Editing IPCC:AR6/WGII/Chapter-16 (section)

== 16.4 Synthesis of Limits to Adaptation across Natural and Human Systems ==

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This section builds on previous IPCC Reports (i.e., AR5, SR15, SROCC, SRCCL) to advance concepts and emphasise remaining gaps in understanding about limits to adaptation. We provide case studies to illustrate these concepts and synthesise regional and sectoral limits to adaptation across natural and human systems that informs key risks ( [[#16.5|Section 16.5]] ) and RFCs ( [[#16.6|Section 16.6]] ). We also identify residual risks—risks that remain after efforts to reduce hazards, vulnerability and/or exposure—associated with limits to adaptation.

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=== 16.4.1 Definitions and Conceptual Advances since AR5 ===

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==== 16.4.1.1 Limits to Adaptation since AR5 ====

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AR5 introduced the concept of limits to adaptation and provided a functional definition that has been used in subsequent Special Reports (SR15, SROCC, SRCCL) and is also used for AR6 (see also Chapter 1).

A limit is defined as the point at which an actor’s objectives or system’s needs cannot be secured from intolerable risks through adaptive actions ( [[#Klein--2014|Klein et al., 2014]] ). Tolerable risks are those where adaptation needed to keep risk within reasonable levels is possible, while intolerable risks are those where practicable or affordable adaptation options to avoid unreasonable risks are unavailable. This highlights that limits to adaptation are socially constructed and based on values that determine levels of reasonable or unreasonable risk as well as on available adaptation options, which vary greatly across and within societies.

Limits are categorised as being either ‘soft’ or ‘hard’. Soft limits may change over time as additional adaptation options that are practicable or affordable become available. Hard limits will not change over time as no additional adaptive actions are possible. When a limit is exceeded, then intolerable risk may materialise and the actor’s objectives or system’s needs may be either abandoned or transformed (Box 16.2).

For human systems, soft and hard limits are largely distinguished by whether or not constraints to adaptation are able to be overcome. Constraints to adaptation (also called barriers) are factors that make it harder to plan and implement adaptation actions, such as limited financial resources, ineffective institutional arrangements or insufficient human capacity. Soft limits are mostly associated with human systems, due in part to the role of human agency in addressing constraints. For natural systems, the magnitude and rate of climate change and capacity of adaptation to such change largely determine the type of limit. Hard limits are largely associated with natural systems and are mostly due to inability to adapt to biophysical changes.

Using this understanding of limits, subsequent Special Reports have assessed relevant literature ( [[#Mechler--2020|Mechler et al., 2020]] ). SR15 identifies several regions, sectors and ecosystems—including coral reefs, biodiversity, human health, coastal livelihoods, Small Island Developing States, and the Arctic—that are projected to experience limits at either 1.5°C or 2°C. SRCCL states that land degradation due to climate change may result in limits to adaptation being reached in coastal regions and areas affected by thawing permafrost. SROCC details that risks of climate-related changes in the ocean and cryosphere may result in limits for ecosystems and vulnerable communities in coral reef environments, urban atoll islands and low-lying Arctic locations before the end of this century in case of high-emissions scenarios.

A key area of advancement since AR5 is how incremental adaptation and transformational adaptation relate to limits to adaptation. Incremental adaptation maintains ‘the essence and integrity of a system or process at a given scale’, while transformational adaptation ‘changes the fundamental attributes of a social-ecological system’ ( [[#IPCC--2018b|IPCC, 2018b]] ). Both incremental and transformational adaptation may expand the adaptive possibilities for a system, providing additional adaptation options after a system reaches a soft limit ( [[#Felgenhauer--2015|Felgenhauer, 2015]] ; [[#Pelling--2015|Pelling et al., 2015]] ; [[#Termeer--2017|Termeer et al., 2017]] , see also Chapters 1 and 17; [[#Alston--2018|Alston et al., 2018]] ; [[#Panda--2018|Panda, 2018]] ; [[#Mechler--2021|Mechler and Deubelli, 2021]] ). However, it is critical to note that adaptation, whether incremental or transformational, must support securing an actor’s objectives or system’s needs from intolerable risks. Once objectives or needs have been abandoned or transformed, a limit to adaptation has occurred. However, objectives or needs may change over time as values of a society change ( [[#Taebi--2020|Taebi et al., 2020]] ), thus adding further complexity to assessing limits to adaptation.

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==== 16.4.1.2 Residual Risk since AR5 ====

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The term ‘residual risk’ was not assessed in detail in AR5 and was used interchangeably with other terms, including ‘residual impacts’, ‘residual loss and damage’ and ‘residual damage’. SR15 includes discussion of residual risks without an explicit definition and relates these to L oss and D amage and limits to adaptation, concluding that residual risks rise as global temperatures increase from 1.5°C to 2°C. SRCCL refers to residual risks arising from limits to adaptation related to land management. Such residual risk can emerge from irreversible forms of land degradation, such as coastal erosion when land completely disappears, collapse of infrastructure due to thawing of permafrost, and extreme forms of soil erosion. SROCC advanced the conceptualisation of residual risk and integrated it within the risk framework, defining residual risk as the risk that remains after actions have been taken to reduce hazards, exposure and/or vulnerability. Residual risk is therefore generally higher where adaptation failure, insufficient adaptation or limits to adaptation occur. We use the SROCC definition of residual risk for our assessment in the following sections and identify residual risks that are associated with limits to adaptation.

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'''Box 16.2 | Linking Adaptation Constraints, Soft and Hard Limits'''
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McNamara et al. (2017) provides an example of community-scaled adaptation that highlights how constraints affect limits, the relationship between soft and hard limits, and the potential need to abandon or transform objectives. Community members of Boigu Island, Australia, are already adapting to perceived climate change hazards—including sea level rise and coastal erosion—to secure their objective of sustaining livelihoods and way of life in their current location. Existing seawall and drainage systems provide inadequate protection from flooding during high tides, leading residents to elevate their houses to prevent damages. However, these adaptation measures have proved to be insufficient. Standing saltwater for extended periods of time after floods has resulted in losses and damages, including erosion of infrastructure, increased soil salinity, and heightened public health concerns. Additional adaptation efforts are constrained by scarcity of elevated land, which inhibits movement of infrastructure within the community, and lack of financial, technical and human assets to improve coastal protection measures.

These constraints are leading to a soft limit to adaptation, where risks would become unreasonable as sea levels continue to rise and practicable and affordable adaptation options are limited to currently available approaches. This soft limit could be overcome through addressing constraints and allowing further adaptation to take place, such as providing financial, technical and human resources for more effective coastal protection and drainage systems that would reduce flooding. However, if the effectiveness of these new adaptation measures decreases as sea levels rise further and if constraints are not able to be overcome, another soft limit may be reached. Eventually, if constraints are not addressed, no further adaptation measures are implemented, and climate hazards intensify, the area could become uninhabitable. This would then be a hard limit for adaptation; there would be no adaptation options available that would allow the community to sustain livelihoods and way of life in its present location. This hard limit to adaptation may necessitate abandoning the objective of remaining in the community. The objective of the community may then transform to sustaining their livelihoods in a less vulnerable location, which would necessitate relocation. However, such transformation of the community’s objectives may be hindered by the expressed resistance of residents to migrate, due to their strong sense of place.

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=== 16.4.2 Insights from Regions and Sectors about Limits to Adaptation ===

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Here we provide example case studies to highlight constraints that may lead to soft limits, potential incremental and transformational adaptation options that may overcome soft limits, evidence of hard limits, and residual risks.

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==== 16.4.2.1 Small Island Developing States ====

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An expanding volume of empirical research highlights existing adaptation constraints that may lead to soft limits in Small Island Developing States (SIDS). Investigation of national communications among 19 SIDS found that financial constraints, institutional challenges and poor resource endowments were the most frequently reported as inhibiting adaptation for a range of climate impacts ( [[#Robinson--2018b|Robinson, 2018b]] ). Governance, financial and information constraints such as unclear property rights and lack of donor flexibility have led to hasty implementation of adaptation projects in Kiribati, whereas in Vanuatu and the Solomon Islands, limited awareness of rural adaptation needs and weak linkages between central governance and local communities have resulted in an urban bias in resource allocation ( [[#Kuruppu--2015|Kuruppu and Willie, 2015]] ). Limited availability and use of information and technology also present constraints to adaptation; many SIDS suffer from lack of data and established routines to identify losses and damages, and the combination of poor monitoring of slow-onset changes and influence of non-climatic determinants of observed impacts challenges attribution ( [[#Thomas--2018|Thomas and Benjamin, 2018]] ). The fact that climate information is often available only in the English language represents another common constraint for island communities ( [[#Betzold--2015|Betzold, 2015]] ). Although Indigenous and local knowledge systems can provide important experience-based input to adaptation policies ( [[#Miyan--2017|Miyan et al., 2017]] ), socio-cultural values and traditions such as attachment to place, religious beliefs and traditions can also constrain adaptation in island communities, particularly for more transformational forms of adaptation ( [[#Ha’apio--2018|Ha’apio et al., 2018]] ; [[#Oakes--2019|Oakes, 2019]] ).

Soft limits to adaptation for coastal flooding and erosion are already being experienced in Samoa owing largely to financial, physical and technological constraints ( [[#Crichton--2018|Crichton and Esteban, 2018]] ). While sea walls have been erected to minimise coastal erosion, these defences need regular upgrading and replacement as high swells, tropical cyclones and constant wave action erode their effectiveness. The high costs of installing, upgrading and enlarging such infrastructure has led to sea walls only being used in specific locations, leaving communities that are beyond the extent of these measures exposed to inundation and erosion. Native tree replanting has also been implemented, but coastal flooding and erosion persist as large swells lead to high failure rates of replanting efforts. Across SIDS, adaptation to coastal flooding and erosion in particular is increasingly facing soft limits due to high costs, unavailability of technological options and limited physical space or environmental suitability for hard engineering or ecosystem-based approaches ( [[#Mackey--2018|Mackey and Ware, 2018]] ; [[#Nalau--2018|Nalau et al., 2018]] ).

Retreat and relocation constitute transformative adaptation options, although evidence of permanent community-scale relocation in response to climate change remains limited at present ( [[#Kelman--2015|Kelman, 2015]] ; [[#McNamara--2015|McNamara and Des Combes, 2015]] ). Material and emotional cost of emigration as well as loss of homeland, nationhood, and other intangible assets and values imply that relocation is generally considered a last resort ( [[#Jamero--2017|Jamero et al., 2017]] ) and may mean abandoning objectives of remaining in existing locations, hence exceeding adaptation limits.

Hard limits in SIDS are mostly due to adaptation being unable to prevent intolerable risks from escalating climate hazards such as SLR and related risks of flooding and surges, severe tropical cyclones, and contamination of groundwater. Emerging evidence suggests that shortage of water and land degradation have already contributed to migration of multiple island communities in the Pacific ( [[#Handmer--2019|Handmer and Nalau, 2019]] ).

Residual risks for SIDS include loss of marine and terrestrial biodiversity and ecosystem services, increased food and water insecurity, destruction of settlements and infrastructure, loss of cultural resources and heritage, collapse of economies and livelihoods, and reduced habitability of islands ( [[IPCC:Wg2:Chapter:Chapter-3#3.5.1|Section 3.5.1]] , [[IPCC:Wg2:Chapter:Chapter-15#15.3|Section 15.3]] ).

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==== 16.4.2.2 Agriculture in Asia ====

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Lack of financial resources is found to be a significant constraint that contributes to soft limits to adaptation in agriculture across Asia. Although smallholder farmers are currently adapting to climate impacts, lack of finance and access to credit prevents upscaling of adaptive responses and has led to losses ( [[#Bauer--2013|Bauer, 2013]] ; [[#Patnaik--2015|Patnaik and Narayanan, 2015]] ; [[#Bhatta--2016|Bhatta and Aggarwal, 2016]] ; [[#Loria--2016|Loria, 2016]] ). Other constraints further contribute to soft limits, including governance and associated institutional factors such as ineffective agricultural policies and organisational capacities ( [[#Tun%20Oo--2017|Tun Oo et al., 2017]] ), information and technology challenges such as limited availability and access to technologies on the ground ( [[#Singh--2018|Singh et al., 2018]] ), socio-cultural factors such as the social acceptability of adaptation measures that are affected by gender ( [[#Huyer--2016|Huyer, 2016]] ; [[#Ravera--2016|Ravera et al., 2016]] ), and limited human capacity ( [[#Masud--2017|Masud et al., 2017]] ). A wide range of pests and pathogens are predicted to become problematic to regional food crop production as average global temperatures rise ( [[#Deutsch--2018|Deutsch et al., 2018]] ), increasing crop loss across Asia for which farmers are already experiencing a variety of adaptation constraints, including financial, economic and technological challenges ( [[#Sada--2014|Sada et al., 2014]] ; [[#Tun%20Oo--2017|Tun Oo et al., 2017]] ; [[#Fahad--2018|Fahad and Wang, 2018]] ). Extreme heatwaves are projected in the densely populated agricultural regions of South Asia, leading to increased risk of heat stress for farmers and resultant constraints on their ability to implement adaptive actions ( [[#Im--2017|Im et al., 2017]] ). However, socioeconomic constraints appear to have a higher influence on soft limits to adaptation in agriculture than biophysical constraints ( [[#Thomas--2021|Thomas et al., 2021]] ). For example, an examination of farmers’ adaptation to climate change in Turkey found that constraints related to access to climate information and access to credit will likely limit the yield benefits of incremental adaptation ( [[#Karapinar--2020|Karapinar and Özertan, 2020]] ). In Nepal, conservation policies restrict traditional grazing inside national parks, which promotes intensive agriculture and limits other cropping systems that have been implemented as climate change adaptation ( [[#Aryal--2014|Aryal et al., 2014]] ).

In Bangladesh, small and landless farm households are already approaching soft limits in adapting to riverbank erosion ( [[#Alam--2018|Alam et al., 2018]] ). While wealthier farming households can implement a range of adaptation responses, including changing planting times and cultivating different crops, poorer households have limited access to financial institutions and credit to implement such measures. Their adaptation responses of shifting to homestead gardening and animal rearing are insufficient to maintain their livelihoods, and these households are more likely to engage in off-farm work or migrate.

Palao et al.. (2019) identify the possible need for transformational adaptation in Asian-Pacific agricultural practices due to changes in biophysical parameters as global average temperatures rise. In this context, transformational adaptation would consist of changing farming locations to different provinces or different elevations for the production of specific crops or introducing new farming systems. Nearly 50% of maize in the region along with 18% of potato and 8% of rice crops would need to either be shifted in location or use new cropping systems, with the most significant transformation being needed in China, India, Myanmar and the Philippines. For maize suitability by 2030, seven provinces in the east and northeast of China are projected to experience over 50% reduction in suitability, and two northern states in India may experience 70% reduction in suitability. Cassava and sweet potato may play a critical role in food resilience in these areas, as these crops are more resilient to climate change ( [[#Prain--2019|Prain and Naziri, 2019]] ).

In terms of hard limits, the rate and extent of climate change is critical as agriculture is climate-dependent and sensitive to changes in climate parameters. [[#Poudel--2017|Poudel and Duex (2017)]] document that over 70% of the springs used as water sources in Nepalese mountain agricultural communities had a decreased flow, and approximately 12% had dried up over the past decade. While there are some adaptation measures to address reduced water availability—such as the introduction of water-saving irrigation technology among Beijing farmers to alleviate water scarcity in metropolitan suburbs ( [[#Zhang--2019|Zhang et al., 2019]] )—these actions still depend on some level of water availability. If climate hazards intensify to the point where water supply cannot meet agricultural demands, hard limits to adaptation will occur.

Residual risks associated with agriculture in Asia include declines in fisheries, aquaculture and crop production, particularly in South and Southeast Asia ( [[IPCC:Wg2:Chapter:Chapter-10#10.3|Section 10.3.5]] ), increased food insecurity ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.5|Section 10.4.5]] ), reductions of farmers’ incomes by up to 25% ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.5|Section 10.4.5]] ), loss of production areas ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.5|Section 10.4.5]] ) and reduced physical work capacity for farmers—between 5% and 15% decline in south-southwest Asia and China under RCP8.5 ( [[IPCC:Wg2:Chapter:Chapter-5#5.12.4|Section 5.12.4]] ).

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==== 16.4.2.3 Livelihoods in Africa ====

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For livelihoods dependent on small-scale rain-fed agriculture in Africa, climate hazards include floods and droughts. However, governance, financial and information/awareness/technology challenges are identified as the most significant constraints leading to soft limits, followed by social and human capacity constraints ( [[#Thomas--2021|Thomas et al., 2021]] ). Finance and land tenure constraints restrict Ghanaian farmers when considering adaptation responses due to climate variability ( [[#Guodaar--2017|Guodaar et al., 2017]] ). Similarly, in East Africa, farmers with small pieces of land have limited economic profitability, making it difficult to invest in drought and/or flood management measures ( [[#Gbegbelegbe--2018|Gbegbelegbe et al., 2018]] ).

Increasing droughts and floods require costlier adaptation responses to reduce risks, such as using drought-tolerant species ( [[#Berhanu--2015|Berhanu and Beyene, 2015]] ) and coping strategies for flood-prone households ( [[#Schaer--2015|Schaer, 2015]] ; [[#Musyoki--2016|Musyoki et al., 2016]] ), resulting in soft limits for poorer households who cannot afford these responses. In Namibia, weak governance and poor integration of information, such as disregarding knowledge of urban and rural residents in flood management strategies, has resulted in soft limits to adaptation, leading to temporary or permanent relocation of communities ( [[#Hooli--2016|Hooli, 2016]] ). Shortage of land—namely high population pressure and small per capita land holding—leads to continuous cultivation and results in poor soil fertility. This low productivity is further aggravated by erratic rainfall causing soft limits as farmers cannot produce enough and must depend on food aid ( [[#Asfaw--2019|Asfaw et al., 2019]] ).

Relocation due to flooding is discussed as a transformation adaptation action taken in Botswana where the government decided to permanently relocate hundreds of residents to a nearby dryland area ( [[#Shinn--2014|Shinn et al., 2014]] ). Some residents permanently relocated, whereas others only temporarily relocated against the government’s instructions. Such relocation processes must attend to micro-politics and risks of existing systemic issues of inequality and vulnerability.

In terms of hard limits, land scarcity poses a hard limit when implementing organic cotton production, an adaptation response supporting sustainable livelihoods ( [[#Kloos--2014|Kloos and Renaud, 2014]] ).

Residual risks associated with livelihoods in Africa include poorer households becoming trapped in cycles of poverty ( [[IPCC:Wg2:Chapter:Chapter-9#9.9.3|Section 9.9.3]] ), increased rates of rural–urban migration ( [[IPCC:Wg2:Chapter:Chapter-9#9.8.4|Section 9.8.4]] ), decline of traditional livelihoods such as in agriculture (Sections 9.9.3, 9.11.3.1) and fisheries ( [[IPCC:Wg2:Chapter:Chapter-9#9.11.1.2|Section 9.11.1.2]] ), and loss of traditional practices and cultural heritage ( [[IPCC:Wg2:Chapter:Chapter-9#9.9.2|Section 9.9.2]] ).

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=== 16.4.3 Regional and Sectoral Synthesis of Limits to Adaptation ===

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==== 16.4.3.1 Evidence on Limits to Adaptation ====

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There is ''high agreement'' and ''medium evidence'' that there are limits to adaptation across regions and sectors. However, much of the available evidence focuses on constraints that may lead to limits at some point with little detailed information on how limits may be related to different levels of socioeconomic or environmental change ( ''high confidence'' ). Figure 16.7 assesses evidence on constraints and limits for broad categories of region and sector. Small islands and Central and South America show most evidence of constraints being linked to adaptation limits across sectors, while ocean and coastal ecosystems and health, well-being and communities show most evidence of constraints being linked to limits across regions ( ''medium confidence'' ) ''.''

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'''Figure 16.7 |''' '''Evidence on constraints and limits to adaptation by region and sector.''' Data from [[#Thomas--2021|Thomas et al. (2021)]] , based on 1682 scientific publications reporting on adaptation-related responses in human systems. See SM16.1 for methods. '''Low evidence:''' &lt;20% of assessed literature has information on limits; literature mostly focuses on constraints to adaptation. '''Medium evidence:''' between 20% and 40% of assessed literature has information on limits; literature provides some evidence of constraints being linked to limits. '''High evidence:''' &gt;40% of assessed literature has information on limits; literature provides broad evidence of constraints being linked to limits.

There are clusters of evidence with additional details on limits to adaptation, as detailed in Table 16.3. Evidence on limits to adaptation is largely focused on terrestrial and aquatic species and ecosystems, coastal communities, water security, agricultural production, and human health and heat ( ''high confidence'' ).

Beginning at 1.5°C, autonomous and evolutionary adaptation responses by terrestrial and aquatic species and ecosystems face hard limits, resulting in biodiversity decline, species extinction and loss of related livelihoods ( ''high confidence'' ). Interventionist adaptation strategies to reduce risks for species and ecosystems face soft limits due to governance, financial and knowledge constraints ( ''medium confidence'' ) ''.''

As sea levels rise and extreme events intensify, coastal communities face soft limits due to financial, institutional and socioeconomic constraints reducing the efficacy of coastal protection and accommodation approaches and resulting in loss of life and economic damages ( ''medium confidence'' ) ''.'' Hard limits for coastal communities reliant on nature-based coastal protection will be experienced beginning at 1.5°C ( ''medium confidence'' ).

Beginning at 3°C, hard limits are projected for water management measures, leading to decreased water quality and availability, negative impacts on health and well-being, economic losses in water and energy dependent sectors and potential migration of communities ( ''medium confidence'' ).

Soft and hard limits for agricultural production are related to water availability and the uptake and effectiveness of climate-resilient crops, which is constrained by socioeconomic and political challenges ( ''medium confidence'' ) ''.''

Adaptation measures to address risks of heat stress, heat mortality and reduced capacities for outdoor work for humans face soft and hard limits across regions beginning at 1.5°C and are particularly relevant for regions with warm climates ( ''high confidence'' ).

'''Table 16.3 |''' Adaptation limits and residual risks for select actors and systems. Asterisks indicate confidence level

{| class="wikitable"
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! Actor/system at risk

! Adaptation limits

! Residual risks

|-
| Terrestrial species in islands at risk to loss of habitat

| Hard: autonomous adaptation unable to overcome loss of habitat and lack of physical space ( c ) (Box [https://www.ipcc.ch/chapter/16#CCP1.1 CCP1.1] )

| Biodiversity decline, local extinctions, half of all species currently considered to be at risk of extinction occur on islands (Box CCP 1.1)

|-
| Terrestrial species across Africa at risk to habitat changes

| Hard: beyond 2°C, many species will lack suitable climate conditions by 2100 despite migration and dispersal ( c ) ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.4.1|Section 9.6.4.1]] )

| 9% of species face complete range loss ( a ), mountaintop endemics and species at poleward boundaries of African continent at risk of range loss due to disappearing cold climates ( c ) ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.4.1|Section 9.6.4.1]] )

|-
| African aquatic organisms at risk to habitat changes

| Hard: thermal changes above optimal physiological limits will reduce available habitats ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.2.4|Section 9.6.2.4]] )

| Greater risks of loss of endemic fish species than generalist fish species ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.2.4|Section 9.6.2.4]] )

|-
| African coastal and marine ecosystems at risk to habitat changes

| Hard: at 2°C, bleaching of east African coral reefs ( c ) ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.2.3|Section 9.6.2.3]] )

| Over 90% of east African coral reefs destroyed at 2°C ( c ) ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.2.3|Section 9.6.2.3]] )

|-
| Coral reefs at risk to oceanic changes

| Hard: coral restoration and management no longer effective after 2°C ( c ), enhanced coal and reef shading no longer effective after 3°C ( b ) (Figure 3.23)

| Loss of more than 80% of healthy coral cover, loss of livelihoods dependent on coral reefs ( c ) (Figure 3.23, Table 8.7)

|-
| Cold-adapted species whose habitats are restricted to polar and high mountaintop areas at risk to loss of climate space

| Hard: evolutionary responses unable to keep pace with the rate of climate change and degraded state of ecosystems (Sections 2.6.1, [https://www.ipcc.ch/chapter/16#CCP1.2.4.2 CCP1.2.4.2] )

| Species extinctions in the case of species losing their climate space entirely on a regional or global scale (Sections 2.6.1, [https://www.ipcc.ch/chapter/16#CCP1.2.4.2 CCP1.2.4.2] )

|-
| Ecosystems in North America at risk to multiple climate hazards

| Soft: governance constraints hinder implementation of adaptation strategies

Hard: some species unable to adapt (Table 14.8)

|
|-
| Ecosystems and species at risk to multiple climate hazards

| Soft: financial and knowledge constraints lead to limits for interventionist approaches such as translocation of species or ecosystem restoration Hard: some habitats unable to be effectively restored ( [[IPCC:Wg2:Chapter:Chapter-2#2.6.6|Section 2.6.6]] )

| Species extinctions and changes, irreversible major biome shifts ( [[IPCC:Wg2:Chapter:Chapter-2#2.6.6|Section 2.6.6]] )

|-
| Coastal settlements in Australia and New Zealand at risk to sea level rise

| Soft and hard: limits in the efficacy of coastal protection and accommodation approaches as sea levels rise and extreme events intensify (Box 11.5)

| With 1–1.1 m of sea level rise, value of coastal urban infrastructure at risk in Australia is AUD 164 to &gt;226 billion, while in NZ it is NZD 43 billion. Sea level rise will also result in significant cultural and archaeological sites disturbed and increasing flood risk and water insecurity with health and well-being impacts on Australia’s small northern islands (Box 11.5)

|-
| Human settlements in

coastal areas in the 1-in-100-year floodplain at risk to coastal flooding

| Soft: socioeconomic, institutional and financial constraints may lead to soft limits well in advance of technical limits of hard engineering measures (Sections [https://www.ipcc.ch/chapter/16#CCP2.3.4 CCP2.3.2] , [https://www.ipcc.ch/chapter/16#CCP2.3.4 CCP2.3.4] ) Hard: Nature-based measures (e.g., restoration of coral reefs, mangroves, marshes) reach hard limits beginning at 1.5°C of global warming. Retreat strategies reach hard limits as availability and affordability of land decreases (CCPs 2.3.2.3, 2.3.5)

| At 3°C, globally up to 510 million people and up to USD 12,739 billion in assets at risk by 2100 (Section CCP 2.2.1)

|-
| Communities in small islands at risk to freshwater shortages

| Hard: domestic freshwater resources unable to recover from increased drought, sea level rise and decreased precipitation by 2030 (RCP8.5+ ice sheet collapse), 2040 (RCP8.5) or 2060 (RCP4.5) (Box 4.2, [[IPCC:Wg2:Chapter:Chapter-4#4.7.2|Section 4.7.2]] )

| Migration of communities due to water shortages with impacts on well-being, community cohesion, livelihoods and people–land relationships (Box 4.2)

|-
| Communities in North America at risk to poor water quality

| Soft: financial and technological constraints lead to limits in ability to treat water for harmful algal blooms (Table 14.8)

|
|-
| Communities in Western and Central Europe at risk to water shortages

| Hard: at 3°C, geophysical and technological limits reached in Southern Europe ( [[IPCC:Wg2:Chapter:Chapter-13#13.10.3|Section 13.10.3.3]] )

| At 3°C, two-thirds of the population of Southern Europe at risk to water security with significant economic losses in water- and energy-dependent sectors ( b ) (Sections 13.2.2, 13.6, 13.10.2.3)

|-
| Communities in Central and South America at risk to water shortages

| Soft: improved water management as an adaptation strategy unable to overcome lack of trust and stakeholder flexibility, unequal power relations and reduced social learning ( [[IPCC:Wg2:Chapter:Chapter-12#12.5.3.4|Section 12.5.3.4]] )

| Increasing competition and

conflict associated with high economic losses ( b ); glacier shrinkage leading to loss of related livelihoods and cultural values ( [[IPCC:Wg2:Chapter:Chapter-12#12.5.3.1|Section 12.5.3.1]] , Table 8.7)

|-
| Agricultural production in Europe at risk to heat and drought

| Soft: above 3°C, unavailability of water will limit irrigation as an adaptation response ( c ) (Sections 13.5.1, 13.10.2.2)

| At 3–4°C, yield losses for maize may reach up to 50% ( b ) (Sections 13.5.1, 13.10.2.2)

|-
| Crops at risk to temperature increase

| Soft: socioeconomic and political constraints limit uptake of climate-resilient crops ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.3|Section 5.4.4.3]] ) Hard: after 2°C, cultivar changes unable to offset global production losses ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.1|Section 5.4.4.1]] )

| Costs of adaptation and residual damages are USD 63 billion at 1.5°C. USD 80 billion at 2°C and USD 128 billion at 3°C, with greater risks and damages in tropical and arid regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.1|Section 5.4.4.1]] )

|-
| Human health in Europe at risk to heat

| Soft: many adaptation measures will not be able to fully mitigate overheating in buildings with high levels of global warming ( c ) ( [[IPCC:Wg2:Chapter:Chapter-13#13.6.2.3|Section 13.6.2.3]] ) Hard: above 3°C, people and health systems unable to adapt ( c ) (Sections 13.6.2.3, 13.7.2, 13.7.4, 13.10.2.1, 13.8)

| At 1.5°C, 30,000 annual deaths due to extreme heat with up to 90,000 annual deaths at 3°C in 2100 ( c ) ( [[IPCC:Wg2:Chapter:Chapter-13#13.7.1|Section 13.7.1]] ); at 3°C, thermal comfort hours during summer will decrease by as much as 74% in locations in southern Europe ( c ) ( [[IPCC:Wg2:Chapter:Chapter-13#13.6.1.5|Section 13.6.1.5]] )

|-
| Human health at risk to heat

| Soft: socioeconomic constraints limit adaptation responses to extreme heat ( [[IPCC:Wg2:Chapter:Chapter-7#7.4.2.6|Section 7.4.2.6]] , Table 8.7)

| Globally, the impact of projected climate change on temperature-related mortality is expected to be a net increase under RCP4.5 to RCP8.5, even with adaptation, particularly for regions with warm climates ( d ) ( [[IPCC:Wg2:Chapter:Chapter-7#7.3.1|Section 7.3.1]] , Table 8.7)

|-
| South Asian settlements at risk to coastal flooding, drought, sea level rise and heatwaves

| Soft and hard: at 4.5°C, maximum temperature is expected to exceed survivability threshGold across most of South Asia, particularly relevant for outdoor work ( a ) (Table 10.6)

| At RCP4.5, 25–50% of population affected; at RCP8.5, more than 50% of population affected; at 4.5°C of warming, increase in heat-related deaths of 12.7% in South Asia ( a ) (Table 10.6)

|-
| Tourism in Europe reliant on snow at risk to higher levels of warming

| Soft: at 3°C, snowmaking as an adaptation measure limited by biophysical and financial constraints ( c ) (Sections 13.6.1.4, 13.6.2.3)

| Damages in European tourism with larger losses in Southern Europe ( c ) ( [[IPCC:Wg2:Chapter:Chapter-13#13.6.1.4|Section 13.6.1.4]] )

|-
| Rapidly growing towns/cities and smaller cities at risk to range of climate hazards

| Soft: governance and financial constraints lead to limits in ability to adapt (Sections 6.3, 6.4)

|
|}

Notes:

(a) ''low confidence''

(b) ''medium confidence''

(c) ''high confidence''

(d) ''very high confidence.''

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==== 16.4.3.2 Constraints Leading to Limits to Adaptation ====

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Across regions and sectors, a range of constraints (Figure 16.8) are identified as leading to limits to adaptation, particularly financial constraints and constraints related to governance, institutions and policy ( ''high confidence'' ) ''.'' While individual constraints may appear straightforward to address, the combination of constraints interacting with each other leads to soft limits that are difficult to overcome ( ''high confidence'' ). The interplay of many different constraints that lead to limits makes it difficult to categorise limits beyond being either soft or hard.

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[[File:fe4f469f42e1850d2b04e644d89e7d1c IPCC_AR6_WGII_Figure_16_008.png]]

'''Figure 16.8 |''' '''Constraints associated with limits by region and sector.''' Data from [[#Thomas--2021|Thomas et al. (2021)]] , based on 1682 scientific publications reporting on adaptation-related responses in human systems. See SM16.1 for methods. Constraints are categorised as: (1) economic: existing livelihoods, economic structures, and economic mobility; (2) social/cultural: social norms, identity, place attachment, beliefs, worldviews, values, awareness, education, social justice, and social support; (3) human capacity: individual, organisational, and societal capabilities to set and achieve adaptation objectives over time including training, education, and skill development; (4) governance, institutions and policy: existing laws, regulations, procedural requirements, governance scope, effectiveness, institutional arrangements, adaptive capacity, and absorption capacity; (5) financial: lack of financial resources; (6) information/awareness/technology: lack of awareness or access to information or technology; (7) physical: presence of physical barriers; and (8) biologic/climatic: temperature, precipitation, salinity, acidity, and intensity and frequency of extreme events including storms, drought, and wind. '''Insufficient data:''' there is not enough literature to support an assessment (fewer than five studies available); '''Minor constraint:''' &lt;20% of assessed literature identifies this constraint; '''Secondary constraint:''' 20–50% of assessed literature identifies this constraint; '''Primary constraint:''' &gt;50% of assessed literature identifies this constraint.

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<span id="climate-change-impacts-financial-constraints-and-limits-to-adaptation"></span>
==== 16.4.3.3 Climate Change Impacts, Financial Constraints and Limits to Adaptation ====

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Across regions and sectors, financial constraints are identified as significant and contributing to limits to adaptation, particularly in low-to-middle-income countries ( ''high confidence'' ) (Sections 3.6.3, 4.7.2, 5.14.3, 6.4.5, 7.4.2, 8.4.5, 12.5.1, 12.5.2, 15.6.1, 15.6.3, Figure 16.8, Table 16.4, Section [https://www.ipcc.ch/chapter/16#CCP2.4.2 CCP2.4.2] ). Impacts of climate change may increase financial constraints ( ''high confidence'' ) and contribute to soft limits to adaptation being reached ( ''medium confidence'' ). Table 16.5 details climate impact observations that point to potentially substantial negative impacts on the availability of financial resources for different regions.

At the national level, negative macroeconomic responses to climate change may limit the availability of financial resources, impede access to financial markets and stunt economic growth ( ''high confidence'' ). Economic growth has been shown to decline under higher temperatures ( [[#Burke--2015|Burke et al., 2015]] ; [[#Kahn--2019|Kahn et al., 2019]] , [[#16.5.2.3|Section 16.5.2.3.4]] ) and following extreme events ( [[#Hsiang--2014|Hsiang and Jina, 2014]] ; [[#IMF--2017|IMF, 2017]] ), particularly for medium- and low-income developing countries ( [[IPCC:Wg2:Chapter:Chapter-18#18.1|Section 18.1]] ). The most severe impacts of climate-related disasters on economic growth per capita have been observed in developing countries, although authors note a publication bias in the reporting of negative effects ( [[#Klomp--2014|Klomp and Valckx, 2014]] ). Substantial immediate output losses and reduced economic growth due to extreme events have been observed in both the short and long term ( [[#16.2.3|Section 16.2.3]] ). Estimates of the duration of negative effects of climate-related disasters differ, with some analyses suggesting that, on average, economies recover after 2 years ( [[#Klomp--2016|Klomp, 2016]] ) and others finding negative effects of cyclones to persist 15–20 years following an event ( [[#Hsiang--2014|Hsiang and Jina, 2014]] ; [[#IMF--2017|IMF, 2017]] ). Rising climate vulnerability has also been shown to increase the cost of debt ( [[#Kling--2018|Kling et al., 2018]] ). Rising climatic risks negatively affect developing countries’ ability to access financial markets ( [[#Cevik--2020|Cevik and Jalles, 2020]] ), and their disclosure may result in capital flight (Cross-Chapter Box FINANCE in Chapter 17). Overall, the direct and indirect economic effects of climate change represent a major risk to financial system stability ( [[IPCC:Wg2:Chapter:Chapter-11#11.5.2|Section 11.5.2]] ). These risks and effects may further limit the availability of financial resources needed to overcome constraints, in particular for developing countries.

Sectoral studies indicate that climate impacts will result in higher levels of losses and damages and decreases in income, thereby increasing financial constraints ( ''medium confidence'' ). Yield losses for major agricultural crops are expected in nearly all world regions (Figure 5.7). Decreases in estimated marine fish catch potential and large economic impacts from ocean acidification are expected globally, leading to the risk of revenue loss ( [[IPCC:Wg2:Chapter:Chapter-5#5.8.3|Section 5.8.3]] ). Losses of primary productivity and farmed species of shellfish are expected in tropical and subtropical regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.9.3.2.2|Section 5.9.3.2.2]] ). Economic losses have been observed in the power generation sector and transport infrastructure ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.6.3.8|Section 10.4.6.3.8]] ), including economic losses from floods in urban areas ( [[IPCC:Wg2:Chapter:Chapter-4#4.2.4|Section 4.2.4.5]] ). However, some positive sectoral climate change impacts have been identified for the timber and forestry sector ( [[IPCC:Wg2:Chapter:Chapter-5#5.6.2|Section 5.6.2]] ), for primary productivity and farmed species of shellfish in high-latitude regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.9.3.2.2|Section 5.9.3.2.2]] ) and for agriculture in high-latitude regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.1.1|Section 5.4.1.1]] ).

At the household or community level, climate impacts may increase financial constraints ( ''high confidence'' ) ''.'' Impacts on agriculture and food prices could force between 3 and 16 million people into extreme poverty ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ). Within-country inequality is expected to increase following extreme weather events ( [[#16.2.3.6|Section 16.2.3.6]] and Chapter 8). Households affected by climate-related extreme events may be faced with continuous reconstruction efforts following extreme events ( [[#Adelekan--2015|Adelekan and Fregene, 2015]] ) or declines in critical livelihood resources in the agriculture, fisheries and tourism sectors ( [[#Forster--2014|Forster et al., 2014]] , [[IPCC:Wg2:Chapter:Chapter-3#3.5.1|Section 3.5.1]] ). Further erosion of livelihood security of vulnerable households creates the risk of poverty traps, particularly for rural and urban landless (Sections 8.2.1, 8.3.3.1), for example in Malawi and Ethiopia ( [[IPCC:Wg2:Chapter:Chapter-9#9.9.3|Section 9.9.3]] ). Levels of labour productivity and economic outputs are projected to decrease as temperatures rise particularly in urban areas ( [[IPCC:Wg2:Chapter:Chapter-6#6.2.3.1|Section 6.2.3.1]] ). At the same time, higher utilities demand under higher urban temperatures exerts additional economic stresses on urban residents and households. Substantial, negative impacts on the livelihoods of over 180 million people are expected from changes to African grassland productivity ( [[IPCC:Wg2:Chapter:Chapter-5#5.5.3.1|Section 5.5.3.1]] ). In Western Uzbekistan, farmers’ incomes are at risk of declining ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.5.3|Section 10.4.5.3]] ). For SIDS, loss of livelihoods is expected due to negative climatic impacts on coastal environments and resources ( [[IPCC:Wg2:Chapter:Chapter-3#3.5.1|Section 3.5.1]] ). Negative effects on households from extreme events can also persist in the long term and in multiple dimensions. Exposure to disasters during the first year of life significantly reduces the number of years of schooling and increases the chances of being unemployed as an adult and living in a multidimensionally poor household ( [[#González--2021|González et al., 2021]] ).

'''Table 16.5 |''' Evidence of climate change impacts affecting availability of financial resources.

{| class="wikitable"
|-
! Region

! Evidence of climate change impacts affecting availability of financial resources

|-
| Africa

| Negative consequences for economic growth and GDP growth rate from higher average temperatures and lower rainfall ( c ) (Sections 9.9.1.1, 9.9.2, 9.9.3)

Economic losses from damage to infrastructure in the energy, transport, water supply, communication services, housing, health and education sectors (observed) (Sections 9.7.2.2, 9.8.2)

|-
| Asia

| High coastal damages due to sea level rise (China, India, Korea, Japan, Russia) ( c ) ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.6.3.4|Section 10.4.6.3.4]] )

Decline in aquaculture production ( [[IPCC:Wg2:Chapter:Chapter-10#10.4.5.2.1|Section 10.4.5.2.1]] )

Loss of coastal ecosystem services (Bangladesh) ( [[IPCC:Wg2:Chapter:Chapter-5#5.9.3.2.4|Section 5.9.3.2.4]] )

|-
| Australasia

| Loss of wealth and negative impacts on GDP (Sections 11.5.1.2, 11.5.2.2)

High disaster costs (observed in Australia, New Zealand) ( [[IPCC:Wg2:Chapter:Chapter-11#11.5.2.1|Section 11.5.2.1]] )

|-
| Central and South America

| High costs of extreme events relative to GDP (observed in Guatemala, Belize) ( [[IPCC:Wg2:Chapter:Chapter-12#12.3.1.4|Section 12.3.1.4]] )

Decrease in growth of total GDP per capita and total income and labour income from one standard deviation in the intensity of a hurricane windstorm ( [[IPCC:Wg2:Chapter:Chapter-12#12.3.1.4|Section 12.3.1.4]] )

|-
| Europe

| Negative combined effect of multiple risks on economy for Europe in total ( b ) (Sections 13.9.1, 13.10.2)

Negative combined effect of multiple risks on economy for Southern Europe ( c ) (Sections 13.9.1, 13.10.2)

High economic costs in agriculture and construction following heatwaves and flooding (Sections 6.2.3.2, 7.4.2.2.1)

|-
| North America

| Small but persistent negative economy wide effect on GDP (observed in the USA and Mexico) ( b ) (Box 14.5)

Economic risks associated with high-temperature scenarios ( c ) (Box 14.5)

Small but persistent positive economy wide effect on GDP (observed in Canada) ( b ) (Box 14.5)

Significant economic costs for urban, natural and ecosystem infrastructure (USA) ( [[IPCC:Wg2:Chapter:Chapter-6#6.2.5|Section 6.2.5.9]] )

High economic damages for a subset of sectors from high warming (southern and southeastern USA) (Box 14.5)

Adverse effects on municipal budgets due to costly liabilities, and disruption of financial markets (Box 14.5)

|-
| Small islands

| High economic costs relative to GDP from extreme events, particularly tropical cyclones (observed) ( [[IPCC:Wg2:Chapter:Chapter-15#15.3.4.1|Section 15.3.4.1]] )

Negative long-term implications of extreme events for state budgets ( [[IPCC:Wg2:Chapter:Chapter-8#8.2.1.4|Section 8.2.1.4]] )

Inundation of almost all port and harbour facilities (Caribbean) ( [[IPCC:Wg2:Chapter:Chapter-15#15.3.4.1|Section 15.3.4.1]] )

|}

Notes:

(a) ''low confidence''

(b) ''medium confidence''

(c) ''high confidence''

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