Chapter 16: Key Risks across Sectors and Regions
16.1 Introduction and Framing
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16.1.4 Drivers of Exposure and Vulnerability
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16.1.4.3 Poverty Trends and Socioeconomic Inequalities within and across Societies
Climate risks are also strongly related to other inequalities, often but not always intersecting with poverty. AR5 found with very high confidence that differences in vulnerability and exposure arise from multi-dimensional inequalities, often produced by uneven development processes. These inequalities relate to geographic location, as well as economic, political and socio-cultural aspects, such as wealth, education, race/ethnicity, religion, gender, age, class/caste, disability and health status (Oppenheimer et al., 2014). Since AR5, a number of studies have confirmed and refined this assessment, especially also regarding socioeconomic inequality and poverty (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017; Pelling and Garschagen, 2019; Hallegatte et al., 2020). Poor people more often live in exposed areas such as wastelands or riverbanks (Garschagen and Romero-Lankao, 2015; Winsemius et al., 2018). Also, poor people lose more of their total wealth to climatic hazards, receive less post-shock support from their often-times equally poor social networks, and are often not covered by social protection schemes (Leichenko and Silva, 2014; Hallegatte et al., 2016). Countries with high inequality tend to have above-average levels of exposure and vulnerability to climate hazards (BEH UNU-EHS, 2016). Many socioeconomic models used in climate research have been found to have a limited ability to capture and represent the poor at a larger scale (Rao et al., 2019; Rufat et al., 2019). However, an analysis of 92 countries found that relative income losses and other climate change impacts were disproportionately high among the poorest (Hallegatte and Rozenberg, 2017, see Section 16.2.6). There have also been advances in detecting and attributing the impacts of climate change and vulnerability at household scale and specifically on women’s agency and adaptive capacity (Rao et al., 2019). The distribution of impacts and responses (adaptation and mitigation) affects inequality, not just between countries but also within countries (e.g., Tol, 2020) and between different people within societies. Distribution has so far largely been thought of in a geographical sense, but identifying those most at risk requires an additional focus on the social distribution of impacts, responses, and resilience, as influenced for instance by differential social protection coverage (Tenzing, 2020).
16.3 Synthesis of Observed AdaptationRelated Responses
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16.3.2 Adaptation-Related Responses by Human Systems
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16.3.2.5 Observed Maladaptation and Co-benefits
Evidence on realised co-benefits of implemented adaptation responses with other priorities in the SDGs is emerging among the areas of poverty reduction, food security, health and well-being, terrestrial and freshwater ecosystem services, sustainable cities and communities, energy security, work and economic growth, and mitigation (Table 16.2) (high confidence). Evidence on co-benefits of adaptation for mitigation is particularly strong, and is observed in various agricultural, forestry and land use management practices like agroforestry, climate-smart agriculture and afforestation (Kremen and Miles, 2012; Christen and Dalgaard, 2013; Mbow et al., 2014; Locatelli et al., 2015; Suckall et al., 2015; Wichelns, 2016; Kongsager, 2018; Debray et al., 2019; Loboguerrero et al., 2019; Morecroft et al., 2019; Chausson et al., 2020) as well as in the urban built environment (Perrotti and Stremke, 2020; Sharifi, 2020). Evidence on co-benefits of implemented responses for other SDG priority areas is less developed, however, in the areas of education, gender inequality and reduced inequalities, clean water and sanitation, industry, innovation and infrastructure, consumption and production, marine and coastal ecosystem protection, and peace, justice, and strong institutions. This indicates a gap between some assumed likely co-benefits of adaptation and empirical evidence on the realisation of these co-benefits within the context of implemented adaptation responses (Berga, 2016; Froehlich et al., 2018; Gattuso et al., 2018; Morris et al., 2018; Chausson et al., 2020; Karlsson et al., 2020; Krauss and Osland, 2020).
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16.3.3 Knowledge Gaps in Observed Responses
Table 16.2 | Observed examples of maladaptation and co-benefits from adaptation-related responses in human systems
| Implemented adaptations |
Observed maladaptation |
References |
| Built environment |
| Top-down technocratic adaptation with no consideration for ecosystem biodiversity, local adaptive capacity and gender issues |
Ignored the complexities of the landscapes and socio-ecological systems; constrained autonomous adaptation due to time and labour demands of public work; increased gender vulnerability; hamper women’s water rights (South Africa); altered local gender norms (Ethiopia); led to a mismatch that undermine local-level processes that are vital to local adaptive capacity (Rwanda) |
Cartwright et al. (2013); Goulden et al. (2013); Nordhagen and Pascual (2013); Carr and Thompson (2014); Nyamadzawo et al. (2015); Ruiz-Mallen et al. (2015); Djoudi et al. (2016); Gautier et al. (2016); Gundersen et al. (2016); Barnett and McMichael (2018); Kihila (2018); Mersha and van Laerhoven (2018); Clay and King (2019); Currenti et al. (2019); Yang et al. (2019) |
| Migration and relocation |
| Certain autonomous, forced and planned relocation Temporary resettlement (India) |
Expansion of informal settlements in cities (Solomon Islands); relocation to areas prone to landslide and soil erosion or insufficient housing (Fiji); disproportionate burden on vulnerable communities (China); temporary relocation created gender inequality associated with minimal privacy; poor access to private toilets; sexual harassment; reduced sleep; insufficient or food rationing; exploitation and abuse of children (India); inadequate funding and governance mechanism for community-based relocation caused loss of culture, economic decline and health concerns (Alaska); relocation of supply chain to reduce exposure to climate change resulted in adverse outcomes for communities along the supply chain |
Monnereau and Abraham (2013); Maldonado et al. (2014); Pritchard and Thielemans (2014); Averchenkova et al. (2016); Lei et al. (2017); Barnett and McMichael (2018); Currenti et al. (2019) |
16.4 Synthesis of Limits to Adaptation across Natural and Human Systems
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16.4.2 Insights from Regions and Sectors about Limits to Adaptation
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16.4.2.2 Agriculture in Asia
Lack of financial resources is found to be a significant constraint that contributes to soft limits to adaptation in agriculture across Asia. Although smallholder farmers are currently adapting to climate impacts, lack of finance and access to credit prevents upscaling of adaptive responses and has led to losses (Bauer, 2013; Patnaik and Narayanan, 2015; Bhatta and Aggarwal, 2016; Loria, 2016). Other constraints further contribute to soft limits, including governance and associated institutional factors such as ineffective agricultural policies and organisational capacities (Tun Oo et al., 2017), information and technology challenges such as limited availability and access to technologies on the ground (Singh et al., 2018), socio-cultural factors such as the social acceptability of adaptation measures that are affected by gender (Huyer, 2016; Ravera et al., 2016), and limited human capacity (Masud et al., 2017). A wide range of pests and pathogens are predicted to become problematic to regional food crop production as average global temperatures rise (Deutsch et al., 2018), increasing crop loss across Asia for which farmers are already experiencing a variety of adaptation constraints, including financial, economic and technological challenges (Sada et al., 2014; Tun Oo et al., 2017; Fahad and Wang, 2018). Extreme heatwaves are projected in the densely populated agricultural regions of South Asia, leading to increased risk of heat stress for farmers and resultant constraints on their ability to implement adaptive actions (Im et al., 2017). However, socioeconomic constraints appear to have a higher influence on soft limits to adaptation in agriculture than biophysical constraints (Thomas et al., 2021). For example, an examination of farmers’ adaptation to climate change in Turkey found that constraints related to access to climate information and access to credit will likely limit the yield benefits of incremental adaptation (Karapinar and Özertan, 2020). In Nepal, conservation policies restrict traditional grazing inside national parks, which promotes intensive agriculture and limits other cropping systems that have been implemented as climate change adaptation (Aryal et al., 2014).
16.5 Key Risks across Sectors and Regions
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16.5.1 Defining Key Risks
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Table 16.4 | Key constraints associated with limits to adaptation for regions
| Region |
Key constraints associated with limits to adaptation |
| Africa |
Financial constraints inhibit implementation of a variety of adaptation strategies including ecosystem-based adaptation (Section 9.11.4.2) and adoption of drought-tolerant crops by farmers (Section 9.12.3).
Information constraints (including limited climate science information), governance constraints (such as communication disconnects between national, district and community levels) and human capacity constraints (limited capacities to analyse threats and impacts) are identified as negatively affecting the implementation of adaptation policies (Section 9.13.1).
Social/cultural constraints (social status, caste and gender) also affect adaptation in contexts with deep-rooted traditions (Section 9.12.4).
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16.5.2 Identification and Assessment of Key Risks and Representative Key Risks
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16.5.2.3 Assessment of Representative Key Risks
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16.5.2.3.4 Risk to living standards (RKR-D)
This RKR includes risks to (i) aggregate economic output at the global and national levels, (ii) poverty and (iii) livelihoods, and their implications for economic inequality. It is informed by key risks identified by regional and sectoral chapters. Risks are potentially severe as measured by the magnitude of impacts in comparison with historical events or as inferred from the number of people currently vulnerable. (...)
iii) Climate change poses severe risks to livelihoods at low levels of warming, high exposure/vulnerability and low adaptation in climate-sensitive regions, ecosystems and economic sectors (high confidence), where severity refers to the disruption of livelihoods for tens to hundreds of millions of additional people (Arnell and Lloyd-Hughes, 2014; Liu et al., 2018). More widespread severe risks would occur at high levels of warming (with high exposure/ vulnerability and low adaptation) where there is additional potential for one or more social or ecological tipping points to be triggered (Cai et al., 2015; Cai et al., 2016b; Kopp et al., 2016; Steffen et al., 2018; Lenton et al., 2019), and for severe impacts on livelihoods to cascade from relatively more climate-sensitive to relatively less climate-sensitive sectors and regions (medium confidence) (Lawrence et al., 2020). Severity assessment is based on the current magnitude of exposure and vulnerability across multiple social and ecological systems, projected future exposure and vulnerability, and the rate at which hazard frequency or intensity is expected to increase (Otto et al., 2017; Roy et al., 2018; Li et al., 2019, Section 8.5). Without effective adaptation measures, regions with high dependence on climate-sensitive livelihoods—particularly agriculture and fisheries in the tropics and coastal regions—would be severely impacted even at low levels of warming (high confidence) (Hoegh-Guldberg et al., 2018b; Roy et al., 2018). For example, it is estimated that 330– 396 million people could be exposed to lower agricultural yields and associated livelihood impacts at warming between 1.5°C and 2°C (Byers et al., 2018). Risks to the 200 million people with livelihoods derived from small-scale fisheries would also be severe, given sensitivity to ocean warming, acidification and coral reef loss occurring beyond 1.5°C (Cheung et al., 2018b; Froehlich et al., 2018; Free et al., 2019; Barnard et al., 2021). Livelihoods in highly exposed locations, such as Small Island Developing States, low-lying coastal areas, arid or semiarid regions, the Arctic, and urban informal settlements or slums, are particularly vulnerable (Ford et al., 2015c; Hagenlocher et al., 2018; Ahmadalipour et al., 2019; Tamura et al., 2019). Within populations, the poor, women, children, the elderly and Indigenous populations are especially vulnerable due to a combination of factors, including gendered divisions of paid and/or unpaid labour, as well as barriers in access to information, skills, services or resources (Bose, 2017; Thomas et al., 2019b; Anderson and Singh, 2020; Adzawla and Baumüller, 2021) (high confidence). Future structural transformation could moderate risk severity by improving adaptive capacity, creating livelihoods in less climate-sensitive sectors, or by enabling sustainable migration to less climate-sensitive locations (Henderson et al., 2017; Roy et al., 2018). However, successful risk moderation would depend upon simultaneous avoidance of both climate-change-related and mitigation-related (Doelman et al., 2019; Fujimori et al., 2019; Doelman et al., 2020) or maladaptation-related risks (Magnan et al., 2016; Benveniste et al., 2020; Schipper, 2020).
16.5.2.3.8 Risks to peace and to human mobility (RKR-H)
Mobility is a universal strategy for pursuing well-being and managing household risks (Section 7.2.6; Cross-Chapter Box MIGRATE in Chapter 7, UN, 2018) and, where it occurs in a safe and orderly fashion, can reduce social inequality and facilitate sustainable development (Franco Gavonel et al., 2021). Involuntary mobility constitutes a key risk because it implies reduced human agency with high potential for significant economic losses and non-material costs, an unequal gender burden, and amplified vulnerability to other RKRs (Schwerdtle et al., 2018; Adger et al., 2020; Maharjan et al., 2020; Piggott-McKellar et al., 2020). Climate change also may erode or overwhelm human capacity to use mobility as a coping strategy, producing involuntarily immobile populations (Adams, 2016). A severe impact is when a large share of an affected population is forcibly displaced or prevented from moving, relative to normal mobility patterns, at local to global scale. However, because mobility may be a favourable mechanism for reducing risk or an adverse outcome of risk, depending on the circumstances under which it occurs, it is not possible to specify a simple quantitative threshold for when impacts become severe.
16.5.4 RKR Interactions
Interactions at the RKR level (Figure 16.11, panel A)—climate change will combine with pre-existing socioeconomic and ecological conditions (grey blocks on the left-hand-side of panel A in Figure 16.10) to generate direct and second-order effects (black plain arrows) both on the structure and/or functioning of ecosystems (RKR-B) and on some natural processes such as the hydrologic cycle (RKR-G), for example. This then translates into implications not only for biodiversity but also for natural resources that support livelihoods, which will in turn affect food security (especially food availability; RKR-F), water security (especially access to adequate quantities of acceptable quality water; RKR-G) and the living standards of already vulnerable groups and aggregate economic outputs at the global level (RKR-D). CIDs (IPCC, 2021) will also directly affect infrastructure that are critical to ensure some basic conditions for economies to function (RKR-C), for example through transportation within and outside the country, energy production and international trade. Such disturbances to socioecological systems and economies pose climate-related risks to human health (RKR-E) as well as to peace and human mobility (RKR-H). Indeed, while health is concerned with direct influence of climate change, for example through hotter air temperatures impacting morbidity and mortality or the spatial distribution of disease vectors such as mosquitos, it is also at risk of being stressed by direct and secondary climate impacts on living standards, food security and water security (RKR-D, RKR-F, RKR-G, respectively). Increased poverty, increased hunger and limited access to drinkable water are well-known drivers of poor health conditions. The role of impact cascades is even more prominent in the case of peace and human mobility (RKR-H), even though the scientific literature does not conclude on any clear and direct climate influence on armed conflict and human migration. Rather, climate-induced degradation of natural resources that are vital for subsistence agriculture and fisheries, transformational and long-term consequences on livelihoods (e.g., new risks, increasing precarious living conditions, gendered inequity, etc.), and erosion of social capital due to exacerbated tension within and between communities are considered among the main drivers of armed conflicts and forced displacement, therefore highlighting links with water security (RKR-G) and living standards (RKR-D), for example.
16.6 Reasons for Concern Across Scales
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16.6.1 Key Risks and Sustainable Development
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16.6.1.1 Links between Key Risks and Sustainable Development Goals
Within the AR6 cycle, the three IPCC Special Reports have all considered the relationships between climate change impacts and actions and the SDGs. SR15 discussed priorities for sustainable development in relation to climate adaptation efforts (Section 5.3.1, SR15); synergies and trade-offs of climate adaptation measures (Section 5.3.2, SR15); and the effect of adaptation pathways towards a 1.5°C warmer world (Section 5.3.3 SR15). The SRCCL considered impacts of desertification on SDGs 1 (no poverty), 2 (zero hunger), 13 (climate), 15 (life on land) and 5 (gender) (IPCC, 2019a, Figure 3.9). Trade-offs and synergies between SDGs 2 (zero hunger) and 13 (climate action) at the global level were recognised (IPCC, 2019a, Section 5.6.6, Figure 5.16). Various integrated response options, interventions and investments were also evaluated within the SDG framework (IPCC, 2019a, Section 6.4.3). The SROCC (Chapter 5) concluded that climate change impacts on the ocean, overall, will negatively affect achieving the SDGs, with 14 (life below water) being most relevant (Singh et al., 2019).
Many linkages between SDG 13 (climate action) and other SDGs have been identified (very high confidence) (Blanc, 2015; Kelman, 2015; Northrop et al., 2016; Hammill and Price-Kelly, 2017; ICSU, 2017; Mugambiwa and Tirivangasi, 2017; Dzebo et al., 2018; Major et al., 2018; Nilsson et al., 2018; Sanchez Rodriguez et al., 2018). In addition, interactions between different climate change actions and SDGs, and interactions among SDGs themselves, have also been assessed (Nilsson et al., 2016; IPCC, 2018a; McCollum et al., 2018; Fuso-Nerini et al., 2019; IPCC, 2019b; Cernev and Fenner, 2020). The Cross-Chapter Box GENDER in Chapter 18 assessment indicates the importance of gender considerations in achieving success and benefits in adaptation efforts. Aligning climate change adaptation to the SDGs could bring potential co-benefits and increased efficiency in funding, and reduce the gap between adaptation planning and implementation (very high confidence) (IPCC, 2018a; Sanchez Rodriguez et al., 2018; IPCC, 2019b; IPCC, 2019a).
16.6.1.2 Results, Implications and Gaps
Linkages between the 17 SDGs and the eight RKRs (Figure 16.12 bottom left panel) are mapped to the regional SDG status (Figure 16.12 bottom right panel) and related to the CIDs (Figure 16.12 top left panel). Interconnections between CIDs and RKRs are complicated by the possibility of concurrent weather events, extremes and longerterm trends. Risks are compounded by existing vulnerabilities (Iwama et al., 2016; Thomas et al., 2019b; Birkmann et al., 2021) and cascading consequences (Pescaroli and Alexander, 2015; Pescaroli and Alexander, 2018; Yokohata et al., 2019) (see, for example, Sections 3.4.3.5, 5.12, 6.2.6, 7.2.2.2) as well as interactions. The level of challenges faced in attaining the SDGs is one metric for assessing vulnerability and lack of capacity to manage risks (Cernev and Fenner, 2020). Other metrics are also available (Parker et al., 2019; Garschagen et al., 2021b; Birkmann et al., 2022). From Figure 16.12, aside from SDG13 (climate action), the strongest connections and risk challenges are with zero hunger (SDG2), sustainable cities and communities (SDG11), life below water (SDG14), decent work and economic growth (SDG8), no poverty (SDG1), clean water and sanitation (SDG6) and good health and well-being (SDG3) (high confidence). Other SDGs have strong linkages with specific RKRs, for example, terrestrial and marine ecosystems with life on land (SDG15); infrastructure (RKR-C) with industry, innovation and infrastructure (SDG9) and affordable and clean energy (SDG7); living standards (RKR-D) with gender equality (SDG5); and peace and human mobility (RKR-H) with peace, justice and strong institutions (SDG 16) (high confidence).
[...] The analysis of RKR linkages to SDGs is also useful in identifying gaps and susceptibilities, especially for developing future climate resilient development targets. This aspect is discussed further in Chapter 18. Gaps may arise as SDG targets and indicators are not specifically focused on systems affected by climate change risks or impacts. For example, in the SRCCL Section 7.1.2, Hurlbert et al. (2019) noted the absence of an explicit goal for conserving freshwater ecosystems and ecosystem services in the SDGs. Such gaps (Tasaki and Kameyama, 2015; Guppy et al., 2019) are inevitable as the current SDG targets and indicators focus on overall sustainable development. As another example, projected increases in frequency and intensity of hot temperature extremes are likely to result in increased heat-related illness and mortality, yet heat extremes are not called out as an SDG indicator under SDGs 3 (good health and well-being) or 13 (climate action). The gaps on climate-related metrics for impacts on health are just beginning to be evaluated (Lloyd and Hales, 2019, see also Section 7.1.6). The current SDG 13 (climate action) targets also do not specifically track the possibility of differential impacts on society from disasters and extreme weather events (RFC2). For example, the first indicator (Section 13.1.1.1), ‘Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population’, does not include any requirement for disaggregated data, unlike several other socioeconomic and population SDG indicators, making it difficult to track the different effects that climate-related disasters are expected to have on men, women and children across different segments of society, relevant for distributional impacts (RFC3) (see also Section 8.3, Cross-Chapter Box GENDER in Chapter 18). The risk consequences identified and discussed in each RKR (Section 16.5.2) provide useful entry points for identifying indicators and metrics for monitoring and evaluating specific impacts of key climate change risks. In addition, the sector and region chapters have considered various adaptation responses relevant to the SDGs (see, for example, Sections 3.6, 4.7.5, 5.13.3, 8.2.1.6, 10.6.1, 13.11.4, 14.6.3) with relevant metrics for evaluation.
Figure 16.12 | Linkages between the projected climatic impact drivers (CIDs) by region, Sustainable Development Goals (SDGs) by region, and the representative key risks (RKRs).
Please refer to page 2492 to see Figure 16.12, which mentions gender.
