Editing IPCC:AR6/WGII/Chapter-8 (section)

==== 8.4.5.6 Future Challenges for Vulnerability and Livelihood Security due to Adaptation Limits of People and Ecosystems ====

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Communities and livelihoods with higher exposure to the risks posed by climate change and with lower adaptive capacity will experience a higher burden of L&amp;D in comparison to others ( [[#Tschakert--2017|Tschakert et al., 2017]] ). In Asia (Indonesia) and the Arctic region, a decline in marine fisheries by approximately 3 million tonnes per degree of warming is expected to have severe negative regional impacts, especially on Indigenous People ( [[#Cheung--2016|Cheung et al., 2016]] ).

It is projected that climate change impacts on the incidence of disasters will push 122 million additional people into extreme poverty with global temperature increase by 2030 ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Jafino--2020|Jafino et al., 2020]] ). It is also expected that around 330–396 million people will experience lower agricultural yields at warming beyond 1.5°C ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), most of them in South Asia and sub-Saharan Africa (Chapter 16; [[#Roy--2018|Roy et al., 2018]] ; [[#World%20Bank--2019a|World Bank, 2019a]] ). There is also ''medium evidence'' that tens to hundreds of millions of people that are dependent upon climate-sensitive livelihoods could out-migrate as a consequence of global temperature increasing, mostly in Africa, Asia and Latin America—posing additional risks to unsustainable urbanisation and group conflict (Chapter 16; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ).

The multi-intersectionality of inequalities (socioeconomic, caste, ethnicity, among others) and marginalisation, result in differential capacity to avoid risks, which is particularly limited amongst the most vulnerable communities who are in, or at the brick of falling into, poverty traps, which then also affects future generations ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ). For instance, the poorest communities in the Global South, who are dependent upon thriving ecosystems for health, food, water and energy, are disproportionately more exposed to temperature extremes and droughts, compromising food and water security ( [[#Byers--2018|Byers et al., 2018]] ). There are also inequalities associated with opportunities to adapt to risks that are unevenly distributed among global regions, with richer and more equal societies in the Global North presenting superior capacities than Global South communities, sectors, ecological systems and species, where the most detrimental climate change impacts are experienced ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ). The climate-sensitive livelihoods of poor and vulnerable communities in the Global South, and the unprecedented ecosystems losses are examples of multiple limits of adaptation that emerge simultaneously and are also linked to the differential access to assets and resources, such as physical (propriety, income), social (health, age, education) cultural (shared community values and norms, ethnicity), ecological (linked to land use change and productivity) and institutional (market, policies and governance) ( [[#Roy--2018|Roy et al., 2018]] ; [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ; [[#Olsson--2019|Olsson et al., 2019]] ). The adaptation limits emerge mostly in countries in Global South, and disproportionately affect specific groups, with high poverty incidence, that are constrained by inadequate financial resources and institutional instruments ( [[#Tian--2018|Tian and Lemos, 2018]] ; [[#Volpato--2019|Volpato and King, 2019]] ), including lack of understanding and preparedness of the risks posed by climate change ( [[#Ayeb-Karlsson--2016|Ayeb-Karlsson et al., 2016]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ).

In other situations, adaptation limits to household livelihoods emerge from ecological thresholds associated with global warming temperatures, such as deterioration of land and water resources, extinction of species and biodiversity that can lead to systemic crop failures, declining fisheries productivity and water availability and substantial risks to households’ livelihoods ( [[#Roy--2018|Roy et al., 2018]] ). However, it is also important to note that limits are associated with development, technology and cultural norms and values that can change over time to enhance or reduce the capacity of systems to avoid limits ( [[#Adger--2014|Adger et al., 2014]] ; [[#Roy--2018|Roy et al., 2018]] ). It could also include aspects of maintaining security of air or water quality, as well as equity, cultural cohesion and preservation of livelihoods ( [[#Adger--2014|Adger et al., 2014]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ). For soft limits, however, adaptation options could become available in the future through changing attitudes or values or as a result of innovation or other resources becoming available to most vulnerable and poor actors, households and countries. However, when compounded with lack of finance, and high costs associated with disasters, poverty and environmental degradation, soft limits could become hard ones in the future (see Figure 8.5; [[#Gracia--2018|Gracia et al., 2018]] ).

Table 8.6, built from SR1.5°C ( [[#Roy--2018|Roy et al., 2018]] ), illustrates how ecological thresholds and socioeconomic determinants are linked to soft and hard adaptation limits and what the potential and magnitude of livelihoods risks will be in the future. For instance, in the SR1.5°C ( [[#IPCC--2018b|IPCC, 2018b]] ) and Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) ( [[#IPCC--2019b|IPCC, 2019b]] ), hard limits are expected with global warming beyond 1.5°C associated with the loss of coral reefs, that will lead to substantial loss of income and livelihoods for coastal communities ( [[#Roy--2018|Roy et al., 2018]] ; [[#Mechler--2019b|Mechler et al., 2019b]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). The loss of coral reefs around the remote islands of Boigu in Australia is affecting low-lying communities facing financial, institutional ( [[#Evans--2016|Evans et al., 2016]] ) and cultural place-based attachment adaptation limits ( [[#McNamara--2017|McNamara et al., 2017]] ). Another hard limit to adaptation with implications for income, and culture- and place-based livelihoods is related to the sensitivity of fish to global temperature increase, with losses in fish reproduction expected to be 10% (SSP1–1.9) to about 60% (SSP5–8.5), potentially cascading into severe risks for fisheries livelihoods ( [[#Dahlke--2020|Dahlke et al., 2020]] ). In West African fisheries, the loss of coastal ecosystems and productivity are estimated to require 5–10% of countries’ GDP in adaptation costs ( [[#Zougmoré--2016|Zougmoré et al., 2016]] ), incurring financial limits in poor countries to avoid socioeconomic risks. The SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ) showed that scientific knowledge limitations can constrain management of coastlines, mainly in the context of lack of data, affecting most of the vulnerable and poor communities in the Global South ( [[#Perkins--2015|Perkins et al., 2015]] ; [[#Sutton-Grier--2015|Sutton-Grier et al., 2015]] ; [[#Wigand--2017|Wigand et al., 2017]] ; [[#Romañach--2018|Romañach et al., 2018]] ). Hard and soft adaptation limits are challenging to define, given the rate and intensity of climate change hazards and the mitigation and adaptation options available, but also the level and rate of non-climatic stresses increasing vulnerabilities and undermining adaptive capacity of poorest members of society and sensitive ecosystems ( ''medium evidence, high agreement'' ) ( [[#Klein--2014|Klein et al., 2014]] ; [[#Roy--2018|Roy et al., 2018]] ).

'''Table 8.6 |''' Synthesis of hard and soft limits to adaptation and risks to livelihoods, equity and sustainability adapted from [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] of SR1.5°C ( [[#Roy--2018|Roy et al., 2018]] ).

{| class="wikitable"
|-
! Determinant

! Nature of barrier to livelihood adaptation

! Magnitude + Indicator

! Soft limit

! Hard limit

! Confidence level

based on number of papers

|-
| colspan="6"| ''Socioeconomic and human-geographical determinants''

|-
| Gender-based inequality or discrimination

| Gender-based inequalities constrain women’s access to resources, thus limiting ability to invest in adaptive capacity and heightening vulnerability.

| World Bank: 62.151% [Employment in agriculture, female (% of female employment) (modelled International Labour Organization (ILO) estimate) – Low income, 2020]; 25.409% [Employment in agriculture, female (% of female employment) (modelled ILO estimate)].

| X

|
| \*** ''high'' (≥ 10 papers)

|-
| Poverty and socioeconomic inequality

| Poverty and lack of financial resources constrain ability to invest in livelihood diversification, resilience and adaptive capacity.

| World Bank: 10% [Poverty headcount ratio at USD 1.90 d −1 (2011 PPP) (% of population)]; 26.498% [Employment in agriculture (% of total employment) (modelled ILO estimate)]; 58.783% [Employment in agriculture (% of total employment) (modelled ILO estimate) – Low income], Low-income countries, 2020.

| X

|
| \*** ''high'' (≥ 10 papers)

|-
| Indigeneity and other cultural place-based attachments

| Indigenous and other populations with strong cultural or economic attachments to place face barriers to adaptation due to non-economic losses associated with migration, urbanisation and some forms of livelihood transformation.

| SIDS total population of around 65 million ( [[#UN-OHRLLS--2015|UN-OHRLLS, 2015]] ); 476 million indigenous people worldwide ( [[#World%20Bank--2016|World Bank, 2016]] ).

|
| X

| \*** ''high'' (≥ 10 papers)

|-
| Arctic hunting and fishing communities

| Residents of arctic regions dependent on hunting and fishing livelihoods interrelated cultural and economic vulnerability due to risk crossing arctic ecosystem thresholds and tipping points.

| Global arctic population, around 4 million (Larsen, 2015).

| X

| X

| \*** ''high'' (≥ 10 papers)

|-
| Urban slum and informal settlement populations

| Residents of slums and informal urban settlements are particularly vulnerable due to limited infrastructure and limited employment opportunities.

| 33.331% [Population living in slums (% of urban population)], World, 2009; It is estimated that 50–57 million urban Africans (47% (44–50%) of the urban population analysed) were living in unimproved housing in 2015, mostly in sub-Saharan Africa ( [[#Tusting--2019|Tusting et al., 2019]] ).

| X

|
| \*** ''high'' (≥ 10 papers)

|-
| colspan="6"| ''Ecological determinants''

|-
| Glacier retreat

| Seasonal water scarcity and/or glacial lake outburst floods pose a serious threat for highly exposed and vulnerable smallholders in the Peruvian Andes ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ). Tibetan Plateau region will reach peak water between 2030 and 2050 ( [[#Yao--2020|Yao et al., 2020]] ).

| The flow decrease of the Tibetan Plateau region will affect water availability for several countries, affecting a population of 1.7 billion people and a GDP of USD 12.7 trillion (Yao et al. 2019). In 2050, the number of people that will be living in water-scarce regions will increase to 2.7–3.2 billion ( [[#Luterbacher--2020|Luterbacher et al., 2020]] ). As of 2010, 27% of global population (~1.9 billion people) lived in severely water-scarce areas ( [[#Luterbacher--2020|Luterbacher et al., 2020]] ).

| X

| X

| \*** ''high'' (≥ 10 papers)

|-
| Loss of coral reefs

| Loss of 70–90% of tropical coral reefs by mid-century under 1.5°C scenario (total loss under 2°C scenario) (see SR1.5°C, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] , Sections 3.4.4; 3.5.2.1; Box 3.4; ( [[#Magnan--2019|Magnan et al., 2019]] ); [[#Roy--2018|Roy et al., 2018]] , [[IPCC:Wg2:Chapter:Chapter-5#5.2|Section 5.2]] ).

| Coral reef fisheries-dependent and coastal livelihoods, sustain 6 million direct fishing jobs and more than USD 6 billion in revenues globally ( [[#Teh--2013|Teh et al., 2013]] ), often among disadvantaged populations ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). In tropical regions, there are 1.3 billion people living by coast and depending upon fisheries for food and livelihoods ( [[#Sale--2014|Sale et al., 2014]] ). In Africa and Asia over 400 million people are dependent upon protein intake from fisheries ( [[#Hoegh-Guldberg--2019b|Hoegh-Guldberg et al., 2019b]] ). Approximately 850 million people live within 100 km of reefs and more than 275 million reside within 30 km, many of whom are likely to be highly dependent on coral reefs, especially those who look to these marine ecosystems for food and livelihoods ( [[#Burke--2011|Burke et al., 2011]] ).

|
| X

| \*** ''high'' (≥ 10 papers)

|-
| Biodiversity loss

| Terrestrial species on average lose 20–27% of their range at 1.5°C (significantly higher range losses projected for some species at 2°C) (see SR1.5°C, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] , [[IPCC:Wg2:Chapter:Chapter-3#3.4.3.2|Section 3.4.3.2]] ; [[#de%20Coninck--2018|de Coninck et al., 2018]] , [[IPCC:Wg2:Chapter:Chapter-4#4.3.2|Section 4.3.2]] ).

Tropical forests (vegetation shifts due mainly to drying), high-latitude and altitude ecosystems and Mediterranean-climate ecosystems (high vulnerability).

| Forest-dependent livelihoods of 1.6 billion rural people (in 2012) are likely to be affected to risks of terrestrial forest and biodiversity loss ( [[#Newton--2020|Newton et al., 2020]] ).

|
| X

| \** ''medium'' (5–9 papers)

|-
| Ocean acidification and warming

| Large-scale changes in oceanic systems (temperature, acidification) inflict damage and losses on livelihoods, income, cultural identity and health for island and coastal-dependent communities at 1.5°C (potential for higher losses increases from 1.5°C to 2°C and above) (see SR1.5°C, ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ); ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ); ( [[#Roy--2018|Roy et al., 2018]] ).

| 500 million people who derive food, income, coastal protection and a range of other services from coral reefs ( [[#Hoegh-Guldberg--2017|Hoegh-Guldberg et al., 2017]] ).

| X

| X

| \** ''medium'' (5–9 papers)

|-
| Sea level rise (SLR)

| SLR and increased wave run up, combined with increased aridity and decreased freshwater availability, at 1.5°C warming potentially leaving several atoll islands uninhabitable (see IPCC SR1.5°C [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] , Box 3.5; [[#de%20Coninck--2018|de Coninck et al., 2018]] , Cross-Chapter Box 4.1). SLR is projected to affect human health and well-being, cultural and natural heritage, freshwater, biodiversity, agriculture and fisheries ( [[#IPCC--2018b|IPCC, 2018b]] ; [[#WHO--2018|WHO, 2018]] ; [[#IDMC--2019|IDMC, 2019]] ; [[#McMichael--2020|McMichael et al., 2020]] ).

| It is projected that ~316–411 million people in 2060 will be living in areas affected by SLR, with most in South and Southeast Asia and in Africa ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). The number of people at risk of floods will increase from its current level of 1.2 billion to 1.6 billion by 2050 ( [[#Luterbacher--2020|Luterbacher et al., 2020]] ).

It is estimated that 6–8% of Latin America and the Caribbean’s population, face high risk associated with coastal hazards ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ).

|
| X

| \*** ''high'' (≥ 10 papers)

|-
| Heat stress

| It is expected that by 2070 over 30% of global poor population will be living outside the human thermal comfort, beyond adaptive capacity. This will also affect crop and livestock productivity ( [[#Xu--2020|Xu et al., 2020]] ).

| Currently 30% of the global population is exposed to deadly heat waves and this percentage by 2100 is projected to increase to ~ 48% under a drastic mitigation scenario to ~ 74% under a scenario of growing emissions. ( [[#Mora--2017|Mora et al., 2017]] ).

Heat stress contributes to deaths and health problems among the elderly and children. Specifically, heat stress is currently responsible for 38,000 annual deaths mostly among the elderly, and 48,000 from diarrhoea, 60,000 from malaria and 95,000 from childhood undernutrition ( [[#WHO--2014a|WHO, 2014a]] ; [[#Roy--2018|Roy et al., 2018]] ).

|
| X

| \** ''medium'' (5–9 papers)

|}

The recent evidence shows that adaptation limits can also be associated with financial and institutional mechanisms, and related to structural poverty and inequalities among rural farmers in India ( [[#Singh--2019a|Singh et al., 2019a]] ) and among low-income countries ( [[#Tenzing--2020|Tenzing, 2020]] ), agro-pastoralist communities ( [[#Volpato--2019|Volpato and King, 2019]] ), women ( [[#Balehey--2018|Balehey et al., 2018]] ), informal slum settlements in Latin America ( [[#Núñez%20Collado--2020|Núñez Collado and Wang, 2020]] ) and informal workers in Southeast Asia ( [[#Balehey--2018|Balehey et al., 2018]] ). For SIDS, multiple adaptation limits also emerge as a combination of political–institutional and cultural aspects ( [[#Robinson--2020|Robinson and Wren, 2020]] ), such as preserving national identity and sovereignty in the context of migration in the Marshall Islands ( [[#Bordnera--2020|Bordnera et al., 2020]] ). A widespread narrative is that an increase in migration in SIDS, given sea level rise and global temperature increase by 2050, is inevitable, desirable and economically necessary. Many more people will be exposed to migration and affected by multiple forms of physiological and emotional stress ( [[#Bordnera--2020|Bordnera et al., 2020]] ). In the same way, the Mohawk community of Kanesatake, Canada, is faced with institutional and socio-political adaptation limits such as lack of land ownership rights, insurance and social institutions ( [[#Fayazi--2020|Fayazi et al., 2020]] ).

New emerging considerations to ecological limits to adaptation associated with severe glacier retreat in the Peruvian Andes, is expected to reduce lake discharge by 2–11% (7–14%) by 2050 (2100). This will affect smallholders farmers, through crop yield failures and severely reduced hydropower capacity ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ). In addition, the study showed a very high risk of glacier lakes being affected by GLOFs under RCP8.5, posing serious threat to rural people’s livelihoods ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ).

Table 8.6 represents different types of adaptation limits (soft or hard) that emerge over time, sometimes concomitantly, that are leading to severe risks to livelihoods in a high poverty, unequal and hotter future, especially among poor and vulnerable populations, and within those Indigenous People, women and children (see [[IPCC:Wg2:Chapter:Chapter-16#16.5.2.3|Section 16.5.2.3.4]] ). The confidence statements are assessed through the evidence on papers as high (≥10 papers), medium (5–9 papers) and low (≤ 4 papers) to ensure traceability on the nature of livelihoods barriers and ecological thresholds associated with ‘soft’ or ‘hard’ limits to adaptation under a warming global world. The determinants of livelihood barriers are linked to ''gender-based inequality or discrimination, poverty and inequality, indigeneity and cultural place attachment, artic hunting and fishing'' , and ''urban slum and informal settlements'' incurring soft and hard limits to adaptation. The ecological thresholds assessed are associated with ''glacier retreat, loss of coral reefs, biodiversity loss, ocean acidification and warming, sea level rise'' and ''heat stress'' incurring hard limits to adaptation and severe risks to people’s livelihoods. The severity of risks to livelihoods is assessed using a magnitude indicator of the current number of people exposed and vulnerable to climate-sensitive livelihoods. The supporting literature is listed in Table SM8.1.

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