AR6: Impacts, Adaptation and Vulnerability

Chapter 8: Poverty, Livelihoods and Sustainable Development

Executive Summary

Mitigation and adaptation responses to climate change influence inequalities, poverty and livelihood security and thereby aspects of climate justice (medium confidence). Improving coherence between adaptations of different social groups and sectors at different scales can reduce maladaptation, enable mitigation and advance progress towards climate resilience (medium confidence). The poor typically have low carbon footprints but are disproportionately affected by adverse consequences of climate change and also lack access to adaptation options. In many cases, the poor and most vulnerable people and groups are most adversely affected by maladaptation (medium evidence, high agreement). Climate justice and rights-based approaches are increasingly recognised as key principles within mitigation and adaptation strategies and projects (medium confidence). Narrowing gender gaps can play a transformative role in pursuing climate justice (medium confidence). Climate resilient development is therefore closely coupled with issues of climate justice. Synergies between adaptation and mitigation exist, and these can have benefits for the poor (medium confidence). {8.4, 8.4.5.5, 8.6}

8.1 Introduction

As a starting point, this chapter examines linkages between climate change, specific climate-related hazards and impacts on multidimensional poverty, vulnerability and livelihoods. Past assessments have identified the linkages between climate change, poverty, livelihoods and human vulnerability, and shown how climate change leads to differential consequences for different communities and populations. The IPCC Fifth Assessment Report (AR5) identified socially and geographically disadvantaged people exposed to persistent inequalities at the intersection of various dimensions of discrimination based on gender, age, ethnicity, class and caste (IPCC, 2014a). AR5 also showed evidence that climate change is a universal driver and multiplier of risk that shapes dynamic interactions between these factors. Climate change is one stressor that shapes dynamic and differential livelihood trajectories. Also, the IPCC Special 1.5°C Report (IPCC SR 1.5°C) underscored with very high confidence that global mean temperature, harm and human well-being losses are increasing substantially (HoeghGuldberg et al., 2018; Roy et al., 2018). 

8.2 Detection and Attribution of Observed Impacts and Responses

8.2.1 Observed Impacts of Climate Change with Implications for Poverty, Livelihoods and Sustainable Development

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8.2.1.3. Observed Differential Vulnerability to Climate Change, and Loss and Damage

A focus in the chapter is on the intersections between climate hazards and differential vulnerability resulting in actual and potential economic and non-economic losses (Section  8.3, 8.4; Thomas et  al., 2019). Increasingly, intersections of age, gender, socioeconomic class, ethnicity and race are recognised as important to the climate risks and differential impacts and losses experienced by vulnerable, marginal and poor in societies (high confidence).(Section  8.2,2.3; CCB GENDER in Chapter 18; Nyantakyi-Frimpong and Bezner-Kerr, 2015). For example, linkages between wildfires and gendered norms and values are real-world examples (Walker et al., 2021). A broader climate agenda which considers social structures and power relations intersecting with climate change extremes is important (Versey, 2021), in order to understand disproportionate impacts of climate hazards, observed and future losses and vulnerability (see Figure 8.3).

Figure 8.3 |  Illustration of the relationship between climate hazards, their impacts (including economic and non-economic losses and damages) and human systems leading to systemic vulnerability. We need to understand who is vulnerable, where, at what scale and why. We cannot just look at the climate hazard (e.g., wild fires, floods, droughts, sea level rise, etc.) but must also look at who is being affected by these hazards and factors that make people and groups vulnerable (e.g., poverty, uneven power structures, disadvantage and discrimination due to, for example, social location and the intersectionality or the overlapping and compounding risks from ethnicity or racial discrimination, gender, age, or disability, etc.) (see also Cross-Chapter Box GENDER in Chapter 18; Section 5.12).

Please refer to page 1193 to see Figure 8.3, which mentions gender. 

8.2.1.4 Climate-related Hazards, Livelihood Transitions and Migration

On the other hand, climate change impacts widen the range of households willing or needing to engage in migration to include those less able to bear the costs of urban migration (Afifi et al., 2016; Hunter and Simon, 2017). The effect is also greater urban poverty, and a higher social burden of migrants seeking urban wages (Singh, 2019). Evidence suggests that poor households often move in desperation to make ends meet. In the context of climate hazards, such as coastal inundation and salinity, economic necessity often drives working-age adults in poor households to seek outside earnings (Dasgupta et al., 2016). Labour migration in the context of climate change is also gendered, and as more men seek employment opportunities away from home, women are required to acquire new capacities to manage new challenges, including increasing vulnerability to climate change (Banerjee et al., 2019b).

8.2.1.7 Linkages Between Climate Change Impacts and Sustainable Development Goals

Many of the observed outcomes of climate change, for example, migration, are also outcomes of multidimensional poverty in lowincome countries (Burrows and Kinney, 2016). Future impacts may be better understood if the vulnerability and the capacity for adaptation is understood to be rooted in a sustainable development context (see Box 8.2). The UN Sustainable Development Goals (SDGs), which aim to reduce poverty and inequality, and identify options for achieving development progress, also provide insight on reducing climate vulnerability (United Nations, 2015). First, climate change impacts may undermine progress toward various SDGs (medium confidence), primarily poverty reduction (SDG1), zero hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others (medium evidence, high agreement). In both developing and high-income countries, climate change hazards in connection with other non-climatic drivers already accelerate trends of wealth inequality (SDG 1) (Leal Filho et al., 2020b). Climate impacts on SDGs illustrate the complex interrelations in development. For example, in regions encountering obstacles to SDGs, characterised by high levels of inequality and poverty, such as in Africa, Central Asia and Central America, climate change is exacerbating water insecurity (SDG 6), which may then also drive food insecurity (SDG 2), impacting the poor directly (e.g., via crop failure), or indirectly (e.g., via rising food prices) (Conway et al., 2015; Hertel, 2015; Cheeseman, 2016; Rasul and Sharma, 2016). There is a pressing need to address poverty issues, since these may negatively influence the implementation of all SDGs (Leal Filho et al., 2021a).

At the same time, there is increasing evidence that successful adaptation depends on equitable development and climate justice; for example, gender inequality (SDG 5) and discrimination (SDG 16) are among the barriers to effective adaptation (high confidence) (Bryan et  al., 2018; Onwutuebe, 2019; Garcia et al., 2020). Likewise, both climatic and non-climatic threats to development, such as conflict (SDG 16), may seriously undermine capacity to formulate and implement adaptation policies, and design planning pathways (Hinkel et  al., 2018). The risk of conflict associated with climate change has great potential to undermine other development goals (Box 8.4). Where sustainable development lags and human vulnerability is high, there is also often also a severe adaptation gap (Figure 8.12; Birkmann et al., 2021a). The SDGs may provide important cues on how to close the adaptation gap: climate action needs to be prioritised where past and future climate change impacts threaten SDGs, and where investment in SDGs improve capacity for adaptation (see Section 8.6).

8.2.2 Poverty–Environment Traps and Observed Responses to Climate Change with Implications for Poverty, Livelihoods and Sustainable Development

Across all geographical regions, there is evidence that anthropogenic climate change is hindering poverty alleviation and thereby constraining responses to climate change in five main ways:

• By worsening living conditions (Hallegatte et  al., 2017; Hsiang et al., 2017)
• By threatening food and nutrition security due to undernutrition and reduced opportunities for income generation (Burke et al., 2015)
• By disrupting access to basic ecosystems services such as rainwater, soil moisture (reducing the productivity of agricultural land) or via the depletion of habitats (e.g., mangroves, fishing grounds) that particularly vulnerable and poor people are depending on (Malhi et al., 2020)
• By creating favourable conditions for the spread of vectortransmitted diseases (Liang and Gong, 2017)
• By threatening underlying gender inequalities exacerbated by climate impacts, such as access and control to productive inputs and reinforcing social-cultural norms that discriminate against gender, age groups, social classes and race (Singh et al., 2019b).

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8.2.2.2 Observed Impacts and Implications for Structural Inequalities, Gender and Access to Resources

This section examines the mutual reinforcement of climate change impacts and structural inequalities. There is robust evidence that negative impacts and harm posed by climate change are also a result of social and political processes and existing structural inequalities (Sealey-Huggins, 2018). Climate change encompasses unevenly distributed impacts on women, youth, elderly, Indigenous Peoples, communities of colour, urban poor and socially excluded groups, exacerbated by unequal distribution of resources and poor access for some (Rufat et al., 2015; McNeeley, 2017; Sealey-Huggins, 2018). Structurally disadvantaged people, who are subject to social, economic and political inequalities resulting historically from discrimination, marginality or disenfranchisement because of gender, age, ethnicity, class, language, ability and/or sexual orientation, are disproportionately vulnerable to the negative impacts of climate change hazards (Kaijser and Kronsell, 2014; Otto et al., 2016). High levels of vulnerability at national scale (see Section 8.3) are often linked to complex histories, including long-term economic dependencies established and reinforced in the context of colonisation.

Links between climate change, structural racism and development are less well established as an element of disproportionate impacts of climate change (Sealey-Huggins, 2018). Discrimination is not restricted to structural racism and includes discrimination of all kinds, including that of gender and caste, because of which a considerable population is directly bound to suffer the harsh impacts of the climate change. The climate change and gender literature has come a long way in demonstrating concrete examples of how structural inequalities operate. The political and micro-political aspects and how they interact with structural inequalities are also important to understand vulnerability. Henrique and Tschakert (2020) shows how the many adaptation efforts benefit powerful actors, while further entrenching the poor and disadvantaged in cycles of dispossession. This critical analysis recommends acknowledging injustices, embracing deliberation and nurturing responsibility for human and more-thanhuman others. Garcia et al. (2020) describes the socio-political drivers of gendered inequalities that produce discriminatory opportunities for adaptation. They use an intersectional subjectivities lens to examine how entrenched power dynamics and social norms related to gender create barriers to adaptation, such as lack of resources and agency. The analysis shows a pronounced dichotomy as women experience the brunt of these barriers and a persistent power imbalance that positions them as ‘less able’ to adapt than men.

Historical marginality and exclusion are context-specific conditions that shape vulnerability (Leichenko and Silva, 2014). There is also robust evidence that gender inequalities contribute to climate vulnerability, and that consideration of gender is a key approach to climate justice (see Cross-Chapter Box  GENDER in Chapter 18). There is robust evidence for the differentiated impacts of climate change and climateorientated policies on women (McOmber, 2020). For example, Friedman et  al. (2019) show that, in Ghana, homogeneous representations of women farmers and a technical focus of climate-orientated policy interventions may threaten to further marginalise the most vulnerable and exacerbate existing inequalities. Climate change impacts can also heighten existing gender inequalities (Jost et  al., 2016; Glazebrook et al., 2020). On the one hand, climate change impacts can be gendered as a result of customary roles in society, such as triple workloads for women (i.e., economic labour, household and family labour, and duties of community participation), and occupational hazards from gendered work indoors and outdoors (Murray et al., 2016). On the other, climate change hazards interact with changing gender roles in society, such as urban migration of both men and women in ways that break with tradition (Bhatta et al., 2016).

Gender influences the way that people also experience loss and process psychological and emotional distress of losses, such as mortality of children and other relatives in climate-related disasters (Chandra et al., 2017).Women’s capacities are often constrained due to their roles in their household and society, institutional barriers and social norms. These constraints result in low adaptive capacity of women, which make them more vulnerable to hazards. As more men seek employment opportunities away from home, women are required to acquire new capacities to manage new challenges, including risks from climate change. Banerjee et  al. (2019b) finds that capacitybuilding interventions for women staying behind, which aimed to strengthen autonomous adaptation measures (e.g. precautionary savings and flood preparedness), also positively influenced women to approach formal institutions. Besides, the intervention households were more likely to invest a part of the precautionary savings in flood preparedness measures than control households.

8.2.3 Observed Impacts and Responses and their Relevance for Decision Making

Table 8.3 |  Some common barriers in implementing climate change responses and their implications.

8.3 Human Vulnerability, Spatial Hotspots, Observed Loss and Damage, and Livelihood Challenges

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8.3.1 Assessments of Risk and Vulnerability

These quantitative global assessments that have emerged within the last decades have not been sufficiently assessed in former IPCC reports, for example, in terms of the agreement on spatial hotspots or in terms of regional clusters of vulnerability and the linkages between past societal impacts and levels of vulnerability. The assessed literature shows that conditions and phenomena that characterise systemic vulnerability (hazard independent vulnerability), such as high levels of poverty and gender inequality, limited access to basic infrastructure services or state fragility are highly relevant for understanding societal impacts of climatic hazards and future risks of climate change (e.g., Cutter et al., 2003; ADB, 2005; Cutter and Finch, 2008; World Bank, 2008; UNISDR, 2009; Crawford et al., 2015; Rufat et al., 2015; Carrao et  al., 2016; Gupta, 2016; Rahman, 2018; Andrijevic et  al., 2020; Jamshed et  al., 2020a; Feldmeyer et  al., 2021; Garschagen et  al., 2021). These factors and context conditions also influence individual vulnerability at household or community level. Access to basic services, such as water and sanitation, are linked to human rights and if not granted increase the likelihood that people disproportionately suffer from climate-induced hazards, due to their pre-existing lack of access to such services. In addition, increasing climate hazards further constrain the access to such services (United Nations, 2018; Kohlitz et al., 2019; Gupta et al., 2020). 

8.3.2 Global Hotspots of Human Vulnerability to Climate Change

8.3.2.1 Hotspots and Spatial Patterns of Multidimensional Vulnerability

While different assessments use different sets of indicators, most of the global assessments with national-scale resolution (Birkmann and Welle, 2016; Kreft et al., 2016; Feldmeyer et al., 2017; Hallegatte et  al., 2017; Eckstein et  al., 2019; INFORM, 2019; ND-GAIN, 2019; Garschagen et  al., 2021), contain indicators that cover different aspects of economic poverty, inequality, access to basic infrastructure services, education and human capital (e.g., adult literacy rate) and some also include issues of gender inequality, specific vulnerable groups or insurance against extreme events. The assessments also differ, for example, in terms of their consideration of aspects of governance, such as corruption and conflict, or the consideration of social safety nets, such as insurance coverage, or the number of people affected by hazards (Feldmeyer et al., 2017; INFORM, 2019), as well as in terms of the consideration of losses experienced in the past or issues such as biodiversity as an aspect of adaptive capacity (Hallegatte et  al., 2017; Birkmann et  al., 2022). Moreover, the assessments differ in terms of the consideration of specific indicators and the inclusion or non-inclusion of specific hazard exposure (Welle and Birkmann, 2015; Hallegatte et al., 2017; INFORM, 2019; ND-GAIN, 2019; Birkmann et al., 2022). 

However, it is also important to note that vulnerability assessments do have their limitations (Heesen et  al., 2014; Rufat et  al., 2019). For example, in high-income countries, specific groups can be highly vulnerable to climate change due to marginalisation and discrimination due to ethnicity or gender. Gender inequality, for example, is also high in some countries classified in the literature as having low vulnerability (see Birkmann et al., 2021a; Birkmann et al., 2022). Nevertheless, these countries have, in theory, sufficient financial resources and governance capacities to deal with these challenges, while this is different for many country clusters classified as highly vulnerable. 

8.3.2.1.2 People residing in most vulnerable versus least vulnerable regions

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Figure 8.6 |  Global map of vulnerability. This map shows the relative level of average national vulnerability as calculated by global indices (INFORM and WRI see details in 8.3.2). Areas shaded light yellow are on average the least vulnerable and those shaded darker red are the most vulnerable. The map combines information about the level of vulnerability (independent of the population size) with the population density (see legend) to show where both high vulnerability and high population density coincide. The map reveals that there are densely populated areas of the world that are highly vulnerable, but also highly vulnerable populations in more sparsely populated areas. There are also highly vulnerable communities and populations in countries with overall low vulnerability as shown with local case studies alongside the map. The pie charts show the number of deaths (mortality) per hazard (storm, flood, drought, heatwaves and wildfires) event per continental region based on EM-DAT Data (CRED, 2020). The size of the pie chart represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that over the past decade, there were significantly more fatalities per hazard in the more vulnerable regions, e.g., Africa and Asia. The analysis of the data shown in this map revealed that over 3.3 billion people are living in countries classified as very highly and highly vulnerable, while approximately 1.8 billion people live in countries with low and very low vulnerability (Birkmann et al., 2022). These vulnerability values are based on the average of the vulnerability components of the INFORM Index (INFORM, 2019) and WorldRiskIndex (Birkmann and Welle, 2016; Feldmeyer et al., 2017) with updated data from 2019 classified into five classes using the quantile method. Other studies applied more vulnerability classes within their assessment and therefore provide slightly different numbers (Birkmann et al., 2021a). However, despite different calculation methods, the conclusion remains that there are significantly more people residing in countries with very high and highly vulnerability compared to those living in countries classified as having low or very low vulnerability.

Please refer to page 1211 to see Figure 8.6, which mentions gender. 

Figure 8.7 |  The figure shows selected aspects of human vulnerability, such as extreme poverty and inequality, and access to health care and basic infrastructure as regional averages. These vulnerability aspects are a selection of indicators from the indicator systems (the INFORM Risk Index and WorldRiskIndex 2019) used for the global vulnerability map (Figure 8.6). These normalized indicator scores were averaged for each region and classified into three levels of severity using the natural breaks method. This figure provides a more differentiated picture about the various dimensions of vulnerability that different regions and countries face and the severity of such challenges in each region. Such vulnerability challenges increase the risk of severe adverse impacts of climate change and related hazards (Birkmann et al., 2022).

Please refer to page 1211 to see Figure 8.7, which mentions gender equality. 

8.3.3.1 The Implications of Vulnerability for Past and Present Livelihood Impacts of Climate Change

Climate change impacts add to livelihood challenges and can further increase inequality and poverty (see Section 8.2.1), whose root causes are social, institutional and governance related. Various regional clusters of high vulnerability (see Figure  8.6) are also influenced by historical processes, such as colonialism and power relations that made people and countries vulnerable (Schell et al., 2020). Thus, vulnerability to climate change is not primarily linked to the degree of exposure to climate change impacts, but determined by societal structures and development processes that shape context and individual vulnerability (see Section 8.3.2), and values and lived experiences of climate hazards (Djoudi et al., 2016; Walker et al., 2021). Intersectionality approaches are central to grasping differential vulnerability (Thomas et  al., 2019) for past and present livelihood impacts of climate change (see Figure 8.3; Section 8.2.2.2). Assessing observed local conditions and livelihood impacts and shifts requires us to consider reinforcing social phenomena such as age, gender, class, race and ethnicity, which shape social inequalities and experiences of the world and also intersect with climate hazards and vulnerability (Walker et al., 2021).

8.3.3.2 Economic and Non-economic Losses and their Relevance for Poverty and Livelihoods

Impacts of climate change are affecting the economic and noneconomic dimensions of people’s lives, including subsistence practices of communities that are experiencing decreases in agriculture productivity and quality, water stress, increases in pests and diseases, disruption to culture, and emotional and psychological distress, to cite just a few (Savo et al., 2016). For example, the cumulative effects of slow-onset events threaten food security especially among the poor in Latin America and the Caribbean—regions which face the largest gender gap in terms of food security globally (Zuñiga et al., 2021). In general for Global South countries, the global average temperature warming (including the Paris target of 1.5°C) means substantially higher warming and including higher frequency and magnitude of extreme events, that will result in significant impacts on societal vulnerability (Aitsi-Selmi and Murray, 2016; Djalante, 2019).

8.3.5 Economic and Non-economic Losses and Damages Due to Climate Change and their Implications for Livelihoods and Livelihood Shifts

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8.3.5.1 Livelihood Shifts Resulting from L&D from Climate Change

In summary, this section has moved beyond the IPCC WGII AR5 in laying out structural elements of vulnerability and climate-related vulnerability hotspots globally, such as poverty, lack of access to basic services, gender inequality and undernourishment. The assessment provides new quantitative evidence about the global spatial distribution of systemic human vulnerability and therewith underscores that various hotspots of countries classified as having very high or high vulnerability emerge in regional clusters. In addition, the number of people living in very highly and highly vulnerable country contexts is significantly higher in some assessments, with even twice as many as the number of people living in countries classified as having low and very low vulnerability. The evidence suggests that statistically relevant differences in fatalities per hazard event are not just a product of the hazard event, but also strongly linked with the level of vulnerability of the region or community exposed. The assessment of non-economic losses has also received little attention in past IPCC Assessment Reports, therefore this section has provided new insights on how (next to measurable economic losses) non-economic losses and intangible losses emerge. These non-economic losses represent an important dimension of societal impacts of climate change that has not sufficiently captured so far within standard damage or postdisaster assessments. Finally, the section provides evidence about the existing adaptation gap in terms of differential vulnerabilities and various non-economic losses already experienced.

8.4.5.2 Future risks, vulnerabilities, differentiated inequalities and livelihood shifts

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 et al., 2014; Hallegatte and Rozenberg, 2017; 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 et al., 2020b; 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 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; Birkmann et al., 2021a; Birkmann et al., 2022).

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

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

8.4.5 Projected Risks for Livelihoods and Consequences for Equity and Sustainability

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8.4.5.3 Future Limits to Adaptation

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 et al., 2014; Barnett et al., 2016; 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 et  al., 2015; O’Neill et  al., 2017b; Roy et  al., 2018; HoeghGuldberg 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 et al., 2018; Roy et al., 2018). Moreover, the L&D from climate change impacts are also felt heavily by women, children and elderly given the intersectionality with socioeconomic and gender inequalities (Li et al., 2016; Roy et al., 2018). For instance, gender and wealth inequality offers challenges to scale up the Maasai pastoralist community autonomous adaptive practices (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 et al., 2018).

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

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 et al., 2014) and key findings from SR1.5°C (Hoegh-Guldberg et  al., 2018; Roy et  al., 2018), SROCC (IPCC, 2019b), and SRCCL (IPCC, 2019a). The AR5 WGII risk tables (IPCC, 2014b), updated in SR1.5°C (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 et al., 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 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 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 et  al., 2019; 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 et al., 2014; Roy et al., 2018). 

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) and Africa (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 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 et al., 2017) and these greatly influence the ability to build adaptive capacities for future hazards (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 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 and Smucker, 2018), ethical background and geography (Piggott-McKellar et al., 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 et al., 2018; Mechler et al., 2020). 

Table 8.6 |  Synthesis of hard and soft limits to adaptation and risks to livelihoods, equity and sustainability adapted from Chapter 5 of SR1.5°C (Roy et al., 2018).

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

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 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 climatesensitive livelihoods. The supporting literature is listed in Table SM8.1.

8.4.5.7 Compounding Future Risks on Equity and Sustainability

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 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 et al., 2014; Pinho et al., 2015; 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 agropastoralists to cope with climate extremes (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 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 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 Filho et al., 2020a; Walker et al., 2020).

8.5 Adaptation Options and Enabling Environments for Adaptation with a Particular Focus on the Poor, Different Livelihood Capitals and Vulnerable Group

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8.5.2 Enabling Environments for Adaptation in Different Socioeconomic Contexts

8.5.2.1 Factors that Support Enabling Environments for Adaptation

An increase in finance mobilised, however, does not automatically equate to adaptation interventions on the ground, nor does it guarantee the effectiveness of those adaptations deployed (BerrangFord et al., 2021). Unintended negative consequences may arise due to lack of understanding of the drivers of vulnerability (such as gender inequality or inequitable access to natural resources), non-involvement of marginalised local groups, retrofitting adaptation into existing development agendas, and insufficiently defining adaptation success (Eriksen et al., 2021). A 2017 study estimated that less than 10% of climate finance committed from international, regional and national climate funds to developing countries between 2003 and 2016 went to locally focused projects, suggesting a need to rethink approaches if the most affected groups are to build sufficient resilience to the impacts of climate change (Soanes et al., 2017). 

8.5.2.3 Human Capital

Adaptations that support human health and well-being require investments in physical assets and infrastructure linked to water and sanitation (see Chapter 4), particularly in rapidly urbanising areas in the Global South, alongside specific pro-poor investment strategies given disproportionate climate change impacts on women (see CrossChapter Box GENDER in Chapter 18), other marginalised groups and low-income households who lack access to healthcare. Climate change facilitates the spread of vector-borne diseases such as malaria, as well as illnesses such as meningitis (Rocklöv and Dubrow, 2020). Impacts on health are also experienced, through food insecurity resulting from climate change, including malnutrition, as well as through loss of livelihoods, making it more difficult to afford and to access health services. Health aspects are considered in-depth in Chapter 7, but we underscore the importance of a rights-based approach to adaptation in supporting the right to health and food in the context of inequality. 

8.5.2.4 Physical Capital

Physical capital in the form of technology is increasingly supporting climate change adaptation, despite that innovations can be rolled out under high uncertainty, opening up new risks (e.g., hacking). Moreover, deployment of technology is closely tied to other forms of capital, especially human capital, and innovations cannot just be rolled out in the absence of suitable institutional and technical support and training. Similarly, access to finance is vital. Some technological adaptations require a pre-existing level of infrastructure and literacy, raising important questions about inequality (Taylor, 2018). Rotz et al. (2019) warn of automation impacts on rural labour, especially in places with high youth unemployment, while Taylor (2018) notes that social classes and gender are impacted differently by technological change, and failure to address underlying inequalities will shape who becomes vulnerable. Adequate testing of technologies in terms of their applicability to different contexts is also required, ensuring they do not become maladaptive when applied at scale. 

8.7 Conclusion

The chapter shows that intersectionality approaches are becoming increasingly central to grasping how differential vulnerability to climate hazards is experienced by different social groups. Intersectionality recognises that age, gender, class, race and ethnicity are reinforcing social phenomena, shaping social inequalities and experiences of the world, and also intersect with climate hazards and vulnerability. Our assessment reveals the central role of maladaptation with robust new evidence on negative consequences of interventions on different social groups. Well-intentioned adaptation can exacerbate past and existing vulnerabilities and undermine livelihoods. There is also evidence that, despite maladaptation, inclusive and sustainable development at the local level can reduce vulnerability.