responsible for assets of USD 150 trillion have expressed support for the TCFD (TCFD, 2020).
40 An analysis of reports to the TCFD in 2016 showed that 83% of companies report on physical risks of
41 climate change, and of these 82% reported on strategies to adapt to some of the identified risks (Goldstein et
42 al., 2019). The same analysis also noted that: (i) the total of estimates of assets at risk were two orders of
43 magnitude lower than generally accepted estimates of total financial risk; (ii) a minority of companies
44 consider risks outside of their own operations or in their value chains; (iii) most underestimate or do not
45 estimate the costs of adaptation; and (iv) many assume linear impacts and responses, neglecting the potential
46 for tipping points or acceleration in risk and potentially transformative adaptation requirements. At this
47 stage, TCFD has influenced many companies' thinking and comprehension of physical climate risk, but it
48 appears too early to assess whether this has driven substantive responses to manage these risks.
49
50 17.4.3 Enabling Condition 2: Finance
51
52 Finance has long been recognised as an important enabling and catalysing factor for adaptation, climate
53 resilient development and climate risk management. In Chapter 17, financing for adaptation and climate risk
54 management is covered in the extended cross chapter box, Financing for Adaptation and Resilience (FAR),
55 below. The Cross-Chapter Box aims to highlight key emerging evidence on financing of adaptation,
56 covering both public and private sources and instruments. Climate finance is also covered in a dedicated
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1 chapter in the WGIII Report (WGIII AR6 Chapter 15), and readers should refer to this Chapter for a more
2 comprehensive assessment of this subject from both a mitigation and adaptation perspective.
3
4
5 [START CROSS-CHAPTER BOX FINANCE HERE]
6
7 Cross-Chapter Box FINANCE: Finance for Adaptation and Resilience
8
9 Authors: Mark New, Madeleine Rawlins, David Viner, Charlene Watson, Lily Burge, Lionel Mok, Lauren
10 Arendse, Vita Karoblyte, Liane Schalatek and Neha Rai and Baysa Naran, So-Min Cheong, Nicoletta
11 Giulivi.
12
13 Introduction
14 This Cross-Chapter Box reports on: (i) new evidence on the finance needed for adaptation and resilience, and
15 uncertainties in these estimates; (ii) the emerging public and private climate finance architecture; (iii) the
16 status of financing for AR, including sources, total flows, regional and sectoral distributions, (iv) equity
17 considerations; (iv) opportunities and challenges for financing adaptation and resilience during and after the
18 COVID-19 pandemic. This Cross-Chapter Box does not focus on finance for mitigation, which is covered in
19 WGIII Chapter 15, nor the economic damages of climate change or financial aspects of Loss and Damage,
20 which are covered in Cross-Working Group Box ECONOMIC (Chapter 16) and Cross-Chapter Box LOSS
21 (this chapter), respectively.
22
23 Successive reports of the IPCC (Vellinga et al., 2001; Mimura et al., 2008; Yohe et al., 2008; Klein et al.,
24 2014) and the AR6 Special Reports have noted the importance of finance as an enabler for adaptation, across
25 both developed and developing nations. While various definitions for climate finance have been suggested,
26 and the UNFCCC has yet to have an agreed definition, the IPCC (see Glossary) defines climate financing as
27 "the financial resources devoted to addressing climate change by all public and private actors from global to
28 local scales, including international financial flows to developing countries to assist them in addressing
29 climate change. [It] aims to reduce net greenhouse gas emissions and/or to enhance adaptation and increase
30 resilience to the impacts of current and projected climate change. Finance can come from private and public
31 sources, channelled by various intermediaries, and is delivered by a range of instruments, including grants,
32 concessional and non-concessional debt, and internal budget reallocations". Adaptation and resilience are
33 often used interchangeably in climate finance discussions, although adaptation is a process while resilience
34 (to climate risk) is the ability to progress towards desired outcomes in face of impacts from a changing
35 climate (see Section 1.2.1).
36
37 [START BOX CROSS-CHAPTER BOX FINANCE.1 HERE]
38
39 Box Cross-Chapter Box FINANCE.1: The 100 Billion Climate Finance Commitment to Developing
40 Countries
41
42 At COP16 in Copenhagen in 2009, developed country Parties to the UNFCCC committed to a goal of jointly
43 mobilizing USD 100 billion per year by 2020 to address the climate change needs of developing countries
44 (UNFCCC, 2009). This was in response to a threat by developing countries to walk out of the negotiations,
45 as they perceived developed country support to be lagging and lacking in ambition (Roberts et al., 2021).
46 The commitment was formalized in the Cancun Agreements (Decision 1/CP.16) in 2010 and was reaffirmed
47 as a key element of the Paris Agreement in 2015 (Article 9, paragraph 4). At COP26 in 2021, formal
48 deliberations will begin on a new climate finance goal to be adopted in 2025; the current USD 100 billion
49 target will serve as the annual minimum until 2025 (Chhetri et al., 2020).
50
51 The "100 Billion" does not represent the total need to respond to climate change in developing countries, nor
52 the global cost across all countries, as is sometimes interpreted in the literature and media. As shown below
53 in this Cross-Chapter Box, the estimated cost of adaptation for developing countries ranges 15-411 billion
54 USD per year for climate change impacts out to 2030, with the majority of estimates being well above 100
55 billion.
56
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1 Proposed sources for the developed country commitment included "a wide variety of sources, public and
2 private, bilateral and multilateral, including alternative sources of finance" and several instruments
3 including grants and loans. Nonetheless, there remain differences of opinion on the types of finance that
4 should count towards this goal, with several issues identified (high confidence) (Bodnar et al., 2015;
5 Bhattacharya et al., 2020; Roberts et al., 2021), including: (i) counting non-grant finance, such as market and
6 concessional loans (public and private), where developing countries ultimately have to repay the investment;
7 (ii) what is counted as "climate" by different funders, especially when climate is not the prime objective; (iii)
8 the extent to which some funds are "new and additional" rather than a repurposing of development finance.
9
10 Progress towards the 100 Billion target has shown an upward trend over the last several years (high
11 confidence), but will fall short in 2020, even when the most generous criteria are included (high confidence).
12 In 2017/18, the most recent year for which data have been comprehensively analysed, estimates using
13 different (but overlapping) data sources and methods were in the range 48-75 billion USD per year,
14 compared to 45-75 in 2015/16 and 41-52 in 2013/14 (Carty et al., 2020; SM17.3; CPI, 2020; OECD, 2020;
15 UNFCCC, 2020). The distribution between adaptation and mitigation has remained strongly weighted
16 towards mitigation, although the proportion allocated to adaptation has increased from 17-25% in 2013/14 to
17 19-30% in 2017/18 (high confidence). One analysis that excludes debt repayments indicates that the debt-
18 adjusted flows are about half the total flows reported above, of which circa 31-33 % was for adaptation
19 between 2015/16 and 2017/18 (Carty et al., 2020).
20
21 [END BOX CROSS-CHAPTER BOX FINANCE.1 HERE]
22
23 Adaptation Finance Needs
24
25 Estimates of global, regional, or national finance needs for adaptation and resilience vary depending on both
26 analysis approach, the level of climate change, and the geographic and sectoral scope of analysis (high
27 confidence) (UNEP, 2016; Chapagain et al., 2020; UNEP, 2020). Recent estimates have adopted one of main
28 approaches: (i) aggregation of individual case studies, along with scaling to generate global or regional costs;
29 (ii) analysis of NDC adaptation cost estimates (Weischer et al., 2016; Hallegatte et al., 2018); (iii) integrated
30 assessment model simulation of impacts and adaptation costs (Markandya and Gonz醠ez-Eguino, 2019;
31 Chapagain et al., 2020).
32
33 All approaches suffer from limitations that can cause both over and underestimates, including incomplete
34 coverage of sectors and risks, inability to account for autonomous/unreported adaptation; incorrect cost
35 estimations; soft and hard limits to adaptation; balance between adaptation, mitigation, and residual cost;
36 benefits and co-benefits on cost; and learning and innovation as climate change progresses (UNEP, 2020).
37 Global or developing region estimates based on scaling NDC data is particularly uncertain, as most NDCs
38 did not specify how the costs were calculated. Also, scaling from a relatively small set of NDCs with costs to
39 the global scale is not particularly robust, indicating a need for more transparency and better guidance for
40 calculating adaptation costs (Watkiss et al., 2015b; Zhang and Pan, 2016; Hallegatte et al., 2018; AfDB,
41 2019).
42
43 Most estimated of adaptation cost in the literature are for developing countries. Chapagain et al. (2020)
44 assessed various estimates of adaptation for developing countries, under different emissions scenarios for
45 2030 and 2050. The median estimates (and range) from these studies are 127 (15-411) and 295 (47-1088)
46 billion USD per year for climate change impacts out to 2030 and 2050, respectively (see SM17.3). All but
47 one study report adaptation costs higher than the 70-100 billion estimated in 2010 by the World Bank (World
48 Bank, 2010).
49
50 The cost of adaptation for developed countries is rarely reported; most literature either reports a global cost
51 or developing country costs, or costs for a specific country or sector. Baarsch et al. (2015), using an IAM,
52 report adaptation annual costs (2012 prices) in 2030 (and 2050) as 272 (660) billion globally and 205 (521)
53 in developing countries only under the RCP2.6 scenario, indicating that developed country costs are around
54 25 (21) % of total cost.
55
56
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FINAL DRAFT Chapter 17 IPCC WGII Sixth Assessment Report
1 Figure Cross-Chapter Box FINANCE.1: Comparison of recent studies that estimated developing country
2 adaptation costs in billion USD (in 2005 prices) per year, for 2030 and 2050. Figure based on Chapagain et al. (2020).
3 Major studies are World Bank (2010), Chapagain et al. (2020), UNEP (2016), Baarsch et al. (2015) and Markandya and
4 Gonz醠ez-Eguino (2019). The solid-coloured bars are based on RCP2.6 and pattern-bars are based on RCP 8.5; the
5 width of the bars indicates the range of estimates (maximum and minimum) produced in each study.
6
7
8 In addition to global estimated adaptation costs, there are many studies that have focused on specific regions,
9 countries, or sectors, such as estimated adaptation cost for coastal environments, water related infrastructure,
10 urban infrastructure, agriculture, energy (UNEP, 2014; Watkiss et al., 2015b; UNEP, 2016). Examples of
11 such estimates are reported in various chapters in this report and summarised in SM17.3.
12
13 Estimating the benefit of adaptation, in terms of damage avoided, remains challenging. For example, Ricke
14 et al. (2018) show that the social cost of carbon (monetary damage per tCO2 emitted) varies by up to two
15 orders of magnitude depending on country, socio-economic scenario, damage function, total GHG forcing,
16 and local climate change. In addition, non-monetary benefits such as cultural identity, sacred places, human
17 health and lives are often ignored (Tschakert et al., 2017; Serdeczny, 2019; see also Cross-Working Group
18 Box ECONOMIC in Chapter 16; Cross-Chapter Box LOSS, this chapter). Recent case studies and global
19 level analyses continue to support the conclusion in IPCC AR5 WGII Chapter 17 (Chambwera et al., 2014)
20 that the benefits of adaptation generally remain larger than the costs (medium confidence), but the cost-
21 benefit ratio varies widely by context and assumptions (OECD, 2015; Global Commission on Adaptation,
22 2019; WRI, 2019)
23
24 The Climate Finance Landscape
25
26 The adaptation and resilience finance landscape spans multiple sources, intermediaries, instruments, and
27 recipients, operating across global to sub-national scales (Buchner et al., 2019; Carter, 2020; Watson and
28 Schalatek, 2021). Public finance is provided by national and subnational governments and distributed
29 directly by government or intermediaries such as development finance institutions and climate funds, either
30 nationally or internationally. Private finance comes from five main sources: commercial financial
31 institutions (banks), institutional investors (including asset managers, insurance companies, and pension
32 funds), other private equity (venture capital and infrastructure funds), non-financial corporations such as
33 renewable energy or water companies, individual households and communities. Across these different
34 sources, the main instruments used are grants, concessional debt, market debt, internal budget allocation,
35 including personal savings in households, and insurance. Public and private sources of funding can be
36 blended into a single instrument, for example for insurance where public funds provide capital for both
37 sovereign catastrophe instruments and microinsurance (Jarzabkowski et al., 2019) or for concessional loans.
38 Similarly, public finance is often ultimately be derived from commercial debt instruments such as bonds.
39
40 International public climate finance
41
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1 International public climate finance flows are realised through bilateral and multilateral channels (Watson
2 and Schalatek, 2021) where contributions to these channels are received from Annex II and non-Annex I
3 countries (UNFCCC SCF, 2018; Buchner et al., 2019). Annex II countries contribute as part of their
4 commitments in the Paris Agreement, while non-Annex I countries commit climate finance through these
5 channels on a voluntary basis (Pickering et al., 2015; Roberts and Weikmans, 2017; Egli and St黱zi, 2019).
6 Bilateral intermediaries include development cooperation agencies and national development banks. These
7 institutions often have long standing development-cooperation experience, and offer climate change projects,
8 facilities and financial instruments based on their differing mandates, structures and priorities (Atteridge et
9 al., 2009; Buchner et al., 2019).
10
11 Multilateral channels include the UNFCCC financial mechanisms, such as the Green Climate Fund, and the
12 multilateral development banks (MDBs), such as the World Bank. Both pool contributor resources before
13 committing such resources for climate change projects and programmes. Funding through multilateral
14 channels promotes recipient country engagement in the governance and prioritisation of funding decisions,
15 with concurrent processes in the multilaterals often existing to support country ownership of funded climate
16 action (Ciplet et al., 2013; Ha et al., 2016).
17
18 There are five multilateral climate change funds of the UNFCCC and Paris Agreement financial
19 mechanisms. There are further multilateral climate change funds that are not governed by the UNFCCC or
20 Paris Agreement, the largest of which is the World Bank governed Climate Investment Funds (Watson and
21 Schalatek, 2021). Some of the major multilateral climate change funds have been established with a specific
22 focus on adaptation, while some bilateral donors have thematic or sectoral priorities. Multilateral climate
23 change funds operate through accredited implementing entities. These have historically been multilateral in
24 nature, such as the development banks, but recent years have seen a rise in the accreditation of national and
25 regional institutions (UNFCCC SCF, 2018). In addition to programming funds from external sources, such
26 as through the multilateral climate change funds, the MDBs also raise and programme their own climate
27 finance (UNFCCC SCF, 2018; MDBs, 2019).
28
29 Several major multilateral climate change funds work through grant-only programmes, whereas others
30 include concessional loan, equity and guarantee instruments. The broader suite of instruments used by the
31 MDBs includes grant, investment loan, equity, guarantee, line of credit, policy-based financing and results-
32 based financing (MDBs, 2019).
33
34 Public funding of a concessional nature that flows from Annex II to non-Annex I countries supports research
35 and capacity building and can also facilitate private finance flows into climate action, with the intention to
36 avoid creating a high debt burden in developing countries, in response to climate impacts for which they
37 have little historic responsibility (Watson, 2016; Carter, 2020; Schalatek, 2020). Less concessional public
38 finance flows include other official flows that are not developmental in nature and can be trade related,
39 including for example export credits.
40
41 Critiques of the public climate finance architecture are aimed at the overlapping mandates of the institutions
42 programming climate finance, particularly the multilateral climate funds, and the challenges in accessing
43 funding (Nakhooda et al., 2014; Amerasinghe et al., 2017; Pickering et al., 2017). However, Pickering et al.
44 (2017) further note that institutional fragmentation of climate finance could result in more flexibility,
45 resilience and innovation. There have also been important governance changes leveraged by some of these
46 funds and instruments, such as integration of gender considerations into projects (Schalatek, 2020).
47
48 Private financing of adaptation and resilience
49
50 There is an increasing focus on the role of the private sector to support large-scale financing of adaptation
51 and resilience (UNEP, 2016; UNEP, 2018). To date it has been difficult to track adaptation and resilience
52 finance within the private sector (UNEP, 2016)as it is either not disclosed or not easily identifiable, since it is
53 often built into capital and operating expenditure and is not a standalone investment. Several private
54 mechanisms are emerging as important sources of climate finance (Gupta et al., 2014; Eccles and Krzus,
55 2018; Miller et al., 2019).
56
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1 Green, social impact and resilience bonds are similar to traditional bonds - fixed-income financial
2 instruments raised on commercial markets by companies, governments or financial institutions - but the
3 proceeds are used to fund activities that have positive environmental, social or climate benefit (Tuhkanen,
4 2020). Green bonds align to voluntary principles, such as the Green Bond Principles set out by the
5 International Capital Market Association, the Climate Bonds Initiative's Climate Resilience Principles
6 (Sartzetakis, 2020)Given the voluntary nature and lack of standardization of green bond principles, there are
7 concerns around their additionality and there is also a lack of data on how green bonds contribute to a scaling
8 up of green projects (Dupre et al., 2018).
9
10 Green bond annual issuance reached 260 billion in 2019 (CBI, 2020) but, as of 2018, only 3-5% (USD 12
11 billion) of green bond total proceeds can be explicitly traced to climate resilience related efforts (CBI, 2019).
12 Examples of AR focused bonds include those issued by Fiji in 2017, dedicating 91% of spending to
13 adaptation and resilience (Shukla and Peyraud, 2017; Ministry of Economy, 2019), and by the European
14 Bank for Reconstruction and Development's 2019 Climate Resilience Bond for USD 700 million to finance
15 climate resilient infrastructure, commercial operations, agriculture or ecological systems (EBRD, 2019).
16
17 Dedicated investment vehicles are equity funds that are created to invest in products and services that
18 enhance resilience and reduce risks. An example is the Climate Resilience and Adaptation Finance and
19 Technology Transfer Facility that is proposed as a USD 500 million private equity fund to invest in
20 companies providing climate resilience solutions for developing countries. Initial funding has been provided
21 by donors (Miller et al., 2019).
22
23 Balance sheet finance occurs when an entity directly invests in resilience and adaptation rather than as a
24 separate project. This source of funding may be from exiting reserves, reallocation from other budget lines,
25 or via external commercial finance, but the investment is financed by the firm rather than as a separate
26 project (Gupta et al., 2014; Buchner et al., 2019).
27
28 Insurance can play an important role in managing residual climate risks at any given level of adaptation, but
29 insurers can also be important r risk assessment and risk reduction as part of any insurance package
30 (Jarzabkowski et al., 2019; Chapter 11.3.8.3). While traditional indemnity insurance is important for repair
31 and rebuilding of damaged property and infrastructure, parametric insurance has become increasingly
32 popular for supporting rapid post-disaster responses such as drought, hurricane damage and flooding.
33 Examples include sovereign insurance facilities such as African Risk Capacity and the Caribbean
34 Catastrophe Risk Insurance Facility (Broberg, 2019) as well as weather-index insurance targeted at
35 individuals, especially in agriculture (Greatrex et al., 2015; Isakson, 2015; Surminski et al., 2016; Jensen and
36 Barrett, 2017; Fischer, 2019). The role of insurance as a climate risk management option, as well as
37 limitations, is covered in more depth in Section 17.2 and Cross-Chapter Box LOSS (this Chapter).
38
39 Mainstreaming physical climate risks and resilience in the private sector
40
41 The data on tracked climate finance and green bond issuance for adaptation and resilience both show a
42 substantial gap between the adaptation needs and the finance deployed. Scaling up these instruments is
43 unlikely to close this gap given the challenges with financing adaptation projects, particularly from the
44 private sector. There is therefore a need for more systematic action to manage climate risks and mainstream
45 climate change considerations (Miller et al., 2019).
46
47 The financial case for mitigation investment can often be demonstrated through revenues from, for example,
48 the sale of renewable electricity. On contrast, the benefits from investment in adaptation and resilience are
49 typically considered in terms of avoided losses and cost benefit ratios. For example, the Global Commission
50 on Adaptation (2019) estimates that the overall rate of return on investments in improved resilience is very
51 high, with benefit-cost ratios ranging from 2:1 to 10:1, and in some cases even higher.
52
53 The private sector is becoming increasingly aware of the need to assess physical climate risks to avoid the
54 long-term risks to assets and enhance climate resilience. The task force on climate-related financial
55 disclosures (TCFD) is likely to create additional pressure from investors for companies to identify, manage
56 and reduce risks from climate change (Eccles and Krzus, 2018; ERM and CBEY, 2018; Tuhkanen, 2020).
57
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1 A key factor for the impact of the TCFD on mainstreaming of physical climate risks and demonstrating the
2 case for investment in adaptation and resilience will be how investors systematically incorporate physical
3 climate risks, adaptation, and resilience into their investment decisions. The Coalition for Climate Resilient
4 Investment (DFID et al., 2019) was established to look at this from the private sector viewpoint and is
5 working to systematically incorporate resilience into cash flow modelling and asset valuation practices, so
6 that investors may quantify the investment in resilience for an asset and the benefits associated with reduced
7 costs and more reliable revenue streams.
8
9 Recent trends in climate finance flows
10
11 Considerable progress has been made in tracking climate finance since AR5, but substantial gaps remain,
12 especially regarding domestic public finance and private sector balance sheet investment in adaptation
13 (Section 17.5.1.5; CPI, 2020; Richmond et al., 2020). The best documented information comes from
14 international climate funds, which provide detail at the project level. Most bilateral and multilateral
15 investment institutions report on whether debt, grants and other instruments are for climate projects, but with
16 less detail. Private finance is harder to track, as reporting is voluntary; even for green bonds, where
17 certification identifies the range of sectors a bond aims to cover, reporting of how the bond is spent is
18 infrequent.
19
20 The Climate Policy Initiative (CPI) has been tracking climate finance since 2009, allowing for trends to be
21 assessed; however, trends reported are a function of both real changes in finance and changes in methods and
22 information sources (Richmond et al., 2020). Total climate finance tracked by CPI has increased from USD
23 364 billion per year in 2010/11 to 579 billion in 2017/18 (SM17.3). Tracked finance remained relatively
24 constant from 2010/11 to 2013/14 but has increased steeply in more recent years. The proportion of finance
25 allocated to adaptation has remained small throughout, between 4 and 8% (high confidence); a further 1-2%
26 of global finance has been classified as "multiple-objectives". The large majority of tracked adaptation
27 finance is from public sources (high confidence), with only 2% coming from private sources in 2017/18
28 (CPI, 2020). This is at least partly because of the difficulty in demonstrating financial (as opposed to public
29 good and avoided damages) return on investment for adaptation.
30
31 The majority of the most recently (2017/18) tracked adaptation and multiple-objective finance was supplied
32 through public donors, largely through grants, concessional and non-concessional instruments (Figure
33 FAR.1). Most finance (44,1%) was spent transregionally (allocated in specific projects to recipients in more
34 than a single region). For regionally specific funding, Sub-Saharan Africa, South Asia, along with the Latin
35 America & Caribbean region, received the largest gross amounts, although Oceania has received the greatest
36 per-capita funding. The largest proportion of AR funding has been allocated to increasing the resilience of
37 infrastructure, energy, and the built environment, followed by agriculture, forestry, and natural management,
38 and then water and wastewater.
39
40 Across financial instruments, sub-Saharan Africa received the highest relative proportion through grants
41 (38%), followed by the Latin America & Caribbean region (23%), with other non-OECD regions receiving
42 between 16 and 10% (SM17.3). Concessional debt as a proportion of the regional total varies from 84% in
43 South Asia to as low as 29% in Latin America & Caribbean, which has the highest proportion of non-
44 concessional debt (48%).
45
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1
2 Figure Cross-Chapter Box FINANCE.2: The flow and distribution of globally tracked adaptation and resilience
3 finance in 2018 from different sources, through different instruments into different sectors and regions. Each strand
4 shows the relative proportion of finance flowing from one category to another (for example from private or public
5 sources to different instruments). Categories from left to right are: (a) whether the finance is solely for adaptation or for
6 adaptation and other objectives, including mitigation (multiple objectives); (b) whether the finance comes from public
7 or private sources; (c) the financing instrument; (d) the broad sectoral allocation; (e) the geographical distribution of
8 funding (proportion of total in % and per-capita allocation). Based on data collated by CPI (2020).
9
10
11 The importance of public and private finance for adaptation and resilience
12
13 Adaptation finance provided by international public mechanisms remains the core source of tracked flows in
14 support of adaptation and resilience to developing countries (Micale et al., 2018; UNEP, 2018), although
15 these public funds alone are insufficient to meet rapidly growing needs and constitute only a minority share
16 of all public climate finance flows (UNEP, 2016; Global Commission on Adaptation, 2019).
17
18 Public mechanisms can play a role in leveraging private sector finance for adaptation by addressing real and
19 perceived regulatory, cost and market barriers through blended finance approaches, public-private
20 partnerships or innovative financial instruments and structuring in support of private sector requirements for
21 risk management and guaranteed investment returns (Pillay et al., 2017; Miller et al., 2019).
22
23 There is growing agreement on the sectors (such as infrastructure, agriculture or water management) and
24 approaches (contingency finance or insurance) where private sector adaptation investments alone, or
25 leveraged by public mechanisms, might be best targeted, such as by reducing the risk of providing financial
26 services for adaptation investments to domestic micro-, small-, and medium enterprises or agricultural
27 smallholders, many of them women (Biagini and Miller, 2013; Chambwera et al., 2014; Pauw et al., 2016;
28 Global Commission on Adaptation, 2019; Miller et al., 2019; Resurrecci髇 et al., 2019; Richmond et al.,
29 2020). A remaining open question is how to allocate limited public adaptation funds in a way that is
30 equitable, effective and efficient between mobilizing private investments and safeguarding adequate financial
31 support for necessary adaptation efforts, such as the provision of public goods, which the private sector will
32 not invest in (Fankhauser and Burton, 2011; Abadie et al., 2013; Baatz, 2018; Omari-Motsumi et al., 2019).
33
34 Many adaptation interventions in the most vulnerable countries, communities and people provide no
35 adequate financial return on investments and can therefore can only be funded with highly concessional
36 public finance. Grant support is most appropriate for measures such as capacity building, planning, public
37 policy and regulatory reforms, disaster risk management and response, community engagement or support
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1 for social safety nets, and for addressing social vulnerabilities, including poverty or gender inequality, which
2 constrain adaptation (Grasso, 2010a; Pillay et al., 2017; Agrawal et al., 2019; Buchner et al., 2019).
3 Access to adequate adaptation grant finance is further constrained because several public mechanisms
4 provide grants only for the additional costs of adaptation measures compared to a development baseline in
5 the absence of climate impacts. Calculating the incremental costs of adaptation measures imposes additional
6 time and resource burden on the most vulnerable recipients, who are often faced with data gaps or technical
7 capacity constraints (Chambwera et al., 2014; GCF, 2018; UNEP, 2018; Omari-Motsumi et al., 2019).
8 An exact delineation of respective costs for adaptation and development components is difficult and might
9 be unsuitable as many adaptation measures are intrinsically linked to development. It may also prevent
10 realizing necessary synergies between both (McGray et al., 2007; Smith et al., 2011; Denton et al., 2014;
11 Resch et al., 2017; Micale et al., 2018).
12
13 Equality and fairness in climate finance
14
15 Climate finance literature recognises that poor and least developed households, communities, and countries
16 are most affected and marginalized by climate change, and least responsible for its causes, but receive
17 relatively little financial support for adaptation (Chapter 15; Chapter 8; Olsson et al., 2014; Rozenberg and
18 Hallegatte, 2015; Hallegatte et al., 2016; Rai and Fisher, 2017; Shakya and Byrnes, 2017).
19
20 Several factors affecting fair and just financing in developing countries have been identified in recent
21 literature (Klein et al., 2014; Colenbrander et al., 2018; Mfitumukiza et al., 2019; Khan et al., 2019a; Doshi
22 and Garschagen, 2020). First, financing is skewed in favour of mitigation, and therefore towards fast-
23 growing upper- and middle-income countries offering the biggest gains in emission reductions, especially in
24 Southeast Asia, but also in Sub-Saharan Africa (Rai et al., 2016). Further, as much of current finance uses
25 debt-based instruments, mitigation projects are further preferred as returns are more assured (Lee and Hong,
26 2018; Carty et al., 2020).
27
28 Second, the requirement of many funders for readiness and fiduciary capacity means that LDCs have been
29 less able to access finance, despite many support mechanisms being offered. Additionally, geopolitical
30 preferences of some countries mean that some developing countries are preferred to others for bilateral
31 funding (Doshi and Garschagen, 2020). This is exacerbated for private sector investment, where lower credit
32 ratings make finance more expensive, and increasing understanding of exposure to physical climate risks
33 could lead to `capital flight' from most vulnerable countries (Global Commission on Adaptation, 2019;
34 Miller et al., 2019; Cooper, 2020).
35
36 Third, within climate-vulnerable countries, very little is channelled to local communities who need it most;
37 the few analyses available suggest that less than 10% of total climate finance supports decentralized actions
38 (Rai et al., 2016; Soanes et al., 2017). Reasons include: (i) lack of consideration of procedural equity in
39 programme design (Grasso, 2010b; Wang and Gao, 2018; Venn, 2019; Khan et al., 2019a); (ii) finance being
40 managed by multilateral implementers, rather than agencies that are closer to local communities; (iii) the
41 higher transaction costs of decentralized projects in low-income communities reduce their attractiveness to
42 funders as well as the ability of local organisations to meet the fiduciary standards (Fonta et al., 2018;
43 Omari-Motsumi et al., 2019).
44
45 It has been proposed that, as middle-income countries can leverage mitigation finance from the private
46 sector, targeting scarce public finance towards LDCs and SIDS may be necessary to ensure sufficient funds
47 reach these countries (Steele, 2015). Matching domestic climate spending with international support is one
48 way to ensure LDCs get the funds they need (Grasso, 2010b; Bird, 2014). Targeting specific marginalized
49 communities and women within countries can also help make climate finance more effective and fairer, such
50 as the Asian Development Bank's efforts to make lending portfolios more inclusive and pro-poor (ADB,
51 2018).
52
53 Post-COVID recovery packages, debt relief and finance for adaptation and resilience
54
55 Recent literature has highlighted the opportunity that COVID recovery packages offer for environmentally
56 sustainable, low carbon and climate resilient economic growth (Forster et al., 2020; Hepburn et al., 2020;
57 Hanna et al., 2021). Assessment of whether this is indeed happening is limited, although the few available
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1 studies suggest that that this opportunity is not being realised in many nations (O'Callaghan and Murdock,
2 2021; VIVID Economics, 2021). One study of the G20 and 10 other nations suggested that stimulus
3 packages would have net negative environmental impact in two thirds of these countries (VIVID Economics,
4 2021), while another showed that around half of G20 recovery investment targeted at energy has had gone
5 towards fossil fuels, rather than to cleaner energy sources (Dibley et al., 2021).
6
7 Concerns have also been raised about the interactions between debt service, COVID economic recession and
8 post COVID recovery in developing countries (Simmons et al., 2021; Volz et al., 2021). Debt service grows
9 as a proportion of national budget during recession, reducing scope for investment in recovery, is a self-
10 reinforcing cycle. It has been suggested that linking debt-relief to Paris-aligned objectives can act as an
11 additional source of climate finance (Fenton et al., 2014). The G20 has begun addressing this debt crisis
12 through its Debt Service Suspension Initiative and the Common Framework for Debt Treatments (IMF,
13 2020). It has been suggested that these initiatives could be expanded to prioritize climate-focused debt-relief
14 instruments and to include more countries (Steele and Patel, 2020; Volz et al., 2021). If debt-relief is used to
15 invest in national instrument for green and inclusive recovery, national ownership of the use of the finance
16 can occur, avoiding some of the negative connotations of historical debt restructuring (Volz et al., 2021).
17
18 [END CROSS-CHAPTER BOX FINANCE HERE]
19
20
21 17.4.4 Enabling Condition 3: Knowledge and Capacity
22
23 17.4.4.1 Overview of Knowledge Systems
24
25 AR5 emphasized the importance of knowledge systems as an enabling condition for decision making, as did
26 earlier ARs, all of which include a focus on the policy-relevance of knowledge (Section 1.1.4) First
27 introduced in IPCC reports in AR4, the term "knowledge system" is used extensively in AR5 and the SRs.
28 The discussion below follows a widely-cited definition of knowledge systems as sets of interacting "agents,
29 practices and institutions that organize the production, transfer and use of knowledge" (Cornell et al., 2013:
30 61). This definition emphasizes the social nature of knowledge and the importance of the link between
31 knowledge and action, rather than presenting knowledge simply as information about past, present and future
32 states of the world which can be of use to decision-makers.
33
34 This definition of knowledge systems indicates the importance of capacity--the ability and the motivation to
35 use knowledge for action--since capacity is an important feature which allows knowledge systems to
36 function. Capacity is a necessary enabling condition for knowledge to be put to use in adaptation activities
37 (high confidence), as shown across sectors such as water (Section 4.5.2), food security (Sections 5.12.3,
38 5.14.3), cities and settlements (Sections 6.4.2, 6.4.4) and health and well-being (Sections 7.1.3, 7.2.6), and
39 across regions, including Africa (Sections 9.13.1, 9.14.5), Asia (Sections 10.3.6, 10.4.4) and North America
40 (Section 14.4.5).
41
42 Some research on knowledge systems retains the earlier attention to information as a resource for decision-
43 makers. A major focus, discussed elsewhere in this chapter, has been increasing the precision about the
44 certainty, likelihood, and the confidence with which certain statements are made in relation to underlying
45 evidence (See Cross-Chapter Box DEEP in this Chapter). This topic, which was first introduced in AR4,
46 advanced significantly in AR5 (Mach et al., 2017).
47
48 In addition to these characteristics of information, the social and organizational aspects of knowledge
49 systems have also been the subject of recent research. One strand of this discussion emphasizes the
50 distinctiveness of different knowledge systems, often focusing on three types of knowledge: scientific,
51 Indigenous, and local, and the latter two sometimes grouped as "traditional" knowledge (See Cross-Chapter
52 Box INDIG in Chapter 18). This strand emphasizes the specific forms of knowledge production and
53 circulation in each type. Another strand of discussion emphasizes the networks of interactions between
54 different groups. This strand follows the influential "Knowledge systems for sustainable development" (Cash
55 et al., 2003), which was cited in Chapter 2, 7 and 8 in WGII AR5; Cash et al. (2003) emphasizes the usability
56 and acceptability of scientific knowledge, and underscores the relations between knowledge producers and
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1 users. The discussion in 17.4.4 on knowledge as an enabling factor integrates these two strands of discussion
2 of knowledge systems.
3
4 It was well established in AR5 and SRs that a component of knowledge systems for good climate decision-
5 making is the production of "information on climate, its impacts, potential risks, and vulnerability" which
6 can "be integrated into an existing or proposed decision-making context" (Jones et al., 2014: 200). Also
7 important are two other components of knowledge: of response options and knowledge of other enabling
8 conditions, particularly governance and finance, which were mentioned less frequently and more indirectly
9 in AR5 and SR1.5, SROCC and SRLAND. Decision-makers assess the feasibility of different alternatives
10 (see Cross-Chapter Box FEASIB) and develop strategies for the implementation and modification of the
11 alternative, requiring a level of knowledge of the governance, policy and finance landscapes at national
12 (Tanner et al., 2019; Lopes et al., 2020; Roberts et al., 2020) and international scales (Woodruff, 2018).
13
14 Examples of the importance of these other two components--knowledge of response options and knowledge
15 of enabling conditions--are provided by networks of cities, including internal institutional networks (Aylett,
16 2015), intermunicipal networks (e.g., those supported by ICLEI- Local Governments for Sustainability and
17 the international United Cities and Local Governments (UCLG) network), transnational municipal networks
18 (e.g. 100 Resilient Cities, Asian Cities Climate Change Resilience Network (ACCCRN), and city to city
19 regional transdisciplinary learning networks (Ndebele-Murisa et al., 2020). These networks generate and
20 exchange knowledge which can be critical to decision-makers for understanding and evaluating the
21 feasibility of different response options, identifying synergies across sectors, and mainstreaming adaptation
22 to climate change (Haupt et al., 2020). However, the question of how to finance such network activities
23 remains under-studied (Bracking, 2021; See Box 17.3).
24
25 In addition to these general considerations of knowledge systems, research since AR5 has contributed to the
26 understanding of specific types of knowledge. Scientific knowledge is thoroughly discussed in Chapter 1,
27 especially in Section 1.3 Understanding and Evaluating Climate Risk, which shows recent advances in the
28 well-established IPCC categories of observation of past conditions and model-based projections of future
29 conditions. We add here a consideration of a new area within scientific knowledge, artificial intelligence,
30 which offers new methods for producing information that can be incorporated into knowledge systems.
31
32 Applying Artificial Intelligence (AI) to climate change is predominantly in the area of climate modelling and
33 forecasting, inclusive of weather extremes (Monteleoni et al., 2013; Jones, 2017; Huntingford et al., 2019).
34 Recent efforts conceptualize the potential uses of AI for mitigation and adaptation (Rolnick et al., 2019;
35 Cheong et al., 2020b) in addition to forecasting (Rolnick et al., 2019; Chattopadhyay et al., 2020; Cheong et
36 al., 2020b; Prabhat et al., 2021). There are very few cases to assess AI applications in these domains given
37 that AI is a new field for climate change impact and adaptation. To this date, sectoral applications of AI
38 relevant to climate change adaptation and risk reduction mainly have advanced in the areas of crop yields,
39 early warning system, and water management.
40
41 These sectoral advances using AI employ various learning techniques inclusive of supervised and
42 unsupervised learning, multimodal learning and transfer learning techniques to generate more accurate
43 predictions than afforded by traditional climate projection methods (Cheong et al., 2020b; Camps-Valls et
44 al., 2021). AI applications use finer resolution data such as sub-daily weather-related data, remote and
45 wearable sensor data, text data, and real-time survey data. They are fed into neural networks and
46 semi/unsupervised learning to configure detailed and more precise predictions of climate change impact on
47 crop yields (Crane-Droesch, 2018), early warning (Moon et al., 2019), impact of extreme heat on older
48 adults (Cheong et al., 2020a), poverty in Africa (Oshri et al., 2018), and multi-scale water management
49 combining blockchain technology with remote water sensors (Lin et al., 2018).
50
51 Indigenous knowledge and local knowledge are thoroughly covered in SROCC (Abram et al., 2019; IPCC,
52 2019c; IPCC, 2019e-b) and in Section 1.3.3. We here add relevant points to decision making, and an
53 additional form of knowledge, practitioner knowledge.
54
55 Indigenous knowledge and local knowledge are gaining recognition at multiple scales (Kleiche-Dray and
56 Waast, 2016; David-Chavez and Gavin, 2018; Nakashima et al., 2018). Of note is their association with
57 ecosystem-based adaptations, showcasing the long-term place-based knowledge of Indigenous peoples
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1 (Johnson et al., 2015; Walshe and Argumedo, 2016; Carter, 2019; Mazzocchi, 2020). These knowledges and
2 practices can be an important enabling condition in decision making processes, complementing scientific
3 information by identifying impacts (Fern醤dez-Llamazares et al., 2017; Katz et al., 2020), emphasizing
4 values to consider (Huambachano, 2018), offering solutions (Chanza and de Wit, 2016; Cuaton and Su,
5 2020; Orlove et al., 2020), guiding land use and resource management (Brond韟io et al., 2021) and filling
6 gaps in scientific knowledge (Hiwasaki et al., 2014; Audefroy and S醤chez, 2017; Makondo and Thomas,
7 2018; Son et al., 2019; Latulippe and Klenk, 2020; Wheeler et al., 2020a).
8
9 Practitioner knowledge--the pragmatic, practice-based knowledge that comes from the regular exercise of
10 craft or professional work--was also acknowledged briefly in AR5 (Jones et al., 2014) and treated
11 significantly in SROCC (Abram et al., 2019). Practitioner knowledge resembles local knowledge in that it is
12 acquired through participation in activities, and yet it differs from local knowledge, which is often place-
13 based and tied directly to specific landscapes and communities. Local knowledge typically covers a variety
14 of environmental domains. Practitioner knowledge may be shared with people in different locations and is
15 often more focused on a narrower set of work activities. Recent calls have recommended bringing
16 practitioners more fully into the IPCC assessment process, to promote more effective decision-making
17 (Howarth et al., 2018).
18
19 Practitioner knowledge makes significant contributions to decision-making by broadening the range of
20 alternatives which are considered and by bringing in understandings of systems to the selection and
21 implementation of alternatives. Such knowledge is applicable to a large number domains, including
22 biodiversity management (Teng� et al., 2014; Rathwell et al., 2015), and natural hazard risk management in
23 urban settings, as reported in Denmark (Madsen et al., 2019), the US (Matsler, 2019), Canada (Yumagulova
24 and Vertinsky, 2019), Mexico (Aguilar-Barajas et al., 2019), and the Caribbean (Ramsey et al., 2019). Other
25 contexts, all at regional scales, include watershed management in Peru (Ostovar, 2019), livestock
26 management in Finland (Rasmus et al., 2020), agricultural adaptation in a context of water scarcity in Iran
27 (Zarei et al., 2020), and the water-energy nexus in the US (Gim et al., 2019).
28
29 Literature indicates the importance of effective governance for promoting integration of local and
30 practitioner knowledge with scientific knowledge (high confidence). This integration is most extensive, and
31 promotes a wider consideration of alternatives, where governance arrangements promote ongoing exchanges
32 of information and discussion of solutions, whether through formal mechanisms such as regional committees
33 (Gim et al., 2019; Ostovar, 2019; Rasmus et al., 2020; Zarei et al., 2020) or informal mechanisms such as
34 personal networks and local discussion groups (Madsen et al., 2019; Yumagulova and Vertinsky, 2019).
35 Where such arrangements are absent, practitioner knowledge is side-lined from the formulation and
36 implementation of decisions (Aguilar-Barajas et al., 2019; Matsler, 2019; Ramsey et al., 2019).
37
38 17.4.4.2 Co-production and Other Composite Knowledge Systems
39
40 There is strong evidence that composite knowledge systems � characterized by interactions between the
41 producers and potential users of climate change information -- can help facilitate climate-related decision
42 making (Prokopy and Power, 2015; Richards, 2018; Ramsey et al., 2019). Several institutional forms and
43 structures have been created to link scientific knowledge, Indigenous knowledge, and local and practitioner
44 knowledge, to climate change decision making.
45
46 17.4.4.2.1 Co-production
47 The co-production of knowledge by different actors provides important avenues for exchanging and
48 integrating climate-related knowledge in decisions made across society (high confidence). Though many
49 definitions of co-production have been offered in recent years (Bremer and Meisch, 2017; Vincent et al.,
50 2018; Bremer et al., 2019; Harvey et al., 2019a), most describe a set of individuals or organizations who
51 work together to generate a set of products that entail new knowledge products and that guide action (Miller
52 and Wyborn, 2020). Some major forms of co-production include action research (Baztan et al., 2017;
53 Laursen et al., 2018; Zanocco et al., 2018a), trans-disciplinarity (Howarth and Monasterolo, 2016; Wamsler,
54 2017; Lanier et al., 2018; Scott et al., 2018; Knapp et al., 2019; Young et al., 2019a); rapid assessment
55 processes (Atkinson et al., 2018b); and participatory integrated assessments (Howarth et al., 2018; Krkoska
56 Lorencov� et al., 2018; Bitsura-Meszaros et al., 2019; Carter et al., 2019a; Cremades et al., 2019; Leitch et
57 al., 2019; Mart韓ez-Tag黣馻 et al., 2020; Section 17.3.1.3.1).
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1
2 Co-production promotes iterative dialogue, experimentation, the tailoring of knowledge to context, needs
3 and priorities, and learning, often promoting integration of Indigenous knowledge, local knowledge and
4 practitioner knowledge with scientific knowledge (high confidence). It generally entails long-lasting ties and
5 fully inclusive partnerships between different parties (Kench et al., 2018). Governance measures and
6 adequate financing can act as enablers of such co-production. This integration is most extensive, and
7 promotes a wider consideration of alternatives where governance arrangements promote ongoing exchanges
8 of information and discussion of solutions, whether through formal mechanisms such as regional committees
9 (Gim et al., 2019; Ostovar, 2019; Rasmus et al., 2020; Zarei et al., 2020) or informal mechanisms such as
10 personal networks and local discussion groups (Madsen et al., 2019; Yumagulova and Vertinsky, 2019).
11 Where such arrangements are absent, practitioner knowledge is side-lined from the formulation and
12 implementation of decisions (Orleans Reed et al., 2013; Aguilar-Barajas et al., 2019; Matsler, 2019; Ramsey
13 et al., 2019).
14
15 An important mechanism of co-production is the boundary organization, a knowledge-producing
16 organization comprised of individuals who reflect different disciplines or knowledge systems and who
17 represent different activities, sectors or forms of governance (Blades et al., 2016; Graham and Mitchell,
18 2016; Guido et al., 2016; Jeuring et al., 2019; Serrao-Neumann et al., 2020; Zarei et al., 2020). Boundary
19 organizations themselves can be linked into boundary chains (Lemos et al., 2014; Meyer et al., 2015;
20 Kirchhoff et al., 2015a; Pretorius et al., 2019; Daniels et al., 2020). When individuals and organizations from
21 different disciplinary backgrounds and missions coordinate their activities informally, the resulting ties have
22 been termed `knowledge networks' (Ziaja and Fullerton, 2015; Brugger et al., 2016; Guido et al., 2016;
23 Davies et al., 2018; Klenk, 2018; Muccione et al., 2019; Ziaja, 2019). When such networks interact with
24 each other, the resulting associations have been called "communities of practice," which can work to
25 collectively shape information to shared contextual circumstances (Orsato et al., 2018; Wang et al., 2019b).
26
27 There is extensive evidence that co-production can generate useful climate knowledge (Djenontin and
28 Meadow, 2018; Bisbal, 2019; Ryan and Bustos, 2019; Hewitt et al., 2020; Jack et al., 2020; Lavorel et al.,
29 2020; Ruiz-Mall閚, 2020) and that it can increase the likelihood that knowledge will be used in decision-
30 making (Vogel et al., 2016; Prokopy et al., 2017; Skelton et al., 2017; Sylvester and Brooks, 2020). Co-
31 production is not without its costs, since it requires more time, money, facilitation expertise and personal
32 commitment from participants than more conventional modes of knowledge production (Lemos et al., 2018;
33 Sletto et al., 2019; Wamsler et al., 2019; Blair et al., 2020). Some research has shown ways to decrease the
34 costs of co-production for participants, such as funding and time to enable and sustain interactions and to
35 build trust and legitimacy, or to create boundary organizations (Young et al., 2016; Klenk et al., 2017).
36
37 Co-production is supported by project cycles that provide for the involvement of stakeholders from the
38 outset (Daly and Dilling, 2019; Brady and Leichenko, 2020); flexible research agendas that do not assume a
39 climate related question (Daniels et al., 2020); support for interactivity and reflexivity (Araujo et al., 2020),
40 and, institutionalizing incentives which address the different values, norms, perceptions and work patterns of
41 scientists, policy-makers and civil society representatives (Cvitanovic et al., 2015; Vincent et al., 2015;
42 Bruno Soares and Dessai, 2016; Singh et al., 2017; Djenontin and Meadow, 2018; Norstr鰉 et al., 2020;
43 Turnhout et al., 2020). Certain roles, such as policy entrepreneurs (Tanner et al., 2019), embedded
44 researchers (Pretorius et al., 2019) and knowledge brokers (Cvitanovic et al., 2015), can facilitate co-
45 production.
46
47 17.4.4.2.2 Climate services
48 Climate services (refer to CWG Box on Climate Services) can be important enablers of climate risk
49 management, provided they are credible, relevant and usable (high confidence), and will become
50 increasingly important as human influence on weather and climate extremes grows across all regions
51 (Chapter 11; Fischer et al., 2021; IPCC, 2021). Climate services are more effective and more widely used
52 when they are tailored to specific decisions and decision-makers (high confidence). Sustained iterative
53 engagement between climate information users, producers and translators can improve the quality of the
54 information and the decision-making and avoid maladaptation (medium confidence).
55
56 Historically, climate services have been organized by climate information providers, based in
57 meteorological, hydrological, and agricultural faculties and services, serving to improve through climate risk
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1 management, including the use of historical information, monitoring, seasonal forecasts, and long-term
2 climate projections (Hewitt et al., 2012; Blome, 2017; Bessembinder et al., 2019; Vaughan et al., 2019b).
3
4 Recent research on climate services shows that transdisciplinary knowledge co-production is a key enabler,
5 starting to shift emphasis from the creation of climate services products to climate services processes
6 (Vincent et al., 2018; Carter et al., 2019b; Daniels et al., 2020), potentially increasing uptake and
7 sustainability (Norstr鰉 et al., 2020). This shift is a result of the recognition of benefits which a co-
8 production approach can offer, in addition to the provision of information; information; these additional
9 benefits include building confidence, capacities, learning, knowledge, social capital, institutional capacity,
10 stakeholder relationships, social networks, beneficial management practices, and strengthened institutions
11 (Bruno Soares and Dessai, 2016; Djenontin and Meadow, 2018; Bremer et al., 2019).
12
13 Cross-Chapter Box 12.2 in WGI AR6, Climate information for climate services, shows that users are widely
14 distributed across civil society. Relevant users of climate services include humanitarian organizations
15 (Coughlan de Perez and Mason, 2014; Harvey et al., 2019b), government offices (Mahon et al., 2019),
16 international agencies (Perkins and Nachmany, 2019), and the private sector (Beckett, 2016; Hudson et al.,
17 2019). Climate services currently exist at local, national, regional, and international scales, at time scales
18 which range from sub-seasonal to decadal and longer (White et al., 2017; Hewitt et al., 2020) and in a range
19 of different sectors (Bruno Soares and Buontempo, 2019). Agriculture is the sector with the largest number
20 of examples (Zebiak et al., 2015; Burke and Emerick, 2016; Cliffe et al., 2016; Haigh et al., 2018;
21 Buontempo et al., 2020); others include health (Ghebreyesus et al., 2010; Ballester et al., 2016), forestry
22 (Caurla and Lobianco, 2020), fisheries (Busch et al., 2016), disaster risk reduction (Street et al., 2019), and
23 water resources management (van Vliet et al., 2015; Golding et al., 2019). Evaluations of the extent to which
24 climate services are accessed, used, and deliver benefits to decision makers remain in an initial stage
25 (Perrels, 2020), though studies suggest that these contributions vary widely depending on context. A review
26 of evaluation of weather and climate agricultural services in Africa, for instance, found that most farmers use
27 climate services when they are available , but that on-farm outcomes varied, with some farmers
28 experiencing yield losses and others gains upward of 60% (Vaughan et al., 2019a). Other studies express
29 concern that large climate service projects have run for decades at significant expense, without adequate
30 evaluation at all (Gerlak et al., 2020).
31
32 Recent reviews (Carr and Onzere, 2018; Hewitt et al., 2020) provide evidence that the use of climate services
33 is affected by (a) the quality, reliability and skill of the climate information (Zebiak, 2019); (b) the fit,
34 tailoring and contextualization of that information with respect to the specific decision-making needs of
35 particular users (Clarkson et al., 2019); (c) the mode and method by which the service is communicated
36 (Golding et al., 2017); and (d) the characteristics of the users themselves � including the users' access to
37 resources that would allow them to alter their decisions based on the information provided (Clarkson et al.,
38 2019).
39
40 A related literature characterizes the extent to which the development, reach and effectiveness of climate
41 services is affected by factors that can be termed `climate service governance' (Stegmaier et al., 2020).
42 Elements of this governance include the arrangements by which those parties engage with each other
43 (Vaughan et al., 2016; Daniels et al., 2020) and the financial arrangements, and associated responsibilities,
44 which support the service (Louren鏾 et al., 2015; Bruno Soares and Buontempo, 2019). Though governance
45 varies by context, evidence suggests that engaging a range of experts and potential users in the co-design and
46 co-production of climate services increases the use and utility of services (Lemos et al., 2014; Pope et al.,
47 2017; Masuda et al., 2018; Harvey et al., 2019b). However, some studies warn that even with broad and
48 inclusive participation, power differentials can create barriers to co-production reducing the usefulness
49 information products (Alexander et al., 2020) and the neglect of non-meteorological sources of information
50 which may also possess useful predictive power (Coughlan de Perez et al., 2019).
51
52 A small but growing number of papers consider the business models that support climate services,
53 including, for instance, the role of open data (Iturbide et al., 2019; Chimani et al., 2020), the standards or
54 institutional mandates by which users come to understand the credibility and legitimacy of certain services
55 (Bruno Soares and Buontempo, 2019), and the role of public-private partnerships (Cortekar et al., 2020).
56 While the commercialization of climate services holds significant promise that more and more specifically
57 targeted services will be provided, there is not yet agreement on which business models best support this in
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1 different contexts. There is also concern that commercialization of climate services may disadvantage under-
2 resourced actors at the expense of wealthier or more powerful ones (Webber, 2017; Webber and Donner,
3 2017; Cortekar et al., 2020). It has been noted that some climate services, such as weather forecasts and early
4 warnings, are an example of a public good, best provided by public agencies (high confidence) (Sutter, 2013;
5 Kitchell, 2016; Hansen et al., 2018).
6
7 17.4.4.2.3 Capacity and motivation within knowledge systems
8 Knowledge of climate change influences decision-making not only by providing information but also by
9 increasing the motivation to act and by promoting behaviour change. Evidence from many sectors (including
10 water (4.5.2), ocean and coastal ecosystems (3.6.2), and agriculture (5.4.2) and regions (including Africa
11 (9.8.4), Asia (10.4.6), and North America (10.4.5) show that building capacity (e.g. adaptive capacity,
12 institutional capacity, education/training in human capacity) can support adaptation and limited governance
13 capacity can constrain it (high confidence). An emerging area of research examines the contribution of
14 building capacity within public and technical organizations and agencies to draw on Indigenous knowledge
15 and local knowledge (Adger et al., 2017; Hochman et al., 2017; Bacud, 2018). A number of factors influence
16 the effect of knowledge on motivation and behaviour change, including values and education.
17
18 Decision-makers who shape options for managing climate risk can evaluate stakeholders' capacities and
19 motivations to participate in the implementation process of these options. Stakeholder engagement in climate
20 change risk management supports successful adaptation (Gray et al., 2014; Elsawah et al., 2015; Siders,
21 2017; Giordano et al., 2020). Research in psychology and related fields shows that the cognitive mechanisms
22 by which individuals and organizations process climate information influence this capacity, motivation and
23 engagement (Grothmann and Patt, 2005; Grothmann et al., 2013; Masud et al., 2016; Nelson et al., 2016;
24 Takahashi et al., 2016; H黦el and Davies, 2020; Grothmann and Michel, 2021).
25
26 The perception of climate change as a major threat that requires action has increased since AR5, reflecting
27 both the growth of information about climate change and the processing of that information (Lee et al., 2015;
28 Fagan and Huang, 2019). Global social movements play an important role in raising public awareness of
29 climate urgency (Thackeray et al., 2020). Climate change concern plays an important role in decision-
30 making outcomes which entail public participation (Lammel, 2015; Chiang, 2018; van Valkengoed and Steg,
31 2019; Arikan and G黱ay, 2020). Nonetheless, public risk perception varies sharply on spatial and temporal
32 scales, reflecting environmental changes, social influences (Kousser and Tranter, 2018; Rousseau and
33 Deschacht, 2020), economic capacities (Arikan and G黱ay, 2020) and culture (Noll et al., 2020), as well as
34 individual characteristics (van Valkengoed and Steg, 2019). The importance of values and norms is
35 demonstrated by recent research which highlights how intrinsic motivation (altruistic, self-transcendental and
36 ecocentric values) (Corner et al., 2014; Braito et al., 2017; Xiang et al., 2019; Bouman et al., 2020) and
37 extrinsic social motivation (e.g., economic gains and social desirability) (van Valkengoed and Steg, 2019)
38 can drive action.
39
40 Recent research shows the importance of education as a predictor of risk perception, motivation and action.
41 Education level is the strongest predictor of public awareness of climate change risk in a study across 119
42 countries of public awareness of climate change risk (Lee, 2015), though this relationship varies in different
43 nations, and is influenced by mediating variables (Muttarak and Chankrajang, 2015; Blennow et al., 2016)
44 (Ballew et al., 2020). Knowledge and awareness of climate change are correlated with the motivation to
45 undertake action on climate change (Hornsey and Fielding, 2017). The integration of climate science in
46 educational curricula has been shown to be effective (Hess and Maki, 2019; Molthan-Hill et al., 2019),
47 including approaches such as integration of the complex system approach (Jacobson et al., 2017),
48 experiential climate change education (Siegner, 2018), including climate games (O'Garra et al., 2021;
49 Pfirman et al., 2021), massive open online courses , and informal science learning centres (Geiger et al.,
50 2017).
51
52 Attention to behavioural change of individuals has grown since AR5, including cases which address both
53 adaptation and mitigation (e.g. dietary changes, modification of buildings, transport alternatives) (Azadi et
54 al., 2019; Fischer, 2019; Willett et al., 2019; Sharifi, 2020; Sharifi, 2021). The interventions to promote
55 behavioural change can be bottom-up, initiated by individuals, communities, non-governmental
56 organizations or the private sector, or top-down, coming from governments at various levels (Robertson and
57 Barling, 2015; Stern et al., 2016). They are supported by a number of mechanisms, including education,
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1 information strategies, and campaigns, financial incentives, regulatory processes and legislation (Rosenow et
2 al., 2017; Creutzig et al., 2018; Carlsson et al., 2019). These behavioural changes contribute significantly to
3 effective risk management.
4
5 17.4.5 Enabling Condition 4: Catalysing Conditions
6
7 A clear difference between enabling conditions and catalysing conditions is emerging in the climate
8 mitigation literature (Hermwille et al., 2019; Michaelowa et al., 2021), with some examples in the adaptation
9 literature as well (Madsen et al., 2019; Booysen et al., 2019a; Bolorinos et al., 2020). Though enabling
10 conditions are necessary preconditions that allow response options to be formulated and implemented, their
11 presence alone does not guarantee that these response options will occur in a timely fashion or at a scale
12 commensurate with the risk, or even that they will occur at all. Catalysing conditions address this deficit in
13 advancing action. They serve to overcome the inertia that often operates as a barrier to action and motivate
14 individuals and organizations to initiate or accelerate action. Different forms of catalyzing conditions,
15 described below, lead individuals and organizations to weigh more seriously the costs of delaying action or
16 keeping action at low levels. Catalysing conditions focus the attention of individuals and organizations on
17 particular risks, leading actors to augment their decision-making processes and to allocate financial and
18 social resources to respond to those risks. This attention and deliberation can lead to more frequent and
19 potentially substantial adaptations, whether through more extensive action on existing forms of adaptation or
20 through the adoption of entirely new adaptations (Bolorinos et al., 2020).
21
22 The first two catalysing conditions described below address the costs of delaying action. Urgency increases
23 the awareness of individuals and organizations of such costs, while windows of opportunity, including
24 extreme events, are time-bound periods during which certain actions are possible, but after which they are
25 more difficult or impossible. The other two conditions stimulate new forms or levels of action by promoting
26 or directing step changes from one policy or management regime to another (Solecki et al., 2017). Litigation
27 over adaptation issues, for example, can open new lines of action or close off old ones, while catalysing
28 agents advance action through a variety of means (e.g., communicating the urgency of climate action,
29 revising agendas for action, expanding coalitions which undertake action). As detailed below, these four
30 catalysing conditions can operate together as well as separately to promote more prompt and extensive
31 adaptations.
32
33 17.4.5.1 Urgency
34
35 Urgency can catalyse action for individuals and organizations. A moderate level of urgency serves as an
36 important driver of climate action, but both high and low levels of urgency impede response (high
37 confidence). Wilson and Orlove (2021) review five experimental and twenty observational papers that
38 examine the relationship between urgency and levels of response in climate decision-making, across a range
39 of settings: from individuals and households, to communities, managed ecosystems, sub-national regions and
40 international river basin. Urgency in the papers is defined primarily through objective and subjective time
41 pressure, including the recognition of the costs of delaying action and the importance of using windows of
42 opportunity during which new forms and higher levels of response are possible. All the experimental papers
43 and all but three of the observational papers provide support for an inverted U-shaped relationship between
44 urgency and response intensity (including motivation and action), with higher levels of response at
45 intermediate levels of urgency and lower levels of response at low or high levels of urgency (Figure 17.8).
46 The general shape of this relationship also is supported for other decision domains by a well-established line
47 of research within psychology (Heitz, 2014; Zakay, 2014; Prem et al., 2017).
48
49
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1
2 Figure 17.8: A moderate level of urgency serves as an important driver of climate action, but both high and low levels
3 of urgency impede response [derived from Wilson and Orlove (2021)].
4
5
6 The synthesis of the studies on urgency offers two central lessons for policy makers, community groups, and
7 others involved in addressing climate change. First, that greater levels of response to climate change-induced
8 challenges can be motivated by communication strategies that move decision makers from low to moderate
9 levels of urgency (high confidence). In the case of drought, a number of studies show that urgent messages
10 promote water conservation, especially when these messages are repeated, perceived as trustworthy, and
11 linked to concrete suggestions for action (Gonzales and Ajami, 2017; Joubert and Ziervogel, 2019; Kam et
12 al., 2019; Booysen et al., 2019a; Booysen et al., 2019b; Bolorinos et al., 2020). These effects are also
13 demonstrated in experimental studies of adaptation planning in contexts including European flood
14 preparations (Madsen et al., 2019; Pot et al., 2019), and Pacific Island coastal planning (Donner and Webber,
15 2014).
16
17 Second, very high levels of urgency are a barrier to effective action (medium confidence), because last-
18 minute actions to reduce risk during crises can create haste and panic, often leading to insufficient
19 deliberation. In these cases, decision-makers fail to consider a full range of alternative actions, make rash
20 choices and poorly mobilize available resources (Asfaw et al., 2019; Robins, 2019; Gee, 2020). Given that
21 climate decision makers in many regions and sectors are experiencing greater pressure to act; this finding
22 suggests the existence of windows for planning and action during which climate risks have led to moderate
23 levels of urgency, but before these risks have resulted in urgency exceeding some upper threshold (see
24 17.4.5.2).
25
26 In addition, these studies point to potential weaknesses as well as strengths in strategic communication to
27 modulate urgency. Such messages may instead lead to lower levels of response if they induce very high
28 levels of urgency (Asfaw et al., 2019), though this effect may be somewhat mitigated by messages that
29 simultaneously increase recipients' sense of self-efficacy or they are experienced in the specific risk domain
30 discussed in the messages (Bodin et al., 2019). Future research on the relationships between urgency and
31 effective risk management could help refine the measurement of urgency, how the relationship varies in
32 different contexts, the role of different forms of messaging about urgency and action (Fesenfeld and
33 Rinscheid, 2021), as well as the effects of urgency on decision-making by high-level decision-makers within
34 polities and by climate social movements.
35
36 17.4.5.2 Windows of Opportunity
37
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1 Windows of opportunity are time-bounded periods during which conditions are present for advancing and
2 often accelerating climate adaptation strategies. They can act as significant catalysing conditions for climate
3 action and are connected to a range of possible outcomes from small incremental shifts to larger scale more
4 profound transformation adaptations (Novalia and Malekpour, 2020).
5
6 Windows can open because of extreme weather events (Birkmann and Fernando, 2008), political shifts, such
7 as new institutions, new laws and regulations, and presence of a new policy entrepreneur or new policies
8 (Haasnoot et al., 2013; Bell and Morrison, 2015), relevant and achievable policy goals, and emergence of
9 new knowledge (Abunnasr et al., 2013), and close after the initial causes recede and become less efficacious.
10 They also serve as focusing events whereby a coalition of groups address specific policy questions or
11 response options (Rudel, 2019). Recognizing that windows of opportunity often catalyze action does not
12 mean that action outside such windows is insignificant or impossible.
13
14 Extreme events such as disasters often act as proximate drivers of windows of opportunity (Birkmann and
15 Fernando, 2008; McSweeney and Coomes, 2011). Climate disasters in a specific location become significant
16 windows for new debate, policymaking and financing (McSweeney and Coomes, 2011). Extreme events also
17 can facilitate change at locations distant from the most impacted site when remote actors gain perspective on
18 their own risks (Friedman et al., 2019; Solecki et al., 2019). Factors that facilitate extreme events driving
19 proactive as opposed to reactive responses include access to relevant risk and vulnerability data, pre-existing
20 experience with similar events, and appropriate governance (Brown et al., 2017a). Page and Dilling (2020)
21 find that worldview or ideology plays a central role in sense-making and in shaping what organizational
22 decision-makers `see' in terms of acceptable actions in response to an extreme event.
23
24 Significant variation is present across the mix and intensity of conditions that promotes action through a
25 window of opportunity. Capacity to respond to is a function of the presence of enabling conditions as well as
26 tools and methods to aid decision-making (Shi et al., 2015). Political activism provides windows of
27 opportunity for climate adaptation (Lauer and Eguavoen, 2016; see also 17.4.5.3.1).
28
29 Sudden shifts in institutions and legal framework can also catalyse climate action. For example, the year
30 2015 included a series of international frameworks such as the Sendai Framework for Disaster Risk
31 Reduction 2015-2030 (van Niekerk et al., 2020; Hofmann, 2021), the 2030 Agenda for Sustainable
32 Development, which established the Sustainable Development Goals (Sanchez Rodriguez et al., 2018), and
33 the Paris Climate Agreement, which dramatically enhanced the promotion and implementation of altered the
34 conditions under which climate adaptation occurred.
35
36 17.4.5.3 Climate Litigation on Adaptation
37
38 Litigation for loss and damage from climate change was first noted as a potential motivator for emissions
39 reduction in AR4 and AR5 noted that litigation was pending but not tested and that while legal systems were
40 beginning to define the boundaries of responsibility for climate change, it was `unclear liability exists'. The
41 SR1.5 (IPCC, 2018a) reported, with high confidence, that litigation risks of government and business had
42 increased and the SRCCL (IPCC, 2019b) noted that recent developments in climate attribution improve the
43 ability to detect human influence on climate and broaden liability.
44
45 Since AR5 there has been growing recognition of the potential of litigation for failure to take measures to
46 adapt to climate change to drive climate risk management (Banda and Fulton, 2017; Peel et al., 2017;
47 Bouwer, 2018). Litigation cases on adaptation and loss and damage comprise about one third of those
48 covered in the literature (Setzer and Vanhala, 2019a). Reasons for this growth are: (i) the growing gap
49 between projected climate change impacts and current adaptation efforts (Stezer and Byrnes, 2019) and (ii)
50 expanded legal duty of government, business, and others to manage foreseeable harms (Marjanac and Patton,
51 2018). Climate change litigation is expanding geographically into the Americas, Asia (and the Pacific
52 region), and Europe with several cases brought in low- and middle-income countries (Stezer and Byrnes,
53 2019) (See Table 17.7).
54
55 Lawsuits against private entities contribute to articulating climate change as a legal and financial risk
56 (medium confidence) (Peel and Osofsky, 2015; Ganguly et al., 2018; McCormick et al., 2018; Peel and
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1 Osofsky, 2018). Even if unsuccessful, Estrin (2016) concludes they are important in underlining the high
2 level of public concern.
3
4 Climate-related, legal, financial disclosure requirements are improving investment decision making of
5 corporations as well as augmenting ex post liability for failure to consider climate change risk in decision
6 making. Organizations are required to disclose governance around climate related risks (impact of climate
7 change on businesses, products, services, supply or value chain, adaptation and mitigation activities,
8 investment in research and development and operations). This functions as a vehicle for identifying
9 climate-related risk and the organization's resilience strategy taking into consideration different climate-
10 related scenarios including a 2癈 or lower scenario (Sarra, 2018). Institutions such as the G20 (Carney,
11 2019), the American Bar Association (Brammer and Chakrabarti, 2019), the European Commission (Zadek,
12 2018) have adopted or endorsed these standards.
13
14
15 Table 17.7: Examples of types of climate-related litigation
Litigation Type Detail and Examples Supporting Literature
Challenge government Challenging government or administrative planning (Banda and Fulton,
decisions for not considering decisions for failure to consider, or adequately address, 2017; Peel et al.,
climate change risks climate change in relation to developing and protecting 2017; Bouwer, 2018;
coastal zones, water stressed regions, flood prone areas, or Clarke and Hussain,
decisions affecting endangered species whose habitat is at 2018)
risk. For example, the Victorian Civil and Administrative
Tribunal in Australia rejected a planned housing project in
a coastal area, citing the risks from climate change
(Gippsland Coastal Bd. v. South Gippsland Sc & Ors
(No2), 2008).
Petitions to act Constitutional petitions to force governments to take (Banda and Fulton,
adaptation measures. As an example, in Leghari v. 2017; Ashgar Leghari
Pakistan a farmer initiated public interest litigation against v. Federation of
federal and provincial governments for failure to develop Pakistan, April 2015;
climate change resilience through adaptation to floods, Ashgar Leghari v.
droughts and other impacts because it violated his rights to Federation of
life and dignity. The High Court of Lahore found for Mr. Pakistan, September
Leghari and created a commission to develop and 2015)
implement a wide range of adaptation actions.
Regulatory proceedings Environmental groups and city and state officials (Consolidated Edison
intervened in the application of the electric utility serving Co., 2019)
Failure to act by public New York City, Consolidated Edison Company, to the
authorities New York State Public Service Commission for a rate
increase. The intervenors argued that the company was not
Failure by private sector to adequately preparing for flooding, heat waves and other
consider climate change climate-related impacts. As a result, the Commission
directed the company to undertake a study of its
vulnerability to climate change, and write and implement a
plan to address these risks.
Liability of public authorities for failure to undertake (Banda and Fulton,
necessary adaptation actions to avoid damage to life or 2017; Peel et al.,
property especially where statutory framework is proven 2017; Bouwer, 2018)
ineffective or out of step with international commitments;
in some areas these are class action suits. An example is
private lawsuits for failure of a built environment to
consider adaptation needs in a built environment (energy
efficiency works, overheating because of increased
temperatures).
Examples include: (i) A citizen suit against ExxonMobil (Benjamin, 2017;
for failure to adapt Everett Terminal to the impacts of Stezer and Byrnes,
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adaptation in their business climate change including increased precipitation, sea level 2019; Street and Jude,
practice rise and storm surges occurring with increasing frequency; 2019; Wasim, 2019;
(ii) A citizen suit against. Shell Oil Products US alleging Conservation Law
Shell failed to incorporate climate risks in its investment in Foundation v. Exxon
a bulk storage and fuel terminal in Rhode Island, USA; Mobil Corporation,
(iii) Shareholder action against ExxonMobil for failure to 2021)
report climate risks or complying with recommendations
to do so and for issuing misleading corporate disclosure
relied on by investors; (iv) A suit brought an NGO, the
Conservation Law Foundation, against Exxon Mobil
alleging that the company had taken insufficient
precautions to protect a major oil tank farm near Boston,
USA, from coastal storms that are worsened by climate
change, creating a danger of an oil spill into Boston
Harbour. The U.S. Court of Appeals for the First Circuit
ruled in 2021 that the lawsuit could proceed, and that the
NGO could attempt to make out its case that Exxon Mobil
should take greater precautions.; (v) Government and
citizen claims for public nuisance against fossil fuel
companies for the costs of adaptation such as
infrastructure to protect against sea level rise.
Youth public trust claims Government inter-generational liability for inadequate (Schneider et al.,
climate change mitigation and adaptation efforts. Our 2017; Bouwer, 2018)
Children's Trust (a non-profit organization) and others
brought an action against the United States and several
executive branch individuals in 2015 claiming damages
for their loss of the environment and the defendant's
failure to preserve a habitable climate system. Similarly, a
public trust claim could be brought in a coastal town for
failure to adapt to climate change.
Human rights claims Human rights may be a powerful tool for organizing and (Hall and Weiss,
unifying adaptation decision making, especially for the 2012; Peel and
most vulnerable, through enforcement mechanisms of Osofsky, 2018; Setzer
progressive realization as well as ex post liability (see and Vanhala, 2019b;
Chapter 8). For example, a persons' right to food implores Stezer and Byrnes,
state parties to take necessary actions to alleviate hunger 2019)
caused by climate change; during natural and other
disasters rights to water, and life are impacted; sea-level
rise and storm surges impact many coastal settlements and
the right to adequate housing and an adequate standard of
living. This is in part due to increasing acceptance of the
impact of climate change on health, livelihoods, shelter
and fundamental rights.
1
2
3 17.4.5.4 Catalysing Agents
4
5 Individuals and organizations often serve as catalysing agents of climate risk decision-making. They promote
6 greater levels of new forms of climate action by communicating the urgency of climate action and by
7 developing coalitions which undertake action. Agents include individuals, organisations or collectives, or
8 multiple organizations linked together.
9
10 17.4.5.4.1 Social movements and other mobilizations
11 Recent studies of climate-related social movements show that they can act as catalysing agents which
12 promote action to manage climate-related risks (medium confidence). However, these studies use varying
13 definitions of climate movements within the broader context of environmental movements. A prominent
14 topic of research is the rapidity and the large scale of the proliferation of these movements around the world,
15 primarily in urban settings but also in rural and Indigenous contexts (Claeys and Delgado Pugley, 2017).
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1
2 These movements usually focus on climate mitigation but sometimes include adaptation. Their social bases
3 include groups which had not previously been active in climate politics, notably children and youth, as well
4 as sectors with long traditions of environmental activism, such as women and Indigenous peoples (see Cross-
5 Chapter Boxes GENDER and INDIG in Chapter 18). Much of the literature on youth movements traces the
6 emergence of the movements themselves (Sanson et al., 2019; Treichel, 2020), their framings of climate
7 change as a social justice issue (Holmberg and Alvinius, 2019) and their presence in demonstrations and on
8 social media (Boulianne et al., 2020). Climate action catalysed by youth and other climate movements
9 include visible international events such as the signing of Declaration on Children, Youth, and Climate
10 Action at COP25 in Madrid 2019 (Han and Ahn, 2020), as well as national efforts, including lawsuits, and
11 local events such as in tree-planting and waste reduction initiatives (Bandura and Cherry, 2019).
12
13 A recent review examines 2743 cases around the world of mobilizations for environmental justice causes
14 (Scheidel et al., 2020); roughly half the cases occurred between 1970 and 2007, and half between 2008 and
15 2019. Of these environmental mobilizations, 17% are directly related to climate and energy, and others are
16 related to climate-sensitive issues (15% for biomass and land use, 14% for water management). This study
17 reports the proportion of positive outcomes for different strategies, defined as meeting the goals of the
18 movements, which generally align with climate adaptation and sustainable resource management. These
19 rates vary from 10% for negotiated solutions to 34% for court decisions. It notes the corresponding higher
20 rates of failure, as well as the costs borne by the movements, which include criminalization (20% of cases),
21 violence (18%) and assassination (13%). These costs are significantly higher for Indigenous communities
22 that engage in these mobilizations.
23
24 At a global scale, climate movements succeeded in pressing for the greater recognition of the importance of
25 Indigenous knowledge within international agreements (Tormos-Aponte and Garc韆-L髉ez, 2018) but did
26 not achieve the major reforms of climate finance which they sought (Khan et al., 2019a); these differing
27 outcomes reflect the sensitivity of the issues and the formation of coalitions which supported or opposed the
28 movements. At national and local scales, one review of US cases reports limited effectiveness of climate
29 movements because of the ability of governmental agencies to coopt them (Pulido et al., 2016), while
30 another review in Pakistan shows a number of successes, because the movements were able to build alliances
31 with other public sector and community groups (Shawoo and McDermott, 2020).
32
33 17.4.5.4.2 Policy leaders and entrepreneurs
34 Policy leaders, often described as policy entrepreneurs within the scholarly literature, are individuals in
35 positions of leadership who set agendas and build coalitions to drive decision-making processes, and hence
36 can function as catalysers of climate adaptation (Petridou and Mintrom, 2020). Political leaders who have
37 taken on climate change as a key policy issue function as policy entrepreneurs at international, national and
38 sub-national levels. City officials including mayors and other executives often play the role of climate policy
39 entrepreneurs, while the absence of effective leadership negatively affects adaptation success (Becker and
40 Kretsch, 2019). Such entrepreneurs can be important forces for change in both reactive contexts following an
41 extreme or focusing event and in proactive context. They can be effective especially in contexts where they
42 navigate and link together formal and informal networks of complex climate governance systems (Tanner et
43 al., 2019). Their capacity to act has been increased when they and their institutions are embedded within
44 partnership networks (Bellinson and Chu, 2019). It is in these contexts that the leadership and position of a
45 policy entrepreneur becomes even more catalytic when operating at the interface of formal and informal
46 networks (Mintrom, 2019; Stone, 2019).
47
48 Sub-national actors and city officials including mayors and other executives are among the individuals most
49 often described and assessed as climate policy entrepreneurs (Kalafatis and Lemos, 2017). City level climate
50 policy entrepreneurs often operate using their own experience, connections, and persistence to address issues
51 of importance to their constituency. Climate risk concerns are often inherently local and in turn local
52 decision-makers perceive it being appropriate to engage. Conversely, the absence of effective leadership
53 negatively affects adaptation success (Kalafatis and Lemos, 2017; Becker and Kretsch, 2019). Urban climate
54 policy entrepreneurs operate in four key spheres of policy development and implementation: attention and
55 support seeking strategies; linking strategies (e.g., coalition building); relational management strategies (e.g.,
56 networking and trusting building); and arena strategies including timing (Brouwer and Huitema, 2018). The
57 presence and operation of urban climate policy entrepreneurs is positively associated in settings with
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1 multiple jurisdictions and across differing spatial scales (Kalafatis and Lemos, 2017; Renner and Meijerink,
2 2018). It is these contexts that their capacity to operate simultaneously at the interface of multiple networks
3 is particularly valuable for promoting climate action. Urban climate policy entrepreneurs can directly engage
4 with a range of constituent groups and offer and promote climate adaptation strategies that can have direct
5 impact on the daily lives of these residents and their interests.
6
7
8 [START BOX 17.3 HERE]
9
10 Box 17.3: Climate Risk Decision-Making in Settlements: From Incrementalism to Transformational
11 Adaptation
12
13 Cities are important sites of experimentation where the integration and management of adaptation decision-
14 making complexity often takes place. These actions provide early evidence of what aspects of complex
15 climate risk management decision-making functions well, but also what does not work (Revi et al., 2020).
16 Cities are seen as locales where case examples of transformative adaptation can be examined (Rosenzweig
17 and Solecki, 2018; Vermeulen et al., 2018). Cities act as testbeds of how to integrate climate response into
18 issues of equity, health, resource allocation, and sustainability in ways that utilize innovative use of new and
19 emerging decision-support tools, methods and protocols.
20
21 Risk management has been an integral part of the community development and settlement building process.
22 Three key sets of drivers influence risk management decision-making in cities (Solecki et al., 2017). These
23 include 1) root � i.e., cultural norms and social traditions; 2) context � i.e., policy and governance conditions
24 and 3) proximate � i.e., extreme events. Settlements have developed informal and formal strategies including
25 climate protection levels to respond to local conditions of climate risk and hazards. In formal contexts, these
26 strategies are contextualized in local climate change action plans (Araos et al., 2016a; Stults and Woodruff,
27 2017; Reckien et al., 2018a; Singh et al., 2021) and defined around a set of evaluation tools and methods and
28 building codes, standards, and regulations (see discussion in 17.4.4).
29
30 Climate change has begun to alter the environmental baseline of cities changing their risk and hazard
31 profiles. In recent years, national and local risk management can benefit from assessments of current
32 decision-making strategies and from evaluations of opportunities for change in risk management policy.
33 These changes can be adjustments of existing policies or transitions to a new policy for current (i.e.,
34 conditions already experienced by getting worse) or emerging risks (i.e., conditions not previously or widely
35 experienced but now increasingly present).
36
37 With increasing impacts of climate change, settlements of all sizes are considering how to make their
38 communities more resilient to climate risk (see Cross-Working Group Box URBAN in Chapter 6; Araos et
39 al., 2016a; Araos et al., 2017; Reckien et al., 2018a). In many settlements demands for heightened resiliency
40 are being coupled with opportunities to enhance the social and economic equity and quality of life of
41 residents. Transformational adaptation (transformational, as being outcome-oriented; Vermeulen et al., 2018)
42 and associated adjustments to the urban risk management decision-making requires an integration of climate
43 resiliency pathways and conditions of sustainable development (Mendizabal et al., 2018). At the same time,
44 growing conflict is present between requirements for greater resiliency and continued economic
45 development, in particular in low-income environments (Ahenkan et al., 2020). Cities and their residents
46 have the capacity to transform their own governance and decision-making systems (Birkmann et al., 2014;
47 Chu, 2018; Romero-Lankao et al., 2018). Furthermore, cities have recognized the opportunity and demand to
48 transform in order to be more ambitious (Mendizabal et al., 2018) and more successful, more equitable
49 (Reckien et al., 2018b) and better able to connect the climate action to the sustainable development process
50 (Singh et al., 2021).
51
52 In some cases, transformational adaptation is associated with large-scale, top-down, formal decision
53 processes leading to significant policy shifts. For coastal cities this might include actions to build massive
54 flood protection systems (as opposed to simple increase of existing structures) (Albers et al., 2015; Hinkel et
55 al., 2018; Ajibade, 2019; see also Section 2.3.5, Cross-Chapter Paper 2) or policies to encourage managed
56 retreat from increasing at risk locations (Hino et al., 2017; Rulleau and Rey-Valette, 2017). In more extreme
57 instances, the relocation of cities is presented as a possibility, such as planned for the city of Jakarta
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1 (Garschagen et al., 2018b). However, acceptability of top-down approaches to relocation are usually low and
2 bottom-up drivers of relocation are important, especially to avoid inequitable outcomes (Mach and Siders,
3 2021). Intensity of extreme events and changing risk perceptions and expectations of property prices have
4 been identified as important behavioural drivers of voluntary relocation (de Koning et al., 2019; de Koning
5 and Filatova, 2020). Yet, when not supported by equitable public adaptation policies, the transformational
6 adaptation left to the influence of autonomous adaptation and market institutions alone leads to climate
7 gentrification low-income households are priced out from the hazard-free zones (de Koning and Filatova,
8 2020).
9
10 These circumstances also have revealed potential advances in decision-making by encouraging greater
11 participation, more effective generation and use of information and data, and more prominent inclusion of
12 questions of social and economic equity (Ziervogel et al., 2017; Reckien et al., 2018b; Solecki et al., In
13 Press). Adaptation planning and decision-making, in general, within cities has increasingly focused on
14 actively engaging residents in participatory and neighbourhood scale co-production processes (Broto et al.,
15 2015; Sarzynski, 2015; Wamsler, 2017; Foster et al., 2019). However, engaging residents in risk
16 management and adaptation has not always led to transformative decision-making and resiliency, but can at
17 times also reinforce existing maladaptive systems (D'Alisa and Kallis, 2016).
18
19 Now increasing amounts of data are being collected via surveys or in participatory settings next to advanced
20 methods, such as using citizen science, big data and AI, to integrate these social dimensions of climate
21 adaptation decisions in cities in formal models (Abebe et al., 2019; Taberna et al., 2020). Linking to social
22 data on individual decisions, risk perceptions, social norms, and governmental policy, advanced social
23 models trace and quantify how adaptation in cities evolve and would cumulatively induce transformational
24 change. Although wider application of these models is outstanding there is opportunity to simulate and learn
25 from the integration of social and behavioural data with political and cultural norms (de Koning and
26 Filatova, 2020).
27
28 Although non-urban areas could in many instances act in the same way as urban areas, the density of people,
29 assets, infrastructure, and economical values drives cities to act as testbeds, implement adaptation, and strive
30 for resiliency. Cities are showcases for the larger environmental systems of governments that also support
31 mitigation ambition of national actors and are therefore demanding to be recognized as valuable actors in the
32 international negotiations, highlighting their contribution in emissions reductions (Chan et al., 2015; Hale,
33 2016), e.g., in the preparation for the first Global Stocktake of the Paris Agreement in 2023 (see Cross-
34 Chapter Box PROGRESS in this Chapter).
35
36 [END BOX 17.3 HERE]
37
38
39 17.5 Adaptation Success and Maladaptation, Monitoring, Evaluation and Learning
40
41 17.5.1 Adaptation Success and Maladaptation
42
43 17.5.1.1 The Adaptation-Maladaptation Continuum
44
45 As evidence on adaptation implementation grows (Berrang-Ford et al., 2021; Eriksen et al., 2021), there is a
46 need to examine the outcomes of adaptation (Ford et al., 2011) for effectiveness, adequacy, justice/ equity in
47 both outcomes and process, as well as synergies and trade-offs with mitigation, ecosystem functioning, and
48 other societal goals. There is also a growing recognition of the observed and potential negative consequences
49 of some adaptation interventions, often referred to as maladaptation (Juhola et al., 2016; Magnan et al., 2016;
50 Schipper, 2020; Eriksen et al., 2021). This section advances a new framing to allow for an improved
51 assessment of the potential positive or negative outcomes of adaptation options, therefore allowing
52 navigation of the adaptation-maladaptation continuum.
53
54 17.5.1.1.1 Defining and assessing success in adaptation vis a vis maladaptation
55 The highly contextual nature of adaptation, a multitude of applied definitions of adaptation (e.g cost
56 effectiveness versus outcomes), its overlaps with development interventions, and the long time horizons over
57 which outcomes accrue, deter a universal definition of adaptation success (Dilling et al., 2019a; section
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1 17.5.1.2; Owen, 2020; Singh et al., 2021). Moser and Boykoff (2013), Olazabal et al. (2019b), and Sherman
2 and Ford (2013) suggest criteria against which successful adaptation could potentially be tracked. The
3 literature is converging to suggest that successful adaptation broadly refers to actions and policies that
4 effectively and substantially reduce climate vulnerability, and exposure to and/or impacts of climate risk
5 (Noble et al., 2014; Juhola et al., 2016), while creating synergies to other climate-related goals, increasing
6 co-benefits to non-climate-related goals (such as current and future economic, societal, and other
7 environmental goals) and minimize trade-offs (Grafakos et al., 2019) across diverse objectives, perspectives,
8 expectations, and values (Eriksen et al., 2015; Gajjar et al., 2019a; Owen, 2020) (high confidence).
9
10 Maladaptation refers to current or potential negative consequences of adaptation-related responses that lead
11 to an increase in the climate vulnerability of a system, sector, or group (Barnett and O'Neill, 2010) by
12 exacerbating or shifting vulnerability or exposure now or in the future (Antwi-Agyei et al., 2014; Noble et
13 al., 2014; Juhola et al., 2016; Magnan et al., 2020) and eroding sustainable development (Juhola et al., 2016).
14 Conceptually, maladaptation differs from `failed' or `unsuccessful' adaptation (Schipper, 2020), which
15 "describes a failed adaptation initiative not producing any significant detrimental effect" (Magnan et al.,
16 2016: 648). Several frameworks have been proposed to explain and better assess maladaptation (Hallegatte,
17 2009; Barnett and O'Neill, 2010; Magnan, 2014; Magnan et al., 2016; Gajjar et al., 2019b). In order to limit
18 the risk of maladaptation, a common focus of these frameworks is on intentionally avoiding negative
19 consequences of adaptation interventions, anticipating detrimental lock-ins and path dependence, and
20 minimizing spatio-temporal trade-offs.
21
22 The adaptation literature challenges the simplistic dichotomy of interventions being either successful or
23 maladaptive (e.g. Moser and Boykoff, 2013; Singh et al., 2016; Magnan et al., 2020; Schipper, 2020). There
24 is no clear cut boundary between these two categories; rather, successful adaptation and maladaptation need
25 to be considered the two ends of a continuum of risk management strategies (Figure 17.9) emphasising that:
26
27 no options are "bad" or "good" a priori with respect to reducing climate risk/vulnerability.
28 positive and negative outcomes of adaptation depend on local context specificities (including the
29 presence/ absence of enabling conditions [1]), how adaptation is planned and implemented, who is judging the
30 outcomes (i.e. adaptation decision-maker, planner, implementer or recipient) and when adaptation outcomes
31 are assessed.
32 ex ante assessment of where options fall on the continuum can help anticipate maladaptive outcomes.
33
34 Along the adaptation-maladaptation continuum, adaptation options can score high or low on different
35 outcome criteria identified in this section as: benefits to the number of people, benefits to ecosystem
36 services, equity outcomes (for marginalized ethnic groups, gender, low-income populations),
37 transformational potential and contribution to GHG emission reduction (see SM 17.1 for full descriptions).
38 Importantly, the outcome of the assessment, and consequently location of a given adaptation option along
39 this continuum, is dynamic, depending on multiple components including changes in the characteristics of
40 climate hazards and the effects of iterative risk management. Unfortunately, this temporal dimension is
41 understudied in the literature (including studying thresholds or speed), preventing advances on this specific
42 point.
43
44
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1
2 Figure 17.9: Successful adaptation and maladaptation are conceptualised as the two end points of a continuum, with
3 adaptation options being located along the continuum based on outcome criteria (how they benefit humans and
4 ecosystems; how they contribute to or hinder equity goals; whether they enable transformative change to climatic risks,
5 and synergies and trade-offs with climate mitigation). As indicated in SM 17.1 and figure 17.9, adaptation options
6 might rate largely positive and slightly negative across outcome criteria (tending towards successful adaptation), while
7 other adaptation options might have small positive aspects and larger negative ones across different outcome criteria
8 (tending towards maladaptation). The figure draws on Singh et al. (2016); Magnan et al. (2020), and Schipper (2020).
9
10
11 17.5.1.1.2 Empirical evidence on success of adaptation vis a vis maladaptation
12 Although the empirical evidence on current and potential successful adaptation and maladaptation remains
13 small and fragmented (Magnan et al., 2020; Berrang-Ford et al., 2021; see Section 17.3.2 in this Chapter),
14 the above framing allows for moving a step further in assessing the potential contribution of a wide range of
15 adaptation-related options to success or maladaptation.
16
17 According to an assessment (Figure 17.10; see SM 17.1 for full descriptions) of maladaptation-relevant
18 outcome dimensions, here called criteria, i.e. benefits to people, benefits to ecosystem services, benefits to
19 equity (marginalized ethnic groups, gender, low-income populations), transformational potential, and
20 contribution to GHG emission reduction, no option is located at one or the other end of the adaptation-
21 maladaptation continuum (Figure 17.10, right panel), showing that all options have some maladaptation
22 potential, i.e. trade-offs (very high confidence). This is also shown by the wide confidence bars of most
23 options (right panel) signifying that most adaptation can be done in a way that involves a higher or a lower
24 risk of maladaptation (medium confidence; see also Table 17.2). The option of `coastal infrastructure'
25 signifies the highest risk for maladaptation. While it can be an efficient adaptation option in highly densely
26 populated areas (Oppenheimer et al., 2019; CCP2.3), it has potential tradeoffs for natural system functioning
27 and human vulnerability over time. The option most widely associated with successful adaptation is `nature
28 restoration', closely followed by `social safety nets' and options relating to `farm/ fishery practices', and
29 `diets/ food waste' (high confidence).
30
31 Some options show the dominant influence of certain criteria (Figure 17.10, central panel rows). For
32 example, `availability of health infrastructure' and `access to health care' are dominated by the criterion
33 `greenhouse gas emissions'. Similarly, `spatial planning' carries a high risk of disadvantages to marginalized
34 ethnic and low-income groups. This means that these adaptations could be transformed into successful
35 adaptations more easily than others, if attention is paid to the dominant criterion. For example, if healthcare
36 could be provided with low GHG emissions it would move closer towards successful adaptation (high
37 confidence). For other options, the criteria's influence is more evenly distributed, as illustrated for the
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1 `diversification of livelihoods' and the three options to address climate risks to peace and mobility, denoting
2 multiple entry points to reduce the risk of maladaptive outcomes for these options.
3
4 Some criteria score highly across a number of options (Figure 17.10, central panel columns), showing that
5 many adaptations do not pay attention to different trade-offs. For example, particular attention should be
6 paid to prioritising benefits to low-income groups and leveraging the transformational potential of adaptation
7 (having the largest number of large circles), i.e. many evaluated options become maladaptive by
8 exacerbating the vulnerability of low-income groups and by fortifying the status-quo (medium confidence).
9 On the contrary, most evaluated adaptation options are widely applicable across populations (benefits to
10 humans), and deliver ecosystem services, while some also respect gender equity (largest number of small
11 bubbles across options), through these criteria a number of adaptation options contribute to a higher potential
12 for successful adaptation (high confidence).
13
14
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1
2 Figure 17.10: The potential contribution of 24 adaptation-related options to maladaptation and successful
3 adaptation. The figure builds on evidence provided in the underlying sectoral and regional chapters and the Cross-
4 Chapter Papers (SM17.1) to map 24 adaptation options identified as relevant to the eight Representative Key Risks (see
5 Ch16.5) onto the adaptation-maladaptation continuum. It assesses the potential contribution of each of these adaptation
6 options to successful adaptation and the risk of maladaptation. The figure permits a review of options in multiple ways:
7 a) Looking at adaptation options (first column) one can see which adaptation options score highest across the criteria
8 (the central rows). Results by options show which ones carry the highest risk of maladaptation (=largest circles per
9 row). b): Looking at criteria (top centre) one can see which criteria seem to be most influential to contribute to
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1 maladaptation outcomes (=largest circles per central column). c) The panel on the right: Merging the scores of each
2 adaptation option across criteria helps highlighting whether the options are likely to end up as successful adaptation or
3 maladaptation.
4
5
6 The results displayed in Figure 17.10 are not rigorous predictions but illustrate the maladaptive potential of
7 options based on a synthesis of literature from underlying WGII chapters and cross-chapter papers. This
8 leads to findings for general situations, potentially obscuring critical contextual specificities which can
9 mediate successful adaptation or maladaptation outcomes. In a certain context, Figure 17.10 will appear
10 different. Moreover, the analysis is based on a static interpretation of adaptation outcomes, while risk and
11 risk reduction are dynamic. The current, underlying literature does not help understanding the temporal
12 dimension of the options, their flexibility or risk of lock-in, and related potential contribution to long-term
13 maladaptation or successful adaptation. The added value of the analysis lies in the approach to assess the
14 potential contribution to maladaptation or successful adaptation (via the seven criteria at the top of the
15 figure), rather than in the final results themselves. This overview illustrates how in a particular context and
16 for particular groups of people, adaptation options and their location on the adaptation-maladaptation
17 continuum can be assessed for a set of outcome dimensions, focuses on assessing potential contributions per
18 and across criteria, as well as per and across options (critical information to support the identification of
19 adaptation pathways; Cross-Chapter Box DEEP in this Chapter).
20
21 17.5.1.1.3 Enabling successful adaptation and pre-empting maladaptation
22 Considering evidence on enabling successful adaptation in the sectoral (Chapters 2-8) and regional chapters
23 (9-15), four conditions stand out as particularly key to enabling adaptation success: recognitional equity and
24 justice, including the integration of Indigenous and local communities and knowledge; procedural equity and
25 justice; distributive equity and justice; and flexible and strong institutions that seek integration of climate
26 risk management with other policies and address long-term risk reduction goals (Table 17.8). For a wider
27 discussion of enablers for adaptation and climate risk management, see Section 17.4.
28
29 Recognitional equity and justice: Recognitional justice focuses on inclusion and agency, i.e. examining
30 who is recognised as a legitimate actor and how their rights, needs and interests are acknowledged and
31 incorporated into action (Singh et al., 2021).
32
33 A global assessment of 1682 papers on adaptation responses yields that low-income groups (high agreement,
34 37% of 1682 articles), women (medium agreement, 20% articles), Indigenous peoples (10%), the elderly
35 (8%), youths (5%), racial and ethnic minorities (4%), and migrants (4%) were the most frequently
36 considered groups in adaptation responses. Individuals with disabilities are the least considered, with only
37 1% of articles including this group. There is a category of "other" capturing characteristics of social
38 disadvantage that are distinct from the categories above. This includes, for example, spatially marginalized
39 populations (e.g., groups relegated to flood-prone or cyclone-prone areas) and groups marginalized due to
40 marital status or assets (education, farm size, and land tenure) (Araos, in press).
41
42 Procedural equity and justice: Participation is employed to enable procedures that aim to redress power
43 imbalances, which are assumed to be the root causes of vulnerability (i.e. the reasons that lead certain people
44 and places to be differentially vulnerable to climate risks) (Tschakert and Machado, 2012; Shackleton et al.,
45 2015; Schlosberg et al., 2017; Ziervogel et al., 2017). However, participation is often constrained by gender
46 (Cross-Chapter Box GENDER, Ch 18), social status, unequal citizenship (as concerns education, access to
47 information, finance and media) (Wallimann-Helmer et al., 2019), entrenched political interests (Shackleton
48 et al., 2015; Chu et al., 2017), power dynamics (Rusca et al., 2015; Taylor and Bhasme, 2018; Kita, 2019;
49 Omukuti, 2020; Taylor and Bhasme, 2020), or institutional shortcomings (Nightingale, 2017, in Nepal),
50 which allow the most powerful access to funding and reinforce marginalisation of the powerless (Schipper et
51 al., 2014; Khatri, 2018; McNamara et al., 2020). Vulnerability is also sometimes used as a pretext to exclude
52 groups from participation, often because vulnerable groups do not own land, lack legal status, time, or the
53 ability to commit labour or material inputs for adaptation, all drivers of vulnerability in the first place
54 (Nyantakyi-Frimpong and Bezner Kerr, 2015; Camargo and Ojeda, 2017; Nagoda and Nightingale, 2017;
55 Nightingale, 2017; Thomas and Warner, 2019; Mikulewicz, 2020).
56
57 Reporting from the global assessment of equity considerations in adaptation, procedural equity and justice,
58 was slightly more often mentioned (~52%) than not (~48) (medium agreement). However, the robustness of
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1 the evidence on inclusion of vulnerable and marginalized groups in the planning of adaptation responses is
2 low (63%) (high agreement). Only for ~6% of the articles that provide evidence for inclusion of vulnerable
3 groups the robustness of evidence is high (low agreement). Globally, the category of low-income (~25%)
4 and women (~13%) are most often included, although the robustness remains low. Most of the robust
5 evidence comes from Africa and Asia, where adaptation responses mostly focus on low-income and women
6 groups in the food (28%) and poverty (32%) sectors (medium agreement). With regards to other vulnerability
7 categories, such as disabled populations, almost negligible evidence was found for the inclusion of this
8 group, globally. There is also little reporting of procedural equity in community-based or ecosystem-based
9 responses (Araos, in press).
10
11 Distributive equity and justice: Attention to distributional equity and justice aims to ensure that adaptation
12 interventions do not exacerbate inequities (Atteridge and Remling, 2018) and that the benefits and burdens of
13 interventions are distributed fairly (Tschakert et al., 2013; Reckien et al., 2017; Reckien et al., 2018b; Pelling
14 and Garschagen, 2019).
15
16 A global assessment of 1682 papers on adaptation (Araos, in press) finds that about 60% of articles
17 mentioned at least one vulnerable group being involved in the implementation of adaptation or targeted by it
18 (medium confidence). Low-income groups (high agreement, 37% of 1682 articles) and women (medium
19 agreement, 20% articles) are the most frequently mentioned. Particularly in sectors and regions that
20 incorporated coping measures in their adaptation response (Poverty, Food, Africa, Asia, Central & South
21 America), these groups are prevalent. In sectors where responses were more strategic or planned, such as in
22 cities, terrestrial and water, a larger proportion of articles (51%, 47% and 47% of articles respectively)
23 vulnerable groups were not frequently included in the response (medium agreement). There was also a stark
24 difference in inclusion of marginalized and vulnerable groups between high-income and low-income
25 countries regions, with the majority of the responses from Australia, Europe and North America, not
26 including marginalized groups (high agreement with 70%, 69% & 55% of articles respectively), showing the
27 need for increasing attention in particular on a cross-sectoral and cross-regional relation (Araos, in press).
28
29 Flexible and strong institutions: There is medium confidence that flexible institutions can enable adoption
30 of new adaptation measures or course-correct established ones based on ongoing monitoring and evaluation,
31 which is key to avoiding potential maladaptation (e.g. Granberg and Glover, 2014, in Australia; Magnan et
32 al., 2016; Torabi et al., 2018; Gajjar et al., 2019a, in India). Cross-sectoral, cross-jurisdictional and cross-
33 spatial institutional frameworks enable successful adaptation by improving the ability of societies to respond
34 to changes in their environment in a timely manner. The latter points to the vital role of monitoring and
35 evaluation, as the tool to detect change in risk and vulnerability, together with environmental or societal
36 conditions determining risk and the effectiveness, efficiency, adequacy, or success of adaptation responses.
37
38
39 Table 17.8: Key factors that enable successful adaptation. The evidence and examples draw on the underlying sectoral
40 and regional chapters as well as a synthesis of adaptation literature.
Enablers What this Key characteristics Examples and traceability
enables
Recognitional Pluralising the - Focuses on inclusion and agency, i.e., who is - Co-production of knowledge
justice ambit of who is recognised as a legitimate actor and how their and inclusion of Indigenous and
'counted' as rights, needs and interests are acknowledged local knowledge (Loboguerrero
vulnerable, and incorporated into adaptation (Chu and et al., 2018; Dannenberg et al.,
drawing on Michael, 2018; Singh et al., 2021). 2019, Cross-Chapter Box ILK;
multiple - Acknowledges how differential vulnerability Ziervogel et al., 2019).
knowledge to climate change stems from historical and - Co-production of knowledge
systems structural inequalities, which can unevenly and inclusion of marginalized
distribute adaptation benefits, especially for groups across sectors, see e.g., in
the poorest and the most marginalized the health sector (Ch 7), food
(Tschakert and Machado, 2012; Shackleton et systems (Ch 5) and fire
al., 2015; Schlosberg et al., 2017; Ziervogel et management (Ch 12).
al., 2017; Eriksen et al., 2021).
- Informs more equitable adaptation priorities
(Ziervogel et al., 2017), legitimizes adaptation
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actions (Myers et al., 2018; Ellis and
Tschakert, 2019), supports inclusion of
marginalized groups (Chu and Michael, 2018)
(medium confidence).
Procedural Differential - Ensures that processes of representation and - Participation of multiple
justice participation participation in adaptation planning, stakeholders enables co-
and power for prioritisation and implementation are inclusive production of adaptation
Distributive more inclusive (Holland, 2017; Reckien et al., 2017; Reckien strategies and devolution of
justice adaptation et al., 2018b) (medium confidence). decision-making (Ziervogel,
planning and - Enable adaptations to advance more quickly 2019) and often, even if not
implementation and generate higher levels of wellbeing (e.g. always (D'Alisa and Kallis,
Dannenberg et al., 2019 comparing cases of 2016), a higher level of
strategic retreat), while also benefiting poorer transformational adaptation (and
households (Chu and Michael, 2018). more ambitious local mitigation
- Higher participation can enable more goals) (Cattino and Reckien, in
legitimate outcomes, greater awareness about press).
societal problems addressed, larger - Participatory processes can
willingness for community cooperation, and have more equitable outcomes as
increased individual behavioural change evidenced in informal
(Burton and Mustelin, 2013). settlements (Ziervogel, 2019,
- Participation in design and implementation South Africa), small farmers
of adaptation projects can be a critical element (Loboguerrero et al., 2018,
for avoiding maladaptive outcomes (Taylor, Colombia); migrants (Gajjar et
2015; Nightingale, 2017; Forsyth, 2018; al., 2019b, India), and
Mikulewicz, 2019). deliberative dialogues (Ojha and
et al., 2019).
- But participation does not
always address unequal power
relations (e.g. Buggy and
McNamara, 2016; Karlsson et
al., 2017).
Delivering - Ensures that adaptation interventions do not - Women and men have very
adaptation for exacerbate inequities (Atteridge and Remling, different access to mobile
vulnerable 2018) and that the benefits and burdens of phones, entailing lower
groups and interventions are distributed fairly (Tschakert responsiveness with climate
correcting et al., 2013; Reckien et al., 2017; Reckien et services among women (Partey
structural al., 2018b; Pelling and Garschagen, 2019). et al., 2020, across Africa).
vulnerabilities - However, low levels of commitment to - Slow progress on prioritizing
distributive justice, e.g. when justice is one of distributional and procedural
many goals of adaptation instead of the prime justice limits the expansion of
one, are insufficient to promote equitable adaptation funding to poorest
distribution of benefits and harms (medium and most vulnerable social
evidence, high agreement) (Anguelovski et al., groups and nations (Khan et al.,
2016; Pulido et al., 2016; Weinstein et al., 2019a).
2019; Shawoo and McDermott, 2020). - Focussing only on distributive
justice alone is less effective
than a holistic integration of
recognitional and procedural
justice (limited evidence,
medium agreement); e.g., only
including poor households as
recipients provides benefits to
wealthier households, in sectors
such as insurance for herders in
Mongolia (Taylor, 2016b), urban
water supply in Malawi (Rusca
et al., 2017), informal urban
settlements in Kenya (Pelling
and Garschagen, 2019), and
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forest management in Cambodia
(Work et al., 2019).
Flexible and Seeks policy - Institutional flexibility allows a society to - Capacity building of adaptation
strong integration, respond quickly to the demands of a changing funders, planners, and
institutions dynamic risk environment by developing new institutions or implementers and reorienting
management, adjusting existing ones quickly (Davis, 2010); existing institutions to make
and account for possibly avoiding lock-ins and addressing decisions under uncertainty,
long-term future climate risks (very high evidence, high institute long-term climate risk
goals agreement) (Levi-Faur, 2012; Sherman and management that goes beyond
Ford, 2013; Boyd and Juhola, 2015; Magnan typical political/ planning cycles,
et al., 2016). and develop learning
- Stability (and familiarity) is often desired in mechanisms between sectors,
governance arrangements and balancing the actors, and projects needed
need for stability with goals of flexibility, (Moser and Boykoff, 2013;
without causing rigidity is key (Craig et al., Granberg and Glover, 2014 in
2017, in USA; Ch 11). This is possible Australia; Boyd and Juhola,
through deliberate, consultative changes that 2015 in cities; Ziervogel, 2019
build awareness, develop shared norms, rules, in Africa and; Olazabal et al.,
and goals, and develop inclusive decision- 2019b in India; Ch 3 Oceans; Ch
making processes (Ch 3). 10; Ch 11; Ch 12).
- Flexible institutions enable
adoption of new adaptation
measures or course-correct based
on ongoing M&E (e.g. Granberg
and Glover, 2014 in Australia;
Magnan et al., 2016; Torabi et
al., 2018; Gajjar et al., 2019a in
India) (medium evidence, high
agreement).
- Sectoral or spatial policy
integration (Chu et al., 2017;
section 17.6; Hino et al., 2017;
Robinson and Wren, 2020);
integration of jurisdictional
frameworks of different agencies
(Poesch et al., 2016; Ch 5; Ch
9); and adaptive and flexible
legal systems, which
disaggregate socio-ecological
systems into smaller components
(Arnold and Gunderson, 2013;
Wenta et al., 2019) are key
enablers.
1
2
3 17.5.2 Adaptation Monitoring, Evaluation & Learning
4
5 17.5.2.1 Purpose of Monitoring and Evaluation
6
7 Adaptation responses have been observed in every region and across a wide variety of sectors (Ch16.3), but
8 little evidence exists of their outcomes in terms of climate risk reduction (high confidence) (Ch 1.4.3; Ford
9 and Berrang-Ford, 2016; Tompkins et al., 2018; Berrang-Ford et al., 2021; Eriksen et al., 2021; UNEP,
10 2021a). To advance on that, the Paris Agreement is encouraging countries to engage in "Monitoring and
11 evaluating and learning from adaptation plans, policies, programmes and actions" (UN, 2015, Article 7.9d).
12 Monitoring and Evaluation (M&E) is the systematic process of collecting, analyzing and using information
13 to assess the progress of adaptation and evaluate its effects--e.g., risk reduction outcomes, co-benefits and
14 trade-offs--mostly during and after implementation (AR6 Glossary). Distinctions between monitoring and
15 evaluation typically view monitoring as a continuous process of tracking implementation and informing
16 management to allow for corrective action including in situations of deep uncertainty (see Cross-Chapter
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1 Box DEEP in this Chapter) while evaluation is described as a more comprehensive assessment of
2 achievements, unintended effects and lessons learned carried out at certain point in time (OECD, 2002).
3 Monitoring and evaluation is an important part of the adaptation process (Figure 1.9). It can help to generate
4 information on adaptation success or maladaptive outcomes.
5
6 M&E of adaptation is undertaken for different purposes, including: (1) understanding whether responses
7 have achieved their intended objectives and contributed to a reduction in climate risks and vulnerability or to
8 an increase of adaptive capacity and resilience, (2) informing ongoing implementation and future responses,
9 and (3) providing upward and downward accountability (Preston et al., 2009; UNFCCC, 2010a; Pringle,
10 2011; Spearman and McGray, 2011). M&E is also commonly linked to learning (section 17.5.2.7). By
11 continuously monitoring implementation, e.g., to assess whether adaptation is on track or needs to be
12 accelerated-- M&E can aid decision-making under uncertainty. Adaptation M&E is distinct from tracking
13 financial flows related to adaptation since financial accounting does not provide information on
14 implementation and outcomes (17.5.2.5; Adaptation Partnership, 2012; World Bank Independent Evaluation
15 Group, 2012).
16
17 17.5.2.2 Adaptation M&E Approaches
18
19 Adaptation M&E can be conducted for various purposes and in a wide variety of different contexts ranging
20 from the local to the global level (McKenzie Hedger et al., 2008; UNFCCC, 2010a; Spearman and McGray,
21 2011). The context and specific purpose of M&E determine what information needs to be generated, and
22 together with the available resources also determine the suitability of particular approaches and methods
23 (Leiter, 2016; Leiter, 2017). Several frameworks and approaches have been proposed for M&E of adaptation
24 and climate resilience (Bours et al., 2014d; Schipper and Langston, 2015; Adaptation Committee, 2016;
25 ODI, 2016; Cai et al., 2018; Gregorowski et al., 2018) including sector-specific ones for agriculture (FAO,
26 2017; FAO, 2019a; FAO, 2019b), health (Ebi et al., 2018), ecosystem-based adaptation (Donatti et al., 2018;
27 Donatti et al., 2020; GIZ, 2020a) and cities (section 6.4.6).
28
29 Adaptation M&E generally seeks to answer whether implementation is taking place and what effects it has
30 (figure 17.11). Accordingly, M&E can focus on the processes, activities and outputs or on their outcomes
31 and ultimate impacts (Harley et al., 2008; Pringle, 2011; Ford et al., 2013). Most of the available guidance
32 for the development of adaptation M&E systems is aimed at the household, local or project level (Pringle,
33 2011; Villanueva, 2012; Olivier et al., 2013; CARE, 2014; BRACED, 2015; Leiter, 2016; Jones, 2019b) with
34 only limited guidance for national or cross-sectoral M&E systems (Price-Kelly et al., 2015) or frameworks
35 that are applicable at different scales (Brooks et al., 2014). The available guidebooks take users through a
36 series of steps which are synthesized in Figure 17.11.
37
38
39 17-92 Total pages: 156
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1 Figure 17.11: Adaptation M&E and learning as part of the adaptation process (based on Hammill et al., 2014a; Price-
2 Kelly et al., 2015; Leiter, 2016). This figure shows the main steps involved in developing an adaptation M&E system
3 where the context informs the purpose of M&E which in turn determines the information needs. To achieve the M&E
4 purposes, the chosen approach and data sources need to be able to generate the needed information which needs to be
5 communicated in a suitable way to the target audiences.
6
7
8 The majority of adaptation M&E efforts have so far focused on processes and outputs rather than on
9 achieved outcomes, e.g. climate risks, vulnerability, well-being or development (Droesch et al., 2008; GIZ
10 and Adelphi, 2014; UNDP Cambodia, 2014; Fawcett et al., 2017) (high confidence) or use a combination
11 thereof (Brooks et al., 2011; Brooks et al., 2014). Newly emerging approaches include perception-based
12 measurements and the use of data collected via mobile phones (Jones et al., 2018; Jones, 2019a), which can
13 be collected frequently (Clare et al., 2017a; Knippenberg et al., 2019; Jones and Ballon, 2020). Such
14 advances call into question the common reliance on "objective" indicators defined from an external
15 perspective. Instead, they suggest that multiple complementary approaches combined with higher frequency
16 data collection produce a more elaborate picture of the effects of adaptation and resilience responses (Jones
17 and d'Errico, 2019; Knippenberg et al., 2019; Singh et al., 2019; Jones, 2019a; see Cross-Chapter Box
18 PROGRESS in this Chapter) (medium confidence).
19
20 Central to designing, monitoring and evaluating adaptation responses is outlining how activities are expected
21 to lead to intended objectives, e.g., via a theory of change (Bours et al., 2014c; Oberlack and al., 2019).
22 Theories of change or similar change models provide a basis to decide what to measure but more attention
23 needs to be paid to how theories of change are constructed and who is involved (Mason and Barnes, 2007;
24 Forsyth, 2018). Participatory approaches can support understanding how climate risks affect the respective
25 population, how these risks interact with social and cultural processes, and how responses could most
26 effectively address climate risks (Conway et al., 2019). Inclusive M&E systems can facilitate ownership and
27 enhance the meaningfulness and usability of the generated information (CARE, 2014; Faulkner et al., 2015).
28 Meaningfulness is not associated with a particular approach or method but depends on whether the chosen
29 M&E design fits the M&E purpose and the information needs of the intended audience (Fisher et al., 2015;
30 Leiter, 2017). Effective communication of M&E findings and feedback into decision making processes is
31 essential to achieve the respective M&E purpose and facilitate learning (section 17.5.2.7).
32
33 17.5.2.3 Adaptation Indicators and Indices
34
35 A set of all-purpose and globally applicable standard indicators that could comprehensively measure
36 adaptation does not exist (high confidence) (IPCC, 2014a; Leiter and Pringle, 2018). A wide variety of
37 indicators have been used to assess adaptation and its results (CARE, 2010; Harvey et al., 2011; Lamhauge
38 et al., 2013; Brooks et al., 2014; Hammill et al., 2014b; M鋕inen et al., 2018; HM Government, 2019).
39 Literature has also noted unrealistic expectations of what indicators can accomplish. For instance, decisions
40 involving competing political interests would not be adequately informed through simple indicators; and
41 learning requires knowledge of how and why change has happened, something that indicators often do not
42 capture (Hinkel, 2011; Bours et al., 2014d). Indicators can also become misguided incentives and might steer
43 attention away from what matters (Leiter and Pringle, 2018; Hallegatte and Engle, 2019; Klonschinski,
44 2021). Surveys, scorecards, interviews and focus groups are alternative methods of gaining insights on
45 adaptation progress (Brooks et al., 2014; Porter et al., 2015; Das, 2019; McNamara et al., 2020).
46
47 The difficulties of assessing adaptation and an emphasis on short-term results have contributed to the
48 common practice of relying on easily quantifiable indicators rather than assessing actual changes, i.e.
49 outcomes and impacts (World Bank Independent Evaluation Group, 2012; Fisher et al., 2015). In fact,
50 indicators used by international climate funds largely measure outputs which provide little evidence of the
51 actual effectiveness of adaptation, i.e. its outcomes and impacts (GCF Independent Evaluation Unit, 2018;
52 Leiter et al., 2019; Pauw et al., 2020).
53
54 Indices, the combination of multiple indicators into a single score, are common products of risk and
55 vulnerability assessments to compare countries or other entities, often in the form of rankings or maps
56 (Preston et al., 2011; Reckien, 2018; de Sherbinin and et al., 2019). They can indicate changes in
57 vulnerability over time within their respective conceptualisation of vulnerability or risk. The construction of
58 indices including indicator selection, their weighting, normalisation and data sources have a profound impact
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1 on their scores (Reckien, 2018). Research has consistently found large discrepancies between country
2 vulnerability rankings (Brooks et al., 2005; Eriksen and Kelly, 2007; Leiter et al., 2017b; Visser et al., 2020).
3 Reviews of vulnerability and resilience indices identified "substantial conceptual, methodological and
4 empirical weaknesses" (F黶sel, 2010: 8) and a widespread lack of validation (Cai et al., 2018). Using
5 countries as a unit of analysis also masks significant subnational variation (Otto et al., 2015;
6 Mohammadpour et al., 2019). Individual indices therefore "fail to convene a robust guidance for policy
7 makers" (Muccione et al., 2017: 4) and should not present the sole basis for policy decisions (Brooks et al.,
8 2005; Leiter and Pringle, 2018). Due to their limitations (Singh et al., 2017), the OECD suggests that indices
9 are primarily used for "initiating discussion and stimulating public interest" (OECD, 2008: 13).
10
11 17.5.2.4 Empirical Evidence of National Adaptation M&E Systems
12
13 Tracking the implementation of national adaptation plans is essential for understanding their effectiveness,
14 i.e. the progress made in addressing climate risks, and can support assessing the success of adaptation and
15 the risk of maladaptation. Over 60 countries have developed or started developing national adaptation M&E
16 systems, although less than half are yet reporting on implementation (Leiter, 2021b; Table 17.9). Country-
17 specific adaptation M&E systems vary considerably regarding their legal mandate, purpose, content,
18 involved actors and types of reporting (Hammill et al., 2014a; EEA, 2015; Leiter, 2015; Leiter et al., 2017a;
19 EEA, 2020). In most cases, they focus primarily on monitoring implementation rather than assessing
20 outcomes, although some are linked to national climate risk or vulnerability assessments (e.g. in Germany
21 and the United Kingdom) (EEA, 2018). At least 15 countries have published evaluations of national
22 adaptation plans which help inform the development of successive adaptation plans or strategies (Table
23 17.9). Nevertheless, there is only limited empirical evidence of the ability of M&E systems to facilitate
24 action or increase the level of ambition of revised policies. More research is needed to determine the quality
25 of national adaptation M&E systems and how well they support the policy cycle.
26
27 Under the Paris Agreement countries are encouraged to provide information on adaptation including its
28 adequacy and effectiveness (M鰄ner et al., 2017; Adaptation Committee, 2021). National adaptation M&E
29 systems can inform both national as well as international reporting and contribute to the global stocktake (see
30 Cross-Chapter Box PROGRESS in this Chapter; Craft and Fisher, 2015; Leiter et al., 2017a). Guidance for
31 and examples of national adaptation progress assessments are provided by Price-Kelly et al. (2015); Brooks
32 et al. (2014); Brooks et al. (2019); EEA (2015); GIZ (2017); Karani (2018); and van R黷h and Sch鰊thaler
33 (2018). Global assessments of adaptation progress have so far often focused on adaptation planning and, to a
34 lesser extent, implementation whilst evidence of the collective effect of adaptation globally remains limited
35 (high confidence) (UNEP, 2021a; Cross-Chapter Box PROGRESS in this Chapter).
36
37
38 Table 17.9: Countries in different stages of developing or operating a national adaptation M&E system as of 1 August
39 2021 (Source: Leiter (2021b). Countries can appear twice if they have published both a progress report and an
40 evaluation.
National adaptation M&E system
Stage Definition Country
Under Early stage Tangible steps have been undertaken to Benin, Cook Islands, Jordan, Paraguay,
develop- develop a national adaptation M&E Sri Lanka, Uganda
system, for example a stocktake of
ment relevant existing data sources and
engagement with stakeholders on the
objectives of the M&E system
Advanced Details of the adaptation M&E system Albania, Bulgaria, Cameroon, Canada,
stage have been developed, including, for Colombia, Ethiopia, Fiji, Grenada,
instance, institutional arrangements, Indonesia, Moldova, Morocco,
indicators and data sources, but it has not Mozambique, Nauru, Peru, Rwanda,
yet been applied Senegal, St.Lucia, St. Vincent and the
Grenadines, Suriname, Thailand, Togo,
Tonga, Turkey, Vietnam
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In Adaptation A progress report on the implementation Austria, Belgium (Flanders), Brazil,
operation progress of the national adaptation plan or Burkina Faso, Cambodia, Chile, Cyprus,
report strategy has been published France, Germany, Japan, Kenya, Kiribati,
published Lithuania, Mexico, Netherlands (Delta
Programme), Norway, Portugal, Slovakia,
Spain, South Africa, South Korea,
Switzerland, United Kingdom
Evaluation An evaluation of the implementation of Belgium, Cambodia, Chile, Czech
published the national adaptation plan or strategy Republic, Finland, France, Germany,
Ireland, Mexico, Netherlands,
has been undertaken and published. Philippines, South Korea, Spain,
Switzerland, United Kingdom
1
2
3 17.5.2.5 Challenges of Assessing Adaptation
4
5 To date, literature has largely focused on aspects prior to implementation such as assessments of climate
6 vulnerability and risks or appraisals of adaptation options (Sietsma et al., 2021; Cross-Chapter Box
7 Adaptation). To understand adaptation progress, the assessment of implemented adaptation actions and their
8 outcomes requires more attention (very high confidence) (Cross-Chapter Box PROGRESS in this Chapter).
9
10 Outcomes on risk reduction are typically expressed in ways that are specific to the respective sector or
11 context (e.g., as agricultural yields, health benefits or reduced water stress) highlighting that "adaptation has
12 no common reference metrics in the same way that tonnes of GHGs or radiative forcing values are for
13 mitigation" (IPCC, 2014a: 856). Assessments of adaptation progress therefore need to specify what they are
14 measuring and how they are measuring it. The way adaptation is conceptualised, e.g. as a continuum
15 between successful adaptation and maladaptation (Section 17.1.1) and the way adaptation is framed, e.g. as a
16 technical challenge or a political process (Juhola et al., 2011; Bassett and Fogelman, 2013; Eriksen et al.,
17 2015), shape the understanding of progress and its subsequent measurement (Singh et al., 2021).
18
19 Furthermore, people can be differently affected even in the same location due to, amongst others, differential
20 vulnerability amongst the population (Reckien and Petkova, 2019; Thomas et al., 2019). Different views and
21 values can also affect what it means to adapt (Few et al., 2021). Assessments of adaptation progress therefore
22 need to be transparent and reflective about how they define and measure adaptation and account for
23 culturally and geographic contingent concepts of what it means to adapt in light of the global diversity of
24 livelihoods and concepts.
25
26 The lack of knowledge on adaptation progress is associated with further measurement challenges including
27 that avoided impacts are difficult to measure and that risk levels change over time, meaning what is effective
28 today may not be effective in the future (Brooks et al., 2011; Pringle, 2011; Spearman and McGray, 2011;
29 Villanueva, 2012; Bours et al., 2014a). Moreover, adaptation is embedded in complex political and social
30 realities where power and politics shape outcomes and where simplistic views of how adaptation would take
31 place may be ill-conceived (Nightingale, 2017; Mikulewicz, 2018; Mikulewicz, 2020). In practice this means
32 that theories of change of adaptation projects may miss important causes of risks and could subsequently
33 lead to inaccurate assessments (Forsyth, 2018). Measuring adaptation is therefore a matter of understanding
34 drivers of vulnerability and risk and of designing responses and M&E systems accordingly (UNFCCC,
35 2019a, section V).
36
37 The importance of context and the dependence on viewpoints make comparative assessments of adaptation
38 across nations, regions or responses challenging. Comparison requires a consistent conceptualisation of
39 adaptation, comparable units of analysis and access to relevant datasets (Ford et al., 2015; Ford and Berrang-
40 Ford, 2016). Comparative adaptation policy assessments to date often lack clarity in concepts and
41 explanatory variables (Dupuis and Biesbroek, 2013; Biesbroek R, 2018a). The trade-off between
42 standardisation and context-specificity also complicates attempts to aggregate adaptation progress across
43 scales to the national or global level (Leiter and Pringle, 2018; Cross-Chapter Box PROGRESS in this
44 Chapter).
45
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1
2 [START CROSS-CHAPTER BOX PROGRESS HERE]
3
4 Cross-Chapter Box PROGRESS: Approaches and Challenges to Assess Adaptation Progress at the
5 Global Level
6
7 Authors: Matthias Garschagen, Timo Leiter, Robbert Biesbroek, Alexandre K. Magnan, Diana Reckien,
8 Mark New, Lea Berrang-Ford, So Min Cheong, Lisa Schipper, Robert Lempert
9
10 This Cross-Chapter Box responds to a growing demand for assessing global climate change adaptation
11 progress, which currently faces the challenge of lacking consensus on how adaptation progress at this level
12 can be tracked (high confidence). The box therefore assesses the rationale and methodological approaches
13 for understanding adaptation progress globally across sectors and regions. It discusses strengths and
14 weaknesses of existing approaches and sources of information, with a view towards informing the first
15 Global Stocktake of the Paris Agreement in 2023.
16
17 Rationale for assessing adaptation progress at the global level
18
19 Global assessments of adaptation are expected to help answer key questions of climate policy (Ford et al.,
20 2015; UNEP, 2017; Adaptation Committee, 2021) (low evidence, high agreement,), including: Do the
21 observed, collective investments in adaptation lead humanity to being better able to avoid or reduce the
22 negative consequences from climate change? Where is progress being made and what gaps remain in the
23 global adaptation response to climate risks?
24
25 Whilst more than 170 countries have policies that address adaptation (Nachmany et al., 2019b; 17.4.2), very
26 few have operational frameworks to track and evaluate implementation and results (Leiter, 2021a; 17.5.2.4).
27 In Europe, for example, most countries have adopted a national adaptation plan or strategy, but only few are
28 tracking whether ambitions are realised (EEA, 2020; 13.11.2). Moreover, climate risks are interconnected
29 across scales, regions and sectors (Eakin et al., 2009; Challinor et al., 2017; Cross-Chapter Box INTERREG
30 in Chapter 16; Hedlund et al., 2018) (high confidence), complicating causal attribution. National assessments
31 of progress usually do not assess private sector and non-governmental adaptation and barely account for
32 climate risks that transcend across borders, for example through supply chains or shared ecosystems (EEA,
33 2018; Benzie and Persson, 2019). In addition, adaptation action in one place or time can potentially lead to
34 negative effects elsewhere (externalities) (Magnan and Ribera, 2016; Atteridge and Remling, 2018; 17.5.1).
35 Hence, determining the collective adequacy and effectiveness (see Figure 1.7 in Chapter 1) of adaptation
36 responses is different from simple aggregates of national and sub-national information (UNEP, 2017).
37
38 Assessing global progress on adaptation is therefore of high relevance to the scientific community, to policy
39 makers and other actors. Global assessments serve different information needs than local assessments and
40 their meaningfulness depends on the chosen approaches and their limitations. Aggregated global assessments
41 of adaptation progress are therefore not meant to substitute place-specific ones but to complement them to
42 enhance the knowledge base on adaptation beyond actions by or within individual countries. The Paris
43 Agreement stipulates a Global Stocktake to be undertaken every five years to assess the collective progress
44 towards its long-term goals including on adaptation (UNFCCC, 2015, Article 14). Yet very few scientific
45 studies have addressed the adaptation-specific aspects of the Global Stocktake (Craft and Fisher, 2018;
46 Tompkins et al., 2018) and there are different views and options on how assessing global progress could take
47 place (high confidence).
48
49 Considerations in designing global adaptation assessments
50
51 A number of key considerations for the design of global adaptation assessment approaches are discussed in
52 the literature (Ford and Berrang-Ford, 2016; Berrang-Ford et al., 2017). Some of these involve trade-offs,
53 e.g. global applicability vs. context-specificity, for which there is no simple solution. Design considerations
54 directly depend on the objectives of global adaptation assessments, which can differ between actors and can
55 include e.g. providing transparency, enabling accountability, understanding effectiveness, or guiding policy
56 development (Section 17.5.2.1). The underlying objectives determine the suitability of approaches and the
57 data requirements.
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1
2 Comparability
3 Global assessments may have the objective to compare adaptation over time and across sectors and regions
4 (Ford et al., 2015). Such comparison requires a consistent definition of concepts (Hall, 2017; Berrang-Ford
5 et al., 2019) and the identification of variables that are both generic enough to be applicable from one context
6 to another and specific enough to illustrate national circumstances. To date, finding such balance has proven
7 to be challenging (Dupuis and Biesbroek, 2013). The context-dependence of adaptation outcomes poses
8 limits for meaningful comparisons. Even people exposed to the same climate hazard may be differentially
9 affected due to varying levels of vulnerability and resilience (Jones et al., 2018; Thomas et al., 2019),
10 meaning that perceptions on adaptation outcomes can also differ (Jones and d'Errico, 2019).
11
12 Aggregation
13 The aggregation of data from local or regional to global scales can take different forms ranging from
14 qualitative synthesis to quantitative aggregation which may involve condensing a diverse set of variables into
15 a single score (Leiter, 2015; 17.5.2.3). In contrast to climate change mitigation, adaptation does not have a
16 global reference metric against which adaptation levels could be assessed to identify progress or gaps.
17 Experience from the Global Environment Facility, for example, has shown that mechanical aggregation
18 based on standardized indicators fails to capture what makes the greatest difference on the ground (Chen and
19 Uitto, 2014).
20
21 Results: Input, process, output or outcome
22 Adaptation progress at any spatial scale can in principle be assessed in terms of input (e.g. resources spent),
23 process (i.e. the way adaptation is organized), output (i.e. adaptation capacities and actions) and outcomes
24 (i.e. actual changes induced) (Section 17.5.2.2). Due to the challenges inherent in measuring adaptation
25 outcomes (Sections 16.3, 17.5.1 and 17.5.2.5), most global assessments to date have focused on outputs, e.g.
26 whether countries have adopted adaptation plans (Berrang-Ford et al., 2021; UNEP, 2021a) (high
27 confidence). Understanding the effectiveness of adaptation responses globally requires a way to
28 conceptualize and capture outcomes, for example in terms of effective climate risk reduction, whilst avoiding
29 simplifications that mask maladaptation at the global level, e.g. where climate risks are shifted to other
30 countries, sectors or population groups (Cross-Chapter Box INTERREG in Chapter 16, Section 17.5.1).
31
32 Data
33 Global assessments typically require global availability of consistent data, be it quantitative or qualitative,
34 which has proven to be a constraining factor for attempts to assess global adaptation (high confidence). For
35 example, many countries face difficulties in reporting adequately on progress in implementing the Sendai
36 Framework and risk-related SDGs (UNDRR, 2019: vi). The availability of data also influences which
37 variables can be eventually selected in an assessment. This limitation can affect the ability to meet the initial
38 objectives and lead to biases in the framing and interpretation of assessment outcomes. For some variables,
39 an alternative to relying on nationally provided data can be to develop new global datasets (Magnan and
40 Chalastani, 2019), or utilising data from Earth Observation (Andries et al., 2018). Adaptation is hence faced
41 with a dilemma between globally available yet generic data and regionally or locally more detailed yet
42 patchy data (high confidence).
43
44 Assessment of existing approaches to assess adaptation progress at the global level
45
46 Only few global assessments of adaptation progress across sectors have been undertaken to date (high
47 confidence). They focus, for example, on whether countries have progressed their adaptation policies and
48 actions over time (Lesnikowski et al., 2015; Nachmany et al., 2019b), the extent of implemented adaptation
49 globally (Leiter, 2021a; Leiter, 2021b), and the type and actors of responses (Berrang-Ford et al., 2021),
50 evidence for reduced vulnerability to climate-related hazards (Formetta and Feyen, 2019; UNDRR, 2019) or
51 adaptation planning in cities across the globe (Araos et al., 2016a; Reckien et al., 2018a; Olazabal et al.,
52 2019a). Each of these assessments draw on different approaches and data, and all have particular potential
53 but also limitations (Table Cross-Chapter Box PROGRESS.1) (high confidence). The application of differing
54 approaches shows that there is no single `best' approach or data source to assess global progress on
55 adaptation (high confidence). Existing global assessments have provided valuable insights into the extent and
56 types of responses and their level of planning and implementation (16.3.2.4). They do, however, not provide
57 comprehensive and robust answers so far on whether climate risk and vulnerability have been reduced
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1 (Berrang-Ford et al., 2021) (high confidence). As a result, combining different approaches and integrating
2 data on climate risk levels, policy measures, implemented actions and their effects on climate risk reduction
3 is currently regarded the most robust approach (Berrang-Ford et al., 2019) (medium evidence, high
4 agreement).
5
6
7 Table Cross-Chapter Box PROGRESS.1: Key approaches and data sources used for global adaptation assessments.
Approach / Data source Potential added-value Limitations
Systematic assessment of adaptation Provides an indication of the Not a representative sample; biased
responses reported in academic literature status, trends and gaps in towards responses published in
(e.g. systematic reviews, evidence synthesis, adaptation responses scientific literature; excludes grey
meta-analysis, large-n comparative studies) literature; some topics and regions
Examples: not well covered; challenges in
Berrang-Ford, 2011 #188}, Global terms of comparability and
Adaptation Mapping Initiative (Berrang- aggregation; inconsistency in
Ford et al., 2021) definitions and use of concepts;
English language bias
Self-reported progress documents by Context-specific information; May only be available every few
years; content is sensitive to
countries (e.g. National Communications, official government documents political and policy changes;
possible bias towards positive
Biennial Transparency Reports or domestic enable assessments of national examples; challenges in terms of
comparability and aggregation;
progress and evaluation) progress inconsistency in definitions and
use of concepts
Examples:
(Gagnon-Lebrun and Agrawala, 2007;
Lesnikowski et al., 2015; Lesnikowski et al.,
2016; Leiter, 2021a)
Self-reported information from the private Provides an indication of the Sample biased towards larger
sector (e.g. information on actions taken in status, trends and gaps in companies; challenges in terms of
response to climate risks within the context adaptation responses by the comparability and aggregation;
of climate-related financial disclosure or in private sector; complements potential inconsistencies in
company reports). information published in the definitions and use of concepts
Examples: scientific literature; could enable
(Committee on Climate Change, 2017; better understanding of supply
Street and Jude, 2019; UNFCCC, 2021), chain risks
responses reported under Climate-related
Financial Disclosure
Project documents and evaluations (e.g. Detailed information on context, Actual implementation can differ
from climate funds or implementing intended or achieved results and from what was proposed;
organisations) activities fragmented picture of
Examples: local/regional actions; results may
(Leiter, 2021b); (Eriksen et al., 2021) be challenging to aggregate;
challenges in terms of
comparability and aggregation;
inconsistency in definitions and
use of concepts
Existing global data sets of mostly Comparable information based Global data availability constrains
quantitative indicators on globally defined indicators indicator choice; reporting burden
Examples: for new indicators; trade-off
United Nations (UN, 2016a; UN, 2016b; between global applicability and
UN, 2019; UNDRR, 2019) national circumstances; usefulness
and meaningfulness of global
indicators is contested (Leiter and
Pringle, 2018; Lyytim鋕i et al.,
2020; Pauw et al., 2020).
Tracking financial flows Comparable data on financial No information about
implementation of measures and
Examples: flows directed at adaptation; their adaptation effect (Eriksen et
al, 2021), i.e. it tracks inputs, not
(CPI, 2019), (OECD, 2018a), (MDBs, 2019) standardised methodologies (e.g.
OECD RIO markers; climate
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finance tracking method of outputs or outcomes; inconsistency
multilateral development banks; in what gets counted as adaptation
chapter 17.5.2.6; Cross-Chapter finance (Donner et al., 2016; Doshi
Box FINANCE in this Chapter) and Garschagen, 2020); evidence
of over-reporting (Michaelowa and
Michaelowa, 2011; Weikmans et
al., 2017)
1
2
3 Conclusion -- Combining approaches for assessing adaptation progress at the global level
4
5 Understanding to what extent the world is on track to adapt to climate change impacts and risks globally is a
6 pressing question in scientific and policy communities, especially in light of the Global Stocktake under the
7 Paris Agreement. Important considerations for a robust assessment framework (e.g. consistency), as well as
8 the associated scientific challenges (e.g. aggregation, externalities, breath vs. depth of data) and the role of
9 underlying objectives (e.g. on the contested issue of comparability) are increasingly understood (high
10 confidence). There is also a growing and diverse body of information on adaptation progress, although most
11 assessments of global progress undertaken to date focus on processes and outputs (e.g. policies and plans)
12 rather than outcomes (i.e. risk reduction). A variety of approaches and data sources are employed, such as
13 systematic reviews of observed adaptation, formal communications by Parties to the UNFCCC, and project
14 documents to international funding agencies. Novel approaches, including big data tools (Ford et al., 2016;
15 Biesbroek et al., 2020), are also being explored but still have to prove their practical value. Each approach
16 and source of information can contribute additional knowledge, but also demonstrates limitations, so that
17 there is no single `best' approach (high confidence). Yet to date, the international community has not
18 sufficiently explored the relative strengths and weaknesses of different approaches and their applicability,
19 and therefore their potential synergies in complementing each other. Triangulated assessments have only
20 rarely been applied (high confidence) due to multiple conceptual and methodological challenges, despite
21 their potential for increasing the robustness of knowledge. One overarching conclusion of this Cross-Chapter
22 Box therefore is that the combination of different approaches will provide a more comprehensive picture of
23 global adaptation progress than is currently available from individual approaches (low evidence, high
24 agreement).
25
26 [END CROSS-CHAPTER BOX PROGRESS HERE]
27
28
29 17.5.2.6 Tracking Adaptation Finance
30
31 Adaptation finance tracking is capturing the financial flows associated with adaptation. It can indicate how
32 much is being spent on adaptation, where funds are going to, and whether spending matches allocated
33 budgets. Thus, adaptation finance tracking can provide useful information for decision making, but it does
34 not provide information on the achievements resulting from the invested funds. Accordingly, it can
35 complement, but not substitute, M&E of actions and outcomes. Adaptation finance tracking can be applied
36 domestically (Guzm醤 et al., 2017; Guzm醤 et al., 2018) as well as internationally, for instance by developed
37 countries to report on the goal to mobilize US$100 billion a year by 2020 in climate finance (UNFCCC SCF,
38 2018). Data on adaptation finance can be used alongside information on planning and implementation to
39 assess adaptation progress (UNEP, 2021a).
40
41 Tracking adaptation finance requires defining what counts as adaptation. Different definitions can lead to
42 large variations in the estimated amount of adaptation finance (Donner et al., 2016; Hall, 2017). A further
43 challenge is how to account for adaptation that is mainstreamed, i.e. where adaptation-specific investments
44 form only part of a larger programme or budget line, or where actions contribute to adaptation without being
45 labelled as adaptation. These challenges limit the direct comparability between adaptation and mitigation
46 finance (UNFCCC, 2019a). In fact, tracking adaptation finance differs from tracking mitigation finance since
47 activities cannot be a-priori assumed to constitute adaptation but instead have to be assessed for their linkage
48 to climate risks in a particular context (MDBs & IDFC, 2018). Methods for adaptation finance tracking
49 continue to be further developed aiming at better comparability and completeness (Richmond and
50 Hallmeyer, 2019; Richmond et al., 2021).
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1
2 Various methods are used to track adaptation finance, which makes comparisons between adaptation finance
3 figures challenging (UNFCCC SCF, 2018; Weikmans and Roberts, 2019). For example, multilateral
4 development banks use a different methodology than countries do under the OECD Development Assistance
5 Committee (DAC) (see Box 17.4; MDBs, 2019). One of the differences concerns the treatment of partially
6 adaptation-relevant projects, namely whether only parts or the full amount of a given project volume are
7 counted as adaptation finance (see e.g. MDBs, 2019). Under the OECD DAC methodology, countries often
8 use a fixed percentage (e.g., 50% of the total project value) whereas the MDB methodology attempts for a
9 project-specific estimation of the adaptation-relevant proportion (MDBs & IDFC, 2018). Another aspect is
10 whether tracking distinguishes between financial instruments, e.g., grants or loans. Different accounting
11 rules can lead to large differences in reported amounts of adaptation finance and to a lack of comparability
12 between providers (Weikmans and Roberts, 2019). Studies identified an over-reporting (i.e., counting non-
13 adaptation related finance) by a factor of two to three, which suggests the need for a more consistent and
14 transparent accounting system (Weikmans et al., 2017; CARE, 2021).
15
16 Good coverage of adaptation finance data exists around international public finance flows, predominantly
17 official development assistance flows from OECD DAC members and from multilateral development banks.
18 Less data exists around domestic public finance and private finance flows to adaptation activities, but data
19 sources continue to be further expanded e.g. through climate change expenditure tagging and city-level data
20 (Weikmans et al., 2017; UNFCCC SCF, 2018; Richmond et al., 2021). Recent estimates of adaptation
21 finance are provided in UNFCCC SCF (2018); Macquarie et al. (2020); and in Cross-Chapter Box FAR.
22
23
24 [START BOX 17.4 HERE]
25
26 BOX 17.4: The Rio Markers Methodology to Track Climate Finance
27
28 The OECD Development Assistance Committee (DAC) introduced a methodology to track the amount of
29 bilateral official development assistance (ODA) that is targeting climate change mitigation and/or adaptation.
30 It distinguishes whether activities have adaptation as a "principal" objective (score "2"), as a "significant"
31 objective (score "1"), or as not targeting it (score "0") (OECD, 2016). The associated project value is
32 counted in full, in part, or not counted as adaptation finance, respectively. Countries count the volume of
33 partial adaptation projects (score "1") to a different extent which limits comparability and can lead to over-
34 reporting (OECD, 2019a). The first data on this "adaptation marker" became available in 2012 for the
35 financial flows of 2010. It forms the basis for developed countries' reporting to the UNFCCC Secretariat on
36 their financial commitments towards developing countries (Weikmans and Roberts, 2019).
37
38 While a guidebook with requirements for adaptation as a principle or significant objective has been
39 developed (OECD, 2016), several studies have shown that OECD DAC donors tend to overestimate the
40 number of activities in their portfolio that genuinely have adaptation objectives (Michaelowa and
41 Michaelowa, 2011; Weikmans et al., 2017; CARE, 2021). Hence, the amount of adaptation finance from
42 public sources may be lower than reported. The use of just three categories leads to a broad range of the
43 extent of adaptation being concentrated in the middle category ("significant objective"). Accordingly, the
44 category "principle objective adaptation" provides a more robust predictor of the relevance of an activity to
45 adaptation (Donner et al., 2016).
46
47 [END BOX 17.4 HERE]
48
49
50 17.5.2.7 Evaluation and Learning
51
52 Most adaptation M&E frameworks and tools proposed to date refer to monitoring rather than evaluation
53 (high confidence) (Adaptation Committee, 2016). Evaluations are envisioned to go beyond monitoring by
54 examining how and why results have been achieved and what could be improved (Brousselle and Buregeya,
55 2018; V鋒鋗鋕i and Verger, 2019). Evaluations of adaptation outcomes are still rare, particularly
56 quantitative impact evaluations (Weldegebriel and Prowse, 2013; Das, 2019; B閚� et al., 2020). Impact
57 evaluations of adaptation need to address several methodological as well as practical challenges (Dinshaw et
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1 al., 2014; Fisher et al., 2015; B閚� et al., 2017; Puri et al., 2020). Different types of evaluations are
2 appropriate for different evaluation questions (Silvestrini et al., 2015). Evaluations of the available evidence
3 of effective adaptation in particular topics or sectors have emerged more recently, for instance on
4 mainstreaming (Runhaar et al., 2018) and agricultural climate services (Vaughan et al., 2019a). Impact
5 evaluations of capacity building measures are important because capacity building is assumed to lead to
6 adaptation, but its actual effects are seldom examined (Mortreux and Barnett, 2017; Alpizar F and
7 Meiselman, 2019). If well designed and utilised for learning, evaluations can play an important role in
8 improving adaptation responses (Hild閚, 2011).
9
10 Learning requires information about how and why change occurred and what experiences have been made
11 (Feinstein, 2012). M&E is frequently associated with learning, but it is rarely made explicit how learning is
12 supposed to take place (Armitage et al., 2008; Baird et al., 2015; Borras and H鴏und, 2015). The design of
13 adaptation M&E systems can support learning by gathering relevant information and disseminating it in a
14 way that is accessible and effectively linked to decision making processes (Spearman and McGray, 2011;
15 Villanueva, 2012; Fisher et al., 2015). Options include institutionalised feedback mechanisms, peer learning
16 and knowledge sharing events, a learning culture and ways to gather in-depth insights beyond indicators
17 (ibid; Oswald and Taylor, 2010). Since AR5, adaptation programmes and funds such as the BRACED
18 programme, the Adaptation Fund, the Climate Investment Funds and the Green Climate Fund have created
19 knowledge-sharing units and provide resources to support learning activities(BRACED, 2015; Roehrer and
20 Kouadio, 2015; Adaptation Fund, 2016; Leavy et al., 2018; CIF, 2020; Puri et al., 2020), but there is little
21 information about their longer-term effectiveness.
22
23
24 17.6 Managing and Adapting to Climate Risks for Climate Resilient Development
25
26 Actions to ameliorate a climate risk have consequences beyond the immediate effects on exposure or
27 vulnerability to a hazard. They may aim to combat many risks, could adversely interact with other risks and
28 actions, or may be nested within a suite of actions across many risks. Some actions may have negative
29 consequences for climate resilient development. In this broader context, the effectiveness of adaptations for
30 supporting climate resilient development is now better articulated (Box 17.1). Importantly, adaptations need
31 to be designed to not only combat current and future climate risks, but also ensuring that they do not lock in
32 undesirable pathways in the future as risks develop and change (very high confidence) (17.2, 17.3.1, 17.5).
33 Effective management of climate risks will therefore be dependent on satisfactorily managing current
34 climate risks (Box 17.1, 17.2, 17.5), coupled with assessing prognoses for future climate risks, and
35 developing responses in advance for reducing those risks to tolerable residual levels (very high confidence)
36 (1.4, 1.6, 16.6, 17.2, Box 16.1; e.g. water risks - 4.7.1). The dynamic nature of risk (Viner et al., 2019;
37 Simpson et al., 2021; 16.3, 16.6) also means that the contribution of current adaptations to ameliorating
38 future risks needs to be regularly reviewed (high confidence) (17.5.2). Across the Working Group II report
39 are examples of how managing adaptations to ameliorate climate risks can negatively or positively affect
40 sustainable development, thereby impacting the potential for climate resilient development discussed in
41 Chapter 18. Drawing on the assessment of sectoral and regional chapters in this report, this section examines
42 three broad components for orienting decision-making for climate adaptation towards climate resilient
43 development.
44
45 17.6.1 Need for Integrated Risk Management
46
47 The complex, interacting and compounding nature of climate risks means that single risks cannot be
48 managed in isolation (very high confidence) (16.5, Figure 16.11; 17.3.2; Nhamo et al., 2018), including
49 accounting for potential risks arising from adaptations (Simpson et al., 2021). Regional examples of needs
50 for cross-sectoral integrated management include the water-energy-food nexus in Africa (10.5.1), Asia
51 (10.6.3), Australasia (11.6), Europe (13.2.2) and North America (Table 14.8), and ecosystem-oriented
52 adaptations and/or nature-based solutions, in Africa (9.6.5), Asia (10.4.2), Australasia (Box 11.4, 11.3.5),
53 Central and South America (12.5.1), Europe (13.3.2), North America (14.6.1, Box14.3) and Small Islands
54 (15.5.4). The cross-sectoral interactions within humans systems, including impacts on cities, settlements and
55 infrastructure, are reflected in those subjects as well as for health in Africa (9.10.2), Asia (10.4.5),
56 Australasia (11.3.6), Central & South America (12.5.6), Europe (13.7.2), North America (14.6.1), and Small
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1 Islands (15.6.2), and poverty and livelihoods in Africa (9.11.3), Asia (10.4.5, 10.5), Australasia (11.4),
2 Central & South America (12.5.7), Europe (13.8.2), North America (14.6.1), and Small Islands (15.3.4).
3 These examples demonstrate that the emergence of climate risks can be at different rates, different time
4 horizons, and the interactions between risks vary from region to region (very high confidence). The need to
5 manage these risks in an integrated manner is readily identified in the Water-Energy-Food nexus (Box 9.5).
6 However, in terms of climate resilient development, the need for integration is demonstrated by the diverse
7 and interacting impacts of climate risks on ecosystems (2.7, 3.6), cities (6.2.3, 6.2.4, Box 6.2, 6.3), health
8 (7.4), and poverty and livelihoods (8.6).
9
10 17.6.2 Strategies for Managing a Portfolio of Climate Risks
11
12 Since WG2 AR5, new methods for simultaneously considering multiple societal and sectoral objectives,
13 climate risks and adaptation options have emerged (17.3.2; Adam et al., 2014; Hadka et al., 2015; Garner et
14 al., 2016; Rosenzweig et al., 2017; Giupponi and Gain, 2017a; Stelzenmuller et al., 2018; Marchau et al.,
15 2019), including methods for accounting for different sources of uncertainty and types of risk (17.3.1;
16 Giupponi and Gain, 2017a). Different decision-making approaches can be complementary (high confidence)
17 (17.3.1; Kwakkel et al., 2016) and multiple approaches will likely be necessary in managing the risks across
18 sectors, over different spatial scales, and over short to long time scales (medium confidence) (Cross-Chapter
19 Box PROGRESS in this Chapter; Girard et al., 2015; Rouillard and Spray, 2016).
20
21 Deciding on which adaptations to adopt when managing climate risks inevitably needs examination of trade-
22 offs in outcomes (very high confidence) (17.3.1, 17.5.1; Cross-Chapter Box FEASIB in Chapter 18). A
23 current difficulty with integrated assessments is to develop a set of metrics that are appropriately scaled for
24 the different sectors or outcomes to be compared (e.g., 12.5.2.6; 17.3.1; 17.5.2; Cross-Chapter Box
25 PROGRESS in this Chapter). For climate resilient development, dimensions of poverty, equity, justice, and
26 health need to be factored into analyses (Box 17.1, 17.5), many of which are difficult to quantify (high
27 confidence) (18.2.4). Moreover, uncertainties on the interactions within and between sectors can make trade-
28 off analyses uneven in their precision across sectors and uncertain as to the outcome of an implemented
29 adaptation (medium confidence) (4.7.2, 17.4, 17.5).
30
31 Expertise and resources for using tools and approaches for integrated risk management varies between the
32 developed and developing countries (high confidence) (e.g. 4.7.2). Exploration of adaptation scenarios can
33 be derived from Earth System Models (high confidence) (e.g. 4.7.1.2, 11.7.3.1). However, the feasibility of
34 possible adaptations and the degree to which they are likely to be effective (Box 17.1) will require further
35 exploration as success will depend on appropriate enabling conditions including institutional support and
36 capacity, available financial resources and knowledge, and suitable conditions for stakeholder participation
37 (high confidence) (17.4). The current levels of uncertainty surrounding the effectiveness of many adaptation
38 options (17.5.2; Cross-Chapter Box PROGRESS in this Chapter) means that decision-making approaches
39 applicable to deep uncertainty (Cross-Chapter Box DEEP in this Chapter; 17.3.1) will apply in many if not
40 most cases (medium confidence). An early step in identifying suitable integrated pathways for managing
41 climate risks, establish `no regrets' anticipatory options in a timely manner, and avoiding path dependencies,
42 is to jointly map the steps for adapting to sectoral risks, and determine suitable ways to avoid maladaptations
43 arising (high confidence) (17.3.1, Cross-Working Group Box URBAN in Chapter 6 and Cross-Chapter
44 Boxes DEEP in this Chapter). The application of Dynamic Adaptive Pathway planning has been successfully
45 used in this way in Australasia (11.7.3) and Europe (13.6.2.2, 13.10.2) (Lawrence et al., 2019a; Haasnoot et
46 al., 2020a). Current experience suggests that synergies between sectors can save resources and effort (limited
47 evidence) (13.11.2). Iterative processes can then enhance adaptation programs by including more detailed
48 modelling and updated knowledge as the experience is acquired (17.3.1).
49
50 17.6.3 Mainstreaming Climate Risk Management in Support of Climate Resilient Development
51
52 This chapter has assessed and detailed a number of decision-making tools (17.3) and enabling mechanisms
53 and catalysing conditions (17.4) that could be used in mainstreaming the management of climate risk and
54 adaptation in the sustainable development of communities, different sectors and nations. Since AR5, the
55 challenges facing the management of climate risks have been articulated (Adger et al., 2018;
56 Balasubramanian, 2018) and greater clarity on the steps that could be taken to better mainstream adaptation
57 has been developed (high confidence) (Cuevas, 2016; Giupponi and Gain, 2017a; Gomez-Echeverri, 2018;
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1 Sanchez Rodriguez et al., 2018). Nevertheless, the choice of decision processes is recognized as being
2 dependent on a variety of local factors influencing development (Ayers et al., 2014; Szabo et al., 2016).
3
4 Adaptation strategies or plans, some of which incorporate elements of climate resilient development, have
5 been developed in many jurisdictions from local (Cuevas, 2016; Araos et al., 2016a; Reckien et al., 2018a;
6 G鰌fert et al., 2019) to provincial/state (Warnken and Mosadeghi, 2018) to national governments (Markolf
7 et al., 2015; CSIRO, 2018; Warnken and Mosadeghi, 2018; Brown et al., 2018a; Table 17.9). National
8 Adaptation Plans have been a requirement under the UNFCCC and establish the general approach taken by
9 nations for adapting to climate change (Woodruff and Regan, 2019). Integrated risk assessments and
10 adaptation processes are being developed but with much less experience evident in their implementation
11 (high confidence) (Wise et al., 2014; Woodruff and Stults, 2016; Brown et al., 2018a).
12
13 National Adaptation Plans (NAPs) submitted to the UNFCCC have been reviewed for quality by Woodruff
14 and Regan (2019). In their review, Woodruff & Regan used a number of indicators grouped within
15 established "quality principles". They found that the plans were more oriented at the strategic level or at the
16 level of specific projects rather than identifying methods for resolving cross-sectoral or cross-jurisdictional
17 interactions or issues (medium confidence). A key recommendation from their review and supported by other
18 studies (e.g. Abutaleb et al., 2018) is that plans would be improved greatly by having inputs from multiple
19 government agencies and multiple sectors (medium confidence), which could provide the basis for planning
20 and review of integrated adaptation. Also, the plans need greater attention to implementation (9.4.1, 11.8,
21 13.11.2), and the identification of metrics by which success (17.5.1) and performance can be measured
22 (Cross-Chapter Box PROGRESS in this Chapter), a common issue for adaptation planning generally (e.g.
23 12.5.2.6, 17.5).
24
25 Hence, satisfactorily managing intersecting climate risks in different settings, of which RKRs provide
26 examples, is central to achieving sustainable development (high confidence) (16.6.4), requiring integrated
27 risk management within and across regions, jurisdictions, sectors and ecosystems (high confidence) (more
28 cross references please CCP5.4.2; CCP5.4.3). Iterative processes will enable measuring progress and
29 updating adaptation at a satisfactory rate, in order to account for the different needs within regions and across
30 sectors at different times (high confidence). The degree to which equity and justice will be achieved will
31 be determined by the participatory processes in deciding on suitable adaptation options, the investment in the
32 adaptation processes and the coordination and collaboration built amongst institutions and people across
33 regions (high confidence).
34
35
36 [START FAQ17.1 HERE]
37
38 FAQ17.1: Which guidelines, instruments and resources are available for decision-makers to recognize
39 climate risks and decide on the best course of action?
40
41 Guidelines, instruments, and resources to identify options for managing risks, and support decisions on the
42 most suitable course of actions to take, can be collectively referred to as decision-support frameworks. These
43 can include data services, decision-support tools, processes for making decisions and methods for monitoring
44 and evaluating progress and success. Data services enable the identification, location and timing of risks that
45 could manifest with negative impacts, as well as potential opportunities. Often, these are termed `climate
46 services' and assist with mapping hazards and how they are changing. Decision-support tools range from
47 qualitative approaches to determine overlap of areas of concern with those hazards in the future, to more
48 quantitative and dynamic simulation approaches that enable dynamic stress-testing of adaptation options and
49 strategies to determine if proposed plans for adapting to the future could be successful. An important
50 consideration is whether options for risk management or capitalisation on opportunities will limit options and
51 flexibility for responding to unforeseen events in the future. If these options have a negative effect on other
52 areas of concern, then they could be identified in these planning scenarios as maladaptations, and therefore
53 avoided.
54
55 A great challenge for decision-makers is how to choose effective options when the future is
56 uncertain. Uncertainty can arise not just in the statistical error of the magnitude of risk but also in the nature
57 and consequence of risk from uncertainty about mechanisms that link areas of concern to hazards,
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1 uncertainty in the decision processes itself and so on. Methods are available to help develop no-regret
2 options, commonly referred to as "decision making under conditions of deep uncertainty".
3 Decision-support frameworks are most successful when they are iterative, integrative, and consultative.
4 Rather than a single decision be made, and an action taken, there are processes for making the best decision
5 possible then monitoring progress toward delivering a successful outcome. Given a set of suitable indicators
6 with regular monitoring, decisions can be revised, updated, or changed as the future unfolds and foundations
7 for the original decision tested. This is important because climate responses need to be initiated well in
8 advance of them being needed due to the time required to implement suitable responses. These forward-
9 looking approaches allow errors to occur and corrections made before problems arise. They also enable
10 action to be taken without having to wait for the circumstances to arise, which if this were to occur could
11 result in only limited reactions being available and the outcomes then dependent upon recovery from events
12 rather than proactive planning and avoidance of events. Integrated approaches to risk management are
13 available to help manage portfolios of interacting risks, including the potential for compounding and
14 cascading risks when climate-related events arise.
15
16 Managing uncertainty with forward-looking processes needs to be more deliberative and oriented towards
17 building trust in a collaborative process. Building relationships through informal, bottom-up processes
18 enables this to occur. Top-down planning processes are important for ensuring the management of risks and
19 opportunities do not end up with maladaptations and that the approaches are equitable and proportional to
20 that which is needed to manage the risks.
21
22 [END FAQ17.1 HERE]
23
24
25 [START FAQ17.2 HERE]
26
27 FAQ17.2: What financing options are available to support adaptation and climate resilience?
28
29 What do we mean by "climate finance"?
30 The UNFCCC has no formally agreed definition of climate finance. The current IPCC definition is: "the
31 financial resources devoted to addressing climate change by all public and private actors from global to
32 local scales, including international financial flows to developing countries to assist them in addressing
33 climate change" (see Annex I: Glossary).
34
35 What needs to be financed?
36 Financial resources might be needed for a range of adaptation and resilience building activities. These
37 include research, education and capacity building; development of laws, regulations, and standards;
38 provision of climate services and other information; reducing the vulnerability of existing assets, activities,
39 and services; and ensuring future development - such as new infrastructure, settlements, health services and
40 business activities - is climate resilient. Finance is also needed to recover and rebuild from the damage of
41 climate hazards that cannot be completely avoided through adaptation. Adaptation actions can be undertaken
42 by many different actors, alone or in partnership, including national and sub-national governments, public
43 and private utilities, businesses of varying size, communities, households, and individuals.
44
45
46 Table FAQ17.2.1 Examples of adaptation and resilience activities that might need to be financed
Training of agricultural extension officers so that their A new urban development requires higher standards (and
advice to small-holder farmers can support up-front costs) for buildings, roads, stormwater systems,
implementation of climate adapted agriculture. water re-use and to be resilient to expected changes in
Additional financial support is needed for the costs of heavy rainfall, runoff, temperature, and water supply
farmers transitioning to climate resilient agricultural reliability.
practices.
A water utility requires capital expenditure to increase A catastrophe risk insurance facility is established to
supply through a desalination plant and to reduce leakage provide post-disaster (drought, hurricane, flooding, pest
from its reticulation system in response to a scenario of outbreaks) recovery finance to national governments.
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reduced surface water availability and an increase in The facility requires capital to be able to underwrite the
customers. insurance products it offers.
1
2
3 How much finance is needed?
4 The amount of adaptation finance depends on global, regional, and local factors, including: the amount and
5 timing of global warming, how this translates into impacts and adaptation needs across the world; the levels
6 of adaptation already in place; the type of risk being adapted to; and the adaptation options being chosen,
7 including whether the adaptation required is incremental or transformational.
8
9 The most mentioned figure for finance need is the developed countries commitment to provide USD 100
10 billion per year by 2020 to support developing countries efforts in mitigation and adaptation. Negotiations
11 will start in 2021 on updating this amount for 2025. While sometimes thought to represent the actual cost of
12 responding to climate change in developing countries, this is not the case. More recent estimates of the
13 global cost of adaptation by 2030 across developed and developing countries range between about USD 80-
14 300 billion per year.
15
16 What types of finance are available?
17 Four main types (or instruments) of finance are currently being used to support adaptation. These different
18 types are not mutually exclusive; grants can be combined with loans to provide blended finance.
19
20
21 Table FAQ17.2.2 The main instruments through which adaptation is being financed
Grants provide finance without any repayment Concessional loans require partial repayment of the
requirements. Most grants for adaptation have been finance provided. These involve either capital
provided by multilateral funds such as the Green Climate repayment coupled to below market interest rates or
Fund or a fund managed by a single OECD country such capital repayment only. Concessional finance is almost
as Germany's International Climate Initiative. Some entirely provided through multilateral development
countries have national climate or environment funds banks such as the World Bank. This finance is
that provide grants for their own climate adaptation particularly important for developing countries where
actions. Grants are also provided by philanthropic market interests are high due to poor credit ratings or
foundations and sometimes by companies as part of their other risk factors, or where the return on investment is
environmental and social responsiveness mandate. too low make a commercial loan viable.
Non-concessional loans (or debts) are commercial Budget reallocation does not require raising of new
instruments, where capital repayment and market interest finance; rather it involves moving funds already secured
rates apply. These may be provided through away from other purposes towards adaptation. In
development banks or private banks. Green bonds are a government, this might involve reallocation towards
relatively new form of market loan, designed to meet flood defence. In the private sector a company might
climate and other environmental sustainability criteria in move budget from marketing, research and development,
terms of how the proceeds are used. In recent years or perhaps dividends, towards increasing the climate
green bonds have offered better interest than ordinary resilience of operation, infrastructure or their value
bonds due to oversubscription by investors who are chain.
looking to move towards environmentally sustainable
investment portfolios.
22
23
24 Where are different types of finance most useful?
25 Grants are useful for a range of adaptation actions where it is hard to generate a financial return. These
26 include capacity building activities, piloting new adaptation innovations, high risk investment settings, or
27 projects where there are considerable non-financial benefits. In contrast loans and other debt instruments can
28 often support larger investments, for example for scaling out of successful pilot projects or for building
29 adaptation and resilience into general development investment. To date, a large proportion of international
30 climate finance for adaptation in developing countries, especially in sub-Saharan Africa and Oceania has
31 been grant led, sourced from OECD public funds, indicating that in many instances financing via loans is
32 either considered too risky by the commercial investment sector or it has been hard to demonstrate sufficient
33 return on investment.
34
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1
2
3 Figure FAQ17.2.1. The distribution of adaptation finance across different regions and different types of finance in
4 2015-2016, as tracked the Climate Policy Initiative. The size of each circle represents the amount of finance, with
5 amount in billions USD superimposed. Based on data tracked by the Climate Policy Initiative.
6
7
8 [END FAQ17.2 HERE]
9
10
11 [START FAQ17.3 HERE]
12
13 FAQ17.3: Why is adaptation planning along a spectrum from incremental to transformational
14 adaptation important in a warming world?
15
16 In a warming world, incremental adaptation, i.e. proven standard measures of adaptation, will not always
17 suffice to adjust to the negative impacts from climate change leading to substantial residual risks and, in
18 some cases, the breaching of adaptation limits; transformational adaptation, involving larger system-wide
19 change (as compared to in system change), will increasingly be necessary as a complement for helping
20 individuals and communities to cope with climate change. As an example of incremental adaptation, a
21 farmer may decide to use drought-tolerant crops to deal with increasing occurrences of heatwaves. With
22 further warming and increases in heat waves and drought, however, the impacts of climate change may
23 necessitate the consideration of system-wide change, such as moving to an entirely new agricultural system
24 in areas where the climate is no longer suitable for current practices; or switching to livestock rearing. Where
25 on-site adaptation becomes infeasible and pull factors exist, the farming households may decide to seek
26 employment in other sectors, which may also lead to migration for work. As another example, physical
27 protection through sea walls to stop coastal flooding is a proven adaptation measure. With further projected
28 flooding due to increasing sea level rise attributable to climate change transformational city planning, that
29 would systemically change how flood water is managed throughout the whole city requiring deeper
30 institutional, structural, and financial support, may become necessary. Also, the deliberate relocation of
31 settlements (managed retreat) is seeing attention in the face of increasingly severe coastal or riverine
32 flooding in some regions. While transformational adaptation is increasingly being considered in theory and
33 planning, implementation is only beginning to see attention.
34
35 [END FAQ17.3 HERE]
36
37
38 [START FAQ17.4 HERE]
39
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1 FAQ17.4: Given the existing state of adaptation, and the remaining risks that are not being managed,
2 who bears the burden of these residual risks around the world?
3
4 A warming climate brings along increasing risks, part of which can be reduced or insured. What remains is
5 called residual risks and needs to be retained by households, the private and public sectors. People living in
6 conflict-affected areas benefit only marginally from adaptation investments by governments, private sector,
7 or other institutions. These people bear most of the changing climate risks themselves. Higher-income
8 countries generally have invested heavily in structural adaptation to make sure people are not exposed to
9 extreme events (e.g. dykes) and have developed a variety of private or public insurance systems to finance
10 the risk of the most rare or extreme events. In other, middle or lower-income countries, these very extreme
11 events are less likely to be insured, and the impacts are borne by the most vulnerable people. Absent risk
12 reduction or insurance, coping with residual risks generally means reducing consumption (e.g. food) or
13 drawing down assets (selling machinery, houses etc), which all can bring along longer-term adverse
14 developmental implications. Adaptation investments in low-income countries tend to focus more heavily on
15 increasing capacity and reducing vulnerability; people remain exposed to the changing climate risks, and
16 bear the burden of reacting and responding.
17
18 [END FAQ17.4 HERE]
19
20
21 [START FAQ17.5 HERE]
22
23 FAQ17.5: How do we know whether adaptation is successful?
24
25 Adaptation aims to reduce exposure and vulnerability to climate change by responding to dynamic and
26 multi-scalar combinations of climatic risks. What might be seen as successful at one scale or at one point in
27 time might not be at another, particularly if climate risks continue to rise. Moreover, the benefits of
28 adaptation interventions may not reach all intended beneficiaries or everyone affected by climate impact and
29 risk, causing different people to have different views on how successful adaptation has been.
30
31 There is, therefore, no universal way to measure adaptation success, but there is high agreement that success
32 is associated with a reduction of climate risks and vulnerabilities (for humans and ecosystems) and an
33 equitable balancing of synergies and trade-offs across diverse objectives, perspectives, expectations, and
34 values. Adaptation that is successful is also commonly expected to be inclusive of different socio-economic
35 groups, especially the most vulnerable, and to be based on flexible and integrative planning processes that
36 take into account different climate scenarios.
37
38 Conceptually, the opposite of successful adaptation is maladaptation, i.e. when adaptation responses produce
39 unintended negative side effects such as exacerbating or shifting vulnerability, increasing risk for certain
40 people or ecosystems, or increasing greenhouse gas emissions. Among the adaptation options assessed in this
41 report (Figure FAQ 17.5.1), physical infrastructure along coasts (e.g., sea walls) has the highest risk for
42 maladaptation over time through negative side-effects on ecosystem functioning and coastal livelihood
43 opportunities. However, such adaptations may appear valuable in the short and even longer term for already
44 densely populated urban coasts, demonstrating that an adaptation can be differently judged based on the
45 context it is implemented in (Figure FAQ 17.5.1). Many other adaptation options have a larger potential to
46 contribute to successful adaptation (Figure FAQ17.5.1), such as nature restoration, providing social safety
47 nets, and changing diets/ minimizing food waste.
48
49
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1
2 Figure FAQ17.5.1: Contribution of adaptation options to potentially successful adaptation and to the risk of
3 maladaptation. Note: A similar figure is part of Ch17.5.2.
4
5
6 Assessments of adaptation need to be transparent about how they are measuring success. Monitoring and
7 Evaluation (M&E) can be used to track progress and evaluate success and to identify if course corrections
8 during adaptation implementation are needed to achieve the envisaged objectives. Given the diversity of
9 adaptation actions and contexts, no one-size-fits-all approach to M&E and no common reference metrics for
10 adaptation exist. To date, assessments of progress of adaptation have often focused on processes and outputs
11 (i.e. actions taken, such as adaptation plans adopted) that are easier to measure than the effects of these
12 actions in terms of long-term reduction of risks and vulnerabilities. However, knowledge about the outcomes
13 in terms of reducing climate risk, impact and vulnerability is critically required to know if adaptation has
14 been successful.
15
16 Tracking progress, in particular outcomes and impacts of adaptation, involves a number of challenges. First,
17 in order to determine progress over time, risk and vulnerability assessments need to be repeated at least once
18 after starting an adaptation process. This is rarely done, as it demands resources that are usually not factored
19 into the adaptation response. Second, attributing changes in climate risks and vulnerabilities to the adaptation
20 response is often difficult due to other influencing factors, such as socio-economic development over time.
21 Expected causal relationships between responses and their outcomes should already be outlined during the
22 adaptation planning phase, for example by mapping the way from activities to outcomes, and they should be
23 monitored during implementation. Third, as adaptation can occur in multiple forms and target multiple
24 temporal and spatial scales, the engagement of a diversity of stakeholders is vital to understand how
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1 responses enable adaptation and adaptation success across vulnerable groups. Though, stakeholder
2 engagement can be time intensive and costly, in particular when reaching out to populations that are usually
3 not part of policy and planning processes it can support evaluating co-benefits and trade-offs of adaptation
4 responses. Consideration and analysis of co-benefits and trade-offs along with a focus on short, medium, and
5 long time horizons of adaptation goals, which is usually possible through flexible and strong institutions,
6 facilitate successful adaptation and reduce the likelihood of maladaptation.
7
8 [END FAQ 17.5 HERE]
9
10
11
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