FINAL DRAFT Chapter 8 IPCC WGII Sixth Assessment Report
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2 Chapter 8: Poverty, Livelihoods and Sustainable Development
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4 Coordinating Lead Authors: Joern Birkmann (Germany), Emma Liwenga (Tanzania), Rajiv Pandey
5 (India)
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7 Lead Authors: Emily Boyd (Sweden), Riyanti Djalante (Indonesia), Fran鏾is Gemenne (Belgium), Walter
8 Leal Filho (Germany), Patricia Fernanda Pinho (Brazil), Lindsay Stringer (United Kingdom), David
9 Wrathall (USA)
10
11 Contributing Authors: Stavros Afionis (Greece), Liana Anderson (Brazil), Desalegn Ayal (Ethiopia),
12 Connor Joseph Cavanagh (Norway), Jon Ensor (United Kingdom), Harald Heubaum (United Kingdom),
13 Monirul Islam (Bangladesh), Rachel James (United Kingdom), Emma li Johansson (Sweden), Murukesan
14 Krishnapillai (The Federated States of Micronesia), Joanna M. McMillan (Germany/Australia); Nicholas P.
15 Simpson (South Africa), Jamon Van Den Hoek (USA), Emmanuel Raju (Denmark)
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17 Review Editors: Taikan Oki (Japan), Marta G. Rivera-Ferre (Spain), Taha Zatari (Saudi Arabia)
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19 Chapter Scientists: Ali Jamshed (Germany/Pakistan), Joanna M. McMillan (Germany/Australia),
20 Mohammad Ravankhah (Germany/Iran)
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22 Date of Draft: 1 October 2021
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24 Notes: TSU Compiled Version
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26
27 Table of Contents
28
29 Executive Summary..........................................................................................................................................3
30 8.1 Introduction ..............................................................................................................................................6
31 8.2 Detection and Attribution of Observed Impacts and Responses .........................................................8
32 8.2.1 Observed impacts of climate change with implications for poverty, livelihoods and sustainable
33 development ............................................................................................................................8
34 Box 8.1: Climate Traps: A Focus on Refugees and Internally Displaced People .....................................15
35 Box 8.2: Livelihood Strategies of Internally Displaced Atoll Communities in Yap .................................18
36 8.2.2 Poverty-environment traps and observed responses to climate change with implications for
37 poverty, livelihoods and sustainable development................................................................20
38 Box 8.3: COVID-19 Pandemic.......................................................................................................................21
39 Box 8.4: Conflict and Governance ................................................................................................................24
40 8.2.3 Observed impacts and responses and their relevance for decision-making.................................28
41 8.3 Human Vulnerability, Spatial Hotspots, Observed Loss and Damage and Livelihood Challenges29
42 8.3.1 Assessments of risk and vulnerability ...........................................................................................29
43 8.3.2 Global hotspots of human vulnerability to climate change ..........................................................31
44 8.3.3 Livelihood impacts, shifting livelihoods and the challenges for equity and sustainability in the
45 context of climate change......................................................................................................43
46 8.3.4 Observed disproportionate impacts according to economic and non-economic losses and
47 damages due to climate change ............................................................................................44
48 8.3.5 Economic and non-economic losses and damages due to climate change and their implications
49 for livelihoods and livelihood shifts......................................................................................49
50 Box 8.5: Western Cape Region in South Africa: Drought Challenges to Equity and Sustainability .....53
51 8.4 Future Vulnerabilities, Risks and Livelihood Challenges and Consequences for Equity and
52 Sustainability ........................................................................................................................................... 55
53 8.4.1 Future exposure, climate change vulnerability and poverty at the global scale ..........................55
54 8.4.2 The influence of future climate change impacts on future response capacities............................56
55 8.4.3 The influence of climate change responses on projected development pathways ........................57
56 8.4.4 Social tipping points in the context of future climate change .......................................................58
57 8.4.5 Projected risks for livelihoods and consequences for equity and sustainability ..........................59
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1 Box 8.6: Social dimensions of the Amazonia Forest Fires and Future Risks............................................65
2 8.5 Adaptation Options and Enabling Environments for Adaptation with a Particular Focus on the
3 Poor, Different Livelihood Capitals and Vulnerable Groups ............................................................74
4 8.5.1 Adaptation options to climate change hazards focusing on vulnerable groups ...........................75
5 8.5.2 Enabling environments for adaptation in different socio-economic contexts...............................77
6 Box 8.7: Addressing Inequalities in National Capabilities: Common but Differentiated Responsibilities
7 and Respective Capabilities Relating to Adaptation and the Paris Agreement ...............................77
8 Box 8.8: Cyclone Aila in Bangladesh: Impact, Adaptation and Way Forward........................................86
9 8.6 Climate Resilient Development for the Poor and Pro-poor Adaptation Finance: Ensuring Climate
10 Justice and Sustainable Development...................................................................................................89
11 8.6.1 Synergies and trade-offs between adaptation and mitigation in different sectors with
12 implications for poverty, livelihoods and sustainable development .....................................90
13 8.6.2 Decision making approaches for Climate Resilient Development................................................99
14 8.6.3. Future adaptation finance and social and economic changes within the context of poverty,
15 livelihoods, equity, equality and justice..............................................................................102
16 Box 8.9: Adaptation Fnancing for the Poor and the Need for Systems Transition: Eastern Indonesian
17 Islands ....................................................................................................................................................103
18 8.7 Conclusion .............................................................................................................................................104
19 FAQ 8.1: Why are people who are poor and disadvantaged especially vulnerable to climate change
20 and why do climate change impacts worsen inequality? ..................................................................107
21 FAQ 8.2: Which world regions are highly vulnerable and how many people live there? .....................107
22 FAQ 8.3: How does and will climate change interact with other global trends (e.g., urbanization,
23 economic globalization) and shocks (e.g., COVID-19) to influence livelihoods of the poor? ........108
24 FAQ 8.4: What can be done to help reduce the risks from climate change, especially for the poor? ..109
25 FAQ 8.5: How do present adaptation and future responses to climate change affect poverty and
26 inequality? .............................................................................................................................................109
27 References......................................................................................................................................................111
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1 Executive Summary
2
3 Adverse impacts of climate change, development deficits and inequality exacerbate each other. Existing
4 vulnerabilities and inequalities intensify with adverse impacts of climate change (high confidence1). These
5 impacts disproportionately affect marginalised groups, amplifying inequalities and undermining sustainable
6 development across all regions (high confidence). Due to their socio-economic conditions and the broader
7 development context, many poor communities, especially in regions with high levels of vulnerability and
8 inequality, are less resilient to diverse climate impacts (high confidence) {8.2.1, 8.2.2, 8.3.2, 8.3.3}
9
10 Under all emissions scenarios, climate change reduces capacities for adaptive responses and limits
11 choices and opportunities for sustainable development. Higher levels of global warming lead to greater
12 constraints on societies. Climate change increases the threat of chronic and sudden onset development
13 challenges, such as poverty traps and food insecurity (high confidence). Adaptation interventions and
14 transformative solutions that prioritize inclusive and wide-ranging climate resilient development and the
15 reduction of poverty and inequality are increasingly seen as necessary to minimize loss and damage from
16 climate change (high confidence) {8.2.1, 8.2.2, 8.3.1, 8.3.2, 8.3.3}.
17
18 Observed societal impacts of climate change, such as mortality due to floods, droughts and storms, are
19 much greater for regions with high vulnerability compared to regions with low vulnerability, which
20 reveals the different starting points that regions have in their move towards climate resilient development
21 (high confidence). Observed average mortality from floods, drought and storms is 15 times higher for
22 countries ranked as very high vulnerable, such as Mozambique, Somalia, Nigeria, Afghanistan and Haiti
23 compared to very low vulnerable countries, such as UK, Australia, Canada and Sweden in the last decade
24 (high confidence). Over 3.3 billion people are living in countries classified as very highly or highly
25 vulnerable, while 1.8 billion people live in countries with low or very low vulnerability. The population in
26 most vulnerable countries is projected to increase significantly by 2050 and 2100, while the population in
27 countries with low vulnerability is projected to decrease or grow only slightly. Vulnerability is a result of
28 many interlinked issues concerning poverty, migration, inequality, access to basic services, education,
29 institutions and governance capacities often made more complex by past developments, such as histories of
30 colonialism (high confidence) {8.3.2, 8.3.3}.
31
32 A growing range of economic and non-economic losses have been detected and attributed to climate
33 extremes and slow onset events under observed increases in global temperatures (medium evidence, high
34 agreement). If future climate change under high emissions scenarios continues and increases risks, without
35 strong adaptation measures, losses and damages will likely2 be concentrated among the poorest vulnerable
36 populations (high confidence). The intersection of inequality and poverty presents significant adaptation
37 limits, resulting in residual risks for people/groups in vulnerable situations, including women, youth, elderly,
38 ethnic and religious minorities, Indigenous People and refugees. Climate change is likely to force economic
39 transitions among the poorest groups, accelerating the switch from agriculture to other forms of wage labour,
40 with implications for labour migration and urbanization (medium evidence, high agreement). Under an
41 inequality scenario (SSP4) the projected number of people living in extreme poverty may increase by 122
42 million by 2030 (medium confidence) {8.2, 8.3.4, 8.4.1, 8.4.5, Map 8.8, Box 8.5, 16.5.2.3.4}
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44 Both climate change and vulnerability threaten the achievement of the UN Sustainable Development
45 Goals (SDGs) (medium confidence). This undermines progress toward various goals such as no poverty
1 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust;
and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very
low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of
agreement are correlated with increasing confidence.
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result:
Virtually certain 99�100% probability, Very likely 90�100%, Likely 66�100%, About as likely as not 33�66%,
Unlikely 0�33%, Very unlikely 0�10%, and Exceptionally unlikely 0�1%. Additional terms (Extremely likely: 95�
100%, More likely than not >50�100%, and Extremely unlikely 0�5%) may also be used when appropriate. Assessed
likelihood is typeset in italics, e.g., very likely). This Report also uses the term `likely range' to indicate that the assessed
likelihood of an outcome lies within the 17-83% probability range.
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1 (SDG1), zero hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others
2 (medium evidence, high agreement). Gender inequality and discrimination are among the barriers to
3 adaptation (high confidence) {8.2.1�8.4.5}. Also maladaptation can lead to additional complex and
4 compounding future risks and threaten sustainable development (high confidence) {8.4.5.5, 8.2.1.7}
5
6 Under higher emissions scenarios and increasing climate hazards, the potential for social tipping points
7 increases (medium confidence). Even with moderate climate change3 people in vulnerable regions will
8 experience a further erosion of livelihood security that can interact with humanitarian crises, such as
9 displacement and forced migration (high confidence) and violent conflict, and lead to social tipping points
10 (medium confidence). Social tipping points can also be coupled with environmental tipping points {8.3,
11 8.4.4}.
12
13 Vulnerable population groups in most vulnerable regions have the most urgent need for adaptation (high
14 confidence). The most vulnerable regions are particularly located in East, Central and West Africa, South
15 Asia, Micronesia and Melanesia and in Central America (high confidence). These regions are
16 characterized by compound challenges of high levels of poverty, a significant number of people without
17 access to basic services, such as water and sanitation and wealth and gender inequalities as well as
18 governance challenges. Areas of high human vulnerability are characterized by larger transboundary regional
19 clusters (high confidence). Additional support and structures are needed to reduce the existing gaps between
20 future adaptation needs and current capacities, and to support transitions from vulnerable livelihood with
21 adequate integration of the Indigenous Knowledge and Local Knowledge systems. Greater investments are
22 required under higher levels of global warming and of inequality (RCP 4.5; RCP8.5 and SSP4) (high
23 confidence) {8.3, 8.4, Box 8.6}.
24
25 The direct and indirect consequences of the COVID-19 pandemic have worsened inequalities within
26 societies, thereby increasing existing vulnerabilities to climate change and further limiting the ability of
27 marginalized communities to adapt (medium confidence). The COVID-19 pandemic is expected to increase
28 the adverse consequences of climate change since the financial consequences have led to a shift in priorities
29 and constrain vulnerability reduction (medium confidence). Moreover, the COVID-19 pandemic is also
30 influencing the capacities of governmental institutions in developing nations to support planned adaptation
31 and poverty reduction of most vulnerable people/groups, since the crisis also means significant reductions in
32 tax revenues (high confidence) {8.3, 8.4�8.4.5.5}.
33
34 Those with climate-sensitive livelihoods and precarious livelihood conditions are often least able to adapt,
35 afforded limited adaptation opportunities and have little influence on decision making (high confidence).
36 Enabling environments that support sustainable development are essential for adaptation and climate
37 resilient development (high confidence). Enabling and supportive environments for adaptation share
38 common governance characteristics, including multiple actors and assets, and multiple centres of power at
39 different levels and an effective vertical and horizontal integration between levels (high confidence).
40 Enabling conditions can support livelihood strategies that do not undermine human wellbeing (medium
41 confidence) {8.5.1, 8.5.2, 8.6.3, 5.13}.
42
43 Mitigation and adaptation responses to climate change influence inequalities, poverty and livelihood
44 security and thereby aspects of climate justice (medium confidence). Improving coherence between
45 adaptations of different social groups and sectors at different scales can reduce maladaptation, enable
46 mitigation and advance progress towards climate resilience (medium confidence). The poor typically have
47 low carbon footprints but are disproportionately affected by adverse consequences of climate change and
48 also lack access to adaptation options. In many cases, the poor and most vulnerable people/groups are most
49 adversely affected by maladaptation (medium evidence, high agreement). Climate justice and right based
50 approaches are increasingly recognized as a key principle within mitigation and adaptation strategies and
51 projects (medium confidence). Narrowing gender gaps can play a transformative role in pursuing climate
52 justice (medium confidence). Climate resilient development is therefore closely coupled with issues of
53 climate justice. Synergies between adaptation and mitigation exist and these can have benefits for the poor
54 (medium confidence) {8.4, 8.4.5.5, 8.6}.
3 meaning low or moderate emission scenarios
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2 There is increasing evidence that nature-based solutions (e.g., urban green infrastructure, ecosystem-
3 based management) can provide important livelihood options and reduce poverty while also supporting
4 mitigation and adaptation (medium confidence). However, the trade-offs over time between nature-based
5 solutions and their dynamics are insufficiently understood. Appropriate governance, including
6 mainstreaming and policy coherence, supported by adaptation finance that targets the poor and marginalised,
7 is essential for adaptation and climate compatible development (medium confidence) {8.5.2, 8.6.3, 5.14}.
8
9
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1 8.1 Introduction
2
3 The impacts of climate change have already significantly affected the livelihoods and living conditions,
4 especially of the poorest and most vulnerable, and will continue to undermine development during the
5 coming century. This chapter assesses the societal consequences of climate change and related hazards in
6 terms of adverse and irreversible consequences for the most vulnerable. To understand societal consequences
7 of climate change we assess impacts through the perspective of vulnerability, poverty and livelihoods of
8 people and identify why climate events trigger sudden and slow-onset disasters, and how the most severe,
9 acute and chronic impacts cause and deepen human suffering. We also examine issues of climate justice.
10 Understanding and engaging with climate justice requires a plural focus on the historical social and
11 institutional relations and inequalities which produce climate change, cause people to be vulnerable to
12 climate hazards, and shape responses to them (Newell et al., 2021). An assessment of observed impacts on
13 the poorest and their strategies for adaptation carries important lessons for inclusive, broad-based solutions
14 to climate change.
15
16 As a starting point, this chapter examines linkages between climate change, specific climate-related hazards
17 and impacts on multidimensional poverty, vulnerability and livelihoods. Past assessments have identified the
18 linkages between climate change, poverty, livelihoods and human vulnerability, and shown how climate
19 change leads to differential consequences for different communities and populations. The IPCC Fifth
20 Assessment Report (AR5) identified socially and geographically disadvantaged people exposed to persistent
21 inequalities at the intersection of various dimensions of discrimination based on gender, age, ethnicity, class
22 and caste (IPCC, 2014a). AR5 also showed evidence climate change is a universal driver and multiplier of
23 risk that shapes dynamic interactions between these factors. Climate change is one stressor that shapes
24 dynamic and differential livelihood trajectories. Also, the IPCC Special 1.5癈 report underscored with very
25 high confidence that global mean temperature, harm and human wellbeing losses are increasing substantially
26 (Hoegh-Guldberg et al., 2018; Roy et al., 2018).
27
28 This chapter builds on this, examining equitable development, robust institutions and poverty reduction as
29 essential inputs to societies' capacity for adaptation (i.e., closes the adaptation gap) in order to avoid losses
30 and damages from climate change. It assesses quantitative spatio-temporal information on human
31 vulnerability at a global scale and for specific sub-regions, livelihood groups and communities at the local
32 level. The chapter assesses the newest literature on how multidimensional poverty and human vulnerability
33 to climate change is measured and also examined the agreement of different index systems in terms of global
34 hotspots of human vulnerability.
35
36 In addition, the chapter explores how climate change affects different livelihoods and livelihood assets and
37 also examines factors that characterize vulnerability to climate change, focusing on different dimensions of
38 human vulnerability and its sub-systems (e.g., access to infrastructure services). In this context the chapter
39 also assesses quantitative data to map human vulnerability as well as economic and non-economic losses that
40 are highly relevant for understanding adverse impacts of climate change.
41
42 The chapter assesses the newest scientific knowledge on how the most vulnerable and marginalized people
43 are experiencing different climate influenced hazards and changes, how these groups prepare for and adapt to
44 these changes. Hence, it examines how climate change intersects with broader processes of development. It
45 also considers the various impacts of climate change on the livelihoods of the poorest, the capabilities, assets
46 and activities required for a means of living. It examines the institutional conditions that promote livelihood
47 resilience in the face of climate change. Quantitative analysis and qualitative data on observed adverse
48 climate change impacts and future projections and trends in vulnerability show that societal impacts of
49 climate change cannot solely be explained by looking at temperature changes or climatic hazards alone.
50
51 The chapter provides due consideration as how societal impacts of climate change are emerging as a result of
52 climatic changes, development and vulnerability. In this regard, it also explores how past and present
53 conditions of poverty, inequality and vulnerability determine observed and future societal impacts of climate
54 change, including future adaptive capacities of societies exposed to climate change. It highlights new entry
55 points to address climate risks and adaptation needs through the targeted reduction of poverty, inequity and
56 vulnerability, linking particularly global quantitative information with local livelihood-oriented qualitative
57 information.
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2 The chapter also outlines new approaches for identifying social tipping points, meaning moments of rapid,
3 destabilizing change across scales that can complement the discussion about physical tipping points in the
4 climate system. It also addresses new perspectives on the baselines for assessing future vulnerabilities, and
5 potential for irreversible losses, emphasizing not only economic but also non-economic losses, which are
6 linked to past and present development trajectories. There is mounting evidence on non-economic losses,
7 including the loss of land, livelihoods, social networks, cultural values and the irreversible degradation of
8 ecosystem functions, as observed, for example, in parts of the Amazon. Non-economic losses are intertwined
9 with economic losses to influence human health, nutrition, wellbeing and social stability, and therefore also
10 influence present and future vulnerabilities and adaptive capacities. Non-economic losses from climate
11 change disproportionately affect the poor. People in vulnerable situations are often disproportionally affected
12 as they are less resilient and have less access to institutional support (including protection mechanisms) and
13 coping strategies. This knowledge is key for informing integrated strategies for sustainable livelihood
14 transitions and adaptation.
15
16 The chapter assesses newer literature about the synergies and trade-offs for the poorest and most vulnerable
17 people/groups between adaptation mitigation, and sustainable development strategies, which societies must
18 negotiate in order to pursue Climate Resilient Development. It explores synergies and mismatches in key
19 development sectors that the poorest rely on, including agriculture, forestry and energy. It identifies the
20 development strategies, elements of institutional design and financial mechanisms likely able to support risk
21 reduction and adaptation. Our assessment reveals that successful adaptation is not solely a question of levels
22 of funding, but depends on broader institutional design that determine societal development and enabling
23 conditions for adaptation to and mitigation of climate change. An assessment of enabling conditions for
24 adaptation supports the finding that more convergent, integrated and comprehensive approaches to
25 adaptation are needed. The chapter concludes that climate justice requires consideration of the legal,
26 institutional and governance frameworks that significantly determine whether adaptation is successful in
27 addressing the needs of the poor.
28
29 Thus, intersections between climate hazards and socioeconomic development are assessed from the point of
30 view of vulnerability, poverty, livelihoods and inequality (see Figure 8.1). Chapter 8 adopts this wider
31 perspective to examine the differential nature of observed and future disproportionate vulnerabilities (i.e.,
32 who is most susceptible to climate hazards and events, where, at the core to understanding of what scale and
33 why?) as well as the inequalities inherent in adaptation and mitigation solutions as part of a wider climate
34 justice perspective adopted in Chapter 8, and challenges for climate resilient development.
35
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2 Figure 8.1: The lens of chapter 8 to better understand the human dimension of climate change at the nexus of climate
3 change, climate hazards and socio-economic development.
4
5
6 Finally, our assessment points towards the fact that human vulnerability to climate change is a complex and
7 multifaceted phenomenon that is often influenced by historic development processes, such as structures that
8 originated with colonization. Also, recent global shocks not directly related to climate change, such as the
9 COVID-19 pandemic and its socio-economic consequences, impact climate vulnerability and inequitable
10 impacts occurring between countries and within countries. Recent studies show that COVID-19, and other
11 social, economic and political crises, have worsened the circumstances of the poor and further marginalized
12 them.
13
14 Overall, the chapter is a key in terms of understanding societal impacts of climate change and factors that
15 determine the various differential adverse consequences of climate change on societies. The information
16 presented and assessed in the chapter is fundamental for informing adaptation and risk reduction strategies,
17 since climatic information alone cannot explain sufficiently why some regions, societies or groups are
18 suffering significantly more under climate change compared to others. Concepts such as vulnerability,
19 intersectionality and climate justice provide important insights on how societal impacts of climate change are
20 influenced and determined by broader societal development contexts.
21
22
23 8.2 Detection and Attribution of Observed Impacts and Responses
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25 8.2.1 Observed impacts of climate change with implications for poverty, livelihoods and sustainable
26 development
27
28 This section reports on new evidence on the observed impacts of climate change to livelihoods and the poor
29 since the previous assessment (IPCC, 2014a). New evidence provides additional insight into the
30 interlinkages between climate change, poverty and livelihoods, and affords this assessment with greater
31 confidence. New evidence has been evaluated according to climate change hazard categories developed for
32 the AR6 (IPCC, 2021), and summarized in Figure 8.2.
33
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1 8.2.1.1 Interactions between climate hazards and non-climatic stressors affecting livelihoods
2
3 New evidence highlights the potential for multi-hazard risks to push the poor into persistent traps of extreme
4 poverty (R鋝鋘en et al., 2016). Risk of extreme impoverishment increases for low-income people
5 experiencing repeated and successive climatic events, whereby before they have recovered from one disaster,
6 they face another impact (Forzieri et al., 2016). Cascading and compounding risks arise from multiple
7 climate hazards producing 'overlaying impacts,' for example, in mountainous regions, where the combination
8 of glacier recession and extreme rainfall result in landslides (Martha et al., 2015). There is robust evidence
9 that this effect has been observed around slow- and rapid-onset climate events related to drought, i.e., rising
10 temperatures, heatwaves, and rainfall scarcity, with devastating consequences for agriculture(Vogt et al.,
11 2018; Bouwer, 2019). Particularly the urban and rural landless poor face difficulties rebuilding assets
12 following one-off disasters or a series of shocks (Garcia-Aristizabal et al., 2015).
13
14 Climate change is one driver among many that challenges livelihoods of the rural poor, including economic
15 transitions associated with industrialization and urbanization, and also governance failures such as unclear
16 property rights and civil conflict (e.g., Nyantakyi-Frimpong and Bezner-Kerr, 2015). Recent research adds
17 evidence about the ways that climate hazards impact non-climatic stressors with implications for poverty
18 reduction (Nelson et al., 2016). The risk that climate hazards may push the poor into persistent extreme
19 poverty intensify with stagnant wages, rising costs of living, mobility traps, and ethnic or religious
20 discrimination (Cramer et al., 2014; Carter et al., 2016). Likewise in both urban and rural environments, non-
21 climatic factors related to governance exacerbate the impacts of climate events among the poorest, including
22 poor service provisioning (e.g., waste collection), poor urban planning (e.g., waste water drainage), and
23 water management failures (Di Baldassarre et al., 2010; Leal Filho et al., 2018) as well as poor rangeland
24 management, intensification of farming land uses (i.e. overgrazing, deforestation), degradation of wetlands,
25 shortage of water and soil erosion in rural areas (Olsson et al., 2019).
26
27 A key risk for the poor is shocks to specific livelihood assets that may force low-income groups into
28 persistent poverty traps (Figure 8.4; Chambers and Conway, 1992; Cinner et al., 2018) but evidence also
29 suggests that climate change impacts are also driving transient forms of poverty, i.e. a modality of poverty
30 which is recurring (Angelsen et al., 2014). Recurrent poverty is, for instance, seen in relation to crop losses
31 and decreasing agricultural production when income losses worsen living conditions (Ward, 2016; Kihara et
32 al., 2020). Recent research shows that climate change impacts may exacerbate poverty indirectly through
33 increasing cost of food, housing and healthcare, among other rising costs borne by the poor (Islam et al.,
34 2014; Ebi et al., 2017; Hallegatte et al., 2018) (high confidence). Severe adverse impacts of climate change at
35 present and future risks may result from permanent, sudden, destabilizing changes accompanying climate
36 events such as decreases in food security, large-scale migration, changes in labour capacity or conflict
37 (Bentley et al., 2014). Overall, there is more evidence that even under medium warming pathways, climate
38 change risks to poverty would become severe if vulnerability is high and adaptation is low (limited evidence,
39 high agreement) (see Section 16.5.2.3.4)
40
41 Reliable and precise estimates of the impacts of climate change on persistent poverty are difficult to
42 generate, e.g., due to data scarcity and data gaps (Hallegatte et al., 2015; Hallegatte et al., 2018; Kugler et al.,
43 2019). However, progress has been made towards detection and attribution of climate change impacts on the
44 poorest by linking standard climate observations in low-income countries with new non-traditional forms of
45 data (including Indigenous Knowledge, historical archival data, satellite imagery, and data from digital
46 devices) (Kuffer et al., 2016; Lu et al., 2016; Bennett and Smith, 2017; Steele et al., 2017).
47
48 8.2.1.2. Links between climate-related hazards, observed losses, poverty and inequality globally
49
50 There is high confidence that climate-related hazards, including both slow-onset shifts and extreme events,
51 directly affect the poor through adverse impacts on livelihoods (see Figure 8.2), including reductions and
52 losses of agricultural yields, impacts on human health and food security, destruction of homes, and loss of
53 income (Hallegatte et al., 2015; Connolly-Boutin and Smit, 2016). One of the key factors that drives
54 disproportionate impacts among poor households globally is lost agricultural income (high confidence)
55 (Hallegatte et al., 2015; Islam and Winkel, 2017). Also of concern are the impacts of climate hazards to
56 human health, which is a primary resource that the poor rely on (Figure 8.2). There are only few robust
57 global estimates of observed income losses to the poor that comprehensively account for all climate hazards;
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1 nevertheless, (Hallegatte and Rozenberg, 2017), estimating average impacts of climate change on incomes of
2 the poor, found that across 92 developing countries, the poorest 40% of the population experienced losses
3 that were 70% greater than the losses of people with average wealth.
4
5
6 a)
7
8
9 b) c)
10
11 Figure 8.2: Summary of confidence on the observed impacts of 23 climate hazards on 9 key livelihood resources on
12 which the poor depend most. Panel A displays 207 confidence statements on the total set of livelihood impacts. Based
13 on a standardized assessment of available literature since the AR5 (2014), each impact category was assigned a
14 confidence statement based on weight of evidence; high confidence is represented with HC, medium confidence with
15 MC and low confidence with LC. An average numerical confidence score is assigned for impacts from each climate
16 hazard, and for each livelihood resource category, representing total risk. Panel B depicts the "high risk" cluster of
17 livelihood impacts, where confidence is highest. Panel C represents the spatial distribution of relative confidence.
18 Hotspots represent highest confidence of observed livelihood impacts; however the absence of spatial information
19 reflects not an absence of observed livelihood risk, but the relative weight of evidence sampled in this assessment
20 exercise.
21
22
23 Overall, our assessment shows (see Figure 8.2) high confidence that two categories of climate hazards pose
24 high risk to a broad range of livelihood resources that the poor rely on: warming trends and droughts (Figure
25 8.2b). Two key livelihood resource categories 璴ife, bodily health and food security, and crop yield
26 (representing agricultural productivity) are most at risk to a broad range of climate hazards (high confidence,
27 Figure 8.2b). In addition to warming and drought, both pluvial and fluvial flooding, severe storms, and sea
28 level rise represent a high-risk cluster for livelihood impacts (high confidence, Figure 8.2b).
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1 Figure 8.2 reflects the fundamental threat that climate hazards pose to the survival of plants, livestock, fish as
2 well as the human bodies on which livelihoods depend (high confidence) (see Horton et al., 2021). The
3 dependence of livelihoods on biological, ecological and human survival depicted in Figure 8.2 is also treated
4 in Chapter 5. Likewise, impacts to livelihood resources can be compared to impacts to other key assets (see
5 AR6 WGI Chapter 12, Section 12.3 and Table 12.2).
6
7 It is revealed that warming trends and droughts pose greatest risks to the widest array of livelihood resources,
8 and are particularly detrimental to crops and human health, a long-term requirement for livelihoods and
9 wellbeing (high confidence) (see Figure 8.2b; Section 8.4.5.3; Section 16.5.2.3.4; Campbell et al., 2018). A
10 wide range of hazards also threaten the survival of fish and livestock that livelihoods depend on (high
11 confidence, Figure 8.2b), as well as other sources of income for the poor. Salinity is a secondary hazard
12 related to droughts, coastal flooding and sea level rise, and poses a fundamental risk to agriculture (high
13 confidence). There is also robust evidence for rainfall variability driving short-term impacts to agricultural
14 productivity as well as permanent loss of agriculture (high confidence).
15
16 While severe storms, pluvial and riverine floods, and coastal floods primarily impact private livelihood
17 resources, such as homes and income (high confidence, Figure 8.2b), warming and droughts also affect
18 common pool resources, such as rangeland, fisheries and forests (high confidence, Figure 8.2b). Multiple
19 hazards undermine ecosystems that Indigenous Peoples and poor communities depend on for food security
20 and income and have sustainably managed over the long-term, such as forests, grazing land, and marine
21 fisheries (Barange et al., 2014; Leichenko and Silva, 2014; B閚� et al., 2016; Jantarasami et al., 2018).
22
23 Highest confidence for observed livelihood impacts is spatially concentrated in South Asia, Africa, North
24 America, and to a lesser extent Small Island States (SIDS) (Figure 8.2c). The hazards most prevalent in all
25 regions include warming trends, droughts and sea level rise (Figure 8.2c), and undermine crop productivity,
26 crop varieties, and cropland in most regions (high confidence). Along coastlines, climate hazards threaten
27 livelihoods particularly exposed to extreme weather, flooding, and sea level rise, and where poor populations
28 are heavily dependent on agriculture and fisheries (high confidence). One third of total sampled evidence on
29 livelihood impacts was observed in just three countries--Nepal, India and Bangladesh--indicating
30 accumulating experience with livelihood impacts in South Asia (Figure 8.2c). However, this spatial
31 representation of confidence does not mean that observed livelihood impacts are not occurring in other
32 regions as well. Relative to South Asia, in Central Asia and the Caribbean, for example, the weight of
33 evidence of livelihood impacts though lighter is still robust. Among industrialized nations, there is highest
34 confidence that climate change has impacted livelihood resources in the United States.
35
36 8.2.1.3. Observed differential vulnerability to climate change, and loss and damage
37
38 The negative impacts of climate change on groups of vulnerable and/or marginalized communities generate
39 so-called `residual impacts' and residual risks that can remain a challenge in their lives (Warner and Van der
40 Geest, 2013; James et al., 2014; Klein et al., 2014; Boyd et al., 2017). Such `unacceptable' losses and
41 damages include the loss of income sources, food insecurity, malnutrition, permanent impacts to health and
42 labour productivity, loss of life, loss of homelands, among others (McNamara and Jackson, 2019; Schwerdtle
43 et al., 2020). The literature on loss and damage provides evidence not only on economic dimensions of
44 global losses and damages, but also experiences of non-economic losses from the impacts of climate change,
45 (see detail in Section 8.3; Barnett et al., 2016; Roy et al., 2018; McNamara and Jackson, 2019). The extreme
46 events that have occurred in recent years highlight the potential for loss and damage, including 2019's
47 Cyclone Kenneth, the strongest in the recorded history of the African continent, which made landfall in
48 northern Mozambique causing 45 deaths and destroying approximately 40,000 houses, leaving hundreds of
49 thousands at risk of acquiring waterborne diseases such as cholera during a prolonged recovery period
50 (Cambaza et al., 2019).
51
52 In parallel to evidence on loss and damage, the science of climate event attribution has evolved from a
53 theoretical possibility into a subfield of climate science. As attribution science strengthens, with it the
54 evidence base linking greenhouse gas emissions to extreme heat events, heavy rainfall and wind storms
55 grows and becomes more robust (Otto et al., 2016; Stott et al., 2016; Otto et al., 2018a; Otto, 2020; Clarke et
56 al., 2021; van Oldenborgh et al., 2021a; van Oldenborgh et al., 2021b; Verschuur et al., 2021).
57
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1 Climate justice questions arise about the observed differential losses and damages due to climatic hazards to
2 affected populations in close connection with their vulnerability (Wrathall et al., 2015). Individual extreme
3 weather events attributable to climate change result in losses and damages in communities and societies,
4 which allow a quantification of the differential impacts of such events on different groups (Hoegh-Guldberg
5 et al., 2019a). Considering the disproportionate adverse impacts of climatic hazard on most vulnerable
6 groups and regions and their relatively minor contribution to anthropogenic climate change (Mora et al.,
7 2018; Robinson and Shine, 2018), it is evident that vulnerability reduction and adaptation to climate change
8 have also to be seen as an issue of climate justice and climate just development (Byers et al., 2018).
9
10 Probabilistic attribution allows an assessment of people's future climate risks and estimates about the costs
11 of successfully adapting to them (James et al., 2014; James et al., 2019). To answer questions about impacts
12 on people, the vulnerable and poor in particular, requires attribution, vulnerability and adaptation science to
13 move far beyond understanding physical events and incorporate information (including Indigenous
14 Knowledge and Local Knowledge) on people's vulnerability and capacities, and exposure and losses
15 resulting from discrete events (Bellprat et al., 2019). Attribution science is therefore highly compatible with
16 risk management tools (i.e., risk reduction, risk transfer, insurance, risk pooling, recovery, rehabilitation, and
17 compensation) suggested in policy (James et al., 2019).
18
19 New observations provide greater evidence on the role of extreme poverty and global inequality, most of the
20 detrimental direct impacts of climate change (e.g., rising food insecurity) disproportionately affecting the
21 Global South (Hasegawa et al., 2018; Mbow et al., 2019; Khan and Zhang, 2021) compared with the Global
22 North. Poor populations in many countries are also disproportionately facing extreme losses and damage
23 from heatwaves, flooding and tropical weather extremes (Gamble et al., 2016). New case studies, such as the
24 European heatwave of 2018, illustrate significant negative impacts across crop production in the Global
25 North (Beillouin et al., 2020), livestock value chain (FAO, 2018; Godde et al., 2021), and fishing (Plag醤yi,
26 2019). Heatwave-induced intense fires can cause property damage, physical injury and death, as well as
27 health and psychological harm of the victims. Heatwaves also create ideal conditions for the prevalence of
28 certain pathogens, increase the risk of temperature related health problems, and exacerbate many pre-existing
29 diseases (Rossiello and Szema, 2019).
30
31 A focus in the chapter is on the intersections between climate hazards and differential vulnerability resulting
32 in actual and potential economic and non-economic losses (Section 8.3, 8.4; Thomas et al., 2019).
33 Increasingly intersections of age, gender, socio-economic class, ethnicity and race are recognised as
34 important to the climate risks and differential impacts and losses experienced by vulnerable, marginal and
35 poor in societies (high confidence).(Section 8.2,2.3; Cross-Chapter Box GENDER in Chapter 18; Nyantakyi-
36 Frimpong and Bezner-Kerr, 2015). For example, linkages between wildfires and gendered norms and values
37 are real-world examples (Walker et al., 2021). A broader climate agenda which considers social structures
38 and power relations intersecting with climate change extremes is important (Versey, 2021), in order to
39 understand disproportionate impacts of climate hazards, observed and future losses and vulnerability (see
40 Figure 8.3).
41
42
43
44 Figure 8.3: This is a schematic figure to illustrate the relationship between risk and impacts from climate change
45 (including economic and non-economic losses and damages) and human systems lead to systemic vulnerability. We
46 need to understand who is vulnerable, where, at what scale and why. We cannot just look at the climate hazard (e.g.,
47 wild fires, floods, droughts, sea-level rise, etc.) but must also look at who is being affected by these hazards and factors
48 that make people/groups vulnerable (e.g., poverty, uneven power structures, disadvantage and discrimination due to, for
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1 example, social location and the intersectionality or the overlapping and compounding risks from ethnicity or racial
2 discrimination, gender, age, or disability, etc.) (see also Cross-Chapter Box GENDER in Chapter 18 and Section 5.12).
3
4
5 Extreme events (e.g., heatwaves, cold periods, icy conditions) occurring in the Global North illustrate that
6 such events cause disproportionate impacts among aging populations, due to their immobility, isolation,
7 infrastructure deficiencies and poor health assistance (Carter et al., 2016; Reckien et al., 2018). A well-
8 known example is the heatwave in 2003 that killed thousands of elderly citizens across Europe (Poumadere
9 et al., 2005; Garc韆-Herrera et al., 2010; Laaidi et al., 2011). More recently, in the Nordic region, elderly
10 populations are experiencing distress associated with heatwaves and extreme cold events, with significant
11 increases in morbidity and mortality due to cardiovascular and respiratory failure, showing that both age and
12 underlying health issues intersect with climate change impacts (Carter et al., 2016; Li et al., 2016). The
13 elderly also experience severe impacts from extreme winter seasons, such as in Finland, where of the from 3
14 000 deaths associated with extreme winter weather and 50 000 injuries associated with slippery from
15 pavement conditions, the majority were people over 65 years old (Carter et al., 2016). Adaptation to extreme
16 events including heatwaves, cold periods and icy conditions in the Global South and North will increase
17 energy demand and the individuals' carbon footprint across all income levels (van Ruijven et al., 2019).
18
19 The 2018 US National Climate Assessment has identified the fact that south-eastern United States is already
20 experiencing more frequent and longer summer heatwaves, and by 2050, rising global temperatures are
21 expected to mean that cities in the south-eastern part of the United States of America may experience
22 extreme heat (USGCRP, 2018). This includes disadvantaged African American communities who are more
23 exposed and hence disproportionately experiencing the impacts of climate change (Shepherd and KC, 2015;
24 Marsha et al., 2018). The historically discriminated Sami as an example of Indigenous People in Northern
25 Sweden, and Maasai in Africa are examples of Indigenous People who also face climate risks and have
26 limited resources, capacity or power to respond (Leal Filho et al., 2017; Persson et al., 2017)
27
28 8.2.1.4 Climate-related hazards, livelihood transitions and migration
29
30 Agricultural livelihoods of the rural poor, especially in Africa, Asia and Latin America, are already in
31 transition due to the forces of industrialization, urbanization and economic globalization (De Brauw et al.,
32 2014; Tacoli et al., 2015), and scientific evidence shows that climate change is accelerating livelihood
33 transitions from rural agricultural production to urban wages (Cai et al., 2016; Cattaneo and Peri, 2016;
34 Kaczan and Orgill-Meyer, 2020).
35
36 There is now robust evidence from virtually every region on earth showing that the livelihood impacts from
37 a multitude of climate hazards are driving people to diversify rural income sources (Figure 8.2; Cross-
38 Chapter Box MIGRATE in Chapter 7). Rural households frequently accomplish the goal of livelihood
39 diversification with an increasing reliance on migration, urban wage labour and remittances (Marchiori et al.,
40 2012; Bohra-Mishra et al., 2014; Gray and Wise, 2016; Nawrotzki and DeWaard, 2016; Banerjee et al.,
41 2019a). What is different about rural-to-urban livelihood transitions under climate change impacts is that
42 they accelerate both rural and urban stratification of wealth (Barrett and Santos, 2014; Thiede et al., 2016).
43 On the one hand, climate change impacts on rural livelihoods increase the necessity of migration as in
44 income strategy, accelerating migration (Cai et al., 2016) even while households that cannot select
45 individuals for migration become more impoverished (Suckall et al., 2017; Nawrotzki and DeWaard, 2018).
46
47 On the other hand, climate change impacts widen the range of households willing or needing to engage in
48 migration to include those less able to bear the costs of urban migration (Afifi et al., 2016; Hunter and
49 Simon, 2017). The effect is also greater urban poverty, and a higher social burden of migrants seeking urban
50 wages (Singh, 2019). Evidence suggests that poor households often move in desperation to make ends meet.
51 In the context of climate hazards such as coastal inundation and salinity, economic necessity often drives
52 working-age adults in poor households to seek outside earnings(Dasgupta et al., 2016). Labour migration in
53 the context of climate change is also gendered, and as more men seek employment opportunities away from
54 home, women are required to acquire new capacities to manage new challenges, including increasing
55 vulnerability to climate change (Banerjee et al., 2019b).
56
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1 Migration and displacement are directly induced by the impacts of climate change (high confidence, Cross-
2 Chapter Box MIGRATE in Chapter 7), however, migration responses to climate change are differentiated
3 across the spectrum of households' wealth. In well-off households, migration can be used as a way to
4 support income diversification through remittances (Gemenne and Blocher, 2017). High levels of poverty
5 mean that a large part of African populations do not have sufficient resources to be mobile (Borderon et al.,
6 2019; Leal Filho et al., 2020b). The poorest households, conversely, will typically lack the resources that
7 would allow them to migrate in ways that maintain an acceptable standard of living, and may find
8 themselves unable or unwilling to move in the face of climate change impacts (Sam et al., 2021).
9
10 There is high agreement and robust evidence that climate change impacts also have a major influence on key
11 enabling conditions for migration, such as sociodemographic, economic and political factors (Abel et al.,
12 2019; Borderon et al., 2019), and that climate change impacts to development and governance may affect
13 how people migrate (Wrathall et al., 2019; Cross-Chapter Box MIGRATE in Chapter 7). Mobility, which
14 was considered as most viable climate change adaptation strategy to poor pastoralists, is restricted due to the
15 political marginalization of pastoral groups, land privatization, governments' decentralisation policies, and
16 plantation investment (Blench, 2001; Randall, 2015; Leal Filho et al., 2020b). While migration can be an
17 adaptation response to climate change impacts (Black et al., 2011; Gemenne and Blocher, 2017), climate
18 change impacts can also act as a direct driver of forced displacement (Marchiori et al., 2012). Societal groups
19 that are forced to involuntarily migrate in response to climate change impacts may lack resources to invest in
20 planned relocation mainly due to lack of good governance systems (Reckien et al., 2018). For people
21 displaced by climate change impacts, policy interventions have a determining influence on migration
22 outcomes, such as the numbers of migrants, the timing of migration and destinations (Gemenne and Blocher,
23 2017; Wrathall et al., 2019).The process of displacement and forced migration leaves people more exposed
24 to climate change related extreme weather events, particularly in low income countries which often host the
25 highest number of displaced people (Adger et al., 2018).
26
27 Climate change may be accelerating livelihood transitions and migration in ways that accelerate urbanization
28 (Adger et al., 2020). Although a range of climate hazards are noted for accelerating rural-to-urban livelihood
29 transitions (see Cross-Chapter Box MIGRATE in Chapter 7), a key theme to emerge across many case
30 studies is the impact of rising temperatures on agricultural productivity (Mueller et al., 2014; Cattaneo and
31 Peri, 2016; Call et al., 2017; Wrathall et al., 2018). In other words, when people cannot farm due to rising
32 temperatures (and related stressors), they migrate. In this context, migration as a livelihood diversification
33 strategy may evolve and take multiple forms over time (Bell et al., 2019), such as temporary migration
34 (Mueller et al., 2020), seasonal migration (Gautam, 2017), or permanent migration (Nawrotzki et al., 2017),
35 but generally conforms to existing patterns of migration (Curtis et al., 2015).
36
37 A key concern for the poor are climate change impacts that undermine livelihood diversification and
38 resilience, narrowing the set of available livelihood alternatives (Tanner et al., 2015; Bailey and Buck, 2016;
39 Perfecto et al., 2019).
40
41 8.2.1.5 The long-lasting effects of climate change on poverty and inequality
42
43 New studies document the long-term effects of climate change impacts on people's livelihoods that persist
44 long after a hazard event. For example, in Mali, 30 years after 1982-1984, the period of most intense drought
45 during the protracted late 20th century drying of the Sahel, the impact of drought on livelihoods and food
46 security is still recognizable. The most food secure households associated with persistent drought induced
47 famine were those that diversified livelihoods away from subsistence agriculture during and after the famine
48 (Giannini et al., 2017). Meanwhile, a larger fraction of households with fewer livelihood activities, lower
49 food security with higher reliance on detrimental nutrition-based coping strategies (such as reducing the
50 quantity or quality of meals) were those unable to diversify livelihoods 30 years previously. Sufficient time
51 has passed to consider the long-term outcomes for the poor in extreme cases featured in previous IPCC
52 assessments, including Hurricane Katrina (2005) (e.g., Fussell, 2015; Raker et al., 2019) and Hurricane
53 Mitch (1998) (e.g., Alaniz, 2017), forewarning that recovery is complex and requires significant sustained
54 long-term investment in `soft' aspects of development, including community organization and mental health
55 (O'Neill et al., 2020; Fraser et al., 2021).
56
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1 The IPCC Special Report on 1.5癈 concluded that climate change has already increased the probability and
2 intensity of individual extreme weather events occurring(Roy et al., 2018), and our new baseline
3 consideration should be that serious climate change impacts are already being experienced by the most
4 vulnerable, with long-term implications for development (Box 8.1; Roy et al., 2018). In both developing and
5 developed countries the disproportionate impacts of the compounding effects of climate change on
6 development are felt by the most disadvantaged. For example, the residual impacts of storms like Hurricane
7 Maria (see Section 8.2.1.1) illustrate how rising temperatures, extreme weather events, coral bleaching, and
8 sea level rise come together and create compounding hazard-cascades to leave long-lasting effects on the
9 lives of the poor, as well as their food and water security, health, livelihoods and prospects for sustainable
10 development--not only in developing countries (Adger et al., 2014; Olsson et al., 2014; Hoegh-Guldberg et
11 al., 2018; Roy et al., 2018), but also in highly inequitable industrialized countries within the same region
12 (Gamble et al., 2016). According to the US National Climate Assessment (USGCRP, 2018). damages caused
13 to communities by Hurricanes Irma and Maria in 2017 sparked unprecedented humanitarian crises.
14 Hurricane Maria, a category 5 hurricane, passed through Dominica, St Croix, and Puerto Rico and is
15 considered the worst climate disaster in recorded history to affect those islands (Rodr韌uez-D韆z, 2018).
16 Approximately 200,000 people migrated from Puerto Rico to the mainland US in the weeks following the
17 storm (Alexander et al., 2019). Estimates for direct and indirect casualties in Puerto Rico point out a total of
18 4645 excess deaths, equivalent to a 62% increase in the mortality rate (Kishore et al., 2018). The example of
19 Hurricane Maria and Puerto Rico illustrates that vulnerability is part of a long history of discrimination and
20 colonial governance which led to greater impacts on the island (Moleti et al., 2020). In Puerto Rico, the
21 economic costs of the collapse of the island's energy, water, transport, and communication infrastructures
22 are estimated to range from $25 to $43 billion (USD in 2017), further indebting the island, and putting its
23 long-term development at risk. Meanwhile the economic impacts of Hurricanes Irma and Maria on the
24 Caribbean region are estimated between $27 and $48 billion, and have long-term implications for state
25 budgets, infrastructure supporting development of the poorest.
26
27 New evidence provides little expectation of net positive impacts of climate change for the poor (Hallegatte et
28 al., 2015). Nevertheless, some benefits of climate change adaptation include improved disaster preparedness,
29 the accumulation of social assets, economic benefits of agricultural diversification, and benefits associated
30 with migration, as well as the political benefits of collective action (Pelling et al., 2018). In contrast,
31 wealthier tiers of society facing climate change impacts are more able to liquidate assets to avoid losses from
32 climate change, to be formally compensated for losses (Fang et al., 2019), and employ social positions to
33 leverage gains from adaptation (Nadiruzzaman and Wrathall, 2015).
34
35 The poor frequently suffer the direct and indirect impacts of climate change, including the cost of adopting
36 adaptive measures (Atteridge and Remling, 2018; Bro et al., 2020). Costs to the poor may also include the
37 secondary impacts of first order adaptation activities, including the livelihood consequences to people
38 migrating due to climate change impacts. The poor frequently bear indirect impacts of adaptation
39 interventions, such as flood protection barriers, which may displace flood waters away from high-income
40 populations toward poorer communities (Mustafa and Wrathall, 2011). Adaptation programming may also
41 indirectly affect the poor as public resources are drawn into risk reduction interventions, and away from
42 spending on social welfare and safety nets (Eriksen et al., 2015). Measures to enhance social welfare and
43 safety nets themselves help enhance the poor's resilience to climate impacts because they focus on non-
44 climatic stressors affecting livelihoods, which interact with climate hazards. Therefore, diverting attention
45 away from safety nets may in fact undermine adaptation efforts (Leichenko and O'Brien, 2019; Tenzing,
46 2020).
47
48
49 [START BOX 8.1 HERE]
50
51 Box 8.1: Climate Traps: A Focus on Refugees and Internally Displaced People
52
53 A population of concern, extremely vulnerable to climate change impacts with limited capacity to adapt, are
54 those displaced and resettled in the course of conflict or disaster, either internally or across borders (Burrows
55 and Kinney, 2016). The risk for refugees and internally displaced people (IDPs) is two-fold: on the one hand,
56 refugee and IDP settlements are disproportionately concentrated in regions (e.g., Central Africa and the Near
57 East) that are exposed to higher-than-average warming levels and specific climate hazards, including
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1 temperature extremes and drought. On the other, these populations frequently inhabit settlements and legal
2 circumstances that are intended to be temporary but are protracted across generations, and at the same time,
3 face legal and economic barriers on their ability to migrate away from climate impacts. (Adams, 2016;
4 Devictor and Do, 2016). Large concentrations of these settlements are located in the Sahel, the Near East and
5 Central Asia, where temperatures will rise higher than the global average, and extreme temperatures will
6 exceed thresholds for safe habitation (Figure Box8.1.1). Already largely dependent on state and humanitarian
7 intervention, these immobile populations will require interventions to safely maintain residence in areas
8 exposed to climate hazards. Adaptation planning should prioritize immobile populations living in an already
9 destabilized development context, on improving their capacities to deal with the further consequences of
10 climate change.
11
12
13
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1 Figure Box 8.1.1: The global distribution of the United Nations High Commissioner for Refugees (UNHCR) refugee
2 and internally displaced people (IDP) settlements (as of 2018) overlaid with annual mean near surface air temperature
3 (癈) in 2040-2059 under RCP8.5.
4
5
6 Refugees and IDPs fit into a global category of extremely structurally vulnerable people that are missing
7 from standard poverty assessments, officially uncounted or uncountable using traditional census and survey
8 methods (Carr-Hill, 2013). These include highly mobile populations, internally displaced by war and
9 environmental hazards (UNHCR, 2020; IDMC, 2021); itinerant labourers; urban poor in informal
10 settlements (Lucci et al., 2018); unauthorized migrants living in countries where they do not hold citizenship
11 (Passel, 2006); guest workers (Reichel and Morales, 2017); the homeless and institutionalized (Caton et al.,
12 2007); rural nomadic, pastoralist or landless populations (Randall, 2015); Indigenous Peoples and forest
13 dwelling communities (Galappaththi et al., 2020); among others. Frequently living without social safety nets,
14 such as health care and formal education, these uncounted or `missing millions' are vulnerable to problems
15 associated with acute and chronic poverty, such as the spread of infectious disease and malnutrition (Ezeh et
16 al., 2017). Because these `missing' populations are not counted, they are frequently not a part of planning
17 (Carr-Hill, 2013), including adaptation planning. In any particular national context, these missing
18 populations may represent a small fraction of the population (about 5% in South Asian countries), however
19 cumulatively hundreds of millions of people may be missing from official estimates (Carr-Hill, 2013). Over
20 the last decade, techniques for estimating the locations, numbers and socioeconomic status of missing
21 populations have moved beyond census and nationally representative household surveys, leveraging
22 advances in satellite imagery (Kuffer et al., 2016; Bennett and Smith, 2017) and data from mobile digital
23 devices (Jean et al., 2016; Xie et al., 2016; Steele et al., 2017).
24
25 [END BOX 8.1 HERE]
26
27
28 8.2.1.6 Interactions between climate hazards and social-ecological thresholds
29
30 Climate change threatens to rapidly transform unique and threatened ecosystems (RFC1), such as tropical
31 rain forests, coral reefs, arctic and high-mountain ecosystems, as well as the Indigenous and forest-dwelling
32 people whose livelihoods, cultures and identities are dependent on these ecosystems. In recent years, the case
33 of Amazonia illustrates how such systems are transforming, with detrimental consequences for Indigenous
34 Peoples, and the vital role that Indigenous Peoples serve in protecting vulnerable ecosystems (Ricketts et al.,
35 2010; Box 8.6). Globally, Indigenous territories cover the greatest area of remaining tropical forest in
36 comparison to other protected areas, and encompass the bulk of Earth's biodiversity, and are the locus for a
37 number of key ecosystem services across spatial and temporal scales(Walker et al., 2020). Specifically, in
38 2014 Indigenous territories and other protected areas represented the equivalent of 58.5% of all the carbon
39 stored in the Brazilian Amazon biome and had the lowest deforestation rate (2.1%) and fire incidences,
40 evidencing the effectiveness in safeguarding important ecosystems services and wellbeing (Nogueira et al.,
41 2018). It is estimated that Indigenous territories in the Brazilian Amazon contribute at least US$5 billion
42 each year to the global economy through food and energy production, greenhouse emissions offsets, and
43 climate regulation and stability (Siqueira-Gay et al., 2020). Given the high incidence of poverty of the
44 Amazonian countries and high proportion of traditional and Indigenous Peoples, remoteness and neglected
45 governance place these unique ecosystems and Indigenous populations as highly vulnerable to climate
46 change impacts (Pinho et al., 2014; Brond韟io et al., 2016; Mansur et al., 2016; Kasecker et al., 2018).
47 Despite their importance, the survival of Indigenous Peoples in the Amazon is on the brink in the wake of
48 increasing deforestation, land conflicts and invasions, cattle ranching, mining, fire incidence, health
49 problems, and human rights violation (Ferrante and Fearnside, 2019). There is increasing evidence that both
50 economic and non-economic losses and damages are currently and will be unevenly experienced by
51 populations in vulnerable conditions, such as children, women, Indigenous Peoples and traditional
52 communities (Pinho, 2016; Lapola et al., 2018; Roy et al., 2018; Eloy et al., 2019; Machado-Silva et al.,
53 2020). Increasing wildfires inside protected areas, in particular, territories of Indigenous Peoples and
54 traditional communities, is worrisome and presents challenges for the future of unique and threatened socio-
55 ecological systems, and the ecosystem services they provide. The Amazonian Indigenous territories and
56 protected areas can deliver protection of biodiversity and important ecosystem services if appropriate
57 governance mechanisms are in place and their land tenure rights and livelihoods are secured (Steege et al.,
58 2015). The role of enabling environments is discussed in Section 8.5.
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1
2 8.2.1.7 Linkages between climate change impacts and sustainable development goals (SDGs)
3
4 Many of the observed outcomes of climate change, for example migration, are also outcomes of
5 multidimensional poverty in low income countries (Burrows and Kinney, 2016). Future impacts may be
6 better understood if the vulnerability and the capacity for adaptation is understood to be rooted in a
7 sustainable development context (see Box 8.2). The UN Sustainable Development Goals (SDGs), which aim
8 to reduce poverty and inequality, and identify options for achieving development progress, also provide
9 insight on reducing climate vulnerability (United Nations, 2015). Firstly, climate change impacts may
10 undermine progress toward various SDGs (medium confidence), primarily poverty reduction (SDG1), zero
11 hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others (medium evidence,
12 high agreement). In both developing and high-income countries, climate change hazards in connection with
13 other non-climatic drivers already accelerate trends of wealth inequality (SDG 1) (Leal-Filho et al., 2020).
14 Climate impacts on SDGs illustrate the complex interrelations between development. For example, in
15 regions encountering obstacles to SDGs, characterized by high levels of inequality and poverty, such as in
16 Africa, Central Asia and Central America, climate change is likely to exacerbate water insecurity (SDG 6),
17 which may then also drive food insecurity (SDG 2), impacting the poor directly (i.e. via crop failure), or
18 indirectly (e.g. via rising food prices) (Conway et al., 2015; Hertel, 2015; Cheeseman, 2016; Rasul and
19 Sharma, 2016). There is a pressing need to address poverty issues, since these may negatively influence the
20 implementation of all SDGs (Leal Filho et al., 2021a).
21
22 At the same time, there is increasing evidence that successful adaptation depends on equitable development
23 and climate justice; for example, gender inequality (SDG 5) and discrimination (SDG 16) are among the
24 barriers to effective adaptation (high confidence) (Bryan et al., 2018; Onwutuebe, 2019; Garcia et al., 2020).
25 Likewise, both climatic and non-climatic threats to development, such as conflict (SDG 16), may seriously
26 undermine capacity to formulate and implement adaptation policies, and design planning pathways (Hinkel
27 et al., 2018). The risk of conflict associated with climate change has great potential to undermine other
28 development goals (Box 8.4). Where sustainable development lags and human vulnerability is high, there is
29 also often also a severe adaptation gap (Figure 8.12; Birkmann et al., 2021a). The SDGs may provide
30 important cues on how to close the adaptation gap: climate action needs to be prioritized where past and
31 future climate change impacts threaten SDGs, and where investment in SDGs improve capacity for
32 adaptation (see Section 8.6).
33
34
35 [START BOX 8.2 HERE]
36
37 Box 8.2: Livelihood Strategies of Internally Displaced Atoll Communities in Yap
38
39 On Yap Island in the Federated States of Micronesia, displaced atoll communities have been under
40 considerable pressure due to climate change. This is because of the island's vulnerability, as a result of its
41 weak economic status, and the little access it has to technologies that may support adaptation efforts. This
42 trend is seen in many Small Island Developing States (SIDS) (see also Chapter 15). On small islands and
43 remote atolls where resources are often limited, recognizing the starting point for action is critical to
44 maximizing benefits from adaptation. They do not have uniform climate risk profiles, and not all adaptations
45 are equally appropriate in all contexts (Nurse et al., 2014) (high confidence).
46
47 The recurrences of natural hazards (e.g., El Nino driven tropical storms, associated coastal erosion and
48 saltwater or seasonal droughts leading to water scarcity) and crises threaten food and nutrition security
49 through impacts on traditional agriculture, leading to income losses and causing the forced migration of
50 coastal communities to highlands in search of better living conditions. As many of the projected climate
51 change impacts are unavoidable, implementing some degree of adaptation becomes crucial for enhancing
52 food and nutrition security, strengthening livelihoods, preventing poverty traps, and increasing the resilience
53 of coastal communities to future climate risks (Krishnapillai, 2018).
54
55 With support from the US Department of Agriculture and USAID, the Cooperative Research and Extension
56 wing of the College of Micronesia- Federated States of Micronesia Yap Campus has been providing
57 outreach, technical assistance and extension education to regain food and nutrition security and stability by
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1 improving the soil and cultivating community vegetable gardens as well as indigenous trees and traditional
2 crops. This program implemented a three-pronged adaptation model to boost household and community
3 resilience under harsh conditions on a degraded landscape, hence addressing poverty risks and promoting
4 more sustainable livelihoods (Meyer and Jose, 2017).
5
6 The following three strategies- a) gender-focused capacity development on soil health management, b) good
7 practices in Sustainable Land Management and c) income generation activities were employed to mitigate
8 crop production losses and increase resilience to climate influenced hazard events within the 258 hectares of
9 degraded lands in Gargey Village.
10
11 The project first focused on increasing the capacity development for 1,100 residents of Gargey Village,
12 including women and youth, in order to create a base of community knowledge for soil health management.
13 Training on soil health management including the following: use of cover crops and improved fallow,
14 legumes, composting and agroforestry systems, mulching, minimum tillage, and contour farming, as well as
15 altering production practices (planting time, spacing, pest and disease, harvesting time), alternative crop
16 production methods (container gardening, raised bed gardening, small plot intensive farming), hands-on
17 training on compost preparation, and seed germination.
18
19 Dissemination and use of good practices in Sustainable Land Management (SLM)
20
21 Following capacity building, the project trained villagers on the use of SLM practices to further soil
22 resilience during ongoing and acute precipitation events. The SLM practices focused on volcanic soil
23 management and compost preparation and use, along with the planting of native trees and crops. The
24 protective soil cover was improved through cover crops, crop residues or mulch, and crop diversification
25 through rotations. Local salt-tolerant crop varieties were introduced. Seed packets and seedlings were
26 distributed to ensure a continuous supply of resilient traditional plants and to provide for sustainable post-
27 disaster recovery.
28
29 Income generation activities
30
31 The project also included training to increase the incomes of households by training household members in
32 the cultivation of vegetables using various alternative crop production methods. Households were then able
33 to sell their vegetables in the local markets.
34
35 Less hunger and more cash from leafy vegetables is a concept adopted at the household level to not only
36 reduce poverty, but also to empower displaced communities to address the dilemma of malnutrition.
37 Practices include growing a variety of nutritious vegetables as part of a large crop portfolio and using
38 alternative crop production methods, such as small-plot intensive farming using container gardening or
39 raised-bed gardening (Krishnapillai and Gavenda, 2014). In addition, focusing efforts on increasing the
40 sustainable production of staple crops confers significant nutritional benefits.
41
42 More households in the settlements are consuming vegetables since home gardeners started harvesting
43 regularly and sharing their produce with extended families or selling them for income generation. The
44 location-specific, community-based adaptation model improved food and nutrition security and livelihoods
45 (Krishnapillai, 2017). People can access more nutritious and reliable food sources, and they are growing their
46 own food and selling their surplus, creating new optimism about their future.
47
48 The climate-smart agriculture package increased land cover by more than 50% within Gargey village. This
49 includes the planting of 42 varieties of native trees and crops. Current major crops that are being successfully
50 grown at this location include coconut, breadfruit, mango, noni, chestnut, pineapple, sugarcane, land taro,
51 tapioca, and sweet potato, among others. There have been additional benefits in terms of improvement in
52 water availability. These activities directly benefited the resilience and food security of more than 1,000
53 residents in Gargey Village, and lessons learned from this project have helped to scale up similar projects at
54 3 locations in Yap that have experienced equivalent climate-damaging processes.
55
56 Overall, this case study illustrates the benefits of promoting resilient crop production in Gargey Village, as
57 an example of displaced atoll communities. Innovative and sustainable CSA strategies offered broader
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1 insights and lessons for enhancing adaptive capacity and resilience, on a degraded landscape. The coherent
2 strategies and methods employed strengthened livelihood opportunities by improving access to services,
3 knowledge, and resources. By its concurrent focus on enhancing food security through traditional crops,
4 coupled with nutrient-rich vegetables, promoting rainwater-harvesting systems and water conservation, and
5 promoting resilient household livelihood opportunities, atoll communities brought together crucial elements
6 needed to reduce vulnerabilities, and to better cope with disasters and climate extremes while embracing the
7 traditional culture. The location-specific yet knowledge-intensive CSA methods deployed, offered
8 opportunities for atoll communities to revitalize themselves, overcoming barriers while adjusting to new
9 landscapes.
10
11 [END BOX 8.2 HERE]
12
13
14 8.2.2 Poverty-environment traps and observed responses to climate change with implications for
15 poverty, livelihoods and sustainable development
16
17 Across all geographical regions, there is evidence that anthropogenic climate change is hindering poverty
18 alleviation and thereby constraining responses to climate change in five main ways:
19 a) by worsening living conditions (Hallegatte et al., 2017; Hsiang et al., 2017),
20 b) by threatening food and nutrition security due to undernutrition and reduced opportunities for
21 income generation (Burke et al., 2015),
22 c) by disrupting access to basic ecosystems services such as rainwater, soil moisture (reducing the
23 productivity of agricultural land) or via the depletion of habitats (e.g., mangroves, fishing grounds)
24 that particularly vulnerable and poor people are depending on (Malhi et al., 2020),
25 d) by creating favourable conditions for the spread of vector-transmitted diseases (Liang and Gong,
26 2017).
27 e) and by threatening underlying gender inequalities exacerbated by climate impacts such as access and
28 control to productive inputs and reinforcing social-cultural norms that discriminate against gender,
29 age groups, social classes and race (Singh et al., 2019b).
30
31 Responses to observed impacts such as glacier melt, sea level rise and increases in the frequency of extreme
32 weather events such as droughts, hurricanes and floods need to take into account how they influence other
33 policy issues and sectors, including poverty alleviation, human health and well-being (Orimoloye et al.,
34 2019), water/energy and the built environment (Andri et al., 2018), transportation and mobility (Markolf et
35 al., 2019), agriculture (Hertel and Lobell, 2014) and biodiversity/ecosystems (Nogu閟-Bravo et al., 2019),
36 only to mention a few. Recent literature provides evidence that impacts of climate change together with non-
37 climatic drivers can create poverty-environment traps that may increase the probability of long-term and
38 chronic poverty (Figure 8.4; Hallegatte et al., 2015; Djalante et al., 2020; Malhi et al., 2020; McCloskey et
39 al., 2020) (high confidence) (see Figure 8.4).
40
41
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1
2 Figure 8.4: Schematic representation of a poverty-environment traps that can increase chronic poverty.
3
4
5 In addition, observed climate change responses, including autonomous and planned adaptation, can
6 exacerbate poverty and vulnerability (Eriksen et al., 2021). There is robust evidence that planned responses
7 to climate change, such as large scale adaptation projects, in some context can also increase vulnerability due
8 to the reinforcement of inequalities and the effects of further marginalization (Fritzell et al., 2015; Eriksen et
9 al., 2021). There is increasing evidence that also the responses to indirect impacts of climate change, such as
10 to shifts in marine or terrestrial ecosystems due to climate change (Seddon et al., 2016) affect different
11 groups differently and impact poverty and livelihood security. Apart from influences on agriculture trends
12 (Reichstein et al., 2014) and changes in yields (Reyes-Fox et al., 2014; Craparo et al., 2015), climate change
13 has significant (direct and indirect) impacts on livelihood assets and resources such as forests, livestock
14 production and fisheries, which may undermine the livelihoods security in the medium- and long-run.
15
16
17 [START BOX 8.3 HERE]
18
19 Box 8.3: COVID-19 Pandemic
20
21 During the COVID-19 pandemic, countries such as India were affected by with hydro-meteorological
22 hazards (Raju, 2020) making it extremely difficult to handle a public health crisis in the context of
23 compounding risks and cascading hazards (Phillips et al., 2020). The COVID-19 pandemic can increase the
24 adverse consequences of climate change, since it has the potential to delay some key adaptation actions. On
25 the other hand, the pandemic also highlights the importance of better preparedness to the impacts of climate
26 change (Djalante et al., 2020). Overall, the COVID-19 pandemic has worsened the economic situation within
27 many countries and local communities particularly for already marginalized groups (Gupta et al., 2021). The
28 accumulation of crises, such as the COVID-19 pandemic alongside climate change impacts, underscore the
29 fact that stressors do not occur in isolation, but are interlinked, with clear implications for structural
30 vulnerability and adaptation options available to the poorest (Sultana, 2021). Responses to COVID-19 has
31 led to significant economic and social distress within and across societies and local communities, especially
32 in poorer countries. The direct health and economic impacts of the lockdowns have further limited the ability
33 of many people across the developing world to pursue income-generating activities, and sustain livelihoods
34 that are already affected by climate hazards. In addition, poor or most vulnerable groups face further
35 marginalization due to misinformation that these groups transmit the virus to other wealthier groups and
36 areas. The pandemic has intensified inequalities in both developing countries (FAO, 2020) and in
37 industrialised nations (Anderson et al., 2020; McCloskey et al., 2020) whereby vulnerable groups are
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1 especially affected (Raju et al., 2021). Whereas different models and scenarios contain different data and
2 figures, an agreement exists that it is likely that socio-economic impacts are particularly severe within
3 selected global regions and areas that are already characterized by a rather high level of human vulnerability
4 (see also Section 8.3). This also implies that the capacity of people to prepare for present and future climate
5 change impacts will further decrease within these countries and population groups under the direct and
6 indirect consequences of the COVID-19 pandemic.
7
8 Moreover, the COVID-19 pandemic has not only influenced climate change research (Leal Filho et al.,
9 2021b) but is also influencing the capacities of governmental institutions and nations to support planned
10 adaptation and poverty reduction favouring the most vulnerable groups, since the crisis also means among
11 other issues a significant reductions in tax revenues (Clemens and Veuger, 2020). COVID-19 may also force
12 people to seek alternative sources of income that can lead to the further erosion of long term adaptive
13 capacities. In many settings, the pandemic has had significant impact on businesses and SMEs (Schmid et
14 al., 2021). The important role of governmental support for buffering crises and periods of income loss of
15 individual households (e.g., unemployment) and private businesses (e.g., SMEs) has also been demonstrated
16 during the COVID-19 pandemic in OECD countries (OECD, 2020b).
17
18 Livelihood disruptions and an increasing probability of higher levels of poverty and of structural
19 vulnerability in various countries have already been observed (Laborde et al., 2020b). These vulnerabilities
20 and the new layers created by the pandemic must be seen with an intersectional lens (Raju, 2019; Sultana,
21 2021).
22
23 In addition, the COVID-19 pandemic has also revealed the unequal access to vaccine and the importance of
24 national state institutions to buffer negative impacts, for example of the lock downs or in terms of
25 unemployment. The COVID-19 pandemic recovery also sets some basis for a stronger narrative towards a
26 green recovery approach (Djalante et al., 2020; Forster et al., 2020).
27
28 [END OF BOX 8.3]
29
30
31 8.2.2.1 Characteristics of responses
32
33 Many of the observed responses to climate change aim to reduce exposure of people to climate-related
34 hazards, such as flood defences, sea walls and embankments (Gralepois et al., 2016), rather than aimed at
35 specifically addressing structural vulnerability to climate change, which means the root causes of
36 vulnerability (e.g., Mikulewicz, 2020; McNamara et al., 2021a). Evidence emerges that responses to impacts
37 of climate change should consider next to the physical climate event, also historical, institutional root causes
38 that make people or systems vulnerable. However, addressing structural vulnerability must be balanced with
39 the political context and the range of options available to people, communities or countries (see Section 8.3).
40 Political frameworks need to consider both types of responses, to revive democratic debate and citizenship
41 (Pepermans et al., 2016). In addition to reducing poverty and vulnerability, planned climate change
42 responses must also be intersectoral, in order to increase their effectiveness. This requires higher levels of
43 vertical and horizontal coordination and integration (GIZ, 2019). Horizontal coordination encompasses for
44 example the integrated coordination of responses to climate change across different sectors, which requires
45 suitable governance structures and processes that allow for such a coordination (Di Gregorio et al., 2017;
46 Burch et al., 2019). Vertical integration is needed in order to ensure that effective responses also include
47 different levels of governance and benefit from knowledge at different scales. The inclusion of local
48 knowledge within national or provincial adaptation strategies requires such linkages and vertical
49 coordination. Overall, there is an increasing body of literature that highlights the importance of improved
50 integration and coordination also in order to promote a higher effectiveness of strategies and an improved
51 consideration of social justice and climate justice when designing and implementing responses (Levy and
52 Patz, 2015).
53
54 However, evaluating the effectiveness, social impacts and social justice of climate change responses is not
55 uniform across locations, nations and regions for three principal reasons:
56 a) temporal dimensions of responses: effective and appropriate climate change responses require that
57 strategies and responses are tested in a specific context and that ongoing learning and adaptive
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1 management is a necessary to avoid maladaptation or other unintended consequences (Eriksen et al.,
2 2021),
3 b) goal of responses: responses may have distinct and locally specific goals, such as reducing
4 vulnerability(Sarker et al., 2019), which is distinct from increasing resilience (Alam et al., 2018).
5 Vulnerability reduction and the increase of resilience (i.e., raising the ability to cope) are two
6 different goals and often involve different processes, and
7 c) level of responses: there is a need to ascertain the relevant level at which the responses are needed or
8 expected (e.g., the individual level, community level, regional level). This analysis, however, also
9 needs to consider the differential capacities of people, for example, the limited capacities of poor
10 people or constrained capacities of most vulnerable countries (see also Section 8.3).
11
12 Effective responses to climate change impacts for one group could impose higher costs and negative
13 consequences for other groups, in terms of shifts in exposure and vulnerability. This category of response is
14 known as maladaptation. Maladaptation actions defined in the IPCC SR1.5癈 (IPCC, 2018b) and in the Land
15 Report (IPCC, 2019a) are the ones that usually have unintended consequence, and can lead to increased
16 negative risk to poor population mostly in the global south to climate hazards by either increasing
17 greenhouse gas (GHG) emissions and or by increasing the vulnerabilities to climate change with diminished
18 welfare, now and in the near future (Roy et al., 2018). For example, migration to urban centres can represent
19 a significant adaptation opportunity for the migrants themselves, but can also increase the vulnerability of
20 their community of origin or destination (for example, through a depletion of the workforce or an addition
21 pressure on environmental resources and infrastructure respectively) (Gemenne and Blocher, 2017). Some
22 types of observed responses to climate change may not yield long-term benefits. For example, food imports
23 during droughts or adverse climate conditions are not a fully adequate response, since they may alleviate a
24 problem on the one hand (i.e., an imminent food shortage due to crop failure), but on the other, lead to no
25 long-lasting improvements in physical conditions and create new dependencies that can increase
26 vulnerability in the long-run (Zimmermann et al., 2018).
27
28 In the AR5, the maladaptation outcomes emerge when impacts of climate change impacts and risks are
29 disproportionately born by the poorest populations (Olsson et al., 2014). Since then, most maladaptation
30 evidence emerges as a consequence of failure to address root causes of vulnerabilities that emerge under high
31 and multiple forms of inequalities. In fact, the literature shows that adaptation practices can indeed
32 redistribute vulnerabilities and increase risks to already poor and marginalized people with risk to
33 maladaptation outcomes mainly in the Global South countries (Atteridge and Remling, 2018).
34
35 The maladaptation outcomes also emerge when responses are not equitable at the policy level, and
36 exacerbate the precarity of vulnerable populations by excluding them from benefits and support, while
37 attending to the needs of people of the most enfranchised segments of society (Thomas and Warner, 2019);
38 Asplund and Hjerpe 2020). In Tanzania, the political marginalization of pastoralist access to critical riparian
39 wetlands and increasing expansion of agriculture may result in adaptation pathways that heighten risk for
40 these groups while reducing risk for others (Smucker et al., 2015). Salim et al. (2019) found that adaptation
41 to flooding in Jakarta privileges political economic elites, while poor infrastructure in poorest
42 neighbourhoods exacerbates loss of assets, housing and displacements (Salim et al., 2019). In Bangladesh,
43 intense and consecutive flooding led to that national and regional-level adaptation plans, that resulted in
44 maladaptive trajectories as local poverty context and precarities of properties are not carefully considered
45 and disconnected from local autonomous practice (Rahman and Hickey, 2019).
46
47 Overall, the assessment shows that understanding impacts of climate change should not be limited to the
48 analysis of direct impacts or physical changes under different climatic conditions, but needs also account for
49 the distributional effects that responses to climate change may imply. For example, responses implemented
50 in order to benefit one sector or social group (e.g., farmers), should not undermine the wellbeing of others
51 (e.g., pastoralists). Documented cases of maladaptation (see Eriksen et al., 2021), hint towards the fact that
52 responses to climate change can exacerbate in some cases existing inequality and may discourage other types
53 of responses (see also Section 8.5 and 8.6). Furthermore, responses to similar climate change impacts and
54 hazards may be extremely differentiated according to various social contexts (see Section 8.3). In some cases
55 responses to climate change (e.g., relocation programmes) can even trigger social tipping points when
56 climate change responses lead to major social transformations, such as forced displacement (see Section 8.4).
57
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1 Also the influence of new global phenomena, such as urbanization, issues of urban health (Schmid and Raju,
2 2020) and the consequences of the COVID-19 pandemic need to be considered when assessing actual and
3 potential consequences of different responses to climate change. For example, inequalities, vulnerabilities
4 and poverty pockets are expected to change and increase, particularly in urban areas in countries with rapid
5 urbanization processes and high levels of poverty (Djalante et al., 2020), hence urban and urbanization trends
6 need more attention. Urbanization processes add another level of complexity (Raju et al., 2021). This is
7 particularly the case in rapidly growing medium-sized cities in Africa that at present do not have sufficient
8 the resources to cope and adapt and to implement climate sensitive land-use planning (Birkmann et al.,
9 2016).
10
11
12 [START BOX 8.4 HERE]
13
14 Box 8.4: Conflict and Governance
15
16 Climate change impacts carry the risk of amplifying or aggravating existing tensions within and between
17 communities or countries (Sakaguchi et al., 2017). There is however little evidence for a universal direct
18 causal linkage between climate change and violent conflicts (Mach et al., 2019). The triggering of conflicts
19 related to climate impacts is strongly determined by contextual factors, such as the type of government or the
20 level of development (Mach et al., 2019). A study of 156 countries (Abel et al., 2019) showed that an
21 increase in periods of drought exacerbate the risk of conflict, especially in democratic countries. This
22 influence was particularly marked during the period 2010-2012 in countries of Western Asia and Northern
23 Africa which were undergoing political transformations such as the Arab Spring. Conflict can then represent
24 people's discontent in governments' inefficient responses to climate impacts (Abel et al., 2019). Research
25 has noted conditions under which climate change can increase risk of armed conflict, which include ethnic
26 exclusion, agricultural dependence, large populations, insufficient infrastructure, dysfunctional local
27 institutions, and low levels of development (von Uexkull et al., 2016; Ide et al., 2020).
28
29 Since the AR5, there is robust evidence of the socially-destabilizing measures and high-risk income
30 alternatives that the world's poorest commonly take to cope with the impacts of climate change on
31 livelihoods (Blattman and Annan, 2016). To avoid impoverishment, households often pursue risky livelihood
32 alternatives, with high potential for return on investment (Sovacool et al., 2018), but which in some cases
33 undermine environmental quality (Bolognesi et al., 2015), violate laws (Ahmed et al., 2019), contradict
34 social norms (Hagerman and Satterfield, 2014), erode institutions (Sovacool et al., 2018), or affect intra- and
35 inter-community cooperation (Nadiruzzaman and Wrathall, 2015). At the same time, a narrowing of
36 livelihood options carries a strong potential for participation and association with violent non-state
37 organizations and movements, either criminal or ideological (Nett and R黷tinger, 2016). In order to reduce
38 the risk of instability and violence associated with climate change, a broadening of livelihood options among
39 the most vulnerable people appears as an effective policy approach (Miguel et al., 2004).
40
41 The determinants of violence in the context of climate shocks are primarily poor institutional planning and
42 response to impacts, such as the capacity of a government to respond to and manage environmental risk
43 (Selby et al., 2017). In Latin America, for example, evidence on social conflicts related to disputes over
44 access water use in the context of drought and decreasing water availability point to institutional failures,
45 such as poor, inequitable or corrupt water governance (Poupeau et al., 2017). Such observation is not
46 confined to low income countries. In industrialised countries, failure of governments to address climate
47 change is likely to fuel discontent, a condition in which violent outcomes are possible (Ide et al., 2020).
48
49 In this regard, specific attention ought to be paid to how responses to climate change exacerbates inequalities
50 within societies and create tensions between different groups--typically between those who are able to
51 protect themselves from climate change impacts and those who do not have sufficient resources and/or are
52 not prioritised in the responses to climate change. Frequently the possibility of migration from climate
53 change is conflated with conflict outcomes from climate change; however there is limited evidence and low
54 agreement that climate change and migration will result in increased conflict (Okpara et al., 2016b), while
55 there is robust evidence and medium agreement that climate change can exacerbate existing tensions, which
56 can in turn result in political violence and an increase in asylum-seeking (Marchiori et al., 2012). In the
57 future, conflict in the context of climate change impacts may increase the number of migrants seeking
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1 asylum, although at present there is scant empirical evidence for this (Schutte et al., 2021). Recent evidence
2 also provides support for social conflict around inequitable climate mitigation policy as well (e.g., fossil fuel
3 subsidies and emissions reductions targets) (Rentschler, 2016).
4
5 In recent years, research on the climate-security nexus has developed considerably, and has highlighted risks
6 pertaining to conflicts, geo-political rivalries, critical infrastructure, terrorism or human security (Gemenne
7 et al., 2014). While different studies have identified have identified strong past correlations between climatic
8 variations (of temperature and rainfall in particular) and the occurrence of violent conflicts (Hsiang et al.,
9 2013), while others have stressed the need for stronger explanatory models or the risk of a selection bias
10 (Benjaminsen et al., 2012; Solow, 2013; Buhaug et al., 2014).
11
12 While climate change may increase armed conflict risks in certain contexts (Mach et al., 2019), responses to
13 climate change will be crucial to mitigate these risks. Poor institutional responses can directly drive violence,
14 and there is robust evidence that inequitable responses further exacerbate marginalisation, exclusion or
15 disenfranchisement of some populations, which are commonly recognized drivers of violent conflict.
16
17 As a ray of hope, robust evidence suggests environmental problems (related to climate change) can be dealt
18 with cooperatively, hence leading to more positive and peaceful relations between groups (Wolf et al., 2003;
19 Ide, 2019). To avert violent outcomes induced by climate change, stronger local and national climate
20 adaptation institutions within vulnerable societies, and stronger cooperative resource governance
21 mechanisms between vulnerable countries (such as transboundary water governance agreements) are needed.
22
23 [END BOX 8.4 HERE]
24
25
26 Table 8.1 and Table 8.2 present a summary of a set of common climate change responses observed,
27 classified according to their main approach. All these responses demand a certain level of commitment, the
28 support of adequate policies, and enough budget for their implementation (Archie et al., 2018). The observed
29 climate change adaptation responses--differentiated along urban and rural settings--underscore the very
30 different nature of various responses and the need for cross-sectoral approaches.
31
32
33 Table 8.1: Selected observed climate change adaptation responses in urban and rural areas commonly associated with
34 positive implications for poverty, livelihoods and sustainable development
Modality of response Impacts to urban communities Impacts to rural communities (e.g., farmers,
pastoralists)
Integrated natural resource Better conservation of green Conservation of natural resources e.g., water,
management
(e.g., van Noordwijk, 2019) areas and reduced exposure to soil, pasture, forest, wildlife, biodiversity,
floods aquatic life.
Disaster Risk Management Pre-disaster risk management Disaster risk management may play an
(e.g., Mall et al., 2019) and post-disaster risk important role to avoid or limit the impacts of
management measures reduce floods, droughts and other extreme events
loss of life and damage to
property
Structural/physical Improving physical/structural Flood defences may help to prevent property
improvements
(e.g., Vallejo and Mullan, measures to prevent property losses, planting of trees may stabilize slopes,
2017)
damages and foster ecosystems reduce soil erosion and siltation, rainwater
integrity harvesting increases water availability,
protection of biotopes supports biodiversity
Relocation of vulnerable Moving vulnerable Reduces the exposure of vulnerable
communities
(e.g., McNamara and Des communities before and during communities to climate change and extremes
Combes, 2015)
climate-induced hazards may hazards e.g., floods and droughts, lessen their
reduce loss of life vulnerability, improve access to better
resources and build their capacity to adjust to a
new context
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Education and Communication Public education and Fosters awareness creation, reducing the
(e.g., Monroe et al., 2017) awareness, improved degree of vulnerability to certain climate
communication may reduce the induced hazards and help build the capacity to
damages and losses from adapt
adverse impacts of climate
change and from extreme
events
1
2
3 While Table 8.1 shows selected adaptation responses, Table 8.2 shows selected mitigation responses that
4 highlight that some mitigation responses (e.g., increasing energy efficiency) have a potential benefit also for
5 the poor or more vulnerable groups for example through the reduction of costs for electricity. Both tables
6 underscore that climate change mitigation and adaptation responses are strongly interlinked with broader
7 development issues (industrial production, land-use planning, education, etc.) at different scales.
8
9
10 Table 8.2: Selected climate change mitigation responses
Modality of response Impacts to urban communities Impacts to rural communities (e.g., farmers
pastoralists)
Land use planning Helps to reduce greenhouse Helps to reduce pressure on the natural
(e.g., Frose and Schiling,
2019) gas (GHG) emissions and resources (deforestation, land filling, damaging
support environmental wetland) and promotes carbon sequestration
conservation, preventing urban
heat islands
Improving industrial processes Unlocks many opportunities In rural settings, industrialization and
(e.g., van Vuuren et al., 2018) for improvement, including technological innovation may directly assist
the optimised use of energy, vulnerable communities through provision of
reuse of waste in the inputs e.g., water storage, drip irrigation,
production, reducing GHG forecast information, or reuse of biowaste in
emissions, use of biomass and agriculture or energy production, hence
more efficient equipment reducing costs and pollution levels
Renewable Energy Reduction of GHG emissions Some options (e.g., solar, wind) may help to
(e.g., Cronin et al., 2018)
and reduction of the cost of reduce deforestation, reduce GHG emissions
electricity and promote healthier air within households
Energy efficiency Efficient end-user's energy Efficient end-user's energy utilization leads to
(e.g., Abrahamse and Shwom, utilization reduces energy natural resource conservation and a reduction
2018) wastage, reduces costs and of GHG emissions
lowers carbon emissions
Local/individual actions Can contribute to reduce Fosters personal and community motivation to
(e.g., Shaffril et al., 2018; carbon footprints manage individually and communally owned
Tvinnereim et al., 2018) resources. Helps to reduce GHG emission and
foster resources conservation
11
12
13 8.2.2.2 Observed impacts and implications for structural inequalities, gender and access to resources
14
15 This section examines the mutual reinforcement of climate change impacts and structural inequalities. There
16 is robust evidence that negative impacts and harm posed by climate change are also a result of social and
17 political processes and existing structural inequalities (Sealey-Huggins, 2018). Climate change encompasses
18 unevenly distributed impacts on women, youth, elderly, Indigenous Peoples, communities of colour, urban
19 poor and socially excluded groups, exacerbated by unequal distribution of resources and poor access for
20 some (Rufat et al., 2015; McNeeley, 2017; Sealey-Huggins, 2018). Structurally disadvantaged people, who
21 are subject to social, economic and political inequalities resulting historically from discrimination,
22 marginality or disenfranchisement because of gender, age, ethnicity, class, language, ability and/or sexual
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1 orientation, are disproportionately vulnerable to the negative impacts of climate change hazards (Kaijser and
2 Kronsell, 2014; Otto et al., 2016). High levels of vulnerability at national scale (see Section 8.3) are often
3 linked to complex histories, including long-term economic dependencies established and reinforced in the
4 context of colonization.
5
6 Links between climate change, structural racism and development are less well established as an element of
7 disproportionate impacts of climate change is relatively new (Sealey-Huggins, 2018). Discrimination is not
8 restricted to structural racism and includes discrimination of all kinds including that of gender and caste
9 because of which a considerable population is directly bound to suffer the harsh impacts of the climate
10 change. The climate change and gender literature has come a long way demonstrating concrete examples of
11 how structural inequalities operate. The political and micro-political aspects and how they interact with
12 structural inequalities are also important to understand vulnerability. Henrique and Tschakert (2020) shows
13 how the many adaptation efforts benefit powerful actors while further entrenching the poor and
14 disadvantaged in cycles of dispossession. This critical analysis recommends acknowledging injustices,
15 embracing deliberation, and nurturing responsibility for human and more-than-human others. Garcia et al.
16 (2020) describes the socio-political drivers of gendered inequalities that produce discriminatory
17 opportunities for adaptation. It utilises an intersectional subjectivities lens to examine how entrenched power
18 dynamics and social norms related to gender create barriers to adaptation, such as lack of resources and
19 agency. The analysis shows a pronounced dichotomy as women experience the brunt of these barriers and a
20 persistent power imbalance that positions them as `less able' to adapt than men.
21
22 Historical marginality and exclusion are context-specific conditions that shape vulnerability (Leichenko and
23 Silva, 2014). There also exists robust evidence that on gender inequalities contribute to climate vulnerability,
24 and that attention to gender is a key approach to climate justice (see Cross-Chapter Box GENDER in
25 Chapter 18) and includes robust evidence on the differentiated impacts of climate change and climate-
26 oriented policies on women (McOmber, 2020). For example, Friedman et al. (2019) show in Ghana that
27 homogeneous representations of women farmers and technical focus of climate-orientated policy
28 interventions may threaten to further marginalize the most vulnerable and exacerbate existing inequalities.
29 Climate change impacts can also heighten existing gender inequalities (Jost et al., 2016; Glazebrook et al.,
30 2020). On the one hand, climate change impacts can be gendered as a result of customary roles in society,
31 such as triple workloads for women (i.e., economic labour, household and family labour as well as duties of
32 community participation), and occupational hazards from gendered work indoors and outdoors (Murray et
33 al., 2016). On the other, climate change hazards interact with changing gender roles in society, such as urban
34 migration of both men and women in ways that break with tradition (Bhatta et al., 2016).
35
36 Gender influences the way that people also experience loss and process psychological and emotional distress
37 of losses, such as mortality of children and other relatives in climate-related disasters (Chandra et al.,
38 2017).Women's capacities are often constrained due to their roles in their household and society,
39 institutional barriers and social norms. These constraints result in low adaptive capacity of women, which
40 make them more vulnerable to hazards. As more men seek employment opportunities away from home,
41 women are required to acquire new capacities to manage new challenges, including risks from climate
42 change. Banerjee et al. (2019b) finds that capacity-building interventions for women staying behind, which
43 aimed to strengthen autonomous adaptation measures (e.g. precautionary savings and flood preparedness),
44 also positively influenced women to approach formal institutions. Besides, the intervention households were
45 more likely to invest a part of the precautionary savings in flood preparedness measures than control
46 households.
47
48 Next to the direct differential impacts of climate change on different social groups, the impacts of climate
49 change can also exacerbate inequality due to the lower access and limited ability to benefit from services
50 provided by ecosystems. The marginalised poor people often significantly depend on the access to
51 surrounding environments, natural resources and ecosystem services for their livelihoods, for leisure or
52 cultural practices. Thus shifts in such resources, for example, due to the bleaching of coral reefs or shifts in
53 fish stock also cause severe challenges and risks to these communities (Leal Filho, 2018; Le, 2019), see also
54 (UNTTSDCC, 2014).
55
56 Overall, the assessed literature highlights that climate change impacts are not emerging in isolation from
57 development context and development pathways. Economic and social ramifications mean that they may
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1 exacerbate poverty and marginalization (Finkbeiner et al., 2018; Dogru et al., 2019). Choudhary et al. (2019)
2 and Orimoloye et al. (2019) highlight how the effects of climate change can be even more prejudicial to poor
3 countries, who in most cases already suffer from weak governance, high prevalence of informal settlements
4 and lack of resources. Health, livelihood assets and economy are examples of aspects that will worsen as a
5 result of the negative impacts of climate change and failure to provide opportunities for sustainable
6 adaptation (United Nations, 2015). These facts highlight the importance of mitigation and adaptation
7 measures especially in these regions characterized by high levels of vulnerability (see also Section 8.3).
8
9 8.2.3 Observed impacts and responses and their relevance for decision-making
10
11 Many countries base their adaptation strategies on National Adaptation Programmes of Action (NAPs),
12 which often correlate different levels of decision-making and governance (Golrokhian et al., 2016). Whereas
13 the involvement of national governments is needed for designing appropriate responses to climate change,
14 recent studies underscore the need to also consider Local Knowledge and Indigenous Knowledge within
15 adaptation and risk reduction strategies, thus fostering stronger linkages with local communities, leading to
16 an improved vertical integration between different strategies, programs and different actors (Ford et al.,
17 2016; Vij et al., 2017; Singh et al., 2020). The relevance of addressing the issue of vulnerability and poverty
18 to reduce the climate change risks has been demonstrated within the assessed literature on the impact of
19 climate change (Hallegatte et al., 2017). In this regard, it is noticeable that not many National Adaptation
20 Programmes of Actions explicitly aim to reduce poverty, even though poverty reduction is associated with
21 vulnerability reduction to climate change (Demski et al., 2017).
22
23 Next to issues of observed impacts and responses to climate change, it is important to assess observed
24 barriers in implementing climate change responses. The discussion of barriers is complemented later in the
25 chapter with an assessment of the enabling environments for adaptation (see Section 8.5.1). Some of the
26 most common barriers outlined in the scientific literature are summarised in Table 8.3.
27
28
29 Table 8.3: Some common barriers in implementing climate change responses and their implications
Dimensions Barriers in implementing effective climate change Implications
responses
Governance Unfavourable political frameworks (Gupta, 2016). Governance structures can undermine autonomous
adaptation (Section 8.4 Table 8.6); Inability to
include gender differentiated vulnerabilities in
governance schemes (Bryan et al., 2017).
Social Attitudes to risks and cultural values may hamper Social norms of reciprocity and cohesion may erode
responses (Billi et al., 2019). as a consequence of climate change responses
(Volpato and King, 2019); Socio-cultural conditions
as key barriers to gender differentiated support to
impact reduction (Bryan et al., 2017).
Institutional Limited availability coordination and prioritisation Lack of anticipatory risks undermining local's effort
processes (Patterson and Huitema, 2019). to cope with hazards (Singh et al., 2019a).
Behavioural Psychological distress may cause insecurity and The psychological distress associated to loss of
behaviour of some groups may increase attachment to a place has also been observed among
vulnerability (Van Lange et al., 2018). vulnerable communities in regions such as South
Asia (Maharjan et al., 2020)
Financial Limited financial resources to support adaptation
projects (Khan et al., 2019). The lack of financial resources and assets among
urban poor increase their exposure and
vulnerabilities to the increasing climate hazards
(Salim et al., 2019)
Structural Unsuitable infrastructure may increase exposure Structural Marginalization of Indigenous people and
(Chinowsky et al., 2015; Vallejo and Mullan, 2017). their local knowledge can exacerbate risks of
maladaptation among SIDs countries (McNamara
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(McNamara and Prasad, 2014; Aipira et al., 2017;
Granderson, 2017). Infrastructure projects to adapt
to climate change impacts may increase the
vulnerability of poor slum people
Technical Lack of access to technologies which may support The highest level of illiteracy among women
adaptation (e.g., climate services) (Bel and Joseph, prevent their engagement to access technology and
2018). risk reductions in vulnerable communities (Balehey
et al., 2018)
1
2
3 There are various characteristics of responses to climate change, which aim to protect livelihoods and
4 prevent poverty expansion (i.e., an enlargement of the group of people already affected by poverty). Some of
5 them are:
6 a) Timely: meaning that responses need to take place within a matter of weeks or months and not over
7 years (Wise et al., 2014)
8 b) Targeted: with a focus on the affected communities and groups, to help alleviate the pressures they
9 are under; (e.g., Aleksandrova, 2020)
10 c) Sustainable: with long-lasting results leading to self-sufficiency of the affected communities and
11 their resource base, as opposed to short-term ones relying on external support (e.g., Caetano et al.,
12 2020)
13 d) Integrated: the impact of climate change is multifaceted and far reaching and requires the
14 engagement of various actors e.g., the vulnerable community, government agencies, local and
15 international nongovernmental organisations, civil societies, media (Ayal et al., 2020)
16
17 Finally, responses as those outlined in Table 8.1 and Table 8.2, need to ensure the active participation of
18 local stakeholders taking into account their diverse interests, so that they are grounded in reality. In addition,
19 responses need to be complemented with operational procedures and timeframes so that they can be more
20 systematically pursued and implemented (Alves et al., 2020).
21
22
23 8.3 Human Vulnerability, Spatial Hotspots, Observed Loss and Damage and Livelihood Challenges
24
25 This section assesses the literature on vulnerability--the assessment of vulnerability at global and national
26 scales--and explores economic and non-economic losses of people and livelihoods exposed to and impacted
27 by climate change. The section examines how climate change threatens livelihoods and juxtaposes global
28 and local level assessments of vulnerability based on empirical data at different scales. The analysis of recent
29 literature underscores that climate change impacts and adaptation needs cannot be understood by looking at
30 climate change only. Vulnerability and livelihood security are seen as an important component to understand
31 the human dimension of climate change (Rhiney et al., 2016; Cardona, 2017; Byers et al., 2018; Eriksen et
32 al., 2020; Wisner, 2020; Birkmann et al., 2021a; Cole et al., 2021).
33
34 Linkages between global and individual vulnerability and livelihood security, including aspects of
35 intersectionality are also assessed. Overall, the sub-chapter reveals that different countries, societies and
36 specific groups within a society have very different starting points on their move towards climate resilience.
37
38 8.3.1 Assessments of risk and vulnerability
39
40 Conventional assessments of risks and the benefits of adaptation and risk reduction measures in the context
41 of climate change primarily focus on the financial value of the avoided losses (in US Dollars) and the assets
42 that are going to be protected from adverse consequences of climate change or extreme events due to specific
43 measures (e.g., dyke construction). Even though these assessments fall short of measuring the real costs of
44 addressing climate change impacts (see DeFries et al., 2019), they often support the definition of priorities in
45 terms of protecting economic values and assets. However, these assessments do not sufficiently account for
46 how climate change impacts and imposes risks on poor people, nor does it capture issues of climate justice
47 and more complex societal impacts and future risks. For example, various observed losses in the context of
48 climate change can not sufficiently be expressed in terms of an economic value (see Section 8.3.5), but these
49 items or assets are highly relevant for various people with limited economic resources (Hallegatte et al.,
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1 2017). Consequently, the assessment of risks from climate change facing particularly poor people requires
2 comprehensive assessments of human vulnerability, resilience and the impacts of climate change on human
3 wellbeing going beyond a simple temperature societal-impact understanding. Knowledge about methods and
4 approaches to assess human or human-environmental vulnerability and livelihood security, including aspects
5 of intersectionality, is important in order to explore whether or not adaptation and development programmes
6 are able to reduce vulnerability. The body of literature on these issues has grown significantly since the AR5
7 publication (IPCC, 2014a; Moser, 2014).
8
9 Literature since AR5 underscores that approaches to assess resilience, vulnerability, human wellbeing
10 include global assessments that can inform strategies and priority settings for adaptation and risk reduction in
11 the context of climate change (high confidence) (WHO, 2014b; Young et al., 2015; Feldmeyer et al., 2017;
12 GIZ and BMZ, 2017; Hallegatte et al., 2017; Birkmann et al., 2021a; Garschagen et al., 2021; Toolkit, 2021).
13
14 These quantitative global assessments that emerged within the last decades have not sufficiently been
15 assessed in former IPCC reports, for example in terms of the agreement on spatial hotspots or in terms of
16 regional clusters of vulnerability and the linkages between past societal impacts and levels of vulnerability.
17 The assessed literature show that conditions and phenomena that characterize systemic vulnerability (hazard
18 independent vulnerability), such as high levels of poverty and gender inequality, limited access to basic
19 infrastructure services or state fragility are highly relevant for understanding societal impacts of climatic
20 hazards and future risks of climate change (e.g., Cutter et al., 2003; ADB, 2005; Cutter and Finch, 2008;
21 World Bank, 2008; UNISDR, 2009; Crawford et al., 2015; Rufat et al., 2015; Carrao et al., 2016; Gupta,
22 2016; Rahman, 2018; Andrijevic et al., 2020; Jamshed et al., 2020a; Feldmeyer et al., 2021; Garschagen et
23 al., 2021). These factors and context conditions also influence individual vulnerability at households or
24 community level. Access to basic services, such as water and sanitation are linked to human rights and that if
25 not granted increase the likelihood that people disproportionately suffer from climate induced hazards, due to
26 their pre-existing lack of access to such services. In addition, increasing climate hazards further constrain the
27 access to such services (United Nations, 2018; Kohlitz et al., 2019; Gupta et al., 2020).
28
29 There is an increasing evidence base that successful adaptation and risk reduction strategies need to
30 acknowledge not only climate change and/or specific climate hazards (sea-level rise, flooding, droughts,
31 etc.), but also human vulnerability and existing adaptation gaps and thereby the different starting points that
32 societies or different groups have towards climate resilience (see UNEP, 2016; Birkmann et al., 2021a).
33 Recent reports underscore that development and capacity indicators are useful to assess the broader
34 adaptation challenges and adaptive capacities at global scale independent of a specific climatic hazard.
35 Examples include the percentage of population with access to improved water sources and improved
36 sanitation, the number of physicians per 1000 people or the dependency ratio (UNEP, 2018). These
37 indicators are also part of more comprehensive vulnerability assessments, such as those assessed within this
38 section namely the vulnerability components of the INFORM risk index (e.g., INFORM, 2019) and of the
39 WorldRiskIndex (e.g., Birkmann and Welle, 2016; Birkmann et al., 2021a; Feldmeyer et al., 2021). Recent
40 literature underscores that measuring vulnerability is seen as key for assessing factors that significantly
41 determine actual and future adverse consequences of climate change and complex risks (Cutter and Finch,
42 2008; Cardona et al., 2012; de Sherbinin et al., 2019; Peters et al., 2019; Jamshed et al., 2020c; Visser et al.,
43 2020; Feldmeyer et al., 2021). However, there is also important critique on indicator based assessments of
44 vulnerability (see de Sherbinin et al., 2019; Rufat et al., 2019; Visser et al., 2020), particularly with regard to
45 issues of validation and its use in decision-making processes. Nevertheless, we observe an emerging
46 agreement in the literature that resilience building and adaptation to climate change has to be informed by
47 climate and multidimensional assessment of the vulnerability of people, different groups and coupled
48 human-environmental systems, including both quantitative and qualitative assessment approaches (IPCC,
49 2014b; UNEP, 2018; Singleton et al., 2021; Birkmann et al., 2022). Since, interdependencies between
50 regional (supranational/sub-continental), national, community and individual vulnerability have often been
51 overlooked, the chapter assesses both global and regional vulnerability as well as local livelihood
52 vulnerabilities.
53
54 While past research regarding the nexus between climate change and poverty focused often on vulnerable
55 groups in rural areas of low income countries (de Sherbinin, 2014; IPCC, 2014a; Barbier and Hochard,
56 2018), new global mega-trends, such as urbanization, underscore the need to assess both rural and urban
57 communities and their vulnerability. In many rapidly growing cities in the global south, access to land and to
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1 housing is a challenge particularly for the poor and marginalized, contributing to a further increase in
2 informal settlements that often emerge in highly hazard-exposed areas (Jeschonnek et al., 2014; Rana et al.,
3 2021). In addition, migration from rural areas to urban centres, also due to increasing adverse impacts of
4 climate change on rural livelihoods, can add another level of complexity (Flavell et al., 2020). Moreover, the
5 context in which such urbanization processes take place is key. For example, rapidly growing medium-sized
6 cities, for example in West-Africa, often do not have sufficient financial, technical and institutional resources
7 to adapt urban structures to climate change (Birkmann and Welle, 2016; Birkmann et al., 2016; de Sherbinin
8 et al., 2017). Hence, vulnerability in urban contexts is an emerging issue for international, national and local
9 adaptation programmes. Rather than focusing on mega-cities and their exposure as primary hotspots, more
10 attention has to be given to rapidly growing small- and medium-sized cities and their adaptation needs from
11 the perspective of vulnerability reduction and poverty.
12
13 8.3.2 Global hotspots of human vulnerability to climate change
14
15 8.3.2.1 Hotspots and spatial patterns of multi-dimensional vulnerability
16
17 The assessment of literature published since the AR5 suggests that alongside already deteriorated specific
18 conditions that determine individual vulnerability and livelihood security to climate change (see Section 8.2),
19 high levels of poverty, lack of access to basic services (human rights to water and sanitation), poor
20 governance, and conflicts are important factors that characterise vulnerability and systemic human
21 vulnerability in particular (EC-DRMKC, 2020; Wisner, 2020; Feldmeyer et al., 2021; Garschagen et al.,
22 2021; GIZ, 2021). These context conditions within a country or region limit the access to effective
23 adaptation options particularly for the poor and marginalized groups.
24
25 Recent studies underscore that human vulnerability--thus the predisposition to be adversely affected--is
26 largely determined by past and present development processes, rather than by the occurrence of individual
27 events (Wisner, 2016; Cutter, 2018; Birkmann et al., 2020). Also the consequences of the COVID-19
28 pandemic will create newly poor particularly in countries that are already characterized by high levels of
29 vulnerability (see Box 8.3; Laborde et al., 2020b; Lakner et al., 2020).
30
31 Quantitative studies and assessments published since AR5 provide additional insights about human
32 vulnerability to climate change and resilience of societies at different scales using different indicator sets and
33 approaches (Feldmeyer et al., 2017; Hallegatte et al., 2017; EC-DRMKC, 2020; Birkmann et al., 2021a;
34 Feldmeyer et al., 2021; Garschagen et al., 2021).
35
36 While quantitative measures of vulnerability are widely used at different scales (Cutter et al., 2016;
37 Garschagen et al., 2021), there are also studies that caution the use of such indices in policy making or risk
38 reduction efforts (Rufat et al., 2019; Spielman et al., 2020). Such assessments of vulnerability have to be
39 internally and externally validated and handled with care when applied in decision-making processes also in
40 terms of their options and limits. At the same time, these assessments capture important conditions and
41 structures that make people more susceptible to various climate hazards and climate change impacts and the
42 relevance of these conditions is confirmed by quantitative impact assessments as well as many case study
43 specific assessments (Welle and Birkmann, 2015; Feldmeyer et al., 2021; Birkmann et al., 2022). For
44 example, the access to basic services (e.g., water and sanitation) (Bollin and Hidajat, 2013; Pandey et al.,
45 2017b; UNEP, 2018; United Nations, 2018; Gupta et al., 2020; Jamshed et al., 2020a), and broader modes of
46 engagement in governance and governance fragility (Crawford et al., 2015; Rahman, 2018; Andrijevic et al.,
47 2020) significantly influence how climatic hazards translate into severe or non-severe losses and harm (see
48 Section 8.5.2).
49
50 The lack of such support structures and resources can severely constrain opportunities of people to cope with
51 and adapt to climate change, since it is not only the climate hazard, but also exposure and particularly the
52 vulnerability of a society, a specific community or an individual household that determine adverse societal
53 consequences of climatic hazards. International vulnerability and resilience assessments show that
54 vulnerability varies across countries of similar wealth or income because multi-dimensional vulnerability,
55 wellbeing and resilience depend on a larger set of factors (Birkmann and Welle, 2016; Hallegatte et al.,
56 2017; INFORM, 2019). In this regard, vulnerability assessment is significantly different from climate
57 exposure mapping.
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1
2 The findings of these global assessments suggest, among other issues, that options to reduce vulnerability
3 and enhance resilience do exist in various countries at different levels, in part irrespective of their income
4 level (Feldmeyer et al., 2017; Hallegatte et al., 2017). Vulnerabilities at national and regional level influence
5 community and individual vulnerability, particularly through structures that determine entitlements, the
6 access to resources and processes of marginalization (Watts and Bohle, 1993; Thomas and Warner, 2019).
7
8 While different assessments use different sets of indicators, most of the global assessments with national
9 scale resolution (Birkmann and Welle, 2016; Kreft et al., 2016; Feldmeyer et al., 2017; Hallegatte et al.,
10 2017; Eckstein et al., 2019; INFORM, 2019; ND-GAIN, 2019; Garschagen et al., 2021), contain indicators
11 that cover different aspects of economic poverty, inequality, access to basic infrastructure services, education
12 and human capital (e.g., adult literacy rate) and some also include issues of gender inequality, specific
13 vulnerable groups or insurance against extreme events. The assessments also differ, for example, in terms of
14 their consideration of aspects of governance, such as corruption and conflict, or the consideration of social
15 safety nets, such as insurance coverage, or the number of people affected by hazards(Feldmeyer et al., 2017;
16 INFORM, 2019), as well as in terms of the consideration of losses experienced in the past or issues such as
17 biodiversity as an aspect of adaptive capacity (Hallegatte et al., 2017; Birkmann et al., 2022). Moreover, the
18 assessments differ in terms of the consideration of specific indicators and the inclusion or non-inclusion of
19 specific hazard exposure (Welle and Birkmann, 2015; Hallegatte et al., 2017; INFORM, 2019; ND-GAIN,
20 2019; Birkmann et al., 2022).
21
22 Recent comparative studies of global assessments of vulnerability show high agreement on the spatial
23 clusters that have very high or very low vulnerability to climate change, compared to larger differences in
24 terms of exposure and risk (Birkmann and Welle, 2016; Hallegatte et al., 2017; INFORM, 2019; Feldmeyer
25 et al., 2021; Garschagen et al., 2021; Schleussner et al., 2021). The comparison of the averaged ranking
26 results at the scale of 'climate regions' using the vulnerability components of the INFORM and the
27 WorldRiskIndex--as two comprehensive global assessment approaches of systemic vulnerability (hazard
28 independent vulnerability) (see Figure 8.5 and Figure 8.6)--also finds a high agreement in terms of most
29 vulnerable regions and regions with low vulnerability (Figure 8.5; Feldmeyer et al., 2021). The assessment at
30 this scale reveals that global hotspots of human vulnerability can be found in climate regions in East Africa,
31 Central Africa and West-Africa. Followed by high vulnerability in Central America and South Asia and
32 South East Asia, for example. Garschagen et al. (2021) in a comparison of further risk indices also found that
33 there is high agreement on global assessments of vulnerability compared to exposure or overall risk.
34
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1
2 Figure 8.5: Aggregated vulnerability map at the scale of climate regions based on the averaged ranking of the
3 INFORM Index's vulnerability component and the averaged ranking of the vulnerability component of the
4 WorldRiskIndex. Based on the rankings of the INFORM index (INFORM, 2019) and the WorldRiskIndex (Birkmann
5 and Welle, 2016; Feldmeyer et al., 2017). The map and diagram show the agreement between the two global
6 vulnerability indices when ranking climate regions according to their vulnerability--darker colours show regions of
7 higher vulnerability. The diagram shows how the 35 climate regions are ranked by each index and also serves as a
8 legend for the map above.
9
10
11 The analysis of vulnerability assessment results of the INFORM Risk Index and WorldRiskIndex at the level
12 of countries also coupled with population data confirms a high agreement on most vulnerable countries and
13 it shows that global hotspots of human vulnerability are not just single countries, but often emerge within
14 regional clusters, particularly in Africa, but also in Asia and Central America (see Figure 8.6 and Birkmann
15 et al., 2021a). These regional clusters (Figure 8.6) are characterized by high levels of vulnerability in terms
16 of socio-economic, demographic, environmental and governance conditions that make people more likely to
17 face adverse consequences once a climate hazard occurs. The internal and external validation of these index
18 systems shows its statistical validity and robustness (Welle and Birkmann, 2015; Marin-Ferrer et al., 2017;
19 Birkmann et al., 2022). It also confirms a quantitative relationship between most vulnerable regions and
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1 fatalities and severely affected people due to climate influenced hazards (Birkmann et al., 2022). The
2 vulnerability map in Figure 8.5 shows the vulnerability level (systemic societal vulnerability) linked to
3 national scale and provides additional information about the population density within these countries. The
4 background map does not show specific vulnerable populations within countries. Selected examples of sub-
5 national human vulnerabilities have been added as additional information in terms of case studies based on
6 information of other chapters within this report (see for example, Box 8.7, Chapter 14 Section 14.4.7,
7 Chapter 13 Section 13.8.1, Chapter 10 Sections 10.3.3 and 10.5.1, Cross-Chapter Paper 6, Section 6.2.7,
8 Chapter 5 Section 5.12 and Chapter 15 Section 15.3.4).
9
10
11
12 Figure 8.6: Global map of vulnerability. This map shows the relative level of average vulnerability as calculated by
13 global indices (INFORM and WRI see details in 8.3.2). Areas shaded light yellow are on average the least vulnerable
14 and those shaded darker brown are the most vulnerable. The map combines information about the level of vulnerability
15 (independent of the population size) with the population density (see legend) to show where both high vulnerability and
16 high population density coincide. The map reveals that there are densely populated areas of the world that are highly
17 vulnerable, but also highly vulnerable populations in more sparsely populated areas. There are also highly vulnerable
18 communities and populations in countries with overall low vulnerability as shown with sub-national case studies
19 alongside the map. The map shows in the pie charts the number of deaths (mortality) per hazard (storm, flood, drought)
20 event per continental region based on EM-DAT Data (CRED, 2020). This reveals that significantly more fatalities per
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1 hazard (droughts, floods, storms) did occur in the past decade in more vulnerable regions. Over 3.3 billion people are
2 living in countries classified as very highly and highly vulnerable, while approximately 2 billion people live in
3 countries with low and very low vulnerability (Birkmann et al., 2022). These vulnerability values are based on the
4 average of the vulnerability components of the INFORM Index (INFORM, 2019) and WorldRiskIndex (Birkmann and
5 Welle, 2016; Feldmeyer et al., 2017) with updated data from 2019 classified into 5 classes using the quantile method.
6 Other studies applied more vulnerability classes within their assessment and therefore provide slightly different
7 numbers (Birkmann et al., 2021a). However, despite different calculation methods, the fact remains that there are
8 significantly more people residing in countries with very high and highly vulnerability compared to those living in
9 countries classified as having low or very low vulnerability.
10
11
12
13 Figure 8.7: Selected aspects of vulnerability. The diagram presents normalized indicator scores for a selection of
14 aspects of human vulnerability aggregated to average values for each region. These indicator scores are based on the
15 vulnerability indices mapped in Figure 8.6 (i.e., the INFORM Risk Index and WorldRiskIndex). This figure provides a
16 more differentiated picture about the various dimensions of vulnerability that different regions and countries face and
17 the severity of such challenges in each region. Such vulnerability challenges increase the risk of severe adverse impacts
18 of climate change and related hazards (Birkmann et al., 2022).
19
20
21 Figure 8.7 provides an aggregated regional overview of selected indicators used within the vulnerability
22 index mapped in Figure 8.6. The overview (Figure 8.7) shows that the many compounded challenges faced
23 by African countries are starkly pronounced, but also on the other regions, especially Asia, Central and
24 South America, and amongst the Small Island States there are several challenges such as inequality,
25 governance issues and displacement which all increase the vulnerability and constrain adaptive capacities of
26 these regions to climate change.
27
28 However, it is also important to note that vulnerability assessments do have their limitations (Heesen et al.,
29 2014; Rufat et al., 2019). For example, also in high income countries specific groups can be highly
30 vulnerable to climate change due to marginalization and discrimination due to ethnicity or gender. Gender
31 inequality for example is also high in some countries classified in the literature as having low vulnerability
32 (see Birkmann et al., 2021a; Birkmann et al., 2022). Nevertheless, these countries have in theory sufficient
33 financial resources and governance capacities to deal with these challenges, while this is different for many
34 country clusters classified as highly vulnerable.
35
36 Countries and regional clusters with low vulnerability (see Figure 8.5 and Figure 8.6), such as Australia and
37 New Zealand or Iceland and North Europe, encompass population groups that are exposed and vulnerable to
38 climate hazards, such as sea-level rise or droughts, but within these regions context conditions exist that
39 allow the negative impacts and losses to be buffered (also for most vulnerable groups). These regions have
40 higher financial and institutional capacities to support people at risk and planned adaptation at a different
41 magnitude within their region, for example, as seen in compensation payments for drought exposed farmers
42 (Hochrainer-Stigler and Hanger-Kopp, 2017; Australian-Government, 2021) or flood affected households in
43 Germany in 2021. Also, the percentage of insured households against climate influenced hazards, such as
44 floods or storms, is significantly higher in these regions (North America, Western Europe) compared to
45 regions such as Western Africa or Micronesia (Welle and Birkmann, 2015; Feldmeyer et al., 2021; Birkmann
46 et al., 2022).
47
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1 While climate change differentially impacts people in vulnerable situations within countries including the
2 poor, children, women, marginalized Indigenous or other ethnic minority people (Rhiney et al., 2016;
3 M閚dez et al., 2020), the global assessment results underscore that in most vulnerable regions and countries
4 very limited resources and structures exist to support these groups when droughts, floods or storms occur and
5 place an additional burden to these groups.
6
7 The assessments of human vulnerability also point towards important adaptation options that are not visible
8 if one focuses on climatic hazards or temperature changes alone (Figure 8.9; D點kers et al., 2015; Cutter et
9 al., 2016; Birkmann et al., 2021a). It is increasingly recognized as fundamental for vulnerability reduction
10 and adaptation are social insurances and infrastructure programmes as well as legislation that improves the
11 access of poor and marginalized groups towards basic infrastructure services and basic security. For
12 example, the "free basic service programme" of the national government of South Africa (GovSA, 2021) is
13 one example where a national government (Government of South Africa) has committed itself to provide a
14 basic amount of free water, electricity and sanitation to low income households, particularly indigent people
15 such as those living in informal settlements or remote rural areas. Coupled with incentives, for example in
16 terms of a higher use of renewable energy e.g., solar home systems in rural areas (see GovSA, 2021) these
17 investments can support vulnerability reduction and mitigation of greenhouse gas emissions. However, there
18 is also critique of the programme design and implementation (see Nel and Rogerson, 2005; Muller, 2008) as
19 is witnessed by ongoing service delivery protests (Mutyambizi et al., 2020). However, the example shows
20 that current national programmes can--even if they are not classified as adaptation measures--provide
21 important entry point to also reduce human vulnerability to climate change.
22
23 The relevance of human vulnerability has also been confirmed by recent assessments. Studies found that the
24 average mortality4 from floods, storms and droughts is 15 times higher in countries and regions ranked as
25 very highly vulnerable (e.g., Mozambique, Somalia, Nigeria, Haiti, Afghanistan) compared to countries with
26 very low vulnerability (e.g., UK, Sweden, Italy, Canada) (Birkmann et al., 2022). Even if one takes solely
27 "high vulnerable countries" such as India, Pakistan and the Philippines (and not "very high" vulnerable
28 countries), mortality is still nine times higher compared to very low vulnerable countries. Similarly, studies
29 further revealed that average number of adversely affected people per hazard event (e.g., loss of the house)
30 are 11 times higher in countries categorized as very high vulnerable compared to very low vulnerable
31 (Birkmann et al., 2022). In addition to floods, droughts and storms, published EM-DAT data for wildfires
32 and heat stress, confirmed higher suffering (higher average mortality) in more vulnerable regions compared
33 to low vulnerable regions, particularly when excluding extreme outliers (CRED, 2020). These findings point
34 towards the fact that in regions identified as highly vulnerable in the assessments even moderate future
35 climate change and future climate hazards are likely to push people further into poverty and lead to
36 significant destabilization processes in terms of livelihoods security (Wallemacq and House, 2018; Birkmann
37 et al., 2022).
38
39 8.3.2.1.1 Historic roots of vulnerability in regions classified as highly vulnerable
40 While increasing attention is given to issues of human vulnerability, less attention has been given to the
41 historical conditions that foster systemic vulnerability of societies. It is important to acknowledge that
42 drivers and root causes of systemic human vulnerabilities and development challenges are not always new,
43 and sometimes--for example in various countries in the Caribbean, Africa and Asia--can be linked to
44 histories of imperialism, colonial structures (Grasham et al., 2019), and subsequent development and
45 governance contexts (Southard, 2017; Zhukova, 2020). Thus, root causes of present structures of human and
46 human-environmental vulnerability have in many cases historic dimensions, for example chronic poverty
47 and structural inequality in Africa (Grasham et al., 2019) or the Caribbean are still influenced by the colonial
48 power-relations outside of these countries making solutions for vulnerability reduction more difficult (see
49 e.g., Douglass and Cooper, 2020). Also national borders, such as in many regions in Africa, sometimes cut
50 through ethnic groups and therewith ignore important interrelations between communities on both sides of
51 the border.
52
53 8.3.2.1.2 People residing in most vulnerable versus least vulnerable regions
4 measured as death per hazard event and calculated by averaging the country values of mortality per event falling in
different vulnerability categories
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1 While global assessments often allow for country rankings, it is similarly important to better understand how
2 many people are living in these different levels of vulnerability. The quantitative assessments underscore that
3 a significantly higher amount of people is living in countries with very high and high vulnerability compared
4 to the population living in countries classified as having low and very low vulnerability. An analysis that
5 measured the vulnerability of countries according to the INFORM Risk Index and the WorldRiskIndex
6 vulnerability-index components, differentiating vulnerability values into 7 vulnerability classes found that
7 nearly twice as many people are living in most vulnerable countries compared to the number living in less
8 vulnerable countries (Birkmann et al., 2021a). Another study that uses the same data and differentiates
9 vulnerability into 5 classes (also considering the lack of coping capacity within the INFORM index, see
10 (Marin-Ferrer et al., 2017)) concludes that about 3.3 billion people are living in countries classified as highly
11 vulnerable, while approximately 2 billion people live in countries with low vulnerability (Birkmann et al.,
12 2022). While these numbers are different, both results underscore that the absolute and relative number of
13 people living in most vulnerable contexts is significantly higher compared to those that live in a country with
14 a low vulnerability status (Birkmann et al., 2021a; Birkmann et al., 2022). These differences have also been
15 observed in former years (Welle and Birkmann, 2015; Feldmeyer et al., 2017).
16
17 That means, even moderate changes in the global mean temperature, as identified in the recent IPCC report
18 SR1.5癈 (IPCC, 2018c) and in scientific literature (Hoegh-Guldberg et al., 2019a), can mean substantial
19 increases in risks for more than 3 billion people due to high levels of vulnerability.
20
21 Overall, there is robust evidence and high agreement in the recent literature that countries and regions
22 classified as highly vulnerable face multiple development challenges at once, in which high levels of poverty
23 interact with limited access to water and sanitation or with high levels of forced migration and in some cases
24 with state fragility making solutions difficult (Hallegatte et al., 2017; Marin-Ferrer et al., 2017; Feldmeyer et
25 al., 2021; Garschagen et al., 2021; Birkmann et al., 2022). High levels of vulnerability within these regional
26 clusters are the product of current development challenges, but are often caused by long and complex
27 histories, including issues of colonization and marginalization, for example, in hotspots in Africa (Birkmann
28 et al., 2021a).
29
30 8.3.2.2 Transboundary vulnerability and adaptation
31
32 Next to the identification of the level of agreement between different vulnerability assessments (Garschagen
33 et al., 2021) and the spatial hotspots, global assessments of vulnerability and adaptation readiness also point
34 towards the need of a transboundary perspective and the need for transboundary cooperation in terms of
35 vulnerability reduction and adaptation (Tilleard and Ford, 2016; Birkmann et al., 2021a). Newer research
36 points towards the fact that various phenomena of vulnerability particularly in highly vulnerable regions spill
37 over national borders and emerge in rather regional clusters, such as forced migration and poverty in West
38 and Central Africa as well as conflicts in the Near East or Asia (IDMC, 2020). That mean regional and
39 transboundary challenges contribute to the formation of systemic human vulnerability, for example, forced
40 migration that is occurring within countries, but also across international borders that is also influenced by
41 climate change (Kaczan and Orgill-Meyer, 2020). In summary, these findings point towards the need for
42 more transboundary approaches in vulnerability and risk reduction, adaptation and development. Recent
43 literature and data presented in Figure 8.6 and (Birkmann and Welle, 2016; Feldmeyer et al., 2017;
44 Hallegatte et al., 2017; INFORM, 2019; Birkmann et al., 2021a) demonstrate the need to strengthen
45 approaches to monitor the regional dimensions of vulnerability and to develop strategies and programmes
46 that allow for transboundary vulnerability and risk reduction and cooperation at different scales, for example,
47 cooperation between national level institutions, but also transboundary networks of cities or communities
48 (Tilleard and Ford, 2016; Benzie and Persson, 2019; Birkmann et al., 2021a). The transnational nature of
49 climate change impacts means that addressing them requires concerted efforts among nations (IPCC, 2014b;
50 Dzebo, 2019).
51
52 In addition, national response strategies for specific transboundary climate influenced hazards, such as river
53 flooding, droughts or coastal flooding can also significantly influence neighbouring countries and can affect
54 exposure and vulnerability of the respective country (Nadin and Roberts, 2018; Booth et al., 2020).
55 Likewise, climate change may affect transboundary resources (e.g., underground water reserves) and
56 transboundary ecosystems (e.g., in terms of the migration of species) (Vij et al., 2017) and thereby further
57 reduces the capacity of vulnerable groups to cope and adapt. In addition, recent research indicates that social
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1 inequities are coupled with access to and quality of environmental resources, also in urban environments--
2 meaning social and environmental justices are interconnected (see Schell et al., 2020).
3
4 Individual adaptation projects to specific climate hazards in regions classified as highly vulnerable are
5 needed, however, recent studies underscore that deeper development challenges need to be addressed in
6 order to make progress towards adaptation and vulnerability reduction and to avoid maladaptation (Eriksen
7 et al., 2021). Adaptation and development projects, such as the construction of a dam as a response to water
8 shortages in one country can significantly influence the exposure to water shortages and the response
9 capacities of another country downstream. Often, transboundary challenges are a result of policy and
10 resource management choices or uncertainty and addressing them requires a greater engagement between
11 governing bodies, which may guide more suitable responses also in the context of climate change adaptation
12 and vulnerability reduction (Earle et al., 2015; Tilleard and Ford, 2016; McLeman, 2018; Birkmann et al.,
13 2021a).
14
15 Most of those countries and regional clusters identified as highly vulnerable have contributed little to the
16 overall amount of greenhouse gas emissions and therefore support for (transboundary) adaptation from the
17 international community is required in these places and for those living under these conditions also in order
18 to support and achieve climate justice.
19
20 8.3.2.3 The effect of higher levels of global warming for most vulnerable regions and specific livelihoods
21
22 Evidence exists that threats to land-based livelihoods and risks of undernutrition increase significantly with
23 higher levels of global warming (Hoegh-Guldberg et al., 2019a). With global warming of 1.5癈 or less,
24 impacts of climate change on livelihoods are still significant, for example, for West Africa and Sahel due to a
25 reduction of area suitable for maize production of about 40%, however, the consequences of global warming
26 of up to 3癈 would mean a high risk of undernutrition for entire regions (see Hoegh-Guldberg et al., 2019a)
27 that are already classified as most vulnerable (see Figure 8.6). That means the consequences of significant
28 warming are a particular challenge for the regional hotspots of vulnerability, since already observed small
29 changes in crop productivity due to increasing droughts or floods or changes in rainfall patterns could lead to
30 severe health risks and undernutrition due to already existing precarious living conditions and due to the
31 limited capacities that people and institutions have to build and enhance coping and adaptive capacities at the
32 level of individual households, communities and even at the level of state institutions (see UNEP, 2018;
33 Birkmann et al., 2021a). The risk of loss of life, displacement and adverse health consequences due to
34 climate change in these most vulnerable regions (such as West Africa, Micronesia, South Asia--see Figure
35 8.5 and Figure 8.6) is higher compared to regions classified as having medium or low vulnerability
36 (Birkmann et al., 2022). Nevertheless, also other regions and countries classified as less vulnerable, for
37 example in Asia, are experiencing disasters and have a relative high share of the global fatalities or losses
38 observed when considering also non-climatic natural hazards (CRED and UNDRR, 2020). In addition,
39 changing climatic hazard and exposure patterns have to be considered, however, the agreement of major
40 global index systems on exposure is significantly lower as compared to vulnerability (Garschagen et al.,
41 2021).
42
43 Moreover, the assessment reveals that in most vulnerable regions a double burden of existing destabilized
44 livelihood conditions and additional climatic hazards is already visible and largely influences societal
45 impacts of climate change. For example, flooding along the White Nile in Uganda and South Sudan hit
46 vulnerable communities that were displaced before because of conflicts and were thus up-rooted again by
47 flooding (IDMC, 2020). Societal impacts and future risks of climate change to societies need to incorporate
48 information about vulnerability and exposure--including capacities of people to cope and adapt (Wisner,
49 2016; Cardona et al., 2020). There is increasing evidence that individual and societal capacities to cope and
50 adapt also depend on how governmental and national institutions can support people at risk (see Section 8.6).
51 For example, climate information services depend on a functioning weather service, likewise, social safety
52 nets as an adaptation strategy require financial resources, which are often absent for most people in highly
53 vulnerable regions. In addition, examples of national programmes that target most vulnerable groups such as
54 the free basic service programme in South Africa show that next to the adaptation to individual hazards,
55 strategies exist that aim to reduce systemic human vulnerability (see GovSA, 2021).
56
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1 At the same time, scientific evidence exists that more intense and frequent climate influenced hazards (e.g.,
2 storms, flooding, droughts, heat stress) can undermine decade-long poverty reduction efforts, particularly in
3 most vulnerable regions (Mysiak et al., 2016; Formetta and Feyen, 2019; Laborde et al., 2020b; Lakner et al.,
4 2020). There is agreement that with global warming of about 3癈 such undermining of poverty reduction
5 efforts will intensify and more regions will face development setbacks due to the spatial and temporal
6 expansion of climate hazards, including the further erosion of capitals that enable people to develop adaptive
7 capacities (high confidence) (see Section 8.5). Such trends can further exacerbate poverty traps (see Section
8 8.2.2). According to a World Bank report, between 32 and 132 million people could fall into extreme
9 poverty in 2030 due to the impacts of climate change (Jafino et al., 2020). Models estimate that at 3癈
10 warming and under an SSP1 there would be an additional 245 million people exposed to poverty. Under an
11 SSP2 this number would increase to 904 million additional people exposed to poverty (SSP2) and under an
12 SSP3 (with significant challenges for equity) about 1718 million additional people could be exposed to
13 poverty (SSP3) in the year 2050 (Byers et al., 2018).
14
15 Overall, the assessments above underscores that adaptation and risk reduction require not only information
16 about changing climatic conditions, but also assessments that capture the development contexts and
17 structural inequality that determine and influence human vulnerability. Strategies that reduce poverty and
18 inequality and that improve the access of people to basic services need to become a higher priority in
19 adaptation and development planning in order to avoid that more than 3 billion people currently and even
20 more in the future are exposed to severe adverse consequences of climate change. Reducing vulnerability to
21 climate change is therefore indispensable for climate justice and just transitions (high confidence).
22
23 8.3.2.4 Compound challenges: vulnerability and state fragility
24
25 Literature in the area of climate change risk management and adaptation highlights the importance of overall
26 governance systems and their functioning and inclusiveness in terms of vulnerability and risk reduction
27 (Burch et al., 2019). Empirical evidence and scientific studies show linkages between issues of governance,
28 conflicts and high levels of state fragility and systemic human vulnerability (see Figure 8.8; Section 8.5.2;
29 Ekl鰓 and Krampe, 2019; Peters et al., 2019; Mawejje and Finn, 2020)
30
31 The comparison of state fragility and vulnerability at the level of regions (UNSD regions) based on the
32 vulnerability information of the INFORM and WRI index systems and information of the Failed State Index
33 indicates clear linkages (see Figure 8.8), meaning that societal development and governance challenges often
34 interact and in many cases are influenced by complex histories (see FFP, 2020; Birkmann et al., 2021a;
35 Feldmeyer et al., 2021). Strategies to reduce systemic vulnerability and multi-dimensional poverty have to
36 account for these broader governance challenges that hamper resilience building and the development of
37 adaptive capacities to climate change at various levels.
38
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1
2 Figure 8.8: Comparison of the vulnerability and state fragility of global regions. The vulnerability values are the
3 average of the vulnerability component of the WorldRiskIndex 2019 (Birkmann et al., 2021a; Feldmeyer et al., 2021)
4 and the vulnerability and lack of coping capacity components of the INFORM Risk Index 2019 (Marin-Ferrer et al.,
5 2017) classified into 5 classes using the equal count method(Birkmann et al., 2022). The state fragility values are based
6 on the Fragile States Index 2019 (FFP, 2020), and regions are based on the intermediate and sub-regions of the United
7 Nations Statistical Division. The size of each circle is proportional to the population (World Bank, 2019b) in the
8 respective region.
9
10
11 Strategies to strengthen adaptation to climate change have therefore to acknowledge these interdependencies
12 between climate change, vulnerability, development and governance (see Section 8.6.5). The results of
13 different global vulnerability assessments and the role of governance conditions underscore that next to
14 individual adaptation projects in specific sectors, integrated strategies and programmes are needed that
15 reduce systemic vulnerability and support enabling conditions for adaptation for most vulnerable groups (see
16 Section 8.6.5).
17
18 8.3.2.5 Trends in vulnerability and poverty in light of climate change and the COVID-19 pandemic
19
20 Literature that assesses trends of poverty and vulnerability as well as exposure to climate change reveals that
21 geographic patterns of poverty and vulnerability are uneven and changing over time (Feldmeyer et al., 2017).
22 However, a robust finding of different studies is that the population growth in most vulnerable country
23 groups and regions "is" and very likely "will be" significantly higher in the future compared to population
24 growth in countries classified as low vulnerable (see Section 8.4.5.2). In summary, a significant increase of
25 population is expected in highly vulnerable countries in the future. In addition, global studies show that by
26 2030 it is expected that almost 50% of the world's poor will be living in countries affected by state fragility,
27 conflict and violence (UNISDR, 2009; Hallegatte et al., 2017).
28
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1 Another important phenomena that modifies trends in vulnerability to climate change and poverty is the
2 COVID-19 pandemic (see Box 8.3). It is likely that the COVID-19 pandemic with its global repercussions
3 will continue to modify and, in many cases, intensify poverty and human vulnerability (Laborde et al.,
4 2020a; Sumner et al., 2020). Recent studies that estimate the impact of COVID-19 on global poverty agree
5 that a significant increase of poverty due to COVID-19 and the respective lockdown of countries is already
6 observed or expected in the near future (Laborde et al., 2020b; Sumner et al., 2020). These studies
7 underscore that 80% of those newly living in extreme poverty (living on under 1.9 USD per day) due to
8 COVID-19 would be located in mainly two global regions: Sub-Saharan Africa and South Asia (Sumner et
9 al., 2020). Consequently, the COVID-19 pandemic is likely to further increase inequality at different scales
10 and increase the burden within regions already characterized by a significant adaptation gap in terms of high
11 vulnerability (see also Figure 8.6). This implies that the capacity of people to prepare for present and future
12 climate change impacts will further decrease within these countries and for specific vulnerable people/groups
13 in these regions.
14
15 Recent scientific studies in the context of climate influenced hazards and disasters also underscore that
16 various regions and countries classified as highly vulnerable are characterized by a high persistence of
17 human vulnerability and chronic poverty (Feldmeyer et al., 2017; UN-DESA, 2020; World Bank, 2020). For
18 example, various highly vulnerable regions in Central, West and East Africa, such as countries like Haiti,
19 Afghanistan, Democratic Republic of Congo, but also Small Island Developing States (SIDS) in Melanesia
20 and Micronesia have been characterized by high levels of poverty for decades (World Bank, 2020). Several
21 of these highly vulnerable regions are also likely to experience a further increase in climate hazards such as
22 sea-level rise in Melanesia and Micronesia and in coastal zones of West- and more severe droughts in Africa
23 (IPCC, 2021).
24
25 There is evidence that in many world regions the exposure to climatic hazards is increasing with additional
26 global warming (Chin-Yee, 2019; Hoegh-Guldberg et al., 2019a; IPCC, 2021). In addition, development
27 patterns and practices such as urbanization and migration to exposed areas, for example, to coastal zones in
28 West-Africa or South Asia is increasing exposure. While the spatial and temporal exposure to impacts from
29 climate change and extreme events increases with higher levels of global warming (Hoegh-Guldberg et al.,
30 2019a), in all global regions and various climate zones (IPCC, 2021), the burden is greater for the most
31 vulnerable regions where people have limited support and capacities to build adaptive capacities for future
32 impacts of climate change.
33
34 In this regard, vulnerability assessment results provide an important additional layer of information for
35 decision making in terms of defining adaptation and risk reduction needs and priorities, as shown in Figure
36 8.9. The figure shows the published climatic information regarding observed changes in agricultural and
37 ecological droughts (IPCC, 2021) combined with a background map of vulnerability. For example, the
38 combined information reveals that even if the agreement on the type of changes in observed changes in
39 droughts is low for North and South-East Africa, it is the high vulnerability in this region that requires urgent
40 attention (see Figure 8.9).
41
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1
2 Figure 8.9: Map with observed changes in agricultural and ecological droughts (IPCC, 2021) overlaid over human
3 vulnerability (see Figure 8.6) provides a more comprehensive overview for defining adaptation priorities.
4
5
6 Recent reports on extreme poverty and human rights (Alston, 2019) show that already millions face
7 malnutrition due to devastating drought. In addition, the linkages between ecosystem vulnerability and
8 human vulnerability and human well-being are important aspects that need more attention, since for example
9 the degradation of marine ecosystems that support food systems for hundreds of millions of people will
10 threaten food security (see for details Cross-Chapter Box Moving Plate chapter 5).
11
12 While the findings of the Alston report underscore the urgency to act in order to protect people's livelihoods,
13 particularly in low income countries, it also shows that extreme poverty (Alston, 2019) and different
14 dimensions of poverty are found in middle and high income countries.
15
16 A study of the World Bank (Hallegatte et al., 2017) estimates that losses in terms of wellbeing are
17 significantly higher compared to actual asset losses experienced (Hallegatte et al., 2017). A higher
18 proportion of the global absolute economic losses occurred in high income countries. About 56% of all
19 disasters reported occurred in high-income countries, while the low-income countries account for 44% of the
20 recorded disasters. However, low income countries account for about 68% of the total deaths reported, high
21 income countries for about 32% (CRED, 2015; see also Section 8.3.2.1). In contrast, average absolute
22 economic losses5 were significantly lower in most vulnerable countries compared to low vulnerable
23 countries (Birkmann et al., 2022). Economic loss trends from EMDAT database (CRED, 2020) must be
24 interpreted with caution. Economic loss data is often incomplete and needs to be improved. However, these
25 differences in terms of economic losses can also be explained in part with significant wealth differences and
26 the monetary value of assets exposed. Consequently, there is a need to critically reflect the measures used to
27 assess loss and damage from climate change. Interestingly, the number of people affected by droughts,
28 floods and storms as a percentage of the total population and per hazard event again points to the
29 disproportional suffering of most vulnerable countries (Birkmann et al., 2022).
5 calculated by averaging the country values of economic losses per event falling in different vulnerability categories
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1
2 Overall, there is robust evidence that at the global scale poor and most vulnerable people particularly in
3 regions classified as highly vulnerable are disproportionately affected by wellbeing losses and loss of life in
4 the context of climate change and climate influenced natural hazards (CRED, 2015; Hallegatte et al., 2017;
5 Birkmann et al., 2022) (high agreement). In this context, also non-economic losses need to receive a higher
6 attention (see Section 8.3.3.2). While there is an emerging understanding that inequality and
7 multidimensional poverty are important determinants of systemic vulnerability to climate change (Dennig et
8 al., 2015; Hallegatte and Rozenberg, 2017; Islam and Winkel, 2017) that affects more than 3 billion people
9 today, only very few countries explicitly aim to reduce poverty and income inequality as an adaptation
10 measure (see e.g., Brazil Ministry of Environment, 2016) (high agreement). Reducing vulnerability is a
11 prerequisite for climate justice and just transitions.
12
13 8.3.3 Livelihood impacts, shifting livelihoods and the challenges for equity and sustainability in the
14 context of climate change
15
16 This section complements the global and regional assessment of vulnerability in the previous section with a
17 more precise assessment of observed local conditions and livelihood impacts and shifts. Firstly, the section
18 reviews linkages between vulnerability and livelihood impacts of climate change broadly. Secondly, it
19 examines the range of observed disproportionate impacts according to economic (e.g., income) and non-
20 economic (e.g., cultural) impacts of climate change. Thirdly, it examines current risks of adaptation limits
21 and compounding effects across social groups and associated livelihood shifts.
22
23 8.3.3.1 The implications of vulnerability for past and present livelihood impacts of climate change
24
25 Climate change impacts add to livelihood challenges and can further increase inequality and poverty (see
26 Section 8.2.1), whose root causes are social, institutional and governance-related. Various regional clusters
27 of high vulnerability (see Figure 8.6) are also influenced by historical processes, such as colonialism and
28 power-relations that made people and countries vulnerable (Schell et al., 2020). Thus, vulnerability to
29 climate change is not primarily linked to the degree of exposure to climate change impacts, but determined
30 by societal structures and development processes that shape context and individual vulnerability (see the
31 above Section 8.3.2), and values and lived experiences of climate hazards (Djoudi et al., 2016; Walker et al.,
32 2021). Intersectionality approaches are central to grasping differential vulnerability (Thomas et al., 2019) for
33 past and present livelihood impacts of climate change (see Figure 8.3 and Section 8.2.2.2). Assessing
34 observed local conditions and livelihood impacts and shifts requires us to consider reinforcing social
35 phenomena such as age, gender, class, race and ethnicity which shape social inequalities and experiences of
36 the world and also intersect with climate hazards and vulnerability (Walker et al., 2021).
37
38 This understanding helps to clarify how social structures, institutions and governance mechanisms matter to
39 address social causes in addition to climate magnifiers while holding them accountable (see Section 8.5). For
40 example, low-elevation coastal zones concentrate high levels of poverty in some specific areas: 90% of the
41 world's rural poor are concentrated in the low-elevation coastal zones of just 15 countries, and this
42 population keeps growing (Barbier, 2015). Yet studies on the economic impacts of climate change and also
43 Integrated Assessment Models typically overlook the distributional effects of these impacts according to
44 vulnerability and exposure and do not sufficiently account for agent and societal heterogeneity (Balint et al.,
45 2017; Sovacool et al., 2021).
46
47 Since the AR5, high confidence is attributed to the fact that the, mostly detrimental, climate change impacts
48 and risks are experienced mainly by the poorest people around the world (Olsson et al., 2014; Roy et al.,
49 2018). There is high confidence that climate change impacts will put a disproportionate burden on low-
50 income households and thus increase poverty levels (IPCC, 2014a; Hallegatte and Rozenberg, 2017).
51
52 There is robust evidence that economic development based on the exploitation of natural resources can
53 significantly increase the vulnerability of communities at the local level. For example, there is a correlation
54 between political arrangements and environmental degradation that brings about both disasters and an
55 increase in disaster risk (Cannon and M黮ler-Mahn, 2010; Pereira et al., 2020) and while development is
56 recognised by some as a key element for adaptation (Cannon and M黮ler-Mahn, 2010).
57
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1 Maladaptation is an important thread given its relevance to assess ways that well intentioned development
2 can exacerbate past and existing vulnerabilities and undermine livelihoods (see Section 8.2.2.1). Evidence
3 shows that some local development projects can undermine resilience and increase the vulnerability of
4 neighbouring communities, leading to maladaptation (Magnan et al., 2016; Schipper, 2020; Eriksen et al.,
5 2021). Development projects can also negatively affect the vulnerability and create new ones of the very
6 community where they are implemented (Burby, 2006; Magnan et al., 2016; Atteridge and Remling, 2018;
7 Thomas and Warner, 2019; Work et al., 2019). Maladaptation has also received growing attention since AR5
8 as projected future climate risk for vulnerable social groups (see Section 8.4.5.5) and in the context of
9 adaptation constraints and trade-offs in climate resilient development (see Section 8.5.1 and 8.6.1), Despite
10 maladaptation, there is however robust evidence that inclusive and sustainable development at the local
11 level, can reduce vulnerability (Cannon and M黮ler-Mahn, 2010; Patnaik et al., 2019).
12
13 8.3.3.2 Economic and non-economic losses and their relevance for poverty and livelihoods
14
15 Economic losses include income and physical assets and non-economic losses include mortality, mobility,
16 mental wellbeing losses from climate change (see Section 8.3.4). The IPCC WGII AR5 primarily associated
17 losses and damages with extreme weather events and economic impacts, and treated it primarily as a future
18 risk. New evidence provides insights into present-day losses and damages from slow-onset impacts (e.g., sea
19 level rise) (Adamo et al., 2021) and non-economic losses (e.g., cultural impacts, emotional and psychological
20 distress) (McNamara et al., 2021b) which previously received much less attention. AR5 had more focus on
21 losses and damages in high-income regions than in regions most at risk, such as Small Island Developing
22 states and Least Developed Countries (LDCs) (van der Geest and Warner, 2020).
23
24 Impacts of climate change are affecting the economic and non-economic dimensions of people's lives,
25 including subsistence practices of communities that are experiencing decreases in agriculture productivity
26 and quality, water stress, increases in pests and diseases, disruption to culture, and emotional and
27 psychological distress, just to cite a few (Savo et al., 2016). For example, the cumulative effects of slow-
28 onset events threaten food security especially among the poor in Latin America and the Caribbean--regions
29 which face the largest gender gap in terms of food security globally (Zu駃ga et al., 2021). In general for
30 Global South countries, the global average temperature warming (including the Paris target of 1.5癈) means
31 substantially higher warming and including higher frequency and magnitude of extreme events, that will
32 result in significant impacts on societal vulnerability (Aitsi-Selmi and Murray, 2016; Djalante, 2019).
33
34 Measuring losses from climate change impacts in terms of poverty and inequality can be difficult, and part of
35 the lack of assessments of non-economic loss and damage can be attributed to the limited observational
36 climate data on poor countries and population impacted, which are mostly concentrated in the Southern
37 hemisphere (Roy et al., 2018).This is also due to the challenges posed by limited data available for assessing
38 attribution (Cramer et al., 2014; Harrington and Otto, 2020; Otto et al., 2020a) and no comprehensive set of
39 adaptation metrics (Otto et al., 2020b). Economic losses and damages from climate change are often assessed
40 and reported after disasters or within crises, however, non-economic losses from climate change are often
41 overlooked as is their relevance for poverty and livelihoods. For those who experience both economic and
42 non-economic losses the impacts of climate change are very real and profound (Tschakert et al., 2017; Roy
43 et al., 2018) Particularly in low-income and most vulnerable regions, it is not the absolute economic loss, but
44 the combination of economic and especially non-economic losses that need to receive higher attention and
45 need to inform adaptation strategies.
46
47 8.3.4 Observed disproportionate impacts according to economic and non-economic losses and damages
48 due to climate change
49
50 Since AR5 a new discourse on Loss and Damage (L&D) has emerged with new typology and elaboration of
51 a definition. L&D has a long and contentious history and is enshrined in the Paris Agreement (see Cross-
52 Chapter Box LOSS in Chapter 17). Despite ambiguity about what constitutes L&D (Boyd et al., 2017), it
53 focuses on how to avert, minimize, and address the negative impacts of climate change, including those that
54 cannot be avoided through adaptation. It can also be thought of as the observed residual risk (and potentially
55 irreversible losses) from climate change when adaptation limits are encountered and mitigation has failed
56 (Boda et al., 2020). L&D is considered a policy mechanism (see Cross-Chapter Box LOSS in Chapter 17). It
57 is also a burgeoning science for loss and damage (Mechler et al., 2019b) which advances the breakdown on
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1 compounding vulnerabilities and highlights the disproportionate effects of climate change on the vulnerable
2 and marginal (see Box 8.5 for illustration of distributional effect of both the drought and responses in the
3 Cape region in South Africa). New evidence provides additional insight into loss and damage from slow
4 onset events related to climate change (sea level rise, drought) (see Anjum and Fraser, 2021; Lund, 2021)
5 For example, (Singh et al., 2021) found growing evidence of urban droughts leading to economic losses
6 (e.g., groundwater over-extraction, financial impacts) and non-economic losses (e.g., conflict, increased
7 drudgery).
8
9 The literature is assessed according to this new L&D typology, which includes both extreme and slow onset
10 events and has a strong emphasis on climate justice and disproportionate impacts of climate hazards (see
11 Figure 8.3) with a new focus non-economic loss and damage.
12
13 8.3.4.1 Economic (e.g., income, assets) impacts of climate change and vulnerability
14
15 While extreme events are not new, the intensity and frequency of extreme events are stacking, leading to
16 additional increase in poverty or vulnerability in some regions, exacerbated by Covid-19, and up against
17 existing development pathways leading to significant impact on economic losses globally (high confidence).
18 There is robust evidence that many African countries experience climate-related losses in terms of loss of
19 crop yields, destroyed homes, food insecurity through increased food prices, and displacement (Box 8.5;
20 Olsson et al., 2014). Attention has been focussed on low income groups, women and children, poor rural
21 communities, and Indigenous Peoples such as the example of the Dupong, an Indigenous Peoples in Ghana
22 using indigenous strategies to limit adverse impacts of climate change-induced water shortages (Opare,
23 2018). In Kenya economic loss and damage during droughts between 2009-11 drought incurred costs
24 including trucking emergency water and food supplies as well as loss of livestock and livelihoods,
25 particularly in areas cross-sectoral economic effects were estimated to reduce GDP by 2.8% per year (King-
26 Okumu et al., 2021a). Past studies have similarly shown that in context of extreme events such as floods or
27 droughts the most commonly sold assets are livestock and land. The sale of property particularly reduces the
28 asset base and creates long-term vulnerabilities to future events and can trigger chronic poverty (high
29 confidence). People may face food shortages in the future from lack of crop production (Opondo, 2013).The
30 sale of cattle affects the household asset base, as well as the important access to animal traction power for
31 farming.
32
33 In South Asia, there is robust evidence of economic impacts of climate change (Cao et al., 2021), for
34 example in the Sundarbans (a transboundary ecosystem with components in both India and Bangladesh, with
35 the problem of unproductive livelihoods being common across residents of both countries) observations
36 show local livelihoods are rapidly becoming unproductive (loss of fish, and increasing salination making
37 agriculture increasingly difficult) (Ghosh, 2018); conditions that are exacerbated by climate change impacts
38 (high confidence). Cyclone and storm surges induced by climate change force saline water into agricultural
39 lands along the coast, which damages crops not only in the year the cyclone hits, but for several years
40 afterwards (Rabbani et al., 2013). They showed in Shyamnagar Upazilla in Satkhira district the proportion of
41 salinity-free farmland has gone down over the past 20 years, from more than 60% to nil (Rabbani et al.,
42 2013). Vietnam has also experienced effects of flooding and salinization in the Mekong delta coupled with
43 rapid social development. Intensified floods and droughts have dramatically resulted in loss of livelihoods in
44 agriculture and fisheries in some areas of the basin (Evers and Pathirana, 2018). In Vietnam the expected
45 salinization increases livelihood shifts into areas that are more risky, such as shrimp farming. Furthermore,
46 the Vietnamese Mekong Delta is characterized by strong migration processes towards cities, particularly Ho
47 Chi Min, meaning that abrupt livelihood shifts are already happening. There are emerging examples of
48 Indigenous Peoples affected by climate change in indigenous farming mountain communities of the Nepal
49 Himalaya. (Sujakhu et al., 2019). The Philippines has experienced extreme events, such as typhoon Haiyan
50 in 2013, which left more than 7353 reported people dead or missing, and damaged or swept away more than
51 1.1million houses and injured more than 27,000 people (McPherson et al., 2015). More than 4 million were
52 displaced. The cost of damages has been estimated at US$864 million with US$435 million for infrastructure
53 and US$440 million for agriculture in affected regions (McPherson et al., 2015).
54
55 Sea-level rise, coastal flooding and surge inundation is an increasingly pressing problem across the urban
56 Pacific, including the urban and coastal population of Vanuatu (McDonnell, 2021). Pacific region islands
57 such as Vanuatu (Handmer and Nalau, 2019) are particularly vulnerable to climate change. Kiribati and
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1 Tuvalu are impacted by exceptionally high tides that affect the urban atolls of South Tarawa and Funafuti,
2 and cyclonic activity causing extensive economic damage in Tuvalu (Curtain and Dornan, 2019). Limited
3 migration opportunities for low-income households can result in forced immobility, and high tides, sea-level
4 rise and cyclonic damages could result in relocation of significant groups of the population.
5
6 A pertinent example of economic losses is the example of the Torres Strait in Australia. This example shows
7 evidence of communities living on remote islands Boigu, a low-lying mud island inundated by the sea during
8 high tides and storm surges, and those most exposed and vulnerable to climate change have limited
9 livelihood assets and face challenges to secure external support with government and others. Place-based
10 values evoke a reluctance to relocate or retreat with economic losses such as community infrastructure,
11 housing, and cultural sites (McNamara et al., 2017). In the Great Barrier Reef, Australia sea level rise and
12 sea level global temperature warming affects fisheries productivity and tourism (Evans et al., 2016).
13 Unprecedented burn area of wild forest fires in Australia between September 2019 and January 2020 (Boer
14 et al., 2020) burnt almost 19 million hectares, destroyed over 3,000 houses, and killed 33 people (Filkov et
15 al., 2020).
16
17 The 2018 European heatwave in Northern and Eastern Europe experienced multiple and simultaneous crop
18 failures--among the highest observed in recent decades (high agreement). These yield losses were
19 associated with extremely low rainfalls in combination with high temperatures between March and August
20 2018 (Beillouin et al., 2020). Across Europe, in 2018 people experienced one of the worst harvests in a
21 generation. Northern and Eastern Europe experienced multiple and simultaneous crop failures--among the
22 highest observed in recent decades. These yield losses were associated with extremely low rainfalls in
23 combination with high temperatures between March and August 2018. This compounding of extreme
24 conditions in 2018 led to one of the highest negative relative yield anomalies at the scale of Eastern and
25 Northern Europe, across a large array of crop species(Beillouin et al., 2020).
26
27 Extreme climate events are disproportionately impacting economies of the most vulnerable everywhere
28 (medium evidence, high agreement). In the United States, Central America and Caribbean, Hurricanes
29 Katrina, Harvey, Irma, Maria and Michael are examples of extreme climate events that have displaced
30 households, destroyed homes, and led to loss of income among the poor and marginalized (Klinenberg et al.,
31 2020). Puerto Rico was devastated by Maria but received less support from the Federal Emergency
32 Management Agency (FEMA) (Garc韆, 2021). Evidence is emerging on unequal governance response in the
33 US versus Puerto Rico (Joseph et al., 2020). Floods, storms and heatwaves have impacted the poorer
34 communities, and even wildfires in California, impact many wealthy groups, also impacted infrastructure
35 used by all, for example, with lengthy electrical power blackouts, but particularly impacted vulnerable to
36 disasters such as undocumented Latino/a and Indigenous immigrants in the case of the Thomas Fire in
37 California's Ventura and Santa Barbara counties(M閚dez et al., 2020) Hurricane Irma in 2017 hit Ragged
38 Island in the Bahamas as a category 5 storm leaving the island in ruins and deemed `unlivable' by its
39 authorities, with most infrastructure left as rubble, no essential utilities remained, schools and health clinics
40 were in ruins and the stench of dead animals was overwhelming. This storm resulted in significant economic
41 loss and damage by the community through loss of their homes, churches, schools, agricultural land, and
42 infrastructure (Thomas and Benjamin, 2020).
43
44 Across South America, groups of farmers, children, elderly, Indigenous Peoples and traditional communities
45 are increasingly exposed to floods, droughts, wild forest fires, losses in crop yields, resulting in significant
46 economic costs (medium evidence, high agreement) (see Box 8.6). Urban communities, in particular those
47 living in informal settlements, are exposed to heatwaves. In Peru, analysis of water risks posed by climate
48 change in the Vilcanota-Urubamba basin, Southern Peru, seasonal water scarcity and `Glacial Lake Outburst
49 Floods' (GLOF), pose a serious threat for highly exposed and vulnerable people. It showed that very high
50 risk potentials of 134 current and another six out of 20 future glacier lakes as potentially highly susceptible
51 to outburst floods. A total of eight existing and one possible future lakes indicate future river discharge could
52 be reduced by some 2-11% (7-14%) until 2050 (2100). Farmers, in particular smallholders risk losses to
53 growing irrigated agriculture and hydropower capacity with effects on water scarcity and food security
54 (Drenkhan et al., 2019).
55
56 There are additional dimensions of economic losses that are of a more diffuse nature. In particular, climate
57 change is also expected to negatively affect labour supply, particularly in temperature exposed industries
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1 (agriculture, mining, manufacturing, construction), due to increases in the number of extreme hot days (Graff
2 Zivin and Neidell, 2014; Garg et al., 2020). Low-income countries have on average a large share of workers
3 in such industries and will thus be especially hard hit. Aside from labour supply, a number of studies also
4 document negative impacts to manufacturing productivity (Acharya et al., 2018; Pogacar et al., 2018;
5 Somanathan et al., 2021). These findings provide a channel to explain macroeconomic consequences of
6 climate change (Burke et al., 2015). However, there are also noneconomic costs in that extreme heat will
7 cause increased discomfort to workers, such as psychological stress, disease and in extreme cases, death
8 among the workforce in developing economies as well as tropical and sub-tropical countries (Ansah et al.,
9 2021).
10
11 8.3.4.2 Non-economic (e.g., mobility, wellbeing)
12
13 Climate change loss and damage presents an existential threat to some (Boyd et al., 2017). For example the
14 Pacific Island Countries have contributed least to total greenhouse gas emissions, the nations of the South
15 Pacific are highly vulnerable to rising sea-levels, tropical cyclones and other climate-related risks (Nand and
16 Bardsley, 2020). For example across Oceania there is significant risk that sea-level rise will lead to forced
17 relocation. Pacific leaders underscore importance of losses including deep connections between their world
18 views and their land, and that leaving their islands can only be considered an option of `last resort'
19 (McDonnell, 2021).
20
21 Non-economic loss and damage (NELD) is values based (subjective and intangible) and relates to norms,
22 social values and highlights intersectional experiences and perspectives on climate risk. The discourse on
23 loss and damage includes a framing of NELD as loss of human and non-human life and mental and physical
24 health and are experienced widely across the world in vastly different ways associated with social values
25 (Tschakert et al., 2019). There are respectable arguments for the case that all life has intrinsic value
26 (Vetlesen, 2019). The NELD framing of climate impacts highlights that not all risks are measurable. While
27 difficult to measure, there are a growing number of cases of non-economic loss and damage globally
28 (medium evidence, high agreement). Illustrative examples of non-economic loss and damage from climate
29 change include the Pacific (McNamara et al., 2021b) and Small Island Developing States (SIDS) in the
30 Caribbean. (Martyr-Koller et al., 2021). For example, the hurricane season in 2017 was particularly extreme
31 resulting in climate-induced displacement with direct implications for non-economic loss and damage,
32 including threats to health and wellbeing and loss of culture and agency (Thomas and Benjamin, 2020).
33
34 In the context of the Pacific Islands NELDs are thought of as interconnected and span human mobility and
35 territory, cultural heritage and Indigenous Knowledge, life and health, biodiversity and ecosystem services,
36 and sense of place and social cohesion (Carmona et al., 2017; Ojwang et al., 2017; McNamara et al., 2021b).
37 There are gaps in our understanding of NELD, much of the evidence is from the Global South and at smaller
38 scales (high agreement), NELD is not explicitly linked to attribution science yet and evidence often lacks
39 coverage on certain groups (Boyd et al., 2017; Carmona et al., 2017; Ojwang et al., 2017). Non-economic
40 losses are often associated with displacements and migration in terms of climate change and human
41 vulnerability (Section 8.2.1.4), studies show that the impacts of extreme flooding, droughts and/or hurricanes
42 and cyclones that can lead to a sense of lost identity and place, and emotional distress, that are hardly
43 assessed dimensions of impacts and risks (Adger et al., 2014; Barnett et al., 2016; Tschakert et al., 2017;
44 Serdeczny et al., 2018). Non-economic losses are particularly relevant for understanding adverse
45 consequences of climate change on the poor and most vulnerable population groups (high confidence). These
46 NELD categories are still overlooked vulnerability assessments and adaptation planning. A novel way to
47 consider NELD in assessments is to interconnect to a sustainable development perspective (Boyd et al.,
48 2017; Boda et al., 2020).
49
50 In order to categorise the different types of non-economic loss and damage that exist (Serdeczny et al.,
51 2016), based on their literature review, the authors come up with a set of systematic categories that capture
52 what is usually thought about as having intrinsic value and according this framing of non-economic loss and
53 damage this includes: human life, sense of place and mobility, cultural artefacts, biodiversity and
54 ecosystems, communal and production sites and agency and identity (Serdeczny et al., 2016; Serdeczny,
55 2019). For example, there is emerging evidence on linkages between slow onset events and mobility
56 decisions, trajectories and outcomes (Zickgraf, 2021). In addition, categories include psychosocial and
57 emotional distress (van Der Geest and Schindler, 2016). For example, research shows potential increased
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1 risk of Intimate Partner Violence (IPV) following disasters, noting that societies that are vulnerable to
2 climate change may need to prepare for the social disasters that can accompany disasters revealed by natural
3 hazards (Malik and Stolove, 2017; Rai et al., 2021).
4
5 Geographical focus on non-economic losses in the literature is mainly on the Global South with studies
6 mainly smaller in scale (high agreement). Many events studied include severe storms, floods and landslides.
7 Key groups affected include low income groups, agropastoralists, women and girls, children and youth,
8 Indigenous Peoples, ethnic and religious minorities. In Europe, the Samis who as a group face significant
9 challenges to health as ecosystems deteriorate (Jaakkola et al., 2018). In Africa, In Zimbabwe storm Idai
10 affected 270,000 people and subsequent flooding and landslides left 340 people dead and many others
11 missing (Chanza et al., 2020). There is evidence of loss of cultural heritage sites where effects of sea-level
12 rise and coastal erosion, the other considering climate change and variability (Brooks et al., 2020). Haile et
13 al. (2013) show flood casualties in Ethiopia include children drowned while playing outside during the 2007
14 flood period although official data is hard to come by (p. 489). Moreover, loss of place was experienced
15 when many of local houses in Itang built from wood, grasses and mud walls, which are easy to reconstruct
16 building economics are not strong enough to withstand an extreme flood and 38% of the surveyed houses
17 were severely damaged by the 2007 flood. These houses were constructed as an adaptation strategy but could
18 not withstand the floods. In Kenya, Opondo (2013) shows loss of human life was the most severe impact of
19 floods. For example, in the focus group discussion with men, `it was reported that a boat capsized on River
20 Nzoia at Siginga and ten people died'. (p. 457). In Mozambique, Brida et al. (2013) show loss of sense of
21 place occurred after flooding in the central districts of Caia and Mopeia, flooding had a devastating impact
22 on homes and livestock (Brida et al., 2013). Health impacts of the forest fire impacts in Amazon basin
23 countries have disproportionately affected vulnerable people/social groups (see Box 8.6).
24
25 In the literature on non-economic loss and damage there are many examples of loss of life (high agreement),
26 one such is in Nepal related to one of the deadliest deadliest landslides in Nepal history resulting in the
27 death-toll of 156 people (van der Geest, 2018). Evidence from landslide Jure and consecutive rainfall in
28 Sindhupalchok in Nepal also indicated that experience with impacts led to harmful mental stress such as fear
29 of new landslides in about 68.4% of people interviewed (van Der Geest and Schindler, 2016). One study in
30 Nepal has shown that almost a quarter (23%) of the households interviewed had sold property including
31 homes, livestock, and heirloom possessions in response to flooding (Bauer, 2013). Human deaths are
32 increasingly associated with losses and damages from tropical cyclones/typhoons Bangladesh, such as the
33 Southern coastal districts of Bangladesh, in particular Khulna and Satkhira (Chiba et al., 2017). Chandra et
34 al. (2017) A case study from Mindanao`, Philippines also reports physical injuries and loss of life in the
35 Philippines from the most powerful typhoon for over a century until 2012, affecting more than six million
36 people, killing at least 1000 people (Eugenio et al., 2016). Beckman and Nguyen (2016) identify the floods
37 2004 pulled away 24 houses in the commune, loss of families when their houses were flushed away.
38
39 An illustrative example is climate-induced loss of wellbeing and (im)mobility in Bhola Slum, an informal
40 settlement in Dhaka, Bangladesh. Research revealed that Internally Displaced People from the southern coast
41 experienced loss of belonging, identity, quality of life and social value produced in people a nostalgia and
42 desire to return home (Ayeb-Karlsson et al., 2020). Another example is of urban climate change justice
43 through the lens of migrants in the Indian cities of Bengaluru and Surat, where experiences of environmental
44 marginality can be attributed to a lack of recognition of citizenship rights and informal livelihood strategies
45 driven by broken social networks and a lack of political voice, as well as heightened exposure to emerging
46 climate risks and economic precariousness. In this case migrants experience extreme forms of climate
47 injustice in their invisibility to formal government and even are actively erased from cities through force or
48 discriminatory development policies (Chu and Michael, 2019). Non-economic loss and damage also includes
49 the loss of social networks that has lasting implications for psychological health as well as for coping with
50 crises following disasters or challenges posed by adverse climate change impacts. For example, many
51 households from Cyclone Aila-affected villages of Dacope and Koyra upazilas of Khulna District in
52 Bangladesh migrated to other places permanently after the cyclone as these affected villages were subject to
53 long-term flooding (e.g., two or three years) following the cyclone. They migrated as they were unable to
54 restore their livelihoods and thus, were unable to secure necessary income for survival (Saha, 2017).
55
56 The examples show the multifaceted nature also of intangible and non-economic losses that people
57 experience in the context of climate change and daily risks they are exposed to. Conventional vulnerability
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1 assessments cover some aspects that are linked to the likelihood to experience non-economic losses, such as
2 aspects of health, governance, education and in some cases also forced migration and the role of social
3 networks. Overall, both elements of this assessment here underscore that it is not just the climatic stressor,
4 but rather the underlying context conditions that decide whether an extreme event translates into a disaster.
5
6 8.3.5 Economic and non-economic losses and damages due to climate change and their implications for
7 livelihoods and livelihood shifts
8
9 This section examines the intersections between losses and damages and livelihood shifts. This requires an
10 examination of the differentiated aspects of livelihoods. Understanding economic (e.g., loss of food crops,
11 infrastructure, assets etc.) and non-economic losses (e.g., health, wellbeing, loss of place, agency) and their
12 consequences for livelihoods is important that the intangible aspects clearly become visible and to receive
13 greater attention in loss assessments and in designing adaptation strategies and programmes. Figure 8.10
14 provides a summary of examples of observed impacts of climate hazards on economic and non-economic
15 capitals and the section assesses livelihood implications across regions. It shows examples of climate hazards
16 attributed to climate change in studies since AR5, across a range of geographical sites for heatwaves,
17 drought, hurricanes, and floods and non-economic losses and damages. The figure 8.10 reveals examples of
18 climate hazards attributed to climate change in studies since AR5 across a range of geographical sites for
19 extreme and slow onset events, such as heatwaves, drought, hurricanes and sea level rise. These are
20 associated with non-economic losses and damages. These figure underscores that non-economic losses and
21 damages lead to significant livelihood threats and livelihood changes. Also limits of adaptation become
22 evident (Chapter 16).
23
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1
2 Figure 8.10: Examples of non-economic loss and damage associated with climate hazards attributed to climate change
3 with background on the global vulnerability and symbols with corresponding detail in the table showing examples
4 where non-economic losses have been documented. It is important to note the following. 1. The figure is not exhaustive
5 in terms of examples of extreme or slow onset events or losses. 2. It does not capture undocumented cases in the
6 scientific literature. 3. It is an illustration of the relationship between unequivocal human induced climate change and
7 intangible losses (Adapted from Boyd et al., 2021).
8
9
10 8.3.5.1 Livelihood shifts resulting from loss and damage from climate change
11
12 While there are limited studies that directly link economic and non-economic loss and damage from climate
13 change to global-scale in livelihood transformations there is robust evidence on the granular linkages, at
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1 community, national and regional levels, between losses, coping strategies and livelihood shifts. Across
2 Africa climate change is impacting crop yields, destroying homes and resulting in loss of infrastructure, and
3 leading to non-economic losses associated with involuntary migration and displacement (Olsson et al.,
4 2014), and loss of livestock and assets (see IPCC SR 1.5癈 Chapter 3 (Hoegh-Guldberg et al., 2018)),
5 resulting in long-term reduction in the capacity for agriculture and land management. For example, in March
6 2019 tropical cyclone Idai in Mozambique, Zimbabwe and Malawi led to substantial losses of agriculture,
7 loss of infrastructure, and lack of access for aid and support, all of which contributed to significant
8 displacement in each country (Fischel de Andrade and de Lima Madureira, 2021). Examples of livelihood
9 impacts include livelihood shifts among Kenyan pastoralists to camel husbandry, resulting from household
10 inequalities in assets and changes in relation to weakening of social norms of reciprocity and social cohesion
11 (Volpato and King, 2019).
12
13 Extreme climatic events pose serious disruptions to local livelihoods and asset bases and requires them to
14 reconstruct, transform and diversify livelihoods (Uddin et al., 2021). Examples of livelihoods shifts across
15 Asia and Southeast Asia (e.g., India, Bangladesh, Vietnam, Philippines) include rural communities in coastal
16 areas, urban settlements that are experiencing economic losses (high confidence) from for example crop
17 failure and reduced access to fish, which contribute to non-economic losses associated with involuntary
18 migration (Ghosh, 2018) and the malnutrition of children (Siddiqi et al., 2011). Chiba et al. (2017) shows in
19 Bangladesh the connection between mental stress and impacts to the fundamental capacity to sustain
20 livelihoods, such as food and a place to live, due to severe damage to houses, homesteads, properties,
21 livestock and crops, loss of family members and relatives, and anxiousness about securing employment and
22 income in the future. In Bangladesh coastal communities experienced losses in livelihood assets due to
23 Cyclones Sidr and Aila (Uddin et al., 2021) and a significant number of cyclone victims were displaced from
24 their homes by severe cyclones. People have had to change their occupations � both intra- and intersectorally
25 � and confronted increased consumption and social costs. The study uncovered differences in impacts
26 between occupations such as farming and fishing, and the latter was likely to change occupation post-
27 disaster. They also show evidence that local people are learning to live with change and uncertainty by
28 nurturing and combining various types of knowledge and social memory, generating diversified livelihood
29 options, and self-organizing to enhance their resilience to future extreme weather events. In Bangladesh
30 Ahmed et al. (2019) found cyclones, riverbank erosion, salinity intrusion, and floods negatively impacted
31 people's lives by reducing their livelihood options. Their study found when there are limits to adaptation
32 strategies many people turn to `illegal livelihoods' included using fine mesh nets to collect shrimp fry in the
33 rivers as well as logging in the Sundarbans. These people include the poorest and vulnerable, and law
34 enforcement only exacerbate their vulnerability. Escarcha et al. (2020), studied impacts of typhoons, floods,
35 and droughts on crop production and effects on livelihoods of cash crop focused rural villages in the
36 Philippines. Their preliminary observations show a shift from crop to livestock production as a buffer
37 activity to recover from crop losses. Farmers changed their farming activities as a multi-adaptive response
38 driven by past experiences of climatic changes, farmers' social relations, household capacity, and resources
39 available.
40
41 In Central Asia, the Sahel and South Asia, three global poverty hotspots, change impacts were shown to
42 undermine traditional knowledge about livelihoods in ways that jeopardise future culture cohesion and sense
43 of place (Tucker et al., 2015). Acosta et al. (2016) identified loss to productive sites in the Philippines with
44 landslides destroying agriculture leaving many farmers without livelihoods. Similarly, Beckman and Nguyen
45 (2016) in Vietnam identified an example where communal dams had been destroyed in floods leading to lack
46 of irrigation for communal sites and local loss of farmland for farming communities. Chandra et al. (2017)
47 identified the vicious cycle between declining agricultural production and conditions of soil erosion due to
48 floods and droughts resulting in decreased crop fertility to productive sites with implications for decline in
49 crop yields, loss of crops and of livelihood assets. Climate change related extreme weather events such as
50 typhoons, floods, and droughts can have detrimental impacts on crop production (high confidence) and in the
51 Philippines and Pakistan have significantly affected the livelihoods of cash crop focused rural villages
52 (Escarcha et al., 2020; Jamshed et al., 2020b). There is an emerging shift from crop to livestock production
53 as a buffer activity to recover from crop losses (Section 5.10.4; Jamshed et al., 2017; Escarcha et al., 2020).
54 As with many examples of livelihood shifts, the viability of the shifts long-term under climate change have
55 yet to be assessed further.
56
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1 In Africa, many communities do already experience drought and flood-related disasters (high confidence)
2 such as those that negatively impact livelihoods and assets the Muzarabani district of Zimbabwe (Mavhura,
3 2017). The Muzarabani community has revived and developed new livelihood strategies to manage risks,
4 including local informal safety nets, local farming practices and the traditional flood-proofing structures.
5 Food security and agriculture productivity are examples of livelihood resources most at risk to climate
6 hazards (see Figure 8.2) (high confidence). An illustration of such risks to cocoa farmers in Ghana includes
7 increased incidences of crop pests and diseases, wilting of cocoa leaves, high mortality of cocoa seedlings
8 which affected expansion and farm rehabilitation, and wilting of cherelles resulting in losses of crop yield.
9 An illustration of livelihood shifts resulting from losses is of farmers shifting to cereals due to the
10 unpredictable climatic patterns and the shortened duration of rainfall. Yet, insecurity with storage, supply
11 chains and low returns from cereal production, coupled with land scarcity in the Western Region, have
12 resulted in a return to cocoa production (Asante et al., 2017).
13
14 Research from Australia shows complex linkages between the impacts of drought on livelihood income,
15 health and cultural heritage, increasing risk of heat stroke, and possibly a link to suicide among male farmers
16 (Alston, 2012; Hanigan et al., 2012; Marshall et al., 2019). The link between agricultural losses and suicides
17 has also been noted in South Asia, including India (Carleton, 2017). Livelihoods are shifting with impacts to
18 wellbeing, as noted by (Evans et al., 2016) showing connections between loss of fishery productivity and
19 impact on tourism sector livelihoods in the Great Barrier Reef region. In Europe, losses to Indigenous
20 Peoples are associated with loss of wellbeing of Sami communities and has forced livelihood shifts from
21 reindeer herding due to loss of ecosystems to support the animals (Persson et al., 2017; Jaakkola et al.,
22 2018). Traditional pastoralist systems are also greatly impacted by cumulative dual challenges of
23 encroachments of other land users and by climate change. Traditional Sami reindeer herding strategies are
24 still practiced, but that rapidly changing environmental circumstances are forcing herders into uncharted
25 territories where traditional strategies and the transmission of knowledge between generations may be of
26 limited use. For example, rotational grazing is no longer possible as all pastures are being used, and changes
27 in climate result in unpredictable weather patterns unknown to earlier generations (Axelsson-Linkowski et
28 al., 2020). These examples show that there are complex factors underpinning the linking loss and damage
29 and shifting livelihoods. Moreover, there are significant challenges to undertake a shift and secure alternative
30 livelihoods.
31
32 Linkages between losses, coping strategies and livelihood shifts in Small Islands (e.g., in the Pacific region
33 Kiribati and Tuvalu, and in the Caribbean the Bahamas) shed light on impacted low-income households. For
34 example, farmers have experienced extensive damage to homes and loss of infrastructure, and experience
35 lack of migration opportunities (Curtain and Dornan, 2019). Evidence is growing that there is also significant
36 loss of cultural heritage in resettlement (Barnett and O'neill, 2012), evidence from Small Islands displaced
37 communities suggests that resettlement can have impacts on sense of place, identity and social fabric, a
38 theme highly relevant to loss, coping and adapting livelihoods, and not only restricted to Small Islands
39 (McNamara et al., 2021b). Roberts (2015) identified loss of communal sites in Kiribati and it is predicted
40 that by 2050 up to 80% of the land on the island of Buariki and 50% of the land on Bikenibeu may be
41 completely inundated and these effects will result in significant loss of livelihoods and displacement.
42 Throughout the Caribbean evidence indicates that there will be an overall reduction in the area of land
43 suitable for crop cultivation, as the region's climate gets progressively warmer and as rainfall becomes more
44 variable (Rhiney et al., 2016).
45
46 The multiple shocks of extreme events reduce crop yields, destroy homes, and lead to loss of infrastructure
47 and displacement (high confidence) and are experienced in South and North America. For example in Peru
48 glacial outbursts have led to loss of livelihoods (Drenkhan et al., 2019). People use a range of coping and
49 adaptation strategies to deal with hazards where they live, such as shifting livelihood activities, inputs or
50 production areas. However, traditional techniques are increasingly failing due to changing weather patterns.
51 Across Peru, findings demonstrate that people use temporary and permanent migration among their many
52 coping and adaptation strategies. Hazards related to water excess have been the key force in destroying
53 homes and driving displacement in Peru. On the flipside, studies demonstrate that water scarcity also
54 threatens livelihoods and thereby influences migration in Peru. While non-climatic reasons for moving
55 dominate migrants' motivations in many areas of Peru, water-related climatic drivers of migration are
56 becoming increasingly relevant (Wrathall et al., 2014). Peru's smallholder farmers and urban poor are not
57 responsible for the climate crisis, yet their lives and cultural heritage are being increasingly jeopardized by
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1 its effects, making improvements in governance an imperative for Peru (Bergmann et al., 2021). Another
2 area of significance is coffee production in Brazil where the majority of Brazilian coffee farms are operated
3 by smallholders, producers with relatively small properties and mostly reliant on family labour (Koh et al.,
4 2020). In the United States (e.g., New Orleans and Puerto Rico) people have lost livelihoods due to displaced
5 households, destroyed homes, and led to loss of income as well as loss of social networks and family
6 networks and loss of cultural heritage. For example, impacts of Hurricane Katrina have led to people being
7 displaced from their employment, many evacuees had to relocate to new areas, which disrupted their social
8 networks and placed them in unfamiliar labour markets, resulting in mental health challenges (Palinkas,
9 2020). There has also been a `climate gentrification' in parts of New Orleans (Aune et al., 2020). Many of
10 those who returned to their pre-Katrina areas had to deal with extensive damage to their homes and to public
11 infrastructure.
12
13 In summary, across regions there are an increasing number of examples of observed economic and non-
14 economic loss and damage from climate change. Adaptation measures need to better incorporate actions to
15 tackle the burgeoning negative social, psychological and wellbeing impacts of climate change (Barnett et al.,
16 2016; Box 8.5). At present, losses from climate change are potentially growing faster than adaptation
17 measures across the globe. It is still uncertain how economic and non-economic losses trigger successful or
18 viable new climate-related livelihood transitions for the poor and people/groups in vulnerable situations in
19 the future (see Section 8.4.4, 8.4.5). In all likelihood, economic losses from climate hazards (e.g., drought)
20 will be compounded by many factors including COVID-19 and other vulnerability drivers. For instance,
21 globally small-scale coffee producers have been destabilised by COVID-19, but also because of history of
22 recurrent (climate) shocks and structural inequalities, and may have to shift into alternative livelihoods
23 (Guido et al., 2020). Coastal communities in Vanuatu have been impacted in the immediate period after
24 COVID-19 showing changes in village populations, loss of cash income, difficulties in accessing food and
25 experiencing shifting pressures on particular resources and habitats (Steenbergen et al., 2020). This trend
26 poses real challenges to equity and sustainability.
27
28
29 [START BOX 8.5 HERE]
30
31 Box 8.5: Western Cape Region in South Africa: Drought Challenges to Equity and Sustainability
32
33 Nature of the drought
34
35 Between 2015 and 2017, the Western Cape region experienced an unprecedented three consecutive years of
36 below average rainfall--leading to acute water shortages, most prominently in the city of Cape Town (Sousa
37 et al., 2018). Anthropogenic climate change made the drought five to six times more likely (Pascale et al.,
38 2020; see also AR6 WGI Chapter 10, Section 10.6.2). The severity of the drought presented new challenges
39 to the existing management and governance capacity to ensure equitable and sustainable water service
40 delivery. The city's water supply infrastructure and demand management practice were unprepared for the
41 `rare and severe' event of three consecutive years of below average rainfall (Wolski, 2018; Muller, 2019).
42 Despite a potential total storage volume of about 900,000 ML of water (enough water for around a year and a
43 half of normal usage, after taking evaporation into account), Cape Town's reservoirs fell from 97% in 2014
44 to less than 20% in May 2018 (Ouweneel et al., 2020; Cole et al., 2021). The drought saw residents queue
45 for water as restrictions were imposed together with threats of closure of water provision to households
46 (Sorensen, 2017; Scheba and Millington, 2018). Poor communication in the early stages of the drought
47 (Hellberg, 2020), and a lack of trust in the administration, contributed to a near-panic situation at the threat
48 of `Day Zero' as dams almost ran dry in the first half of 2018 (Enqvist and Ziervogel, 2019; Simpson et al.,
49 2020c). `Day Zero' was avoided largely through public response, water demand management and the 2018
50 winter rains (Sorensen, 2017; Booysen et al., 2019a; Muller, 2019; Rodina, 2019b; Matikinca et al., 2020).
51 At a household-level, responses to the drought saw everyday residents can display unprecedented degrees of
52 resilience (Sorensen, 2017), including behavioural and attitudinal shifts and technological innovation across
53 the full socio-economic spectrum (Ouweneel et al., 2020). But the private nature of some of these responses
54 extended existing inequality in water access through privileged forms of `gated adaptation' by elites which
55 conventional water governance arrangements were unprepared for (Simpson et al., 2019b; Simpson et al.,
56 2020a).
57
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1 These `climate gating' actions, such as drilling boreholes, secured water access for high-income households
2 and companies, but excluded a large proportion of Cape Town's population who could not afford such
3 private technologies (Simpson et al., 2019a; Simpson et al., 2020b). These responses were unanticipated by
4 the city administration and compounded fiscal challenges faced by the municipality which could no longer
5 use revenues from high-consumption households to cross-subsidise water for low-income households
6 (Simpson et al., 2020a). This shift threatened to undermine the sustainability of the municipal fiscus and
7 general water access ( Box 9.8; Simpson et al., 2019a; Simpson et al., 2020a). In order to recover losses,
8 municipal water tariffs for consumers were raised by 26% in 2018 (Muller, 2018; Simpson et al., 2019a). In
9 addition to decline in tourism, median estimations of the overall economic impact of the drought indicate
10 loss of 27.6 billion South African Rand (US$1.7 billion) translating into 64,810 job losses in the Western
11 Cape, with Cape Town accounting for approximately half of those job losses (DEDAT, 2018). This had a
12 disproportionate impact on unskilled and semi-skilled workers, particularly for those from low- and middle-
13 income households (DEDAT, 2018). The drought also exacerbated the potential for sanitation health risks of
14 the urban poor where tens of thousands of people lack access to safely managed sanitation facilities (Enqvist
15 and Ziervogel, 2019).
16
17 The Day Zero Disaster Plan included prioritising and protecting the poor and most vulnerable communities
18 where critical infrastructure and facilities and vulnerable and informal residential areas would remain
19 connected while higher income residential areas would be cut off (Cole et al., 2021). Yet it is important to
20 recognise that pre-existing deficiencies in service delivery meant water access for the urban poor did not
21 change as significantly during the drought, particularly those in informal settlements who collect water from
22 standpipes (Enqvist and Ziervogel, 2019; Matikinca et al., 2020). For these communities, the negative
23 economic impact of the drought was compounded by the unintended consequences of demand management
24 regulation emanating from the drought response. South Africa ostensibly ensures a constitutional right to
25 water, regardless of ability to pay (Rodina, 2016), 58). Since 2018 however, as a consequence of new water
26 tariffs instituted during the drought, Cape Town residents now have had to `prove their poverty' in order to
27 register as indigent households and access their water right (Scheba and Millington, 2018). Further, since
28 2007 and with increasing effect during the drought, the municipality has installed approximately 250,000
29 water management devices as a credit control and, during the drought, also a consumption control measure.
30 As these have been largely installed in low-income homes, this control measure disproportionately affected
31 poor households (Scheba and Millington, 2018; Enqvist and Ziervogel, 2019).
32
33 Lessons from the drought
34
35 The effect of communication at different stages in the drought highlights how critical information needs to
36 be provided in a format and language that empowers people to act appropriately and collaboratively (Muller,
37 2019; Rodina, 2019b; Rodina, 2019a). Getting political decisions made in a timely fashion and with public
38 support is a long-standing challenge for managers of urban water supplies (Muller, 2017; Muller, 2019). In
39 Cape Town this was further challenged by dependence on a malfunctioning national department for water
40 supply planning, poor coordination between the spheres of government--city, provincial and national
41 governments--and poor collaboration between political representatives, technical experts, and strategic
42 managers (Madonsela et al., 2019; Nhamo and Agyepong, 2019; Rodina, 2019a; Ziervogel, 2019b). This
43 highlights the need to strengthen partnerships and collaboration across sectors and scales of governance
44 (Ziervogel, 2019a) including the adoption of a `whole-of-society' approach that recognises the contributions
45 of non-state actors as adopted in the Cape Town Resilience Strategy (CoCT, 2019; Simpson et al., 2020a).
46 Experienced yet inflexible water management initially operated at a distance from politicians and their
47 citizens here was limited knowledge and capacity in how various municipal departments thought about risk,
48 exposure and vulnerability of Cape Town's highly-differentiated population(Mukheibir and Ziervogel, 2007;
49 Pasquini et al., 2015; Madonsela et al., 2019). In the later stages of the drought, Cape Town's water
50 management department was able to work collaboratively across different departments and with politicians
51 to implement responses.
52
53 The Cape Town case highlights how disaster planning for slow-onset city-wide shocks will be become
54 increasingly important to safeguard equity and sustainability across African cities (Cole et al., 2021). It
55 demonstrates the importance of integrating state and non-state responses to climate change in municipal
56 adaptation and disaster planning (Booysen et al., 2019a; Booysen et al., 2019b; Simpson et al., 2020a),
57 particularly for responses with unintended consequences. Further, water tariff models need to be flexible
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1 enough and have built-in redundancies in order to prioritize the needs of the urban poor and ensure climate
2 responses do not disproportionately affect low-income groups and deepen existing inequalities (Scheba and
3 Millington, 2018; Enqvist and Ziervogel, 2019; Simpson et al., 2019b). Systems and relationships of mutual
4 accountability can also build more effective water management between spheres of government and enhance
5 horizontal collaboration between municipal departments and non-state entities (Ziervogel, 2019b; Ziervogel,
6 2019a).
7
8 [END BOX 8.5 HERE]
9
10
11 In summary, this section has moved beyond the IPCC WGII AR5 in laying out structural elements of
12 vulnerability and climate related vulnerability hotspots globally such as poverty, lack of access to basic
13 services, gender inequality and undernourishment. The assessment provides new quantitative evidence about
14 the global spatial distribution of systemic human vulnerability and therewith underscores that various
15 hotspots of countries classified as very high or high vulnerable emerge in regional clusters. In addition, the
16 number of people living in very high and high vulnerable country contexts is significantly higher in some
17 assessments even twice as many as the number of people living in countries classified as low and very low
18 vulnerable. The evidence suggest that statistically relevant differences in fatalities per hazard events are not
19 just a produce of the hazard event, but strongly linked also with the level of vulnerability of a region or
20 community exposed. The assessment of non-economic losses has also received only little attention in past
21 IPCC Assessment Reports, therefore this sub-chapter provides new insights on how (next to measurable
22 economic losses) non-economic losses and intangible losses emerge. These non-economic losses represent
23 an important dimension of societal impacts of climate change that has not sufficiently captured so far within
24 standard damage or post disaster assessments. Finally, the section provides evidence about the existing
25 adaptation gap in terms of differential vulnerabilities and various non-economic losses already experienced.
26
27
28 8.4 Future Vulnerabilities, Risks and Livelihood Challenges and Consequences for Equity and
29 Sustainability
30
31 Future climate vulnerability and risks to livelihood security are significantly influenced by present and past
32 development trends, equity and sustainability. Consequently, observed impacts covered in previous sections
33 provide essential insight for enhancing future adaptation and risk reduction. Since the AR5, new research
34 approaches incorporate past lessons to project and assess climate change vulnerability and socio-economic
35 conditions into the future. Scenario tools and methods are a powerful approach for integrated assessments of
36 emissions pathways, associated warming and development contexts, helpful in guiding analysis of adaptation
37 policy and planning (Berkhout et al., 2014; Birkmann et al., 2021a). Both quantitative and qualitative
38 scenario approaches that assess future vulnerability and risks as well as livelihood challenges at global,
39 national and local scales allow experts, planners, decision-makers and affected people to articulate and
40 visualize development futures. These approaches can complement emissions pathway scenarios.
41
42 8.4.1 Future exposure, climate change vulnerability and poverty at the global scale
43
44 The Shared Socioeconomic Pathways (SSPs) scenarios orient climate models around possible development
45 pathways that produce future exposure patterns, risk probabilities and vulnerability for future populations
46 (O'Neill et al., 2014; O'Neill et al., 2017a). While the likelihood of any given scenario actually occurring is
47 highly uncertain, they have the advantage of pairing with computational models to generate robust
48 projections about risk profiles in possible futures, and therefore assess the relative influence of different
49 drivers of change. In this way, scenario tools generate pictures of future vulnerability and adaptation
50 pathways, and often have both an analytic and normative function. The decision-making context will
51 determine which specific scenario approach is most appropriate (Rozenberg et al., 2014). Scenarios are
52 limited by stakeholders' imaginations, and as such, new emergent challenges, such as the COVID-19
53 pandemic, are difficult to anticipate in scenario planning. Nevertheless, recent studies and forecasts of the
54 impact of COVID-19 on poverty conclude that in the near and medium-term future major portions of the
55 newly poor will emerge in Sub-Saharan Africa and South Asia (Laborde et al., 2020b; Sumner et al., 2020).
56 Since these countries are already characterized by high levels of absolute poverty and vulnerability to
57 climate change, it is likely that these regions will face more severe challenges in overcoming vulnerability
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1 and will be confronted with a growing adaptation gap. Thus, the implication for scenario planning is that
2 single crises or events, such as the COVID-19 pandemic, might not significantly alter existing
3 vulnerabilities, but rather reinforce them.
4
5 8.4.1.1 Exposure and vulnerability under different scenarios and alternative development pathways
6
7 At the international and national level, the Shared Socio-economic Pathways (SSPs) (O'Neill et al., 2017a)
8 have been developed to outline various development pathways, associated emissions and levels of warming,
9 but also different possible development profiles (i.e., levels of economic growth, poverty, inequality,
10 demographic change, etc.) that are highly relevant for adaptation.
11
12 Studies using the SSPs to understand multidimensional poverty are few but growing, and underscore the
13 impacts of climate change on poverty are extremely sensitive to different levels of warming (Byers et al.,
14 2018). Multi-sector risks approximately double between 1.5篊 and 2篊 GMT change, and double again in a
15 +3癈 world. Comparing a +1.5癈 world pursuing sustainable development (SSP1) to a high-poverty and
16 high-inequality +3癈 world (SSP3), Byers et al. (2018) project substantial increases in populations exposed
17 to drought, water stress, heat stress and habitat degradation (see in detail Byers et al., 2018). While in a
18 +1.5癈 world exposed populations increase by 7-17%, the increase within a +3癈 plus world is 27-51%
19 (Byers et al., 2018; Frame et al., 2018). Populations in Asia and Africa account for more than 80% of the
20 global population exposed to these phenomena, and within South Asia and the Sahel, up to 90% of
21 populations are exposed. Scenario tools help us to understand the burden of increasing multidimensional
22 poverty, and potential for poverty traps, if mitigation and adaptation measures are not taken rapidly and
23 effectively implemented.
24
25 At the national and sub-national levels, studies on development and risk scenarios capture specific
26 challenges, for example, urban growth, demographic change, human health and aging (e.g., Dong et al.,
27 2015; Chapman et al., 2019). In this regard, local scenarios of human vulnerability can inform future
28 strategies for adapting to hazards such as heatwaves in cities under different socio-economic development
29 strategies. These scenario approaches allow to focus on changes in climatic and societal conditions as well as
30 urban transformations. This provides a more comprehensive basis for defining adaptation goals (see Fekete,
31 2019; Birkmann et al., 2021b). Also costs and benefits of different adaptation measures can be assessed
32 against different future scenarios of climatic and societal change.
33
34 Contrasting with `top-down' SSP scenarios, (Berkhout et al., 2014) outline how mesoscale and `bottom-up'
35 scenarios have been developed to inform spatial planning, for example, in the Netherlands. Increasing
36 computational power has opened possibilities for large-scale `bottom-up' simulations of people's livelihoods
37 in the context of evolving climate change impacts, such as the migration decisions of farmers facing drought
38 in Mexico over the coming century (Bell et al., 2019) and livelihood decisions of people facing coastal
39 flooding in Bangladesh to the year 2100 (Bell et al., 2021). Such `bottom-up' scenarios can generate
40 projections about future outcomes, inform mapping and assess future vulnerability, with special emphasis on
41 livelihoods of the poor. Researchers conclude that results of respective scenarios that aim to inform
42 adaptation and risk reduction policies in the context of climate change have to match the frames of the
43 stakeholder (Berkhout et al., 2014; Conway et al., 2019). Scenarios that assess potential future vulnerabilities
44 and future capacities for adaptation require more attention, since many approaches for projecting future
45 climate risk still largely overlook non-climatic drivers that determine future vulnerability and exposure
46 (Windfeld et al., 2019).
47
48 8.4.2 The influence of future climate change impacts on future response capacities
49
50 The influence of climate change also impacts the future response capacities of people and nations to deal
51 with future climate change and climate hazards. Recent studies (Mysiak et al., 2016) conclude that climate
52 change can increase the severity and intensity of crises or even trigger disasters, particularly floods, storms,
53 forest and wildfires, and droughts, have undermined decade-long poverty reduction efforts, particularly in
54 low income and at-risk countries (Djalante, 2019). Climate influenced (disaster) risks are getting more
55 complex and systemic (UNDRR, 2019). The magnitude of global annual average economic losses from
56 natural and climate induced hazards to the built environment alone are estimated in the United Nations
57 Office for Disaster Risk Reduction (UNDRR) Global Assessment Report (2019) comparable with the gross
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1 domestic product (GDP) of the 36th largest economy in the world - the Philippines at that time (in 2015)
2 (UNISDR, 2015; Mysiak et al., 2016). In addition, a World Bank study concludes that losses of human-
3 wellbeing are higher than the overserved economic losses from natural hazards (Hallegatte et al., 2017). In
4 this regard, it is likely that future impacts of climate change, particularly under increasing levels of global
5 warming (above 1.5癈) will also increase non-economic losses (see Section 8.3.2.3) and losses of human-
6 wellbeing that are particularly relevant to most vulnerable groups and the poor.
7
8 Furthermore, the expected future increase in the number of exposed people to climate hazards, such as sea-
9 level rise and coastal flooding is not only determined by changing hazard patterns, but also by regional
10 processes of migration and urbanization for example in Asia and Africa, including an increasing number of
11 urban poor living in low-elevation coastal zones (United Nations, 2018). This can increase again the
12 probability that more people require assistance and support for buffering these effects of climate related
13 hazards, for example in coastal zones. Historical urbanization processes, in coastal cities in Asia (e.g., in
14 China, Vietnam, etc.) and Africa (e.g., in Nigeria) have increased the exposure of people to climate hazards,
15 such as sea-level rise, which by 2100 under RCP8.5 will globally threaten 630 million people, largely in
16 coastal cities (Kulp and Strauss, 2019).
17
18 In addition, Smirnov et al. (2016) conclude that worldwide the number of people exposed to extreme
19 droughts will increase under both the RCP4.5 and the RCP8.5 particularly at the end of the century. The
20 authors assess that under RCP4.5 the average monthly global population exposed to drought will increase
21 between the periods 2008-2017 and 2081-2100 from the mean of 80 million to 212 million, and under
22 RCP8.5 from about 90 million to approximately 472 million people. The research findings underscore that
23 there is a high probability that exposure increases to extreme droughts particularly in regions and countries
24 classified already today as high vulnerable (e.g., Sudan, Nigeria, etc.) (Smirnov et al., 2016). Extreme
25 droughts are expected to further erode coping and adaptive capacities of those already characterized by high
26 levels of vulnerability (see Section 8.3.1). Building adaptive capacities for most vulnerable groups in the
27 future in these areas will be a challenge, since high levels of livelihood insecurity are coupled with high
28 levels of structural vulnerability at national and regional scale (poverty, state fragility, etc.) making planned
29 adaptation support very complex and difficult. Therefore, increasing adaptation gaps at different scales are
30 anticipated in the future.
31
32 Increasing population exposure (e.g., due to urbanization of coastal zones, etc.), coupled with higher
33 frequencies and intensities of specific climate hazards are likely in connection with the existing adaptation
34 gap (e.g., high levels of vulnerability) to compromise development and human security. Recent studies, such
35 as by Harrington (2018), conclude that the actual exposure and the physical individual recognition of some
36 climate hazards, will be higher in low-income countries. The study of Harrington (2018) underscores that
37 changes in extreme heat, for example, will be felt by the average citizen of a low-income country after 1.5篊
38 of global warming and will not be felt by about 40% of people living in high-income nations until well after
39 double the amount of global warming is reached (3癈 increase). In this context, it is important to note that
40 even if a city or place is exposed to heat stress, people experience it quite differently due to different levels
41 of vulnerability and adaptive capacities, such as the ability to afford air conditioning (Barreca et al., 2016).
42 That means well-off populations are better insulated from effects of global warming than poorer or more
43 vulnerable groups, even if they are geographically living in the same exposure zone. These findings
44 underscore that issues of climate justice need to be considered within the problem definition and not solely at
45 the end when designing adaptation strategies. Impacts of future climate hazards (heat stress, flooding, etc.)
46 differ not only due to changes in frequency and intensity of the hazard itself, but also significantly in terms
47 of the opportunities people have to respond and prepare for these hazards and climatic changes at present and
48 in the future. However, it is also important to note the extreme heat stress has also caused significant
49 fatalities in countries classified as low vulnerable, such as seen within the heat wave in Europe in 2003.
50
51 8.4.3 The influence of climate change responses on projected development pathways
52
53 Responses to climate change can have dual effects on development pathways. On the one hand, mitigation
54 and adaptation processes can create significant development opportunities. The potential of mitigation
55 policies for jobs creation, in particular, has been highlighted (Healy and Barry, 2017). However, responses to
56 climate change can also have detrimental effects on future development: mitigation policies such as the
57 building of hydro-electrical dams or the culture of biofuels can lead to communities' dislocation and
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1 populations' resettlement, particularly of disadvantaged groups within a society (de Sherbinin et al., 2011;
2 Eriksen et al., 2021). Adaptation policies can also hinder some development processes: for example, the
3 promotion of migration as an adaptation strategy can lead to communities being deprived of their workforce,
4 and resenting the departure of some of their members (Gemenne and Blocher, 2017), even though they may
5 offer new livelihood opportunities. However, the migration consequences in the context of climate change
6 are often more nuanced and different trade-offs and benefits occur (see Porst and Sakdapolrak, 2020). For
7 example, remittances support family members, at the same time in some cases these can also create
8 imbalances in local markets (Melde et al., 2017). Evidence exists that some climate responses such as small-
9 scale agricultural livelihood adaptation strategies have improved the ability of people to sustain their
10 livelihood and to reduce poverty (Osbahr et al., 2010).
11
12 8.4.4 Social tipping points in the context of future climate change
13
14 Climate change has the potential to trigger major, sudden social transformations, yet there are no clear linear
15 relationships between the magnitude of climate change impacts and the social changes they induce(Steffen et
16 al., 2018). Evidence shows that major destabilizing social transformations (e.g., forced migration) can occur
17 in response to limited climate change impacts, even while major climate change impacts can be mitigated
18 through the resilience of social, political and economic systems and thus yield only minor social impacts.
19
20 In the context of climate change, `tipping points' have been identified as critical thresholds at which a tiny
21 perturbation can qualitatively alter the state or development of a system (Lenton et al., 2008; Lenton et al.,
22 2019). The concept of tipping points is usually associated with large-scale components of the climate system
23 that could be pushed past an irretrievable threshold as a result of human-induced climate change (Lenton et
24 al., 2008), such as the deterioration of Antarctic ice sheets (Pattyn and Morlighem, 2020). Social tipping
25 points refer to similar mechanisms of destabilization resulting from impacts of climate change on human
26 societies at multiple scales and the societal context conditions in which these impacts occur. They are
27 reached when climate change impacts force destabilizing social transformations from one state to another
28 (Lenton et al., 2019): from sporadic losses due to climate change to chronic losses and impoverishment, from
29 peace to violence, from a democracy to an authoritarian regime, from adequate food provisioning to famine,
30 or into forced migration. For example, small variations in the rainfall or temperature can jeopardise
31 livelihoods that are dependent upon subsistence agriculture, which can lead to migration and/or tensions
32 around resources (see Figure 8.11). Social tipping points can also occur when intangible elements that ensure
33 the survival of individuals and communities are eroded or removed. This is the case, for example, when the
34 social fabric of a community falls apart. The Millennium drought in Australia led to higher rates of male
35 suicide, especially among farmers, and droughts in Ghana led to similar outcome when people were forced to
36 drink from the same water source as their animals, which they perceived as robbing them off their human
37 dignity (Bryant and Garnham, 2015; Tschakert et al., 2019).
38
39 In socio-ecological systems, tipping points occur when a (small quantitative) change inevitably triggers a
40 non-linear change in the corresponding social component of the socio-ecological systems, driven by a self-
41 reinforcing positive feedback mechanisms, that inevitably and often irreversibly lead to a qualitatively
42 different state of the social system' (Milkoreit et al., 2018).
43
44
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1 Figure 8.11: A social tipping point is reached when climate impacts push a society towards a state of instability. Those
2 climate impacts are typically aggravated by economic, social and political stressors that reduce adaptive capacity and
3 overwhelm its resilience. Once a social tipping point is reached, a society may experience mutually reinforcing states of
4 economic, social and political instability, leading to cascading disruptions such as livelihoods insecurity, migration and
5 displacement, food insecurity, impoverishment, civil and political conflict, and change of political regimes.
6
7
8 In recent years, significant research efforts have been made to identify early warning signals for social
9 tipping points (Barrett and Dannenberg, 2014; Bentley et al., 2014; Lenton et al., 2019). While some identify
10 early warning signals through time series (Scheffer et al., 2012), others see them in interaction networks and
11 individual thresholds (Barrett and Dannenberg, 2014; McLeman, 2018). Empirical research conducted in a
12 transboundary contentious region--the Jordan river valley--showed that there were significant local and
13 regional differences in the identification of social tipping points (Rodriguez Lopez et al., 2019).
14
15 Empirical evidence shows that social tipping points can be triggered long before climate tipping points are
16 reached. For example, recent research in West Africa shows that migration decisions are often based on the
17 perceptions of environmental changes by local populations rather than on the actual observed changes (De
18 Longueville et al., 2020). The migration of some members of a community can also trigger the migration of
19 the whole group, as the migration of some members can have a strong impact on the community (Gemenne
20 and Blocher, 2017). In other contexts, the expectation of a climate impact can trigger social or political
21 shifts: for example, the expectation of lower snow cover levers can reduce or stop investments in ski resorts.
22 Some planned relocations of populations are already underway in anticipation of future climate impacts (de
23 Sherbinin et al., 2011), while the government of Indonesia decided in 2019 to move its capital city, Jakarta,
24 in anticipation of future floods.
25
26 Shifting livelihoods is a typical adaptation strategy but can also reflect a social tipping point if this shift
27 affects the community as a whole. Therefore, social tipping points should not be confused with the carrying
28 capacity of a community. Whilst the carrying capacity of a community is a fixed, predetermined limit, social
29 tipping points are dynamic, and constantly evolving under the influence of different social and political
30 factors--such as solidarity networks or governance mechanisms. The carrying capacity of a community can
31 evolve over time, but remains a static concept, unlike social tipping points. Social tipping points have also
32 been applied to adaptation, through the concept of adaptation tipping points, which indicate how much
33 pressure a socio-environmental system is able to absorb (Ahmed et al., 2018). Beyond the adaptation tipping
34 point, the efficiency of adaptation responses will be limited, and can even transform into maladaptive
35 options.
36
37 8.4.5 Projected risks for livelihoods and consequences for equity and sustainability
38
39 8.4.5.1 Projected risks for livelihoods
40
41 There is robust evidence with high agreement that future climate change impacts will have severe
42 consequences for poor households, particularly those situated in areas highly exposed to actual or future
43 climate hazards, such as low lying coastal communities (see also Cross-Chapter Paper 1 COASTS), drylands
44 (see also Cross-Chapter Paper 3 DRYLANDS) or remote mountain (see also Cross-Chapter Paper 5
45 MOUNTAINS) settlements with low levels of connectivity to markets, poor infrastructure and high
46 dependence upon poor quality natural capital (Barbier and Hochard, 2018; Gioli et al., 2019). While
47 livelihoods operate in a dynamic context characterised by multiple interacting structures and processes,
48 climate change can act as a risk multiplier. When current livelihood activities become untenable as a result of
49 both long trends and short-term shocks and climate hazards (e.g., droughts, floods), shifting livelihoods is a
50 common response and in many cases can be unavoidable due to the negative consequences of these climate
51 hazards on specific livelihood capitals (see Section 8.5). Such shifts can involve a change in livelihood
52 activities (e.g., continuing in agriculture but growing different kinds of crops), or a change to broader
53 livelihood strategies (e.g., diversifying into handicrafts or paid employment, specialising in one particular
54 activity, or migrating, seasonally or permanently in search of other livelihood opportunities) or even an
55 entire change of the livelihood activity, for example, abandoning agriculture altogether (McLeman and Smit,
56 2006; Black et al., 2011). Shifting livelihoods can therefore involve mobility or take place in situ. Some of
57 these shifts also lead to social tipping points.
58
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1 8.4.5.1.1. Proactive and reactive livelihood shifts and their relevance for future risks due to climate change
2 Livelihood shifts may also take place proactively as new opportunities emerge and reduce climate impacts
3 by providing buffers of financial capital. For example, (Hirons, 2014) assesses artisanal and small-scale
4 mining as an emerging livelihood opportunity in Ghana. Evidence challenges the popular assertion around
5 the idea of wealth seeking for short term profit and reveals an alternative scenario whereby artisanal and
6 small-scale mining can be a poverty-driven activity, particularly in areas in which agricultural employment
7 has not delivered sufficient income or where crops are highly exposed and sensitive to climate change
8 impacts. Income from new livelihood activities can support recovery following specific events (major
9 flooding or drought) linked to climate hazards and climate change. Livelihood shifts therefore take place in a
10 highly dynamic and heterogeneous context. Another example comes from (Okpara et al., 2016a) the Small
11 Lake Chad, Republic of Chad. Fluctuating water levels linked to seasonal flood pulses and droughts were
12 shown to link closely to livelihood dynamics. Lake drying led to new adaptive behaviours based on
13 seasonality (e.g., migration of herders to different areas of the lake shore to access water resources, in line
14 with more predictable seasonal changes) as well as linking to opportunism supported by climate change
15 impacts. For example, during times of lake flooding, new opportunities for fishing opened for people that
16 were otherwise operating primarily as pastoral or agricultural households. However, these kinds of
17 livelihood shifts remain largely reactive and can bring negative as well as positive impacts. In the Lake Chad
18 case, it resulted in social clashes between different groups, while in other examples from Tanzania,
19 livelihood shifts towards extensification of farming led to deforestation (Suckall et al., 2014), which could
20 constitute a maladaptive shift. Such findings have important implications for the types of government and
21 institutional support that can enable livelihood shifts and highlight the need to consider trade-offs for climate
22 change mitigation, as well as with other adaptation options (see Section 8.6).
23
24 8.4.5.2 Future risks, vulnerabilities, differentiated inequalities and livelihood shifts
25
26 Overall, there is high agreement that future climate change impacts are going to worsening poverty and
27 exacerbating inequalities within and between nations with projections that by 2030 these will increase
28 significantly (Olsson et al., 2014; Hallegatte and Rozenberg, 2017; Roy et al., 2018). In addition, the
29 COVID-19 pandemic and consequences linked to measures to reduce the spreading of the virus are likely to
30 increase poverty, particularly in regions already facing high levels of vulnerability and poverty (Laborde et
31 al., 2020b; Sumner et al., 2020).
32
33 Key risks due to future climate change, exposure and vulnerability are difficult to assess and are based on
34 evidence from the past and likely future vulnerabilities and livelihood challenges. The assessment of
35 Representative Key Risks (see Section 16.5.2.3.4) underscores that risks to living standards are potentially
36 severe as measured by the magnitude of impacts in comparison to historical events or as inferred from the
37 number of people currently vulnerable (see in detail Chapter 16). Table 8.4 provides an overview of what is
38 known in the literature assessed about future risks, inequalities and particularly future vulnerabilities,
39 including potential challenges for climate justice and adaptation barriers. For example, barriers for gender,
40 ethnicity and class have been addressed for a long time yet need substantive intervention. Gender, along with
41 many other structural inequalities (Table 8.4) that are deeply rooted, pose future threats to people/groups in
42 vulnerable situations from, for example, the loss of land/assets, exposure to extreme events and so on. These
43 people will also likely be highly exposed to future climate risks unless there are significant and new avenues
44 for action on climate change now. For example, recent studies suggest that the total population of all
45 countries classified as most highly vulnerable is projected to grow significantly. A study using 5
46 vulnerability categories globally concludes that the total population of all countries with very high
47 vulnerability (see Figure 8.6) is projected to increase from 2019 numbers approximately by 102% by 2050
48 (i.e., roughly double) and 257% by 2100, while the population of all countries with very low vulnerability is
49 projected to decrease by 9% by 2050 and 17% by 2100 (based on UN medium probabilistic projections).
50 Another study estimates that the total population of all countries classified at most vulnerable (top 2
51 categories; using 7 vulnerability categories globally) is predicted to increase by 82% by 2050 and 192% by
52 2100. In contrast the population of all countries classified as least vulnerable (bottom 2 categories) is
53 projected to only increase by 9% by 2050 and 1% by 2100 (see in detail UN-DESA, 2019; Birkmann et al.,
54 2021a; Birkmann et al., 2022).
55
56 That means that, based on current population growth estimates and if vulnerability levels are not reduced
57 significantly, more people will be living in more vulnerable context conditions in the future compared to
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1 those living in less vulnerable contexts. This is independent of the development of climatic hazard exposure.
2 If significant reductions of vulnerability are achieved, this projection will change. However, the vulnerability
3 and poverty of some regions and countries has proved over decades to be persistent, such as Haiti or
4 Afghanistan. Consequently, the estimated future population growth is another factor that points towards the
5 urgent need to reduce vulnerability and to narrow the adaptation gap.
6
7 While future adaptation options can also encompass measures or tools that emerge in future, most of the
8 future adaptation options and their relevance for reducing vulnerability, poverty and inequality are known.
9 Evidence exists that the importance of social networks that organise social protection and leverage resources
10 in terms of reducing risks to climate change is increasing, particularly for most vulnerable people/groups in
11 countries that have limited social security measures in place.
12
13
14 Table 8.4: Summary of interlocking categories differentiation future risks, vulnerabilities, inequality and adaptation
Future risks Inequalities Future vulnerabilities, References
future livelihood, future
exposure (examples)
Increasing risk of Gender inequality leaves Increasing future (Singh, 2020; Cross-
displacement and women and girls hidden, vulnerability of Women Chapter Box GENDER in
damages to women forgotten, exposed, resulting and girls due to high Chapter 18)
and girls in floods in displacement impacts and hazard exposure; gender
limited resources, including differentiated vulnerability
social capital and increasing to urban flood in India);
risk of human trafficking Increasing risk of human
trafficking associated with
exposure to future extreme
events
Increasing risks of Differentiation based on Increasing future (Hsu et al., 2021; Section
exacerbating Ethnicity and race leads to vulnerability of 8.3)
inequalities and groups in society less visible, Indigenous Peoples due to
tensions less rights, in particularly exposure to extreme
livelihoods that expose them events. Communities of
to extremes. Unequal access colour are likely to be
to adaptation opportunities exposed to increased
and benefits. climate change impacts,
e.g., differentiated health
impacts on black and
hispanic communities
heat-related mortality
rates and poverty for
neighborhoods in New
York City.
Increasing risk of Class differences in exposure Increasing differentiated (Jones and Boyd, 2011;
loss of homes and and awareness of flood risks. exposure among classes to Fielding, 2018)
assets in the case of Lower caste events such as flooding.
floods disproportionately impacted
by climate change
Risks to loss of lives Religious and beliefs impact Increasing vulnerability to (Schuman et al., 2018)
in cases where there experience of climate change climate change among
is no agency different religious groups.
Risk of premature Age and aging populations. Increasing future (Hsu et al., 2021; Section
mortality, risk of loss Elderly and young are vulnerability among 8.3)
of livelihoods in disproportionately impacted elderly, underage youth
employment by climate change, e.g., and children vulnerable to
heatwave in France 2003 and increasing risks of health
Japan 2018. Youth
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underemployed or in impacts of pollutants or
vulnerable livelihoods could floods, heatwaves
be vulnerable to climate
related risks which adversely Increasing risks to people
affects the economy. with disabilities
disadvantaged exposed to
Risks to mobility in a People with disabilities, for extreme events. (King et al., 2019)
climate extreme instance shows evidence .
emerging in the disaster risk
reduction and humanitarian
sector.
Risks of isolation for Geographical exposure. The Increasing risk and (Section 8.3; Cross-
communities remote location of people and exposure among Chapter Box GENDER in
from centres of societies within a particular communities remote from Chapter 18)
power territory is a determinant of urban centres far from
inequality e.g., disruptions to resources and exposed to
food supplies to the climate impacts
Caribbean when there are
climate extreme events.
Risks of food Differentiation of asset / Increasing risks to tenurial (Section 8.2; Cross-
insecurity ownership / access among landless. If tenurial status Chapter Box GENDER in
groups where unclear status. is unclear, groups may Chapter 18).
experience loss of land
and displacement.
1
2
3 8.4.5.3 Future limits to adaptation
4
5 Local perceptions of losses from adverse effects of climate variability and change can help to assess the
6 magnitude of impacts that individuals and communities have not been able to cope with or adapt to (James et
7 al., 2014; Barnett et al., 2016; McNamara and Jackson, 2019 McNamara et al. 2021, Mecheler et al. 2020).
8
9 The IPCC Special Report on a 1.5癈 warming world shows with high confidence that for the Arctic systems,
10 if average temperature increase exceeds 1.5癈 by the end of the century, compromising people's livelihoods
11 and will exceed limits to adaptation and residual impacts can be expected (Ford et al., 2015; O'Neill et al.,
12 2017b; Roy et al., 2018; Hoegh-Guldberg et al., 2019a). The loss and degradation of the Amazon forest
13 concerning global warming temperatures (beyond 1.5癈) is another clear example of irreversible loss, with
14 significant impact to people's livelihoods today and in the future (Hoegh-Guldberg et al., 2018; Roy et al.,
15 2018). Moreover, the losses and damage from climate change impacts are also felt heavily by women,
16 children and elderly given the intersectionality with socio-economic and gender inequalities (Li et al., 2016;
17 Roy et al., 2018). For instance, gender and wealth inequality offers challenges to scale up the Maasai
18 pastoralist community autonomous adaptive practices (Wangui and Smucker, 2018). These authors found
19 that most female-headed and poorest households couldn't access the land, water for irrigation, and financial
20 assets required to access adaptive practices that are available in the wider community. Consequently, future
21 impacts of climate change are likely to increase rather than decrease inequality based on already observed
22 impacts on adaptive capacities that constrain also future adaptation options particularly for the poor (Roy et
23 al., 2018).
24
25 8.4.5.4 Future livelihood challenges in the context of risks and adaptation limits
26
27 The climate change risks in this section are addressed through the lens of livelihoods, human, food, water,
28 and ecosystem security, building on key impacts and risks since AR5 (Oppenheimer et al., 2014), and key
29 findings from SR1.5癈 (Hoegh-Guldberg et al., 2018; Roy et al., 2018), SROCC (IPCC, 2019b), and
30 SRCCL (IPCC, 2019a). The AR5 WGII risk tables (IPCC, 2014b), updated in SR1.5癈 (Roy et al., 2018)
31 offer an interesting entry point as it shows high confidence on key observed impacts and limits to the
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1 adaptation of natural and social systems that are compounded by the effects of poverty and inequality on
2 water scarcity, ecosystems alteration and degradation, coastal cities in relation to sea-level rise, cyclones and
3 coastal erosion, food systems and human health (Hoegh-Guldberg et al., 2018; Roy et al., 2018). As a
4 consequence, the climate change risks substantially pose negative impacts on climate-sensitive livelihoods of
5 smallholder farmers, fisheries communities, urban poor, Indigenous Peoples, informal settlements, with
6 limits to adaptation evidenced on the loss income, ecosystems, health, and increasing migration (Roy et al.,
7 2018). The compounded effects of socio-economic development patterns and climate change impacts are
8 worse experienced among climate-sensitive ecosystems in the Arctic and Small Island Developing States
9 (SIDS) (Roy et al., 2018). The future risks to these climate-sensitive ecosystems and livelihoods are
10 potentially severe given their current high exposure to climate hazards, and high number of vulnerable of
11 people exposed for example in the SIDS (see also Chapter 16 Living Standard; (Ahmadalipour et al., 2019);
12 Liu and Chen 2021). Residual losses then may be unavoidable for some ecosystems and livelihoods affecting
13 the vulnerable groups of people and countries as consequences of structural poverty, socio-economic,
14 gender, and ethnics inequalities, that marginalize and exclude and limit the development of adaptive capacity
15 for future changes (Olsson et al., 2014; Roy et al., 2018).
16
17 In Small Islands States (SDIS) key risks are represented by losses of livelihoods of coastal settlements,
18 ecosystem services, infrastructure, and economic stability, exhibiting limits to adaptation in face of local's
19 coping strategies capacity (Hoegh-Guldberg et al., 2019a). There is high confidence that sea-level rise in
20 SIDS combined with extreme flooding events will threaten the future livelihoods of coastal communities
21 (Hoegh-Guldberg et al., 2018; Roy et al., 2018).
22
23 In the global south, the increasing heat associated with warming of global temperature represents an
24 important risks due to losses of labour productivity, crop failures and livelihood security, involving
25 economic losses, and health effects as well as increasing deaths that are anticipated to have significant
26 implications for poverty, inequality and equity (Carleton, 2017; Roy et al., 2018). The increasing
27 temperature, droughts, and excessive rain lead to successive crop failures and lack of productivity that are
28 affecting children's growth and health in developing countries (Hanna and Oliva, 2016). Likewise, the
29 expected global temperature increase by the end of the century will have devastating health consequences for
30 children, associated with sea-level rise, heatwaves, and incidence of malaria and dengue, and malnutrition,
31 especially in Asia (Ghosh et al., 2018) and African countries as Chad, Somalia, Niger and Mali (Hanna and
32 Oliva, 2016; Ghosh et al., 2018; Clark et al., 2020).
33
34 The incidence of floods also increases the occurrence of diseases (e.g., diarrhoea and respiratory infections)
35 and undernutrition in children living in informal settlements and slums in Asia (Ghosh, 2018) and Africa
36 (Clark et al., 2020). Women and children are currently bearing the worst impacts from climate hazards, and
37 are unable to move due to assigned gender roles to avoid flooding risks in highly vulnerable slums in
38 Bangladesh, causing them emotional distress and poor living conditions (Ayeb-Karlsson et al., 2020). In this
39 region, the experienced severe floods associated with death, injury, infectious disease, mental and emotional
40 stress and cultural disruptions--dimensions of noneconomic losses are often not accounted for in the disaster
41 relief policies (Chiba et al., 2017) and these severely influence the ability to build adaptive capacities for
42 future hazards (Roy et al., 2018). In the same way, risks to female-headed households with insecurity in
43 tenure rights is greater, as these group were the most affected by flooding in 2018 in Dar es Salaam,
44 Tanzania, that cost 3-4% of the country's gross domestic product (GDP) and affecting 4.5 million people
45 (Erman et al., 2019).
46
47 In the Himalayas mountain range (part of the Hindu-Kush Himalaya, HKH) temperature warming is
48 expected to increase up to 2癈 by 2050 (high confidence), increasing flooding and bringing larger risks to
49 food and water security on mountain communities that are already highly vulnerable given limited livelihood
50 options and supporting infrastructure in these regions (Mishra et al., 2017). In Nepal, agriculture-oriented
51 livelihoods are reported to be negatively affected by an increase in landslide frequency (92.6%) and intensity
52 (97.3%) over a 20 years period (1996-2016) (van Der Geest and Schindler, 2016). The catastrophic landslide
53 in 2014, the material losses experienced by poor households were 14 times greater than their annual gains
54 associated with loss of crops and land; The NELD losses were emotional distress and fear of new event
55 occurrence, showing that most poor households may not fully recover in their lifetime post an extreme event.
56 This example is indicative of the representative future climate risks to these populations; Albeit livelihood
57 diversification is commonly adopted by the poor households to reduce the impacts of extreme rainfall and
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1 landslides smallholders in Nepal, there are limits to these strategies given poor household infrastructure that
2 challenge risk reduction and as so it is expected that migration to neighbouring countries as Bhutan or India
3 will increase (van Der Geest and Schindler, 2016).
4
5 Expected future risks to vulnerable communities and Indigenous Peoples includes losses across a range of
6 impacts. A larger household comparative analysis across countries in Southeast Asia, Africa, and Asia
7 Mountain regions shows that more than 60% of the population reported losses from residual impacts
8 concerning droughts, floods, cyclones, sea-level rise, glacier retreat, and desertification, despite autonomous
9 adaptation involving changing food consumption, and relying on formal aid from government support
10 (Warner and Van der Geest, 2013). Among Indigenous Peoples across the Global South as in the Brazilian
11 Amazon, Australia and Botswana, locally autonomous adaptive measures, were not sufficient to avoid
12 significant losses (some irreversible in case of lost habitats). The barriers and insufficient adaptive capacities
13 are also intrinsically linked to historical marginalization and vulnerability of the population in these countries
14 (Maru et al., 2014).
15
16 In the Arctic, temperature warming, and sea level rising constitute a key risk to the loss of identity and
17 culture of Indigenous People, associated to migration and or relocation due to livelihoods deterioration from
18 coastal erosion, permafrost thaw, and reduced fisheries productivity (Roberts and Andrei, 2015; Roy et al.,
19 2018). These risks and losses often encompass various non-economic losses, such as the loss of identity that
20 cannot be replaced or economically compensated (see also Section 8.3.5).
21
22 Likewise, in the Amazon basin, climate change hazards of severe droughts and floods (high confidence)
23 (Cox et al., 2004; IPCC, 2019a), are exhibiting limits to adaptation among the majority of riverine
24 communities, and smallholders farmers with residual impacts associated with losses of income, fisheries, and
25 agriculture productivity as well as affecting non-economic livelihood dimensions, such as the ability to
26 attend school and losses of place and identity through forced migration (Maru et al., 2014; Pinho et al., 2015;
27 Lapola et al., 2018). Furthermore, the expansion of the agricultural frontier and construction of large dams to
28 supply energy needs in the Amazon basin are amplifying the vulnerabilities and reducing future adaptive
29 capacities, of smallholders, and the fisheries communities to climate risk (Bro et al., 2018; Castro-Diaz et al.,
30 2018). It is expected that the global temperature warming level of 2篊 by 2050 in the Amazon will lead to a
31 significant reduction of major rivers' water flow and leading to further food and water insecurity (Betts et al.,
32 2018) likely to affect forest and river dependent livelihoods in the Region (Box 8.6; Lapola et al., 2018).
33
34 The glacier retreat associated with the increase in global warming temperature has also shown losses that are
35 permanent and related to a sense of belonging and cultural heritage for the Glacier countries but with the
36 most negative livelihood impacts experienced among poor households in the Peruvian Andes and Himalayas
37 (Jurt et al., 2015). The risks for the glacier smallholder's livelihoods are expected to increase in the future
38 once the shrinking glaciers are expected to increase water competition, crop failure, and extreme flooding
39 (Kraaijenbrink et al., 2017). For example, in Bhutan adaptive measures such as changing crops, developing
40 irrigation channels, and sharing water among the community members still insufficient to avoid loss and
41 damage associated with the dramatically reduced water availability (Kusters and Wangdi, 2013; Warner and
42 Van der Geest, 2013). In high Mountain Regions, the intersections of agro-pastoralists marginalization,
43 difficult in access, and ecological sensitivity contribute to residual impacts associated with extreme climate
44 hazards which can lead to irreversible losses and challenge poverty reduction efforts (Mishra et al., 2019).
45
46 In semi-arid West Africa, poor households have in place longer term local adaptation to deal with severe
47 droughts that involves reducing household and cattle water consumption, planting drought-tolerant crops,
48 and adopting integrated crop-livestock for efficiency, with migration either seasonal and or permanent
49 mostly effective (van der Geest et al., 2019). Likewise, Senegal, Ethiopia, and Northern Kenya adaptation
50 have advanced with external government and non-government organisation (NGO) support (Sch鋐er et al.,
51 2019), including technological innovations and insurance to households (Sch鋐er et al., 2019) but not enough
52 in preventing losses to already impoverished households (Sch鋐er et al., 2019).
53
54 There is robust evidence that future risks to climate-sensitive livelihoods as agriculture, livestock and
55 fisheries are amplified by gender, age, and wealth inequalities (Wangui and Smucker, 2018), ethical
56 background and geography (Piggott-McKellar et al., 2020; Thomas and Benjamin, 2020) as well as by
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1 ecological thresholds that challenge autonomous adaptation among vulnerable disadvantaged communities
2 mostly in the Global South (Roy et al., 2018; Mechler et al., 2020).
3
4 The assessment also points towards the fact that there exist strong linkages between national level
5 vulnerability (see e.g., Figure 8.6) and individual vulnerability at household or livelihood scale. Various
6 disadvantaged and marginalized groups or communities within a society are significantly constrained in
7 terms of the ability to build adaptive capacities for future climate change threats due to limited access to
8 resources or government support for planned adaptation. Consequently, these linkages between regional,
9 national and local vulnerability need more attention in research and practical adaptation strategies (vertical
10 integration).
11
12 The next section discusses how risks emerge as a result of the failure in adaptation or when it is not
13 implemented, with particular attention to risks that are impossible to adapt to and lead to inevitable loss and
14 damage among the poor households, livelihoods and countries.
15
16
17 [START BOX 8.6 HERE]
18
19 Box 8.6: Social dimensions of the Amazonia Forest Fires and Future Risks
20
21 The Amazon ecosystem, together with the Arctic, is listed as the first out of five IPCC Reasons for Concern
22 (RFCs) due to climate change, given the high confidence level that different temperature warming and
23 greenhouse emissions will offer significant risks that threaten these unique ecosystems (O'Neill et al., 2017b;
24 Roy et al., 2018). In addition to the scientific evidence, a resurgence of cross-national collective expressions
25 about the fate of the Amazon forest, Indigenous Peoples and traditional communities, in the context of an
26 unprecedented climate crisis and sustainable future, have gained pronounced importance. On 19 August
27 2019, the skies of Sao Paulo State were dark by 3pm due to the formation of a `smoke corridor' associated
28 with the extensive burning of the Amazon forest (Seymour and Harris, 2019). The fire outbreaks were a
29 consequence of multiple factors related to political, social, economic and environmental scenarios
30 concomitant with the weakening of environmental governance such as control and monitoring of
31 deforestation and fire incidences programs (Escobar, 2019; Seymour and Harris, 2019). The deforestation
32 rate and incidences of fire are both increasing in the Amazon of Brazil, Colombia and Peru (Seymour and
33 Harris, 2019). Accordingly, 2019 registered an increase of 60% on the number of cumulative fire count in
34 Brazil, Bolivia and Peru in comparison with the same period in 2018, and a 12% increase in comparison with
35 the same period in an extremely dry year in 2016 (GFED, 2019). In this context, looking at this case study
36 through the lenses of poverty, inequality and the Sustainable Development Goals (SDGs) addresses the
37 compound effect of climate and land-use change in the Amazon forest fires and its cascading impacts and
38 risks on the social domain in the region. There is evidence that both climate and land-use change impacts and
39 risks are disproportionately borne by poor and vulnerable ethnical groups, remote rural communities and
40 poor urban households in the Amazon(Pinho et al., 2015; Brond韟io et al., 2016; Mansur et al., 2016; Pinho,
41 2016).
42
43 Fires are not a natural phenomenon in the Amazon region (Bush et al., 2004; McMichael et al., 2012) albeit
44 used for food security, hunting and religious rituals among Indigenous Peoples and traditional communities
45 (Hecht, 2006; Carmenta et al., 2019; da Cunha, 2020), and also as a widespread technique for land clearing
46 for small and large-scale farms for agriculture (Morello et al., 2019). The dramatically increased forest
47 burning observed in the Amazon recently are the results of illegal land grabbing, the small and large-scale
48 cattle ranching sector and agribusiness practices coupled with loosening land tenure policies and decision
49 making neglect of deforestation and burning monitoring data (Nobre et al., 2016; Lovejoy and Nobre, 2018;
50 Leal Filho et al., 2020a). The fire outbreaks intensified substantially to the point that in August 2019 there
51 were approximately 3500 fires in 148 Indigenous territories (DETER and INPE, 2019; ISA, 2019). Although
52 most of the burning in the Legal Amazon in Brazil occurred on private land of medium and larger sizes
53 (about 67%), around 33% was observed within Indigenous territories and protected areas called conservation
54 units (UCs) (DETER and INPE, 2019; ISA, 2019). In 2019, 40% of the deforestation occurred in public
55 forests, which encompasses undesignated forest lands, Indigenous territories and conservation units (UCs).
56 This deforestation came accompanied by fires: 18% of the 2019 fires occurred on undesignated lands, 7% on
57 Indigenous territories and 6% on UCs, where many traditional populations live (Alencar et al., 2020). It is
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1 also important to note that during 2019, 46% of the deforestation and 52% of the fires occurred on private
2 rural properties and settlements, respectively, where the legal accountability of these crimes is possible. The
3 2020 deforestation rate presented an increase of 47% and 9.5% compared to 2018 and 2019, respectively,
4 and was the highest in the decade (Silveira et al., 2020). The clear-cut inside indigenous territories more than
5 doubled from 2018 to 2019 (Brasilis, 2021) and despite it decreasing from the 2019 rate, during 2020 it was
6 the highest since 2008. It has been demonstrated that on average, at least 50% of yearly active fires being up
7 to 5 km from deforested areas in the same year, reaching 74% during 2019 (Silveira et al., 2020). This
8 means, that fires and deforestation have an increased threat to indigenous population (Oliveira et al., 2020),
9 particularly during the year 2020 and currently in 2021 since, COVID-19 and air pollution from agricultural
10 burnings greatly impacts respiratory health in the Amazon (Morello, 2021).
11
12 Health impacts, economic and non-economic losses
13
14 The health impacts and economic losses estimates are not homogeneously gathered for the entire Amazon
15 basin countries, but some recent evidence associated with this knowledge gap shows the magnitude of the
16 forest fire impacts, as well as where they spatially occur and who are the most affected by it. Fires associated
17 with deforestation in the Amazon have been related to 1065-4714 deaths annually in South America
18 (Reddington et al., 2015). The recent fires in the Amazon basin are directly affecting 24 million Amazonians
19 with the worst impacts felt by children, and the elderly (Machado-Silva et al., 2020). Indigenous Peoples and
20 traditional communities (Fellows et al., 2020). Children under five years old and the elderly in rural areas are
21 respectively 11 and 22 times more affected by the smoke from fire outbreaks and temperature increase in the
22 Amazon (Machado-Silva et al., 2020).
23
24 In the Acre State, the fire incidence coupled with extreme droughts in 2005 and 2010 led to an increase--
25 from 1.2% to 27%--in hospitalizations of children (under 5 years) due to respiratory diseases (Smith et al.,
26 2015). The same evidence was found among the rapidly deforested areas known as `Arc of Deforestation'
27 that have dramatically led to a higher number of respiratory diseases mainly in children under 5 years (do
28 Carmo et al., 2013). There is also evidence for interlinked dynamics between deforestation, urbanization and
29 incidence of fire episodes providing an appropriate environment for Anopheles darlingi vector propagation
30 and the increased incidence of malaria in the region (Hahn et al., 2014). In the 2005 drought, burning in Acre
31 alone recorded 400,000 people affected and the loss of 300,000 hectares of forest with direct costs of US$50
32 million (Brown et al., 2006). In 2010, the fires during the drought were approximately 16 times larger than
33 that in the meteorologically normal years (Campanharo et al., 2019). The estimated total economic loss in
34 2010 was about US$243.36 � 85.05 million, representing 9.07 � 2.46% of Acre's gross domestic product
35 (GDP) (Campanharo et al., 2019). The economic and non-economic losses associated with the impacts of
36 climate change and future risks of fires outbreaks on native food crops (a鏰i, guaran�), livelihoods, tourism,
37 medicinal and spiritual sites, culture, migration patterns, place-based attachments, emotional and mental
38 distress among the most affected and vulnerable population as Indigenous Peoples and traditional
39 communities are still to be fully estimated for the region (Pinho et al., 2015; Brond韟io et al., 2016). Also
40 relevant is a trend of Amazonian forest fires spreading from the southern Brazilian Amazon to Bolivia and
41 Peru, indicating that transboundary burning increases are systemic and will lead to extensive economic
42 losses of wildcrops, infrastructure and livelihoods, and requiring a landscape level approach for deforestation
43 and fire management and control (Kalamandeen et al., 2018).
44
45 Future vulnerabilities and risks for Indigenous Peoples and traditional communities
46
47 In the future, it is expected that by 2030 the incidence of extreme droughts in the Amazon will increase the
48 costs of the health sector associated with treatment costs of respiratory diseases (20%-50%) and malaria
49 incidence (5%-10%) incurring a high social cost as people will be impaired to carry out their livelihoods
50 (Lapola et al., 2018). It is also expected that extreme droughts in the Amazon by 2030 will accelerate and
51 intensify rural (traditional communities and Indigenous Peoples) migration to urban centres where their
52 living standards are expected to decrease once they will occupy marginal areas within larger urban centres
53 (Lapola et al., 2018).
54
55 In terms of adaptation and risk reduction, priority should be given to strengthening multi-scale governance
56 and partnerships among different private and public actors. Also policies at national and sub-national levels
57 are needed, such as control strategies to reduce deforestation and fire incidence, demarcating new Indigenous
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1 territories, payment for ecosystem services (REDD+) and investment in traceability for commodities
2 productive chain market are needed (Morello et al., 2017; Scarano, 2017; Carmenta et al., 2019; Seymour
3 and Harris, 2019). The increase in global temperature level up to 2癈 will exacerbate food and water
4 insecurity in the Amazon (Betts et al., 2018; Hoegh-Guldberg et al., 2018) (medium confidence). Thus,
5 curbing fire incidence and deforestation rate will make it easier for the Indigenous Peoples and traditional
6 and vulnerable population to reach the Sustainable Development Goals (SDGs), especially in terms of
7 reducing poverty (SDG1), food security (SDG2 ), wellbeing and health (SDG3) and protecting terrestrial
8 ecosystem (SDG15) (Roy et al., 2018).
9
10 [END BOX 8.6 HERE]
11
12
13 8.4.5.5 Maladaptation as a projected future risk particularly for the poor and marginalized
14
15 There is increasing evidence that maladaptation can lead to future risks to socio-ecological security when
16 adaptation measures focusing on short -term action that may lead to adverse longer-term impacts to
17 livelihoods and failures to address transboundary scales to avoid negative consequences for social and
18 ecological systems (Warner and Van der Geest, 2013; Roy et al., 2018; Mechler et al., 2019a; see also
19 Section 5.13.3). Hence, maladaptation can intensify and even accelerate future risks as a result from climate
20 change mitigation and adaptation policies when responses to climate change hazards are embedded within
21 `business as usual' development approaches (Work et al., 2019). For instance, in Cambodia, the conventional
22 development strategies intertwined with climate change mitigation and adaptation initiatives are increasing
23 the probability of maladaptive outcomes in a context of high informality, and conflicts among poor farmers
24 exposed and vulnerable to flooding (Work et al., 2019). The potential for maladaptation emerges from the
25 vulnerability of precarious living conditions of poor farmers in informality, not accounted for in climate
26 mitigation and adaptation strategies for irrigation, protected areas management and reforestation projects
27 funded by multilateral donors (Work et al., 2019). (Work et al., 2019). As a consequence, losses emerge
28 despite actions to prevent adverse impacts and maladaptation instead became a vector of increased
29 vulnerability for poor and vulnerable communities (Mechler et al., 2019a).
30
31 The maladaptation outcome also emerges as a failure of adaptation. In Ghana, poor farmers in face of crop
32 yield failure during severe droughts further exacerbate water use for irrigation and livelihood diversification,
33 including selling firewood for charcoal production, forms of maladaptation as it can furthering increasing
34 their vulnerability to climate risks, compromising food production, income generation, and sustainability
35 (Antwi-Agyei et al., 2018b). In Cambodia, governmental adaptation strategies focusing on reforestation and
36 conservation measures are eroding the local biodiversity, and the crop irrigation strategies are compromising
37 scarce water resources and also excluding poor farmers susceptible to flooding from decision-making and
38 benefits (Work et al., 2019). Likewise, in Ethiopia, efforts of adaptation programs to droughts contribute to
39 current unsustainable development trajectories among pastoralist communities, resulting in charcoal
40 production, overgrazing, migration and conflict with other groups and marginalization of livelihood (Magnan
41 et al., 2016). In the Sudan, maladaptation outcomes to the poor population are linked to a dependency on war
42 economy and post- conflict power dynamics that are and will affect sustainability and equity in the context
43 of drought incidence (Young and Ismail, 2019).
44
45 In Bangladesh, a highly expensive Coastal Climate-Resilient Infrastructure Project can potentially increase
46 the vulnerability of urban poor as they will remain in areas that are highly susceptible to flooding brought by
47 sea level rise (Magnan et al., 2016). In Central America, the lack of assessments of future climate variability
48 on crop yield scenarios coupled with lack of policy makers to incorporate autonomous local adaptation
49 practices could lead to an unsustainable trajectory to local communities and risk of maladaptation (Beveridge
50 et al., 2018). In Bhutan, small-scale rice farmers have adopted water -sharing measures to avoid the impacts
51 of reduced and uncertain precipitation levels associated with monsoons, but these measures led to disruptions
52 in social cohesion as conflicts over water sharing escalated (Mathew and Akter, 2015). In the same region,
53 local governments prioritize the glacier retreat as a perceived risk to flooding on dams but overlook the slow
54 and gradual impact of the deficit in precipitation affecting negatively the rice productivity (Mathew and
55 Akter, 2015). In Burkina Faso, a region highly impacted by severe droughts, local communities have become
56 less able to cope with droughts given a decline in cultural pastoralism and increased dependence on crops
57 (van der Geest et al., 2019).
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1
2 As seen, maladaptive responses to droughts, sea-level rise and flooding are negatively affecting poor
3 farmers, pastoralists, and rural and urban informal workers, increasing loss of crops, infrastructure, income,
4 conflict and migration. Given the high risks of maladaptation to poor people this agenda should be given
5 priority among the development sector and planning (Magnan et al., 2016). The categories in Table 8.5 also
6 represent important future compounding and complex risks that can emerge due to maladaptation (high
7 confidence).
8
9
10 Table 8.5: Categories of Maladaptation as future risk and examples of outcomes and world regions based on literature
11 assessment evidence. Confidence Level ** Medium (5-9 papers).
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 The exclusion of rights and access and benefits of adaptation
Sustainability Further ecological degradation and biodiversity loss.
Informality Reinforces vulnerabilities to the poor and marginalized population.
Poverty It increase vulnerabilities and risks of maladaptation.
Scales (Temporal and Spatial) There is negative trade-offs across short and longer term decisions as well as
transboundary issues that increase likelihood of maladaptation.
South Asia and Southeast Asia (Bangladesh, India, Nepal, Maldives, Indonesia
and Thailand) (6) **
Regions Evidence Africa (Ethiopia, Gahna, Malawaii ) (3)
Central America (1)
Global South (2)
Global (1)
12
13
14 8.4.5.6 Future challenges for vulnerability and livelihood security due to adaptation-limits of people and
15 ecosystems
16
17 The risks and future losses of communities and livelihoods with higher exposure to the risks posed by
18 climate change and with lower adaptive capacity will experience a higher burden of loss and damage in
19 comparison to others (Tschakert et al., 2017). In Asia (Indonesia) and Arctic region, a decline of marine
20 fisheries by approximately 3 million metric tons per degree of warming is expected to have severe negative
21 regional impacts, especially on Indigenous People (Cheung et al., 2016).
22
23 It is projected that climate change impacts on incidence of disasters will push 122 million additional people
24 into extreme poverty with global temperatures increase by 2030 (Hallegatte and Rozenberg, 2017; Hoegh-
25 Guldberg et al., 2018; Jafino et al., 2020). It is also expected that around 330-396 million people will be
26 exposed to lower agricultural yields at warming beyond 1.5癈 (Hoegh-Guldberg et al., 2018), most of them
27 in South Asia and Sub-Saharan Africa (Chapter 16; Roy et al., 2018; World Bank, 2019a). There is also
28 medium evidence that tens to hundreds of millions of people that are dependent upon climate-sensitive
29 livelihoods could out-migrate as a consequence of global temperature increasing, mostly in Africa, Asia and
30 Latin America--posing additional risks to unsustainable urbanization and/or group conflict (Chapter 16;
31 Hoegh-Guldberg et al., 2018; Roy et al., 2018).
32
33 The multi-intersectionality of inequalities (socioeconomic, caste, ethnicity, among others) and
34 marginalization, in most of the cases exhibit adaptation limits, emerge through differential capacity to avoid
35 risks that amongst the world's poor and vulnerable communities are highly deficient and at the brick of
36 falling into poverty traps affecting future generations (Hallegatte and Rozenberg, 2017; Roy et al., 2018;
37 Tschakert et al., 2019). For instance, the poorest communities in the Global South, whose livelihoods are
38 dependent upon thriving ecosystems for health, food, water, energy, are disproportionately more exposed to
39 temperature extremes, and droughts compromising the food and water security (Byers et al., 2018). There are
40 also inequalities associated with the opportunities to adapt to risks that are unevenly distributed among
41 global regions, with richer and more equal societies in the Global North presenting superior capacities than
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1 Global South communities, sectors, ecological systems, and species where the most detrimental climate
2 change impacts are experienced (Hoegh-Guldberg et al., 2018; Roy et al., 2018). The climate-sensitive
3 livelihoods of poor and vulnerable communities in the global south, and the unprecedented ecosystems
4 losses are examples of multiple limits of adaptation that emerge simultaneously also linked to the differential
5 access to assets and resources, such as physical (propriety, income), social (health, age, education) , cultural
6 (shared community values and norms, ethnicity), ecological (linked to land use change and productivity) and
7 institutional (market, policies and governance) (Roy et al., 2018; Hoegh-Guldberg et al., 2019a; Olsson et al.,
8 2019). The adaptation limits emerges mostly in the Global South countries, and disproportionately affect
9 specific groups, with high poverty incidence, that are constrained by inadequate financial resources and
10 institutional instruments (Tian and Lemos, 2018; Volpato and King, 2019), including lack of understanding
11 and preparedness of the risks posed by climate change (Ayeb-Karlsson et al., 2016; Maharjan et al., 2020).
12
13 In other situations, adaptation limits to household's livelihoods emerge from ecological thresholds associated
14 with global warming temperatures, such as deterioration of land and water resources, extinction of species
15 and biodiversity that can lead to systemic crop failures, declined fisheries productivity and water availability
16 and substantial risks to households' livelihoods (Roy et al., 2018). However, it is also important to note that
17 limits are associated with development, technology, and cultural norms and values that can change over time
18 to enhance or reduce the capacity of systems to avoid limits (Adger et al., 2014; Roy et al., 2018). It could
19 also include aspects of maintaining security of air or water quality; as well as equity, cultural cohesion, and
20 preservation of livelihoods (Adger et al., 2014; Tschakert et al., 2019). For soft limits, however, adaptation
21 options could become available in the future owing to changing attitudes or values or as a result of
22 innovation or other resources becoming available to most vulnerable and poor actors, households and
23 countries. However, when compounded with lack of finance, and high costs associated with disasters and
24 poverty and environmental degradation, soft limits might become hard ones in the future (see Figure 8.5;
25 Gracia et al., 2018).
26
27 Table 8.6, built from SR1.5癈 (Roy et al., 2018), illustrates how ecological thresholds and socio-economic
28 determinants are linked to soft and hard adaptation limits and what are the potential and magnitude of
29 livelihoods risks in the future. For instance, in the SR1.5癈 (IPCC, 2018b) and SROCC (IPCC, 2019b), hard
30 limits are expected with global warming beyond 1.5癈 associated with the losses of coral reefs, that will lead
31 to substantial loss of income and livelihoods for coastal communities (Roy et al., 2018; Mechler et al.,
32 2019b; Oppenheimer et al., 2019). The loss of coral reefs in remote islands of Boigu in Australia are
33 affecting low-lying communities facing financial, institutional (Evans et al., 2016) and cultural place-based
34 attachment adaptation limits (McNamara et al., 2017). Another hard limit to adaptation and implications for
35 income, and culture-and place-based livelihoods is related to the sensitivity of global fish to global
36 temperature increase with losses of fish reproduction expected to 10% (SSP1�1.9) to about 60% (SSP5�8.5)
37 potentially cascading into severe risks for fisheries livelihoods (Dahlke et al., 2020). In West African
38 fisheries, the loss of coastal ecosystems and productivity are estimated to require 5�10% of countries' gross
39 domestic product (GDP) in adaptation costs (Zougmor� et al., 2016), incurring financial limits in the poor
40 countries to avoid socio-economic risks. The SROCC (IPCC, 2019b) showed that scientific knowledge
41 limitations can constrain management of coastlines, mainly in the context of lack of data with affect most of
42 the vulnerable and poor communities in the global south (Perkins et al., 2015; Sutton-Grier et al., 2015;
43 Wigand et al., 2017; Roma馻ch et al., 2018). The hard and soft adaptation limits are challenging to be
44 defined, given the rate and intensity of climate change hazards and the mitigation and adaptation options
45 available, but also the level and rate of non-climatic stresses increasing vulnerabilities and undermining
46 adaptive capacity of poorest members of society and sensitive ecosystems (medium evidence, high
47 agreement) (Klein et al., 2014; Roy et al., 2018).
48
49 The recent evidence show that adaptation limits can also be associated to financial and institutional
50 mechanisms, and related to structural poverty and inequalities among rural farmers in India (Singh et al.,
51 2019a) and among low-income countries (Tenzing, 2020), agro -pastoralists communities (Volpato and
52 King, 2019), women (Balehey et al., 2018), slum informal settlements in Latin America (Nez Collado and
53 Wang, 2020), and informal workers in Southeast Asia (Balehey et al., 2018). For SIDS countries, multiple
54 adaptation limits also emerge as a combination of political-institutional, and cultural aspects (Robinson and
55 Wren, 2020) such as preserving national identity and sovereignty in the context of migration in the Marshall
56 Islands (Bordnera et al., 2020). The widespread narrative that migration in the SIDS countries given sea level
57 rise and global temperature increase by 2050 is inevitable, desirable and economically necessary, many more
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1 people will be exposed to migration and affected by multiple physiological and emotional distress (Bordnera
2 et al., 2020). In the same way, the Mohawk community of Kanesatake, Canada, is faced with institutional
3 and socio-political adaptation limits such as lack of land ownership rights, insurance and social institutions,
4 to name only a few (Fayazi et al., 2020).
5
6 New emerging considerations to ecological limits to adaptation associated with severe glacier retreat in the
7 Peruvian Andes, is expected to reduce lake discharge by 2-11% (7-14%) until 2050 (2100) affecting
8 smallholders farmers, through crops yield failures and severely reduced hydropower capacity (Drenkhan et
9 al., 2019). Also, the study showed very high risk of glacier lakes affected by Glacial Lake Outburst Floods
10 (GLOF) in RCP8.5, posing serious threat to rural people's livelihoods (Drenkhan et al., 2019).
11
12 Table 8.6 represents different types of adaptation limits (Soft and/or Hard) that emerge over time and
13 sometimes concomitantly and are leading to severe risks to livelihoods in a high poverty, unequal and hotter
14 future, especially among the poor and vulnerable population in that, the Indigenous People, women, and
15 children (see Section 16.5.2.3.4). The confidence statements is assessed through the evidence on papers as
16 High ( 10 papers), Medium (5-9 papers), Low ( 4 papers) to ensure traceability on what are the nature of
17 livelihoods barriers and ecological thresholds associated to `soft" and or "hard" limits to adaption under a
18 warming global world. The determinants of livelihood barriers are linked to: Gender-based inequality or
19 discrimination, poverty and inequality, Indigeneity and cultural place attachment, Artic Hunting and fishing,
20 Urban Slum and Informal Settlements incurring in soft and hard limits to adaptation. The Ecological
21 thresholds assessed are associated to Glacier Retreat, Loss of Coral Reefs, Biodiversity Loss, Ocean
22 Acidification and warming, Sea Level Rise (SLR) and Heat Stress incurring into hard limits to adaptation
23 severe risks to people's livelihoods; The severity of risks to livelihoods is assessed by presenting a
24 magnitude indicator either through the current number of people exposure and vulnerable to climate-
25 sensitive livelihoods. The supporting literature has been provided in the Table SM8.1.
26
27
28 Table 8.6: Synthesis of hard and soft limits to adaptation and risks to livelihoods, equity and sustainability adapted
29 from Chapter 5 of SR1.5癈 (Roy et al., 2018).
Determinant Nature of barrier to Magnitude + Indicator Soft Hard Confidence Level
livelihood adaptation Limit Limit Based on Number
of Papers
Socioeconomic and human-geographical determinants
World Bank: 62.151%
Gender-based inequalities [Employment in agriculture,
Gender-based constrain women's access to female (% of female employment)
inequality or resources, thus limiting
discrimination ability to invest in adaptive (modelled ILO estimate) - Low X ***High ( 10
capacity and heightening income, 2020]; 25.409% papers)
[Employment in agriculture,
vulnerability female (% of female employment)
(modelled ILO estimate)],
World Bank: 10% [Poverty
headcount ratio at $1.90 a day
(2011 PPP) (% of population)];
Poverty and lack of financial 26.498% [Employment in
Poverty and resources constrain ability to agriculture (% of total ***High ( 10
papers)
socioeconomic invest in livelihood employment) (modeled ILO X
inequality diversification, resilience or estimate)]; 58.783% [Employment
adaptive capacity in agriculture (% of total
employment) (modeled ILO
estimate) - Low income], Low
income countries, 2020
Indigenous and other
Indigeneity populations with strong SIDS total population of ca. 65
million(UN-OHRLLS, 2015); 476
and other cultural or economic million indigenous people ***High ( 10
worldwide (World Bank, 2016) papers)
cultural place- attachments to place face X
based barriers to adaptation due to
attachments noneconomic losses
associated with migration,
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urbanisation, and some
forms of livelihood
transformation
Residents of Arctic regions
dependent on hunting and
Arctic hunting fishing livelihoods Global arctic population, ca. 4 X X ***High ( 10
and fishing interrelated cultural and million (Larsen, 2015) papers)
communities economic vulnerability due
to risk crossing Arctic
ecosystem thresholds and
tipping points
33.331% [Population living in
Urban slum Residents of slums and slums (% of urban population)], X ***High ( 10
and informal informal urban settlements World, 2009; It is estimated that papers)
settlement are particularly vulnerable 50�57 million urban Africans
populations due to limited infrastructure (47% (44�50%) of the urban
and limited employment population analysed) were living
opportunities in unimproved housing in 2015
mostly in the sub-Saharan Africa
(Tusting et al., 2019)
Ecological determinants
The flow decrease of the Tibetan
Glacier Retreat Seasonal water scarcity Plateau region would affect water X X ***High ( 10
and/or Glacial Lake Outburst availability for 1.7 billion people papers)
Floods (GLOF) pose a with a gross domestic product
serious threat for highly (GDP) of US$ 12.7 trillion (Yao
exposed and vulnerable et al. 2019).In 2050, the number
smallholders in the Peruvian of people that will be living in
Andes (Drenkhan et al., water scarce regions will increase
2019). Tibetan Plateau to 2.7 to 3.2 billion people
region will reach peak water (Luterbacher et al., 2020). As for
between 2030 and 2050 2010, 27% of global population
(Yao et al., 2020) (~1.9 billion people) lived in
severely water-scarce areas
(Luterbacher et al., 2020).
The Coral reef fisheries-
dependent and coastal livelihoods,
sustain 6 million direct fishing
jobs and more than $6 billion in
revenues globally (Teh et al.,
2013), often among disadvantaged
populations (Hoegh-Guldberg et
Loss of Coral Loss of 70-90% of tropical al., 2018). In tropical regions, X ***High ( 10
reefs coral reefs by mid-century there are 1.3 billion people living papers)
under 1.5癈 scenario (total by coast and depending upon
loss under 2癈 scenario) (see fisheries for food and livelihoods
SR1.5癈 in Chapter 3, (Sale et al., 2014). In Africa and
Sections 3.4.4 and 3.5.2.1, Asia over 400 million people are
Box 3.4 (Hoegh-Guldberg et dependent upon protein intake
al., 2018), (Magnan et al., from fisheries (Hoegh-Guldberg
2019).; Chapter 5, Section et al., 2019b). Approximately 850
5.2 (Roy et al., 2018)). million people live within 100
kilometres of reefs and more than
275 million reside within 30
kilometres, 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 et
al., 2011).
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Terrestrial species on
average lose 20-27% of their
range at 1.5癈 (significantly
higher range losses projected
for some species at 2癈) (see
Biodiversity IPCC SR 1.5癈 Chapter 3, The forest dependent livelihoods X **Medium (5-9
Loss Section 3.4.3.2 (Hoegh- of 1.6 billion rural people (in papers)
Guldberg et al., 2018); 2012) is likely to be affected to
Chapter 4, Section 4.3.2 (de risks of terrestrial forest and
Coninck et al., 2018)). biodiversity loss (Newton et al.,
Tropical forests (vegetation 2020).
shifts due mainly to drying),
and high latitude and altitude
ecosystems and
Mediterranean-climate
ecosystems (high
vulnerability
Large-scale changes in
oceanic systems
(temperature, acidification)
inflict damage and losses to
livelihoods, income, cultural
identity and health for island
and coastal-dependent 500 million people who derive
Ocean communities at 1.5癈 food, income, coastal protection, **Medium (5-9
papers)
acidification (potential for higher losses and a range of other services from X X
and warming increases from 1.5- 2癈 and coral reefs (Hoegh-Guldberg et
above) (see Chapter 3, al., 2017)
Section 3.4.4.2.4 (Hoegh-
Guldberg et al., 2018);
Chapter 4, Section 4.3.5 (de
Coninck et al., 2018);
Section 5.2.2 (Roy et al.,
2018).
Sea level rise and increased
wave run up combined with
Sea level rise increased aridity and It is projected that ~316�411 X ***High ( 10
(SLR) decreased freshwater million people in 2060 will be papers)
availability at 1.5癈 living in areas to be affected by
warming potentially leaving SLR, with most of them in South
several atoll islands and Southeast Asia and in Africa
uninhabitable (see IPCC SR. (Neumann et al., 2015;
1.5癈; Chapter 3, Box 3.5 Oppenheimer et al., 2019).The
(Hoegh-Guldberg et al., number of people at risk of floods
2018); Chapter 4, Cross- will increase from its current level
chapter Box 4.1 (de Coninck of 1.2 billion to 1.6 billion by
et al., 2018)); The projected 2050 (Luterbacher et al., 2020).
SLR is projected to affect It is estimated that 6�8% of Latin
human health and wellbeing, America and the Caribbean's
cultural and natural heritage, population, face high risk
freshwater, biodiversity, associated with coastal hazards
agriculture, and fisheries (Oppenheimer et al., 2019).
(IPCC, 2018b; WHO, 2018;
IDMC, 2019; McMichael et
al., 2020).
Heat Stress It is expected that by 2070 It is projected that by 2100, X **Medium (5-9
over 30% of global poor human mortality from heat will papers)
population will be living affect increase and affect 1/4 of
outside the human thermal the population (-1/448% under
comfort, beyond adaptive drastic mitigation scenario) and to
capacity, and affecting crop almost 1/5 in a higher emission
scenario (-1/474% under a
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and livestock productivity scenario of growing emissions)
(Xu et al., 2020) (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; Roy et al., 2018).
1
2
3 8.4.5.7 Compounding future risks on equity and sustainability
4
5 The compounding future effects on equity and sustainability emerge when multiple stressors linked to
6 environmental and/or climate change, together with underlying structural poverty, exclusion,
7 marginalization, and conflicts creating risks that need to be addressed simultaneously. Compounding risks of
8 climate change received attention in AR5 (Oppenheimer et al., 2014) including risks associated with
9 compound hazards (O'Neill et al., 2017b), and their implications for future risk when repeated impacts erode
10 human and ecosystem capacity, including through transboundary effects. In SRCCL (IPCC, 2019a), land
11 degradation and climate change compounded to highly expose the livelihoods of the poor to climate hazards
12 and caused food insecurity (high confidence), migration, conflict and loss of cultural heritage (low
13 confidence) (Olsson et al., 2019).
14
15 The evidence of compounded risks emerge from specific climate and environmental hazards as in relation to
16 heatwaves, droughts, altered precipitation regimes, and increasing aridity, cyclones, floods, hurricanes and
17 wildfires (Table 8.7). Other evidence shows that the structural poverty and socio-economic inequalities
18 (Lusseau and Mancini, 2019), disability (Sun et al., 2017), corruption (Markkanen, 2019) and isolation
19 (Reyer et al., 2017) (Table 8.7) compound to amplify climate risks among rural and urban poor, smallholder
20 farms, coastal settlements, with health impacts in children's development (Perera, 2017) and urban elderly
21 (Sun et al., 2017). In Tanzania, a greater exposure of households to climate change impacts and risks is
22 associated with increasing land value and variable tenure, compounded with declining farm yields,
23 accelerating the negative effects among the population (R鰏chel et al., 2018). In India, extreme droughts and
24 heatwaves compound extreme poverty, and high dependence on agriculture for income and food production
25 will affect crop productivity, income and increase of food price among smallholder farms (Singh and Leua,
26 2017). Soil degradation and fertility compounded with incidence of droughts increase the vulnerability of
27 already poor smallholders in Mozambique that lack access to technological advances for crop yield
28 management and drought resistance crops (Kidane et al., 2019).
29
30
31 Table 8.7: Effects of compounded risks on the poor. Climate hazards: flooding, hurricanes, drought, heatwave.
32 Confidence level: *** High ( 10 papers); ** Medium (5-9 papers); * Low ( 4 papers); NE (No evidence)
Dimensions of compounding risk effects to 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) *
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Disability (1)
Climate-sensitive livelihoods (1)
Infrastructure (1)
1
2
3 In the context of urbanization, in fast growing cities in Asia, Africa and Latin America that are highly
4 socially and economically unequal, the climate change impacts from events such as flooding, and droughts,
5 are amplified on water crisis mostly among the poor and marginalized population, and challenging
6 governance for risk reduction (Gore, 2015; Dodman et al., 2017; Jiang and O'Neill, 2017; Pelling et al.,
7 2018; Solecki et al., 2018). In the Global South, over 880 million people are living in precarious and
8 informal conditions without access to water and sanitation mostly in sub-Saharan Africa and South Asia (see
9 Chapter 6; Rosenzweig et al., 2018; Satterthwaite et al., 2018; Tusting et al., 2019). In rapid urbanization sub
10 Saharan African countries, around 53 (50�57) million urban inhabitants (50% of urban population ) and 595
11 (585�607) million rural inhabitants (82% of the rural population) are still living in unimproved housing in
12 2015 (Tusting et al., 2019).
13
14 Experienced losses and damage from climate extremes, such as fatalities or economic losses due to droughts
15 or floods (see also Fig. 8.6), also matter for future vulnerability and risk, since the poorest segments of
16 society take longer to recover after shocks (Gupta and Sharma, 2006; van der Geest, 2018). In some cases,
17 poor households might never be able to fully recover from post disaster, especially in the context of
18 increasing global temperature increase (van der Geest, 2018). Another example of compounding effects of
19 climate change to equity and sustainability is migration, which is underpinned by the underlying socio-
20 economic and political context of vulnerability (see Section 8.2).
21
22 In Latin America, compounding effects of climate change impacts (disasters) and armed conflict has
23 contributed to increase forced migration to the point that in 2018 alone, 1.7 million people migrated due to
24 extremes events, four times as high as the number of people leaving their homeland due to armed conflict
25 (Serraglio and Schraven, 2019). In South America, migration within and between countries can stem from
26 climate extremes primarily felt by the poorest and marginalized (by gender, age, ethnicity) populations that
27 might not be able to adapt to the fast pace and scale of changes at the local level (Maru et al., 2014; Pinho et
28 al., 2015; Serraglio and Schraven, 2019). In Mountain Regions, intersections of people's marginalization,
29 difficulty in access, and environmental sensitivity in the context of incidence of climate extremes have
30 combined to reduce the ability of mountain agro-pastoralists to cope with climate extremes (Mishra et al.,
31 2019). Mountain ecosystems are also highly susceptible to disasters and disturbances, which can lead to
32 irreversible loss, and challenge poverty reduction efforts (Mishra et al., 2019) Some risks associated with the
33 degradation and loss of habitats and ecosystem services associated with land use changes and commodities
34 in many countries have compounding impacts on equity and sustainability, associated with permanent losses
35 to the livelihood of poor and marginalized groups such as Indigenous Peoples and traditional communities
36 around the world (Roy et al., 2018). For instance, high deforestation rates and increased forest burning in
37 many of the Amazonian countries are further exposing vulnerable Indigenous Peoples and Traditional
38 populations to health problems, crop failures and shortages of freshwater supply, especially in the context of
39 extreme droughts and non-supportive governance (Leal Filho et al., 2020a; Walker et al., 2020).
40
41 Overall, there is increasing evidence that the compounding effects of climate hazards intertwined with
42 dimensions of poverty, environmental degradation, and inequalities, represent a key risk to equity and
43 sustainability among poor and vulnerable populations (medium evidence and high agreement). Compounding
44 risks - compared to compounding hazards - can also be significantly influenced by societal tipping points and
45 by different factors of human vulnerability that determine underlying destabilization processes of societies
46 and communities exposed to climate change, including issues of governance.
47
48
49 8.5 Adaptation Options and Enabling Environments for Adaptation with a Particular Focus on the
50 Poor, Different Livelihood Capitals and Vulnerable Groups
51
52 This section focuses on adaptation at household and community scales, including options, capacity and
53 enabling environment, which include actions required towards building resilience. The emphasis is on the
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1 decision-making space and governance including the role of the state, private sector and other actors.
2 Successful adaptation requires not only identifying adaptation options and assessing their costs and benefits,
3 but also exploiting available mechanisms for expanding the adaptive capacity of human and natural systems
4 (Klein et al., 2014). At the same time, developing suitable responses to hazards for communities and users of
5 climate services is important in ensuring the success of adaptation measures. But despite this, knowledge
6 about adaptation options, including possible actions that can be implemented to improve adaptation and
7 reduce the impacts of climate change hazards, is still limited.
8
9 8.5.1 Adaptation options to climate change hazards focusing on vulnerable groups
10
11 In light of the severe adverse consequences of climate change for the poorest populations whose livelihoods
12 are frequently dependent on vulnerable ecosystems, it is essential to enhance knowledge about sustainable
13 and appropriate adaptation strategies and measures, as well as recognise and respond to limits to adaptation
14 as reported in AR5 (Somorin, 2010; Noble et al., 2014; Connolly-Boutin and Smit, 2016). There is
15 increasing evidence on the adaptation options that enhance the ability of different socio-ecological systems
16 to become resilient in the long-term in ways that do not exacerbate poverty and inequality, and which
17 adaptations may have little or no impact, or even adverse effects (maladaptation). Analysis of climate
18 hazards can provide an indication of required adaptation strategies, however, most importantly is the focus
19 on exposure, vulnerability, however the novelty of the AR6 is assessing existing response capacities to cope
20 and adapt to climate changes and associated hazards. There is increasing knowledge about the differential
21 adaptation options within and across social groups and the influence of (enabling) conditions that enhance or
22 limit these options.
23
24 From the analysis in the IPCC AR5, there is high agreement that engineered and technological adaptation
25 options are still the most common adaptation responses, although there is increased recognition of the value
26 of ecosystem-based, institutional and social measures, including the provision of climate-linked safety nets
27 for those who are most vulnerable (IPCC, 2014a). It is important to note that climate adaptation measures are
28 increasingly integrated within wider policy, development strategies and spatial planning frameworks. Such
29 integration streamlines the adaptation planning and decision-making process and embeds climate-sensitive
30 thinking in existing and new institutions and organizations across scales and levels.
31
32 In the past decades a number of categories of adaptation options have been identified and are also discussed
33 in Section 8.5. Adaptation options are categorized in various ways, such as in terms of grey and green
34 adaptation or hard and soft measures (Depietri et al., 2013; Chambwera et al., 2014; Grimm et al., 2015).
35 Grey measures refer for example to technological and engineering solutions to improve adaptation of
36 infrastructures or to protect a specific land use or city from adverse consequences of climate hazards (OECD,
37 2018). It is accordingly explained that ecosystem-based approaches, including natural infrastructure, can
38 provide an effective complement or substitute for traditional built (or "grey") infrastructure. For example,
39 watershed restoration can protect sources of drinking water and reduce the need for subsequent treatment.
40 Green measures are often encompassing ecosystem-based (or nature-based) approaches. These make use of
41 the multiple services provided by ecosystems to improve resilience and adaptive capacity or to reduce risk.
42 Soft adaptation measures include policy, legal, social, management and financial measures that can alter
43 human behaviour and support adaptive governance, contributing to improved adaptation capacity, increased
44 awareness and change in values and actions on climate change issues.
45
46 Adaptation actions frequently include deliberate, coordinated, proactive policy decisions based on the
47 awareness that conditions have changed or will change and that action is required to avert impacts or return
48 to, maintain, or achieve a desired state (Carter et al., 1994). Noteworthy, governance provides an important
49 contextual framing, particularly in contexts where it is weak or contested (e.g., some of the Sahel zone). In
50 these cases, it can mean that adaptation options stem largely from the local level. Adaptation processes can
51 be categorised as individual, collective, proactive, reactive, autonomous, coordinated, and natural
52 (Chambwera et al., 2014). Apart from governments, other actors, organizations and institutions (including
53 non-state agencies and private industry actors) also play an important part in adaptation processes,
54 consequently also the discussion of enabling environments for sustainable or successful adaptation has to
55 deal and consider these different scales and actors. For example, while autonomous adaptations are mainly
56 undertaken by private actors, triggered by climate change induced market or welfare changes, planned
57 adaptations can be carried out by both private and public actors. Natural adaptations appear within
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1 ecosystems as a reaction to climate change as well as other factors and incorporate innumerable possible
2 actions that are context specific, ranging from managerial approaches, technological innovations, and
3 ecosystem based approaches (Huq et al., 2004). Sanchez et al. (2017) draws attention to preconceived ideas
4 about some adaptation measures that are either considered good or bad without proper evaluation. It is
5 argued that the association `hard-bad' and `soft-good' is not necessarily true; the impacts of adaptation can
6 only be established through a case-by-case assessment. The decision to select a more or less intensive
7 adaptation measure should integrate all approaches, social, environmental, technical and economic, in a
8 multi-criteria analysis. This analysis should value, inter alia, social and environmental sensitivity, benefits
9 and drawbacks or trade-offs with climate, including all the adaptation options, among them the `no action'
10 alternative.
11
12 Adaptation frequently responds to an observed or anticipated `trigger' for response, such as the looming loss
13 of land to sea level rise (Barnett et al., 2014). Identifying adaptation needs stemming from climate risks and
14 vulnerabilities provides a foundation for selecting a sequence of adaptation options that connect through
15 time, a long-term adaptation pathway (Wise et al., 2014; Turnheim et al., 2015). National, sectoral, or local
16 adaptation plans are likely to include a number of measures that are implemented jointly from across various
17 categories including structural, institutional, and social options. While structural or physical adaptation
18 encompasses measures for the engineered built environment it also can encompass nature based solutions,
19 which include ecosystem based protection measures, for example to buffer risks and hazard exposure to
20 extreme weather events. The category of `soft' adaptation measures--changes in societal values or
21 practices--are often linked to issues of education, information and behavioural changes to support
22 communities within specific adaptation processes to climate change and climate hazards. Institutional
23 adaptation deals with adaptation actions and measures introduced through new legal frameworks, laws and
24 regulations for new institutions or policies for risk reduction and adaptation. This category can also
25 encompass the development of new organizations that have the mandate to support adaptation (Noble et al.,
26 2014). The appropriateness and accessibility of adaptation options under these categories for supporting the
27 poor and most vulnerable groups differs. In many cases large scale structural measures are not affordable for
28 many poor communities. Despite this important potential of Indigenous Knowledge for disaster risk
29 reduction of the communities, it is often shunned by practitioners (Dube and Munsaka, 2018). It is further
30 argued by practitioners that Indigenous Knowledge lacks documentation, it is not found in all generational
31 classes, it is contextualised to particular communities and the knowledge cannot be scientifically validated.
32 However, there is also evidence that both local communities and disaster risk reduction practitioners can
33 benefit from the Indigenous Knowledge of communities (Dube and Munsaka, 2018).
34
35 In practice, adaptation refers to initiatives such as a policy, plan, project or decision that are designed to
36 change and/or respond to something in the context of existing risks and hazards. For example, a farmer
37 might adapt to drought by deciding to harvest their crop earlier; a municipality can decide to build a sea wall
38 to adapt to increased flood risk.
39
40 The increasing complexity of adaptation practice means that institutional learning is an important component
41 of effective adaptation (Noble et al., 2014). It is paramount that approaches to selecting adaptation options
42 continue to emphasize incremental change to reduce impacts while achieving co-benefits. There is increasing
43 evidence that transformative changes may be necessary in order to prepare for climate change impacts and
44 adaptation options in the context of climate hazards (Noble et al., 2014). Transformation for some actors at
45 some levels may equate with incremental change and transitions for other actors and scales. While attention
46 to flexibility and safety margins is becoming more common in selecting adaptation options, many see the
47 need for more urgent and transformative changes in our perception and paradigms about the nature of
48 climate change, adaptation and their relationship to other natural and human systems.
49
50 In this context, there are many potential adaptation options available for marginal change of existing
51 agricultural and other livelihood systems, often variations of existing climate risk management. According to
52 Howden et al. (2007) implementation of these options is likely to have substantial benefits under moderate
53 climate change for some existing cropping systems. Apparently, there are limits to their effectiveness under
54 more severe climate changes. Hence, more systemic changes in resource allocation need to be considered,
55 such as targeted diversification of production systems and livelihoods. Howden et al. (2007) further argue
56 that achieving increased adaptation action will necessitate integration of climate change-related issues with
57 other risk factors, which implies integrating non-climatic factors, such as climate variability and market risk,
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1 and with other policy domains, such as sustainable development. Noteworthy, an increasing number of
2 research programs seek to support adaptation to climate change through the engagement of large-scale
3 transdisciplinary networks that span countries and continents (Cundill et al., 2019).
4
5 Based on analysis of different adaptation options, there is high agreement that the many barriers to effective
6 adaptation will require a comprehensive and dynamic policy approach covering a range of geographical
7 scales and multiple actors across scales, taking into consideration both climatic and non-climatic stress
8 factors (Eriksen et al., 2015). For instance, from the agricultural perspective this could imply the
9 understanding by farmers of change in risk profiles to the establishment of efficient markets that facilitate
10 response strategies. It is also important to note that Science, too, has to adapt employing a range of
11 approaches, based on the fact that multidisciplinary problems require multidisciplinary solutions. Towards
12 enhancing resilience, a focus on integrated rather than disciplinary science alone could be of utmost
13 importance as well as strengthening of the interface with key stakeholders, ranging from decision makers,
14 practitioners, policymakers, and scientists.
15
16 8.5.2 Enabling environments for adaptation in different socio-economic contexts
17
18 8.5.2.1 Factors that support enabling environments for adaptation
19
20 This section assesses the literature on components of the enabling environment for adaptation. The point of
21 departure considers findings in both the SR1.5癈 report, which note that adaptation becomes increasingly
22 difficult (and expensive) at temperatures more than 1.5篊 warmer (IPCC, 2018a), and noting also that (IPCC,
23 2014a) underscores that there is no one-size-fits-all approach to adaptation for all contexts, and that
24 mitigation and adaptation must be pursued in tandem.
25
26 Climate change affects people inequitably, and everyone does not contribute equally to climate change. A
27 range of economic and non-economic impacts can be experienced. This has led some researchers to call for a
28 more central role for rights-based approaches to adaptation, to help secure space for those marginalised from
29 adaptation decision making and to prioritise access to resources and information for those most vulnerable
30 to, or affected by, the social, cultural or economic consequences of climate change (Bee et al., 2013; Da
31 Costa, 2014; Toussaint and Martinez Blanco, 2020; Box 8.7; Section 5.12). In terms of international law, the
32 human rights obligations of states have been subject to multiple recommendations relating to climate change
33 by UN treaty bodies in the reporting period. More broadly, rights-based approaches rely on the normative
34 framework of human rights, requiring adaptation to be non-discriminatory, participatory, transparent and
35 accountable in both formal (e.g., legal and regulatory) and informal (e.g., social or cultural norms) settings
36 and at international, national and sub-national scales (Ensor et al., 2015; Arts, 2017). Sovacool et al. (2015)
37 note that unless critical competing interests are addressed during planning, adaptations may fail to achieve
38 the desired outcomes. This is increasingly seen at a political level within efforts to implement the Paris
39 Agreement, in relation to the principle of `Common but Differentiated Responsibilities and Respective
40 Capacities' (CBDR-RC) (Box 8.7).
41
42
43 [START BOX 8.7 HERE]
44
45 Box 8.7: Addressing Inequalities in National Capabilities: Common but Differentiated Responsibilities
46 and Respective Capabilities Relating to Adaptation and the Paris Agreement
47
48 Common but differentiated responsibilities and respective capabilities (CBDR-RC) is a key principle within
49 the UNFCCC, and attempts to acknowledge countries' diverse development situations. The Convention and
50 its Kyoto Protocol operationalized the principle by committing developed (Annex I) countries to absolute
51 emission reduction or limitation targets and exempting developing countries from any binding reductions in
52 emissions (Huggins and Karim, 2016; Pauw et al., 2019). In contrast, the Paris Agreement distinguishes
53 between 'developed' and 'developing' countries instead of Annex I and non-Annex I countries and
54 acknowledges significant asymmetries and inequalities not only between developed and developing
55 countries, but also between developed and developing countries themselves, both in terms of vulnerability to
56 climate change impacts, and capacity to mitigate the problem. The literature contains extensive analyses of
57 CBDR-RC in relation to equity in mitigation efforts in the post-2020 regime (e.g., Michaelowa and
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1 Michaelowa, 2015; du Pont et al., 2017; Liu et al., 2017; Holz et al., 2018; S鎙en et al., 2019), but little in
2 relation to adaptation, particularly relating to how it plays out in the Paris Agreement.
3
4 The somewhat static interpretation of CBDR-RC prior to the Paris COP was overcome through the
5 introduction of a qualification to the CBDR-RC principle: the phrase `in the light of different national
6 circumstances'. Without changing the original principle, the qualifier adds a dynamic element (Rajamani,
7 2016). Common but differentiated responsibilities and respective capabilities of Parties are therefore
8 recognised not to be `tied to the annexes', but instead evolve alongside national circumstances (Maljean-
9 Dubois, 2016; Voigt and Ferreira, 2016 p.301). The Paris Agreement also recognises context, considering
10 differentiation in relation to each of the Durban pillars, i.e., mitigation, adaptation, finance, technology,
11 capacity building and transparency (Rajamani and Gu閞in, 2017).
12
13 Article 7 of the Paris Agreement acknowledges adaptation as a `global challenge faced by all', recognising,
14 for the first time, a global aspiration of `enhancing adaptive capacity, strengthening resilience and reducing
15 vulnerability to climate change'. It calls for a balance between mitigation and adaptation funding and
16 emphasises the need to provide developing country Parties, especially the most vulnerable, with
17 `[c]ontinuous and enhanced international support' for adaptation. The basis for differentiation under Article
18 7 therefore relies mostly on diverse national circumstances, capabilities and vulnerabilities. Least Developed
19 Countries (LDCs), as well as Small Island Developing States (SIDS), are assumed by the literature, to be part
20 of this category (Maljean-Dubois, 2016).
21
22 The literature offers two main perspectives when evaluating the effectiveness of these provisions on
23 adaptation in the context of the post-Paris climate change regime. One argument follows that the Paris
24 Agreement gives priority attention to the most vulnerable Parties and, unlike previous international
25 agreements in the climate change regime, places adaptation on equal footing to mitigation (Magnan and
26 Ribera, 2016; P閞ez and Kallhauge, 2017; Morgan, 2018). Article 7 is interpreted here as a breakthrough,
27 containing unprecedented provisions that give adaptation prominence and which elevate the importance of
28 undertaking adequate action to cope with current and future climate change impacts. A second view argues
29 that the Article 7 marks little departure from previous efforts to support adaptation efforts in developing
30 countries (Doelle, 2016) or that it could have included stronger provisions, such as a quantitative goal with
31 respect to adaptation needs and costs (Bodansky, 2016).
32
33 The literature nevertheless shows high agreement that other parts of the Paris Agreement do contain
34 consequential provisions on adaptation and the operationalization of the CBDR-RC principle. Those
35 provisions covering financial support are arguably the most pertinent, as they replace the dichotomy between
36 developing countries and developed countries with a trichotomy which also includes `other Parties'
37 (Maljean-Dubois, 2016). While provision of support from developed Parties continues to be mandatory,
38 these `other Parties', apparently developing country Parties, are `encouraged to provide or continue to
39 provide such support voluntarily' (Article 9.2). Parties themselves determine whether they belong to this
40 category. So far, several developing countries have made contributions to the Green Climate Fund, ranging
41 from Indonesia and Mexico to Mongolia and Panama (Green Climate Fund, 2017). Expanding the donor
42 base to these `other parties' and breaking down the wall between donor and recipient countries marks a
43 departure from previous practice, under which developing countries had no formal role in climate finance
44 and support (Bodansky, 2016; Voigt and Ferreira, 2016).
45
46 [END BOX 8.7 HERE]
47
48
49 The scale of analysis, baseline conditions prior to adaptation and scale of action matter too when assessing
50 the key components of an enabling environment for adaptation. At a national scale, it is well established that
51 low income countries are less well positioned to manage climate change impacts, being variously attributed
52 to a lack of institutional, economic or financial capacity to adapt effectively (Tol and Yohe, 2007; Barr et al.,
53 2010). It can be particularly difficult to adapt to drought, for example, when it occurs in the pre-conditions of
54 poor water supplies and sanitation (see Box 8.5 and Section 8.3.2), and in a context of corruption,
55 governance failure and a lack of accountability. Adaptation productivity in higher income countries is further
56 supported by better infrastructure and stronger institutions--low adaptation efficiency is linked to lower
57 government spending, higher inequalities in income distribution and poor governance (Fankhauser and
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1 McDermott, 2014). At smaller scales, even within a single socio-economic setting, different groups require
2 different kinds of adaptation support and exhibit different vulnerabilities to climate change impacts. Huynh
3 and Stringer (2018) found that households vulnerable to climate change impacts linked to sea-level rise and
4 flooding in Da Nang City and Ngu Hanh Son District, Vietnam, had limited access to human, natural,
5 physical, financial and social assets and lacked a diversified livelihood portfolio. An enabling environment
6 for household level adaptation would need to address these factors in this context. However, the same
7 authors found that at District scale, different challenges persisted, including obstacles to multidirectional
8 flows of climate information, poor vertical interplay both upward and downward, and a lack of citizen
9 participation in the governance of climate change.
10
11 Acknowledging that context and scale matter, it is nevertheless possible to set out the core components of a
12 generic enabling environment (Figure 8.12), linking them to the literature on climate change and recognising
13 how they can support adaptation in different socio-economic and environmental settings in which different
14 emphases are required. This broad set of enablers requires different emphases according to the specific
15 context, yet the interdependence between them is universally applicable.
16
17
18
19 Figure 8.12: Core components of the enabling environment for adaptation to climate change (key interactions are
20 illustrated but it should be noted that there are overlaps, interactions and feedbacks both within and between each item;
21 and that different countries have different capacities and starting points in addressing these enablers and the
22 interlinkages between them).
23
24
25 The specific political economy of each country and its underpinning philosophies shape the national political
26 context in which public policy supporting adaptation is developed and implemented. It further shapes the
27 context for private adaptation. Public policy targeting climate change seeks to address market failures,
28 amend policy distortions and offer incentives for private adaptation, as well as provide climate-resilient
29 public goods, climate services and safety nets for the poor and vulnerable (Fankhauser, 2017). In some
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1 countries that have a more stable institutional context, such policies are more straightforward to develop and
2 implement; while in countries with weaker institutions (e.g., those emerging from conflict) a larger role may
3 be needed for regional economic commissions and transnational networks to support the governance of
4 `borderless climate risks' (Benzie and Persson, 2019) particularly where these countries also are most
5 vulnerable to climate change (see also Figure 8.6). To support enabling conditions in highly vulnerable
6 countries that are also characterized by state fragility (see Figure 8.8), funding and projects designed to
7 support adaptation may need to be modified to effectively promote regional cooperation and transboundary
8 adaptation. Nevertheless, such interventions can also reinforce particularly powerful agendas and fail to
9 assist and empower those with the greatest need to adapt(Biermann et al., 2010; Burch et al., 2019)
10 neglecting community voices and sovereignty (Schlosberg and Collins, 2014). It is therefore important that
11 the relevance of people and community empowerment to effectively achieve vulnerability reduction and
12 climate change adaptation is recognised.
13
14 It is also insufficient to consider countries as stand-alone entities, due to links such as those provided by
15 international trade. Taking Europe as an example, the continent has strong links to major trade partners such
16 as India, Indonesia, Nigeria and Vietnam, so failure to assist adaptation in other locations opens up important
17 vulnerabilities through supply chains (Lung et al., 2017). Policies seeking to protect national interests alone
18 (e.g., in terms of food security) are seen as causes of negative impacts at a global scale (Puma et al., 2015;
19 Challinor et al., 2017), with those nations and individuals least able to adapt to evolving climate changes
20 experiencing exacerbation of existing imbalances (Elbehri et al., 2015). Least developed countries are
21 projected to suffer greater import losses in more connected networks (Puma et al., 2015). In the food sector,
22 poorer net food buyers are anticipated to experience the worst impacts of climate change (Gitz et al., 2015).
23
24 Behind each policy are decisions about the magnitude of financial resource investments in specific
25 adaptation actions, and their allocation between different sectors and groups in society, both spatially and
26 temporally. The IPCC has estimated that limiting the rise in global average surface temperatures to 1.5癈
27 would require between $1.6 trillion to $3.8 trillion of annual investment in supply-side energy systems (those
28 that generate energy) between 2016 and 2050 (IPCC, 2018b). Resource allocations however, are shaped by
29 perceptions of the risks of climate change and the urgency of actions, as well as other motivational factors
30 such as descriptive norms and perceived self-efficacy (van Valkengoed and Steg, 2019) and the underlying
31 approaches taken to valuing human wellbeing (e.g., see work from Bhutan on Gross National Happiness and
32 climate change actions (Kamei et al., 2021)).
33
34 An increase in finance mobilised, however, does not automatically equate to adaptation interventions on the
35 ground, nor does guarantee the effectiveness of those adaptations deployed (Berrang-Ford et al., 2021).
36 Unintended negative consequences may arise due to lack of understanding of the drivers of vulnerability
37 (such as gender inequality or inequitable access to natural resources), non-involvement of marginalised local
38 groups, retrofitting adaptation into existing development agendas, and insufficiently defining adaptation
39 success (Eriksen et al., 2021). A 2017 study estimated that less than 10 percent of climate finance committed
40 from international, regional and national climate funds to developing countries between 2003 and 2016 went
41 to locally focused projects, suggesting a need to rethink approaches if the most affected groups are to build
42 sufficient resilience to the impacts of climate change (Soanes et al., 2017).
43
44 The literature shows with high confidence that the poorest groups in society often lose out, and require
45 greater planned adaptation support, having less capacity to adapt than better off groups with easy access to
46 assets (Barbier and Hochard, 2018; Ziervogel, 2019b; Box 8.5). Developing countries such as Burkina Faso,
47 Mali and Zambia are not only among the most vulnerable to climate change, they are also the least able to
48 mobilise the finance needed to adapt to its impacts (ND-GAIN, 2019). Women and girls are often most
49 heavily burdened. When building adaptive capacity these groups can require different support such that their
50 knowledge, capacities and skills can be harnessed, in such a way that does not feminise responsibility and
51 add to their burdens (Clissold et al., 2020; McNamara et al., 2021a).
52
53 There is broad support for the notion, enshrined in the Paris Agreement, that adaptation finance flowing to
54 developing countries of the Global South should primarily benefit the most climate-vulnerable among them
55 due to their limited technical capacity and financial capabilities, yet such countries are often insufficiently
56 considered in funding decisions. There are nevertheless concerns regarding institutional fit: that foreign
57 funding regimes may not map onto more recently developed administrative traditions, leading to dominance
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1 of governance models emanating from donors (Vink and Schouten, 2018). Research has found multilateral
2 donors do not prioritise vulnerable developing countries at the project selection stage and they have received
3 smaller allocations of adaptation finance from bilateral donors than less vulnerable countries (Saunders,
4 2019), leaving the poor vulnerable to climate impacts. The lack of climate finance flowing to LDCs and
5 SIDs (currently 14 and two percent of the total, respectively) is compounded by access issues due to the
6 inability of domestic institutions to meet specific fiduciary standards and other access requirements,
7 insufficient human resource support and the inflexibility of current approaches which are biased in favour of
8 governments and against non-traditional actors such as local enterprise and grassroots organisations (Shakya
9 et al., 2021). Further, vulnerable developing countries shoulder additional financial burden, embodied in
10 higher interest payments to service public and private debt, due to the increased cost of capital brought about
11 by greater exposure to climate risks (Buhr et al., 2018). This has been further exacerbated by the recession
12 and debt distress accompanying the Covid-19 pandemic (Kose et al., 2021). A range of reforms, including
13 comprehensive debt relief by public creditors, green recovery bonds, debt-for-climate swaps and new SDG-
14 aligned debt instruments may address unsustainable debt burdens, freeing up investment in climate
15 adaptation and a green economic recovery (Volz et al., 2020; see Section 8.6.3.1)..
16
17 Greater investment is also needed in the developed countries of the Global North. For example, both the
18 2018 forest fires in Sweden, the 2019-2020 Australian bushfire season and the 2020 forest fire season along
19 the US West Coast were unusually long and severe, resulting in unprecedented damage to natural habitats
20 and human livelihoods and, relatedly, significant economic cost, particularly given interlinkages with other
21 stressors such as Covid-19. While a range of drivers underpin annual fire seasons, including greater water
22 withdrawal and years of fire suppression, early research indicates that climate change increases their
23 likelihood due to long-term warming trends (van Oldenborgh et al., 2021a).
24
25 However, investing in poverty reduction does not necessarily lead to climate change adaptation and where
26 adaptation does result, it does not always reduce vulnerability of the most marginalised, such as documented
27 in case studies from Northeast Brazil (Nelson et al., 2016). Poverty also affects private adaptation options.
28 For example, research from Portugal highlights the importance of private financial assets in helping older
29 adults to adapt to extreme temperatures (Nunes, 2018).
30
31 Policies and investments that are adopted are embedded within the relevant legal and regulatory frameworks,
32 which extend beyond national jurisdictions upward to the regional scale (such as the Southern Africa
33 Development Community's Southern Africa Regional Framework of Climate Change Programmes, (2010))
34 and international scale, for example, UNFCCC, the 2015 Paris Agreement, the Sendai Framework for
35 Disaster Risk Reduction, the New Urban Agenda and the SDGs. Legal and regulatory concerns also extend
36 downward to shape local- and city-scale adaptation efforts (e.g., Sao Paulo's municipal policy and new
37 master plan). Nevertheless, only a minority of countries have dedicated legal frameworks supporting
38 adaptation (Lesnikowski et al., 2017) and these often lack in both precision and obligation--largely because
39 adaptation is a contested global public good but also because adaptation is commonly bundled in with
40 mitigation commitments (Hall and Persson, 2018). Coherence, horizontally and vertically in both policy and
41 law is often lacking. At the same time, bottom-up, private, autonomous adaptation efforts are being better
42 tracked, with different actors motivated by growing experiences of local climate change impacts (Berrang-
43 Ford et al., 2014).While the emergent polycentricity of adaptation governance is beginning to take shape,
44 wherein both state and non-state actors share a common adaptation goal and interact coherently, yet often
45 independently, to advance progress towards it (Morrison et al., 2019), understandings of how various centres
46 of decision making with different degrees of autonomy support an enabling environment for adaptation,
47 remain at a nascent stage . Multiple scales and forms of adaptation occur, with attributes such as self-
48 organisation, appreciation of site-specific conditions, and the need for learning and experimentation,
49 alongside building of trust, increasingly shown to be vital (Dorsch and Flachsland, 2017). Literature
50 indicates that professional and learning networks are important groups supporting adaptation in cities and
51 can help harness resources (Woodruff, 2018); while (Hauge et al., 2019) research in Norway underscores the
52 importance of working across multiple disciplines and the inclusion of actors from different levels of
53 authority in multilevel municipal networks. They found that these factors can help to identify specific
54 adaptation actions as well support knowledge sharing within participating organisations, which in turn helps
55 garner commitment to adaptation and its implementation. They also found that it is important to involve
56 local leaders in polycentric adaptation networks.
57
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1 Among the many institutions, actors and roles associated with successful adaptation, two play an
2 increasingly important role: local governments and the private sector (Noble et al., 2014). These groups often
3 define the flows of information and finance from the top down, as well as supporting the scaling up of
4 community and household adaptation. In some countries, for example, in South America (Argentina, Brazil,
5 Paraguay) vocational agricultural schools, often in remote rural locations, play a key part in knowledge
6 sharing activities that support adaptation. Similar valuable contributions are made by universities through
7 their outreach activities, particularly those offering programs in environmental and agricultural fields. Many
8 actors face a lack of resources and capacity, particularly at the local level. Local institutions, including local
9 governments, non-government organizations (NGOs) and civil society organizations, are hampered by
10 ongoing challenges in gaining support from higher governance levels--from national government or the
11 international community, particularly in developing countries. At the same time, private sector actors, from
12 individual farmers and small/medium enterprises (SMEs) as well as large multinational businesses, will seek
13 to protect and enhance their production systems, supply chains and markets by pursuing adaptation-related
14 opportunities. Yet, while these goals will help expand adaptation activities, they may not align with
15 government or community objectives and priorities without coordination and incentives, and in the process,
16 can reinforce existing capacities, inequalities and power relations (Sovacool et al., 2015). Similarly, an
17 enabling environment for businesses' adaptation is highly differentiated and often requires structural deficits
18 (such as limited market access, finance and transport and communications infrastructure) to be tackled
19 (Gannon et al., 2020).
20
21 The challenges of climate change have driven governments around the world to emphasise climate services
22 as a route to enhance decision-making and reduce climate-related risks, as well as inform adaptation,
23 supporting calls for the right to information (Tall and Njinga, 2013). While there have been some efforts to
24 evaluate the economic impact of climate services alongside other impacts (e.g, Tall et al., 2018), little is
25 known about the institutional contexts in which investments in climate services have taken place, nor those
26 groups that are most vulnerable or marginalised in relation to specific climate risks. Vincent et al. (2017)
27 offer preliminary insights from Malawi, identifying that barriers to improved integration of climate services
28 in national policy planning include factors relating to spatial and temporal scale, accessibility and timing of
29 information provision, credibility and mismatches in time-frames between planning cycles and climate
30 projections. An understanding of the factors that enable climate service investment is important for the
31 development of climate services at local, national and international levels (Vaughan et al., 2017) but this area
32 of literature is not yet well developed.
33
34 Overall, adaptation entails financial (and non-financial) costs not just in implementing adaptation actions,
35 but also in designing, facilitating and preparing for actions--costs to create and maintain an enabling
36 environment (see also Section 8.2.2.3, Cross-Chapter Box LOSS in Chapter 17). Financial and economic
37 investments target the whole range of other types of asset (natural capital, physical capital, human capital,
38 social capital). AR5 reports that aggregate economic losses accelerate with increasing temperatures (IPCC,
39 2014a). Costs may be borne when gaining information (e.g., investments in climate services), while
40 adjustment costs are incurred as adaptations take place. Nevertheless, to enable adaptation, investment is
41 needed in various natural, human, physical and social assets, as considered below. The importance of
42 investment in each of these different types of asset varies according to the scale and livelihood system in
43 need of adaptation and the ways in which livelihood resilience is framed and power is distributed, within
44 each specific setting (Carr, 2020).
45
46 8.5.2.2 Natural capital
47
48 It is well established that climate change compounds the impacts of pressures that humans place on the
49 environment (high confidence) and that environmental degradation can undermine options for adaptation and
50 an enabling environment, with poor and natural resource dependent groups most acutely affected (see e.g.,
51 CCP3 for insights from deserts and semi-arid areas). Sustainable management of natural capital contributes
52 to building resilience and the natural ability of ecosystems to adapt to climate change (IPCC, 2014a) and see
53 also IPCC SROCC Chapter 5, Section 5.3.2 (Bindoff et al., 2019). Some systems like mangroves (found in
54 123 countries, many of which are in the developing world) offer a broad range of vital ecosystem
55 services(Hamza et al., 2020). Mangroves provide regulating services by acting as a natural defence against
56 sea level rise and storm surges; and by sequestering carbon in both the trees and sediments they capture.
57 Provisioning services (e.g., fish, crabs, timber and fuelwood) from mangroves support livelihoods and
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1 livelihood adaptation options especially for those with few other livelihood opportunities, while these
2 systems also provide important habitat (breeding, spawning and nursery grounds for fish) and biodiversity,
3 and offer cultural services in the forms of education, recreation and spiritual benefits (Quinn et al., 2017). As
4 the frequency of events such as hurricanes, storms and typhoons rises with climate change, natural capital
5 assets like mangroves become increasingly important in protecting coastlines and supporting adaptation.
6 While not reducing the hazard itself, the mangroves reduce exposure and in some cases also vulnerability.
7 The literature shows with high confidence that environmental assets support both climate change mitigation
8 (at a large scale) and adaptation (at a smaller scale), particularly for the poorest groups in society who
9 directly depend upon natural capital for their subsistence (e.g., Angelsen et al., 2014). In turn, the legal and
10 regulatory context and institutional set up determines who has access rights to different aspects of the natural
11 resource base. This shows how different aspects of the enabling environment work in tandem to constitute
12 one another.
13
14 In a market economy, human activities tend to exacerbate degradation of natural capital, despite its role in
15 buffering climate change impacts, supporting mitigation and providing adaptation options. Economic agents
16 base their decisions on market prices, even though market prices do not incorporate the costs of deteriorating
17 natural capital because of externalities and other market failures, i.e., environmental degradation is not
18 internalised (Bowen et al., 2012). At the same time, expanding populations, capitalism and consumption
19 choices affect the condition of natural capital, alongside short-termism stemming from poverty, linked to the
20 need for survival. All these factors therefore interact, with the aggregate effect of worsening the impacts of
21 climate change, while also undermining future adaptation options, particularly for the poor. Adaptation
22 policies should, but do not always, compensate for the prevalent market failures. For example, in Melanesia,
23 sea walls have been built out of coral by local people in an attempt to reduce the impacts of rising sea levels,
24 leading to outright destruction of some of the world's most productive and biodiverse coral reefs (Martin and
25 Watson, 2016). Similarly, in the Congo Basin, farmers are adapting to increasingly variable rainfall by
26 expanding their cropping activities into forested areas, releasing carbon into the atmosphere through forest
27 clearance activities and threatening biodiversity. Agricultural land is also being degraded globally (see the
28 IPCC's SRCCL (IPCC, 2019a)), and this too closes down adaptation and livelihood options for the poorest,
29 natural resource dependent populations, while jeopardising food security, biodiversity and human health at
30 wider scales. An enabling environment for adaptation therefore demands investment in sustaining natural
31 capital at multiple scales, internalising the costs of degradation, as well as establishing the necessary legal
32 and regulatory frameworks (and associated enforcement) to reduce its degradation(IPBES, 2018).
33
34 The literature increasingly shows that approaches such as nature-based solutions (NBS) and ecosystem-based
35 adaptation (see Chapter 2 and Chapter 6) can offer value for money in tackling climate change from both a
36 mitigation and adaptation standpoint (Seddon et al., 2020). According to the Global Commission on
37 Adaptation, a global investment of $1.8 trillion between 2020 and 2030 into adaptation measures such as
38 early warning systems, climate-resilient infrastructure, improved dryland agriculture, mangrove protection,
39 and resilient water resources can yield $7.1 trillion in total net benefits (Global Commission on Adaptation,
40 2019). NBS operate by harnessing natural processes, sometimes in combination with technological or
41 engineered solutions. Examples encompass green public spaces and parks (Sahakian and Anantharaman,
42 2020), green infrastructure, such as urban forests and street trees (Richards and Edwards, 2017) which create
43 shade and reduce urban heat island effects whereby urban areas are warmer than their surroundings (Depietri
44 et al., 2013), and support human health and wellbeing by keeping people in cities more closely linked with
45 nature (Gulsrud et al., 2018). NBS also encompasses blue infrastructure including constructed wetlands,
46 bioswales, rain gardens etc., which can reduce flood risks (Haase, 2015). While the literature is generally
47 positive about the ability of NBS to support climate risk reduction and deliver multiple other benefits
48 (Connop et al., 2016) such as green job opportunities, improved provision of recreational space, cleaner air,
49 habitat provision and increased property values (Emmanuel and Loconsole, 2015), more research is required
50 to specifically assess and evaluate the conditions and contexts in which these kinds of potential benefits are
51 realised and how they can be mainstreamed into policy (Frantzeskaki et al., 2019). Similarly, there is limited
52 evidence on unintended consequences (e.g., methane production, creation of habitat for disease vectors,
53 increased human-wildlife conflict) and how these can be avoided (Wolch et al., 2014).
54
55 8.5.2.3 Human capital
56
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1 Successful adaptation requires support to be directed towards human capital and socio-economic capabilities
2 and competences, in terms of education, knowledge, experience, health and wellbeing, and migration,
3 enabling people to contribute meaningfully towards development (Bowen et al., 2012). At the same time,
4 strong human capital and investment in actions that build human capacities to deal with climate change, can
5 further enhance adaptation activities linked to other capitals, and contribute positively to overall disaster risk
6 reduction.
7
8 Analyses of educational attainment distributions with datasets reaching back as far as 1970 show that
9 improving educational attainment in people of working age has been the most consistent and significant
10 driver of economic growth globally (Lutz et al., 2008), showing the importance of the right to education.
11 Education has further supported sustainable development by fostering empowerment, yielding access to
12 information (including on climate change) and has clear links to other aspects of human capital, including
13 health and mortality (Samir and Lutz, 2017). There is medium evidence and high agreement that education
14 reduces vulnerability and enhances adaptive capacity (Frankenberg et al., 2013; Sharma et al., 2013), with
15 high agreement that climate change impacts can have negative effects on existing levels of human capital,
16 with some development pathways affected more than others (Samir and Lutz, 2017). Education can help to
17 shape people's risk perception and assessment, as well as affecting knowledge sharing and the development
18 of problem-solving abilities (Striessnig et al., 2013).
19
20 At the same time, Indigenous Knowledge and Local Knowledge can inform adaptation actions (Apgar et al.,
21 2018), but is poorly integrated into formal educational systems and in some cases is insufficient to adapt to
22 new hazards that are emerging as a consequence of climate change. Education further feeds into livelihood
23 options, with close relationships between people's earning capacities, the livelihood choices they can make
24 and their levels of financial capital. It also supports food security (Lutz et al., 2004). There is medium
25 evidence that climate change can undermine human capital and education. For example, studies have shown
26 that higher temperatures reduce exam educational performance (Park, 2020), while extreme weather events
27 such as snow storms disrupt learning, yielding long lasting and multidimensional effects (Maccini and Yang,
28 2009; Cho, 2017; Graff Zivin et al., 2018).
29
30 As well as studies examining formal education, a large body of research has focused on social learning and
31 its role in building adaptive capacity through joint knowledge production and reflexivity. Foregrounding the
32 need for continuous changes in response to emerging conditions, this literature identifies the potential of
33 shared learning for co-constructing policy and practice responses to complex, multi-stakeholder
34 environmental problems, and highlights both the necessity and challenge of including non-dominant values,
35 knowledge and expertise in adaptation decision making, considering the role of power dynamics therein
36 (Collins and Ison, 2009; Ensor and Harvey, 2015; Phuong et al., 2017; Apgar et al., 2018; Brymer et al.,
37 2018; Fisher and Dodman, 2019). A growing body of evidence also links to on organisational learning and
38 adaptation. It was found that organisations' adaptive behaviours, like those of households and individuals, do
39 not operate in a vacuum, with organisations' behaviours shaped by policy and market conditions amongst
40 other factors. Mudombi et al. (2017) highlight further barriers in their study in South Africa, linked to
41 inadequate resourcing, political interference, governance shortcomings and knowledge/expertise gaps within
42 organisations, alongside short timeframes for implementing projects.
43
44 Adaptations that support human health and wellbeing require investments in physical assets and
45 infrastructure linked to water and sanitation (see Chapter 4), particularly in rapidly urbanizing areas in the
46 Global South, alongside specific pro-poor investment strategies given disproportionate climate change
47 impacts on women (See Cross-Chapter Box GENDER in Chapter 18), other marginalized groups and low
48 income households who lack access to healthcare. Climate change facilitates the spread of vector borne
49 diseases such as malaria, as well as illnesses such as meningitis (Rockl鰒 and Dubrow, 2020). Impacts on
50 health are also experienced, through food insecurity resulting from climate change, including malnutrition, as
51 well as through loss of livelihoods, making it more difficult to afford and to access health services. Health
52 aspects are considered in-depth in chapter 7 but we underscore the importance of a rights based lens on
53 adaptation in supporting the right to health and food in the context of inequality.
54
55 A key dimension of human capital is local understanding of climate risk, which includes knowledge systems
56 outside western scientific approaches. For millennia, local communities have relied heavily upon culturally
57 accumulated Indigenous Knowledge participating in landscapes as stewards of their environment, engaged in
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1 profoundly detailed livelihood strategies that deal with natural hazards (Ajayi and Mafongoya, 2017).
2 Indigenous Knowledge systems as they are embedded in culture, and are passed from generation to
3 generation in various ways: livelihoods, traditions, spiritual practices and oral tradition, cultural identity, and
4 historical memory. Indigenous Knowledge is known or learnt from experience, or acquired through
5 observation and practice, and handed down from generation to generation. It is acknowledged that
6 Indigenous Peoples communities, particularly those in hazard-prone areas, have developed a profound
7 understanding and knowledge of disaster prevention and mitigation, early warning, preparedness and
8 response, and post disaster recovery. While Indigenous Knowledge systems, themselves, are an
9 indispensable dimension of capacity for adaptation, and where threatened represent a major risk to
10 Indigenous Peoples communities. While still robust among Indigenous Peoples in many parts of Africa, Asia
11 and Latin America, Indigenous Knowledge is not well reflected or incorporated in assessments such as this,
12 and stands in danger of being lost as its custodians are passing away.
13
14 Indigenous Knowledge about natural hazards enables communities at risk to take steps to reduce climate
15 risk. Indigenous Knowledge systems are locally indispensable resources for adaptation to climate change, yet
16 are often misunderstood and undervalued. Generally, Indigenous Peoples and other local groups hold
17 relevant local-scale knowledge about environmental change, the impacts of those changes on ecosystems and
18 livelihoods at local scales, and possible locally effective adaptive responses. However, it is important that
19 Indigenous Knowledge and Local Knowledge is situated within knowledge from other scales in order to
20 assess its broader relevance and applicability (Ahlborg and Nightingale, 2012). Some authors suggest
21 including Indigenous Knowledge in the IPCC assessment process should be of high priority, as it is
22 becoming increasingly relevant for climate services (high confidence) (Strauss and Orlove, 2003; Crate and
23 Nuttall, 2009; Crate, 2011). Their knowledge can draw attention to climate baselines and change, and
24 identify adaptation priorities, such as plant and animal species that should be protected given local contextual
25 environmental considerations. For example, using Indigenous Knowledge in weather and climate prediction,
26 local communities in different parts of Tanzania have been coping with and adapting to increased climate
27 variability normally manifested in the form of increased frequency and magnitude of various exigencies
28 including droughts and floods, and outbreak of pests and diseases (Kijazi et al., 2013). Prediction of
29 impending hazards has been an integral part of Indigenous Peoples' adaptation strategies. Various
30 environmental and astronomical indicators are used to predict rainfall, including plant phenology, behaviour
31 and movement of birds, animal and insects are widely used in many parts of Tanzania (Kijazi et al., 2013).
32
33 There are efforts in developing adaptation plans that utilize local knowledge. Local knowledge-based
34 adaptation is focused primarily on the use of traditional knowledge to increase adaptive capacity at the
35 community level and less on integration (Mimura et al., 2014). Hence, there is need to increase effectiveness
36 of policy processes that work towards integration of local and scientific knowledge (Nakashima et al., 2013;
37 IPCC, 2014a).
38
39 8.5.2.4 Physical capital
40
41 Ensuring sufficient investment in physical capital is vital to support development pathways at the national
42 level, but for the poorest and most marginalised in society, physical capital represents an invaluable source
43 of adaptation options (Hallegatte et al., 2019). Physical capital constitutes assets such as land, roads and
44 other infrastructure (e.g., water supplies, electricity, mobile phone connectivity), housing and other
45 buildings, as well as the materials and tools needed to make a living (e.g., farming, forestry and fishing
46 equipment, transportation vehicles, technology). It can also help to foster a sense of place, and can support
47 wellbeing. Climate change impacts on physical capital are often widespread, as well as economically and
48 emotionally costly, particularly when communities afflicted by hardship (inadequate levels of sustainable
49 human development through access to essential public goods and services and access to income
50 opportunities (Abbott and Pollard, 2004).
51
52 Given the massive scale of investments required to build and sustain physical capital at the state level, it is
53 imperative to ensure physical capital decisions take into account climate resilience; not least because
54 retrofitting and replacing are both highly costly. The World Bank estimates that adapting over the period
55 2010-2050 to a world that is 2 癈 warmer by 2050 will cost $70 billion to $100 billion per annum, with the
56 infrastructure sector accounting for the largest share of costs (World Bank, 2010). At the same time, every $1
57 invested in preventive measures can save $5 of repairs (PRIF, 2013). While adequate financing and technical
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1 expertise are required, as well as foresight in planning and design and climate risk screening, successful
2 adaptation relating to physical capital also demands legal and institutional enablers (e.g., development and
3 enforcement of building codes and regulations; roll out of insurance options; planning restrictions to reduce
4 construction in locations that are highly exposed to climate hazards etc). In some situations, these are
5 lacking. For example, low-lying least developed countries such as Bangladesh, as well as small island
6 nations, regularly suffer from climate events such as floods, typhoons, cyclones, hurricanes and saline
7 intrusion (see chapter 15 on small islands). Hazards such as typhoons cause substantial damage and
8 destruction, impede mobility, reduce connectivity, disrupt communications, food, water and energy supplies
9 and render people homeless and without the assets they rely on to make a living. In the absence of adequate
10 legal and institutional enablers, as well as livelihood assets, it makes the maintenance of physical capital far
11 more challenging, as the case of Cyclone Aila in Box 8.8 demonstrates.
12
13
14 [START BOX 8.8 HERE]
15
16 Box 8.8: Cyclone Aila in Bangladesh: Impact, Adaptation and Way Forward
17
18 Historically, southern coastal Bangladesh, where the 1970 Bhola Cyclone killed 500,000 people, has been
19 considered among the most climate vulnerable environments on Earth. However in recent decades, extreme
20 weather events, like Cyclone Aila, though still destructive and destabilizing, have resulted in lower death
21 tolls thanks to a concerted investment in flood mitigation infrastructure, a dense network of cyclone shelters
22 and a robust early warning system (Chowdhury et al., 1993; Paul, 2009). Cyclone Aila struck the south-west
23 coast of Bangladesh on 25 May 2009 with a wind speed of 120km/hour (Islam and Hasan, 2016). With tidal
24 surges of up to 6.5 m, occurring over dry pre-monsoon soils, 11 coastal districts and more than 3.9 million
25 people were affected (United Nations, 2010), 190 people died, and 7,100 people suffered injuries (Saha,
26 2017).
27
28 Aila greatly damaged the region's physical capital, including 6000 km of roads and 17,000 km of
29 embankments. The cyclone polluted and damaged sources of drinking water and destroyed 243,000 houses
30 and thousands of schools (Mallick et al., 2017; Paul and Chatterjee, 2019). In Satkhira and Khulna districts
31 alone, 165,000 houses were destroyed and households were forced to live on damaged embankments in
32 makeshift shanties(UNDP, 2015). Many people had to live in these temporary shelters for years (Saha,
33 2017). Aila occurred during a high tide and the surge of saline water inundated not only the roads,
34 embankments and houses but also vast areas of agricultural field and shrimp farms (Paul and Chatterjee,
35 2019) leaving many areas waterlogged for months (Abdullah et al., 2016; Mallick et al., 2017). The effect of
36 saline water logging inside embankments caused further harm to houses, roads, and culverts, adding more
37 barriers to the post-disaster reconstruction activities (Roy, 2020). In the same area, tube-wells were damaged.
38 Women had to travel up to 2 km every day to collect safe water, spending 30�90 minutes on this activity
39 daily (Alam and Rahman, 2019). The distribution of costs across different socio-economic groups was not
40 always as expected. A study in Aila affected Koyra sub-district of Khulna found that households with higher
41 incomes were more vulnerable to Aila in both relative and absolute terms compared to middle- and low-
42 income groups mainly due to damage to shrimp farming which underpinned their livelihoods (Abdullah et
43 al., 2016). This highlights how specialised livelihoods can leave people more vulnerable as they have fewer
44 options. However, the same study found that the damage to physical capital such as fishing nets and boats
45 was statistically significantly greater for middle- and low-income groups. Damage to houses was statistically
46 significantly more among poorer households followed by middle and higher-income groups.
47
48 A range of coping and adaptation actions were enacted in response to losses of and damage to physical
49 capital (Table Box8.8.1). Actions varied across the different affected areas and were taken by the households
50 themselves, by the Government, and NGOs.
51
52
53 Table Box 8.8.1: Coping and adaptation actions enacted in the cyclone Aila affected area in response to losses of and
54 damage to physical capital
Coping and adaptation actions Action group References
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Human migration--mostly forced due to loss Households (Abdullah et al., 2016; Mallick et al.,
of houses as well as other resources and 2017; Paul and Chatterjee, 2019)
livelihood activities (Alam et al., 2015)
Alternative livelihood activities such as crafts, Households (Alam et al., 2015)
and honey and wood collection from the
Sundarbans, due to irreparable damage to
fishing gear
Saving money for house repairs or construction Households
Underground storage of emergency items such Households (Alam et al., 2015)
as foods, matchbox, cooker and cooking fuel
Selection of high land to build shelter along Households (Alam et al., 2015)
both sides of the embankments
Tree plantation in the homestead periphery to Households (Alam et al., 2015)
protect the house from gusty winds and to use
as a source of wood for house
repair/construction
Increasing height of the house plinth Households (Alam et al., 2015)
Changing of house roofing material from Households (Alam et al., 2015)
thatched to corrugated iron sheet or asbestos
Informally allowing people to harvest Forest (Abdullah et al., 2016)
Sundarbans forest wood without any charge so Department
they could make makeshift houses
Rainwater harvesting using plastic or clay pots NGOs and (Sultana and Mallick, 2015)
and artificial aquifer tube-wells for securing Households
drinking water.
Replacement of mud walls of houses with NGOs and (Sultana and Mallick, 2015)
wood or bamboo sticks to enhance durability households
Making thick shelterbelts along coastal NGOs and (Rahman and Rahman, 2015)
embankments households
1
2
3 The impacts of some of these adaptations, particularly engagement in new livelihood activities after Aila,
4 were varied, with income of the affected households increasing in some cases and decreasing in others. In
5 Koyra, the income of the poorest and middle-income households increased by 16 % and 4% respectively,
6 while the income of richer households (many of whom lost physical capital assets that they use to pursue
7 their livelihoods) decreased by 50% (Abdullah et al., 2016).
8
9 Research into adaptation projects led by various actors has shown that adaptations taken by the households
10 and community themselves are effective only to address typical challenges (such as seasonal shifts in
11 temperature or rainfall) but are less effective in addressing extreme events that have long-lasting impacts.
12 This is mainly due to lack of adequate resources and institutional support (Alam et al., 2015). At the same
13 time, some coping mechanisms are harmful in the longer term, for example, harvesting Sundarbans forest
14 wood after Aila for reconstruction could have negative impacts on the forest.
15
16 As of 2017, many of the affected areas had not yet been able to recover from the effects of Aila (Paul and
17 Chatterjee, 2019). A transformative approach needs to be taken not only to help them recover in livelihoods
18 terms, but also to support people's wellbeing. Suggestions of physical interventions that are needed include
19 higher and stronger dykes, cyclone-resistant housing, active maintenance and strict policing of embankment
20 use and good governance (Abdullah et al., 2016). Enabling formal institutions could help, for instance, by
21 improving the climate-resilience of physical capital (e.g., by developing and enforcing building codes for
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1 houses). Other institutional mechanisms could help to improve access to low interest credit, prevent
2 maladaptation, improve enforcement of laws, and provide insurance. However, such institutional reforms
3 need to be co-developed with local people and incorporate local cultural mechanisms (Islam and Nursey-
4 Bray, 2017). Future adaptation strategies also need to take into account the limits to autonomous adaptation
5 (i.e. that without external intervention) and differential level of impacts and adaptive capacities among
6 different groups of households in the Aila affected areas. This example illustrates the importance of a more
7 comprehensive approach to resilience building, and the need to better understand the interlinkages between
8 the core components of an enabling environment for adaptation (see Figure 8.12).
9
10 [END BOX 8.8 HERE]
11
12
13 Physical capital in the form of technology is increasingly supporting climate change adaptation, despite that
14 innovations can be rolled out under high uncertainty, opening up new risks (e.g., hacking). Moreover,
15 deployment of technology is closely tied to other forms of capital, especially human capital, and innovations
16 cannot just be rolled out in the absence of suitable institutional and technical support and training. Similarly,
17 access to finance is vital. Some technological adaptations require a pre-existing level of infrastructure and
18 literacy, raising important questions about inequality (Taylor, 2018). Rotz et al. (2019) warn of automation
19 impacts on rural labour, especially in places with high youth unemployment, while Taylor (2018) notes that
20 social classes and gender are impacted differently by technological change, and failure to address underlying
21 inequalities will shape who becomes vulnerable. Adequate testing of technologies in terms of their
22 applicability to different contexts is also required, ensuring they do not become maladaptive when applied at
23 scale.
24
25 Similarly, technology must always be grounded in an appreciation of the cultural context. Research in the
26 European Arctic with the Indigenous Sami Peoples found that use of GPS technology on reindeer, together
27 with supplementary feeding, offer useful adaptations for some herders. However, there are fears such
28 technologies may, over time, reduce the skills, cultural knowledge and Indigenous adaptations of the Sami
29 (Andersson and Keskitalo, 2017), as, for example, reindeer become more tame through supplementary
30 feeding, affecting their range selection. Overall, technology and other adaptations should seek not to erode
31 Sami culture's adaptive capacity (Vuojala-Magga et al., 2011; Risvoll and Hovelsrud, 2016), particularly
32 because reindeer grazing as a land management practice can play a useful climate change mitigation role too.
33 Reindeer grazing protects tundra from tree line and bush encroachment, while summer grazing increases
34 surface albedo by delaying snowmelt (Jaakkola et al., 2018).
35
36 8.5.2.4.1 Socio-cultural factors
37 Social and cultural factors are closely linked to values, beliefs and identities (Heimann and Mallick, 2016)
38 and mediate the ways in which people respond to climate variability and change (Adger et al., 2013). There
39 is limited evidence but medium agreement about the importance and role of social and cultural factors in
40 shaping adaptation, in terms of both the need to adapt and the way it is presented and communicated,
41 although evidence is somewhat mixed in terms of how experiences of weather affect opinions and
42 perceptions of climate change (Howe et al., 2019). Research also highlights the importance of context in
43 understanding relations between perceptions of risks and behaviour, arguing that power relations and other
44 obstacles and opportunities play a vital role in shaping actions (Rufat et al., 2020). In general, nonetheless,
45 adaptation is spurred when people perceive that there is an action they can take to make a difference
46 (Kuruppu and Liverman, 2011; Mayer and Smith, 2019), although it cannot be assumed that action will be
47 taken if the socio-cultural setting is not amenable and it contravenes the values underlying people's
48 perceptions (Kwon et al., 2019). Research testing for the effect of beliefs on behavioural change from 48
49 countries highlighted the need for policy leaders to present climate change as solvable yet challenging, if
50 fatalistic beliefs that act as barriers to adaptation were to be reduced (Mayer and Smith, 2019). This
51 demonstrates how beliefs do not always reinforce actions, even when risks are perceived. Similarly, research
52 from Burkina Faso working with the Fulbe ethnic group found that cultural norms restricted engagement in
53 four of the most successful livelihood strategies that support adaptation to climate change (labour migration,
54 working for development projects, gardening and female engagement in economic activities) (Nielsen and
55 Reenberg, 2010). Cultural factors therefore play an important but under-researched role in adaptation.
56
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1 Social factors in the context of adaptation, by contrast, are more widely studied. The literature on adaptation
2 and the role of social capital as an enabler is diverse. There is high confidence that during disasters, social
3 capital plays an important role in linking those who are affected to external supports and resources, while on
4 small islands social networks can be dense and support adaptation (Petzold and Ratter, 2015), with
5 traditional knowledge and societal cohesion helping small island communities to have self-belief and build
6 resilience even in the absence of external interventions (Nunn and Kumar, 2018). Even the development of
7 weak ties (e.g., one-way information transfer) can lead to establishment of mutual collaboration relations that
8 can be more easily draw on in times of climate change related shocks and stresses (Ingold, 2017), while
9 collective shared disaster experiences can cause new social groups to emerge and spur action, linked to a
10 perceived common fate (Ntontis et al., 2020). However, this can exacerbate inequalities and create new ones,
11 with those who are more connected having enhanced access to, for example, shelters following storm
12 evacuations or earthquakes (Rahill et al., 2014). In adapting to more incremental changes, social capital has
13 been shown to increase shared Local Knowledge and awareness, support participatory processes and
14 strengthen ties to corporate and political institutions, increasing their responsiveness to local concerns, as
15 shown by examples from Aldrich et al. (2016). They describe how in Houma, Louisiana, located west of
16 New Orleans, rising sea levels and hurricane risks have drawn on and built social capital at the community
17 level. Having what was perceived locally as insufficient federal government support, residents, church
18 groups and town council members collaborated to spur adaptation. Community mobilisation led to
19 construction of self-funded levees and water projects to protect 200,000 residents from storm surges. Projects
20 include marshland restoration, the elevation of existing housing, improved pumping systems and canal
21 drainage, as well as buyouts and relocations of businesses and housing that has been repetitively damaged.
22 Funds were raised from households through donations via a self-imposed sales tax. While this example
23 paints a positive picture of the role of social capital and collective action in adaptation activities, it also raises
24 questions about the coherence of actions across levels, again, highlighting a role for polycentric governance
25 if risks of maladaptation are to be reduced. The danger in the example presented here is that should federal
26 plans in future conflict with the community level work, local efforts may have been in vain if installations
27 have to be removed. This highlights the importance of careful evaluation of all adaptation options on an
28 ongoing basis.
29
30 Further warnings about social capital as an adaptation enabler come from Acosta et al. (2016) who recognise
31 that it may be detrimental to private adaptation in some cases. Their research in rural Ethiopia found that
32 qualitative measures of trust predict contributions to public goods, supporting theories about collective
33 action, but that the effects of social capital are not homogenous: it can be helpful in some contexts, but
34 unhelpful, or even detrimental in others. This led them to highlight the need for policymakers to consider
35 these potentially different outcomes. Other research, also from Ethiopia, suggested that households with
36 more social capital are more specialised in their livelihood strategies. This could leave them more vulnerable
37 to climate change impacts (as per the cyclone Aila example where shrimp farmers were specialised and hit
38 hardest by the cyclone's impacts), though social capital acts as a kind of informal insurance (Wuepper et al.,
39 2018).
40
41
42 8.6 Climate Resilient Development for the Poor and Pro-poor Adaptation Finance: Ensuring
43 Climate Justice and Sustainable Development
44
45 This section evaluates climate-resilient development (CRD) focussing on potential synergies between
46 adaptation and mitigation in different sectors, decision making approaches and adaptation finance especially
47 for the poor. It examines whether climate change response options, meaning mitigation and adaptation, in
48 different development sectors, create development synergies or trade-offs for low-income households and
49 people living in poverty.
50
51 The link between development and climate change was not evaluated comprehensively until the first decades
52 of the twenty first century (Figure 8.13; Klein et al., 2005; Tol, 2005). Until recently mitigation and
53 adaptation, the two primary approaches to climate action, have been dealt with separately in climate change
54 science and policy (Landauer et al., 2015). Nevertheless, synergistic "co-benefits" between mitigation and
55 adaptation may be enhanced, and trade-offs reduced, through the holistic empirical evaluation of actions for
56 climate change response (Runhaar et al., 2018). The synergetic effect of mitigation and adaptation has been
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1 documented for a few interventions across the globe, however, evidence-based quantification of the
2 synergies and trade-offs are rare.
3
4 Where co-benefits have emphasized identifying mitigation-adaptation synergies, a key turn has been
5 evaluating Climate Compatible Development (CCD), `development that minimises the harm caused by
6 climate change impacts, while maximising the many human development opportunities presented by a low
7 emission, more resilient future' (Mitchell and Maxwell, 2010). CCD calls for triple wins, resulting in
8 synergies between mitigation-adaptation-development through single interventions (Figure 8.13; Ellis and
9 Tschakert, 2019). Climate compatible development offers specific entry points for identifying ways on how
10 to strengthen synergies between mitigation and adaptation particularly within the context of low income
11 countries. Effective integration of emission reductions and accommodation actions for mitigation and
12 adaptation can be win-win strategies and may be cost-efficient (Runhaar et al., 2018) and have the potential
13 to create opportunities to foster sustainable development (Denton et al., 2014).
14
15
16
17 Figure 8.13: Climate Resilient Development (CRD). Actions and strategies consider both Climate Compatible
18 Development and Climate Action.
19
20
21 This assessment identifies and evaluates approaches to Climate Resilient Development (CRD) "that
22 deliberately adopt mitigation and adaptation measures to secure a safe climate, meet basic needs, eliminate
23 poverty and enable equitable, just and sustainable development". The body of literature on the synergies and
24 trade-offs between adaptation, mitigation, poverty, equity and sustainable development has grown steadily
25 since the AR5 (IPCC, 2014a). The IPCC Special Report on the impacts of global warming of 1.5癈 (IPCC,
26 2018c), suggests that `Limiting warming to 1.5癈 can be achieved synergistically with poverty alleviation
27 and improved energy security and can provide large public health benefits through improved air quality,
28 preventing millions of premature deaths'.
29
30 Implementing the integrative concept of CRD will likely produce transformative benefits affecting the
31 poorest populations primarily (Roy et al., 2018; Leal Filho et al., 2019). The risks of transformative actions
32 to the poor are diminished when undertaken in the context of good governance at multiple levels, within
33 existing top-down and bottom-up processes, and making use of available levers of policy, technology,
34 education and financial/economic systems (Stringer et al., 2020).
35
36 8.6.1 Synergies and trade-offs between adaptation and mitigation in different sectors with implications
37 for poverty, livelihoods and sustainable development
38
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1 8.6.1.1 Climate Resilient Development
2
3 Climate Resilient Development relies on identifying synergies between different strategies and actions in the
4 field of climate change, primarily between mitigation actions with adaptation benefits (Locatelli et al., 2015),
5 adaptation actions with mitigation benefits (Denton et al., 2014; S醤chez and Izzo, 2017), processes that
6 promote both mitigation and adaptation measures, and policies and strategies that promote integrated
7 mitigation and adaptation measures (Zhao et al., 2018). At the same time, adaptation and mitigation actions
8 can be evaluated in terms of their co-benefits, the social, economic or other benefits of actions in addition to
9 avoiding climate change impacts (Karlsson et al., 2020). The clearest co-benefits of mitigation are associated
10 with economic development through low-carbon industrialization (IPCC, 2014c; Jakob et al., 2014; Lu,
11 2017). Co-benefits can include contributing to economic growth, reducing competition for resources,
12 improved integration of scientific input to policy development and implementation, or improving political
13 participation and social licensing in large-scale projects (e.g., hydropower) (Hennessey et al., 2017).
14 Adaptation can support mitigation and contribute to co-benefits in various ways: ensuring development-
15 based natural resource management (Denton et al., 2014; Suckall et al., 2015; Reang et al., 2021) ,
16 integrating water resources management (Liang et al., 2016; Sharifi, 2021), practicing sustainable agriculture
17 (Bustamante et al., 2014; Duguma et al., 2014a; Di Gregorio et al., 2017; Reang et al., 2021), ensuring the
18 protection of ecosystem services (Pandey et al., 2017a; Baumber et al., 2019), conserving biodiversity (Di
19 Gregorio et al., 2017; Loboguerrero et al., 2019; Smith et al., 2019) and managing bioenergy resource
20 (Dovie, 2019).
21
22 The key challenge for CRD is addressing climate change from the perspective of development: addressing
23 the fundamental development obstacles that limit capacity for adaptation. Where development is not
24 sustainable, especially if it is not equitable, capacity for adapting is greatly reduced--a phenomenon known
25 as the adaptation gap (Figure 8.14; Birkmann et al., 2021a; UNEP, 2021). Figure 8.14 depicts the effect of
26 development trajectories (as described in the Shared Socioeconomic Pathways framework) on capacity for
27 adaptation, a key determinant of eventual outcomes. Achieving CRD through coupling adaptation with
28 equitable sustainable development under and low emissions profiles that limit warming to 1.5癈 (i.e.,
29 sustainability scenario) is necessary to close the adaptation gap. Even if emissions are kept low and 1.5癈
30 emissions targets are achieved, if poverty and inequality remain high, then impacts are likely to remain high
31 and may overwhelm capacity for adaptation. High poverty and high inequality in a society (i.e., inequality
32 scenario) reduce the likelihood that countries are able to manage risk and avoid residual impacts, such as also
33 documented in the assessment above (see Sections 8.2, 8.3, 8.4). Unsustainable development trajectories
34 reduce capacity for adaptation and may result in highly unequally distributed residual impacts from climate
35 change. Even despite rapid, equitable development and modest emissions reductions efforts necessary to
36 limit warming to 2癈 (i.e. the middle of the road scenario), there is still risk of unequal distribution of
37 impacts. Under all high emissions scenarios (>3癈 warming), universal residual impacts are unavoidable.
38
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1
2 Figure 8.14: Conceptual figure illustrating the link between sustainable development and the adaptation gap. Even if
3 emissions are kept low, if poverty and inequality remain high, then impacts are likely to remain high and may
4 overwhelm capacity for adaptation.
5
6
7 Mitigation planning has not sufficiently considered poverty reduction policies, the basis for narrowing the
8 adaptation gap (see also Figure 8.14). Many synergies between climate change mitigation and poverty
9 reduction have been identified, although sometimes with limited evidence. The mitigation measures that have
10 been most evaluated include clean development mechanisms (CDM), programs aimed at reduction of
11 emissions from deforestation and forest degradation (REDD+), voluntary carbon offsets and biofuel
12 production. However, while these mitigation programs stimulate economic growth, they may contribute to
13 processes that trade-off against equitable development and threaten to further impoverish forest
14 communities, such as large-scale land acquisitions (Carter et al., 2017; Schaafsma et al., 2021) and fortress
15 conservation (see IPCC SR 1.5癈 Chapter 5 (Roy et al., 2018) and see also Chapter 6 of this report).
16
17 The IPCC Special Report on Climate Change and Land (IPCC, 2019a) states that agriculture, food
18 production and deforestation are major drivers of climate change and calls for coordinated action to tackle
19 climate change that can simultaneously improve land, food security and nutrition, and help to end hunger.
20 There are five land challenges identified including climate change mitigation, adaptation, desertification,
21 land degradation and food security. This report identified three major categories of climate response options
22 that show promise for achieving mitigation and increasing capacity for adaptation while addressing poverty:
23 sustainable land management options, value chain management and risk management options (IPCC, 2019a).
24 For example, programs supporting no-till agriculture and residue retention allows small-scale farmers to
25 participate in mitigation and adaptation activities, with long-term benefits to soil health and food, energy and
26 water security (Wright et al., 2014). Likewise, the installation of a solar powered drip irrigation system
27 simultaneously reduces emission, improves water security and increases farmers' income; (Locatelli et al.,
28 2015). Response options in terms of sustainable land management options, and value chain and risk
29 management involves interlinkages between land-based climate strategies, synergies and trade-offs (see
30 Chapter 6). On the other hand, a key trade-off for consideration CRD is the potential for maladaptation,
31 where one adaptation intervention at one time, location or sector could increase the vulnerability at another
32 time, location or sector, or increase the vulnerability of the target group to future climate change (medium
33 evidence, high agreement) (Eriksen et al., 2011). A cause of increasing concern to adaptation planners, the
34 understanding of maladaptation has changed subtly to recognize that it arises inadvertently, from poorly
35 planned adaptation actions, but also from carefully deliberated decisions where wider considerations place
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1 greater emphasis on singular or short-term outcomes ahead of broader, longer-term threats, or that discount,
2 or fail to consider, the full range of interactions arising from the planned actions across scales (Eriksen et al.,
3 2021). Research identifies the challenge of avoiding maladaptation as one of reducing long-term structural
4 vulnerability. Accordingly, one can consider that CCD and maladaptation as two sides of the same coin.
5 Scholars of `sustainable adaptation' define it as adaptation that contributes to socially and environmentally
6 sustainable development pathways, which takes into account both social justice and environmental integrity
7 (Eriksen et al., 2011). The parallels in maladaptation include the underlying drivers of vulnerability, namely
8 socio-environmental processes such as conflict, marginalization, economic restructuring, exploitation,
9 institutional fragility, etc (Antwi-Agyei et al., 2018b; Neef et al., 2018).
10
11 Harnessing opportunities for mitigation, adaptation and development in an effective manner may lead to
12 `triple-wins' under CRD, though empirical evidence is extremely rare for such `triple-wins' strategies that
13 address mitigation, adaptation and development in an effective manner (Tompkins et al., 2013). Integration
14 of mitigation, adaptation and development is being initiated and operationalised through projects by several
15 developing countries for achieving main national development priorities, such as poverty reduction,
16 increased employment opportunities, energy security, transportation (Denton et al., 2014; Stringer et al.,
17 2014). Important follow-on questions from that are pressing social questions about how trade-offs are
18 deliberated, who wins and losses and who decides (see Section 8.4 and Ellis and Tschakert, 2019). Likewise,
19 the efficiency, effectiveness and feasibility trade-offs of climate policies must be considered (i.e., can
20 programs in developing countries be economically efficient and provide opportunities to achieve sustainable
21 development targets for developing countries?) (Dang et al., 2003). Moreover, questions about co-benefits
22 must consider the benefit-cost ratio of mitigative versus adaptive action for assets saved from destruction by
23 climate impacts, for example (Stadelmann et al., 2014). Implementing a mitigation or adaptation option may
24 affect positively or negatively, directly or indirectly, the feasibility and effectiveness of other options such as
25 soil management leads to soil organic carbon (Locatelli et al., 2015; de Coninck et al., 2018). Farmers and
26 local people are often also being encouraged to undertake mitigation and adaptation activities leading to long
27 term benefits such as cultivation of no-till wheat with residue retention leading to low emission along with
28 energy and water saving (Wright et al., 2014).
29
30 Moreover, regulatory structure for evaluation of mitigation and adaptation actions is required for
31 understanding the co-benefits of these two actions such as choice of adaptation actions can be made
32 according to their effectiveness per unit of money invested such as economic assets saved from destruction
33 of climate change impacts and benefits can be evaluated in terms of economies, people, and the environment
34 such as human lives and health protected contrary to the emission reduction by mitigation strategies
35 (Stadelmann et al., 2014). .
36
37 8.6.1.2. Climate Resilient Development Synergies and Trade-offs by Sector
38
39 Some sectors--such as agriculture, forestry, energy--are found to have more potential for CRD synergies
40 than others, although trade-offs are also identified. Climate-smart agriculture, carbon-forestry programmes
41 and the water-energy-climate nexus show trade-offs across levels and sectors with identified winners and
42 losers (high confidence) (IPCC, 2018a). Mitigation can be designed to provide opportunities for enhanced
43 adaptation with comparable co-benefits, even while adaptation portfolios can maximize co-benefits around
44 sustainable resource management that reduce emissions (Dovie, 2019). Climate policy integration can be
45 considered as the integration of multiple policy objectives, governance arrangements and policy processes of
46 climate change mitigation and adaptation along with other policy domains (Di Gregorio et al., 2017) as well
47 as sector policies integrating climate change adaptation and mitigation (England et al., 2018). Integrating
48 climate policies may require balancing multiple sectoral goals, such as REDD+ projects, climate smart
49 agriculture, water sector strategies, national policies on climate change and national conservation plans
50 (Duguma et al., 2014a). Within the scientific discourse increasing attention is given to the question of the
51 synergies and mismatches between mitigation and adaptation policies.
52
53 The assessed literature underscores that for synergies to be realized, mitigation and adaptation policies must
54 be institutionally supported within a multi-level governance architecture (national to sub-national to
55 municipal levels) with other priorities, and identify sustainable financing mechanisms within the country or
56 via the international community (Dovie and Lwasa, 2017). Integrating and mainstreaming adaptation and
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1 mitigation across agencies within countries can bridge the divide between climate policy and sustainable
2 development (Venema and Rehman, 2007).
3
4 The Paris Agreement recognized that the agreement will reflect equity and common but differentiated
5 responsibilities (CBDR-RC) of national circumstances, (Voigt and Ferreira, 2016). The Paris Climate
6 Agreement should be broadened to include mitigation co-benefits (Dovie, 2019). Integrating adaptation with
7 mitigation may possibly contribute to amend or reduce the discursive rift between climate policy and
8 sustainable development (Venema and Rehman, 2007).
9
10 Integrated climate change actions or responses can be inefficient and infeasible in the absence of enabling
11 conditions, including the policy conditions that reinforce unified climate action, and sustainable financial
12 mechanism for implementation of the programs and policies (Duguma et al., 2014b). In the absence of strong
13 coordination, integrating mitigation and adaptation may undermine the overall or individual objectives of
14 either climate response (Kongsager, 2018). A lack of coordination in mitigation and adaptation may also
15 exacerbate the threats of climate change to sustainable development (Ayers and Huq, 2009; Kongsager,
16 2018). Therefore, for successful integration of CRD, it is necessary to move beyond considering either
17 adaptation or mitigation towards better understanding the linkages between adaptation and mitigation
18 projects and policies at multiple levels of governance, to identify potential trade-offs in projects and policies
19 (Suckall et al., 2015) and to identify the enabling conditions for designing and implementing action leading
20 to synergies (Denton et al., 2014; Kongsager, 2018).
21
22 Despite the potential effectiveness and efficiency of integrating mitigation and adaptation under a common
23 CRD framework, gaps persist in our knowledge about the enabling conditions for synergies, due to the
24 limited number of examples and even fewer evaluations. Potential benefits may be achieved by pursuing
25 multi-level governance approaches, that means integrating decision-making at the local level with
26 coordination at other levels, by actors and agencies simultaneously pursuing multiple other priorities (see
27 Section 8.5.2 Shaw et al., 2014). For example, pursuing climate-resilient land-use pathways integrating
28 climate policy within the land use sector requires a governance policy environment that combines multiple
29 policy aims, including urban growth, soil conservation and water management alongside mitigation and
30 adaptation. Facilitating climate resilient land use pathways combining the aims of climate change adaptation,
31 mitigation and sustainable development requires a governance environment requires: i) internal climate
32 policy coherence between mitigation and adaptation objectives and policies; ii) external climate policy
33 coherence between climate change and development objectives; iii) vertical policy integration that to
34 mainstreams climate change into sectoral policies; and; iv) overarching governance structures that facilitate
35 horizontal policy integration cross-sectoral coordination by overarching governance structures for cross-
36 sectoral coordination (Di Gregorio et al., 2017) as well as sector policies integrating climate change
37 adaptation and mitigation (England et al., 2018).
38
39 Within sector policies and economic sectors (such as land-use, transportation, and technology) mitigation
40 and adaptation have many positive, negative, direct and indirect linkages within and beyond the sector
41 (Locatelli et al., 2015). The land-use sector, for example, includes agriculture and forestry and encompasses
42 the management of a mosaic of interacting urban environments and ecosystems with a diversity of cultural
43 and institutional attributes (Locatelli et al., 2015). The land-use sector is key to climate adaptation, where
44 policy coordination can enhance food production, regulate urban microclimates, affect water security, and, in
45 the case of mangroves, buffer the impacts of extreme climate events in coastal areas (Locatelli et al., 2015).
46 City-level actions can also be pivotal for reduction in emissions and improvement in resilience (UCLG,
47 2015) such as zoning and planning that promotes green development and green and efficient energy use.
48 Urban planning and transport policies are crucial to support a transition towards a low-carbon and resilient
49 future (Ford et al., 2018) such as means of transportation as public and private transport facilities are crucial
50 for emission reduction.
51
52 CRD may require multi-sectoral coordination, including public-private partnerships (Campbell et al., 2018).
53 In the food system, for example, under a CRD framework transformative actions may require (1) incentives
54 for expanded private sector activities and/or public-private partnerships; (2) publicly-backed credit and/or
55 insurance; (3) public institutional support for strong local organisations and networking; (4) climate-
56 informed weather advisories and early warning systems; (5) digital investments in technological
57 transformation for agriculture (e.g., "digital agriculture" and virtual markets); (6) investments in climate-
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1 resilient and low-emission practices and technologies (Duguma et al., 2014b); (7) prioritisation and pathways
2 of change; (8) capacity and enabling policy and institutions are crucial with careful consideration of trade-
3 offs between adaptation and mitigation, and amongst other SDGs for achieving SDG13 `urgent action to
4 combat climate change and its impacts' (Campbell et al., 2018). Moreover, the risks of transformative
5 actions to the farmers is addressed by strong good governance at multiple levels, combining top-down and
6 bottom-up processes along with by a mix of levers that combine policy, technology, education and
7 awareness-raising, dietary shifts and financial/economic mechanisms, attending to multiple time dimensions
8 (Stringer et al., 2020).
9
10 8.6.1.2.1 Agriculture and food production
11 Integrated CRD approaches in agriculture, such as climate smart agriculture (CSA), can reduce trade-offs
12 and exploit synergies with biodiversity and food security to reduce the risk of climate change (Di Gregorio et
13 al., 2017; Loboguerrero et al., 2019). There are many technologies and approaches in agriculture that
14 leverage synergies relevant for CRD, including agroecology (Pandey et al., 2017a; Saj et al., 2017), climate
15 smart agriculture (CSA), climate smart landscapes, organic agriculture mitigating climate change,
16 conservation agriculture, ecological intensification and sustainable intensifications, which in many cases aim
17 to address both adaptation and mitigation to climate change simultaneously (Kongsager, 2018). From these
18 approaches, a number of scalable agriculture technologies have emerged that simultaneously achieve
19 mitigation and adaptation goals, such as reducing water consumption while maintaining grain yield,
20 including alternate wetting and drying (AWD) irrigation technology (Liang et al., 2016) and aerobic rice
21 production (Wichelns, 2016). Likewise, a number of these approaches have been supported within
22 international and national institutional frameworks (e.g., through incentives) to harness synergies (Kongsager
23 et al., 2016).
24
25 Climate-smart agriculture (CSA) is discussed in the scientific literature as an approach that could transform
26 agricultural production systems and food value chains in line with sustainable development and food security
27 under climate change. However, concerns and critique have been raised, such as the insufficient
28 consideration of the access to entitlements within CSA and the question who wins and loses when applying
29 CSA in different country contexts (see Karlsson et al., 2017; Sain et al., 2017). CSA has three main
30 objectives: sustainably increase agricultural productivity and incomes; adapt and build resilience to climate
31 change and reduce and/or remove greenhouse gas emissions (FAO, 2017). Various CSA technologies are
32 capable of improving crop yields, increasing net income, increasing input use efficiencies and reducing
33 emissions (Khatri-Chhetri et al., 2017). However, up-take and adoption of CSA by local farmers in poor
34 developing countries remains a challenge (Palanisami et al., 2015) due to the difficulty of identifying and
35 prioritising of technologies suiting local climate risks and accommodating the farming practices of locals
36 (Dougill et al., 2017; Khatri-Chhetri et al., 2017). An analysis of CSA implementation in Mali, for example,
37 identified major challenges to policy makers' efforts to adopt CSA, including difficulties identifying CSA
38 options and portfolios, valuing them, and prioritizing investments (Andrieu et al., 2017).
39
40 Potential opportunities from CSA may also result from Integration of "technological packages" (Totin et al.,
41 2018), which include new market structures; knowledge infrastructure and agriculture extension services;
42 and capacity building programs (Dougill et al., 2017; Totin et al., 2018); institutional support for key
43 enabling programs, such as crop insurance, agro-advisories and rainwater harvesting (Khatri-Chhetri et al.,
44 2017). CSA is able--if carefully designed--to achieve transformative "triple wins" for climate and
45 development when it is accompanied by new governance architectures that are socially inclusive and
46 respectful of traditions and livelihoods, and accommodate traditional institutions that underpin the
47 bargaining power of the poorest and most vulnerable groups (Karlsson et al., 2017).
48
49 Conservation Agriculture (CA), another framework for achieving CRD, is based on three synergistic
50 principles: a) soil management to reduce soil physical disturbance and reduce its degradation; b) crop
51 management such as residue management to protect the soil top layers; and c) genetic management to
52 increase agricultural systems' biodiversity and in consequences their resilience (DeLonge and Basche, 2017).
53 In the cereal systems of the Indo-Gangetic Plains, India, Conservation Agriculture has increased crop yields,
54 returns from crop cultivation, input-use efficiency, in spite of heat stress even while reducing GHGs
55 emissions (Sapkota et al., 2015). However, also challenges with CA are documented in the scientific
56 literature. For example, an evaluation of CA in Malawi noted that adoption of CA was challenged by weak
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1 integration of CA in agricultural policies; lack of institutional arrangements of promoters; and farmers'
2 experiences (Chinseu et al., 2019).
3
4 Locally appropriate agroecological practices have clear potential to increase the resilience of livelihoods and
5 enhance adaptation to climate change at field and farm levels across a wide range of contexts, often with
6 significant mitigation co-benefits (Sinclair et al., 2019). Relatedly, agroforestry systems are the intentional
7 integration of trees and shrubs into crop and animal production systems to solve societal challenges
8 including climate change (Raymond et al., 2017). For example, in the tropics, such systems offer viable
9 opportunities to mitigate and adapt to climate change for farmers through transforming into resilient farming
10 systems and improving farm economy while securing environmental benefits to local and global
11 communities (Swamy and Tewari, 2017). In Western Africa, the high plant functional diversity of
12 agroforestry systems with a mix of trees and crops having different roles, such as shade provision, soil
13 fertilization, fruit production, or timber value, maximises benefits and allows alternative adaptation strategies
14 (Tschora and Cherubini, 2020). In spite of various benefits of agroforestry, the expansion of existing areas of
15 agroforestry and the establishment of new agroforestry systems has remained limited (Martineau et al.,
16 2016), mainly due to a lack of institutional supports, a lack of expert support to ensure adequate
17 management, weak capacity for monitoring and regulation, and a lack of financial support (Hern醤dez-
18 Morcillo et al., 2018).
19
20 The enabling conditions for the expansion of agroforestry include training and expert support programmes
21 for managers and sharing of best practices (Ashraf et al., 2015; Hern醤dez-Morcillo et al., 2018; Tschora and
22 Cherubini, 2020). Other scalable frameworks integrating food and agriculture within CRD include
23 Sustainable Intensification (SI), which emphasizes sustainable practices to safeguard sustainable use of
24 natural resources, and meet the growing demand for agricultural production, even while building resilience
25 (Thierfelder et al., 2018). Integrated Agricultural Systems (IAS) aim to increase farm diversity and lower
26 reliance on external inputs, enhancing nutrient cycling and increasing natural resource use efficiency (Smith
27 et al., 2017), and may have the potential to enhance resilience against climate change impacts and risks (Gil
28 et al., 2017). Policy frameworks that aim to integrate any of these approaches climate actions must account
29 for the costs associated throughout the up-take and adoption process (Gil et al., 2017).
30
31 8.6.1.2.2 Livestock
32 As the consumption of animal protein and products rises along with global standards of living, CRD will
33 require transformations in livestock-centred livelihoods. Livestock are a key contributor to global food
34 security especially in marginal lands where animal products are a unique source of energy, protein and
35 micronutrients (FAO, 2017; IPCC, 2019a), but also contribute disproportionately to the total annual
36 anthropogenic GHG emissions globally and influence climate through land use change, processing and
37 transport by emitting CO2; animal production by increasing methane emissions; and feed production,
38 manure by emitting CO2, nitrous oxide, and methane, (Rojas-Downing et al., 2017). Mitigation of livestock
39 emissions can be achieved by implementation of various technologies and practices such as improving diets
40 to reduce enteric fermentation, improving manure management, improvement in animal nutrition and
41 genetics (Rojas-Downing et al., 2017); altering land use for grazing and feed production, feeding practices,
42 manure treatment and herd size reduction (Zhang et al., 2017). Adaptation strategies in the livestock sector
43 include changes in animal feeding, genetic manipulation, alterations in species and/or breeds (Zhang et al.,
44 2017); shifting to mixed crop-livestock systems (Rojas-Downing et al., 2017), production and management
45 system modifications, breeding strategies, institutional and policy changes, science and technology advances,
46 and changing farmers' perception and adaptive capacity (USDA, 2013).
47
48 Policies supporting sustainable rangeland management and the livelihood strategies of rangeland users have
49 an outsized influence on both development and climate action (Gharibvand et al., 2015). Climate change
50 adaptation, mitigation practices and livestock production can be supported by policies that encourage
51 diversification of livestock animals (within species), support sustainable foraging and feed varieties (Rivera-
52 Ferre et al., 2016), strengthen institutions such as agricultural support programs, markets and intra- and inter-
53 regional trade (Zhang et al., 2017). For example, sustainable pastoralism can contribute to mitigation both by
54 increasing carbon sequestration through improved soil management and by reducing methane emissions
55 through changing the mix and distribution of the herd. Likewise sustainable pastoralism can also contribute
56 to adaptation by changing grazing management, introducing alternative livestock breeds, pest management,
57 and modified production structures (Joyce et al., 2013). Another example of rangeland adaptation is
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1 diversifying the use of rangelands such as supplementing with payments for ecosystem services, carbon
2 sequestration, tourism or supplementary assistance for all land based activities (Gharibvand et al., 2015).
3 However, challenges for climate smart livestock production systems remain due to a lack of information,
4 limited access to technology and insufficient capital (FAO, 2017). Small-holders in cropping and livestock
5 systems in Saharan Africa and South Asia, for example, face obstacles obtaining climate change mitigation
6 and adaptation synergies due to poor access to markets and relevant knowledge, land tenure insecurity and
7 the common property status of most grazing resources (Descheemaeker et al., 2016). Consequently, the
8 appropriateness of these strategies and measures need to be further evaluated, particularly in terms of their
9 usefulness for the poor and most vulnerable.
10
11 Different farming and pastoral systems can achieve reductions in the emissions intensity of livestock
12 products. Depending on the farming and pastoral systems and level of development, reductions in the
13 emissions intensity of livestock products may lead to absolute reductions in GHG emissions (IPCC, 2019a)
14 (medium confidence). Significant synergies exist between adaptation and mitigation, for example, through
15 sustainable land management approaches (high confidence). {4.8, 5.3.3, 5.5.1, 5.6}.
16
17 8.6.1.2.3 Forestry
18 Forests can support CRD in rural communities and households: they support consumption of energy, food
19 and fibre; provide a safety net in cases of shocks; fill gaps during seasonal shortfalls; and are a means to
20 accumulate assets and provide support to emerge out of poverty (Angelsen et al., 2014; Adams et al., 2020).
21 Forest ecosystems are an essential element of climate change mitigation and adaptation, with the potential
22 for synergy and conflict between the two climate action objectives (Morecroft et al., 2019). However, there
23 are varied perspectives on the role of the forests, with some treating conservation and forest management
24 practices as a barrier to livelihood resilience (Few et al., 2017) despite the broader role of forest management
25 in climate mitigation (Houghton, 2012).
26
27 Forestry mitigation projects such as forest conservation, reduced deforestation, protected area management
28 and sustainable forest management, can promote adaptation and can also have consequences for the
29 development objectives of other sectors (for example, expansion of farmland) (Smith et al., 2014). REDD+
30 (reducing emissions from deforestation and forest degradation and fostering conservation, sustainable
31 management of forest and enhancement of carbon stocks) is a payment programmes and may provide
32 adaptation benefits by enhancing households' economic resilience (Sills et al., 2014; Duchelle et al., 2018)
33 and also produce positive livelihood impacts through the employment benefits of supporting conservation
34 and sustainable management of forests (Caplow et al., 2011). Furthermore, the management of ecosystem
35 services may contribute to both mitigation and adaptation. For example, REDD+ projects, such as mangrove
36 conservation and restoration simultaneously contribute to carbon storage and diversification of incomes and
37 economic activities. At the same time, mangroves protect coastal areas against flooding and hydrological
38 variations, improving capacity for adaptation in local livelihoods (Locatelli et al., 2016).
39
40 However, while studies of existing REDD+ programs noted the moderately encouraging impacts for
41 mitigation and small or insignificant impacts for adaptation options (especially well-being), they underscored
42 the potentially damaging impacts to local livelihoods (Milne et al., 2019; Skutsch and Turnhout, 2020) and
43 suggested improved engagement with local communities, increased funding to strengthen the interventions
44 on the ground, and more attention to both mitigation and adaptation outcomes in implementation for
45 achieving the benefits of REDD+ program (Duchelle et al., 2018). Moreover, to effectively counter local
46 threats to forests and biodiversity and attain positive biodiversity and development outcomes, REDD+
47 programs must be focused on better institutional support for governance, coordinating interventions and
48 monitoring of plans, as well as making explicit linkages between REDD+ activities and national biodiversity
49 conservation efforts (Panfil and Harvey, 2016) and assuring a fair distribution of benefits to local
50 communities (Myers et al., 2018). An analysis of country-specific REDD+ programs in Cameroon for
51 synergistic approaches to REDD+ with other national goals such as poverty reduction identifies two
52 principal modes of strategic interaction management among actors. The first prioritizes relates to specific
53 structures for designing REDD+ giving high priority to social safeguards, and the second relates to
54 programming that builds trust, communication and confidence of participants creating an environment for
55 enabling management through commitment and behavioural interaction by creating an overarching
56 institutional framework and unilateral management (Somorin et al., 2016).
57
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1 To achieve CRD, forestry conservation strategies need to be driven by climate action and forest management
2 policies that benefit both ecological and human systems, and above all, involve forest communities in
3 program and project implementation (Cordeiro-Beduschi, 2020). Synergies between mitigation and
4 adaptation of the forestry sector can be enhanced by considering on-the-ground contexts of constraints and
5 social trade-offs that may undermine implemented actions (Few et al., 2017). However, the lack of
6 knowledge about trade-offs and synergies at the local level and between local and global scales makes this
7 challenging.
8
9 Despite these constraints, forestry can serve as a foundation for CRD when adaptation and mitigation
10 activities are effectively integrated from the stage of policy formulation with consideration of specific
11 institutional structures and procedures that can assist to facilitate such integration (Locatelli et al., 2015).
12 Effectively integrated adaptation and mitigation activities can be achieved by encouraging collaboration
13 between the two activities, promoting research on the impacts of the integrated activities, their cost-
14 effectiveness and their synergies within the complex setting of risks and uncertainty concerning the
15 magnitude of climate change impacts (Bakkegaard et al., 2016), along with facilitating participation of
16 communities in the two activities and defining forest policies (Ngum et al., 2019). Moreover, international
17 donors and funds are also critical to guide countries to identify adaptation-mitigation synergies, through
18 consultation processes, dialogue and awareness raising (Locatelli et al., 2016). Moreover, in order to be
19 effective, nature-based climate solutions such as mixed species plantation, forest expansion and REDD+,
20 must be people-centric and respond to the needs of the rural and Indigenous Peoples who manage
21 ecosystems for their livelihoods while supporting at the same time the biodiversity of the ecosystems
22 (Temperton et al., 2019; Fleischman et al., 2020).
23
24 8.6.1.2.4 Energy
25 The continued dependence on fossil energy sources for economic development is the primary source of
26 increasing GHGs (Hansen et al., 2017). There is an emerging agreement in terms of the importance of the
27 bioenergy sector for climate change mitigation (Jackson et al., 2016; Hansen et al., 2017), however, the
28 options and limitations in terms of transforming the energy systems to support both mitigation and
29 adaptation are still contested.
30
31 About 1 billion people globally (12.5% of the world's population) do not have access to electricity (World
32 Bank, 2021), and yet access to electricity is required for basic adaptation strategies, such as the use of air
33 conditioning and fans in homes and working spaces to mitigate heat stress and enable healthier lives,
34 daytime activities, and night-time sleep quality. Electrification enables farmers to mechanically pump water
35 from the underground to boost agricultural productivity, stabilise yields and make food security less reliant
36 on erratic rainfall patterns and less vulnerable to dry spells. Access to electricity enables the spread of
37 valuable information through television, radio, computers, and smartphones, including weather forecasts and
38 disasters prevention and response (Dagnachew et al., 2018). The increasing access to electricity facilitates
39 the SDG 7 coupled with other SDGs and societal goals, including mitigation of climate change (van Vuuren
40 et al., 2018) through reducing energy consumption by the use of efficient technology and appliances.
41 Electricity access can be an important enabler of adaptation action for different purposes in different sectors
42 (Mastrucci et al., 2019).
43
44 Low-carbon development strategies can also be compatible with ecological sustainability, as proponents of
45 bioenergy have claimed. Bioenergy can contribute to reducing emissions and energy inefficiencies in
46 agricultural food and bioenergy sectors, even while safeguarding food and energy security. However, recent
47 literature also points towards significant tensions and mismatches between increasing bioenergy on
48 agricultural land and local livelihoods and food security (Yildiz, 2019). A growing list of studies have
49 documented the detrimental trade-offs between small-holder food systems and large-scale biofuel
50 production, which include dispossession and impoverishment of small-holder farmers, food insecurity, food
51 shortages, and social instability (Hunsberger et al., 2017). Nevertheless, synergies between bioenergy and
52 food security can be promoted by integrated resource management designed to improve both food and water
53 security and access to bioenergy; investments in technology, rural extension, promotion of stable prices to
54 incentivize local production; use of double cropping and flex crops that provide food and energy (Souza et
55 al., 2017).
56
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1 Trade-offs of bioenergy can be minimised by replacing land-intensive first generation biofuels (e.g., oil
2 palm) with second and subsequent generations (e.g., microalgae). However, there are costs of relying on
3 `sustainable biofuels' as most of the agricultural and non-agricultural land would be needed for cultivation of
4 biofuels along with reduction in pattern of energy consumption as well as attainment to a significant
5 reduction in population (Gomiero, 2015). Contrasting impacts on environmental, economic and social
6 sustainability are reported for production and use of biofuels (Azapagic and Perdan, 2011) ranging from
7 positive impacts such as reduction in GHG emissions, energy security and rural development and negative
8 impacts such as risk of increase of food prices, the risk of increase in GHG emissions through direct and
9 indirect land-use change from production of biofuel feedstocks, as well as the risks of degradation of land,
10 forests, water resources and ecosystems (UNEP, 2009). Biofuel production may cause loss of biodiversity
11 (Jeswani et al., 2020) and may also impact on various ecosystem services, such as land, water and food,
12 however biofuel production and use may pollute air, water and soil (Scovronick and Wilkinson, 2014). The
13 collective benefits of biofuels may be realized by developing future policies based on integrated systems
14 view with clear understanding about the interactions across sectors and land uses by analysing complete
15 value chains (Jeswani et al., 2020).
16
17 Clean sources of energy such as solar and wind can facilitate both mitigation and adaptation. For example, in
18 South Africa, clean sources of energy provide energy security with huge water savings along with creation of
19 employment, proximity to point-of-use and, in many cases, less reliance on concentrated sources of energy
20 (Mpandeli et al., 2018). Overall, the increased use of thermal solar panels contributes to reducing GHG
21 emissions and improves air quality as well as providing benefits to the community and the environment. The
22 differential adoption of solar panels can be managed by simultaneous investment in other technologies that
23 utilize renewable energy along with investment in solar panels (Kaya et al., 2019). Development of a smart
24 electricity grid connected to a renewable energy source reduces GHG emissions and decreases vulnerability
25 to climate change by enhancing response to changing conditions and providing more reliable service to the
26 population (Hennessey et al., 2017). Moreover, in the policy development for a low-carbon and climate
27 resilient power system, a local nexus between mitigation and adaptation can be explored (Handayani et al.,
28 2020). For example, use of efficient fuel in urban areas facilitates air pollution reduction and also provides
29 health benefits for urban populations (Ramaswami et al., 2017). Green buildings substantially reduce energy
30 consumption and also improve indoor environmental quality and thus contribute to mitigation and provide
31 societal value in terms of health (MacNaughton et al., 2018). Besides, green roofed building contributes to
32 keeping local temperatures cooler during the hot days and thereby reducing energy use for air-conditioning
33 and thus contributing to both mitigation and adaptation (Sharma et al., 2016).
34
35 Positive synergies between adaptation and mitigation in the energy sector can include changes in production
36 technologies and utilization of technologies by various industries, change in consumer or corporate
37 behaviour, and the development of policies that alter the energy sector activities sufficiently to achieve a
38 combination of reduced GHGs emissions and increased benefits for communities (Morand et al., 2015).
39 However, the policy perspective must be based on the country circumstances, especially urbanization,
40 economic growth and energy consumption matching with the income level of the country (Wang et al.,
41 2018).
42
43 8.6.2 Decision making approaches for Climate Resilient Development
44
45 A range of different traditional economic decision support tools can be used to help guide resource allocation
46 in relation to climate change adaptation (e.g., cost benefit analysis, cost-effectiveness analysis, multi criteria
47 analysis) (Watkiss et al., 2016), with a strong focus on monetary values and the present and near-term. There
48 are also tools to assess uncertainty (e.g., iterative risk management) and to guide decision making under
49 uncertainty over longer time frames (through e.g., real options analysis, robust decision making involving
50 substantial numbers of scenarios, portfolio analysis and rule based decision support for uncertainty where
51 maximum regrets are minimised). Use of these tools nevertheless requires human capital and skills and more
52 commonly they are applied to public rather than private (individual/ household) adaptation decision
53 processes. Tools grounded in economics can lack sufficient consideration of which groups in society might
54 gain and lose out from particular options (Sovacool et al., 2015; Stringer et al., 2019), neglecting to
55 appreciate non-monetary factors (like wellbeing) which are non-economic, less tangible and harder to put a
56 value on (see Section 8.3).
57
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1 This section lists several groups of the strategies, ranging from mainstreaming and coherence, to dealing
2 with the complexities through broader and innovative governance and scale, to provision of funding and the
3 associated cost and benefit analysis, through focussing on the community and addressing underlying equity
4 through transformational adaptation.
5
6 8.6.2.1 Policy coherence, policy integration and broader governance approaches
7
8 Mainstreaming and policy coherence is one of the most proposed strategies in dealing with adaptation and
9 mitigation as a coherent approach, in the context of good governance. Politics, power and interests influence
10 the prospects of achieving integrated climate policy and development goals in practice (Naess et al., 2015).
11 Institutional incoherence has led to inefficiency and ineffectiveness (Di Gregorio et al., 2017). To achieve
12 more coherent institutions and synergies, four major enabling conditions have been identified: (1) planned
13 and/or existing national laws, policies and strategies; (2) existing and planned financial means and measures;
14 (3) institutional arrangements in the country with specific reference to climate change issues; and (4) planned
15 and/or existing plans, programmes and initiatives in the country (Kabisch et al., 2016). Another strategy
16 offered is to develop a `dual track approach' at local/municipality/city level through having a local climate
17 plan and/or mainstreaming plan (Duguma et al., 2014b). This can lead to effective implementation of climate
18 actions and diffusion of climate issues into local sector policies (Reckien et al., 2019). Effective climate
19 policy integration (CPI) calls for four ways of coherence (Di Gregorio et al., 2017), namely between internal
20 coherence (mitigation and adaptation policies objectives and policies), external coherence (climate change
21 and development objectives), vertical integration (mainstream climate change into sectoral policies) and
22 horizontal integration (overarching governance structures for cross-sectoral coordination).
23
24 Progress of policy integration varies from the global to local level. Progress in mainstreaming and coherence
25 is emerging globally and has slowly made it down to the national level (Di Gregorio et al., 2017). Adaptation
26 and mitigation should be mainstreamed into planning and implementation on food security programmes, and
27 cross-cutting oversights are required to integrate land restoration, climate policy, food security and disaster
28 risk management into a coherent policy framework (Woolf et al., 2015).
29
30 There has been an increase in the literature examining adaptation and mitigation synergy in the Nationally
31 Determined Contributions (NDCs) submitted by countries to the UNFCCC. Agriculture and energy are the
32 two priority sectors for which there have been significant pledges and commitments from countries, with, to
33 some extent, good alignment between adaptation and mitigation. This alignment can provide good
34 opportunities to integrate both into national sectoral policies (Antwi-Agyei et al., 2018a). This suggests that
35 inclusive and sustainable economic and social development can be achieved if national governments focus
36 on developing coherent, cross-sector approaches that deliver potential triple wins of mitigation, adaptation
37 and development.
38
39 Different governance approaches such as polycentric governance, adaptive governance, multi-level
40 governance, collaborative governance, or network governance are increasingly utilised to understand the
41 processes of transitioning towards CRD. The potential of polycentric governance approaches for promoting
42 both climate mitigation and adaptation is well established (Cole, 2015; Abbott, 2017; Morrison et al., 2017a;
43 Warner et al., 2018). Polycentric governance deals with active steering of local, regional, national, and
44 international actors and instigates learning from experience across multiple actors, levels of decision-making,
45 and temporal scales (Ostrom, 2010). It is the source of power to achieve collective goals. Polycentric actors
46 have the framing power, power by design and pragmatic power (Morrison et al., 2017b). It offers new
47 opportunities for climate action through more opportunities for communication, trust-building, policy
48 experimentation and learning (Cole, 2015). Adaptive governance is understood as various interactions
49 between actors, networks, organizations, and institutions toward achieving a desired state of social-
50 ecological systems (Chaffin et al., 2014). It requires a structure of nested institutions, diversity at different
51 levels, connected by formal and informal social networks (Dietz et al., 2003). As Brunner and Lynch (2010)
52 observe, the emergence of community-based initiatives in addressing climate change marks the emergence of
53 adaptive governance.
54
55 8.6.2.2 The water-energy-food-nexus approach
56
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1 Increasing demands for water, energy, food and materials are putting pressure on resource supply, and hence
2 the nexus approach can inform transition pathways for interlinked resource systems (Johnson et al., 2019).
3 Nexus approach, especially the water-energy-food nexus, is used to examine synergies and trade-offs
4 between adaptation and mitigation (Howells and Rogner, 2014). As reviewed by (Wiegleb and Bruns, 2018),
5 early use of the concept was by the World Economic Forum in 2008 where it was emphasised that issues of
6 economic growth need to be considered within water, energy and food resource systems. This was later
7 published as Water Security: The Water-Food-Energy-Climate Nexus. Another key activity was the
8 Bonn2011 Nexus conference. Then in 2015, The Nexus Dialogue Programme was held by the UN and EU
9 Commissions as an approach to implement the SDGs. UN Water underscores the water-food-energy nexus
10 as central to development (Newell et al., 2019). It notes that demand for water, food and energy are rising
11 due to a growing population, rapid urbanisation, changing diets and economic growth, and in most cases, the
12 lack of knowledge on water-food-energy nexus has often led to mismatches in prioritization and decision-
13 making which hinders sustainable development (Mitra et al., 2020). It is important to note, however, benefits
14 of nexus approach are not always easily quantified and often accrue to local communities over time (Amjath-
15 Babu et al., 2019).
16
17 A well-coordinated and integrated nexus approach offers opportunities to build resilient systems while
18 harmonising interventions, mitigating trade-offs and hence improving sustainability (Biggs et al., 2015). This
19 can be achieved through greater resource mobilisation and coordination, policy convergence across sectors,
20 and targeting nexus points in the broader landscape (Mpandeli et al., 2018). Studies utilizing the nexus
21 approach to climate change in different places show considerably different results. In the Southern African
22 Region, climate change is already affecting water-energy-food resources and exerting further pressure on
23 already scarce resources. It is proposed that adaptation can be achieved through cross-sectoral management
24 of resources, by adopting water management practices, by aiming to produce more food and energy with less
25 water resources, and through the adoption of cleaner and renewable sources of energy resulting in saving
26 water and ensuring energy security in a region that depends on hydro and coal energy sources (Mpandeli et
27 al., 2018). A study in developing Asian countries (Bangladesh, India and Vietnam), found following factors
28 inhibit ability to govern the nexus consideration (i) absence of institutional coordination; (ii) influence of
29 political priorities on decisions rather than use of scientific knowledge to shape the decisions; (iii) lack of
30 capacity to understand interlinkages between sectors; (iv) lack of multi-stakeholder engagement in planning
31 and decision-making processes; and (v) lack of incentive mechanisms and adequate finance to support the
32 approach` (Bao et al., 2018). Applying the nexus approach on the Hindu-Kush Himalayan region identified
33 three challenges: increasing population and declining agricultural land, stagnating or declining food
34 production, and increasingly water and energy intensive food production despite water and energy scarcity
35 (Rasul and Sharma, 2016). Nexus smart adaptation policies need to be complemented with system-wide
36 adaptation, policy coherence and sectoral coordination, and targeting poverty and vulnerability linkages
37 (Rasul and Sharma, 2016).
38
39 8.6.2.3 Community-based approach
40
41 Another important strategy to better determine impacts of adaptation and mitigation and promote inclusivity,
42 ensure transparency and accountability is a community based approach. This approach also supports
43 adaptation and mitigation indirectly through the strengthening of capacity and social capital. For example, in
44 Bangkalan, Indonesia, the presence of high social capacity and readily available free agricultural inputs are
45 the two decisive factors for effective climate change mitigation and adaptation as well as enhancing
46 community livelihood (Sunkar and Santosa, 2018). The calls for considering Indigenous Knowledge and
47 Indigenous People to support integrated strategies in adaptation and mitigation are increasing (Ford et al.,
48 2016; Altieri and Nicholls, 2017; Brugnach et al., 2017). Detailed knowledge of local socio-ecological
49 contexts may offer transformational processes to harness synergies (Thornton and Comberti, 2017). A study
50 in the Ukraine on cooperatives shows that it offers a well-established livelihood strategy and means to
51 support agriculture small holders. Moreover, social capital fulfils key roles in the process of capacity
52 building and implementation of sustainable measures (Kopytko, 2018). In Indonesia, a well-known program
53 focussing on community-led adaptation and mitigation activities is Proklim. It empowers communities to
54 learn about climate change impacts, record data and plan actions for climate change (Muttaqin and Yulianti,
55 2019). Multi-stakeholder, participatory planning processes are beneficial to help farmers to screen and
56 prioritise rural livelihood strategies in Indonesia. The necessity of CRD is reflected in standard development
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1 interventions: water management, intensification and diversification of agriculture and aquaculture,
2 education, health, food security and skill building for farmers (Wise et al., 2016).
3
4 8.6.3. Future adaptation finance and social and economic changes within the context of poverty,
5 livelihoods, equity, equality and justice
6
7 8.6.3.1 Coverage of adaptation finance
8
9 There is still some debate on what qualifies as adaptation finance and how such finance should be measured
10 (UNFCCC, 2016). According to the Climate Policy Initiative, adaptation finance is `finance with the aim of
11 improving preparation and reducing climate-related risk and damage, for both human and natural systems, as
12 short-term climate impacts will continue to exact economic, social, and environmental costs even if
13 appropriate mitigation actions are taken.' (CPI, 2019). According to UNEP, the annual costs of adaptation in
14 developing countries could range from $140 billion to $300 billion by 2030. Globally, adaptation costs are
15 estimated to be even greater, with up to $500 billion per year by 2050 under a business-as-usual scenario
16 (UNEP, 2021). While global climate finance flows reached $579 billion on average over the 2017/18 period,
17 there has been a continued heavy imbalance in favour of mitigation finance, with adaptation finance totalling
18 around $30 billion (compared to $532 billion for mitigation), or five percent of tracked climate finance. The
19 World Bank has however, committed itself to increase direct adaptation finance to $50 billion over the 2020-
20 25 period, putting the Bank's adaptation finance in developing countries on par with its mitigation
21 investments (World Bank, 2019a). Adaptation finance is also growing alongside finance for actions with
22 both mitigation and adaptation benefits, for example in forestry or agriculture, which rose to just over $12
23 billion (CPI, 2019), as well as increasing focus on adaptation and cross-sectoral projects. Looking only at
24 climate finance flows from developed to developing countries, the OECD estimates a total of $78.9 billion
25 mobilised in 2018, with mitigation accounting for 70 percent ($55 billion) of the total, adaptation 21 percent
26 ($16.8 billion) and cross-cutting finance making up the remainder(OECD, 2020a).
27
28 Adaptation finance funds actions to adapt to the impacts of climate change, yet such actions are heavily
29 context-, scale- and time-specific. Many mitigation actions in the energy sector can be easily quantified and
30 employed across different jurisdictions. For example, solar photovoltaic (PV) presents an established way
31 across a multitude of countries to produce low-carbon energy at a profit and reduce global GHG emissions.
32 Adaptation needs, however, vary greatly from location to location and short-term solutions, for example
33 investments in irrigation technologies to improve water availability for specific crops in a growing season,
34 may differ from longer-term solutions, for example switching to different crops altogether. Benefits are not
35 always easily quantified and often accrue to local communities over time rather than to investors looking for
36 the kind of returns realised in mitigation actions.
37
38 Development finance institutions (DFIs) mainly draw on market-rate loans and, to a lesser extent,
39 concessional lending and grants to finance adaptation actions. There are regional differences in the choice of
40 instruments, too, owing to the degree of economic development: while most of the adaptation finance
41 flowing to the Asia-Pacific is market-rate debt, the vast majority of adaptation finance flowing to sub-
42 Saharan Africa is in the form of concessional debt or grants (Richmond et al., 2020).
43
44 Globally, the main sectors benefiting from adaptation finance to date include water and waste water
45 management; agriculture, forestry, land use, and natural resource management; disaster risk management;
46 and infrastructure, energy, and other built environment (Oliver et al., 2018). In recent years, this finance has
47 moved away from a concentration on water and wastewater management to spread out more evenly across
48 the sectors. Between 2015/16 and 2017/18, investment in water and wastewater management dropped from
49 $11 billion to $9 billion, while investment in agriculture, forestry, land use and natural resource management
50 grew from $5 billion to $7 billion, and investment in disaster risk management more than doubled from $3
51 billion to $7 billion (CPI, 2019). In addition, while mitigation actions are more easily delineated, for example
52 wind farms in the energy sector, adaptation measures often need to be mainstreamed across a number of
53 sectors and investment decisions.
54
55 There are strong interconnections between nature-based solutions, climate adaptation and mitigation actions.
56 Ecosystem-based adaptation is a nature-based solution that uses ecosystem services to help communities
57 adapt to climate change. Examples of such approaches were covered in Section 8.5.2.2. For example,
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1 mangrove restoration provides both climate mitigation (as carbon sinks) and adaptation to climate change
2 (increasing the resilience of coastal communities) while also supporting the implementation of a range of
3 other SDGs (for example through increased food security). Research has found that without mangroves,
4 global flood damage costs would increase by more than $65 billion a year (Men閚dez et al., 2020). There is,
5 therefore, an urgent need to invest in a range of nature-based solutions.
6
7
8 [START BOX 8.9 HERE]
9
10 Box 8.9: Adaptation Fnancing for the Poor and the Need for Systems Transition: Eastern Indonesian
11 Islands
12
13 Summary
14
15 A 4-year project in Nusa Tenggara Barat Province, Indonesia, aimed to stimulate an adaptation pathways
16 process. The goal was to support climate resilient development in a context with low stakeholder capacity,
17 high poverty, and rapid environmental and social change. On these archipelagic islands, livelihoods are
18 predominantly rural, far from political and urban centres. The project focused on the integrated top-down
19 and bottom-up development planning that could enable climate resilient development at the local level,
20 linked to provincial and national plans.
21
22 Lessons learned
23
24 � Substantial gradients in both climate and livelihoods in the island geographies necessitate fine-scale
25 planning and make it difficult to scale up.
26 � Infrastructural investments, including roads, ports, and irrigation, are crucial to climate-resilient
27 development. If not well designed, such investments are prone to maladaptation, such as exposure to
28 sea level rise.
29 � Although some development interventions are delivering climate resilience, such outcomes are often
30 haphazard, rather than strategically conceived, coordinated, and delivered. (Butler et al., 2016)
31
32 [END BOX 8.9 HERE]
33
34
35 New financial instruments can help to support investment in, for example, ecosystem-based adaptation. For
36 example, green bonds have shown their ability to raise significant amounts of capital in support of projects
37 with environmental/ climate benefits. The green bond market has quickly developed since the European
38 Investment Bank launched the first green bond in 2007, with issuance growing to $257.7 billion in 2019, up
39 more than 50 percent on the previous year (CPI, 2019). Most green bonds focus on energy, buildings and
40 transport infrastructure but green bond issuance to support sustainable agriculture and forestry has grown
41 from $208 million in 2013 to $7.4 billion in 2018 (Wilkins, 2019). The Seychelles issued the world's first
42 `blue' bond in 2018 with the support of the World Bank. Similar to green bonds, blue bonds earmark the use
43 of bond proceeds for specific purposes, here the sustainable use of marine resources (World Bank, 2018). In
44 2019, the European Bank for Reconstruction and Development issued the world's first ever dedicated
45 climate resilience bond, raising $700 million. The five-year bond will be used to finance the Bank's projects
46 in climate resilient infrastructure (e.g., water, energy and transport), climate-resilient business, commercial
47 operations, climate-resilient agriculture and ecological systems (Bennett, 2019). While these issuances are
48 still small compared to the overall green bond market, their rapid growth points to enormous opportunities
49 for ecosystem-based adaptation.
50
51 Despite the growth of official adaptation funding at international and national levels, for the world's poorest,
52 adaptation to the impacts and opportunities of climate change frequently occurs in response to losses and
53 damages at the individual or household scale, without coordination at larger institutional scales (Section 8.3,
54 8.4; Barrett, 2014). Discussions of adaptation finance often occur in the context of dwindling resources, and
55 trade-offs: triage decisions about the other investments that societies can tolerate suspending (Warner and
56 Van der Geest, 2013; Tanner et al., 2015). In many poor, vulnerable countries, complex governance
57 challenges, such as budget austerity or corruption, hamper the provision of such support. In the absence of
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1 adaptation funding for the poor coordinated at higher scales, the costs of adaptation are borne by the poor at
2 community, kin-group and household scales. Bearing the cost of adaptation, thus, can become, in the short-
3 term, an erosive process of coping that ultimately increases the likelihood that communities and households
4 will remain trapped in poverty (Antwi-Agyei et al., 2018b). In the long-term, these measures financing
5 adaptation may be maladaptive, meaning they ultimately leave the poor at greater risk of experiencing
6 climate change impacts (Section 8.4.5; Rahman and Hickey, 2019). Such circumstances highlight the
7 governance gap that drive the poorest to rely on extreme measures to finance adaptation.
8
9 Since the AR5, there is greater documentation of the extreme measures and high-risk income alternatives
10 that the world's poorest commonly take to finance adaptation (Dawson, 2017; Ahmed et al., 2019). While
11 still a controversial topic, clear examples of extreme adaptation finance measures include:
12
13 � unauthorized international migration (McLeman, 2018)
14 � informal small-scale mining of precious metals and minerals (Hilson and Van Bockstael, 2012;
15 Osumanu, 2020)
16 � illegal poaching of flora and fauna, including participation in illegal timber harvesting (Bolognesi et
17 al., 2015)
18 � illegal, unregulated or unreported (IUU) fishing, including within marine protected areas, or the
19 coastal zones of neighbouring countries(Tanner et al., 2014)
20 � utilizing livelihood resources, such as boats, in smuggling activities, including drug and arms
21 trafficking(Belhabib et al., 2020)
22 � participation in piracy, extortion or kidnapping economies (Staff, 2017)
23 .
24
25 Enabling conditions for formal adaptation finance for the poorest are needed to reduce reliance on high-risk,
26 extra-legal sources of income (see Section 8.5.2). In general, the antidote to this emerging problem is access
27 to living wages that the poor can rely on to finance adaptation. There are few examples of pro-poor
28 mechanisms, programs or institutions that prioritize coordinated, access to credit for proactively adapting
29 livelihoods of the poor (Agrawal and Perrin, 2009). Institutions can reduce incentives for vulnerable people
30 to engage in high risk activities by including them in the process of adaptation governance, which aims not
31 only at supporting sustainable livelihood practices (such as farming, fishing and forestry), but also
32 guaranteeing land tenure (Wrathall et al., 2019). Critical for risk reduction to the poor is also the ability of
33 authorities across multiple spatial and temporal scales to maintain social protection that are able to reduce the
34 dependency of illegal sources of income at the same time facilitate adaptation (Tenzing, 2020). A range of
35 tools exists for opening access to credit to poor and marginalized people whose livelihoods are most highly
36 vulnerable (Ribot, 2013): climate insurance tools that are designed and targeted at the poorest and which
37 have been properly assessed to ensure they do not undermine other coping strategies such as risk spreading,
38 programs that ease access or subsidize loans for adaptation, mobile banking and mobile-based financial and
39 risk-management tools, impact pay-outs in the form of direct transfers, and institutional supports for
40 hometown associations. International governance arrangements, such as the Warsaw International
41 Mechanism on Loss and Damage, might aim primarily to clear the financing gap between global financial
42 and risk management institutions and the pocketbooks of the poorest (Wrathall et al., 2015).
43
44
45 8.7 Conclusion
46
47 The chapter has moved beyond the IPCC WGII AR5 in that the chapter lays out structural elements of
48 vulnerability and provides quantitative information about climate-related vulnerability hotspots globally
49 complemented with the assessment of poverty, local livelihood vulnerability and sustainable development.
50 Also the assessment of non-economic losses and enabling and supportive environments for adaptation are
51 new aspects.
52
53 The chapter provides additional evidence on the livelihood resources at local levels that have been impacted
54 by different climate hazards, and globally, that specific hazards (namely, drought and rising temperatures)
55 are more threatening and destabilizing to livelihoods than others. There is robust evidence that coping and
56 adaptive capacities erode with increasing global mean temperature (GMT)--substantial differences are
57 expected between a GMT increase of less than 1.5癈 compared to an increase of more than 3癈-- and the
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1 frequencies of climate hazards, such as heat waves, droughts or floods is likely to increase substantially.
2 Nevertheless, this assessment also revealed that the adverse impacts of climate change for livelihoods and
3 multidimensional poverty differ substantially between different population groups exposed to climate
4 hazards, based on the socio-economic and governance context. Consequently, societal impacts of climate
5 change need to be understood in the broader context of development and the development challenges that
6 influence exposure, vulnerability and adaptation.
7
8 There is robust evidence of the impacts of all climate hazards on the key livelihood resources that the poor
9 depend on. There is high confidence that two climate hazards pose high risk to a broad range of livelihood
10 resources: warming trends and droughts. Meanwhile, the livelihood resources that are globally at greatest
11 risk include people's bodily health, food security and agricultural productivity (high confidence). Evidence
12 suggests that the fundamental challenge of climate change to livelihoods is that rising temperatures, drought
13 and other hazards endanger human life, and the lives of plants and animals that humans rely on to survive
14 (high confidence). There is now robust evidence that the impacts of climate change on livelihoods are
15 driving people to migrate in search of alternative incomes, and this tendency will increase with rising
16 temperatures. Of greatest concern are people whose development context is compromised by war, conflict
17 and extreme poverty and inequality, such as refugee populations and displaced people.
18
19 This chapter reports quantitative evidence about human vulnerability and therefore identifies various spatial
20 hotspots of vulnerability emerging in regional clusters, and reports that significantly more people are living
21 in highly vulnerable context conditions compared to those living in low vulnerability contexts. The
22 assessment revealed that approximately more than 3 million people are living in countries classified as very
23 highly or highly vulnerable (depending on the assessment method and the number of classes used and
24 countries included). In contrast approximately 2 billion people reside in low or very low vulnerable country
25 contexts. Studies estimate the population in the most vulnerable regions to almost double by the year 2100
26 (Section 8.4.5.2). When near-term estimates are used, the population growth in highly vulnerable countries is
27 still significantly higher compared to less vulnerable countries. Consequently, this assessment points towards
28 the fact that even if we do not know how societal or community vulnerability will develop in specific areas,
29 it is highly likely that in the future, more people will live in destabilized and highly vulnerable country
30 contexts compared to the population today. However, it is important to note that the scientific literature also
31 underscores that trends in vulnerability differ significantly between different world regions and within
32 countries.
33
34 The chapter also advances knowledge in terms of the interconnections between human vulnerability and
35 observed losses and adverse consequences. The assessment shows that statistically relevant differences in
36 observed fatalities per hazard events can not only be explained by hazard intensity and frequency, but are
37 also linked to different levels of vulnerability of a region exposed. Despite all uncertainties about future
38 change, the assessed literature clearly provides an accurate picture of the expected societal impacts of
39 climate change, the requirements for successful adaptation, and the need to address the adaptation gap
40 through the perspective of vulnerability.
41
42 The chapter shows that intersectionality approaches are becoming increasingly central to grasping how
43 differential vulnerability to climate hazards is experienced by different social groups. Intersectionality
44 recognises that age, gender, class, race and ethnicity are reinforcing social phenomena shaping social
45 inequalities and experiences of the world which also intersect with climate hazards and vulnerability. Our
46 assessment reveals the central role of maladaptation with robust new evidence on negative consequences of
47 interventions on different social groups. Well intentioned adaptation can exacerbate past and existing
48 vulnerabilities and undermine livelihoods. There is also evidence that despite maladaptation, inclusive and
49 sustainable development at the local level can reduce vulnerability.
50
51 Since AR5 loss and damage has taken much more central stage in sustainable development, policy and
52 poverty and livelihoods discourse. While there is ambiguity about what constitutes loss and damage the
53 chapter illustrates there is new evidence of observed losses and damages, including slow-onset impacts (e.g.,
54 sea level rise and drought). Our assessment reveals that there exists a body of literature that explicitly
55 addresses non-economic losses and that these are experienced everywhere now due to human induced
56 climate change. These are coupled with advancements in the science of extreme event attribution with new
57 focus on adaptation metrics and vulnerability assessments.
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1
2 This assessment also identifies emerging evidence of linkages between extreme and slow onset events, non-
3 economic loss and damage, and livelihood shifts. Evidence suggests that losses are leading to a range of
4 shifts in livelihoods, which may be easier for some social groups than others and with implications for
5 livelihoods security across transboundary scales. Yet, climate change is only one driver. Untangling the
6 drivers of vulnerability is also critical with use of intersectionality approaches. Our quantification of
7 vulnerability hotspots supports this concern and it will be critical to seek further knowledge on the extent of
8 livelihood shifts among the most vulnerable resulting from specific non-economic loss and damage, for
9 whom, where and at what scale. Gaps in knowledge highlight this as an area that needs further work in order
10 to develop and understand further the full extent and reach of the relationships between extreme and slow
11 onset climate events, non-economic losses, and shifting livelihoods.
12
13 This chapter builds from AR5 and 1.5癈 Report on key limits to the adaptation of natural and social systems
14 since that are compounded by the effects of poverty and inequality such as on water scarcity, ecosystems
15 alteration and degradation, coastal cities in relation to sea-level rise, cyclones and coastal erosion, food
16 systems and human health (high confidence). The climate change risks substantially pose negative impacts
17 on climate-sensitive livelihoods of smallholder farmers, fisheries communities, Indigenous People, urban
18 poor, informal settlements, with limits to adaptation evidenced on the loss income, ecosystems, health, and
19 increasing migration (high confidence). It also addresses how ecological thresholds and socio-economic
20 determinants of vulnerabilities are linked to soft and hard adaptation limits, including the potential and
21 magnitude to livelihoods risks in the future. For instance, a hard limit associated to losses of coral reefs at
22 1.5癈 warmer world will lead to substantial loss of income and livelihoods for coastal communities (high
23 confidence), including loss of culture and place-based attachment (medium confidence). The adaptation hard
24 limits are expected for the Arctic ecosystem, whose threshold will affect residents of Arctic regions
25 dependent on hunting and fishing livelihoods (high confidence). New emerging considerations to ecological
26 limits to adaptation such as severe glacier retreat and Amazon Forest dieback, is expected to affect the
27 livelihoods of smallholder's farmers, and Indigenous People through crops yield failures, biodiversity loss,
28 reduced hydropower capacity and heath (medium evidence). While a knowledge gap remains on the
29 projected risks of increasing global temperature to climate-sensitive livelihoods among global south
30 countries and specific groups of people, current observations show negative impacts to livelihoods for tens to
31 hundreds of millions of people. Thus, without sustainable, equitable and urgent adaptation measures,
32 maladaptation risks are likely to further increase vulnerability, marginalization, and ecological tipping points
33 among the poor within countries (medium confidence).
34
35 Evidence on the kinds of enabling environment required paints a complex picture. The assessment highlights
36 the interaction of different capital assets with the broader context of key enablers in shaping the overall
37 enabling environment for adaptation, which itself is highly context-dependent. In this regard, countries
38 present different starting points for adaptation, with some requiring, for example, more of an emphasis on
39 institutional capacity building; others requiring transformation to the broader legal and political conditions.
40 Capitals are not necessarily substitutable but rather act as an assemblage in shaping both perceptions of
41 climate risk and the necessity and appropriateness of actions. At the same time there is robust evidence that
42 livelihoods that depend strongly on natural capital for both subsistence and as a source of income are
43 particularly sensitive to climate risks; and are where perhaps adaptive actions are most urgently needed, even
44 with smaller rises in temperature under the most optimistic scenarios. This applies to both the global south
45 and the global north. Investments in any form of capital asset to support adaptation need to be mindful of
46 reinforcing existing inequalities and introducing new ones, particularly if transformation takes place. This
47 also underscores the importance of inclusive, polycentric governance in ensuring the voices of all groups are
48 heard and that wide ranging knowledge types are incorporated in decision making, nevertheless recognising
49 that trade-offs are inevitable.
50
51 The chapter also highlights and provides quantitative evidence that adaptation strategies need to go beyond
52 the idea of adapting to warming levels only. Adaptation strategies have to reduce the adaptation gap and
53 therewith reduce human vulnerability independent of a specific climatic hazard. It has been shown that
54 adaptation strategies that explicitly address poverty, inequities and consider also right based approaches can
55 generate co-benefits for resilience building of most vulnerable groups and for sustainable development.
56
57
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1 [START FAQ8.1 HERE]
2
3 FAQ 8.1: Why are people who are poor and disadvantaged especially vulnerable to climate change
4 and why do climate change impacts worsen inequality?
5
6 Poor people and their livelihoods are especially vulnerable to climate change because they usually have
7 fewer assets and less access to funding, technologies and political influence. Combined, these constraints
8 mean they have fewer resources to adapt to climate change impacts. Climate change impacts tend to worsen
9 inequalities due to the fact that they disproportionately affect disadvantaged groups. This in turn further
10 increases their vulnerability to climate change impacts and reduces their ability to cope and recover.
11
12 Climate change and related hazards (e.g., droughts, floods, heat stress, etc.) affect many aspects of people's
13 lives--such as their health, access to food and housing, or their source of income such as crops or fish
14 stocks--and many will have to adapt their way of life in order to deal with these impacts. People who are
15 poor and have few resources with which to adapt are thus much more seriously negatively affected by
16 climate-related hazards. If a person or community are not able to cope and adapt to climate-related hazards,
17 this is referred to as `vulnerability'. For example, if someone who is very rich has their house washed away
18 in a flood, this is terrible, but they often have more resources to rebuild, have insurances that support
19 recovery and maybe even build a house that is no longer in a flood-prone area. Whereas for someone who is
20 very poor and who does not live in a state that provides support, the loss of their house in a flood could mean
21 homelessness. This example shows that the same climate hazard (flood) can have a very different impact on
22 people depending on their vulnerability (their capacity to cope and adapt to hazards).
23
24 It is not just poverty that can make people more vulnerable to climate change and climate-related hazards.
25 Disadvantage due to discrimination, gender and income inequalities and lack of access to resources, for
26 example, those with disabilities or of minority groups, can mean these groups have fewer resources with
27 which to prepare and react to climate change and to cope with and recover from its adverse effects and are
28 therefore more vulnerable. This vulnerability can then increase due to climate change impacts in a vicious
29 cycle, unless adaptation measures are supported and made possible.
30
31 [END FAQ8.1 HERE]
32
33
34 [START FAQ8.2 HERE]
35
36 FAQ 8.2: Which world regions are highly vulnerable and how many people live there?
37
38 A mix of multiple development challenges, such as poverty, hunger, conflict and environmental degradation,
39 make countries and whole regions vulnerable to climate change. Many of the people in the most vulnerable
40 situations and in the most vulnerable regions are also highly exposed to climate hazards, such as droughts,
41 floods or sea-level rise at present and will become increasingly so in the future. Studies estimate that around
42 1.6 to 3.3 billion people are living in regions classified as highly vulnerable to climate change impacts,
43 which is almost twice as many as the approximately 0.8 to 2 billion people who reside in regions classified
44 as least vulnerable. The most vulnerable regions include East, Central and West Africa, South Asia,
45 Micronesia and Melanesia and in Central America.
46
47 When a country or region is considered `vulnerable' to climate change this means that climate hazards (e.g.,
48 drought, flood, heatwaves) have a very negative impact because there are a high number of people in these
49 areas lacking the ability or opportunity to cope and adapt to such events, due, for example, to high average
50 poverty, inequality and lack of institutional support. This vulnerability could be due to many different
51 development challenges that all come together and influence each other, such as poverty, lack of access to
52 basic infrastructure services, high numbers of uprooted people, state fragility, low or below average life
53 expectancy and biodiversity degradation. These structural social issues often affect regions for many decades
54 and make it difficult for the state and for individuals to respond to climate change and climate-related
55 hazards.
56
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1 For example, if a region is already characterized by poverty and struggling to feed its population and provide
2 adequate access to basic infrastructure services such as water and sanitation, this makes them vulnerable. If
3 this region is then faced with an increased number of extremely dry years, this exposes them to drought and
4 will make things even harder and cause more hunger, poverty and worsen health--these are climate impacts.
5 Most vulnerable regions are in Africa, as well as in South Asia, the Pacific and the Caribbean. In these
6 regions there are often multiple neighbouring countries that all are highly vulnerable, for example in Central-
7 and West-Africa. These regional clusters require special attention.
8
9 There are also highly vulnerable groups and individuals within less vulnerable regions. For example,
10 marginalised, disadvantaged and poor minorities within highly affluent cities. Programmes that aim to
11 support adaptation to climate change need to focus on reducing the vulnerability of individuals, groups,
12 countries and regions.
13
14 [END FAQ8.2 HERE]
15
16
17 [START FAQ8.3 HERE]
18
19 FAQ 8.3: How does and will climate change interact with other global trends (e.g., urbanization,
20 economic globalization) and shocks (e.g., COVID-19) to influence livelihoods of the poor?
21
22 A range of local, regional and global economic and political processes already underway have put at risk
23 the livelihoods of the poor (which include urbanization, industrialization, technological transformation,
24 monetization of rural economies, increasing reliance on wages, and inequality at national and international
25 levels), and climate change intersects with these processes.
26
27 The world's poorest already struggle providing for themselves and their families in their pursuit of
28 livelihoods. Despite hard work there are many factors beyond an individual's control that can make earning a
29 living very difficult. Climate change is one problem among many that put stress on livelihoods. Poor and
30 marginal groups disproportionately bear impacts of climate change, in ways that accelerate transitions from
31 traditional livelihoods, such as rural farming, to wage jobs in urban areas. Where adaptation measures are
32 insufficient and where the poor are excluded from decision-making, these livelihood transitions can be
33 severely destabilizing.
34
35 For example, climate change may alter the frequency or intensity of hazards that threaten the viability of a
36 community's traditional farming or fishing livelihoods. Local farmers or fishers are then forced to adapt how
37 they farm or fish or abandon livelihood practices entirely. The latter may mean migrating to a city to find
38 work. As many communities face the same challenge, this intersects with a global trend that is affecting
39 billions of lives and livelihoods--urbanization--as seen in the rapid growth of informal settlements at the
40 peripheries of cities around the world, particularly rapidly growing megacities in Africa, Asia and Latin
41 America. These developments will be accelerated by negative impacts of climate change and increase risks
42 that larger segments of the population enter conditions of persistent poverty.
43
44 At the same time, people whose livelihoods have been upended by climate change are subject to new threats,
45 such as the global COVID-19 pandemic, which has shined a light on the plight of the most vulnerable
46 people. Disproportionately severely impacted by COVID-19 were for example the elderly, Indigenous
47 Peoples and Communities of Colour and also the indirect economic consequences particularly hit the poor.
48 Hence, COVID-19 demonstrates that the livelihoods of the poorest and most marginalized are vulnerable to
49 other global trends beyond climate change. Also, most severe impacts are expected in regions that are
50 already characterized by high levels of systemic human vulnerability.
51
52 [END FAQ8.3 HERE]
53
54
55 [START FAQ8.4 HERE]
56
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1 FAQ 8.4: What can be done to help reduce the risks from climate change, especially for the poor?
2
3 Public and private investment in different types of assets can help reduce risks from climate change. Exactly
4 which assets require investment depends on the specific situation. However, the provision of access to basic
5 services, such as water and sanitation, education and health care as well as the importance of reducing
6 inequity is shown within the assessment for many regions. The poor have fewer resources to invest, so in
7 poorer countries greater public investment is needed. Legal, social, political, institution and economic
8 interventions can alter human behaviour, though care must be taken that these do not amplify existing
9 inequalities, create new inequalities, or reduce future adaptation options.
10
11 Adaptation can help to reduce risks for the poor and requires both public and private investment in various
12 natural assets (e.g., mangroves, farmland, wetlands); human assets (e.g., health, skills, Indigenous
13 Knowledge), physical assets (e.g., mobile phone connectivity, housing, electricity, technology), financial
14 assets (e.g., savings, credit) and social assets (e.g., social networks, membership of organisations such as
15 farmer cooperatives). Often, the poor have the least to invest, so poverty can reduce adaptation options.
16 Sometimes people migrate as a reaction to floods or droughts, though the poorest groups often lack the
17 resources to move. Exactly what needs investing in to reduce risks varies according to the scale and
18 livelihood system in need of adaptation. In general, risks can be reduced through a range of different
19 technological and engineering approaches (for example, building sea defences to reduce storm surge
20 impacts), as well as ecosystem based approaches (such as replanting mangroves, altering the types of crops
21 grown, changing the timing of farming activities, or using climate smart agriculture or agroforestry
22 approaches).
23
24 At the same time, legal, social, political, institutional and economic solutions can alter human behaviour (for
25 example, through enforcement of building codes to prevent construction on low lying land prone to flooding;
26 timely provision of weather information and early warning systems; knowledge sharing activities, including
27 adaptation strategies grounded in Indigenous Knowledge; crop insurance schemes; incentives such as
28 payments to stop people cutting down trees or to enable them to plant them, and social protection to provide
29 a safety net in times of crisis).
30
31 The poorest groups often require greater public adaptation investments. Efforts to support adaptation need to
32 be mindful of reinforcing existing inequalities and introducing new ones, making sure they are inclusive,
33 culturally sensitive, and that the voices of all groups of people are heard. It is also important that adaptations
34 which reduce immediate risks for the poor do not rule out adaptation options that could help them later on, or
35 which could cause them to increase their emissions. Political will is needed to put people at the centre of
36 climate change risk reduction efforts, including support for their livelihoods.
37
38 [END FAQ8.4 HERE]
39
40
41 [START FAQ8.5 HERE]
42
43 FAQ 8.5: How do present adaptation and future responses to climate change affect poverty and
44 inequality?
45
46 Present adaptation can help to reduce the current and possibly future impacts of climate change. Future
47 responses to climate change can reduce poverty and inequality and even help transition toward climate
48 resilient livelihoods and climate resilient development. Pro-poor adaptation planning is necessary to ensure
49 future risks for the poor are being accounted for and the inequality underlying the poverty is being
50 addressed.
51
52 There are many ways in which poverty and inequality are influenced by climate change. The livelihood
53 sources of the poor are likely to be affected and cumulative effects of losses and damages and may influence
54 future poverty. There are cases when present adaptation worsens future poverty and exacerbates inequality--
55 this is called maladaptation. The risks of maladaptation are greater in societies characterized by high
56 inequality, and in many cases the poor and most vulnerable groups are the ones most adversely affected.
57
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1 Effective decision making in adaptation should be informed by past, present and future climate data,
2 information and scenarios to cater for reliable plans and actions for climate-resilient livelihoods. Adaptation
3 lessons from the past play an important role in decision making regarding responses to climate change. There
4 is an emerging debate on the role of learning, particularly forward-looking (anticipatory) learning, as a key
5 element or important aspect for adaptation and resilience in the context of climate change. Memory,
6 monitoring of key drivers of change, scenario planning, and measuring anticipatory capacity are seen as
7 crucial ingredients for future adaptation and resilience pathways and hence overcoming maladaptation.
8 Moreover, climate resilient development calls for ensuring synergies between adaptation, mitigation and
9 development are maximised, while trade-offs, especially those to the poor, are minimised.
10
11 [END FAQ8.5 HERE]
12
13
14
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