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

=== 8.3.2 Global Hotspots of Human Vulnerability to Climate Change ===

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==== 8.3.2.1 Hotspots and Spatial Patterns of Multidimensional Vulnerability ====

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The assessment of literature published since the AR5 suggests that alongside already deteriorated specific conditions that determine individual vulnerability and livelihood security to climate change (see [[#8.2|Section 8.2]] ), high levels of poverty, lack of access to basic services (human rights to water and sanitation), poor governance and conflicts are important factors that characterise vulnerability and systemic human vulnerability in particular (EC-DRMKC, 2020; [[#Wisner--2020|Wisner, 2020]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#GIZ--2021|GIZ, 2021]] ). These context conditions within a country or region limit the access to effective adaptation options particularly for the poor and marginalised groups.

Recent studies underscore that human vulnerability—thus the predisposition to be adversely affected—is largely determined by past and present development processes, rather than by the occurrence of individual events ( [[#Wisner--2016|Wisner, 2016]] ; [[#Cutter--2018|Cutter, 2018]] ; [[#Birkmann--2020|Birkmann et al., 2020]] ). Also the consequences of the COVID-19 pandemic will create newly poor, particularly in countries that are already characterised by high levels of vulnerability (see Box 8.3; [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Lakner--2020|Lakner et al., 2020]] ).

Quantitative studies and assessments published since AR5 provide additional insights about human vulnerability to climate change and resilience of societies at different scales using different indicator sets and approaches ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; EC-DRMKC, 2020; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ).

While quantitative measures of vulnerability are widely used at different scales ( [[#Cutter--2016|Cutter et al., 2016]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ), there are also studies that caution the use of such indices in policy making or risk reduction efforts ( [[#Rufat--2019|Rufat et al., 2019]] ; [[#Spielman--2020|Spielman et al., 2020]] ). Such assessments of vulnerability have to be internally and externally validated and handled with care when applied in decision-making processes in terms of their options and limits. At the same time, these assessments capture important conditions and structures that make people more susceptible to various climate hazards and climate change impacts. The relevance of these conditions is confirmed by quantitative impact assessments as well as many specific case study assessments ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). For example, the access to basic services (e.g., water and sanitation) ( [[#Bollin--2013|Bollin and Hidajat, 2013]] ; [[#Pandey--2017b|Pandey et al., 2017b]] ; [[#UNEP--2018|UNEP, 2018]] ; [[#United%20Nations--2018|United Nations, 2018]] ; [[#Gupta--2020|Gupta et al., 2020]] ; [[#Jamshed--2020a|Jamshed et al., 2020a]] ) and broader modes of engagement in governance and governance fragility ( [[#Crawford--2015|Crawford et al., 2015]] ; [[#Rahman--2018|Rahman, 2018]] ; [[#Andrijevic--2020|Andrijevic et al., 2020]] ) significantly influence how climatic hazards translate into severe or non-severe losses and harm (see [[#8.5.2|Section 8.5.2]] ).

The lack of such support structures and resources can severely constrain opportunities of people to cope with and adapt to climate change, since it is not only the climate hazard, but also exposure and particularly the vulnerability of a society, a specific community or an individual household that determine adverse societal consequences of climatic hazards. International vulnerability and resilience assessments show that vulnerability varies across countries of similar wealth or income because multidimensional vulnerability, well-being and resilience depend on a larger set of factors ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019). In this regard, vulnerability assessment is significantly different from climate exposure mapping.

The findings of these global assessments suggest, among other issues, that options to reduce vulnerability and enhance resilience do exist in various countries at different levels, in part irrespective of their income level ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ). Vulnerabilities at national and regional-level influence community and individual vulnerability, particularly through structures that determine entitlements, the access to resources and processes of marginalisation ( [[#Watts--1993|Watts and Bohle, 1993]] ; [[#Thomas--2019|Thomas and Warner, 2019]] ).

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

Recent comparative studies of global assessments of vulnerability show ''high agreement'' on the spatial clusters that have very high or very low vulnerability to climate change, compared to larger differences in terms of exposure and risk ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#Schleussner--2021|Schleussner et al., 2021]] ). The comparison of the averaged ranking results at the scale of ‘climate regions’ using the vulnerability components of INFORM and the WorldRiskIndex—as two comprehensive global assessment approaches of systemic vulnerability (hazard independent vulnerability) (see Figures 8.5; 8.6)—also finds a ''high agreement'' in terms of most vulnerable regions and regions with low vulnerability (Figure 8.5; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). The assessment at this scale reveals that global hotspots of human vulnerability can be found in climate regions in East Africa, Central Africa and West Africa, followed by high vulnerability in Central America, South Asia and Southeast Asia, for example. [[#Garschagen--2021|Garschagen et al. (2021)]] in a comparison of further risk indices also found that there is ''high agreement'' on global assessments of vulnerability compared to exposure or overall risk.

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'''Figure 8.5 |''' '''Aggregated vulnerability map at the scale of climate regions based on the averaged ranking of the INFORM Index’s vulnerability component and the averaged ranking of the vulnerability component of the WorldRiskIndex.''' Based on the rankings of the INFORM index (INFORM, 2019) and the WorldRiskIndex ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ). The map and diagram show agreement between the two global vulnerability indices when ranking climate regions according to their vulnerability—darker colours show regions of higher vulnerability. The diagram shows how the 35 climate regions are ranked by each index and also serves as a legend for the map above.

The analysis of vulnerability assessment results of the INFORM Risk Index and WorldRiskIndex [[#footnote-002|4]] at the level of countries coupled with population data confirms a ''high agreement'' on most vulnerable countries. It also shows that global hotspots of human vulnerability are not just single countries, but often emerge within regional clusters, particularly in Africa, but also in Asia and Central America (see Figure 8.6 and [[#Birkmann--2021a|Birkmann et al., 2021a]] ). These regional clusters (Figure 8.6) are characterised by high levels of vulnerability in terms of socioeconomic, demographic, environmental and governance conditions that make people more likely to face adverse consequences once a climate hazard occurs. The internal and external validation of these index systems shows its statistical validity and robustness ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). It also confirms a quantitative relationship between most vulnerable regions and fatalities and severely affected people due to climate-influenced hazards ( [[#Birkmann--2022|Birkmann et al., 2022]] ). The vulnerability map in Figure 8.5 shows the vulnerability level (systemic societal vulnerability) linked to national scale and provides additional information about the population density within these countries. The background map does not show specific vulnerable populations within countries. Selected examples of sub-national human vulnerabilities have been added as additional information in terms of case studies based on information from other chapters within this report (see, for example, Box 8.7; Sections 5.12; 10.3.3; 10.5.1; 13.8.1; 14.4.7; 15.3.4; Cross-Chapter Paper 6.2.7).

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[[File:3dde51887ab6ef849965df6708b7cd62 IPCC_AR6_WGII_Figure_8_006.png]]

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

Figure 8.7 provides an aggregated regional overview of selected indicators used within the vulnerability index mapped in Figure 8.6. The overview shows that the many compounded challenges faced by African countries are starkly pronounced, but also in other regions, especially Asia, Central and South America, and among SIDS, there are several challenges such as inequality, governance issues and displacement, which all increase the vulnerability and constrain adaptive capacities of these regions to climate change.

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

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

Countries and regional clusters with low vulnerability (see Figures 8.5; Figure 8.6), such as Australia and New Zealand or Iceland and North Europe, encompass population groups that are exposed and vulnerable to climate hazards, such as sea level rise or droughts but, within these regions’ context, conditions exist that allow the negative impacts and losses to be buffered (also for most vulnerable groups). These regions have higher financial and institutional capacities to support people at risk and planned adaptation at a different magnitude within their region, for example, as seen in compensation payments for drought exposed farmers ( [[#Hochrainer-Stigler--2017|Hochrainer-Stigler and Hanger-Kopp, 2017]] ; Australian-Government, 2021) or flood affected households in Germany in 2021. Also, the percentage of households insured against climate-influenced hazards, such as floods or storms, is significantly higher in these regions (North America, Western Europe) compared to regions such as Western Africa or Micronesia ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ).

While climate change differentially impacts people in vulnerable situations within countries, including the poor, children, women, marginalised Indigenous or other ethnic minority people ( [[#Rhiney--2016|Rhiney et al., 2016]] ; [[#Méndez--2020|Méndez et al., 2020]] ), the global assessment results underscore that, in most vulnerable regions and countries, very limited resources and structures exist to support these groups when droughts, floods or storms occur and place an additional burden on these groups.

The assessments of human vulnerability also point towards important adaptation options that are not visible if one focuses on climatic hazards or temperature changes alone (Figure 8.9; [[#Dückers--2015|Dückers et al., 2015]] ; [[#Cutter--2016|Cutter et al., 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Fundamental for vulnerability reduction and adaptation are social insurances and infrastructure programmes, as well as legislation that improves the access of poor and marginalised groups to basic infrastructure services and security. For example, the ‘free basic service programme’ of the national government of South Africa (GovSA, 2021) is one example where a national government (Government of South Africa) has committed itself to providing a basic amount of free water, electricity and sanitation to low-income households, particularly indigent people, such as those living in informal settlements or remote rural areas. Coupled with incentives, for example in terms of a higher use of renewable energy (e.g., solar home systems in rural areas) (see GovSA, 2021), these investments can support vulnerability reduction and mitigation of GHG emissions. However, the programme design and implementation has also been criticised (see [[#Nel--2005|Nel and Rogerson, 2005]] ; [[#Muller--2008|Muller, 2008]] ), as is witnessed by ongoing service delivery protests ( [[#Mutyambizi--2020|Mutyambizi et al., 2020]] ). This example shows that current national programmes can—even if they are not classified as adaptation measures—provide important entry points to reduce human vulnerability to climate change.

The relevance of human vulnerability has also been confirmed by recent assessments. The assessment of vulnerability studies and mortality data found that the average mortality [[#footnote-001|5]] from floods, storms and droughts is 15 times higher in regions and countries ranked as very highly vulnerable (e.g., Afghanistan, Haiti, Mozambique, Nigeria, Somalia) compared to regions and countries with very low vulnerability (e.g., Canada, Italy, Sweden, UK) ( [[#Birkmann--2022|Birkmann et al., 2022]] ). These patterns are confirmed by other studies (e.g., [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#CRED%20and%20UNDRR--2016|CRED and UNDRR, 2016]] ; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ) that examined disaster mortality per hazard event in low and lower middle income countries compared to high income countries and therewith also point towards major differences between countries with high and low vulnerability ( [[#Pelling--2004|Pelling et al., 2004]] ; [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#CRED%20and%20UNDRR--2016|CRED and UNDRR, 2016]] ; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ). Even if one takes solely ‘highly vulnerable countries’ such as India, Pakistan and the Philippines (and not ‘very highly’ vulnerable countries), mortality is still nine times higher compared to very low vulnerability countries. Similarly, studies further revealed that average number of adversely affected people per hazard event (e.g., loss of the house) is 11 times higher in regions and countries categorised as having very high vulnerability compared to very low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). In addition to floods, droughts and storms, published EM-DAT data for wildfires and heat stress, confirmed higher suffering (higher average mortality) in more vulnerable regions compared to less vulnerable regions, particularly when excluding extreme outliers ( [[#CRED--2020|CRED, 2020]] ). These findings point towards the fact that in regions identified as highly vulnerable in the assessments even moderate future climate change and future climate hazards are likely to push people further into poverty and lead to significant destabilisation processes in terms of livelihoods security ( [[#Wallemacq--2018|Wallemacq and House, 2018]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ).

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===== 8.3.2.1.1 Historic roots of vulnerability in regions classified as highly vulnerable =====

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While increasing attention is given to issues of human vulnerability, less attention has been given to the historical conditions that foster systemic vulnerability of societies. It is important to acknowledge that drivers and root causes of systemic human vulnerabilities and development challenges are not always new, and sometimes—for example in various countries in Africa, Asia and the Caribbean—can be linked to histories of imperialism, colonial structures ( [[#Grasham--2019|Grasham et al., 2019]] ), and subsequent development and governance contexts ( [[#Southard--2017|Southard, 2017]] ; [[#Zhukova--2020|Zhukova, 2020]] ). Thus, root causes of present structures of human and human–environmental vulnerability often have historic dimensions, for example, chronic poverty and structural inequality in Africa ( [[#Grasham--2019|Grasham et al., 2019]] ) or the Caribbean are still influenced by the colonial power relations outside of these countries making solutions for vulnerability reduction more difficult (see e.g., [[#Douglass--2020|Douglass and Cooper, 2020]] ). In addition, national borders, such as in many regions in Africa, sometimes cut through ethnic groups and therewith ignore important interrelations between communities on both sides of the border.

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===== 8.3.2.1.2 People residing in most vulnerable versus least vulnerable regions =====

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While global assessments often allow for country rankings, it is similarly important to better understand how many people are living in these different levels of vulnerability. The quantitative assessments underscore that a significantly higher number of people live in countries with very high and high vulnerability compared to the population living in countries classified as having low and very low vulnerability. An analysis that measured the vulnerability of countries according to the INFORM Risk Index and the WorldRiskIndex vulnerability index components, differentiating vulnerability values into seven vulnerability classes found that nearly twice as many people are living in most vulnerable countries compared to the number living in less vulnerable countries ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Another study that uses the same data and differentiates vulnerability into five classes (also considering the lack of coping capacity within the INFORM index, see ( [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] )) concludes that about 3.3 billion people are living in countries classified as highly vulnerable, while approximately 1.8 billion people live in countries with low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Additional assessments based on the classification of income groups of countries reveal that approximately 3.6 billion people live in low and lower middle-income countries, which are most vulnerable and disproportionally bear the human costs of disasters due to extreme weather events and hazards (World Bank, 2019b; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ; EC-DRMKC, 2020; [[#UN-DESA--2020a|UN-DESA, 2020a]] ; [[#UN-DESA--2021|UN-DESA, 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). While these numbers are different, both results underscore that the absolute and relative number of people living in most vulnerable contexts is significantly higher compared to those that live in a country with a low vulnerability status ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). These differences have also been observed in former years ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ).

That means, even moderate changes in the global mean temperature, as identified in the recent IPCC report SR1.5°C ( [[#IPCC--2018c|IPCC, 2018c]] ) and in scientific literature ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ), can mean substantial increases in risks for more than 3 billion people due to high levels of vulnerability.

Overall, there is ''robust evidence'' and ''high agreement'' in the recent literature that countries and regions classified as highly vulnerable face multiple development challenges at once, in which high levels of poverty interact with limited access to water and sanitation or with high levels of forced migration and, in some cases, with state fragility making solutions difficult ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). High levels of vulnerability within these regional clusters are the product of current development challenges, but are often caused by long and complex histories, including issues of colonisation and marginalisation, for example, in hotspots in Africa ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ).

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==== 8.3.2.2 Transboundary Vulnerability and Adaptation ====

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Next to the identification of the level of agreement between different vulnerability assessments ( [[#Garschagen--2021|Garschagen et al., 2021]] ) and the spatial hotspots, global assessments of vulnerability and adaptation readiness also point towards the need for a transboundary perspective and transboundary cooperation in terms of vulnerability reduction and adaptation ( [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Newer research points towards the fact that various phenomena of vulnerability, particularly in highly vulnerable regions, spill over national borders and emerge in rather regional clusters, such as forced migration and poverty in West and Central Africa, as well as conflicts in the Near East and Asia ( [[#IDMC--2020|IDMC, 2020]] ). This means that regional and transboundary challenges contribute to the formation of systemic human vulnerability, for example, forced migration that is occurring within countries, but also across international borders that is also influenced by climate change ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ). In summary, these findings point towards the need for more transboundary approaches in vulnerability and risk reduction, adaptation and development. Recent literature and data presented in Figure 8.6 and ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019; [[#Birkmann--2021a|Birkmann et al., 2021a]] ) demonstrate the need to strengthen approaches to monitor the regional dimensions of vulnerability and to develop strategies and programmes that consider transboundary vulnerability in risk reduction and cooperation at different scales. This includes, for example, cooperation between national-level institutions, but also transboundary networks of cities or communities ( [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#Benzie--2019|Benzie and Persson, 2019]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). The transnational nature of climate change impacts means that addressing them requires concerted efforts among nations ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#Dzebo--2019|Dzebo, 2019]] ).

In addition, national response strategies for specific transboundary climate-influenced hazards, such as river flooding, droughts or coastal flooding can also significantly influence neighbouring countries and can affect exposure and vulnerability of the respective country ( [[#Nadin--2018|Nadin and Roberts, 2018]] ; [[#Booth--2020|Booth et al., 2020]] ). Likewise, climate change may affect transboundary resources (e.g., underground water reserves) and transboundary ecosystems (e.g., in terms of the migration of species) ( [[#Vij--2017|Vij et al., 2017]] ) and thereby further reduce the capacity of vulnerable groups to cope and adapt. In addition, recent research indicates that social inequities are also coupled with access to and quality of environmental resources in urban environments—meaning social and environmental justices are interconnected (see [[#Schell--2020|Schell et al., 2020]] ).

Individual adaptation projects to specific climate hazards in regions classified as highly vulnerable are needed. However, recent studies underscore that deeper development challenges need to be addressed in order to make progress towards adaptation and vulnerability reduction and to avoid maladaptation ( [[#Eriksen--2021|Eriksen et al., 2021]] ). Adaptation and development projects, such as the construction of a dam as a response to water shortages in one country can significantly influence the exposure to water shortages and the response capacities of another country downstream. Often, transboundary challenges are a result of policy and resource management choices or uncertainty, and addressing them requires a greater engagement between governing bodies, which may also guide more suitable responses in the context of climate change adaptation and vulnerability reduction ( [[#Earle--2015|Earle et al., 2015]] ; [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#McLeman--2018|McLeman, 2018]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ).

Most of those countries and regional clusters identified as highly vulnerable have contributed little to the overall amount of GHG emissions and therefore support for (transboundary) adaptation from the international community is required in these places and for those living under these conditions in order to support and achieve climate justice.

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==== 8.3.2.3 The Effect of Higher Levels of Global Warming for Most Vulnerable Regions and Specific Livelihoods ====

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Evidence exists that threats to land-based livelihoods and risks of undernutrition increase significantly with higher levels of global warming ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ). With global warming of 1.5°C or less, impacts of climate change on livelihoods are still significant, for example, for West Africa and the Sahel there will be an estimated reduction of the area suitable for maize production of about 40%. The consequences of global warming of up to 3°C would mean a high risk of undernutrition for entire regions (see [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ) that are already classified as most vulnerable (see Figure 8.6). That means the consequences of significant warming are a particular challenge for regional hotspots of vulnerability. Small changes in crop productivity, already observed due to increasing droughts, floods or changes in rainfall patterns, could lead to severe health risks and undernutrition. This is because of existing precarious living conditions and the limited capacities that people and institutions have to build and enhance coping and adaptive capacities at the level of individual households, communities and state institutions (see [[#UNEP--2018|UNEP, 2018]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). The risk of loss of life, displacement and adverse health consequences due to climate change in these most vulnerable regions (such as Micronesia, South Asia, West Africa—see Figures 8.5; 8.6) is higher compared to regions classified as having medium or low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Nevertheless, other regions and countries classified as less vulnerable, for example in Asia, are experiencing disasters and have a relative high share of the observed global fatalities or losses, when considering non-climatic natural hazards ( [[#CRED%20and%20UNDRR--2020a|CRED and UNDRR, 2020a]] ; see also [[#8.3.2.1|Section 8.3.2.1]] ). In addition, changing climatic hazard and exposure patterns have to be considered. However, the agreement of major global index systems on exposure is significantly lower compared to vulnerability ( [[#Garschagen--2021|Garschagen et al., 2021]] ).

Moreover, the assessment reveals that in most vulnerable regions a double burden of existing destabilised livelihood conditions and additional climatic hazards is already visible and largely influences societal impacts of climate change. For example, flooding along the White Nile in Uganda and South Sudan hit vulnerable communities that were displaced due to conflicts and were thus uprooted again by flooding ( [[#IDMC--2020|IDMC, 2020]] ). Societal impacts and future risks of climate change to societies need to incorporate information about vulnerability and exposure—including capacities of people to cope and adapt ( [[#Wisner--2016|Wisner, 2016]] ; Cardona et al., 2020). There is increasing evidence that individual and societal capacities to cope and adapt also depend on how governmental and national institutions can support people at risk (see [[#8.6|Section 8.6]] ). For example, climate information services depend on a functioning weather service. Likewise, social safety nets as an adaptation strategy require financial resources, which are often absent for most people in highly vulnerable regions. In addition, examples of national programmes that target most vulnerable groups, such as the free basic service programme in South Africa, show that next to the adaptation to individual hazards, strategies exist that aim to reduce systemic human vulnerability (see GovSA, 2021).

At the same time, there is scientific evidence that more intense and frequent climate-influenced hazards (e.g., storms, flooding, droughts, heat stress) can undermine decade-long poverty reduction efforts, particularly in most vulnerable regions ( [[#Mysiak--2016|Mysiak et al., 2016]] ; [[#Formetta--2019|Formetta and Feyen, 2019]] ; [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Lakner--2020|Lakner et al., 2020]] ). There is ''high agreement'' that, with global warming of about 3°C, such undermining of poverty reduction efforts will intensify and more regions will face development setbacks due to the spatial and temporal expansion of climate hazards, including the further erosion of capital that enables people to develop adaptive capacities ( ''high confidence'' ) (see [[#8.5|Section 8.5]] ). Such trends can further exacerbate poverty traps (see [[#8.2|Section 8.2.2]] ). According to a World Bank report, between 32 and 132 million people could fall into extreme poverty by 2030 due to the impacts of climate change ( [[#Jafino--2020|Jafino et al., 2020]] ). Models estimate that at 3°C warming and under Shared Socioeconomic Pathway (SSP) 1, there would be an additional 245 million people exposed to poverty. Under SSP2 this number would increase to 904 million additional people exposed to poverty (SSP2) and under SSP3 (with significant challenges for equity) about 1918 million additional people could be exposed to poverty in the year 2050 ( [[#Byers--2018|Byers et al., 2018]] ).

Overall, the assessments above underscore that adaptation and risk reduction require not only information about changing climatic conditions, but also assessments that capture the development contexts and structural inequality that determine and influence human vulnerability. Strategies that reduce poverty and inequality and that improve the access of people to basic services need to become a higher priority in adaptation and development planning in order to avoid more than 3 billion people currently and even more in the future being exposed to severe adverse consequences of climate change. Reducing vulnerability to climate change is therefore indispensable for climate justice and just transitions ( ''high confidence'' ).

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==== 8.3.2.4 Compound Challenges: Vulnerability and State Fragility ====

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Literature in the area of climate change risk management and adaptation highlights the importance of overall governance systems and their functioning and inclusiveness in terms of vulnerability and risk reduction ( [[#Burch--2019|Burch et al., 2019]] ). Empirical evidence and scientific studies show linkages between issues of governance, conflicts and high levels of state fragility and systemic human vulnerability (see Figure 8.8; [[#8.5.2|Section 8.5.2]] ; [[#Eklöw--2019|Eklöw and Krampe, 2019]] ; [[#Peters--2019|Peters et al., 2019]] ; [[#Mawejje--2020|Mawejje and Finn, 2020]] )

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[[File:84ace6fe482c11ce5c7266c2d11c4834 IPCC_AR6_WGII_Figure_8_008.png]]

'''Figure 8.8 |''' '''Comparison of the vulnerability and state fragility of global regions.''' The vulnerability values are the average of the vulnerability component of the WorldRiskIndex 2019 ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ) and the vulnerability and lack of coping capacity components of the INFORM Risk Index 2019 ( [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ) classified into five classes using the equal count method ( [[#Birkmann--2022|Birkmann et al., 2022]] ). The state fragility values are based on the Fragile States Index 2019 ( [[#FFP--2020|FFP, 2020]] ) and regions are based on the intermediate and sub-regions of the United Nations Statistical Division. The size of each circle is proportional to the population (World Bank, 2019b) in the respective region.

The comparison of state fragility and vulnerability at the level of regions (United Nations Statistics Division regions) based on the vulnerability information of the INFORM and WorldRiskIndex systems and information from the Failed State Index indicates clear linkages (see Figure 8.8), meaning that societal development and governance challenges often interact and, in many cases, are influenced by complex histories (see [[#FFP--2020|FFP, 2020]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). Strategies to reduce systemic vulnerability and multidimensional poverty have to account for these broader governance challenges that hamper resilience building and the development of adaptive capacities to climate change at various levels.

Strategies to strengthen adaptation to climate change have therefore to acknowledge these interdependencies between climate change, vulnerability, development and governance (see [[#8.6|Section 8.6.5]] ). The results of different global vulnerability assessments and the role of governance conditions underscore that next to individual adaptation projects in specific sectors, integrated strategies and programmes are needed that reduce systemic vulnerability and support enabling conditions for adaptation for most vulnerable groups (see [[#8.6|Section 8.6.5]] ).

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==== 8.3.2.5 Trends in Vulnerability and Poverty in Light of Climate Change and the COVID-19 Pandemic ====

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Literature that assesses trends of poverty and vulnerability, as well as exposure to climate change, reveals that geographic patterns of poverty and vulnerability are uneven and changing over time ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ). However, a robust finding of different studies is that population growth in most vulnerable country groups and regions ‘is’ and ‘will be’ significantly higher in the future compared to population growth in countries classified as having low vulnerability (see [[#8.4.5.2|Section 8.4.5.2]] ). In summary, a significant increase of population is expected in highly vulnerable countries in the future. In addition, global studies predict that, by 2030, almost 50% of the world’s poor will be living in countries affected by state fragility, conflict and violence ( [[#UNISDR--2009|UNISDR, 2009]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ).

Another important phenomenon that modifies trends in vulnerability to climate change and poverty is the COVID-19 pandemic (see Box 8.3). It is ''likely'' that the COVID-19 pandemic with its global repercussions will continue to modify and, in many cases, intensify poverty and human vulnerability ( [[#Laborde--2020a|Laborde et al., 2020a]] ; [[#Sumner--2020|Sumner et al., 2020]] ). Recent studies that estimate the impact of COVID-19 on global poverty agree that a significant increase of poverty due to COVID-19 and the respective lockdown of countries is already observed or expected in the near future ( [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Sumner--2020|Sumner et al., 2020]] ). These studies underscore that 80% of those newly living in extreme poverty (living on under 1.9 USD d −1 ) due to COVID-19 would be mainly located in two global regions: sub-Saharan Africa and South Asia ( [[#Sumner--2020|Sumner et al., 2020]] ). Consequently, the COVID-19 pandemic is ''likely'' to further increase inequality at different scales and increase the burden within regions already characterised by a significant adaptation gap in terms of high vulnerability (see also Figure 8.6). This implies that the capacity of people to prepare for present and future climate change impacts will further decrease within these countries and for specific vulnerable people or groups in these regions.

Recent scientific studies in the context of climate-influenced hazards and disasters also underscore that various regions and countries classified as highly vulnerable are characterised by a high persistence of human vulnerability and chronic poverty ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#UN-DESA--2020b|UN-DESA, 2020b]] ; [[#World%20Bank--2020|World Bank, 2020]] ). For example, various highly vulnerable regions in Central, West and East Africa, countries such as Afghanistan, Democratic Republic of Congo and Haiti, and also SIDS in Melanesia and Micronesia have been characterised by high levels of poverty for decades ( [[#World%20Bank--2020|World Bank, 2020]] ). Several of these highly vulnerable regions are also ''likely'' to experience a further increase in climate hazards such as sea level rise in Melanesia and Micronesia and in coastal zones of West Africa and more severe droughts in Africa ( [[#IPCC--2021|IPCC, 2021]] ).

There is ''robust evidence'' that in many world regions the exposure to climatic hazards is increasing with additional global warming ( [[#Chin-Yee--2019|Chin-Yee, 2019]] ; [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ; [[#IPCC--2021|IPCC, 2021]] ). In addition, development patterns and practices such as urbanisation and migration to exposed areas, for example, to coastal zones in West Africa or South Asia is increasing exposure. While the spatial and temporal exposure to impacts from climate change and extreme events increases with higher levels of global warming ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ), in all global regions and various climate zones ( [[#IPCC--2021|IPCC, 2021]] ), the burden is greater for the most vulnerable regions where people have limited support and capacities to build adaptive capacities for future impacts of climate change.

In this regard, vulnerability assessment results provide an important additional layer of information for decision making in terms of defining adaptation and risk reduction needs and priorities, as shown in Figure 8.9. The figure shows the published climatic information regarding observed changes in agricultural and ecological droughts ( [[#IPCC--2021|IPCC, 2021]] ) combined with a background map of vulnerability. For example, the combined information reveals that even if the agreement on the type of changes observed in droughts is low for North and southeast Africa, it is the high vulnerability in this region that requires urgent attention (see Figure 8.9).

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[[File:d35a1a467bfe34c59d3637df6676ce1d IPCC_AR6_WGII_Figure_8_009.png]]

'''Figure 8.9 |''' '''Map with observed changes in agricultural and ecological droughts''' '''( [[#IPCC--2021|IPCC, 2021]] ) overlaid over human vulnerability (see Figure 8.''' '''6) provides a more comprehensive overview for defining adaptation priorities.'''

Recent reports on extreme poverty and human rights ( [[#Alston--2019|Alston, 2019]] ) show that millions already face malnutrition due to devastating drought. In addition, the linkages between ecosystem vulnerability and human vulnerability and human well-being are important aspects that need more attention, since, for example, the degradation of marine ecosystems that support food systems for hundreds of millions of people will threaten food security (see for details Cross-Chapter Box MOVING PLATE in Chapter 5).

While the findings of the Alston report underscore the urgency to act in order to protect people’s livelihoods, particularly in low-income countries, it also shows that extreme poverty ( [[#Alston--2019|Alston, 2019]] ) and different dimensions of poverty are found in middle- and high-income countries.

A study of the World Bank ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ) estimates that losses in terms of well-being are significantly higher than actual asset losses experienced ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ). A higher proportion of the global absolute economic losses occurred in high-income countries. About 56% of all disasters reported occurred in high-income countries, while the low-income countries account for 44% of the recorded disasters. However, low-income countries account for about 68% of the total deaths reported, high-income countries for about 32% ( [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ). In contrast, average absolute economic losses [[#footnote-000|6]] were significantly lower in most vulnerable countries compared to low vulnerable countries ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Economic loss trends from EM-DAT database ( [[#CRED--2020|CRED, 2020]] ) must be interpreted with caution. Economic loss data is often incomplete and needs to be improved. However, these differences in terms of economic losses can also be explained in part by the significant wealth differences and the monetary value of assets exposed. Consequently, there is a need to critically reflect on the measures used to assess L&amp;D from climate change. Interestingly, the number of people affected by droughts, floods and storms as a percentage of the total population and per hazard event again points to the disproportionate suffering of most vulnerable countries ( [[#Birkmann--2022|Birkmann et al., 2022]] ).

Overall, there is ''robust evidence'' that at the global scale poor and most vulnerable people, particularly in regions classified as highly vulnerable, are disproportionately affected by well-being losses and loss of life in the context of climate change and climate-influenced natural hazards ( [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ) ( ''high agreement'' ). In this context, non-economic losses also need to receive more attention (see [[#8.3.3.2|Section 8.3.3.2]] ). While there is an emerging understanding that inequality and multidimensional poverty are important determinants of systemic vulnerability to climate change ( [[#Dennig--2015|Dennig et al., 2015]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Islam--2017|Islam and Winkel, 2017]] ) that affects more than 3 billion people today, only very few countries explicitly aim to reduce poverty and income inequality as an adaptation measure (see e.g., [[#Brazil%20Ministry%20of%20Environment--2016|Brazil Ministry of Environment, 2016]] ) ''(high agreement)'' . Reducing vulnerability is a prerequisite for climate justice and just transitions.

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