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

== 8.4 Future Vulnerabilities, Risks and Livelihood Challenges and Consequences for Equity and Sustainability ==

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Future climate vulnerability and risks to livelihood security are significantly influenced by present and past development trends, equity and sustainability. Consequently, observed impacts covered in previous sections provide essential insight for enhancing future adaptation and risk reduction. Since the AR5, new research approaches incorporate past lessons to project and assess climate change vulnerability and socioeconomic conditions into the future. Scenario tools and methods are a powerful approach for integrated assessments of emissions pathways, associated warming and development contexts, helpful in guiding analysis of adaptation policy and planning ( [[#Berkhout--2014|Berkhout et al., 2014]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Both quantitative and qualitative scenario approaches that assess future vulnerability and risks, as well as livelihood challenges at global, national and local scales, allow experts, planners, decision makers and affected people to articulate and visualise development futures. These approaches can complement emissions pathway scenarios.

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=== 8.4.1 Future Exposure, Climate Change Vulnerability and Poverty at the Global Scale ===

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The SSPs scenarios orient climate models around possible development pathways that produce future exposure patterns, risk probabilities and vulnerability for future populations ( [[#O’Neill--2014|O’Neill et al., 2014]] ; [[#O’Neill--2017a|O’Neill et al., 2017a]] ). While the likelihood of any given scenario actually occurring is highly uncertain, they have the advantage of pairing with computational models to generate robust projections about risk profiles in possible futures, and therefore assess the relative influence of different drivers of change. In this way, scenario tools generate pictures of future vulnerability and adaptation pathways, and often have both an analytic and normative function. The decision-making context will determine which specific scenario approach is most appropriate ( [[#Rozenberg--2014|Rozenberg et al., 2014]] ). Scenarios are limited by stakeholders’ imaginations and, as such, new emergent challenges, such as the COVID-19 pandemic, are difficult to anticipate in scenario planning. Nevertheless, recent studies and forecasts of the impact of COVID-19 on poverty conclude that in the near- and medium-term future major portions of the newly poor will emerge in sub-Saharan Africa and South Asia ( [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Sumner--2020|Sumner et al., 2020]] ). Since these countries are already characterised by high levels of absolute poverty and vulnerability to climate change, it is ''likely'' that these regions will face more severe challenges in overcoming vulnerability and will be confronted with a growing adaptation gap. Thus, the implication for scenario planning is that single crises or events, such as the COVID-19 pandemic, might not significantly alter existing vulnerabilities, but rather reinforce them.

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==== 8.4.1.1 Exposure and Vulnerability under Different Scenarios and Alternative Development Pathways ====

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At the international and national level, the SSPs ( [[#O’Neill--2017a|O’Neill et al., 2017a]] ) have been developed to outline various development pathways, associated emissions and levels of warming, but also different possible development profiles (i.e., levels of economic growth, poverty, inequality, demographic change, etc.) that are highly relevant for adaptation.

Studies using the SSPs to understand multidimensional poverty are few but growing, and underscore that the impacts of climate change on poverty are extremely sensitive to different levels of warming ( [[#Byers--2018|Byers et al., 2018]] ). Multi-sector risks approximately double between 1.5°C and 2°C global mean temperature (GMT) change, and double again in a +3°C world. Comparing a +1.5°C world pursuing sustainable development (SSP1) to a high-poverty and high-inequality +3°C world (SSP3), [[#Byers--2018|Byers et al. (2018)]] project substantial increases in populations exposed to drought, water stress, heat stress and habitat degradation (see in detail [[#Byers--2018|Byers et al., 2018]] ). While in a +1.5°C world exposed populations increase by 7–17%, the increase within a +3°C plus world is 27–51% ( [[#Byers--2018|Byers et al., 2018]] ; [[#Frame--2018|Frame et al., 2018]] ). Populations in Asia and Africa account for more than 80% of the global population exposed to these phenomena, and within South Asia and the Sahel, up to 90% of populations are exposed. Scenario tools help us to understand the burden of increasing multidimensional poverty, and potential for poverty traps, if mitigation and adaptation measures are not taken rapidly and effectively implemented.

At the national and sub-national levels, studies on development and risk scenarios capture specific challenges, for example, urban growth, demographic change, human health and ageing (e.g., [[#Dong--2015|Dong et al., 2015]] ; [[#Chapman--2019|Chapman et al., 2019]] ). In this regard, local scenarios of human vulnerability can inform future strategies for adapting to hazards such as heatwaves in cities under different socioeconomic development strategies. These scenario approaches allow us to focus on changes in climatic and societal conditions as well as urban transformations. This provides a more comprehensive basis for defining adaptation goals (see [[#Fekete--2019|Fekete, 2019]] ; [[#Birkmann--2021b|Birkmann et al., 2021b]] ). Costs and benefits of different adaptation measures can be assessed against different future scenarios of climatic and societal change.

Contrasting with ‘top-down’ SSP scenarios, ( [[#Berkhout--2014|Berkhout et al., 2014]] ) outline how mesoscale and ‘bottom-up’ scenarios have been developed to inform spatial planning, for example, in the Netherlands. Increasing computational power has opened possibilities for large-scale ‘bottom-up’ simulations of people’s livelihoods in the context of evolving climate change impacts, such as the migration decisions of farmers facing drought in Mexico over the coming century (Bell et al., 2019) and livelihood decisions of people facing coastal flooding in Bangladesh to the year 2100 ( [[#Bell--2021|Bell et al., 2021]] ). Such ‘bottom-up’ scenarios can generate projections about future outcomes, inform mapping and assess future vulnerability, with special emphasis on livelihoods of the poor. Researchers conclude that results of respective scenarios that aim to inform adaptation and risk reduction policies in the context of climate change have to match the frames of the stakeholder ( [[#Berkhout--2014|Berkhout et al., 2014]] ; [[#Conway--2019|Conway et al., 2019]] ). Scenarios that assess potential future vulnerabilities and future capacities for adaptation require more attention, since many approaches for projecting future climate risk still largely overlook non-climatic drivers that determine future vulnerability and exposure ( [[#Windfeld--2019|Windfeld et al., 2019]] ).

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=== 8.4.2 The Influence of Future Climate Change Impacts on Future Response Capacities ===

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The influence of climate change also impacts the future response capacities of people and nations to deal with future climate change and climate hazards. Recent studies (e.g., [[#Mysiak--2016|Mysiak et al., 2016]] ) conclude that climate change can increase the severity and intensity of crises or even trigger disasters, particularly floods, storms, forest and wildfires, and droughts. These have undermined decade-long poverty reduction efforts, particularly in low-income and at-risk countries ( [[#Djalante--2019|Djalante, 2019]] ). Climate-influenced (disaster) risks are getting more complex and systemic ( [[#UNDRR--2019|UNDRR, 2019]] ). The magnitude of global annual average economic losses from natural and climate-induced hazards to the built environment alone are estimated in the United Nations Office for Disaster Risk Reduction (UNDRR) Global Assessment Report (2015) as being comparable with the GDP of the 36th largest economy in the world at that time—the Philippines (in 2015) ( [[#UNISDR--2015|UNISDR, 2015]] ; [[#Mysiak--2016|Mysiak et al., 2016]] ). In addition, a World Bank study concludes that losses of human well-being are higher than the overserved economic losses from natural hazards ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ). In this regard, it is ''likely'' that future impacts of climate change, particularly under increasing levels of global warming (above 1.5°C) will also increase non-economic losses (see [[#8.3.2.3|Section 8.3.2.3]] ) and losses of human well-being that are particularly relevant to most vulnerable groups and the poor.

Furthermore, the expected future increase in the number of people exposed to climate hazards, such as sea level rise and coastal flooding, is not only determined by changing hazard patterns, but also by regional processes of migration and urbanisation for example in Asia and Africa, including an increasing number of urban poor living in low-elevation coastal zones ( [[#United%20Nations--2018|United Nations, 2018]] ). This can increase again the probability that more people require assistance and support for buffering these effects of climate-related hazards, for example, in coastal zones. Historical urbanisation processes, in coastal cities in Asia (e.g., in China, Vietnam, etc.) and Africa (e.g., in Nigeria) have increased the exposure of people to climate hazards, such as sea level rise, which by 2100 under Relative Concentration Pathway (RCP) 8.5 will globally threaten 630 million people, largely in coastal cities ( [[#Kulp--2019|Kulp and Strauss, 2019]] ).

In addition, [[#Smirnov--2016|Smirnov et al. (2016)]] conclude that worldwide the number of people exposed to extreme droughts will increase under both the RCP4.5 and the RCP8.5 particularly at the end of the century. The authors assess that under RCP4.5 the average monthly global population exposed to drought will increase between the periods 2008–2017 and 2081–2100 from a mean of 80 million to 212 million, and under RCP8.5 from about 90 million to approximately 472 million people. The research findings underscore that there is a high probability that exposure to extreme droughts will increase, particularly in regions and countries already classified as highly vulnerable (e.g., Nigeria, Sudan, etc.) ( [[#Smirnov--2016|Smirnov et al., 2016]] ). Extreme droughts are expected to further erode coping and adaptive capacities of those already characterised by high levels of vulnerability (see [[#8.3.1|Section 8.3.1]] ). Building adaptive capacities for the most vulnerable groups in the future in these areas will be a challenge, since high levels of livelihood insecurity are coupled with high levels of structural vulnerability at national and regional scale (poverty, state fragility, etc.) making planned adaptation support very complex and difficult. Therefore, increasing adaptation gaps at different scales are anticipated in the future.

Increasing population exposure (e.g., due to urbanisation of coastal zones, etc.), coupled with higher frequencies and intensities of specific climate hazards are ''likely'' in connection with the existing adaptation gap (e.g., high levels of vulnerability) to compromise development and human security. Recent studies, such as that by Harrington (2018), conclude that the actual exposure and the physical individual recognition of some climate hazards, will be higher in low-income countries. The study of Harrington (2018) underscores that changes in extreme heat, for example, will be felt by the average citizen of a low-income country after 1.5°C of global warming and will not be felt by about 40% of people living in high-income nations until well after double the amount of global warming is reached (3°C increase). In this context, even if a city or place is exposed to heat stress, people experience it quite differently due to different levels of vulnerability and adaptive capacities, such as the ability to afford air conditioning ( [[#Barreca--2016|Barreca et al., 2016]] ). That means well-off populations are better insulated from effects of global warming than poorer or more vulnerable groups, even if they are geographically living in the same exposure zone. These findings underscore that issues of climate justice need to be considered within the problem definition when designing adaptation strategies, and not solely at the end. Impacts of future climate hazards (heat stress, flooding, etc.) differ not only due to changes in frequency and intensity of the hazard itself, but also significantly in terms of the opportunities people have to respond and prepare for these hazards and climatic changes at present and in the future. However, extreme heat stress has also caused significant fatalities in countries classified as having low vulnerability, such as seen within the heat wave in Europe in 2003.

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=== 8.4.3 The Influence of Climate Change Responses on Projected Development Pathways ===

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Responses to climate change can have dual effects on development pathways. On the one hand, mitigation and adaptation processes can create significant development opportunities. The potential of mitigation policies for job creation, in particular, has been highlighted ( [[#Healy--2017|Healy and Barry, 2017]] ). However, responses to climate change can also have detrimental effects on future development: mitigation policies, such as the building of hydro-electrical dams or the culture of biofuels can lead to communities’ dislocation and populations’ resettlement, particularly of disadvantaged groups within a society ( [[#de%20Sherbinin--2011|de Sherbinin et al., 2011]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). Adaptation policies can also hinder some development processes: for example, the promotion of migration as an adaptation strategy can lead to communities being deprived of their workforce and resenting the departure of some of their members ( [[#Gemenne--2017|Gemenne and Blocher, 2017]] ), even though this may offer new livelihood opportunities. However, the migration consequences in the context of climate change are often more nuanced and different trade-offs and benefits occur (see [[#Porst--2020|Porst and Sakdapolrak, 2020]] ). For example, remittances support family members but, at the same time, can also create imbalances in local markets ( [[#Melde--2017|Melde et al., 2017]] ). Evidence exists that some climate responses, such as small-scale agricultural livelihood adaptation strategies, have improved the ability of people to sustain their livelihood and to reduce poverty ( [[#Osbahr--2010|Osbahr et al., 2010]] ).

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=== 8.4.4 Social Tipping Points in the Context of Future Climate Change ===

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Climate change has the potential to trigger major, sudden social transformations, yet there are no clear linear relationships between the magnitude of climate change impacts and the social changes they induce ( [[#Steffen--2018|Steffen et al., 2018]] ). Evidence shows that major destabilising social transformations (e.g., forced migration) can occur in response to limited climate change impacts, even while major climate change impacts can be mitigated through the resilience of social, political and economic systems, and thus yield only minor social impacts.

In the context of climate change, ‘tipping points’ have been identified as critical thresholds at which a tiny perturbation can qualitatively alter the state or development of a system ( [[#Lenton--2008|Lenton et al., 2008]] ; [[#Lenton--2019|Lenton et al., 2019]] ). The concept of tipping points is usually associated with large-scale components of the climate system that could be pushed past an irretrievable threshold as a result of human-induced climate change ( [[#Lenton--2008|Lenton et al., 2008]] ), such as the deterioration of Antarctic ice sheets ( [[#Pattyn--2020|Pattyn and Morlighem, 2020]] ). Social tipping points refer to similar mechanisms of destabilisation resulting from impacts of climate change on human societies at multiple scales and the societal context conditions in which these impacts occur. They are reached when climate change impacts force destabilising social transformations from one state to another ( [[#Lenton--2019|Lenton et al., 2019]] ): from sporadic losses due to climate change to chronic losses and impoverishment, from peace to violence, from a democracy to an authoritarian regime, from adequate food provisioning to famine, or into forced migration. For example, small variations in the rainfall or temperature can jeopardise livelihoods that are dependent upon subsistence agriculture, which can lead to migration or tensions around resources (see Figure 8.11). Social tipping points can also occur when intangible elements that ensure the survival of individuals and communities are eroded or removed. This is the case, for example, when the social fabric of a community falls apart. The Millennium drought in Australia led to higher rates of male suicide, especially among farmers, and droughts in Ghana led to similar outcome when people were forced to drink from the same water source as their animals, which they perceived as robbing them off their human dignity ( [[#Bryant--2015|Bryant and Garnham, 2015]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ).

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[[File:fb4cc30dfa774e248f99125d4320cf0a IPCC_AR6_WGII_Figure_8_011.png]]

'''Figure 8.11 |''' '''A social tipping point is reached when climate impacts push a society towards a state of instability.''' Those climate impacts are typically aggravated by economic, social and political stressors that reduce adaptive capacity and overwhelm its resilience. Once a social tipping point is reached, a society may experience mutually reinforcing states of economic, social and political instability, leading to cascading disruptions such as livelihood insecurity, migration and displacement, food insecurity, impoverishment, civil and political conflict, and change of political regimes.

In socio-ecological systems, tipping points occur when a (small quantitative) change inevitably triggers a nonlinear change in the corresponding social component of the socio-ecological systems, driven by a self-reinforcing positive feedback mechanism, that inevitably and often irreversibly leads to a qualitatively different state of the social system ( [[#Milkoreit--2018|Milkoreit et al., 2018]] ).

In recent years, significant research efforts have been made to identify early warning signals for social tipping points ( [[#Barrett--2014|Barrett and Dannenberg, 2014]] ; [[#Bentley--2014|Bentley et al., 2014]] ; [[#Lenton--2019|Lenton et al., 2019]] ). While some identify early warning signals through time series ( [[#Scheffer--2012|Scheffer et al., 2012]] ), others see them in interaction networks and individual thresholds ( [[#Barrett--2014|Barrett and Dannenberg, 2014]] ; [[#McLeman--2018|McLeman, 2018]] ). Empirical research conducted in a transboundary contentious region—the Jordan river valley—showed that there were significant local and regional differences in the identification of social tipping points ( [[#Rodriguez%20Lopez--2019|Rodriguez Lopez et al., 2019]] ).

Empirical evidence shows that social tipping points can be triggered long before climate tipping points are reached. For example, recent research in West Africa shows that migration decisions are often based on the perceptions of environmental changes by local populations rather than on the actual observed changes ( [[#De%20Longueville--2020|De Longueville et al., 2020]] ). The migration of some members of a community can also trigger the migration of the whole group, as the migration of some members can have a strong impact on the community ( [[#Gemenne--2017|Gemenne and Blocher, 2017]] ). In other contexts, the expectation of a climate impact can trigger social or political shifts: for example, the expectation of lower snow cover levers can reduce or stop investments in ski resorts. Some planned relocations of populations are already underway in anticipation of future climate impacts ( [[#de%20Sherbinin--2011|de Sherbinin et al., 2011]] ), while the government of Indonesia decided in 2019 to move its capital city, Jakarta, in anticipation of future floods.

Shifting livelihoods is a typical adaptation strategy but can also reflect a social tipping point if this shift affects the community as a whole. Therefore, social tipping points should not be confused with the carrying capacity of a community. While the carrying capacity of a community is a fixed, predetermined limit, social tipping points are dynamic and constantly evolving under the influence of different social and political factors, such as solidarity networks or governance mechanisms. The carrying capacity of a community can evolve over time, but remains a static concept, unlike social tipping points. Social tipping points have also been applied to adaptation, through the concept of adaptation tipping points, which indicate how much pressure a socio-environmental system is able to absorb ( [[#Ahmed--2018|Ahmed et al., 2018]] ). Beyond the adaptation tipping point, the efficiency of adaptation responses will be limited, and can even transform into maladaptive options.

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=== 8.4.5 Projected Risks for Livelihoods and Consequences for Equity and Sustainability ===

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==== 8.4.5.1 Projected Risks for Livelihoods ====

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There is ''robust evidence'' with ''high agreement'' that future climate change impacts will have severe consequences for poor households, particularly those situated in areas highly exposed to actual or future climate hazards, such as low-lying coastal communities (see also Cross-Chapter Paper 1), drylands (see also Cross-Chapter Paper 3) or remote mountain (see also Cross-Chapter Paper 5) settlements with low levels of connectivity to markets, poor infrastructure and high dependence upon poor quality natural capital ( [[#Barbier--2018|Barbier and Hochard, 2018]] ; [[#Gioli--2019|Gioli et al., 2019]] ). While livelihoods operate in a dynamic context characterised by multiple interacting structures and processes, climate change can act as a risk multiplier. When current livelihood activities become untenable as a result of both long trends and short-term shocks and climate hazards (e.g., droughts, floods), shifting livelihoods is a common response and, in many cases, can be unavoidable due to the negative consequences of these climate hazards on specific livelihood capitals (see [[#8.5|Section 8.5]] ). Such shifts can involve a change in livelihood activities (e.g., continuing in agriculture but growing different kinds of crops), or a change to broader livelihood strategies (e.g., diversifying into handicrafts or paid employment, specialising in one particular activity, or migrating, seasonally or permanently, in search of other livelihood opportunities) or even an entire change of the livelihood activity, for example, abandoning agriculture altogether ( [[#McLeman--2006|McLeman and Smit, 2006]] ; [[#Black--2011|Black et al., 2011]] ). Shifting livelihoods can therefore involve mobility or take place ''in situ'' . Some of these shifts also lead to social tipping points.

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===== 8.4.5.1.1 Proactive and reactive livelihood shifts and their relevance for future risks due to climate change =====

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Livelihood shifts may also take place proactively as new opportunities emerge and reduce climate impacts by providing buffers of financial capital. For example, [[#Hirons--2014|Hirons (2014)]] assesses artisanal and small-scale mining as an emerging livelihood opportunity in Ghana. Evidence challenges the popular assertion around the idea of wealth seeking for short-term profit and reveals an alternative scenario whereby artisanal and small-scale mining can be a poverty-driven activity, particularly in areas in which agricultural employment has not delivered sufficient income or where crops are highly exposed and sensitive to climate change impacts. Income from new livelihood activities can support recovery following specific events (major flooding or drought) linked to climate hazards and climate change. Livelihood shifts therefore take place in a highly dynamic and heterogeneous context. Another example comes from the Small Lake Chad, Republic of Chad studied by ( [[#Okpara--2016a|Okpara et al., 2016a]] ). Fluctuating water levels linked to seasonal flood pulses and droughts were shown to link closely to livelihood dynamics. Lake drying led to new adaptive behaviours based on seasonality (e.g., migration of herders to different areas of the lake shore to access water resources, in line with more predictable seasonal changes), as well as linking to opportunism supported by climate change impacts. For example, during times of lake flooding, new opportunities for fishing opened for people that were otherwise operating primarily as pastoral or agricultural households. However, these kinds of livelihood shifts remain largely reactive and can bring negative as well as positive impacts. In the Lake Chad case, it resulted in social clashes between different groups, while in other examples from Tanzania, livelihood shifts towards extensification of farming led to deforestation ( [[#Suckall--2014|Suckall et al., 2014]] ), which could constitute a maladaptive shift. Such findings have important implications for the types of government and institutional support that can enable livelihood shifts and highlight the need to consider trade-offs for climate change mitigation, as well as with other adaptation options (see [[#8.6|Section 8.6]] ).

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==== 8.4.5.2 Future risks, vulnerabilities, differentiated inequalities and livelihood shifts ====

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Overall, there is ''high agreement'' that future climate change impacts are going to worsen poverty and exacerbate inequalities within and between nations, with projections that by 2030 these will increase significantly ( [[#Olsson--2014|Olsson et al., 2014]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Roy--2018|Roy et al., 2018]] ). In addition, the COVID-19 pandemic and consequences linked to measures to reduce the spreading of the virus are ''likely'' to increase poverty, particularly in regions already facing high levels of vulnerability and poverty ( [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Sumner--2020|Sumner et al., 2020]] ).

Key risks due to future climate change, exposure and vulnerability are difficult to assess and are based on evidence from the past and ''likely'' future vulnerabilities and livelihood challenges. The assessment of Representative Key Risks (see [[IPCC:Wg2:Chapter:Chapter-16#16.5.2.3|Section 16.5.2.3.4]] ) underscores that risks to living standards are potentially severe as measured by the magnitude of impacts in comparison to historical events or as inferred from the number of people currently vulnerable (see in detail Chapter 16). Table 8.4 provides an overview of what is known in the literature assessed about future risks, inequalities and particularly future vulnerabilities, including potential challenges for climate justice and adaptation barriers. For example, barriers for gender, ethnicity and class have been addressed for a long time yet need substantive intervention. Gender, along with many other structural inequalities (Table 8.4) that are deeply rooted, pose future threats to people and groups in vulnerable situations from, for example, the loss of land or assets, exposure to extreme events and so on. These people will also ''likely'' be highly exposed to future climate risks unless there are significant and new avenues for action on climate change now. For example, recent studies suggest that the total population of all countries classified as most highly vulnerable is projected to grow significantly. A study using five vulnerability categories globally concludes that the total population of all countries with very high vulnerability (see Figure 8.6) is projected to increase from 2019 numbers approximately by 102% by 2050 (i.e., roughly double) and 257% by 2100, while the population of all countries with very low vulnerability is projected to decrease by 9% by 2050 and 17% by 2100 (based on UN medium probabilistic projections). Another study estimates that the total population of all countries classified at most vulnerable (top two categories; using seven vulnerability categories globally) is predicted to increase by 82% by 2050 and 192% by 2100. In contrast the population of all countries classified as least vulnerable (bottom two categories) is projected to only increase by 9% by 2050 and 1% by 2100 (see in detail [[#UN-DESA--2019|UN-DESA, 2019]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ).

'''Table 8.4 |''' Summary of interlocking categories differentiation future risks, vulnerabilities, inequality and adaptation

{| class="wikitable"
|-
! Future risks

! Inequalities

! Future vulnerabilities, future livelihood, future exposure (examples)

! References

|-
| Increasing risk of displacement and damage to women and girls in floods

| Gender inequality leaves women and girls hidden, forgotten and exposed, resulting in displacement impacts and limited resources, including social capital and increasing risk of human trafficking.

| Increasing future vulnerability of women and girls due to high hazard exposure; gender differentiated vulnerability to urban flooding (in India); increasing risk of human trafficking associated with exposure to future extreme events.

| ( [[#Singh--2020|Singh, 2020]] ; CCB GENDER in Chapter 18)

|-
| Increasing risks of exacerbating inequalities and tensions

| Differentiation based on ethnicity and race leads to groups in society being less visible, with fewer rights, particularly for livelihoods that expose them to extremes. Unequal access to adaptation opportunities and benefits.

| Increasing future vulnerability of Indigenous Peoples due to exposure to extreme events. Communities of colour are ''likely'' to be exposed to increased climate change impacts, e.g., differentiated health impacts on black and Hispanic communities heat-related mortality rates and poverty for neighbourhoods in New York City.

| [[#8.3|Section 8.3]] ; ( [[#Hsu--2021|Hsu et al., 2021]] ; [[#8.3|Section 8.3]] )

|-
| Increasing risk of loss of homes and assets in the case of floods

| Class differences in exposure and awareness of flood risks. Lower caste disproportionately impacted by climate change.

| Increasing differentiated exposure among classes to events such as flooding.

| ( [[#Jones--2011|Jones and Boyd, 2011]] ; [[#Fielding--2018|Fielding, 2018]] )

|-
| Risks to loss of lives in cases where there is no agency

| Religious beliefs impact experience of climate change.

| Increasing vulnerability to climate change among different religious groups.

| ( [[#Schuman--2018|Schuman et al., 2018]] )

|-
| Risk of premature mortality, risk of loss of livelihoods in employment

| Age and ageing populations. Elderly and young are disproportionately impacted by climate change, e.g., heatwave in France 2003 and Japan 2018. Youth underemployed or in vulnerable livelihoods could be vulnerable to climate-related risks, which adversely affects the economy.

| Increasing future vulnerability among elderly, underage youth and children vulnerable to increasing risks of health impacts of pollutants, floods or heatwaves.

| ( [[#Hsu--2021|Hsu et al., 2021]] ; [[#8.3|Section 8.3]] )

|-
| Risks to mobility in a climate extreme

| People with disabilities, for instance; evidence emerging in the disaster risk reduction and humanitarian sector.

| Increasing risks to people with disabilities, who are disadvantaged when exposed to extreme events.

| ( [[#King--2019|King et al., 2019]] )

|-
| Risks of isolation for communities remote from centres of power

| Geographical exposure. The location of people and societies within a particular territory is a determinant of inequality e.g., disruptions to food supplies to the Caribbean when there are climate extreme events.

| Increasing risk and exposure among communities remote from urban centres, far from resources and exposed to climate impacts.

| [[#8.3|Section 8.3]] ; Cross-Chapter Box GENDER in Chapter 18

|-
| Risks of food insecurity

| Differentiation of asset/ownership/access among groups where status is unclear.

| Increasing risks to tenurial landless. If tenurial status is unclear, groups may experience loss of land and displacement.

| [[#8.2|Section 8.2]] ; Cross-Chapter Box GENDER in Chapter 18.

|}

That means that, based on current population growth estimates and if vulnerability levels are not reduced significantly, more people will be living in more vulnerable context conditions in the future compared to those living in less vulnerable contexts. This is independent of the development of climatic hazard exposure. If significant reductions of vulnerability are achieved, this projection will change. However, the vulnerability and poverty of some regions and countries, such as Afghanistan or Haiti, has proved over decades to be persistent. Consequently, the estimated future population growth is another factor that points towards the urgent need to reduce vulnerability and to narrow the adaptation gap.

While future adaptation options can also encompass measures or tools that emerge in future, most of the future adaptation options and their relevance for reducing vulnerability, poverty and inequality are known. Evidence exists that the importance of social networks that organise social protection and leverage resources in terms of reducing risks to climate change is increasing, particularly for most vulnerable people or groups in countries that have limited social security measures in place.

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==== 8.4.5.3 Future Limits to Adaptation ====

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Local perceptions of losses from adverse effects of climate variability and change can help to assess the magnitude of impacts that individuals and communities have not been able to cope with or adapt to ( [[#James--2014|James et al., 2014]] ; [[#Barnett--2016|Barnett et al., 2016]] ; [[#McNamara--2019|McNamara and Jackson, 2019]] McNamara et al. 2021, Mecheler et al. 2020).

The IPCC Special Report on a 1.5°C warming world shows with ''high confidence'' that for the Arctic systems, if average temperature increase exceeds 1.5°C by the end of the century limits to adaptation and residual impacts will be exceeded, compromising people’s livelihoods ( [[#Ford--2015|Ford et al., 2015]] ; [[#O’Neill--2017b|O’Neill et al., 2017b]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ). The loss and degradation of the Amazon forest with global warming temperatures beyond 1.5°C is another clear example of irreversible loss, with significant impact to people’s livelihoods today and in the future ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ). Moreover, the L&amp;D from climate change impacts are also felt heavily by women, children and elderly given the intersectionality with socioeconomic and gender inequalities ( [[#Li--2016|Li et al., 2016]] ; [[#Roy--2018|Roy et al., 2018]] ). For instance, gender and wealth inequality offers challenges to scale up the Maasai pastoralist community autonomous adaptive practices ( [[#Wangui--2018|Wangui and Smucker, 2018]] ). This study found that most female-headed and poorest households could not access the land, water for irrigation and financial assets required to access adaptive practices that are available in the wider community. Consequently, future impacts of climate change are ''likely'' to increase rather than decrease inequality based on already observed impacts on adaptive capacities that constrain future adaptation options, particularly for the poor ( [[#Roy--2018|Roy et al., 2018]] ).

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==== 8.4.5.4 Future Livelihood Challenges in the Context of Risks and Adaptation Limits ====

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The climate change risks in this section are addressed through the lens of livelihoods, human, food, water and ecosystem security, building on key impacts and risks since AR5 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ) and key findings from SR1.5°C ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ), SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ), and SRCCL ( [[#IPCC--2019a|IPCC, 2019a]] ). The AR5 WGII risk tables ( [[#IPCC--2014b|IPCC, 2014b]] ), updated in SR1.5°C ( [[#Roy--2018|Roy et al., 2018]] ) offer an interesting entry point as they show ''high confidence'' on key observed impacts and limits to the adaptation of natural and social systems that are compounded by the effects of poverty and inequality on water scarcity, ecosystem alteration and degradation, coastal cities in relation to sea level rise, cyclones and coastal erosion, food systems and human health ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ). As a consequence, climate change risks pose substantially negative impacts on climate-sensitive livelihoods of smallholder farmers, fisheries communities, urban poor, Indigenous Peoples and informal settlements, with limits to adaptation evidenced by the loss of income, ecosystems and health, and increasing migration ( [[#Roy--2018|Roy et al., 2018]] ). The compounded effects of socioeconomic development patterns and climate change impacts are worst in climate-sensitive ecosystems in the Arctic and SIDS ( [[#Roy--2018|Roy et al., 2018]] ). The future risks to these climate-sensitive ecosystems and livelihoods are potentially severe given their current high exposure to climate hazards, and high number of vulnerable of people exposed for example in the SIDS (see also Chapter 16; [[#Ahmadalipour--2019|Ahmadalipour et al., 2019]] ; [[#Liu--2021|Liu and Chen, 2021]] ). Residual losses then may be unavoidable for some ecosystems and livelihoods affecting the vulnerable groups of people and countries as consequences of structural poverty, socioeconomic, gender and ethnic inequalities, that marginalise and exclude and limit the development of adaptive capacity for future changes ( [[#Olsson--2014|Olsson et al., 2014]] ; [[#Roy--2018|Roy et al., 2018]] ).

In SIDS, key risks are represented by losses of livelihoods of coastal settlements, ecosystem services, infrastructure and economic stability, exhibiting limits to adaptation in the face of local people’s coping strategies capacity ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ). There is ''high confidence'' that sea level rise in SIDS combined with extreme flooding events will threaten the future livelihoods of coastal communities ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ).

In the Global South, the increasing heat associated with warming global temperature represents an important risk due to losses in labour productivity, crop failures and livelihood security. These involve economic losses and health effects, as well as increasing deaths that are anticipated to have significant implications for poverty, inequality and equity ( [[#Carleton--2017|Carleton, 2017]] ; [[#Roy--2018|Roy et al., 2018]] ). The increasing temperature, droughts and excessive rain lead to successive crop failures and reduced productivity that are affecting children’s growth and health in developing countries ( [[#Hanna--2016|Hanna and Oliva, 2016]] ). Likewise, the expected global temperature increase by the end of the century will have devastating health consequences for children, associated with sea level rise, heatwaves, affecting the incidence of malaria and dengue, and malnutrition, especially in Asian ( [[#Ghosh--2018|Ghosh et al., 2018]] ) and African countries, such as Chad, Mali, Niger and Somalia ( [[#Hanna--2016|Hanna and Oliva, 2016]] ; [[#Ghosh--2018|Ghosh et al., 2018]] ; [[#Clark--2020|Clark et al., 2020]] ).

The incidence of floods also increases the occurrence of diseases (e.g., diarrhoea and respiratory infections) and undernutrition in children living in informal settlements and slums in Asia ( [[#Ghosh--2018|Ghosh, 2018]] ) and Africa ( [[#Clark--2020|Clark et al., 2020]] ). Women and children are currently bearing the worst impacts of climate hazards, and are unable to move due to assigned gender roles to avoid flooding risks in highly vulnerable slums in Bangladesh. This results in poor living conditions and causes the women emotional distress ( [[#Ayeb-Karlsson--2020|Ayeb-Karlsson et al., 2020]] ). This region experienced severe floods associated with death, injury, infectious disease, mental and emotional stress and cultural disruptions—dimensions of non-economic losses that are often not accounted for in disaster relief policies ( [[#Chiba--2017|Chiba et al., 2017]] ) and these greatly influence the ability to build adaptive capacities for future hazards ( [[#Roy--2018|Roy et al., 2018]] ). In the same way, risks to female-headed households that have insecure tenure rights are greater. This group was the most affected by flooding in 2018 in Dar es Salaam, Tanzania, costing 3–4% of the country’s GDP and affecting 4.5 million people ( [[#Erman--2019|Erman et al., 2019]] ).

In the Himalayas (part of the Hindu Kush Himalaya, HKH) temperature warming is expected to increase up to 2°C by 2050 ( ''high confidence'' ), increasing flooding and bringing larger risks to food and water security for mountain communities that are already highly vulnerable given limited livelihood options and supporting infrastructure in these regions ( [[#Mishra--2017|Mishra et al., 2017]] ). In Nepal, agriculture-orientated livelihoods are reported to be negatively affected by an increase in landslide frequency (92.6%) and intensity (97.3%) over a 20 years period (1996–2016) ( [[#van%20Der%20Geest--2016|van Der Geest and Schindler, 2016]] ). The catastrophic landslide in 2014 caused material losses associated with loss of crops and land to poor households that were 14 times greater than their annual gains. The NELD losses were emotional distress and fear of new event occurrence, showing that poorest households may not fully recover following an extreme event. This example is indicative of the representative future climate risks to these populations. Livelihood diversification is commonly adopted by poor households and smallholders in Nepal to reduce the impacts of extreme rainfall and landslides. However, there are limits to these strategies given poor household infrastructure that challenge risk reduction, as a result, it is expected that migration to neighbouring countries as Bhutan or India will increase ( [[#van%20Der%20Geest--2016|van Der Geest and Schindler, 2016]] ).

Expected future risks to vulnerable communities and Indigenous Peoples include losses across a range of impacts. A larger household comparative analysis across mountain regions in Africa, Asia and Southeast Asia shows that more than 60% of the population reported losses from residual impacts concerning droughts, floods, cyclones, sea level rise, glacier retreat and desertification, despite autonomous adaptation involving changing food consumption and formal aid from the government ( [[#Warner--2013|Warner and Van der Geest, 2013]] ). Among Indigenous Peoples in the Global South, for example in the Brazilian Amazon, Australia and Botswana, locally autonomous adaptive measures were not sufficient to avoid significant losses (some irreversible in case of lost habitats). The barriers and insufficient adaptive capacities are also intrinsically linked to historical marginalisation and vulnerability of the population in these countries ( [[#Maru--2014|Maru et al., 2014]] ).

In the Arctic, warming temperature and sea level rise constitute key risks to the loss of identity and culture of Indigenous People. This is associated with migration and relocation due to livelihood deterioration resulting from coastal erosion, permafrost thaw and reduced fisheries productivity ( [[#Roberts--2015|Roberts and Andrei, 2015]] ; [[#Roy--2018|Roy et al., 2018]] ). These risks and losses often encompass various non-economic losses, such as the loss of identity, that cannot be replaced or economically compensated (see also [[#8.3.5|Section 8.3.5]] ).

Likewise, in the Amazon basin, climate change hazards of severe droughts and floods ( ''high confidence'' ) ( [[#Cox--2004|Cox et al., 2004]] ; [[#IPCC--2019a|IPCC, 2019a]] ) are revealing limits to adaptation among the majority of riverine communities and smallholder farmers with residual impacts associated with losses of income, fisheries and agricultural productivity, as well as affecting non-economic livelihood dimensions, such as the ability to attend school and losses of place and identity through forced migration ( [[#Maru--2014|Maru et al., 2014]] ; [[#Pinho--2015|Pinho et al., 2015]] ; [[#Lapola--2018|Lapola et al., 2018]] ). Furthermore, the expansion of the agricultural frontier and construction of large dams to supply energy needs in the Amazon basin are amplifying the vulnerabilities and reducing future adaptive capacities of smallholders and the fisheries communities to climate risk ( [[#Bro--2018|Bro et al., 2018]] ; [[#Castro-Diaz--2018|Castro-Diaz et al., 2018]] ). It is expected that a global temperature warming level of 2°C by 2050 in the Amazon will lead to a significant reduction of water flow in major rivers leading to further food and water insecurity ( [[#Betts--2018|Betts et al., 2018]] ). This is affecting forest- and river-dependent livelihoods in the region (Box 8.6; [[#Lapola--2018|Lapola et al., 2018]] ).

The glacier retreat associated with the increase in global warming temperature has also shown losses that are permanent and related to a sense of belonging and cultural heritage for glacier countries. The most negative livelihood impacts are experienced by poor households in the Peruvian Andes and Himalayas ( [[#Jurt--2015|Jurt et al., 2015]] ). The risks for smallholder livelihoods in glaciated regions are expected to increase as the shrinking glaciers result in increased water competition, crop failure and extreme flooding ( [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ). For example, in Bhutan adaptive measures such as changing crops, developing irrigation channels and sharing water among community members are still insufficient to avoid L&amp;D associated with the dramatically reduced water availability ( [[#Kusters--2013|Kusters and Wangdi, 2013]] ; [[#Warner--2013|Warner and Van der Geest, 2013]] ). In high-mountain regions, the intersections of agro-pastoralist marginalisation, difficulty of access and ecological sensitivity contribute to residual impacts associated with extreme climate hazards, which can lead to irreversible losses and challenge poverty reduction efforts ( [[#Mishra--2019|Mishra et al., 2019]] ).

In semiarid West Africa, longer-term local adaptation is in place to help poor households deal with severe droughts. This involves reducing household and cattle water consumption, planting drought-tolerant crops and adopting integrated crop–livestock production for efficiency, with migration being either seasonal and or permanent. These measures are mostly effective ( [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ). Likewise, in Ethiopia, Northern Kenya and Senegal, adaptation has advanced with external government and non-government organisation (NGO) support ( [[#Schäfer--2019|Schäfer et al., 2019]] ). This includes technological innovations and insurance for households ( [[#Schäfer--2019|Schäfer et al., 2019]] ), but is not enough to prevent losses in already impoverished households ( [[#Schäfer--2019|Schäfer et al., 2019]] ).

There is ''robust evidence'' that future risks to climate-sensitive livelihoods, such as agriculture, livestock and fisheries are amplified by gender, age, wealth inequalities ( [[#Wangui--2018|Wangui and Smucker, 2018]] ), ethical background and geography ( [[#Piggott-McKellar--2020|Piggott-McKellar et al., 2020]] ; [[#Thomas--2020|Thomas and Benjamin, 2020]] ), as well as by ecological thresholds that challenge autonomous adaptation among vulnerable disadvantaged communities mostly in the Global South ( [[#Roy--2018|Roy et al., 2018]] ; [[#Mechler--2020|Mechler et al., 2020]] ).

The assessment also points towards the fact that there are strong linkages between national-level vulnerability (e.g., Figure 8.6) and individual vulnerability at household or livelihood scale. Various disadvantaged and marginalised groups or communities within a society are significantly constrained in terms of the ability to build adaptive capacities for future climate change threats due to limited access to resources or government support for planned adaptation. Consequently, these linkages between regional, national and local vulnerability need more attention in research and practical adaptation strategies (vertical integration).

The next section discusses how risks emerge as a result of the failure in adaptation or failure to implement it, with particular attention to risks that are impossible to adapt to and lead to inevitable L&amp;D among poor households, livelihoods and countries.

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==== 8.4.5.5 Maladaptation as a Projected Future Risk Particularly for the Poor and Marginalised ====

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There is increasing evidence that maladaptation can lead to future risks to socio-ecological security. Adaptation measures focusing on short-term action can lead to adverse longer-term impacts to livelihoods and failures to address transboundary scales to avoid negative consequences for social and ecological systems ( [[#Warner--2013|Warner and Van der Geest, 2013]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Mechler--2019a|Mechler et al., 2019a]] ; see also [[IPCC:Wg2:Chapter:Chapter-5#5.13.3|Section 5.13.3]] ). Hence, maladaptation can intensify and even accelerate future risks as a result of climate change mitigation and adaptation policies when responses to climate change hazards are embedded within business-as-usual development approaches ( [[#Work--2019|Work et al., 2019]] ). For instance, in Cambodia, the conventional development strategies intertwined with climate change mitigation and adaptation initiatives are increasing the probability of maladaptive outcomes in the context of high informality, and conflicts among poor farmers exposed and vulnerable to flooding ( [[#Work--2019|Work et al., 2019]] ). The potential for maladaptation emerges from the vulnerability of precarious living conditions of informal poor farmers, not accounted for in climate mitigation and adaptation strategies for irrigation, protected areas management and reforestation projects funded by multilateral donors ( [[#Work--2019|Work et al., 2019]] ). As a consequence, losses emerge despite actions to prevent adverse impacts, and maladaptation instead becomes a vector of increased vulnerability for poor and vulnerable communities ( [[#Mechler--2019a|Mechler et al., 2019a]] ).

The maladaptation outcome also emerges as a failure of adaptation. In Ghana, poor farmers, facing crop yield failure during severe droughts further exacerbated by water use for irrigation have diversified their livelihoods (e.g., selling firewood for charcoal production).This is a form of maladaptation that can further increase their vulnerability to climate risks, compromising food production, income generation and sustainability ( [[#Antwi-Agyei--2018b|Antwi-Agyei et al., 2018b]] ). In Cambodia, governmental adaptation strategies focusing on reforestation and conservation measures are eroding local biodiversity, and crop irrigation strategies are compromising scarce water resources and also excluding poor farmers, who are susceptible to flooding, from decision making and benefits ( [[#Work--2019|Work et al., 2019]] ). Likewise, in Ethiopia, efforts of adaptation programmes to address droughts contribute to current unsustainable development trajectories among pastoralist communities, resulting in charcoal production, overgrazing, migration, conflict with other groups and marginalisation of livelihoods ( [[#Magnan--2016|Magnan et al., 2016]] ). In the Sudan, maladaptation outcomes for the poor population are linked to a dependency on a war economy and post-conflict power dynamics that are and will continue to affect sustainability and equity in the context of drought incidence ( [[#Young--2019|Young and Ismail, 2019]] ).

In Bangladesh, an expensive coastal climate-resilient infrastructure project could potentially increase the vulnerability of urban poor as they will remain in areas that are highly susceptible to flooding brought by sea level rise ( [[#Magnan--2016|Magnan et al., 2016]] ). In Central America, the lack of assessments of future climate variability on crop yield scenarios, coupled with lack of policymakers to incorporate autonomous local adaptation practices, could lead to an unsustainable trajectory for local communities and risk of maladaptation ( [[#Beveridge--2018|Beveridge et al., 2018]] ). In Bhutan, small-scale rice farmers have adopted water-sharing measures to avoid the impacts of reduced and uncertain precipitation levels associated with monsoons. However, these measures led to disruptions in social cohesion as conflicts over water sharing escalated ( [[#Mathew--2015|Mathew and Akter, 2015]] ). In the same region, local governments prioritise the glacier retreat as a perceived risk to flooding from dams, but overlook the slow and gradual impact of the deficit in precipitation that is negatively affecting rice productivity ( [[#Mathew--2015|Mathew and Akter, 2015]] ). In Burkina Faso, a region highly impacted by severe droughts, local communities have become less able to cope with droughts given a decline in cultural pastoralism and increased dependence on crops ( [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ).

As seen, maladaptive responses to droughts, sea level rise and flooding are negatively affecting poor farmers, pastoralists, and rural and urban informal workers, increasing loss of crops, infrastructure, income, conflict and migration. Given the high risks of maladaptation to poor people this agenda should be given priority by development and planning sectors ( [[#Magnan--2016|Magnan et al., 2016]] ). The categories in Table 8.5 also represent important future compounding and complex risks that can emerge due to maladaptation ( ''high confidence'' ).

'''Table 8.5 |''' Categories of maladaptation as future risk and examples of outcomes and world regions based on literature assessment evidence.

{| class="wikitable"
|-
! Categories of risks to maladaptation

! Examples of outcomes

|-
| Uncertainty (climate events)

| Lack of knowledge of future climate extreme events hinder adaptation actions for the poor

|-
| Inequalities

| Exclusion of rights and access, and benefits of adaptation

|-
| Sustainability

| Further ecological degradation and biodiversity loss

|-
| Informality

| Reinforced vulnerabilities of the poor and marginalised populations

|-
| Poverty

| Increased vulnerabilities and risks of maladaptation

|-
| Scales (temporal and spatial)

| Negative trade-offs across short- and longer-term decisions, as well as transboundary issues resulting in increased likelihood of maladaptation

|-
| rowspan="5"| Regional evidence

| South Asia and Southeast Asia (Bangladesh, India, Indonesia, Maldives, Nepal and Thailand) (6) **

|-
| Africa (Ethiopia, Ghana, Malawi ) (3)

|-
| Central America (1)

|-
| Global South (2)

|-
| Global (1)

|}

Notes:

Confidence level ** ''medium'' (5–9 papers).

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==== 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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<span id="compounding-future-risks-on-equity-and-sustainability"></span>
==== 8.4.5.7 Compounding Future Risks on Equity and Sustainability ====

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The compounding future effects on equity and sustainability emerge when multiple stressors linked to environmental and/or climate change, together with underlying structural poverty, exclusion, marginalisation, and conflicts creating risks that need to be addressed simultaneously. Compounding risks of climate change received attention in AR5 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ). This included risks associated with compound hazards ( [[#O’Neill--2017b|O’Neill et al., 2017b]] ) and their implications for future risk when repeated impacts erode human and ecosystem capacity, including through transboundary effects. In SRCCL ( [[#IPCC--2019a|IPCC, 2019a]] ), land degradation and climate change compounded to highly expose the livelihoods of the poor to climate hazards and caused food insecurity ( ''high confidence'' ), migration, conflict and loss of cultural heritage ( ''low confidence'' ) ( [[#Olsson--2019|Olsson et al., 2019]] ).

The evidence of compounded risks emerges from specific climate and environmental hazards, as in relation to heatwaves, droughts, altered precipitation regimes and increasing aridity, cyclones, floods, hurricanes and wildfires (Table 8.7). Other evidence shows that the structural poverty and socioeconomic inequalities ( [[#Lusseau--2019|Lusseau and Mancini, 2019]] ), disability ( [[#Sun--2017|Sun et al., 2017]] ), corruption ( [[#Markkanen--2019|Markkanen, 2019]] ) and isolation ( [[#Reyer--2017|Reyer et al., 2017]] ) (Table 8.7) compound to amplify climate risks among rural and urban poor, smallholder farms, coastal settlements, with health impacts on children’s development ( [[#Perera--2017|Perera, 2017]] ) and urban elderly ( [[#Sun--2017|Sun et al., 2017]] ). In Tanzania, a greater exposure of households to climate change impacts and risks is associated with increasing land value and variable tenure, compounded by declining farm yields, accelerating the negative effects among the population ( [[#Röschel--2018|Röschel et al., 2018]] ). In India, extreme droughts and heatwaves compound extreme poverty and high dependence on agriculture for income and food production will affect crop productivity, income and food prices among smallholder farms ( [[#Singh--2017|Singh and Leua, 2017]] ). In Mozambique, soil degradation and fertility, compounded by incidence of droughts, increase the vulnerability of already poor smallholders who lack access to technological advances for crop yield management and drought-resistant crops ( [[#Kidane--2019|Kidane et al., 2019]] ).

'''Table 8.7 |''' Effects of compounded risks on the poor. Climate hazards: flooding, hurricanes, drought and heatwaves.

{| class="wikitable"
|-
! Dimensions of compounded risk effects on the poor

! Equity

! Sustainability

|-
| Poverty (9)**

| ✓

| ✓

|-
| Environmental (ecological change, soil degradation, fertility and aridity) and socioeconomic changes (8)**

| ✓

| ✓

|-
| Inequalities (4)*

| ✓

|
|-
| Governance (3)*

| ✓

| ✓

|-
| Geographical (isolation) (1)

| ✓

| ✓

|-
| Population growth (3)*

|
| ✓

|-
| Diseases (3)*

| ✓

| ✓

|-
| Uncertainty (1)*

|
|-
| Finance (1)*

|
|-
| Informality urban (2)*

| ✓

| ✓

|-
| Disability (1)*

| ✓

|
|-
| Climate-sensitive livelihoods (1)*

|
| ✓

|-
| Infrastructure (1)*

|
| ✓

|}

Notes:

Confidence level: *** ''high'' (≥10 papers); ** ''medium'' (5–9 papers); * ''low'' (≤4 papers).

In the context of urbanisation, in fast growing cities in Asia, Africa and Latin America that are highly socially and economically unequal, the climate change impacts from events such as flooding and droughts, are amplified as water crises, mostly among the poor and marginalised population, challenging governance for risk reduction ( [[#Gore--2015|Gore, 2015]] ; [[#Dodman--2017|Dodman et al., 2017]] ; [[#Jiang--2017|Jiang and O’Neill, 2017]] ; [[#Pelling--2018|Pelling et al., 2018]] ; [[#Solecki--2018|Solecki et al., 2018]] ). In the Global South, over 880 million people are living in precarious and informal conditions without access to water and sanitation, mostly in sub-Saharan Africa and South Asia (see Chapter 6; [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ; [[#Tusting--2019|Tusting et al., 2019]] ). In rapidly urbanising sub-Saharan African countries, around 53 (50–57) million urban inhabitants (50% of urban population ) and 595 (585–607) million rural inhabitants (82% of the rural population) were still living in unimproved housing in 2015 ( [[#Tusting--2019|Tusting et al., 2019]] ).

L&amp;D from climate extremes, such as fatalities or economic losses due to droughts or floods (see also Figure 8.6) also matter for future vulnerability and risk, since the poorest segments of society take longer to recover after shocks ( [[#Gupta--2006|Gupta and Sharma, 2006]] ; [[#van%20der%20Geest--2018|van der Geest, 2018]] ). In some cases, poor households might never be able to fully recover post-disaster, especially in the context of increasing global temperature increase ( [[#van%20der%20Geest--2018|van der Geest, 2018]] ). Another example of compounding effects of climate change to equity and sustainability is migration, which is underpinned by the underlying socioeconomic and political context of vulnerability (see [[#8.2|Section 8.2]] ).

In Latin America, compounding effects of climate change impacts (disasters) and armed conflict has contributed to forced migration to the point that in 2018 alone, 1.7 million people migrated due to extreme events, four times as many as the number of people leaving their homeland due to armed conflict ( [[#Serraglio--2019|Serraglio and Schraven, 2019]] ). In South America, migration within and between countries can stem from climate extremes, primarily felt by the poorest and marginalised (by gender, age, ethnicity) populations that might not be able to adapt to the fast pace and scale of changes at the local level ( [[#Maru--2014|Maru et al., 2014]] ; [[#Pinho--2015|Pinho et al., 2015]] ; [[#Serraglio--2019|Serraglio and Schraven, 2019]] ). In mountain regions, intersections of people’s marginalisation, difficulty in access and environmental sensitivity in the context of incidence of climate extremes have combined to reduce the ability of mountain agro-pastoralists to cope with climate extremes ( [[#Mishra--2019|Mishra et al., 2019]] ). Mountain ecosystems are also highly susceptible to disasters and disturbances, which can lead to irreversible loss and challenge poverty reduction efforts ( [[#Mishra--2019|Mishra et al., 2019]] ) Some risks associated with the degradation and loss of habitats and ecosystem services associated with land use changes and commodities in many countries have compounding impacts on equity and sustainability, associated with permanent losses to the livelihoods of poor and marginalised groups, such as Indigenous Peoples and traditional communities around the world ( [[#Roy--2018|Roy et al., 2018]] ). For instance, high deforestation rates and increased forest burning in many Amazonian countries are further exposing vulnerable Indigenous Peoples and traditional populations to health problems, crop failures and shortages of freshwater supply, especially in the context of extreme droughts and non-supportive governance ( [[#Leal%20Filho--2020a|Leal Filho et al., 2020a]] ; [[#Walker--2020|Walker et al., 2020]] ).

Overall, there is increasing evidence that the compounding effects of climate hazards intertwined with dimensions of poverty, environmental degradation and inequalities, represent a key risk to equity and sustainability among poor and vulnerable populations ( ''medium evidence'' and ''high agreement'' ). Compounding risks—compared to compounding hazards—can also be significantly influenced by societal tipping points and by different factors of human vulnerability that determine underlying destabilisation processes of societies and communities exposed to climate change, including issues of governance.

<div id="box-8.6" class="h2-container box-container"></div>

'''Box 8.6 | Social dimensions of the Amazonia forest fires and future risks'''
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The Amazon ecosystem, together with the Arctic, is listed as the first of five IPCC Reasons for Concern due to climate change, given the ''high confidence'' level that different temperature warming and GHG emissions will pose significant risks that threaten these unique ecosystems ( [[#O’Neill--2017b|O’Neill et al., 2017b]] ; [[#Roy--2018|Roy et al., 2018]] ). In addition to the scientific evidence, a resurgence of cross-national collective expressions about the fate of the Amazon forest, Indigenous Peoples and traditional communities, in the context of an unprecedented climate crisis and sustainable future, have gained pronounced importance. On 19 August 2019, the skies of Sao Paulo State were dark by 3 pm due to the formation of a ‘smoke corridor’ associated with the extensive burning of the Amazon forest ( [[#Seymour--2019|Seymour and Harris, 2019]] ). The fire outbreaks were a consequence of multiple factors related to political, social, economic and environmental scenarios concomitant with the weakening of environmental governance, such as control and monitoring of deforestation and fire incidences programmes ( [[#Escobar--2019|Escobar, 2019]] ; [[#Seymour--2019|Seymour and Harris, 2019]] ). The deforestation rate and incidences of fire are both increasing in the Amazon of Brazil, Colombia and Peru ( [[#Seymour--2019|Seymour and Harris, 2019]] ). Accordingly, 2019 registered an increase of 60% in the cumulative fire count in Brazil, Bolivia and Peru in comparison with the same period in 2018, and a 12% increase in comparison with the same period in an extremely dry year in 2016 (GFED, 2019). In this context, looking at this case study through the lenses of poverty, inequality and the SDGs, it addresses the compound effect of climate and land use change in the Amazon forest fires and its cascading impacts and risks on the social domain in the region. There is evidence that both climate and land use change impacts and risks are disproportionately borne by poor and vulnerable ethnic groups, remote rural communities and poor urban households in the Amazon ( [[#Pinho--2015|Pinho et al., 2015]] ; [[#Brondízio--2016|Brondízio et al., 2016]] ; [[#Mansur--2016|Mansur et al., 2016]] ; [[#Pinho--2016|Pinho, 2016]] ).

Fires are not a natural phenomenon in the Amazon region ( [[#Bush--2004|Bush et al., 2004]] ; [[#McMichael--2012|McMichael et al., 2012]] ); they are used for food security, hunting and religious rituals by Indigenous Peoples and traditional communities ( [[#Hecht--2006|Hecht, 2006]] ; [[#Carmenta--2019|Carmenta et al., 2019]] ; [[#da%20Cunha--2020|da Cunha, 2020]] ), and also as a widespread technique for land clearing for small- and large-scale farms for agriculture ( [[#Morello--2019|Morello et al., 2019]] ). The dramatically increased forest burning observed in the Amazon recently are the result of illegal land grabbing, the small-a and large-scale cattle ranching sector and agribusiness practices coupled with loosening of land tenure policies and decision makers’ neglect of deforestation and burning monitoring data ( [[#Nobre--2016|Nobre et al., 2016]] ; [[#Lovejoy--2018|Lovejoy and Nobre, 2018]] ; [[#Leal%20Filho--2020a|Leal Filho et al., 2020a]] ). The fire outbreaks intensified substantially to the point that, in August 2019, there were approximately 3500 fires in 148 Indigenous territories (DETER and INPE, 2019; ISA, 2019). Although most of the burning in the Legal Amazon in Brazil occurred on private land of medium and larger sizes (about 67%), around 33% was observed within Indigenous territories and protected areas called conservation units (UCs) (DETER and INPE, 2019; ISA, 2019). In 2019, 40% of the deforestation occurred in public forests, which encompasses undesignated forest lands, Indigenous territories and UCs. This deforestation came accompanied by fires: 18% of the 2019 fires occurred on undesignated lands, 7% on Indigenous territories and 6% on UCs, where many traditional populations live ( [[#Alencar--2020|Alencar et al., 2020]] ). During 2019, 46% of the deforestation and 52% of the fires occurred on private rural properties and settlements, respectively, where legal accountability for these crimes is possible. The 2020 deforestation rate increased by 47% and 9.5% compared to 2018 and 2019, respectively, and was the highest in the decade ( [[#Silveira--2020|Silveira et al., 2020]] ). The clear-cut inside indigenous territories more than doubled from 2018 to 2019 ( [[#Brasilis--2021|Brasilis, 2021]] ) and, despite it decreasing from the 2019 rate, during 2020 it was the highest since 2008. On average, at least 50% of yearly active fires were within 5 km of deforested areas in the same year, reaching 74% during 2019 ( [[#Silveira--2020|Silveira et al., 2020]] ). This means, that fires and deforestation have an increased threat to Indigenous populations ( [[#Oliveira--2020|Oliveira et al., 2020]] ), particularly during the year 2020 and currently in 2021, since COVID-19 and air pollution from agricultural burning greatly impacts respiratory health in the Amazon ( [[#Morello--2021|Morello, 2021]] ).

'''Health impacts, economic and non-economic losses'''
The health impacts and economic losses estimates are not homogeneously gathered for the entire Amazon basin countries, but some recent evidence associated with this knowledge gap shows the magnitude of the forest fire impacts, as well as where they spatially occur and who are the most affected by it. Fires associated with deforestation in the Amazon have been related to 1065–4714 deaths annually in South America ( [[#Reddington--2015|Reddington et al., 2015]] ). The recent fires in the Amazon basin are directly affecting 24 million Amazonians with the worst impacts felt by children and the elderly ( [[#Machado-Silva--2020|Machado-Silva et al., 2020]] ), Indigenous Peoples and traditional communities ( [[#Fellows--2020|Fellows et al., 2020]] ). Children under 5 years old and the elderly in rural areas are respectively 11 and 22 times more affected by the smoke from fire outbreaks and temperature increase in the Amazon ( [[#Machado-Silva--2020|Machado-Silva et al., 2020]] ).

In Acre State, the fire incidence coupled with extreme droughts in 2005 and 2010 led to an increase—from 1.2% to 27%—in hospitalisations of children (under 5 years) due to respiratory diseases ( [[#Smith--2015|Smith et al., 2015]] ). The same evidence was found among the rapidly deforested areas known as the ‘Arc of Deforestation’, with a dramatically higher number of respiratory diseases recorded, mainly in children under 5 years ( [[#do%20Carmo--2013|do Carmo et al., 2013]] ). There is also evidence for interlinked dynamics between deforestation, urbanisation and incidence of fire episodes providing an appropriate environment for ''Anopheles darlingi'' vector propagation and the increased incidence of malaria in the region ( [[#Hahn--2014|Hahn et al., 2014]] ). In the 2005 drought, burning in Acre alone recorded 400,000 people affected and the loss of 300,000 ha of forest with direct costs of USD 50 million ( [[#Brown--2006|Brown et al., 2006]] ). In 2010, the fires during the drought were approximately 16 times larger than those in the meteorologically normal years ( [[#Campanharo--2019|Campanharo et al., 2019]] ). The estimated total economic loss in 2010 was about USD 243.36 ± 85.05 million, representing 9.07 ± 2.46% of Acre’s GDP ( [[#Campanharo--2019|Campanharo et al., 2019]] ). The economic and non-economic losses associated with the impacts of climate change and future risks of fire outbreaks on native food crops (açai, guaraná), livelihoods, tourism, medicinal and spiritual sites, culture, migration patterns, place-based attachments, emotional and mental distress among the most affected and vulnerable population as Indigenous Peoples and traditional communities are still to be fully estimated for the region ( [[#Pinho--2015|Pinho et al., 2015]] ; [[#Brondízio--2016|Brondízio et al., 2016]] ). Also relevant is a trend of Amazonian forest fires spreading from the southern Brazilian Amazon to Bolivia and Peru, indicating that transboundary burning increases are systemic and will lead to extensive economic losses of wild crops, infrastructure and livelihoods, requiring a landscape level approach for deforestation and fire management and control ( [[#Kalamandeen--2018|Kalamandeen et al., 2018]] ).

'''Future vulnerabilities and risks for Indigenous Peoples and traditional communities'''
It is expected that by 2030 the incidence of extreme droughts in the Amazon will increase the costs of the health sector associated with treatment costs of respiratory diseases (20–50%) and malaria incidence (5–10%). This will also incur a high social cost as people will less able to carry out their livelihoods ( [[#Lapola--2018|Lapola et al., 2018]] ). It is also expected that the droughts will accelerate and intensify rural (traditional communities and Indigenous Peoples) migration to urban centres where migrants living standards are expected to decrease once they will occupy marginal areas within larger urban centres ( [[#Lapola--2018|Lapola et al., 2018]] ).

In terms of adaptation and risk reduction, priority should be given to strengthening multi-scale governance and partnerships among different private and public actors. Policies at national and sub-national levels are needed, such as control strategies to reduce deforestation and fire incidence, demarcating new Indigenous territories, payment for ecosystem services (REDD+) and investment in traceability for commodity production chains are needed ( [[#Morello--2017|Morello et al., 2017]] ; [[#Scarano--2017|Scarano, 2017]] ; [[#Carmenta--2019|Carmenta et al., 2019]] ; [[#Seymour--2019|Seymour and Harris, 2019]] ). The increase in global temperature level up to 2°C will exacerbate food and water insecurity in the Amazon ( [[#Betts--2018|Betts et al., 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) ( ''medium confidence'' ). Thus, curbing fire incidence and deforestation rate will make it easier for Indigenous Peoples, traditional and vulnerable populations to reach the SDGs, especially in terms of reducing poverty (SDG1), improving food security (SDG2), improving well-being and health (SDG3) and protecting terrestrial ecosystem (SDG15) ( [[#Roy--2018|Roy et al., 2018]] ).

<div id="8.5" class="h1-container"></div>

<span id="adaptation-options-and-enabling-environments-for-adaptation-with-a-particular-focus-on-the-poor-different-livelihood-capitals-and-vulnerable-groups"></span>