Editing IPCC:AR6/WGII/Chapter-17 (section)

=== 17.5.1 Adaptation Success and Maladaptation ===

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==== 17.5.1.1 The Adaptation–Maladaptation Continuum ====

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As evidence on adaptation implementation grows ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ), there is a need to examine the outcomes of adaptation ( [[#Ford--2011|Ford et al., 2011]] ) for effectiveness, adequacy and justice/equity in both outcomes and process, as well as synergies and trade-offs with mitigation, ecosystem functioning and other societal goals. There is also a growing recognition of the observed and potential negative consequences of some adaptation interventions, often referred to as maladaptation ( [[#Juhola--2016|Juhola et al., 2016]] ; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Schipper--2020|Schipper, 2020]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). This section advances a new framing to allow for an improved assessment of the potential positive or negative outcomes of adaptation options, therefore allowing navigation of the adaptation–maladaptation continuum.

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===== 17.5.1.1.1 Defining and assessing success in adaptation vis a vis maladaptation =====

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The highly contextual nature of adaptation, a multitude of applied definitions of adaptation (e.g., cost effectiveness versus outcomes), its overlaps with development interventions, and the long time horizons over which outcomes accrue, deter a universal definition of adaptation success (Dilling et al., 2019; [[#17.5.1|Section 17.5.1.2]] ; [[#Owen--2020|Owen, 2020]] ; [[#Singh--2021|Singh et al., 2021]] ). [[#Moser--2013|Moser and Boykoff (2013)]] , [[#Olazabal--2019b|Olazabal et al. (2019b)]] and [[#Sherman--2013|Sherman and Ford (2013)]] suggest criteria against which successful adaptation could potentially be tracked. The literature is converging to suggest that successful adaptation broadly refers to actions and policies that effectively and substantially reduce climate vulnerability, and exposure to and/or impacts of climate risk ( [[#Noble--2014|Noble et al., 2014]] ; [[#Juhola--2016|Juhola et al., 2016]] ), while creating synergies to other climate-related goals, increasing benefits to non-climate-related goals (such as current and future economic, societal and other environmental goals) and minimise trade-offs ( [[#Grafakos--2019|Grafakos et al., 2019]] ) across diverse objectives, perspectives, expectations and values ( [[#Eriksen--2015|Eriksen et al., 2015]] ; [[#Gajjar--2019a|Gajjar et al., 2019a]] ; [[#Owen--2020|Owen, 2020]] ) ( ''high confidence'' ).

Maladaptation refers to current or potential negative consequences of adaptation-related responses that lead to an increase in the climate vulnerability of a system, sector or group ( [[#Barnett--2010|Barnett and O’Neill, 2010]] ) by exacerbating or shifting vulnerability or exposure now or in the future ( [[#Antwi-Agyei--2014|Antwi-Agyei et al., 2014]] ; [[#Noble--2014|Noble et al., 2014]] ; [[#Juhola--2016|Juhola et al., 2016]] ; [[#Magnan--2020|Magnan et al., 2020]] ) and eroding sustainable development ( [[#Juhola--2016|Juhola et al., 2016]] ). Conceptually, maladaptation differs from ‘failed’ or ‘unsuccessful’ adaptation ( [[#Schipper--2020|Schipper, 2020]] ), which ‘describes a failed adaptation initiative not producing any significant detrimental effect’ ( [[#Magnan--2016|Magnan et al., 2016]] : 648). Several frameworks have been proposed to explain and better assess maladaptation ( [[#Hallegatte--2009|Hallegatte, 2009]] ; [[#Barnett--2010|Barnett and O’Neill, 2010]] ; [[#Magnan--2014|Magnan, 2014]] ; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Gajjar--2019b|Gajjar et al., 2019b]] ). To limit the risk of maladaptation, a common focus of these frameworks is on intentionally avoiding negative consequences of adaptation interventions, anticipating detrimental lock-ins and path dependence, and minimising spatio-temporal trade-offs/ dis-benefits.

The adaptation literature challenges the simplistic dichotomy of interventions being either successful or maladaptive (e.g., [[#Moser--2013|Moser and Boykoff, 2013]] ; [[#Singh--2016|Singh et al., 2016]] ; [[#Magnan--2020|Magnan et al., 2020]] ; [[#Schipper--2020|Schipper, 2020]] ). There is no clear-cut boundary between these two categories; rather, successful adaptation and maladaptation need to be considered as the two ends of a continuum of risk management strategies (Figure 17.10), emphasising that:

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'''Figure 17.10 |''' '''Successful adaptation and maladaptation are conceptualised as the two end points of a continuum, with adaptation options being located along the continuum based on outcome criteria (how they benefit humans and ecosystems; how they contribute to or hinder equity goals; whether they enable transformative change to climatic risks; and synergies and trade-offs with climate mitigation).''' As indicated in SM 17.1 and Figure 17.10, adaptation options might rate largely positive and slightly negative across outcome criteria (tending towards successful adaptation), while other adaptation options might have small positive aspects and larger negative ones across different outcome criteria (tending towards maladaptation). The figure draws on [[#Singh--2016|Singh et al. (2016)]] , [[#Magnan--2020|Magnan et al. (2020)]] and [[#Schipper--2020|Schipper (2020)]] .

* no options are ‘bad’ or ‘good’ ''a priori'' with respect to reducing climate risk/vulnerability.
* positive and negative outcomes of adaptation depend on local context specificities (including the presence/absence of enabling conditions [1] ), how adaptation is planned and implemented, who is judging the outcomes (i.e., adaptation decision maker, planner, implementer or recipient) and when adaptation outcomes are assessed.
* ''ex ante'' assessment of where options fall on the continuum can help anticipate maladaptive outcomes.

Along the adaptation–maladaptation continuum, adaptation options can score high or low on different outcome criteria identified in this section such as: benefits to the number of people, benefits to ecosystem services, equity outcomes (for marginalised ethnic groups, gender, low-income populations), transformational potential and contribution to GHG emission reduction (see SM 17.1 for full descriptions). Importantly, the outcome of the assessment, and consequently location of a given adaptation option along this continuum, is dynamic, depending on multiple components, including changes in the characteristics of climate hazards and the effects of iterative risk management. Unfortunately, this temporal dimension is understudied in the literature (including studying thresholds or speed), preventing advances on this specific point.

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===== 17.5.1.1.2 Empirical evidence on success of adaptation vis a vis maladaptation =====

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Although the empirical evidence on current and potential successful adaptation and maladaptation remains small and fragmented ( [[#Magnan--2020|Magnan et al., 2020]] ; [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ; see [[#17.3.2|Section 17.3.2]] in this Chapter), the above framing allows for moving a step further in assessing the potential contribution of a wide range of adaptation-related options to success or maladaptation.

According to an assessment (Figure 17.11; see SM 17.1 for full descriptions) of maladaptation-relevant outcome dimensions, here called criteria, that is, benefits to people, benefits to ecosystem services, benefits to equity (marginalised ethnic groups, gender, low-income populations), transformational potential and contribution to GHG emission reduction, no option is located at one or the other end of the adaptation-maladaptation continuum (Figure 17.11, right panel), showing that all options have some maladaptation potential, that is, trade-offs ( ''very high confidence'' ). This is also shown by the wide confidence ranges of most options (right panel) signifying that most adaptation can be done in a way that involves a higher or a lower risk of maladaptation ( ''medium confidence'' ; see also Figure 17.3). The option of ‘coastal infrastructure’ signifies the highest risk for maladaptation. While it can be an efficient adaptation option in highly densely populated areas ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ; [https://www.ipcc.ch/chapter/17#CCP2.3 CCP2.3] ), it has potential trade-offs for natural system functioning and human vulnerability over time. The options most widely associated with successful adaptation are ‘nature restoration’, ‘social safety nets’, ‘change of farm/fishery practice’ and ‘change of diets/reducing food waste’ ( ''high confidence'' ).

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'''Figure 17.11 |''' '''The potential contribution of 24 adaptation-related options to maladaptation and successful adaptation.'''

The figure builds on evidence provided in the underlying sectoral and regional chapters and the Cross-Chapter Papers (SM17.1) to map 24 adaptation options identified as relevant to the eight Representative Key Risks (see [[IPCC:Wg2:Chapter:Chapter-16#16.5|Section 16.5]] ) onto the adaptation–maladaptation continuum. It assesses the potential contribution of each of these adaptation options to successful adaptation and the risk of maladaptation. The figure permits a review of options in multiple ways: (a) looking at adaptation options (first column), one can see which adaptation options score highest across the criteria (the central rows). Results by options show which ones carry the highest risk of maladaptation (largest circles per row); (b): looking at criteria (top centre), one can see which criteria seem to be most influential to contribute to maladaptation outcomes (largest circles per central column); (c) panel on the right: merging the scores of each adaptation option across criteria helps highlight whether the options are likely to end up as successful adaptation or maladaptation.

Some options show the dominant influence of certain criteria (Figure 17.11, central panel rows). For example, ‘availability of health infrastructure’ and ‘access to health care’ are dominated by the criterion ‘greenhouse gas emissions’. Similarly, ‘spatial planning’ carries a high risk of disadvantages to marginalised ethnic and low-income groups. This means that these adaptations could be transformed into successful adaptations more easily than others, if attention is paid to the dominant criterion. For example, if health care could be provided with low GHG emissions, it would move closer towards successful adaptation ( ''high confidence'' ). For other options, the criteria’s influence is more evenly distributed, as illustrated for the ‘diversification of livelihoods’ and the three options to address climate risks to peace and mobility, denoting multiple entry points to reduce the risk of maladaptive outcomes for these options.

Some criteria score highly across a number of options (Figure 17.11, central panel columns), showing that many adaptations do not pay attention to different trade-offs. For example, particular attention should be paid to prioritising benefits to low-income groups and leveraging the transformational potential of adaptation (having the largest number of large circles), that is, many evaluated options become maladaptive by exacerbating the vulnerability of low-income groups and by fortifying the status quo ( ''medium confidence'' ). On the contrary, most evaluated adaptation options are widely applicable across populations (benefits to humans) and deliver ecosystem services, while some also respect gender equity (largest number of small bubbles across options). Through these criteria, a number of adaptation options contribute to a higher potential for successful adaptation ( ''high confidence'' ).

The results displayed in Figure 17.11 are not rigorous predictions but illustrate the maladaptive potential of options based on a synthesis of literature from underlying WGII chapters and cross-chapter papers. This leads to findings for general situations, potentially obscuring critical contextual specificities which can mediate successful adaptation or maladaptation outcomes. In a certain context, Figure 17.11 will appear different. Moreover, the analysis is based on a static interpretation of adaptation outcomes, while risk and risk reduction are dynamic. The current, underlying literature does not help understanding the temporal dimension of the options, their flexibility or risk of lock-in, and related potential contribution to long-term maladaptation or successful adaptation. The added value of the analysis lies in the approach to assess the potential contribution to maladaptation or successful adaptation (via the seven criteria at the top of the figure), rather than in the final results themselves. This overview illustrates how, in a particular context and for particular groups of people, adaptation options and their location on the adaptation–maladaptation continuum can be assessed for a set of outcome dimensions, focusing on assessing potential contributions per and across criteria as well as per and across options (critical information to support the identification of adaptation pathways; Cross-Chapter Box DEEP in this Chapter).

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===== 17.5.1.1.3 Enabling successful adaptation and pre-empting maladaptation =====

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Considering evidence on enabling successful adaptation in the sectoral (Chapters 2–8) and regional chapters (Chapters 9–15), four conditions stand out as particularly key to enabling adaptation success: recognitional equity and justice, including the integration of Indigenous and local communities and knowledge; procedural equity and justice; distributive equity and justice; and flexible and strong institutions that seek integration of climate risk management with other policies and address long-term risk reduction goals (Table 17.7). For a wider discussion of enablers for adaptation and climate risk management, see [[#17.4|Section 17.4]] .

'''Recognitional equity and justice:''' Recognitional justice focuses on inclusion and agency, that is, examining who is recognised as a legitimate actor and how their rights, needs and interests are acknowledged and incorporated into action ( [[#Singh--2021|Singh et al., 2021]] ).

A global assessment of 1682 papers on adaptation responses yields that low-income groups ( ''high agreement'' , 37% of 1682 articles), women ( ''medium agreement'' , 20% articles), Indigenous peoples (10%), the elderly (8%), youths (5%), racial and ethnic minorities (4%), and migrants (4%) were the most frequently considered groups in adaptation responses. Individuals with disabilities are the least considered, with only 1% of articles including this group. There is a category of ‘other’ capturing characteristics of social disadvantage that are distinct from the categories above. This includes, for example, spatially marginalised populations (e.g., groups relegated to flood-prone or cyclone-prone areas) and groups marginalised due to marital status or assets (education, farm size and land tenure) ( [[#Araos--2021|Araos et al., 2021]] ).

'''Procedural equity and justice:''' Participation is employed to enable procedures that aim to redress power imbalances, which are assumed to be the root causes of vulnerability (i.e., the reasons that lead certain people and places to be differentially vulnerable to climate risks) ( [[#Tschakert--2012|Tschakert and Machado, 2012]] ; [[#Shackleton--2015|Shackleton et al., 2015]] ; [[#Schlosberg--2017|Schlosberg et al., 2017]] ; [[#Ziervogel--2017|Ziervogel et al., 2017]] ). However, participation is often constrained by gender (Cross-Chapter Box GENDER in Chapter 18), social status, unequal citizenship (as concerns education, access to information, finance and media) ( [[#Wallimann-Helmer--2019|Wallimann-Helmer et al., 2019]] ), entrenched political interests ( [[#Shackleton--2015|Shackleton et al., 2015]] ; [[#Chu--2017|Chu et al., 2017]] ), power dynamics ( [[#Rusca--2015|Rusca et al., 2015]] ; [[#Taylor--2018|Taylor and Bhasme, 2018]] ; [[#Kita--2019|Kita, 2019]] ; [[#Omukuti--2020|Omukuti, 2020]] ; [[#Taylor--2020|Taylor and Bhasme, 2020]] ) or institutional shortcomings ( [[#Nightingale--2017|Nightingale, 2017]] , in Nepal), which allow the most powerful access to funding and reinforce marginalisation of the powerless ( [[#Schipper--2014|Schipper et al., 2014]] ; [[#Khatri--2018|Khatri, 2018]] ; [[#McNamara--2020|McNamara et al., 2020]] ). Vulnerability is also sometimes used as a pretext to exclude groups from participation, often because vulnerable groups do not own land and lack legal status, time or the ability to commit labour or material inputs for adaptation, all drivers of vulnerability in the first place (Nyantakyi-Frimpong and Bezner Kerr, 2015; [[#Camargo--2017|Camargo and Ojeda, 2017]] ; [[#Nagoda--2017|Nagoda and]] [[#Nightingale--2017|Nightingale, 2017]] ; [[#Nightingale--2017|Nightingale, 2017]] ; [[#Thomas--2019|Thomas and Warner, 2019]] ; [[#Mikulewicz--2020|Mikulewicz, 2020]] ).

Reporting from the global assessment of equity considerations in adaptation, procedural equity and justice was slightly more often mentioned (~52%) than not (~48%) ( ''medium agreement'' ). However, the robustness of the evidence on inclusion of vulnerable and marginalised groups in the planning of adaptation responses is low (63%) ( ''high agreement'' ). Only for ~6% of the articles that provide evidence for inclusion of vulnerable groups was the robustness of evidence high ( ''low agreement'' ). Globally, the categories of low income (~25%) and women (~13%) are most often included, although the robustness remains low. Most of the ''robust evidence'' comes from Africa and Asia, where adaptation responses mostly focus on low-income and women groups in the food (28%) and poverty (32%) sectors ( ''medium agreement'' ). With regard to other vulnerability categories, such as disabled populations, almost negligible evidence was found for the inclusion of this group, globally. There is also little reporting of procedural equity in community-based or ecosystem-based responses ( [[#Araos--2021|Araos et al., 2021]] ).

'''Distributive equity and justice:''' Attention to distributional equity and justice aims to ensure that adaptation interventions do not exacerbate inequities ( [[#Atteridge--2018|Atteridge and Remling, 2018]] ) and that the benefits and burdens of interventions are distributed fairly ( [[#Tschakert--2013|Tschakert et al., 2013]] ; [[#Reckien--2017|Reckien et al., 2017]] ; [[#Reckien--2018b|Reckien et al., 2018b]] ; [[#Pelling--2019|Pelling and Garschagen, 2019]] ).

A global assessment of 1682 papers on adaptation ( [[#Araos--2021|Araos et al., 2021]] ) finds that about 60% of articles mentioned at least one vulnerable group being involved in the implementation of adaptation or targeted by it ( ''medium confidence'' ). Low-income groups ( ''high agreement'' , 37% of 1682 articles) and women ( ''medium agreement'' , 20% articles) are the most frequently mentioned. Particularly in sectors and regions that incorporated coping measures in their adaptation response (poverty, food, Africa, Asia, Central and South America), these groups are prevalent. In sectors where responses were more strategic or planned, such as in cities, terrestrial and water, in a larger proportion of articles (51%, 47% and 47% of articles, respectively) vulnerable groups were not frequently included in the response ( ''medium agreement'' ). There was also a stark difference in inclusion of marginalised and vulnerable groups between high-income and low-income countries or regions, with the majority of the responses from Australia, Europe and North America, not including marginalised groups ( ''high agreement'' with 70%, 69% and 55% of articles, respectively), showing the need for increasing attention in particular on a cross-sectoral and cross-regional relation ( [[#Araos--2021|Araos et al., 2021]] ).

'''Flexible and strong institutions:''' There is ''medium confidence'' that flexible institutions can enable adoption of new adaptation measures or course-correct established ones based on ongoing monitoring and evaluation, which is key to avoiding potential maladaptation (e.g., [[#Granberg--2014|Granberg and Glover, 2014]] , in Australia; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Torabi--2018|Torabi et al., 2018]] ; [[#Gajjar--2019a|Gajjar et al., 2019a]] , in India). Cross-sectoral, cross-jurisdictional and cross-spatial institutional frameworks enable successful adaptation by improving the ability of societies to respond to changes in their environment in a timely manner. The latter points to the vital role of monitoring and evaluation, as the tool to detect change in risk and vulnerability, together with environmental or societal conditions determining risk and the effectiveness, efficiency, adequacy or success of adaptation responses.

'''Table 17.7 |''' Key factors that enable successful adaptation. The evidence and examples draw on the underlying sectoral and regional chapters as well as a synthesis of adaptation literature.

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! Enablers

! What this enables

! Key characteristics

! Examples and traceability

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| Recognitional justice

| Pluralising the ambit of who is ‘counted’ as vulnerable, drawing on multiple knowledge systems

| * Focuses on inclusion and agency, i.e., who is recognised as a legitimate actor and how their rights, needs and interests are acknowledged and incorporated into adaptation ( [[#Chu--2018|Chu and Michael, 2018]] ; [[#Singh--2021|Singh et al., 2021]] ).
* Acknowledges how differential vulnerability to climate change stems from historical and structural inequalities, which can unevenly distribute adaptation benefits, especially for the poorest and the most marginalised ( [[#Tschakert--2012|Tschakert and Machado, 2012]] ; [[#Shackleton--2015|Shackleton et al., 2015]] ; [[#Schlosberg--2017|Schlosberg et al., 2017]] ; [[#Ziervogel--2017|Ziervogel et al., 2017]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ).
* Informs more equitable adaptation priorities ( [[#Ziervogel--2017|Ziervogel et al., 2017]] ), legitimises adaptation actions ( [[#Myers--2018|Myers et al., 2018]] ; [[#Ellis--2019|Ellis and Tschakert, 2019]] ), supports inclusion of marginalised groups ( [[#Chu--2018|Chu and Michael, 2018]] ) ( ''medium confidence'' ).

| * Co-production of knowledge and inclusion of Indigenous and local knowledge ( [[#Loboguerrero--2018|Loboguerrero et al., 2018]] ; [[#Dannenberg--2019|Dannenberg et al., 2019]] , Cross-Chapter Box ILK; [[#Ziervogel--2019|Ziervogel et al., 2019]] ).
* Co-production of knowledge and inclusion of marginalised groups across sectors, see, e.g., in the health sector (Chapter 7), food systems (Chapter 5) and fire management (Chapter 12).

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| Procedural justice

| Differential participation and power for more inclusive adaptation planning and implementation

| * Ensures that processes of representation and participation in adaptation planning, prioritisation and implementation are inclusive ( [[#Holland--2017|Holland, 2017]] ; [[#Reckien--2017|Reckien et al., 2017]] ; [[#Reckien--2018b|Reckien et al., 2018b]] ) ( ''medium confidence'' ).
* Enables adaptations to advance more quickly and generate higher levels of well-being (e.g., [[#Dannenberg--2019|Dannenberg et al., 2019]] comparing cases of strategic retreat), while also benefitting poorer households ( [[#Chu--2018|Chu and Michael, 2018]] ).
* Higher participation can enable more legitimate outcomes, greater awareness about societal problems addressed, larger willingness for community cooperation, and increased individual behavioural change ( [[#Burton--2013|Burton and Mustelin, 2013]] ).
* Participation in design and implementation of adaptation projects can be a critical element for avoiding maladaptive outcomes ( [[#Taylor--2015|Taylor, 2015]] ; [[#Nightingale--2017|Nightingale, 2017]] ; [[#Forsyth--2018|Forsyth, 2018]] ; [[#Mikulewicz--2019|Mikulewicz, 2019]] ).

| * Participation of multiple stakeholders enables co-production of adaptation strategies and devolution of decision-making ( [[#Ziervogel--2019|Ziervogel, 2019]] ) and often, if not always ( [[#D’Alisa--2016|D’Alisa and Kallis, 2016]] ), a higher level of transformational adaptation (and more ambitious local mitigation goals) (Cattino and Reckien, in press).
* Participatory processes can have more equitable outcomes as evidenced in informal settlements ( [[#Ziervogel--2019|Ziervogel, 2019]] , South Africa), small farmers ( [[#Loboguerrero--2018|Loboguerrero et al., 2018]] , Colombia), migrants ( [[#Gajjar--2019b|Gajjar et al., 2019b]] , India) and deliberative dialogues (Ojha and et al., 2019).
* But participation does not always address unequal power relations (e.g., [[#Buggy--2016|Buggy and McNamara, 2016]] ; [[#Karlsson--2017|Karlsson et al., 2017]] ).

|-
| Distributive justice

| Delivering adaptation for vulnerable groups and correcting structural vulnerabilities

| * Ensures that adaptation interventions do not exacerbate inequities ( [[#Atteridge--2018|Atteridge and Remling, 2018]] ) and that the benefits and burdens of interventions are distributed fairly ( [[#Tschakert--2013|Tschakert et al., 2013]] ; [[#Reckien--2017|Reckien et al., 2017]] ; [[#Reckien--2018b|Reckien et al., 2018b]] ; [[#Pelling--2019|Pelling and Garschagen, 2019]] ).
* However, low levels of commitment to distributive justice, e.g., when justice is one of many goals of adaptation instead of the prime one, are insufficient to promote equitable distribution of benefits and harms ( ''medium evidence'' , ''high agreement'' ) ( [[#Anguelovski--2016|Anguelovski et al., 2016]] ; [[#Pulido--2016|Pulido et al., 2016]] ; [[#Weinstein--2019|Weinstein et al., 2019]] ; [[#Shawoo--2020|Shawoo and McDermott, 2020]] ).

| * Women and men have very different access to mobile phones, entailing lower responsiveness with climate services among women ( [[#Partey--2020|Partey et al., 2020]] , across Africa).
* Slow progress on prioritising distributional and procedural justice limits the expansion of adaptation funding to poorest and most vulnerable social groups and nations ( [[#Khan--2019a|Khan et al., 2019a]] ).
* Focusing 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|Taylor, 2016b]] ), urban water supply in Malawi ( [[#Rusca--2017|Rusca et al., 2017]] ), informal urban settlements in Kenya ( [[#Pelling--2019|Pelling and Garschagen, 2019]] ) and forest management in Cambodia ( [[#Work--2019|Work et al., 2019]] ).

|-
| Flexible and strong institutions

| Seeks policy integration and dynamic risk management, and accounts for long-term goals

| * Institutional flexibility allows a society to respond quickly to the demands of a changing environment by developing new institutions or adjusting existing ones quickly ( [[#Davis--2010|Davis, 2010]] ); possibly avoiding lock-ins and addressing future climate risks ( ''very robust evidence'' , ''high agreement'' ) ( [[#Levi-Faur--2012|Levi-Faur, 2012]] ; [[#Sherman--2013|Sherman and Ford, 2013]] ; [[#Boyd--2015|Boyd and Juhola, 2015]] ; [[#Magnan--2016|Magnan et al., 2016]] ).
* Stability (and familiarity) is often desired in governance arrangements, and balancing the need for stability with goals of flexibility without causing rigidity is key ( [[#Craig--2017|Craig et al., 2017]] , in USA; Chapter 11). This is possible through deliberate, consultative changes that build awareness, develop shared norms, rules and goals, and develop inclusive decision-making processes (Chapter 3).

| * Capacity building of adaptation funders, planners and implementers and re-orienting existing institutions to make decisions under uncertainty, institute long-term climate risk management that goes beyond typical political/planning cycles, and develop learning mechanisms between sectors, actors and projects needed ( [[#Moser--2013|Moser and Boykoff, 2013]] ; [[#Granberg--2014|Granberg and Glover, 2014]] in Australia; [[#Boyd--2015|Boyd and Juhola, 2015]] in cities; [[#Ziervogel--2019|Ziervogel, 2019]] in Africa and; [[#Olazabal--2019b|Olazabal et al., 2019b]] in India; [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] Oceans; Chapter 10; Chapter 11; Chapter 12).
* Flexible institutions enable adoption of new adaptation measures or course-correct based on ongoing M&amp;E (e.g., [[#Granberg--2014|Granberg and Glover, 2014]] in Australia; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Torabi--2018|Torabi et al., 2018]] ; [[#Gajjar--2019a|Gajjar et al., 2019a]] in India) ( ''medium evidence'' , ''high agreement)'' .
* Sectoral or spatial policy integration ( [[#Chu--2017|Chu et al., 2017]] ; [[#17.6|Section 17.6]] ; [[#Hino--2017|Hino et al., 2017]] ; [[#Robinson--2020|Robinson and Wren, 2020]] ); integration of jurisdictional frameworks of different agencies ( [[#Poesch--2016|Poesch et al., 2016]] ; Chapter 5; Chapter 9); and adaptive and flexible legal systems which disaggregate socio-ecological systems into smaller components ( [[#Arnold--2013|Arnold and Gunderson, 2013]] ; [[#Wenta--2019|Wenta et al., 2019]] ) are key enablers.

|}

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'''Box 17.3 | Climate Risk Decision-Making in Settlements: From Incrementalism to Transformational Adaptation'''
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Cities are important sites of experimentation where the integration and management of adaptation decision-making complexity often takes place. These actions provide early evidence of what aspects of complex climate risk management decision-making functions well, but also what does not work ( [[#Revi--2020|Revi et al., 2020]] ). Cities are seen as locales where case examples of transformative adaptation can be examined ( [[#Rosenzweig--2018|Rosenzweig and Solecki, 2018]] ; [[#Vermeulen--2018|Vermeulen et al., 2018]] ). Cities act as testbeds of how to integrate climate response into issues of equity, health, resource allocation and sustainability in ways that utilise innovative use of new and emerging decision-support tools, methods and protocols.

Risk management has been an integral part of the community development and settlement building process. Three key sets of drivers influence risk management decision-making in cities ( [[#Solecki--2017|Solecki et al., 2017]] ). These include: (1) root, that is, cultural norms and social traditions; (2) context, that is, policy and governance conditions; and (3) proximate, that is, extreme events. Settlements have developed informal and formal strategies, including climate protection levels, to respond to local conditions of climate risk and hazards. In formal contexts, these strategies are contextualised in local climate change action plans ( [[#Araos--2016a|Araos et al., 2016a]] ; [[#Stults--2017|Stults and Woodruff, 2017]] ; [[#Reckien--2018a|Reckien et al., 2018a]] ; [[#Singh--2021|Singh et al., 2021]] ) and defined around a set of evaluation tools and methods and building codes, standards and regulations (see discussion in [[#17.4.4|Section 17.4.4]] ).

Climate change has begun to alter the environmental baseline of cities, changing their risk and hazard profiles. In recent years, national and local risk management can benefit from assessments of current decision-making strategies and from evaluations of opportunities for change in risk management policy. These changes can be adjustments of existing policies or transitions to a new policy for current (i.e., conditions already experienced by getting worse) or emerging risks (i.e., conditions not previously or widely experienced but now increasingly present).

With increasing impacts of climate change, settlements of all sizes are considering how to make their communities more resilient to climate risk (see Cross-Working Group Box URBAN in Chapter 6; [[#Araos--2016a|Araos et al., 2016a]] ; [[#Araos--2017|Araos et al., 2017]] ; [[#Reckien--2018a|Reckien et al., 2018a]] ). In many settlements, demands for heightened resiliency are being coupled with opportunities to enhance the social and economic equity and quality of life of residents. Transformational adaptation (transformational, as being outcome-oriented; [[#Vermeulen--2018|Vermeulen et al., 2018]] ) and associated adjustments to the urban risk management decision-making require an integration of climate resiliency pathways and conditions of sustainable development ( [[#Mendizabal--2018|Mendizabal et al., 2018]] ). At the same time, growing conflict is present between requirements for greater resiliency and continued economic development, in particular in low-income environments ( [[#Ahenkan--2020|Ahenkan et al., 2020]] ). Cities and their residents have the capacity to transform their own governance and decision-making systems ( [[#Birkmann--2014|Birkmann et al., 2014]] ; [[#Chu--2018|Chu, 2018]] ; [[#Romero-Lankao--2018|Romero-Lankao et al., 2018]] ). Furthermore, cities have recognised the opportunity and demand to transform in order to be more ambitious ( [[#Mendizabal--2018|Mendizabal et al., 2018]] ) and more successful, more equitable ( [[#Reckien--2018b|Reckien et al., 2018b]] ) and better able to connect the climate action to the sustainable development process ( [[#Singh--2021|Singh et al., 2021]] ).

In some cases, transformational adaptation is associated with large-scale, top-down, formal decision processes leading to significant policy shifts. For coastal cities, this might include actions to build massive flood protection systems (as opposed to simple increase of existing structures) ( [[#Albers--2015|Albers et al., 2015]] ; [[#Hinkel--2018|Hinkel et al., 2018]] ; [[#Ajibade--2019|Ajibade, 2019]] ; see also [[IPCC:Wg2:Chapter:Chapter-2#2.3|Section 2.3.5]] , Cross-Chapter Paper 2) or policies to encourage managed retreat from increasing at risk locations ( [[#Hino--2017|Hino et al., 2017]] ; [[#Rulleau--2017|Rulleau and Rey-Valette, 2017]] ). In more extreme instances, the relocation of cities is presented as a possibility, such as planned for the city of Jakarta ( [[#Garschagen--2018b|Garschagen et al., 2018b]] ). However, acceptability of top-down approaches to relocation are usually low, and bottom-up drivers of relocation are important, especially to avoid inequitable outcomes ( [[#Mach--2021|Mach and Siders, 2021]] ). Intensity of extreme events and changing risk perceptions and expectations of property prices have been identified as important behavioural drivers of voluntary relocation ( [[#de%20Koning--2019|de Koning et al., 2019]] ; [[#de%20Koning--2020|de Koning and Filatova, 2020]] ). Yet, when not supported by equitable public adaptation policies, the transformational adaptation left to the influence of autonomous adaptation and market institutions alone leads to climate gentrification low-income households are priced out from the hazard-free zones ( [[#de%20Koning--2020|de Koning and Filatova, 2020]] ).

These circumstances also have revealed potential advances in decision-making by encouraging greater participation, more effective generation and use of information and data, and more prominent inclusion of questions of social and economic equity ( [[#Ziervogel--2017|Ziervogel et al., 2017]] ; [[#Reckien--2018b|Reckien et al., 2018b]] ; Solecki et al., In Press). Adaptation planning and decision-making, in general, within cities has increasingly focused on actively engaging residents in participatory and neighbourhood scale co-production processes ( [[#Broto--2015|Broto et al., 2015]] ; [[#Sarzynski--2015|Sarzynski, 2015]] ; [[#Wamsler--2017|Wamsler, 2017]] ; [[#Foster--2019|Foster et al., 2019]] ). However, engaging residents in risk management and adaptation has not always led to transformative decision-making and resiliency, but can at times also reinforce existing maladaptive systems ( [[#D’Alisa--2016|D’Alisa and Kallis, 2016]] ).

Now increasing amounts of data are being collected via surveys or in participatory settings next to advanced methods, such as using citizen science, big data and AI, to integrate these social dimensions of climate adaptation decisions in cities in formal models ( [[#Abebe--2019|Abebe et al., 2019]] ; [[#Taberna--2020|Taberna et al., 2020]] ). Linking to social data on individual decisions, risk perceptions, social norms and governmental policy, advanced social models trace and quantify how adaptation in cities evolve and would cumulatively induce transformational change. Although wider application of these models is outstanding, there is opportunity to simulate and learn from the integration of social and behavioural data with political and cultural norms ( [[#de%20Koning--2020|de Koning and Filatova, 2020]] ).

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Box 17.3

Although non-urban areas could in many instances act in the same way as urban areas, the density of people, assets, infrastructure and economical values drive cities to act as testbeds, implement adaptation and strive for resiliency. Cities are showcases for the larger environmental systems of governments that also support mitigation ambition of national actors and are therefore demanding to be recognised as valuable actors in the international negotiations, highlighting their contribution in emissions reductions ( [[#Chan--2015|Chan et al., 2015]] ; [[#Hale--2016|Hale, 2016]] ), such as in the preparation for the first Global Stocktake of the Paris Agreement in 2023 (see Cross-Chapter Box PROGRESS in this Chapter).

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