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

=== 17.4.4 Enabling Condition 3: Knowledge and Capacity ===

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==== 17.4.4.1 Overview of Knowledge Systems ====

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AR5 emphasised the importance of knowledge systems as an enabling condition for decision-making, as did earlier ARs, all of which include a focus on the policy relevance of knowledge ( [[IPCC:Wg2:Chapter:Chapter-1#1.1.4|Section 1.1.4]] ). First introduced in IPCC reports in AR4, the term ‘knowledge system’ is used extensively in AR5 and the SRs. The discussion below follows a widely cited definition of knowledge systems as sets of interacting ‘agents, practices and institutions that organize the production, transfer and use of knowledge’ ( [[#Cornell--2013|Cornell et al., 2013]] : 61). This definition emphasises the social nature of knowledge and the importance of the link between knowledge and action, rather than presenting knowledge simply as information about past, present and future states of the world which can be of use to decision makers.

This definition of knowledge systems indicates the importance of capacity—the ability and the motivation to use knowledge for action—since capacity is an important feature which allows knowledge systems to function. Capacity is a necessary enabling condition for knowledge to be put to use in adaptation activities ( ''high confidence'' ), as shown across sectors such as water ( [[IPCC:Wg2:Chapter:Chapter-4#4.5.2|Section 4.5.2]] ), food security (Sections 5.12.3, 5.14.3), cities and settlements (Sections 6.4.2, 6.4.4) and health and well-being (Sections 7.1.3, 7.2.6), and across regions, including Africa (Sections 9.13.1, 9.14.5), Asia (Sections 10.3.6, 10.4.4) and North America ( [[IPCC:Wg2:Chapter:Chapter-14#14.4|Section 14.4.5]] ).

Some research on knowledge systems retains the earlier attention to information as a resource for decision makers. A major focus, discussed elsewhere in this chapter, has been increasing the precision about the certainty, likelihood and confidence with which certain statements are made in relation to underlying evidence (see Cross-Chapter Box DEEP in this Chapter). This topic, which was first introduced in AR4, advanced significantly in AR5 ( [[#Mach--2017|Mach et al., 2017]] ).

In addition to these characteristics of information, the social and organisational aspects of knowledge systems have also been the subject of recent research. One strand of this discussion emphasises the distinctiveness of different knowledge systems, often focusing on three types of knowledge: scientific, Indigenous and local, and the latter are two sometimes grouped as ‘traditional’ knowledge (see Cross-Chapter Box INDIG in Chapter 18). This strand emphasises the specific forms of knowledge production and circulation in each type. Another strand of discussion emphasises the networks of interactions between different groups. This strand follows the influential ‘Knowledge systems for sustainable development’ ( [[#Cash--2003|Cash et al., 2003]] ), which was cited in Chapters 2, 7 and 8 in WGII AR5; [[#Cash--2003|Cash et al. (2003)]] emphasise the usability and acceptability of scientific knowledge, and highlight the relations between knowledge producers and users. The discussion in [[#17.4.4|Section 17.4.4]] on knowledge as an enabling factor integrates these two strands of discussion of knowledge systems.

It was well established in AR5 and SRs that a component of knowledge systems for good climate decision-making is the production of ‘information on climate, its impacts, potential risks, and vulnerability’ which can ‘be integrated into an existing or proposed decision-making context’ ( [[#Jones--2014|Jones et al., 2014]] : 200). Also important are two other components of knowledge: of response options and knowledge of other enabling conditions, particularly governance and finance, which were mentioned less frequently and more indirectly in AR5 and SR1.5, SROCC and SRCCL. Decision makers assess the feasibility of different alternatives (see Cross-Chapter Box FEASIB) and develop strategies for the implementation and modification of the alternative, requiring a level of knowledge of the governance, policy and finance landscapes at national ( [[#Tanner--2019|Tanner et al., 2019]] ; [[#Lopes--2020|Lopes et al., 2020]] ; [[#Roberts--2020|Roberts et al., 2020]] ) and international scales ( [[#Woodruff--2018|Woodruff, 2018]] ).

Examples of the importance of these other two components—knowledge of response options and knowledge of enabling conditions—are provided by networks of cities, including internal institutional networks ( [[#Aylett--2015|Aylett, 2015]] ), intermunicipal networks (e.g., those supported by Local Governments for Sustainability [ICLEI] and the international United Cities and Local Governments [UCLG] network), transnational municipal networks (e.g., 100 Resilient Cities, Asian Cities Climate Change Resilience Network [ACCCRN]) and city-to-city regional transdisciplinary learning networks ( [[#Ndebele-Murisa--2020|Ndebele-Murisa et al., 2020]] ). These networks generate and exchange knowledge which can be critical to decision makers for understanding and evaluating the feasibility of different response options, identifying synergies across sectors and mainstreaming adaptation to climate change ( [[#Haupt--2020|Haupt et al., 2020]] ). However, the question of how to finance such network activities remains under-studied ( [[#Bracking--2021|Bracking, 2021]] ; See Box 17.3).

In addition to these general considerations of knowledge systems, research since AR5 has contributed to the understanding of specific types of knowledge. Scientific knowledge is thoroughly discussed in Chapter 1, especially in [[IPCC:Wg2:Chapter:Chapter-1#1.3|Section 1.3]] ‘Understanding and Evaluating Climate Risk’, which shows recent advances in the well-established IPCC categories of observation of past conditions and model-based projections of future conditions. We add here a consideration of a new area within scientific knowledge, artificial intelligence, which offers new methods for producing information that can be incorporated into knowledge systems.

Applying artificial intelligence (AI) to climate change is predominantly in the area of climate modelling and forecasting, inclusive of weather extremes ( [[#Monteleoni--2013|Monteleoni et al., 2013]] ; [[#Jones--2017|Jones, 2017]] ; [[#Huntingford--2019|Huntingford et al., 2019]] ). Recent efforts conceptualise the potential uses of AI for mitigation and adaptation ( [[#Rolnick--2019|Rolnick et al., 2019]] ; Cheong et al., 2020b) in addition to forecasting ( [[#Rolnick--2019|Rolnick et al., 2019]] ; [[#Chattopadhyay--2020|Chattopadhyay et al., 2020]] ; Cheong et al., 2020b; [[#Prabhat--2021|Prabhat et al., 2021]] ). There are very few cases to assess AI applications in these domains given that AI is a new field for climate change impact and adaptation. To this date, sectoral applications of AI relevant to climate change adaptation and risk reduction mainly have advanced in the areas of crop yields, early-warning systems and water management.

These sectoral advances using AI employ various learning techniques inclusive of supervised and unsupervised learning, multi-modal learning and transfer learning techniques to generate more accurate predictions than afforded by traditional climate projection methods (Cheong et al., 2020b; [[#Camps-Valls--2021|Camps-Valls et al., 2021]] ). AI applications use finer-resolution data such as sub-daily weather-related data, remote and wearable sensor data, text data and real-time survey data. They are fed into neural networks and semi-/unsupervised learning to configure detailed and more precise predictions of climate change impact on crop yields ( [[#Crane-Droesch--2018|Crane-Droesch, 2018]] ), early warning ( [[#Moon--2019|Moon et al., 2019]] ), impact of extreme heat on older adults ( [[#Cheong--2020a|Cheong et al., 2020a]] ), poverty in Africa ( [[#Oshri--2018|Oshri et al., 2018]] ) and multi-scale water management combining blockchain technology with remote water sensors ( [[#Lin--2018|Lin et al., 2018]] ).

Indigenous knowledge and local knowledge are thoroughly covered in SROCC ( [[#Abram--2019|Abram et al., 2019]] ; [[#IPCC--2019c|IPCC, 2019c]] ; [[#IPCC--2019d|IPCC, 2019d]] ) and in [[IPCC:Wg2:Chapter:Chapter-1#1.3.3|Section 1.3.3]] . We here add relevant points to decision-making, and an additional form of knowledge, practitioner knowledge.

Indigenous knowledge and local knowledge are gaining recognition at multiple scales ( [[#Kleiche-Dray--2016|Kleiche-Dray and Waast, 2016]] ; [[#David-Chavez--2018|David-Chavez and Gavin, 2018]] ; [[#Nakashima--2018|Nakashima et al., 2018]] ). Of note is their association with ecosystem-based adaptations, showcasing the long-term place-based knowledge of Indigenous Peoples ( [[#Johnson--2015|Johnson et al., 2015]] ; [[#Walshe--2016|Walshe and Argumedo, 2016]] ; [[#Carter--2019|Carter, 2019]] ; [[#Mazzocchi--2020|Mazzocchi, 2020]] ). These knowledges and practices can be an important enabling condition in decision-making processes, complementing scientific information by identifying impacts ( [[#Fernández-Llamazares--2017|Fernández-Llamazares et al., 2017]] ; [[#Katz--2020|Katz et al., 2020]] ), emphasising values to consider ( [[#Huambachano--2018|Huambachano, 2018]] ), offering solutions ( [[#Chanza--2016|Chanza and de Wit, 2016]] ; [[#Cuaton--2020|Cuaton and Su, 2020]] ; [[#Orlove--2020|Orlove et al., 2020]] ), guiding land use and resource management ( [[#Brondízio--2021|Brondízio et al., 2021]] ) and filling gaps in scientific knowledge ( [[#Hiwasaki--2014|Hiwasaki et al., 2014]] ; [[#Audefroy--2017|Audefroy and Sánchez, 2017]] ; [[#Makondo--2018|Makondo and Thomas, 2018]] ; [[#Son--2019|Son et al., 2019]] ; [[#Latulippe--2020|Latulippe and Klenk, 2020]] ; Wheeler et al., 2020).

Practitioner knowledge—the pragmatic, practice-based knowledge that comes from the regular exercise of craft or professional work—was also acknowledged briefly in AR5 ( [[#Jones--2014|Jones et al., 2014]] ) and treated significantly in SROCC ( [[#Abram--2019|Abram et al., 2019]] ). Practitioner knowledge resembles local knowledge in that it is acquired through participation in activities, and yet it differs from local knowledge, which is often place-based and tied directly to specific landscapes and communities. Local knowledge typically covers a variety of environmental domains. Practitioner knowledge may be shared with people in different locations and is often more focused on a narrower set of work activities. Recent calls have recommended bringing practitioners more fully into the IPCC assessment process, to promote more effective decision-making ( [[#Howarth--2018|Howarth et al., 2018]] ).

Practitioner knowledge makes significant contributions to decision-making by broadening the range of alternatives which are considered and by bringing in understandings of systems to the selection and implementation of alternatives. Such knowledge is applicable to a large number of domains, including biodiversity management ( [[#Tengö--2014|Tengö et al., 2014]] ; [[#Rathwell--2015|Rathwell et al., 2015]] ), and natural hazard risk management in urban settings, as reported in Denmark ( [[#Madsen--2019|Madsen et al., 2019]] ), the USA ( [[#Matsler--2019|Matsler, 2019]] ), Canada ( [[#Yumagulova--2019|Yumagulova and Vertinsky, 2019]] ), Mexico ( [[#Aguilar-Barajas--2019|Aguilar-Barajas et al., 2019]] ) and the Caribbean ( [[#Ramsey--2019|Ramsey et al., 2019]] ). Other contexts, all at regional scales, include watershed management in Peru ( [[#Ostovar--2019|Ostovar, 2019]] ), livestock management in Finland ( [[#Rasmus--2020|Rasmus et al., 2020]] ), agricultural adaptation in a context of water scarcity in Iran ( [[#Zarei--2020|Zarei et al., 2020]] ) and the water–energy nexus in the USA ( [[#Gim--2019|Gim et al., 2019]] ).

Literature indicates the importance of effective governance for promoting integration of local and practitioner knowledge with scientific knowledge ( ''high confidence'' ). This integration is most extensive and promotes a wider consideration of alternatives, where governance arrangements promote ongoing exchanges of information and discussion of solutions, whether through formal mechanisms such as regional committees ( [[#Gim--2019|Gim et al., 2019]] ; [[#Ostovar--2019|Ostovar, 2019]] ; [[#Rasmus--2020|Rasmus et al., 2020]] ; [[#Zarei--2020|Zarei et al., 2020]] ) or informal mechanisms such as personal networks and local discussion groups ( [[#Madsen--2019|Madsen et al., 2019]] ; [[#Yumagulova--2019|Yumagulova and Vertinsky, 2019]] ). Where such arrangements are absent, practitioner knowledge is side-lined from the formulation and implementation of decisions ( [[#Aguilar-Barajas--2019|Aguilar-Barajas et al., 2019]] ; [[#Matsler--2019|Matsler, 2019]] ; [[#Ramsey--2019|Ramsey et al., 2019]] ).

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==== 17.4.4.2 Co-production and Other Composite Knowledge Systems ====

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There is strong evidence that composite knowledge systems—characterised by interactions between the producers and potential users of climate change information—can help facilitate climate-related decision-making ( [[#Prokopy--2015|Prokopy and Power, 2015]] ; [[#Richards--2018|Richards, 2018]] ; [[#Ramsey--2019|Ramsey et al., 2019]] ). Several institutional forms and structures have been created to link scientific knowledge, Indigenous knowledge, and local and practitioner knowledge to climate change decision-making.

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===== 17.4.4.2.1 Co-production =====

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The co-production of knowledge by different actors provides important avenues for exchanging and integrating climate-related knowledge in decisions made across society ( ''high confidence'' ). Though many definitions of co-production have been offered in recent years ( [[#Bremer--2017|Bremer and Meisch, 2017]] ; [[#Vincent--2018|Vincent et al., 2018]] ; [[#Bremer--2019|Bremer et al., 2019]] ; [[#Harvey--2019a|Harvey et al., 2019a]] ), most describe a set of individuals or organisations who work together to generate a set of products that entail new knowledge products and that guide action ( [[#Miller--2020|Miller and Wyborn, 2020]] ). Some major forms of co-production include action research ( [[#Baztan--2017|Baztan et al., 2017]] ; [[#Laursen--2018|Laursen et al., 2018]] ; [[#Zanocco--2018a|Zanocco et al., 2018a]] ), trans-disciplinarity ( [[#Howarth--2016|Howarth and Monasterolo, 2016]] ; [[#Wamsler--2017|Wamsler, 2017]] ; [[#Lanier--2018|Lanier et al., 2018]] ; [[#Scott--2018|Scott et al., 2018]] ; [[#Knapp--2019|Knapp et al., 2019]] ; Young et al., 2019), rapid assessment processes ( [[#Atkinson--2018b|Atkinson et al., 2018b]] ) and participatory integrated assessments ( [[#Howarth--2018|Howarth et al., 2018]] ; [[#Krkoška%20Lorencová--2018|Krkoška Lorencová et al., 2018]] ; [[#Bitsura-Meszaros--2019|Bitsura-Meszaros et al., 2019]] ; [[#Carter--2019a|Carter et al., 2019a]] ; [[#Cremades--2019|Cremades et al., 2019]] ; [[#Leitch--2019|Leitch et al., 2019]] ; [[#Martínez-Tagüeña--2020|Martínez-Tagüeña et al., 2020]] ; [[#17.3.1.3.1|Section 17.3.1.3.1]] ).

Co-production promotes iterative dialogue, experimentation, the tailoring of knowledge to context, needs and priorities, and learning, often promoting integration of Indigenous knowledge, local knowledge and practitioner knowledge with scientific knowledge ( ''high confidence'' ). It generally entails long-lasting ties and fully inclusive partnerships between different parties ( [[#Kench--2018|Kench et al., 2018]] ). Governance measures and adequate financing can act as enablers of such co-production. This integration is most extensive, and promotes a wider consideration of alternatives where governance arrangements promote ongoing exchanges of information and discussion of solutions, whether through formal mechanisms such as regional committees ( [[#Gim--2019|Gim et al., 2019]] ; [[#Ostovar--2019|Ostovar, 2019]] ; [[#Rasmus--2020|Rasmus et al., 2020]] ; [[#Zarei--2020|Zarei et al., 2020]] ) or informal mechanisms such as personal networks and local discussion groups ( [[#Madsen--2019|Madsen et al., 2019]] ; [[#Yumagulova--2019|Yumagulova and Vertinsky, 2019]] ). Where such arrangements are absent, practitioner knowledge is side-lined from the formulation and implementation of decisions ( [[#Orleans%20Reed--2013|Orleans Reed et al., 2013]] ; [[#Aguilar-Barajas--2019|Aguilar-Barajas et al., 2019]] ; [[#Matsler--2019|Matsler, 2019]] ; [[#Ramsey--2019|Ramsey et al., 2019]] ).

An important mechanism of co-production is the boundary organisation, a knowledge-producing organisation composed of individuals who reflect different disciplines or knowledge systems and who represent different activities, sectors or forms of governance ( [[#Blades--2016|Blades et al., 2016]] ; [[#Graham--2016|Graham and Mitchell, 2016]] ; [[#Guido--2016|Guido et al., 2016]] ; [[#Jeuring--2019|Jeuring et al., 2019]] ; [[#Serrao-Neumann--2020|Serrao-Neumann et al., 2020]] ; [[#Zarei--2020|Zarei et al., 2020]] ). Boundary organisations themselves can be linked into boundary chains ( [[#Lemos--2014|Lemos et al., 2014]] ; [[#Meyer--2015|Meyer et al., 2015]] ; [[#Kirchhoff--2015a|Kirchhoff et al., 2015a]] ; [[#Pretorius--2019|Pretorius et al., 2019]] ; [[#Daniels--2020|Daniels et al., 2020]] ). When individuals and organisations from different disciplinary backgrounds and missions coordinate their activities informally, the resulting ties have been termed ‘knowledge networks’ ( [[#Ziaja--2015|Ziaja and Fullerton, 2015]] ; [[#Brugger--2016|Brugger et al., 2016]] ; [[#Guido--2016|Guido et al., 2016]] ; [[#Davies--2018|Davies et al., 2018]] ; [[#Klenk--2018|Klenk, 2018]] ; [[#Muccione--2019|Muccione et al., 2019]] ; [[#Ziaja--2019|Ziaja, 2019]] ). When such networks interact with each other, the resulting associations have been called ‘communities of practice’, which can work to collectively shape information to shared contextual circumstances ( [[#Orsato--2018|Orsato et al., 2018]] ; [[#Wang--2019b|Wang et al., 2019b]] ).

There is extensive evidence that co-production can generate useful climate knowledge ( [[#Djenontin--2018|Djenontin and Meadow, 2018]] ; [[#Bisbal--2019|Bisbal, 2019]] ; [[#Ryan--2019|Ryan and Bustos, 2019]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ; [[#Jack--2020|Jack et al., 2020]] ; [[#Lavorel--2020|Lavorel et al., 2020]] ; [[#Ruiz-Mallén--2020|Ruiz-Mallén, 2020]] ) and that it can increase the likelihood that knowledge will be used in decision-making ( [[#Vogel--2016|Vogel et al., 2016]] ; [[#Prokopy--2017|Prokopy et al., 2017]] ; [[#Skelton--2017|Skelton et al., 2017]] ; [[#Sylvester--2020|Sylvester and Brooks, 2020]] ). Co-production is not without its costs, since it requires more time, money, facilitation expertise and personal commitment from participants than more conventional modes of knowledge production ( [[#Lemos--2018|Lemos et al., 2018]] ; [[#Sletto--2019|Sletto et al., 2019]] ; [[#Wamsler--2019|Wamsler et al., 2019]] ; [[#Blair--2020|Blair et al., 2020]] ). Some research has shown ways to decrease the costs of co-production for participants, such as funding and time to enable and sustain interactions and to build trust and legitimacy, or to create boundary organisations ( [[#Young--2016|Young et al., 2016]] ; [[#Klenk--2017|Klenk et al., 2017]] ).

Co-production is supported by project cycles that provide for the involvement of stakeholders from the outset ( [[#Daly--2019|Daly and Dilling, 2019]] ; [[#Brady--2020|Brady and Leichenko, 2020]] ); flexible research agendas that do not assume a climate related question ( [[#Daniels--2020|Daniels et al., 2020]] ); support for interactivity and reflexivity ( [[#Araujo--2020|Araujo et al., 2020]] ); and institutionalising incentives which address the different values, norms, perceptions and work patterns of scientists, policymakers and civil society representatives ( [[#Cvitanovic--2015|Cvitanovic et al., 2015]] ; [[#Vincent--2015|Vincent et al., 2015]] ; Bruno [[#Soares--2016|Soares and Dessai, 2016]] ; [[#Singh--2017|Singh et al., 2017]] ; [[#Djenontin--2018|Djenontin and Meadow, 2018]] ; [[#Norström--2020|Norström et al., 2020]] ; [[#Turnhout--2020|Turnhout et al., 2020]] ). Certain roles, such as policy entrepreneurs ( [[#Tanner--2019|Tanner et al., 2019]] ), embedded researchers ( [[#Pretorius--2019|Pretorius et al., 2019]] ) and knowledge brokers ( [[#Cvitanovic--2015|Cvitanovic et al., 2015]] ), can facilitate co-production.

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===== 17.4.4.2.2 Climate services =====

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Climate services (refer to CWG Box on Climate Services) can be important enablers of climate risk management, provided they are credible, relevant and usable ( ''high confidence'' ), and will become increasingly important as human influence on weather and climate extremes grows across all regions (Chapter 11; [[#Fischer--2021|Fischer et al., 2021]] ; [[#IPCC--2021|IPCC, 2021]] ). Climate services are more effective and more widely used when they are tailored to specific decisions and decision makers ( ''high confidence'' ). Sustained iterative engagement between climate information users, producers and translators can improve the quality of the information and the decision-making and avoid maladaptation ( ''medium confidence'' ).

Historically, climate services have been organised by climate information providers, based in meteorological, hydrological and agricultural faculties and services, serving to improve through climate risk management, including the use of historical information, monitoring, seasonal forecasts and long-term climate projections ( [[#Hewitt--2012|Hewitt et al., 2012]] ; [[#Blome--2017|Blome, 2017]] ; [[#Bessembinder--2019|Bessembinder et al., 2019]] ; [[#Vaughan--2019b|Vaughan et al., 2019b]] ).

Recent research on climate services shows that transdisciplinary knowledge co-production is a key enabler, starting to shift emphasis from the creation of climate services ''products'' to climate services ''processes'' ( [[#Vincent--2018|Vincent et al., 2018]] ; [[#Carter--2019b|Carter et al., 2019b]] ; [[#Daniels--2020|Daniels et al., 2020]] ), potentially increasing uptake and sustainability ( [[#Norström--2020|Norström et al., 2020]] ). This shift is a result of the recognition of benefits which a co-production approach can offer, in addition to the provision of information; these additional benefits include building confidence, capacities, learning, knowledge, social capital, institutional capacity, stakeholder relationships, social networks, beneficial management practices and strengthened institutions (Bruno [[#Soares--2016|Soares and Dessai, 2016]] ; [[#Djenontin--2018|Djenontin and Meadow, 2018]] ; [[#Bremer--2019|Bremer et al., 2019]] ).

Cross-Chapter Box 12.2 in WGI AR6, ‘Climate information for climate services’, shows that users are widely distributed across civil society. Relevant users of climate services include humanitarian organisations ( [[#Coughlan%20de%20Perez--2014|Coughlan de Perez and Mason, 2014]] ; [[#Harvey--2019b|Harvey et al., 2019b]] ), government offices ( [[#Mahon--2019|Mahon et al., 2019]] ), international agencies ( [[#Perkins--2019|Perkins and Nachmany, 2019]] ) and the private sector ( [[#Beckett--2016|Beckett, 2016]] ; [[#Hudson--2019|Hudson et al., 2019]] ). Climate services currently exist at local, national, regional and international scales, at time scales which range from sub-seasonal to decadal and longer ( [[#White--2017|White et al., 2017]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ) and in a range of different sectors (Bruno [[#Soares--2019|Soares and Buontempo, 2019]] ). Agriculture is the sector with the largest number of examples ( [[#Zebiak--2015|Zebiak et al., 2015]] ; [[#Burke--2016|Burke and Emerick, 2016]] ; [[#Cliffe--2016|Cliffe et al., 2016]] ; [[#Haigh--2018|Haigh et al., 2018]] ; [[#Buontempo--2020|Buontempo et al., 2020]] ); others include health ( [[#Ghebreyesus--2010|Ghebreyesus et al., 2010]] ; [[#Ballester--2016|Ballester et al., 2016]] ), forestry ( [[#Caurla--2020|Caurla and Lobianco, 2020]] ), fisheries ( [[#Busch--2016|Busch et al., 2016]] ), disaster risk reduction ( [[#Street--2019|Street et al., 2019]] ) and water resources management ( [[#van%20Vliet--2015|van Vliet et al., 2015]] ; [[#Golding--2019|Golding et al., 2019]] ). Evaluations of the extent to which climate services are accessed, used and deliver benefits to decision makers remain in an initial stage ( [[#Perrels--2020|Perrels, 2020]] ), though studies suggest that these contributions vary widely depending on context. A review of evaluation of weather and climate agricultural services in Africa, for instance, found that most farmers use climate services when they are available , but that on-farm outcomes varied, with some farmers experiencing yield losses and others gains upward of 60% ( [[#Vaughan--2019a|Vaughan et al., 2019a]] ). Other studies express concern that large climate service projects have run for decades at significant expense, without adequate evaluation ( [[#Gerlak--2020|Gerlak et al., 2020]] ).

Recent reviews ( [[#Carr--2018|Carr and Onzere, 2018]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ) provide evidence that the use of climate services is affected by (a) the quality, reliability and skill of the climate information ( [[#Zebiak--2019|Zebiak, 2019]] ); (b) the fit, tailoring and contextualisation of that information with respect to the specific decision-making needs of particular users ( [[#Clarkson--2019|Clarkson et al., 2019]] ); (c) the mode and method by which the service is communicated ( [[#Golding--2017|Golding et al., 2017]] ); and (d) the characteristics of the users themselves, including the users’ access to resources that would allow them to alter their decisions based on the information provided ( [[#Clarkson--2019|Clarkson et al., 2019]] ).

A related literature characterises the extent to which the development, reach and effectiveness of climate services is affected by factors that can be termed ‘climate service governance’ ( [[#Stegmaier--2020|Stegmaier et al., 2020]] ). Elements of this governance include the arrangements by which those parties engage with each other ( [[#Vaughan--2016|Vaughan et al., 2016]] ; [[#Daniels--2020|Daniels et al., 2020]] ) and the financial arrangements, and associated responsibilities, which support the service ( [[#Lourenço--2015|Lourenço et al., 2015]] ; Bruno [[#Soares--2019|Soares and Buontempo, 2019]] ). Though governance varies by context, evidence suggests that engaging a range of experts and potential users in the co-design and co-production of climate services increases the use and utility of services ( [[#Lemos--2014|Lemos et al., 2014]] ; [[#Pope--2017|Pope et al., 2017]] ; [[#Masuda--2018|Masuda et al., 2018]] ; [[#Harvey--2019b|Harvey et al., 2019b]] ). However, some studies warn that, even with broad and inclusive participation, power differentials can create barriers to co-production, reducing the usefulness of information products ( [[#Alexander--2020|Alexander et al., 2020]] ) and the neglect of non-meteorological sources of information which may also possess useful predictive power ( [[#Coughlan%20de%20Perez--2019|Coughlan de Perez et al., 2019]] ).

A small but growing number of papers consider the business models that support climate services, including, for instance, the role of open data ( [[#Iturbide--2019|Iturbide et al., 2019]] ; [[#Chimani--2020|Chimani et al., 2020]] ), the standards or institutional mandates by which users come to understand the credibility and legitimacy of certain services (Bruno [[#Soares--2019|Soares and Buontempo, 2019]] ), and the role of public–private partnerships ( [[#Cortekar--2020|Cortekar et al., 2020]] ). While the commercialisation of climate services holds significant promise that more and more specifically targeted services will be provided, there is not yet agreement on which business models best support this in different contexts. There is also concern that commercialisation of climate services may disadvantage under-resourced actors at the expense of wealthier or more powerful ones ( [[#Webber--2017|Webber, 2017]] ; [[#Webber--2017|Webber and Donner, 2017]] ; [[#Cortekar--2020|Cortekar et al., 2020]] ). It has been noted that some climate services, such as weather forecasts and early warnings, are an example of a public good, best provided by public agencies ( ''high confidence'' ) ( [[#Sutter--2013|Sutter, 2013]] ; [[#Kitchell--2016|Kitchell, 2016]] ; [[#Hansen--2018|Hansen et al., 2018]] ).

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===== 17.4.4.2.3 Capacity and motivation within knowledge systems =====

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Knowledge of climate change influences decision-making not only by providing information but also by increasing the motivation to act and by promoting behaviour change. Evidence from many sectors (including water ( [[IPCC:Wg2:Chapter:Chapter-4#4.5.2|Section 4.5.2]] ), ocean and coastal ecosystems ( [[IPCC:Wg2:Chapter:Chapter-3#3.6.2|Section 3.6.2]] ), and agriculture ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.2|Section 5.4.2]] ) and regions (including Africa [Section 9.8.4], Asia [Section 10.4.6] and North America [Section 10.4.5] shows that building capacity (e.g., adaptive capacity, institutional capacity, education/training in human capacity) can support adaptation and limited governance capacity can constrain it ( ''high confidence'' ). An emerging area of research examines the contribution of building capacity within public and technical organisations and agencies to draw on Indigenous knowledge and local knowledge ( [[#Adger--2017|Adger et al., 2017]] ; [[#Hochman--2017|Hochman et al., 2017]] ; Bacud, 2018). A number of factors influence the effect of knowledge on motivation and behaviour change, including values and education.

Decision makers who shape options for managing climate risk can evaluate stakeholders’ capacities and motivations to participate in the implementation process of these options. Stakeholder engagement in climate change risk management supports successful adaptation ( [[#Gray--2014|Gray et al., 2014]] ; [[#Elsawah--2015|Elsawah et al., 2015]] ; [[#Siders--2017|Siders, 2017]] ; [[#Giordano--2020|Giordano et al., 2020]] ). Research in psychology and related fields shows that the cognitive mechanisms by which individuals and organisations process climate information influence this capacity, motivation and engagement ( [[#Grothmann--2005|Grothmann and Patt, 2005]] ; [[#Grothmann--2013|Grothmann et al., 2013]] ; [[#Masud--2016|Masud et al., 2016]] ; [[#Nelson--2016|Nelson et al., 2016]] ; [[#Takahashi--2016|Takahashi et al., 2016]] ; [[#Hügel--2020|Hügel and Davies, 2020]] ; [[#Grothmann--2021|Grothmann and Michel, 2021]] ).

The perception of climate change as a major threat that requires action has increased since AR5, reflecting both the growth of information about climate change and the processing of that information ( [[#Lee--2015|Lee et al., 2015]] ; [[#Fagan--2019|Fagan and Huang, 2019]] ). Global social movements play an important role in raising public awareness of climate urgency ( [[#Thackeray--2020|Thackeray et al., 2020]] ). Climate change concern plays an important role in decision-making outcomes which entail public participation ( [[#Lammel--2015|Lammel, 2015]] ; [[#Chiang--2018|Chiang, 2018]] ; [[#van%20Valkengoed--2019|van Valkengoed and Steg, 2019]] ; Arıkan and Günay, 2020). Nonetheless, public risk perception varies sharply on spatial and temporal scales, reflecting environmental changes, social influences ( [[#Kousser--2018|Kousser and Tranter, 2018]] ; [[#Rousseau--2020|Rousseau and Deschacht, 2020]] ), economic capacities (Arıkan and Günay, 2020) and culture ( [[#Noll--2020|Noll et al., 2020]] ), as well as individual characteristics ( [[#van%20Valkengoed--2019|van Valkengoed and Steg, 2019]] ). The importance of values and norms is demonstrated by recent research which highlights how intrinsic motivation (altruistic, self-transcendental and eco-centric values) ( [[#Corner--2014|Corner et al., 2014]] ; [[#Braito--2017|Braito et al., 2017]] ; [[#Xiang--2019|Xiang et al., 2019]] ; [[#Bouman--2020|Bouman et al., 2020]] ) and extrinsic social motivation (e.g., economic gains and social desirability) ( [[#van%20Valkengoed--2019|van Valkengoed and Steg, 2019]] ) can drive action.

Recent research shows the importance of education as a predictor of risk perception, motivation and action. Education level is the strongest predictor of public awareness of climate change risk in a study across 119 countries of public awareness of climate change risk ( [[#Lee--2015|Lee, 2015]] ), though this relationship varies in different nations, and is influenced by mediating variables ( [[#Muttarak--2015|Muttarak and Chankrajang, 2015]] ; [[#Blennow--2016|Blennow et al., 2016]] ) ( [[#Ballew--2020|Ballew et al., 2020]] ). Knowledge and awareness of climate change are correlated with the motivation to undertake action on climate change ( [[#Hornsey--2017|Hornsey and Fielding, 2017]] ). The integration of climate science in educational curricula has been shown to be effective ( [[#Hess--2019|Hess and Maki, 2019]] ; [[#Molthan-Hill--2019|Molthan-Hill et al., 2019]] ), including approaches such as integration of the complex system approach ( [[#Jacobson--2017|Jacobson et al., 2017]] ), experiential climate change education ( [[#Siegner--2018|Siegner, 2018]] ), including climate games ( [[#O’Garra--2021|O’Garra et al., 2021]] ; [[#Pfirman--2021|Pfirman et al., 2021]] ), massive open online courses and informal science learning centres ( [[#Geiger--2017|Geiger et al., 2017]] ).

Attention to behavioural change of individuals has grown since AR5, including cases which address both adaptation and mitigation (e.g., dietary changes, modification of buildings, transport alternatives) ( [[#Azadi--2019|Azadi et al., 2019]] ; [[#Fischer--2019|Fischer, 2019]] ; [[#Willett--2019|Willett et al., 2019]] ; [[#Sharifi--2020|Sharifi, 2020]] ; [[#Sharifi--2021|Sharifi, 2021]] ). The interventions to promote behavioural change can be bottom-up, initiated by individuals, communities, non-governmental organisations or the private sector, or top-down, coming from governments at various levels ( [[#Robertson--2015|Robertson and Barling, 2015]] ; [[#Stern--2016|Stern et al., 2016]] ). They are supported by a number of mechanisms, including education, information strategies, and campaigns, financial incentives, regulatory processes and legislation ( [[#Rosenow--2017|Rosenow et al., 2017]] ; [[#Creutzig--2018|Creutzig et al., 2018]] ; [[#Carlsson--2019|Carlsson et al., 2019]] ). These behavioural changes contribute significantly to effective risk management.

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'''Cross-Chapter Box FINANCE: Finance for Adaptation and Resilience'''
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Authors: Mark New (South Africa), Madeleine Rawlins (UK), David Viner (UK), Charlene Watson (UK), Lily Burge (UK), Lionel Mok (Canada), Lauren Arendse (South Africa), Vita Karoblyte (UK), Liane Schalatek (USA), Neha Rai (UK), Baysa Naran (Mongolia), So-Min Cheong (Republic of Korea), Nicoletta Giulivi (Italy/Guatemala).

'''Introduction'''
This Cross-Chapter Box reports on: (i) new evidence on the finance needed for adaptation and resilience, and uncertainties in these estimates; (ii) the emerging public and private climate finance architecture; (iii) the status of financing for AR, including sources, total flows, regional and sectoral distributions; (iv) equity considerations; (iv) opportunities and challenges for financing adaptation and resilience during and after the coronavirus disease 2019 (COVID-19) pandemic. This Cross-Chapter Box does not focus on finance for mitigation, which is covered in WGIII [[IPCC:Wg2:Chapter:Chapter-15|Chapter 15]] (Kreibiel et al., 20122), nor the economic damages of climate change or financial aspects of Loss and Damage, which are covered in Cross-Working Group Box ECONOMIC (Chapter 16) and Cross-Chapter Box LOSS (this chapter), respectively.

Successive reports of the IPCC ( [[#Vellinga--2001|Vellinga et al., 2001]] ; [[#Mimura--2008|Mimura et al., 2008]] ; [[#Yohe--2008|Yohe et al., 2008]] ; [[#Klein--2014|Klein et al., 2014]] ) and the AR6 Special Reports have noted the importance of finance as an enabler for adaptation, across both developed and developing nations. While the UNFCCC and the UNFCCC has yet to arrive at a formally agreed definition of climate finance, numerous overlapping have been suggested and reported (e.g., [[#Falconer--2014|Falconer and Stadelmann, 2014]] ; [[#UNFCCC--2014|UNFCCC, 2014]] ; [[#Roberts--2017|Roberts and Weikmans, 2017]] ; [[#Munira--2021|Munira et al., 2021]] ). However, there is wide agreement across these definitions that climate finance refers to financial resources devoted to addressing climate change, both mitigation and adaptation to current and projected climate change, and that these resources can come from both public and private sources (high confidence). Climate finance includes, but in most definitions is not restricted to, international financial flows to developing countries. Finance can be delivered through a range of instruments including grants, concessional and non-concessional debt, and internal budget reallocations (high confidence) (Watson and Schalatek, 2019). Adaptation and resilience are often used interchangeably in climate finance discussions, although adaptation is a process, while resilience (to climate risk) is the ability to progress towards desired outcomes in the face of impacts from a changing climate ( [[IPCC:Wg2:Chapter:Chapter-1#1.2.1|Section 1.2.1]] ).

Box Cross-Chapter Box FINANCE.1 | The 100 Billion Climate Finance Commitment to Developing Countries

At the 16th session of the Conference of the Parties (COP16) in Copenhagen in 2009, developed country parties to the UNFCCC committed to a goal of jointly mobilising USD 100 billion yr −1 by 2020 to address the climate change needs of developing countries ( [[#UNFCCC--2009|UNFCCC, 2009]] ). This was in response to a threat by developing countries to walk out of the negotiations, as they perceived developed country support to be lagging and lacking in ambition ( [[#Roberts--2021|Roberts et al., 2021]] ). The commitment was formalised in the Cancun Agreements (Decision 1/CP.16) in 2010 and was re-affirmed as a key element of the Paris Agreement in 2015 (Article 9, paragraph 4). At the 26th session of the Conference of the Parties (COP26) in 2021, formal deliberations will begin on a new climate finance goal to be adopted in 2025; the current USD 100 billion target will serve as the annual minimum until 2025 ( [[#Chhetri--2020|Chhetri et al., 2020]] ).

The ‘100 Billion’ does not represent the total need to respond to climate change in developing countries, nor the global cost across all countries, as is sometimes interpreted in the literature and media. As shown below in this Cross-Chapter Box, the estimated cost of adaptation for developing countries ranges from 15 to 411 billion USD yr −1 for climate change impacts out to 2030, with the majority of estimates being well above 100 billion.

Proposed sources for the developed country commitment included ‘ ''a wide variety of sources, public and private, bilateral and multilateral, including alternative sources of finance'' ’ and several instruments including grants and loans. Nonetheless, there remain differences of opinion on the types of finance that should count towards this goal, with several issues identified ( ''high confidence'' ) ( [[#Bodnar--2015|Bodnar et al., 2015]] ; [[#Bhattacharya--2020|Bhattacharya et al., 2020]] ; [[#Roberts--2021|Roberts et al., 2021]] ), including: (i) counting non-grant finance, such as market and concessional loans (public and private), where developing countries ultimately have to repay the investment; (ii) what is counted as ‘climate’ by different funders, especially when climate is not the prime objective; (iii) the extent to which some funds are ‘new and additional’ rather than a repurposing of development finance.

Progress towards the 100 Billion target has shown an upward trend over the last several years ( ''high confidence'' ), but will fall short in 2020, even when the most generous criteria are included ( ''high confidence'' ). In 2017/2018, the most recent year for which data have been comprehensively analysed, estimates using different (but overlapping) data sources and methods were in the range 48–75 billion USD yr −1 , compared with 45–75 in 2015/2016 and 41–52 in 2013/2014 ( [[#Carty--2020|Carty et al., 2020]] ; SM17.3; [[#CPI--2020|CPI, 2020]] ; [[#OECD--2020|OECD, 2020]] ; [[#UNFCCC--2020|UNFCCC, 2020]] ). The distribution between adaptation and mitigation has remained strongly weighted towards mitigation, although the proportion allocated to adaptation has increased from 17–25% in 2013/2014 to 19–30% in 2017/2018 ( ''high confidence'' ). One analysis that excludes debt repayments indicates that the debt-adjusted flows are about half the total flows reported above, of which circa 31–33% was for adaptation between 2015/2016 and 2017/2018 ( [[#Carty--2020|Carty et al., 2020]] ).

'''Adaptation finance needs'''
Estimates of global, regional or national finance needs for adaptation and resilience vary depending on both analysis approach, the level of climate change, and the geographic and sectoral scope of analysis ( ''high confidence'' ) ( [[#UNEP--2016|UNEP, 2016]] ; [[#Chapagain--2020|Chapagain et al., 2020]] ; [[#UNEP--2020|UNEP, 2020]] ). Recent estimates have adopted one of main approaches: (i) aggregation of individual case studies, along with scaling to generate global or regional costs; (ii) analysis of NDC adaptation cost estimates ( [[#Weischer--2016|Weischer et al., 2016]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ); (iii) integrated assessment model simulation of impacts and adaptation costs ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ; [[#Chapagain--2020|Chapagain et al., 2020]] ).

All approaches suffer from limitations that can cause both over- and underestimates, including incomplete coverage of sectors and risks; inability to account for autonomous/unreported adaptation; incorrect cost estimations; soft and hard limits to adaptation; balance between adaptation, mitigation and residual cost; benefits and co-benefits on cost; and learning and innovation as climate change progresses ( [[#UNEP--2020|UNEP, 2020]] ). Global or developing region estimates based on scaling NDC data is particularly uncertain, as most NDCs did not specify how the costs were calculated. Also, scaling from a relatively small set of NDCs with costs to the global scale is not particularly robust, indicating a need for more transparency and better guidance for calculating adaptation costs ( [[#Watkiss--2015|Watkiss et al., 2015]] ; [[#Zhang--2016|Zhang and Pan, 2016]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#African%20Development%20Bank--2019|African Development Bank, 2019]] ).

Most estimates of adaptation cost in the literature are for developing countries. [[#Chapagain--2020|Chapagain et al. (2020)]] assessed various estimates of adaptation for developing countries, under different emissions scenarios for 2030 and 2050. The median estimates (and range) from these studies are 127 (15–411) and 295 (47–1088) billion USD yr −1 for climate change impacts out to 2030 and 2050, respectively (see SM17.3). All but one study report adaptation costs higher than the 70–100 billion estimated in 2010 by the World Bank ( [[#World%20Bank--2010|World Bank, 2010]] ).

[[File:579274329b4732502644012235f255b5 IPCC_AR6_WGII_Figure_17_Cross-Chapter_Box_FINANCE_1.png]]

'''Figure Cross-Chapter Box FINANCE.1 |''' '''Comparison of recent studies that estimated developing country adaptation costs in billion USD (in 2005 prices) yr''' '''−1''' ''', for 2030 and 2050.''' Figure based on [[#Chapagain--2020|Chapagain et al. (2020)]] . Major studies are [[#World%20Bank--2010|World Bank (2010)]] , [[#Chapagain--2020|Chapagain et al. (2020)]] , [[#UNEP--2016|UNEP (2016)]] , [[#Baarsch--2015|Baarsch et al. (2015)]] and [[#Markandya--2019|Markandya and González-Eguino (2019)]] . The solid-coloured bars are based on RCP2.6, and patterned bars are based on RCP 8.5; the width of the bars indicates the range of estimates (maximum and minimum) produced in each study.

The cost of adaptation for developed countries is rarely reported; most literature either reports a global cost or developing country costs, or costs for a specific country or sector. [[#Baarsch--2015|Baarsch et al. (2015)]] , using an Integrated Assessment Model (IAM), report adaptation annual costs (2012 prices) in 2030 (and 2050) as 272 (660) billion globally and 205 (521) in developing countries only under the RCP2.6 scenario, indicating that developed country costs are around 25% (21%) of total cost.

In addition to global estimated adaptation costs, there are many studies that have focused on specific regions, countries or sectors, such as estimated adaptation cost for coastal environments, water-related infrastructure, urban infrastructure, agriculture and energy ( [[#UNEP--2014|UNEP, 2014]] ; [[#Watkiss--2015|Watkiss et al., 2015]] ; [[#UNEP--2016|UNEP, 2016]] ). Examples of such estimates are reported in various chapters in this report and summarised in SM17.3.

Estimating the benefit of adaptation, in terms of damage avoided, remains challenging. For example, [[#Ricke--2018|Ricke et al. (2018)]] show that the social cost of carbon (monetary damage per tCO 2 emitted) varies by up to two orders of magnitude depending on country, socioeconomic scenario, damage function, total greenhouse gases (GHG) forcing, and local climate change. In addition, non-monetary benefits such as cultural identity, sacred places, human health and lives are often ignored ( [[#Tschakert--2017|Tschakert et al., 2017]] ; [[#Serdeczny--2019|Serdeczny, 2019]] ; see also Cross-Working Group Box ECONOMIC in Chapter 16; Cross-Chapter Box LOSS, this Chapter). Recent case studies and global level analyses continue to support the conclusion in IPCC AR5 WGII [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-17 Chapter 17] ( [[#Chambwera--2014|Chambwera et al., 2014]] ) that the benefits of adaptation generally remain larger than the costs ( ''medium confidence'' ), but the cost–benefit ratio varies widely by context and assumptions ( [[#OECD--2015|OECD, 2015]] ; [[#Global%20Commission%20on%20Adaptation--2019|Global Commission on Adaptation, 2019]] ; [[#WRI--2019|WRI, 2019]] )

'''The climate finance landscape'''
The adaptation and resilience finance landscape spans multiple sources, intermediaries, instruments and recipients, operating across global to sub-national scales ( [[#Buchner--2019|Buchner et al., 2019]] ; [[#Carter--2020|Carter, 2020]] ; [[#Watson--2021|Watson and Schalatek, 2021]] ). Public finance is provided by national and sub-national governments and distributed directly by government or intermediaries such as development finance institutions and climate funds, either nationally or internationally. Private finance comes from five main sources: commercial financial institutions (banks), institutional investors (including asset managers, insurance companies and pension funds), other private equity (venture capital and infrastructure funds), non-financial corporations such as renewable energy or water companies, and individual households and communities. Across these different sources, the main instruments used are grants, concessional debt, market debt, internal budget allocation, insurance, as well as personal savings in households ( ''high confidence'' ). Public and private sources of funding can be blended into a single instrument, for example for insurance where public funds provide capital for both sovereign catastrophe instruments and micro-insurance ( [[#Jarzabkowski--2019|Jarzabkowski et al., 2019]] ) or for concessional loans. Similarly, public finance is often ultimately derived from commercial debt instruments such as bonds.

'''International public climate finance'''
International public climate finance flows are realised through bilateral and multi-lateral channels ( [[#Watson--2021|Watson and Schalatek, 2021]] ) where contributions to these channels are received from Annex II and non-Annex I countries ( [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ; [[#Buchner--2019|Buchner et al., 2019]] ). Annex II countries contribute as part of their commitments in the Paris Agreement, while non-Annex I countries commit climate finance through these channels on a voluntary basis ( [[#Pickering--2015|Pickering et al., 2015]] ; [[#Roberts--2017|Roberts and Weikmans, 2017]] ; [[#Egli--2019|Egli and Stünzi, 2019]] ). Bilateral intermediaries include development cooperation agencies and national development banks. These institutions often have long-standing development-cooperation experience, and offer climate change projects, facilities and financial instruments based on their differing mandates, structures and priorities ( [[#Atteridge--2009|Atteridge et al., 2009]] ; [[#Buchner--2019|Buchner et al., 2019]] ).

Multi-lateral channels include the UNFCCC financial mechanisms, such as the Green Climate Fund, and the multi-lateral development banks (MDBs), such as the World Bank. Both pool contributor resources before committing such resources for climate change projects and programmes. Funding through multi-lateral channels promotes recipient country engagement in the governance and prioritisation of funding decisions, with concurrent processes in the multi-laterals often existing to support country ownership of funded climate action ( [[#Ciplet--2013|Ciplet et al., 2013]] ; [[#Ha--2016|Ha et al., 2016]] ).

There are five multi-lateral climate change funds of the UNFCCC and Paris Agreement financial mechanisms. There are further multi-lateral climate change funds that are not governed by the UNFCCC or Paris Agreement, the largest of which is the World Bank governed Climate Investment Funds ( [[#Watson--2021|Watson and Schalatek, 2021]] ). Some of the major multi-lateral climate change funds have been established with a specific focus on adaptation, while some bilateral donors have thematic or sectoral priorities. Multi-lateral climate change funds operate through accredited implementing entities. These have historically been multi-lateral in nature, such as the development banks, but recent years have seen a rise in the accreditation of national and regional institutions ( [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ). In addition to programming funds from external sources, such as through the multi-lateral climate change funds, the MDBs also raise and programme their own climate finance ( [[#UNFCCC%20SCF--2018|UNFCCC SCF, 2018]] ; [[#MDBs--2019|MDBs, 2019]] ).

Several major multi-lateral climate change funds work through grant-only programmes, whereas others include concessional loan, equity and guarantee instruments. The broader suite of instruments used by the MDBs includes grant, investment loan, equity, guarantee, line of credit, policy-based financing and results-based financing ( [[#MDBs--2019|MDBs, 2019]] ).

Public funding of a concessional nature that flows from Annex II to non-Annex I countries supports research and capacity building and can also facilitate private finance flows into climate action, with the intention to avoid creating a high debt burden in developing countries, in response to climate impacts for which they have little historic responsibility ( [[#Watson--2016|Watson, 2016]] ; [[#Carter--2020|Carter, 2020]] ; [[#Schalatek--2020|Schalatek, 2020]] ). Less concessional public finance flows include other official flows that are not developmental in nature and can be trade related, including, for example, export credits.

Critiques of the public climate finance architecture are aimed at the overlapping mandates of the institutions programming climate finance, particularly the multi-lateral climate funds, and the challenges in accessing funding ( [[#Nakhooda--2014|Nakhooda et al., 2014]] ; [[#Amerasinghe--2017|Amerasinghe et al., 2017]] ; [[#Pickering--2017|Pickering et al., 2017]] ). However, Pickering et al. (2017) further note that institutional fragmentation of climate finance could result in more flexibility, resilience and innovation. There have also been important governance changes leveraged by some of these funds and instruments, such as integration of gender considerations into projects ( [[#Schalatek--2020|Schalatek, 2020]] ).

'''Private financing of adaptation and resilience'''
There is an increasing focus on the role of the private sector to support large-scale financing of adaptation and resilience ( [[#UNEP--2016|UNEP, 2016]] ; [[#UNEP--2018|UNEP, 2018]] ). To date, it has been difficult to track adaptation and resilience finance within the private sector ( [[#UNEP--2016|UNEP, 2016]] ) as it is either not disclosed or not easily identifiable, since it is often built into capital and operating expenditure and is not a standalone investment. Several private mechanisms are emerging as important sources of climate finance (Gupta et al., 2014; [[#Eccles--2018|Eccles and Krzus, 2018]] ; [[#Miller--2019|Miller et al., 2019]] ).

'''''Green, social impact and resilience bonds''''' are similar to traditional bonds—fixed-income financial instruments raised on commercial markets by companies, governments or financial institutions—but the proceeds are used to fund activities that have positive environmental, social or climate benefit ( [[#Tuhkanen--2020|Tuhkanen, 2020]] ). Green bonds align to voluntary principles, such as the Green Bond Principles set out by the International Capital Market Association, the Climate Bonds Initiative’s Climate Resilience Principles ( [[#Sartzetakis--2020|Sartzetakis, 2020]] ). Given the voluntary nature and lack of standardisation of green bond principles, there are concerns around their additionality, and there is also a lack of data on how green bonds contribute to a scaling up of green projects ( [[#Dupre--2018|Dupre et al., 2018]] ).

Green bond annual issuance reached 260 billion in 2019 ( [[#CBI--2020|CBI, 2020]] ), but as of 2018, only 3–5% (USD 12 billion) of green bond total proceeds can be explicitly traced to climate-resilience-related efforts ( [[#CBI--2019|CBI, 2019]] ). Examples of AR focused bonds include those issued by Fiji in 2017, dedicating 91% of spending to adaptation and resilience ( [[#Shukla--2017|Shukla and Peyraud, 2017]] ; [[#Ministry%20of%20Economy--2019|Ministry of Economy, 2019]] ), and by the European Bank for Reconstruction and Development’s 2019 Climate Resilience Bond for USD 700 million to finance climate-resilient infrastructure, commercial operations, agriculture or ecological systems ( [[#EBRD--2019|EBRD, 2019]] ).

'''''Dedicated investment vehicles''''' are equity funds that are created to invest in products and services that enhance resilience and reduce risks. An example is the Climate Resilience and Adaptation Finance and Technology Transfer Facility that is proposed as a USD 500 million private equity fund to invest in companies providing climate resilience solutions for developing countries. Initial funding has been provided by donors ( [[#Miller--2019|Miller et al., 2019]] ).

'''''Balance sheet finance''''' occurs when an entity directly invests in resilience and adaptation rather than as a separate project. This source of funding may be from exiting reserves, re-allocation from other budget lines, or via external commercial finance, but the investment is financed by the firm rather than as a separate project (Gupta et al., 2014; [[#Buchner--2019|Buchner et al., 2019]] ).

'''''Insurance''''' can play an important role in managing residual climate risks at any given level of adaptation, but insurers can also be important r risk assessment and risk reduction as part of any insurance package ( [[#Jarzabkowski--2019|Jarzabkowski et al., 2019]] ; [[IPCC:Wg2:Chapter:Chapter-11#11.3.8.3|Section 11.3.8.3]] ). While traditional indemnity insurance is important for repair and rebuilding of damaged property and infrastructure, parametric insurance has become increasingly popular for supporting rapid post-disaster responses such as drought, hurricane damage and flooding. Examples include sovereign insurance facilities such as African Risk Capacity and the Caribbean Catastrophe Risk Insurance Facility ( [[#Broberg--2019|Broberg, 2019]] ) as well as weather-index insurance targeted at individuals, especially in agriculture ( [[#Greatrex--2015|Greatrex et al., 2015]] ; [[#Isakson--2015|Isakson, 2015]] ; [[#Surminski--2016|Surminski et al., 2016]] ; [[#Jensen--2017|Jensen and Barrett, 2017]] ; [[#Fischer--2019|Fischer, 2019]] ). The role of insurance as a climate risk management option, as well as limitations, is covered in more depth in [[#17.2|Section 17.2]] and Cross-Chapter Box LOSS (this chapter).

'''Mainstreaming physical climate risks and resilience in the private sector'''
The data on tracked climate finance and green bond issuance for adaptation and resilience both show a substantial gap between the adaptation needs and the finance deployed. Scaling up these instruments is unlikely to close this gap given the challenges with financing adaptation projects, particularly from the private sector. There is therefore a need for more systematic action to manage climate risks and mainstream climate change considerations ( [[#Miller--2019|Miller et al., 2019]] ).

The financial case for mitigation investment can often be demonstrated through revenues from, for example, the sale of renewable electricity. On contrast, the benefits from investment in adaptation and resilience are typically considered in terms of avoided losses and cost benefit ratios. For example, the [[#Global%20Commission%20on%20Adaptation--2019|Global Commission on Adaptation (2019)]] estimates that the overall rate of return on investments in improved resilience is very high, with benefit–cost ratios ranging from 2:1 to 10:1, and in some cases even higher.

The private sector is becoming increasingly aware of the need to assess physical climate risks to avoid the long-term risks to assets and enhance climate resilience. The task force on climate-related financial disclosures (TCFD) is likely to create additional pressure from investors for companies to identify, manage and reduce risks from climate change ( [[#Eccles--2018|Eccles and Krzus, 2018]] ; [[#ERM%20and%20CBEY--2018|ERM and CBEY, 2018]] ; [[#Tuhkanen--2020|Tuhkanen, 2020]] ).

A key factor for the impact of the TCFD on mainstreaming of physical climate risks and demonstrating the case for investment in adaptation and resilience will be how investors systematically incorporate physical climate risks, adaptation and resilience into their investment decisions. The Coalition for Climate Resilient Investment ( [[#DFID--2019|DFID et al., 2019]] ) was established to look at this from the private sector viewpoint and is working to systematically incorporate resilience into cash flow modelling and asset valuation practices, so that investors may quantify the investment in resilience for an asset and the benefits associated with reduced costs and more reliable revenue streams.

'''Recent trends in climate finance flows'''
Considerable progress has been made in tracking climate finance since AR5, but substantial gaps remain, especially regarding domestic public finance and private sector balance sheet investment in adaptation ( [[#17.5.1|Section 17.5.1.5]] ; [[#CPI--2020|CPI, 2020]] ; [[#Richmond--2020|Richmond et al., 2020]] ). The best documented information comes from international climate funds, which provide detail at the project level. Most bilateral and multi-lateral investment institutions report on whether debt, grants and other instruments are for climate projects, but with less detail. Private finance is harder to track, as reporting is voluntary; even for green bonds, where certification identifies the range of sectors a bond aims to cover, reporting of how the bond is spent is infrequent.

The Climate Policy Initiative (CPI) has been tracking climate finance since 2009, allowing for trends to be assessed; however, trends reported are a function of both real changes in finance and changes in methods and information sources ( [[#Richmond--2020|Richmond et al., 2020]] ). Total climate finance tracked by CPI has increased from USD 364 billion yr −1 in 2010/2011 to 579 billion in 2017/2018 (SM17.3). Tracked finance remained relatively constant from 2010/2011 to 2013/2014 but has increased steeply in more recent years. The proportion of finance allocated to adaptation has remained small throughout, between 4% and 8% ( ''high confidence'' ); a further 1–2% of global finance has been classified as ‘multiple-objectives’. The large majority of tracked adaptation finance is from public sources ( ''high confidence'' ), with only 2% coming from private sources in 2017/2018 ( [[#CPI--2020|CPI, 2020]] ). This is at least partly because of the difficulty in demonstrating financial (as opposed to public good and avoided damages) return on investment for adaptation.

The majority of the most recently (2017/18) tracked adaptation and multiple-objective finance was supplied through public donors, largely through grants, concessional and non-concessional instruments (Figure FAR.1). Most finance (44.1%) was spent transregionally (allocated in specific projects to recipients in more than a single region). For regionally specific funding, Sub-Saharan Africa and South Asia, along with the Latin America and Caribbean region, received the largest gross amounts, although Oceania has received the greatest per-capita funding. The largest proportion of AR funding has been allocated to increasing the resilience of infrastructure, energy and the built environment, followed by agriculture, forestry and natural management, and then water and wastewater.

Across financial instruments, Sub-Saharan Africa received the highest relative proportion through grants (38%), followed by the Latin America and Caribbean region (23%), with other non-Organisation for Economic Co-operation and Development (OECD) regions receiving between 16% and 10% (SM17.3). Concessional debt as a proportion of the regional total varies from 84% in South Asia to as low as 29% in Latin America and Caribbean, which has the highest proportion of non-concessional debt (48%).

[[File:49c2c94a5683c22f964bcc423661fc9d IPCC_AR6_WGII_Figure_17_Cross-Chapter_Box_FINANCE_2.png]]

'''Figure Cross-Chapter Box FINANCE.2 |''' '''The flow and distribution of globally tracked adaptation and resilience finance in 2018 from different sources, through different instruments into different sectors and regions.''' Each strand shows the relative proportion of finance flowing from one category to another (for example, from private or public sources to different instruments). Categories from left to right are: (a) whether the finance is solely for adaptation or for adaptation and other objectives, including mitigation (multiple objectives); (b) whether the finance comes from public or private sources; (c) the financing instrument; (d) the broad sectoral allocation; (e) the geographical distribution of funding (proportion of total in % and per-capita allocation). Based on data collated by [[#CPI--2020|CPI (2020)]] .

'''The importance of public and private finance for adaptation and resilience'''
Adaptation finance provided by international public mechanisms remains the core source of tracked flows in support of adaptation and resilience to developing countries ( [[#Micale--2018|Micale et al., 2018]] ; [[#UNEP--2018|UNEP, 2018]] ), although these public funds alone are insufficient to meet rapidly growing needs and constitute only a minority share of all public climate finance flows ( [[#UNEP--2016|UNEP, 2016]] ; [[#Global%20Commission%20on%20Adaptation--2019|Global Commission on Adaptation, 2019]] ).

Public mechanisms can play a role in leveraging private sector finance for adaptation by addressing real and perceived regulatory, cost and market barriers through blended finance approaches, public–private partnerships or innovative financial instruments and structuring in support of private sector requirements for risk management and guaranteed investment returns ( [[#Pillay--2017|Pillay et al., 2017]] ; [[#Miller--2019|Miller et al., 2019]] ).

There is growing agreement on the sectors (such as infrastructure, agriculture or water management) and approaches (contingency finance or insurance) where private sector adaptation investments alone, or leveraged by public mechanisms, might be best targeted, such as by reducing the risk of providing financial services for adaptation investments to domestic micro-, small and medium enterprises or agricultural smallholders, many of them women ( [[#Biagini--2013|Biagini and Miller, 2013]] ; [[#Chambwera--2014|Chambwera et al., 2014]] ; [[#Pauw--2016|Pauw et al., 2016]] ; [[#Global%20Commission%20on%20Adaptation--2019|Global Commission on Adaptation, 2019]] ; [[#Miller--2019|Miller et al., 2019]] ; [[#Resurrección--2019|Resurrección et al., 2019]] ; [[#Richmond--2020|Richmond et al., 2020]] ). A remaining open question is how to allocate limited public adaptation funds in a way that is equitable, effective and efficient between mobilising private investments and safeguarding adequate financial support for necessary adaptation efforts, such as the provision of public goods, which the private sector will not invest in ( [[#Fankhauser--2011|Fankhauser and Burton, 2011]] ; [[#Abadie--2013|Abadie et al., 2013]] ; [[#Baatz--2018|Baatz, 2018]] ; [[#Omari-Motsumi--2019|Omari-Motsumi et al., 2019]] ).

Many adaptation interventions in the most vulnerable countries, communities and people provide no adequate financial return on investments and can therefore can only be funded with highly concessional public finance. Grant support is most appropriate for measures such as capacity building, planning, public policy and regulatory reforms, disaster risk management and response, community engagement or support for social safety nets, and for addressing social vulnerabilities, including poverty or gender inequality, which constrain adaptation ( [[#Grasso--2010a|Grasso, 2010a]] ; [[#Pillay--2017|Pillay et al., 2017]] ; [[#Agrawal--2019|Agrawal et al., 2019]] ; [[#Buchner--2019|Buchner et al., 2019]] ).

Access to adequate adaptation grant finance is further constrained because several public mechanisms provide grants only for the additional costs of adaptation measures compared with a development baseline in the absence of climate impacts. Calculating the incremental costs of adaptation measures imposes additional time and resource burden on the most vulnerable recipients, who are often faced with data gaps or technical capacity constraints ( [[#Chambwera--2014|Chambwera et al., 2014]] ; GCF, 2018; [[#UNEP--2018|UNEP, 2018]] ; [[#Omari-Motsumi--2019|Omari-Motsumi et al., 2019]] ).

An exact delineation of respective costs for adaptation and development components is difficult and might be unsuitable as many adaptation measures are intrinsically linked to development. It may also prevent realising necessary synergies between both components ( [[#McGray--2007|McGray et al., 2007]] ; [[#Smith--2011|Smith et al., 2011]] ; [[#Denton--2014|Denton et al., 2014]] ; [[#Resch--2017|Resch et al., 2017]] ; [[#Micale--2018|Micale et al., 2018]] ).

'''Equality and fairness in climate finance'''
Climate finance literature recognises that poor and least developed households, communities and countries are most affected and marginalised by climate change, and least responsible for its causes, but receive relatively little financial support for adaptation (Chapters 15, 8; [[#Olsson--2014|Olsson et al., 2014]] ; [[#Rozenberg--2015|Rozenberg and Hallegatte, 2015]] ; [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Rai--2017|Rai and Fisher, 2017]] ; [[#Shakya--2017|Shakya and Byrnes, 2017]] ).

While the gap between current financial flows to developing countries and their adaptation needs (see Box Cross-Chapter Box FINANCE.1) is a major factor undermining equity and fairness in financing, several other factors that can also affect fair and just financing in developing countries have been identified in recent literature ( [[#Klein--2014|Klein et al., 2014]] ; [[#Colenbrander--2018|Colenbrander et al., 2018]] ; [[#Mfitumukiza--2019|Mfitumukiza et al., 2019]] ; [[#Khan--2019a|Khan et al., 2019a]] ; [[#Doshi--2020|Doshi and Garschagen, 2020]] ). First, financing is skewed in favour of mitigation, and therefore towards fast-growing upper- and middle-income countries offering the biggest gains in emission reductions, especially in Southeast Asia, but also in Sub-Saharan Africa ( [[#Rai--2016|Rai et al., 2016]] ). Further, as much of current finance uses debt-based instruments, mitigation projects are further preferred as returns are more assured ( [[#Lee--2018|Lee and Hong, 2018]] ; [[#Carty--2020|Carty et al., 2020]] ).

Second, the requirement of many funders for readiness and fiduciary capacity means that least developed countries (LDCs) have been less able to access finance, despite many support mechanisms being offered. Additionally, geopolitical preferences of some countries mean that some developing countries are preferred to others for bilateral funding ( [[#Doshi--2020|Doshi and Garschagen, 2020]] ). This is exacerbated for private sector investment, where lower credit ratings make finance more expensive, and increasing understanding of exposure to physical climate risks could lead to ‘capital flight’ from most vulnerable countries ( [[#Global%20Commission%20on%20Adaptation--2019|Global Commission on Adaptation, 2019]] ; [[#Miller--2019|Miller et al., 2019]] ; [[#Cooper--2020|Cooper, 2020]] ).

Third, within climate-vulnerable countries, very little is channelled to local communities who need it most; the few analyses available suggest that less than 10% of total climate finance supports decentralised actions ( [[#Rai--2016|Rai et al., 2016]] ; [[#Soanes--2017|Soanes et al., 2017]] ). Reasons include: (i) lack of consideration of procedural equity in programme design ( [[#Grasso--2010b|Grasso, 2010b]] ; [[#Wang--2018|Wang and Gao, 2018]] ; [[#Venn--2019|Venn, 2019]] ; [[#Khan--2019a|Khan et al., 2019a]] ); (ii) finance being managed by multi-lateral implementers, rather than agencies that are closer to local communities; (iii) the higher transaction costs of decentralised projects in low-income communities reduce their attractiveness to funders as well as the ability of local organisations to meet the fiduciary standards ( [[#Fonta--2018|Fonta et al., 2018]] ; [[#Omari-Motsumi--2019|Omari-Motsumi et al., 2019]] ).

It has been proposed that, as middle-income countries can leverage mitigation finance from the private sector, targeting scarce public finance towards LDCs and SIDS may be necessary to ensure sufficient funds reach these countries ( [[#Steele--2015|Steele, 2015]] ). Matching domestic climate spending with international support is one way to ensure LDCs get the funds they need ( [[#Grasso--2010b|Grasso, 2010b]] ; [[#Bird--2014|Bird, 2014]] ). Targeting specific marginalised communities and women within countries can also help make climate finance more effective and fairer, such as the Asian Development Bank’s efforts to make lending portfolios more inclusive and pro-poor ( [[#ADB--2018|ADB, 2018]] ).

'''Post-COVID recovery packages, debt relief and finance for adaptation and resilience'''
Recent literature has highlighted the opportunity that COVID recovery packages offer for environmentally sustainable, low-carbon and climate-resilient economic growth ( [[#Forster--2020|Forster et al., 2020]] ; [[#Hepburn--2020|Hepburn et al., 2020]] ; [[#Hanna--2021|Hanna et al., 2021]] ). Assessment of whether this is indeed happening is limited, although the few available studies suggest that that this opportunity is not being realised in many nations ( [[#O’Callaghan--2021|O’Callaghan and Murdock, 2021]] ; VIVID [[#Economics--2021|Economics, 2021]] ). One study of the Group of Twenty (G20) and 10 other nations suggested that stimulus packages would have net negative environmental impact in two-thirds of these countries (VIVID [[#Economics--2021|Economics, 2021]] ), while another showed that around half of G20 recovery investment targeted at energy has had gone towards fossil fuels, rather than to cleaner energy sources ( [[#Dibley--2021|Dibley et al., 2021]] ).

Concerns have also been raised about the interactions between debt service, COVID economic recession and post-COVID recovery in developing countries ( [[#Simmons--2021|Simmons et al., 2021]] ; [[#Volz--2021|Volz et al., 2021]] ). Debt service grows as a proportion of national budget during recession, reducing scope for investment in recovery, is a self-reinforcing cycle. It has been suggested that linking debt relief to Paris-aligned objectives can act as an additional source of climate finance ( [[#Fenton--2014|Fenton et al., 2014]] ). The G20 has begun addressing this debt crisis through its Debt Service Suspension Initiative and the Common Framework for Debt Treatments ( [[#IMF--2020|IMF, 2020]] ). It has been suggested that these initiatives could be expanded to prioritise climate-focused debt-relief instruments and to include more countries ( [[#Steele--2020|Steele and Patel, 2020]] ; [[#Volz--2021|Volz et al., 2021]] ). If debt relief is used to invest in national instrument for green and inclusive recovery, national ownership of the use of the finance can occur, avoiding some of the negative connotations of historical debt restructuring ( [[#Volz--2021|Volz et al., 2021]] ).

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