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

== 17.5 Adaptation Success and Maladaptation, Monitoring, Evaluation and Learning ==

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=== 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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[[File:f204079052da564353ff3d5d93a83f2f IPCC_AR6_WGII_Figure_17_010.png]]

'''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]] ).

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| 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).

<div id="17.5.2" class="h2-container"></div>

<span id="adaptation-monitoring-evaluation-learning"></span>
=== 17.5.2 Adaptation Monitoring, Evaluation &amp; Learning ===

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<div id="17.5.2.1" class="h3-container"></div>

<span id="purpose-of-monitoring-and-evaluation"></span>
==== 17.5.2.1 Purpose of Monitoring and Evaluation ====

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Adaptation responses have been observed in every region and across a wide variety of sectors ( [[IPCC:Wg2:Chapter:Chapter-16#16.3|Section 16.3]] ), but little evidence exists of their outcomes in terms of climate risk reduction ( ''high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-1#1.4.3|Section 1.4.3]] ; [[#Ford--2016|Ford and Berrang-Ford, 2016]] ; [[#Tompkins--2018|Tompkins et al., 2018]] ; [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ; [[#UNEP--2021a|UNEP, 2021a]] ). To advance on that, the Paris Agreement is encouraging countries to engage in ‘Monitoring and evaluating and learning from adaptation plans, policies, programmes and actions’ ( [[#UN--2015|UN, 2015]] , Article 7.9d). Monitoring and evaluation (M&amp;E) is the systematic process of collecting, analysing and using information to assess the progress of adaptation and evaluate its effects—for example, risk reduction outcomes, co-benefits and trade-offs—mostly during and after implementation (AR6 Glossary, Annex II). Distinctions between monitoring and evaluation typically view monitoring as a continuous process of tracking implementation and informing management to allow for corrective action including in situations of deep uncertainty (see Cross-Chapter Box DEEP in this Chapter), while evaluation is described as a more comprehensive assessment of achievements, unintended effects and lessons learned carried out at certain point in time ( [[#OECD--2002|OECD, 2002]] ). M&amp;E is an important part of the adaptation process (Figure 1.9). It can help to generate information on adaptation success or maladaptive outcomes.

M&amp;E of adaptation is undertaken for different purposes, including: (1) understanding whether responses have achieved their intended objectives and contributed to a reduction in climate risks and vulnerability or to an increase of adaptive capacity and resilience, (2) informing ongoing implementation and future responses, and (3) providing upward and downward accountability (Preston et al., 2009; [[#UNFCCC--2010a|UNFCCC, 2010a]] ; [[#Pringle--2011|Pringle, 2011]] ; [[#Spearman--2011|Spearman and McGray, 2011]] ). M&amp;E is also commonly linked to learning ( [[#17.5.2.7|Section 17.5.2.7]] ). By continuously monitoring implementation, for example, to assess whether adaptation is on track or needs to be accelerated, M&amp;E can aid decision-making under uncertainty. Adaptation M&amp;E is distinct from tracking financial flows related to adaptation since financial accounting does not provide information on implementation and outcomes ( [[#17.5.2.5|Section 17.5.2.5]] ; [[#Adaptation%20Partnership--2012|Adaptation Partnership, 2012]] ; [[#World%20Bank%20Independent%20Evaluation%20Group--2012|World Bank Independent Evaluation Group, 2012]] ).

<div id="17.5.2.2" class="h3-container"></div>

<span id="adaptation-me-approaches"></span>
==== 17.5.2.2 Adaptation M&amp;E Approaches ====

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Adaptation M&amp;E can be conducted for various purposes and in a wide variety of different contexts ranging from the local to the global level ( [[#McKenzie%20Hedger--2008|McKenzie Hedger et al., 2008]] ; [[#UNFCCC--2010a|UNFCCC, 2010a]] ; [[#Spearman--2011|Spearman and McGray, 2011]] ). The context and specific purpose of M&amp;E determine what information needs to be generated, and together with the available resources also determine the suitability of particular approaches and methods ( [[#Leiter--2016|Leiter, 2016]] ; [[#Leiter--2017|Leiter, 2017]] ). Several frameworks and approaches have been proposed for M&amp;E of adaptation and climate resilience ( [[#Bours--2014d|Bours et al., 2014d]] ; [[#Schipper--2015|Schipper and Langston, 2015]] ; [[#Adaptation%20Committee--2016|Adaptation Committee, 2016]] ; [[#ODI--2016|ODI, 2016]] ; [[#Cai--2018|Cai et al., 2018]] ; [[#Gregorowski--2018|Gregorowski et al., 2018]] ), including sector-specific ones for agriculture ( [[#FAO--2017|FAO, 2017]] ; [[#FAO--2019a|FAO, 2019a]] ; [[#FAO--2019b|FAO, 2019b]] ), health ( [[#Ebi--2018|Ebi et al., 2018]] ), ecosystem-based adaptation ( [[#Donatti--2018|Donatti et al., 2018]] ; [[#Donatti--2020|Donatti et al., 2020]] ; [[#GIZ--2020|GIZ, 2020]] ) and cities ( [[IPCC:Wg2:Chapter:Chapter-6#6.4.6|Section 6.4.6]] ).

Adaptation M&amp;E generally seeks to answer whether implementation is taking place and what effects it has (Figure 17.12). Accordingly, M&amp;E can focus on the processes, activities and outputs or on their outcomes and ultimate impacts ( [[#Harley--2008|Harley et al., 2008]] ; [[#Pringle--2011|Pringle, 2011]] ; [[#Ford--2013|Ford et al., 2013]] ). Most of the available guidance for the development of adaptation M&amp;E systems is aimed at the household, local or project level ( [[#Pringle--2011|Pringle, 2011]] ; [[#Villanueva--2012|Villanueva, 2012]] ; [[#Olivier--2013|Olivier et al., 2013]] ; [[#CARE--2014|CARE, 2014]] ; [[#BRACED--2015|BRACED, 2015]] ; [[#Leiter--2016|Leiter, 2016]] ; [[#Jones--2019b|Jones, 2019b]] ) with only limited guidance for national or cross-sectoral M&amp;E systems ( [[#Price-Kelly--2015|Price-Kelly et al., 2015]] ) or frameworks that are applicable at different scales ( [[#Brooks--2014|Brooks et al., 2014]] ). The available guidebooks take users through a series of steps which are synthesised in Figure 17.12.

<div id="_idContainer054" class="Figure"></div>

[[File:79840ac5ec788348e69a606a4c7948a1 IPCC_AR6_WGII_Figure_17_012.png]]

'''Figure 17.12 |''' '''Adaptation M&amp;E and learning as part of the adaptation process (based on Hammill et al''' '''.''' ''', 2014a; [[#Price-Kelly--2015|Price-Kelly et al., 2015]] ; [[#Leiter--2016|Leiter, 2016]] ).''' This figure shows the main steps involved in developing an adaptation M&amp;E system where the context informs the purpose of M&amp;E, which in turn determines the information needs. To achieve the M&amp;E purposes, the chosen approach and data sources need to be able to generate the needed information, which needs to be communicated in a suitable way to the target audiences.

The majority of adaptation M&amp;E efforts have so far focused on processes and outputs rather than on achieved outcomes such as climate risks, vulnerability, well-being or development ( [[#Droesch--2008|Droesch et al., 2008]] ; [[#GIZ%20and%20Adelphi--2017|GIZ and Adelphi, 2017]] ; UNDP [[#Cambodia--2014|Cambodia, 2014]] ; [[#Fawcett--2017|Fawcett et al., 2017]] ) ( ''high confidenc'' e) or use a combination thereof ( [[#Brooks--2011|Brooks et al., 2011]] ; [[#Brooks--2014|Brooks et al., 2014]] ). Newly emerging approaches include perception-based measurements and the use of data collected via mobile phones ( [[#Jones--2018|Jones et al., 2018]] ; [[#Jones--2019a|Jones, 2019a]] ), which can be collected frequently ( [[#Clare--2017a|Clare et al., 2017a]] ; [[#Knippenberg--2019|Knippenberg et al., 2019]] ; [[#Jones--2020|Jones and Ballon, 2020]] ). Such advances call into question the common reliance on ‘objective’ indicators defined from an external perspective. Instead, they suggest that multiple complementary approaches combined with higher-frequency data collection produce a more elaborate picture of the effects of adaptation and resilience responses ( [[#Jones--2019|Jones and d’Errico, 2019]] ; [[#Knippenberg--2019|Knippenberg et al., 2019]] ; [[#Singh--2019|Singh et al., 2019]] ; [[#Jones--2019a|Jones, 2019a]] ; see Cross-Chapter Box PROGRESS in this Chapter) ( ''medium confidence'' ).

Central to designing, monitoring and evaluating adaptation responses is outlining how activities are expected to lead to intended objectives, for example, via a theory of change ( [[#Bours--2014c|Bours et al., 2014c]] ; Oberlack and al., 2019). Theories of change or similar change models provide a basis to decide what to measure, but more attention needs to be paid to how theories of change are constructed and who is involved ( [[#Mason--2007|Mason and Barnes, 2007]] ; [[#Forsyth--2018|Forsyth, 2018]] ). Participatory approaches can support understanding how climate risks affect the respective population, how these risks interact with social and cultural processes, and how responses could most effectively address climate risks ( [[#Conway--2019|Conway et al., 2019]] ). Inclusive M&amp;E systems can facilitate ownership and enhance the meaningfulness and usability of the generated information ( [[#CARE--2014|CARE, 2014]] ; [[#Faulkner--2015|Faulkner et al., 2015]] ). Meaningfulness is not associated with a particular approach or method but depends on whether the chosen M&amp;E design fits the M&amp;E purpose and the information needs of the intended audience ( [[#Fisher--2015|Fisher et al., 2015]] ; [[#Leiter--2017|Leiter, 2017]] ). Effective communication of M&amp;E findings and feedback into decision-making processes is essential to achieve the respective M&amp;E purpose and facilitate learning ( [[#17.5.2.7|Section 17.5.2.7]] ).

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<span id="adaptation-indicators-and-indices"></span>
==== 17.5.2.3 Adaptation Indicators and Indices ====

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A set of all-purpose and globally applicable standard indicators that could comprehensively measure adaptation does not exist ( ''high confidence'' ) ( [[#IPCC--2014|IPCC, 2014]] ; [[#Leiter--2018|Leiter and Pringle, 2018]] ). A wide variety of indicators have been used to assess adaptation and its results ( [[#CARE--2010|CARE, 2010]] ; [[#Harvey--2011|Harvey et al., 2011]] ; [[#Lamhauge--2013|Lamhauge et al., 2013]] ; [[#Brooks--2014|Brooks et al., 2014]] ; [[#Hammill--2014b|Hammill et al., 2014b]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#HM%20Government--2019|HM Government, 2019]] ). Literature has also noted unrealistic expectations of what indicators can accomplish. For instance, decisions involving competing political interests would not be adequately informed through simple indicators; and learning requires knowledge of how and why change has happened, something that indicators often do not capture ( [[#Hinkel--2011|Hinkel, 2011]] ; [[#Bours--2014b|Bours et al., 2014b]] ). Indicators can also become misguided incentives and might steer attention away from what matters ( [[#Leiter--2018|Leiter and Pringle, 2018]] ; [[#Hallegatte--2019|Hallegatte and Engle, 2019]] ; [[#Klonschinski--2021|Klonschinski, 2021]] ). Surveys, scorecards, interviews and focus groups are alternative methods of gaining insights on adaptation progress ( [[#Brooks--2014|Brooks et al., 2014]] ; [[#Porter--2015|Porter et al., 2015]] ; [[#Das--2019|Das, 2019]] ; [[#McNamara--2020|McNamara et al., 2020]] ).

The difficulties of assessing adaptation and an emphasis on short-term results have contributed to the common practice of relying on easily quantifiable indicators rather than assessing actual changes, that is, outcomes and impacts ( [[#World%20Bank%20Independent%20Evaluation%20Group--2012|World Bank Independent Evaluation Group, 2012]] ; [[#Fisher--2015|Fisher et al., 2015]] ). In fact, indicators used by international climate funds largely measure outputs which provide little evidence of the actual effectiveness of adaptation, that is, its outcomes and impacts ( [[#GCF%20Independent%20Evaluation%20Unit--2018|GCF Independent Evaluation Unit, 2018]] ; [[#Leiter--2019|Leiter et al., 2019]] ; [[#Pauw--2020|Pauw et al., 2020]] ).

Indices, the combination of multiple indicators into a single score, are common products of risk and vulnerability assessments to compare countries or other entities, often in the form of rankings or maps ( [[#Preston--2011|Preston et al., 2011]] ; [[#Reckien--2018|Reckien, 2018]] ; de Sherbinin and et al., 2019). They can indicate changes in vulnerability over time within their respective conceptualisation of vulnerability or risk. The construction of indices, including indicator selection, their weighting, normalisation and data sources, has a profound impact on their scores ( [[#Reckien--2018|Reckien, 2018]] ). Research has consistently found large discrepancies between country vulnerability rankings ( [[#Brooks--2005|Brooks et al., 2005]] ; [[#Eriksen--2007|Eriksen and Kelly, 2007]] ; [[#Leiter--2017b|Leiter et al., 2017b]] ; [[#Visser--2020|Visser et al., 2020]] ). Reviews of vulnerability and resilience indices identified ‘substantial conceptual, methodological and empirical weaknesses’ ( [[#Füssel--2010|Füssel, 2010]] : 8) and a widespread lack of validation ( [[#Cai--2018|Cai et al., 2018]] ). Using countries as a unit of analysis also masks significant sub-national variation ( [[#Otto--2015|Otto et al., 2015]] ; [[#Mohammadpour--2019|Mohammadpour et al., 2019]] ). Individual indices therefore ‘fail to convene a robust guidance for policy makers’ ( [[#Muccione--2017|Muccione et al., 2017]] : 4) and should not present the sole basis for policy decisions ( [[#Brooks--2005|Brooks et al., 2005]] ; [[#Leiter--2018|Leiter and Pringle, 2018]] ). Due to their limitations ( [[#Singh--2017|Singh et al., 2017]] ), the OECD suggests that indices are primarily used for ‘initiating discussion and stimulating public interest’ ( [[#OECD--2008|OECD, 2008]] : 13).

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<span id="empirical-evidence-of-national-adaptation-me-systems"></span>
==== 17.5.2.4 Empirical Evidence of National Adaptation M&amp;E Systems ====

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Tracking the implementation of national adaptation plans is essential for understanding their effectiveness, that is, the progress made in addressing climate risks, and can support assessing the success of adaptation and the risk of maladaptation. Over 60 countries have developed or started developing national adaptation M&amp;E systems, although less than half are yet reporting on implementation ( [[#Leiter--2021b|Leiter, 2021b]] ; Table 17.8). Country-specific adaptation M&amp;E systems vary considerably regarding their legal mandate, purpose, content, involved actors and types of reporting ( [[#Hammill--2014a|Hammill et al., 2014a]] ; EEA, 2015; [[#Leiter--2015|Leiter, 2015]] ; [[#Leiter--2017a|Leiter et al., 2017a]] ; [[#EEA--2020|EEA, 2020]] ). In most cases, they focus primarily on monitoring implementation rather than assessing outcomes, although some are linked to national climate risk or vulnerability assessments (e.g., in Germany and the UK) ( [[#EEA--2018|EEA, 2018]] ). At least 15 countries have published evaluations of national adaptation plans which help inform the development of successive adaptation plans or strategies (Table 17.8). Nevertheless, there is only limited empirical evidence of the ability of M&amp;E systems to facilitate action or increase the level of ambition of revised policies. More research is needed to determine the quality of national adaptation M&amp;E systems and how well they support the policy cycle.

Under the Paris Agreement, countries are encouraged to provide information on adaptation, including its adequacy and effectiveness ( [[#Möhner--2017|Möhner et al., 2017]] ; [[#Adaptation%20Committee--2021|Adaptation Committee, 2021]] ). National adaptation M&amp;E systems can inform both national as well as international reporting and contribute to the Global Stocktake (see Cross-Chapter Box PROGRESS in this Chapter; [[#Craft--2015|Craft and Fisher, 2015]] ; [[#Leiter--2017a|Leiter et al., 2017a]] ). Guidance for and examples of national adaptation progress assessments are provided by [[#Price-Kelly--2015|Price-Kelly et al. (2015)]] , [[#Brooks--2014|Brooks et al. (2014)]] , [[#Brooks--2019|Brooks et al. (2019)]] , EEA (2015), [[#GIZ--2017|GIZ (2017)]] , [[#Karani--2018|Karani (2018)]] and [[#van%20Rüth--2018|van Rüth and Schönthaler (2018)]] . Global assessments of adaptation progress have so far often focused on adaptation planning and, to a lesser extent, implementation, while evidence of the collective effect of adaptation globally remains limited ( ''high confidence'' ) ( [[#UNEP--2021a|UNEP, 2021a]] ; Cross-Chapter Box PROGRESS in this Chapter).

'''Table 17.8 |''' Countries in different stages of developing or operating a national adaptation M&amp;E system as of 1 August 2021 (Source: [[#Leiter--2021b|Leiter, 2021b]] ). Countries can appear twice if they have published both a progress report and an evaluation.

{| class="wikitable"
|-
! rowspan="2"|
! colspan="3"| National adaptation M&amp;E system

|-
! Stage

! Definition

! Country

|-
| rowspan="2"| Under development

| Early stage

| Tangible steps have been undertaken to develop a national adaptation M&amp;E system, for example a stocktake of relevant existing data sources and engagement with stakeholders on the objectives of the M&amp;E system

| Benin, Cook Islands, Jordan, Paraguay, Sri Lanka, Uganda

|-
| Advanced stage

| Details of the adaptation M&amp;E system have been developed, including, for instance, institutional arrangements, indicators and data sources, but it has not yet been applied

| Albania, Bulgaria, Cameroon, Canada, Colombia, Ethiopia, Fiji, Grenada, Indonesia, Moldova, Morocco, Mozambique, Nauru, Peru, Rwanda, Senegal, St. Lucia, St. Vincent and the Grenadines, Suriname, Thailand, Togo, Tonga, Turkey, Vietnam

|-
| rowspan="2"| In operation

| Adaptation progress report published

| A progress report on the implementation of the national adaptation plan or strategy has been published

| Austria, Belgium (Flanders), Brazil, Burkina Faso, Cambodia, Chile, Cyprus, France, Germany, Japan, Kenya, Kiribati, Lithuania, Mexico, the Netherlands (Delta Programme), Norway, Portugal, Slovakia, Spain, South Africa, South Korea, Switzerland, UK

|-
| Evaluation published

| An evaluation of the implementation of the national adaptation plan or strategy has been undertaken and published

| Belgium, Cambodia, Chile, Czech Republic, Finland, France, Germany, Ireland, Mexico, the Netherlands, Philippines, South Korea, Spain, Switzerland, UK

|}

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<span id="challenges-of-assessing-adaptation"></span>
==== 17.5.2.5 Challenges of Assessing Adaptation ====

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To date, literature has largely focused on aspects prior to implementation such as assessments of climate vulnerability and risks or appraisals of adaptation options ( [[#Sietsma--2021|Sietsma et al., 2021]] ; Cross-Chapter Box Adaptation). To understand adaptation progress, the assessment of implemented adaptation actions and their outcomes requires more attention ( ''very high confidence'' ) (Cross-Chapter Box PROGRESS in this Chapter).

Outcomes on risk reduction are typically expressed in ways that are specific to the respective sector or context (e.g., as agricultural yields, health benefits or reduced water stress) highlighting that ‘adaptation has no common reference metrics in the same way that tonnes of GHGs or radiative forcing values are for mitigation’ ( [[#IPCC--2014|IPCC, 2014]] : 856). Assessments of adaptation progress therefore need to specify what they are measuring and how they are measuring it. The way adaptation is conceptualised, for example as a continuum between successful adaptation and maladaptation ( [[#17.1.1|Section 17.1.1]] ), and the way adaptation is framed, for example as a technical challenge or a political process ( [[#Juhola--2011|Juhola et al., 2011]] ; [[#Bassett--2013|Bassett and Fogelman, 2013]] ; [[#Eriksen--2015|Eriksen et al., 2015]] ), shape the understanding of progress and its subsequent measurement ( [[#Singh--2021|Singh et al., 2021]] ).

Furthermore, people can be differently affected even in the same location owing to, among others, differential vulnerability among the population ( [[#Reckien--2019|Reckien and Petkova, 2019]] ; [[#Thomas--2019|Thomas et al., 2019]] ). Different views and values can also affect what it means to adapt ( [[#Few--2021|Few et al., 2021]] ). Assessments of adaptation progress therefore need to be transparent and reflective about how they define and measure adaptation and account for culturally and geographic contingent concepts of what it means to adapt in light of the global diversity of livelihoods and concepts.

The lack of knowledge on adaptation progress is associated with further measurement challenges, including that avoided impacts are difficult to measure and that risk levels change over time, meaning what is effective today may not be effective in the future ( [[#Brooks--2011|Brooks et al., 2011]] ; [[#Pringle--2011|Pringle, 2011]] ; [[#Spearman--2011|Spearman and McGray, 2011]] ; [[#Villanueva--2012|Villanueva, 2012]] ; [[#Bours--2014a|Bours et al., 2014a]] ). Moreover, adaptation is embedded in complex political and social realities where power and politics shape outcomes and where simplistic views of how adaptation would take place may be ill-conceived ( [[#Nightingale--2017|Nightingale, 2017]] ; [[#Mikulewicz--2018|Mikulewicz, 2018]] ; [[#Mikulewicz--2020|Mikulewicz, 2020]] ). In practice, this means that theories of change of adaptation projects may miss important causes of risks and could subsequently lead to inaccurate assessments ( [[#Forsyth--2018|Forsyth, 2018]] ). Measuring adaptation is therefore a matter of understanding drivers of vulnerability and risk and of designing responses and M&amp;E systems accordingly ( [[#UNFCCC--2019a|UNFCCC, 2019a]] , section V).

The importance of context and the dependence on viewpoints make comparative assessments of adaptation across nations, regions or responses challenging. Comparison requires a consistent conceptualisation of adaptation, comparable units of analysis and access to relevant data sets ( [[#Ford--2015|Ford et al., 2015]] ; [[#Ford--2016|Ford and Berrang-Ford, 2016]] ). Comparative adaptation policy assessments to date often lack clarity in concepts and explanatory variables ( [[#Dupuis--2013|Dupuis and Biesbroek, 2013]] ; Biesbroek R, 2018a). The trade-off between standardisation and context specificity also complicates attempts to aggregate adaptation progress across scales to the national or global level ( [[#Leiter--2018|Leiter and Pringle, 2018]] ; Cross-Chapter Box PROGRESS in this Chapter).

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<span id="tracking-adaptation-finance"></span>
==== 17.5.2.6 Tracking Adaptation Finance ====

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Adaptation finance tracking is capturing the financial flows associated with adaptation. It can indicate how much is being spent on adaptation, where funds are going to and whether spending matches allocated budgets. Thus, adaptation finance tracking can provide useful information for decision-making, but it does not provide information on the achievements resulting from the invested funds. Accordingly, it can complement, but not substitute, M&amp;E of actions and outcomes. Adaptation finance tracking can be applied domestically ( [[#Guzmán--2017|Guzmán et al., 2017]] ; [[#Guzmán--2018|Guzmán et al., 2018]] ) as well as internationally, for instance by developed countries to report on the goal to mobilise USD 100 billion yr −1 by 2020 in climate finance ( [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ). Data on adaptation finance can be used alongside information on planning and implementation to assess adaptation progress ( [[#UNEP--2021a|UNEP, 2021a]] ).

Tracking adaptation finance requires defining what counts as adaptation. Different definitions can lead to large variations in the estimated amount of adaptation finance ( [[#Donner--2016|Donner et al., 2016]] ; [[#Hall--2017|Hall, 2017]] ). A further challenge is how to account for adaptation that is mainstreamed, that is, where adaptation-specific investments form only part of a larger programme or budget line, or where actions contribute to adaptation without being labelled as adaptation. These challenges limit the direct comparability between adaptation and mitigation finance ( [[#UNFCCC--2019a|UNFCCC, 2019a]] ). In fact, tracking adaptation finance differs from tracking mitigation finance since activities cannot be ''a priori'' assumed to constitute adaptation but instead have to be assessed for their linkage to climate risks in a particular context (MDBs &amp; IDFC, 2018). Methods for adaptation finance tracking continue to be further developed aiming at better comparability and completeness ( [[#Richmond--2019|Richmond and Hallmeyer, 2019]] ; [[#Richmond--2021|Richmond et al., 2021]] ).

Various methods are used to track adaptation finance, which makes comparisons between adaptation finance figures challenging ( [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ; [[#Weikmans--2019|Weikmans and Roberts, 2019]] ). For example, multi-lateral development banks use a different methodology than countries do under the OECD Development Assistance Committee (DAC) (Box 17.4; [[#MDBs--2019|MDBs, 2019]] ). One of the differences concerns the treatment of partially adaptation-relevant projects, namely whether only parts or the full amount of a given project volume are counted as adaptation finance (see, e.g., [[#MDBs--2019|MDBs, 2019]] ). Under the OECD DAC methodology, countries often use a fixed percentage (e.g., 50% of the total project value), whereas the MDB methodology attempts for a project-specific estimation of the adaptation-relevant proportion (MDBs &amp; IDFC, 2018). Another aspect is whether tracking distinguishes between financial instruments, such as grants or loans. Different accounting rules can lead to large differences in reported amounts of adaptation finance and to a lack of comparability between providers ( [[#Weikmans--2019|Weikmans and Roberts, 2019]] ). Studies identified an over-reporting (i.e., counting non-adaptation-related finance) by a factor of two to three, which suggests the need for a more consistent and transparent accounting system ( [[#Weikmans--2017|Weikmans et al., 2017]] ; [[#CARE--2021|CARE, 2021]] ).

Good coverage of adaptation finance data exists around international public finance flows, predominantly official development assistance flows from OECD DAC members and from multi-lateral development banks. Less data exist around domestic public finance and private finance flows to adaptation activities, but data sources continue to be further expanded, for example through climate change expenditure tagging and city-level data ( [[#Weikmans--2017|Weikmans et al., 2017]] ; [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ; [[#Richmond--2021|Richmond et al., 2021]] ). Recent estimates of adaptation finance are provided in [[#UNFCCC%20SCF--2018|UNFCCC SCF (2018)]] , [[#Macquarie--2020|Macquarie et al. (2020)]] and Cross-Chapter Box FAR in this Chapter.

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==== 17.5.2.7 Evaluation and Learning ====

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Most adaptation M&amp;E frameworks and tools proposed to date refer to monitoring rather than evaluation ( ''high confidence'' ) ( [[#Adaptation%20Committee--2016|Adaptation Committee, 2016]] ). Evaluations are envisioned to go beyond monitoring by examining how and why results have been achieved and what could be improved ( [[#Brousselle--2018|Brousselle and Buregeya, 2018]] ; [[#Vähämäki--2019|Vähämäki and Verger, 2019]] ). Evaluations of adaptation outcomes are still rare, particularly quantitative impact evaluations ( [[#Weldegebriel--2013|Weldegebriel and Prowse, 2013]] ; [[#Das--2019|Das, 2019]] ; [[#Béné--2020|Béné et al., 2020]] ). Impact evaluations of adaptation need to address several methodological as well as practical challenges ( [[#Dinshaw--2014|Dinshaw et al., 2014]] ; [[#Fisher--2015|Fisher et al., 2015]] ; [[#Béné--2017|Béné et al., 2017]] ; [[#Puri--2020|Puri et al., 2020]] ). Different types of evaluations are appropriate for different evaluation questions ( [[#Silvestrini--2015|Silvestrini et al., 2015]] ). Evaluations of the available evidence of effective adaptation, in particular topics or sectors, have emerged more recently, for instance on mainstreaming ( [[#Runhaar--2018|Runhaar et al., 2018]] ) and agricultural climate services ( [[#Vaughan--2019a|Vaughan et al., 2019a]] ). Impact evaluations of capacity building measures are important because capacity building is assumed to lead to adaptation, but its actual effects are seldom examined ( [[#Mortreux--2017|Mortreux and Barnett, 2017]] ; Alpizar F and Meiselman, 2019). If well designed and utilised for learning, evaluations can play an important role in improving adaptation responses ( [[#Hildén--2011|Hildén, 2011]] ).

Learning requires information about how and why change occurred and what experiences have been made ( [[#Feinstein--2012|Feinstein, 2012]] ). M&amp;E is frequently associated with learning, but it is rarely made explicit how learning is supposed to take place ( [[#Armitage--2008|Armitage et al., 2008]] ; [[#Baird--2015|Baird et al., 2015]] ; [[#Borras--2015|Borras and Hølund, 2015]] ). The design of adaptation M&amp;E systems can support learning by gathering relevant information and disseminating it in a way that is accessible and effectively linked to decision-making processes ( [[#Spearman--2011|Spearman and McGray, 2011]] ; [[#Villanueva--2012|Villanueva, 2012]] ; [[#Fisher--2015|Fisher et al., 2015]] ). Options include institutionalised feedback mechanisms, peer learning and knowledge sharing events, a learning culture and ways to gather in-depth insights beyond indicators (ibid; [[#Oswald--2010|Oswald and Taylor, 2010]] ). Since AR5, adaptation programmes and funds such as the BRACED programme, the Adaptation Fund, the Climate Investment Funds and the Green Climate Fund have created knowledge-sharing units and provide resources to support learning activities ( [[#BRACED--2015|BRACED, 2015]] ; [[#Roehrer--2015|Roehrer and Kouadio, 2015]] ; [[#Adaptation%20Fund--2016|Adaptation Fund, 2016]] ; [[#Leavy--2018|Leavy et al., 2018]] ; [[#CIF--2020|CIF, 2020]] ; [[#Puri--2020|Puri et al., 2020]] ), but there is little information about their longer-term effectiveness.

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'''Cross-Chapter Box PROGRESS | Approaches and Challenges to Assess Adaptation Progress at the Global Level'''
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Authors: Matthias Garschagen (Germany), Timo Leiter (Germany/UK), Robbert Biesbroek (the Netherlands), Alexandre K. Magnan (France), Diana Reckien (the Netherlands/Germany), Mark New (South Africa), Lea Berrang-Ford (UK/Canada), So Min Cheong (Republic of Korea), Lisa Schipper (Sweden/USA), Robert Lempert (USA).

This Cross-Chapter Box responds to a growing demand for assessing global climate change adaptation progress, which currently faces the challenge of lacking consensus on how adaptation progress at this level can be tracked ( ''high confidence'' ). The box therefore assesses the rationale and methodological approaches for understanding adaptation progress globally across sectors and regions. It discusses strengths and weaknesses of existing approaches and sources of information, with a view towards informing the first Global Stocktake of the Paris Agreement in 2023.

'''Rationale for assessing adaptation progress at the global level'''
Global assessments of adaptation are expected to help answer key questions of climate policy ( [[#Ford--2015|Ford et al., 2015]] ; [[#UNEP--2017|UNEP, 2017]] ; [[#Adaptation%20Committee--2021|Adaptation Committee, 2021]] ) ( ''limited evidence'' , ''high agreement'' ), including: Do the observed, collective investments in adaptation lead humanity to being better able to avoid or reduce the negative consequences from climate change? Where is progress being made, and what gaps remain in the global adaptation response to climate risks?

While more than 170 countries have policies that address adaptation ( [[#Nachmany--2019b|Nachmany et al., 2019b]] ; [[#17.4.2|Section 17.4.2]] ), very few have operational frameworks to track and evaluate implementation and results ( [[#Leiter--2021a|Leiter, 2021a]] ; [[#17.5.2.4|Section 17.5.2.4]] ). In Europe, for example, most countries have adopted a national adaptation plan or strategy, but only few are tracking whether ambitions are realised ( [[#EEA--2020|EEA, 2020]] ; [[IPCC:Wg2:Chapter:Chapter-13#13.11.2|Section 13.11.2]] ). Moreover, climate risks are interconnected across scales, regions and sectors ( [[#Eakin--2009|Eakin et al., 2009]] ; [[#Challinor--2017|Challinor et al., 2017]] ; Cross-Chapter Box INTERREG in Chapter 16; [[#Hedlund--2018|Hedlund et al., 2018]] ) ( ''high confidence'' ), complicating causal attribution. National assessments of progress usually do not assess private sector and non-governmental adaptation and barely account for climate risks that transcend across borders, for example through supply chains or shared ecosystems ( [[#EEA--2018|EEA, 2018]] ; [[#Benzie--2019|Benzie and Persson, 2019]] ). In addition, adaptation action in one place or time can potentially lead to negative effects elsewhere (externalities) ( [[#Magnan--2016|Magnan and Ribera, 2016]] ; [[#Atteridge--2018|Atteridge and Remling, 2018]] ; 17.5.1). Hence, determining the collective adequacy and effectiveness (see Figure 1.7 in Chapter 1) of adaptation responses is different from simple aggregates of national and sub-national information ( [[#UNEP--2017|UNEP, 2017]] ).

Assessing global progress on adaptation is therefore of high relevance to the scientific community, policymakers and other actors. Global assessments serve different information needs than local assessments, and their meaningfulness depends on the chosen approaches and their limitations. Aggregated global assessments of adaptation progress are therefore not meant to substitute place-specific ones but to complement them to enhance the knowledge base on adaptation beyond actions by or within individual countries. The Paris Agreement stipulates a Global Stocktake to be undertaken every 5 years to assess the collective progress towards its long-term goals, including on adaptation ( [[#UNFCCC--2015|UNFCCC, 2015]] , Article 14). Yet very few scientific studies have addressed the adaptation-specific aspects of the Global Stocktake ( [[#Craft--2018|Craft and Fisher, 2018]] ; [[#Tompkins--2018|Tompkins et al., 2018]] ), and there are different views and options on how assessing global progress could take place ( ''high confidence'' ).

'''Considerations in designing global adaptation assessments'''
A number of key considerations for the design of global adaptation assessment approaches are discussed in the literature ( [[#Ford--2016|Ford and Berrang-Ford, 2016]] ; [[#Berrang-Ford--2017|Berrang-Ford et al., 2017]] ). Some of these involve trade-offs, such as global applicability versus context specificity, for which there is no simple solution. Design considerations directly depend on the objectives of global adaptation assessments, which can differ between actors and can include, for example, providing transparency, enabling accountability, understanding effectiveness or guiding policy development ( [[#17.5.2.1|Section 17.5.2.1]] ). The underlying objectives determine the suitability of approaches and the data requirements.

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Cross-Chapter Box PROGRESS

Comparability

Global assessments may have the objective to compare adaptation over time and across sectors and regions ( [[#Ford--2015|Ford et al., 2015]] ). Such comparison requires a consistent definition of concepts ( [[#Hall--2017|Hall, 2017]] ; [[#Berrang-Ford--2019|Berrang-Ford et al., 2019]] ) and the identification of variables that are both generic enough to be applicable from one context to another and specific enough to illustrate national circumstances. To date, finding such balance has proven to be challenging ( [[#Dupuis--2013|Dupuis and Biesbroek, 2013]] ). The context dependence of adaptation outcomes poses limits for meaningful comparisons. Even people exposed to the same climate hazard may be differentially affected due to varying levels of vulnerability and resilience ( [[#Jones--2018|Jones et al., 2018]] ; [[#Thomas--2019|Thomas et al., 2019]] ), meaning that perceptions on adaptation outcomes can also differ ( [[#Jones--2019|Jones and d’Errico, 2019]] ).

Aggregation

The aggregation of data from local or regional to global scales can take different forms ranging from qualitative synthesis to quantitative aggregation, which may involve condensing a diverse set of variables into a single score ( [[#Leiter--2015|Leiter, 2015]] ; [[#17.5.2.3|Section 17.5.2.3]] ). In contrast to climate change mitigation, adaptation does not have a global reference metric against which adaptation levels could be assessed to identify progress or gaps. Experience from the Global Environment Facility, for example, has shown that mechanical aggregation based on standardised indicators fails to capture what makes the greatest difference on the ground ( [[#Chen--2014|Chen and Uitto, 2014]] ).

''Results: Input, process, output or outcome''

Adaptation progress at any spatial scale can in principle be assessed in terms of input (e.g., resources spent), process (i.e., the way adaptation is organised), output (i.e., adaptation capacities and actions) and outcomes (i.e., actual changes induced) ( [[#17.5.2.2|Section 17.5.2.2]] ). Due to the challenges inherent in measuring adaptation outcomes (Sections 16.3, 17.5.1 and 17.5.2.5), most global assessments to date have focused on outputs, such as whether countries have adopted adaptation plans ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ; [[#UNEP--2021a|UNEP, 2021a]] ) ( ''high confidence'' ). Understanding the effectiveness of adaptation responses globally requires a way to conceptualise and capture outcomes, for example in terms of effective climate risk reduction, while avoiding simplifications that mask maladaptation at the global level, such as where climate risks are shifted to other countries, sectors or population groups (Cross-Chapter Box INTERREG in Chapter 16, [[#17.5.1|Section 17.5.1]] ).

Data

Global assessments typically require global availability of consistent data, be they quantitative or qualitative, which has proven to be a constraining factor for attempts to assess global adaptation ( ''high confidence'' ). For example, many countries face difficulties in reporting adequately on progress in implementing the Sendai Framework and risk-related SDGs ( [[#UNDRR--2019|UNDRR, 2019]] : vi). The availability of data also influences which variables can be eventually selected in an assessment. This limitation can affect the ability to meet the initial objectives and lead to biases in the framing and interpretation of assessment outcomes. For some variables, an alternative to relying on nationally provided data can be to develop new global data sets ( [[#Magnan--2019|Magnan and Chalastani, 2019]] ) or utilise data from Earth Observation ( [[#Andries--2018|Andries et al., 2018]] ). Adaptation is hence faced with a dilemma between globally available yet generic data and regionally or locally more detailed yet patchy data ( ''high confidence'' ).

'''Assessment of existing approaches to assess adaptation progress at the global level'''
Only few global assessments of adaptation progress across sectors have been undertaken to date ( ''high confidence'' ). They focus, for example, on whether countries have progressed their adaptation policies and actions over time ( [[#Lesnikowski--2015|Lesnikowski et al., 2015]] ; [[#Nachmany--2019b|Nachmany et al., 2019b]] ), the extent of implemented adaptation globally ( [[#Leiter--2021a|Leiter, 2021a]] ; [[#Leiter--2021b|Leiter, 2021b]] ), and the type and actors of responses ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ), evidence for reduced vulnerability to climate-related hazards ( [[#Formetta--2019|Formetta and Feyen, 2019]] ; [[#UNDRR--2019|UNDRR, 2019]] ) or adaptation planning in cities across the globe ( [[#Araos--2016a|Araos et al., 2016a]] ; [[#Reckien--2018a|Reckien et al., 2018a]] ; [[#Olazabal--2019a|Olazabal et al., 2019a]] ). Each of these assessments draws on different approaches and data, and all have particular potential but also limitations (Table Cross-Chapter Box PROGRESS.1) ( ''high confidence'' ). The application of differing approaches shows that there is no single ‘best’ approach or data source to assess global progress on adaptation ( ''high confidence'' ). Existing global assessments have provided valuable insights into the extent and types of responses and their level of planning and implementation ( [[IPCC:Wg2:Chapter:Chapter-16#16.3.2.4|Section 16.3.2.4]] ). However, they do not provide comprehensive and robust answers so far on whether climate risk and vulnerability have been reduced ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ) ( ''high confidence'' ). As a result, combining different approaches and integrating data on climate risk levels, policy measures, implemented actions and their effects on climate risk reduction is currently regarded as the most robust approach ( [[#Berrang-Ford--2019|Berrang-Ford et al., 2019]] ) ( ''medium evidence'' , ''high agreement'' ).

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Cross-Chapter Box PROGRESS

'''Table Cross-Chapter Box PROGRESS.1 |''' Key approaches and data sources used for global adaptation assessments.

{| class="wikitable"
|-
! Approach/data source

! Potential added value

! Limitations

|-
| Systematic assessment of adaptation responses reported in academic literature (e.g., systematic reviews, evidence synthesis, meta-analysis, large- ''n'' comparative studies)

Examples:

Berrang-Ford, 2011, Global Adaptation Mapping Initiative, [[#Berrang-Ford--2021|Berrang-Ford et al. (2021)]]

| Provides an indication of the status, trends and gaps in adaptation responses

| Not a representative sample; biased towards responses published in scientific literature; excludes grey literature; some topics and regions not well covered; challenges in terms of comparability and aggregation; inconsistency in definitions and use of concepts; English language bias

|-
| Self-reported progress documents by countries (e.g., National Communications, Biennial Transparency Reports or domestic progress and evaluation)

Examples:

[[#Gagnon-Lebrun--2007|Gagnon-Lebrun and Agrawala (2007)]] ; [[#Lesnikowski--2015|Lesnikowski et al. (2015)]] ; [[#Lesnikowski--2016|Lesnikowski et al. (2016)]] ; [[#Leiter--2021a|Leiter (2021a)]]

| Context-specific information; official government documents enable assessments of national progress

| May only be available every few years; content is sensitive to political and policy changes; possible bias towards positive examples; challenges in terms of comparability and aggregation; inconsistency in definitions and use of concepts

|-
| Self-reported information from the private sector (e.g., information on actions taken in response to climate risks within the context of climate-related financial disclosure or in company reports).

Examples:

[[#Committee%20on%20Climate%20Change--2017|Committee on Climate Change (2017)]] ; [[#Street--2019|Street and Jude (2019)]] ; [[#UNFCCC--2021|UNFCCC (2021)]] , responses reported under Climate-related Financial Disclosure

| Provides an indication of the status, trends and gaps in adaptation responses by the private sector; complements information published in the scientific literature; could enable better understanding of supply chain risks

| Sample biased towards larger companies; challenges in terms of comparability and aggregation; potential inconsistencies in definitions and use of concepts

|-
| Project documents and evaluations (e.g., from climate funds or implementing organisations)

Examples:

[[#Leiter--2021b|Leiter (2021b)]] ; [[#Eriksen--2021|Eriksen et al. (2021)]]

| Detailed information on context, intended or achieved results and activities

| Actual implementation can differ from what was proposed; fragmented picture of local/regional actions; results may be challenging to aggregate; challenges in terms of comparability and aggregation; inconsistency in definitions and use of concepts

|-
| Existing global data sets of mostly quantitative indicators

Examples:

United Nations ( [[#UN--2016a|UN, 2016a]] ; [[#UN--2016b|UN, 2016b]] ; [[#UN--2019|UN, 2019]] ; [[#UNDRR--2019|UNDRR, 2019]] )

| Comparable information based on globally defined indicators

| Global data availability constrains indicator choice; reporting burden for new indicators; trade-off between global applicability and national circumstances; usefulness and meaningfulness of global indicators is contested ( [[#Leiter--2018|Leiter and Pringle, 2018]] ; [[#Lyytimäki--2020|Lyytimäki et al., 2020]] ; [[#Pauw--2020|Pauw et al., 2020]] ).

|-
| Tracking financial flows

Examples:

[[#CPI--2019|CPI (2019)]] , OECD (2018a), [[#MDBs--2019|MDBs (2019)]]

| Comparable data on financial flows directed at adaptation; standardised methodologies (e.g., OECD RIO markers; climate finance tracking method of multi-lateral development banks; [[#17.5.2.6|Section 17.5.2.6]] ; Cross-Chapter Box FINANCE in this Chapter)

| No information about implementation of measures and their adaptation effect (Eriksen et al, 2021), i.e., it tracks inputs, not outputs or outcomes; inconsistency in what gets counted as adaptation finance ( [[#Donner--2016|Donner et al., 2016]] ; [[#Doshi--2020|Doshi and Garschagen, 2020]] ); evidence of over-reporting ( [[#Michaelowa--2011|Michaelowa and Michaelowa, 2011]] ; [[#Weikmans--2017|Weikmans et al., 2017]] )

|}

'''Conclusion—Combining approaches for assessing adaptation progress at the global level'''
Understanding to what extent the world is on track to adapt to climate change impacts and risks globally is a pressing question in scientific and policy communities, especially in light of the Global Stocktake under the Paris Agreement. Important considerations for a robust assessment framework (e.g., consistency), as well as the associated scientific challenges (e.g., aggregation, externalities, breadth versus depth of data) and the role of underlying objectives (e.g., on the contested issue of comparability) are increasingly understood ( ''high confidence'' ). There is also a growing and diverse body of information on adaptation progress, although most assessments of global progress undertaken to date focus on processes and outputs (e.g., policies and plans) rather than outcomes (i.e., risk reduction). A variety of approaches and data sources are employed, such as systematic reviews of observed adaptation, formal communications by Parties to the UNFCCC, and project documents to international funding agencies. Novel approaches, including big data tools (Ford et al., 2016; Biesbroek et al., 2020), are also being explored but still have to prove their practical value. Each approach and source of information can contribute additional knowledge, but also demonstrates limitations, so that there is no single ‘best’ approach ( ''high confidence'' ). Yet, to date, the international community has not sufficiently explored the relative strengths and weaknesses of different approaches and their applicability and, therefore, their potential synergies in complementing each other. Triangulated assessments have only rarely been applied ( ''high confidence'' ) due to multiple conceptual and methodological challenges, despite their potential for increasing the robustness of knowledge. One overarching conclusion of this Cross-Chapter Box therefore is that the combination of different approaches will provide a more comprehensive picture of global adaptation progress than is currently available from individual approaches ( ''limited evidence'' , ''high agreement'' ).

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Cross-Chapter Box PROGRESS

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'''Box 17.4 | The Rio Markers Methodology to Track Climate Finance'''
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The OECD Development Assistance Committee (DAC) introduced a methodology to track the amount of bilateral official development assistance (ODA) that is targeting climate change mitigation and/or adaptation. It distinguishes whether activities have adaptation as a ‘principal’ objective (score ‘2’), as a ‘significant’ objective (score ‘1’) or as not targeting it (score ‘0’) ( [[#OECD--2016|OECD, 2016]] ). The associated project value is counted in full, in part, or not counted as adaptation finance, respectively. Countries count the volume of partial adaptation projects (score ‘1’) to a different extent, which limits comparability and can lead to over-reporting ( [[#OECD--2019|OECD, 2019]] ). The first data on this ‘adaptation marker’ became available in 2012 for the financial flows of 2010. It forms the basis for developed countries’ reporting to the UNFCCC Secretariat on their financial commitments towards developing countries ( [[#Weikmans--2019|Weikmans and Roberts, 2019]] ).

While a guidebook with requirements for adaptation as a principle or significant objective has been developed ( [[#OECD--2016|OECD, 2016]] ), several studies have shown that OECD DAC donors tend to overestimate the number of activities in their portfolio that genuinely have adaptation objectives ( [[#Michaelowa--2011|Michaelowa and Michaelowa, 2011]] ; [[#Weikmans--2017|Weikmans et al., 2017]] ; [[#CARE--2021|CARE, 2021]] ). Hence, the amount of adaptation finance from public sources may be lower than reported. The use of just three categories leads to a broad range of the extent of adaptation being concentrated in the middle category (‘significant objective’). Accordingly, the category ‘principle objective adaptation’ provides a more robust predictor of the relevance of an activity to adaptation ( [[#Donner--2016|Donner et al., 2016]] ).

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