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

== 8.2 Detection and Attribution of Observed Impacts and Responses ==

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

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This section reports on new evidence on the observed impacts of climate change to livelihoods and the poor since the previous assessment ( [[#IPCC--2014a|IPCC, 2014a]] ). New evidence provides additional insight into the interlinkages between climate change, poverty and livelihoods. New evidence has been evaluated according to climate change hazard categories developed for the AR6 ( [[#IPCC--2021|IPCC, 2021]] ), and summarised in Figure 8.2.

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'''Figure 8.2 |''' '''Summary of confidence on the observed impacts of 23 climate hazards on nine key livelihood resources on which the poor depend most.'''

'''(a)''' A total of 207 confidence statements on the total set of livelihood impacts. Based on a standardised assessment of available literature since the AR5 ( [[#IPCC--2014a|IPCC, 2014a]] ), each impact category was assigned a confidence statement based on weight of evidence; ''high confidence'' is represented with HC, ''medium confidence'' with MC and ''low confidence'' with LC. An average numerical confidence score is assigned for impacts from each climate hazard, and for each livelihood resource category, representing total risk.

'''(b)''' The ‘high-risk’ cluster of livelihood impacts, where confidence is highest. '''(c)''' The spatial distribution of relative confidence. Hotspots represent highest confidence of observed livelihood impacts; however, the absence of spatial information reflects not an absence of observed livelihood risk, but the relative weight of evidence sampled in this assessment exercise.

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==== 8.2.1.1 Interactions Between Climate Hazards and Non-climatic Stressors Affecting Livelihoods ====

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New evidence highlights the potential for multi-hazard risks to push the poor into persistent traps of extreme poverty ( [[#Räsänen--2016|Räsänen et al., 2016]] ). Risk of extreme impoverishment increases for low-income people experiencing repeated and successive climatic events, whereby before they have recovered from one disaster, they face another impact ( [[#Forzieri--2016|Forzieri et al., 2016]] ). Cascading and compounding risks arise from multiple climate hazards coinciding to produce impacts, for example, in mountainous regions, where the combination of glacier recession and extreme rainfall result in landslides ( [[#Martha--2015|Martha et al., 2015]] ). There is ''robust evidence'' that this effect has been observed around slow- and rapid-onset climate events related to drought (i.e., rising temperatures, heatwaves and rainfall scarcity), with devastating consequences for agriculture ( [[#Vogt--2018|Vogt et al., 2018]] ; [[#Bouwer--2019|Bouwer, 2019]] ). In particular, the urban and rural landless poor face difficulties rebuilding assets following one-off disasters or a series of shocks ( [[#Garcia-Aristizabal--2015|Garcia-Aristizabal et al., 2015]] ).

Climate change is one driver among many that challenges livelihoods of the rural poor, including economic transitions associated with industrialisation and urbanisation, and also governance failures such as unclear property rights and civil conflict (e.g., [[#Nyantakyi-Frimpong--2015|Nyantakyi-Frimpong and Bezner-Kerr, 2015]] ). Recent research adds evidence about the ways that climate hazards impact non-climatic stressors with implications for poverty reduction ( [[#Nelson--2016|Nelson et al., 2016]] ). The risk that climate hazards may push the poor into persistent extreme poverty intensifies with stagnant wages, rising costs of living, mobility traps, and ethnic or religious discrimination ( [[#Cramer--2014|Cramer et al., 2014]] ; [[#Carter--2016|Carter et al., 2016]] ). Likewise in both urban and rural environments, non-climatic factors related to governance exacerbate the impacts of climate events among the poorest, including poor service provisioning (e.g., waste collection), poor urban planning (e.g., waste water drainage) and water management failures ( [[#Di%20Baldassarre--2010|Di Baldassarre et al., 2010]] ; [[#Leal%20Filho--2018|Leal Filho et al., 2018]] ), as well as poor rangeland management, intensification of farming land uses (i.e., overgrazing, deforestation), degradation of wetlands, shortage of water and soil erosion in rural areas ( [[#Olsson--2019|Olsson et al., 2019]] ).

A key risk for the poor is shocks to specific livelihood assets that may force low-income groups into persistent poverty traps (Figure 8.4; [[#Chambers--1992|Chambers and Conway, 1992]] ; [[#Cinner--2018|Cinner et al., 2018]] ) but research also suggests that climate change impacts are also driving transient forms of poverty, a modality of poverty which is recurring ( [[#Angelsen--2014|Angelsen et al., 2014]] ). Recurrent poverty is, for instance, seen in relation to crop losses and decreasing agricultural production when income losses worsen living conditions ( [[#Ward--2016|Ward, 2016]] ; [[#Kihara--2020|Kihara et al., 2020]] ). Recent research shows that climate change impacts may exacerbate poverty indirectly through increasing cost of food, housing and healthcare, among other rising costs borne by the poor ( [[#Islam--2014|Islam et al., 2014]] ; [[#Ebi--2017|Ebi et al., 2017]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ) ( ''high confidence'' ). Severe adverse impacts of climate change at present and future risks may result from permanent, sudden, destabilising changes accompanying climate events such as decreases in food security, large-scale migration, changes in labour capacity or conflict ( [[#Bentley--2014|Bentley et al., 2014]] ). Overall, there is more evidence that even under medium warming pathways, climate change risks to poverty would become severe if vulnerability is high and adaptation is low ( ''limited evidence, high agreement'' ) (see [[IPCC:Wg2:Chapter:Chapter-16#16.5.2.3|Section 16.5.2.3.4]] )

Reliable and precise estimates of the impacts of climate change on persistent poverty are difficult to generate, for example, due to data scarcity and data gaps ( [[#Hallegatte--2015|Hallegatte et al., 2015]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#Kugler--2019|Kugler et al., 2019]] ). However, progress has been made towards detection and attribution of climate change impacts on the poorest by linking standard climate observations in low-income countries with new non-traditional forms of data (including Indigenous knowledge, historical archival data, satellite imagery, and data from digital devices) ( [[#Kuffer--2016|Kuffer et al., 2016]] ; [[#Lu--2016|Lu et al., 2016]] ; [[#Bennett--2017|Bennett and Smith, 2017]] ; [[#Steele--2017|Steele et al., 2017]] ).

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==== 8.2.1.2 Links Between Climate-related Hazards, Observed Losses, Poverty and Inequality Globally ====

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There is ''high confidence'' that climate-related hazards, including both slow-onset shifts and extreme events, directly affect the poor through adverse impacts on livelihoods (see Figure 8.2), including reductions and losses of agricultural yields, impacts on human health and food security, destruction of homes, and loss of income ( [[#Hallegatte--2015|Hallegatte et al., 2015]] ; [[#Connolly-Boutin--2016|Connolly-Boutin and Smit, 2016]] ). One of the key factors that drives disproportionate impacts among poor households globally is lost agricultural income ( ''high confidence'' ) ( [[#Hallegatte--2015|Hallegatte et al., 2015]] ; [[#Islam--2017|Islam and Winkel, 2017]] ). Also of concern are the impacts of climate hazards to human health, which is a primary resource that the poor rely on (Figure 8.2). There are only few robust global estimates of observed income losses to the poor that comprehensively account for all climate hazards; nevertheless, ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ), estimating average impacts of climate change on incomes of the poor, found that across 92 developing countries, the poorest 40% of the population experienced losses that were 70% greater than the losses of people with average wealth.

Overall, our assessment shows (see Figure 8.2) ''high confidence'' that two categories of climate hazards pose high risk to a broad range of livelihood resources that the poor rely on: warming trends and droughts (Figure 8.2b). Two key livelihood resource categories—life, bodily health and food security, and crop yield (representing agricultural productivity) are most at risk to a broad range of climate hazards ( ''high confidence,'' Figure 8.2b). In addition to warming and drought, both pluvial and fluvial flooding, severe storms and sea level rise represent a high-risk cluster for livelihood impacts ( ''high confidence,'' Figure 8.2b).

Figure 8.2 reflects the fundamental threat that climate hazards pose to the survival of plants, livestock and fish, as well as the people on which livelihoods depend ( ''high confidence'' ) (see [[#Horton--2021|Horton et al., 2021]] ). The dependence of livelihoods on biological, ecological and human survival depicted in Figure 8.2 is also treated in Chapter 5. Likewise, impacts to livelihood resources can be compared to impacts to other key assets (see Working Group I (WGI) [[IPCC:Wg2:Chapter:Chapter-12#12.3|Section 12.3]] ; WGI Table 12.2, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

It is revealed that warming trends and droughts pose greatest risks to the widest array of livelihood resources, and are particularly detrimental to crops and human health, a long-term requirement for livelihoods and well-being ( ''high confidence'' ) (see Figure 8.2B; [[#8.4.5.3|Section 8.4.5.3]] ; [[IPCC:Wg2:Chapter:Chapter-16#16.5.2.3|Section 16.5.2.3.4]] ; [[#Campbell--2018|Campbell et al., 2018]] ). A wide range of hazards also threaten the survival of fish and livestock that livelihoods depend on ( ''high confidence,'' Figure 8.2b), as well as other sources of income for the poor. Salinity is a secondary hazard related to droughts, coastal flooding and sea level rise, and poses a fundamental risk to agriculture ( ''high confidence'' ). There is also ''robust evidence'' for rainfall variability driving short-term impacts to agricultural productivity as well as permanent loss of agriculture ( ''high confidence'' ).

While severe storms, pluvial and riverine floods, and coastal floods primarily impact private livelihood resources, such as homes and income ( ''high confidence,'' Figure 8.2b), warming and droughts also affect common pool resources, such as rangeland, fisheries and forests ( ''high confidence,'' Figure 8.2b). Multiple hazards undermine ecosystems that Indigenous Peoples and poor communities depend on for food security and income and have sustainably managed over the long term, such as forests, grazing land and marine fisheries ( [[#Barange--2014|Barange et al., 2014]] ; [[#Leichenko--2014|Leichenko and Silva, 2014]] ; [[#Béné--2016|Béné et al., 2016]] ; [[#Jantarasami--2018|Jantarasami et al., 2018]] ).

''High confidence'' for observed livelihood impacts is spatially concentrated in South Asia, Africa, North America, and to a lesser extent Small Island Developing States (SIDS) (Figure 8.2c). The hazards most prevalent in all regions include warming trends, droughts and sea level rise (Figure 8.2c), and undermine crop productivity, crop varieties, and cropland in most regions ( ''high confidence'' ). Along coastlines, climate hazards threaten livelihoods particularly exposed to extreme weather, flooding and sea level rise, and where poor populations are heavily dependent on agriculture and fisheries ( ''high confidence'' ). One third of total sampled evidence on livelihood impacts was observed in just three countries—Bangladesh, India and Nepal—indicating accumulating experience with livelihood impacts in South Asia (Figure 8.2c). However, this spatial representation of confidence does not mean that observed livelihood impacts are not occurring in other regions as well. Relative to South Asia, in Central Asia and the Caribbean, for example, the weight of evidence of livelihood impacts though lighter is still ''robust'' . Among industrialised nations, there is ''high confidence'' that climate change has impacted livelihood resources in the USA.

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

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The negative impacts of climate change on groups of vulnerable or marginalised communities generate so-called ‘residual impacts’ and residual risks that can remain a challenge in their lives ( [[#Warner--2013|Warner and Van der Geest, 2013]] ; [[#James--2014|James et al., 2014]] ; [[#Klein--2014|Klein et al., 2014]] ; [[#Boyd--2017|Boyd et al., 2017]] ). Such ‘unacceptable’ L&amp;Ds include the loss of income sources, food insecurity, malnutrition, permanent impacts to health and labour productivity, loss of life and loss of homelands, among others ( [[#McNamara--2019|McNamara and Jackson, 2019]] ; [[#Schwerdtle--2020|Schwerdtle et al., 2020]] ). The literature on L&amp;D provides ''robust evidence'' not only on economic dimensions of global L&amp;Ds, but also experiences of non-economic losses from the impacts of climate change (see detail in [[#8.3|Section 8.3]] ; [[#Barnett--2016|Barnett et al., 2016]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#McNamara--2019|McNamara and Jackson, 2019]] ). The extreme events that have occurred in recent years highlight the potential for L&amp;D, including 2019’s Cyclone Kenneth, the strongest in the recorded history of the African continent, which made landfall in northern Mozambique causing 45 deaths and destroying approximately 40,000 houses, leaving hundreds of thousands at risk of acquiring waterborne diseases such as cholera during a prolonged recovery period ( [[#Cambaza--2019|Cambaza et al., 2019]] ).

In parallel to evidence on L&amp;D, the science of climate event attribution has evolved from a theoretical possibility into a subfield of climate science. As attribution science strengthens, with it the evidence base linking greenhouse gas (GHG) emissions to extreme heat events, heavy rainfall and wind storms grows and becomes more robust ( [[#Otto--2016|Otto et al., 2016]] ; [[#Stott--2016|Stott et al., 2016]] ; [[#Otto--2018|Otto et al., 2018]] ; [[#Otto--2020|Otto, 2020]] ; [[#Clarke--2021|Clarke et al., 2021]] ; [[#van%20Oldenborgh--2021a|van Oldenborgh et al., 2021a]] ; [[#van%20Oldenborgh--2021b|van Oldenborgh et al., 2021b]] ; [[#Verschuur--2021|Verschuur et al., 2021]] ).

Climate justice questions arise about the observed differential L&amp;Ds due to climatic hazards to affected populations in close connection with their vulnerability ( [[#Wrathall--2015|Wrathall et al., 2015]] ). Individual extreme weather events attributable to climate change result in L&amp;Ds in communities and societies, which allow a quantification of the differential impacts of such events on different groups ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ). Considering the disproportionately adverse impacts of climatic hazard on most vulnerable groups and regions and their relatively minor contribution to anthropogenic climate change ( [[#Mora--2018|Mora et al., 2018]] ; [[#Robinson--2018|Robinson and Shine, 2018]] ), it is evident that vulnerability reduction and adaptation to climate change have also to be seen as an issue of climate justice and climate just development ( [[#Byers--2018|Byers et al., 2018]] ).

Probabilistic attribution allows an assessment of people’s future climate risks and estimates about the costs of successfully adapting to them ( [[#James--2014|James et al., 2014]] ; [[#James--2019|James et al., 2019]] ). To answer questions about impacts on people, the vulnerable and poor in particular, requires attribution, vulnerability and adaptation science need to move far beyond understanding physical events and incorporate information (including Indigenous knowledge and local knowledge (IKLK)) on people’s vulnerability and capacities, and exposure and losses resulting from discrete events ( [[#Bellprat--2019|Bellprat et al., 2019]] ). Attribution science is therefore highly compatible with risk management tools (i.e., risk reduction, risk transfer, insurance, risk pooling, recovery, rehabilitation and compensation) suggested in policy ( [[#James--2019|James et al., 2019]] ).

New observations provide greater evidence on the role of extreme poverty and global inequality, most of the detrimental direct impacts of climate change (e.g., rising food insecurity) disproportionately affecting the Global South ( [[#Hasegawa--2018|Hasegawa et al., 2018]] ; [[#Mbow--2019|Mbow et al., 2019]] ; [[#Khan--2021|Khan and Zhang, 2021]] ) compared with the Global North. Poor populations in many countries are also disproportionately facing extreme L&amp;D from heatwaves, flooding and tropical weather extremes ( [[#Gamble--2016|Gamble et al., 2016]] ). New case studies, such as the European heatwave of 2018, illustrate significant negative impacts across crop production in the Global North ( [[#Beillouin--2020|Beillouin et al., 2020]] ), livestock value chain ( [[#FAO--2018|FAO, 2018]] ; [[#Godde--2021|Godde et al., 2021]] ) and fishing ( [[#Plagányi--2019|Plagányi, 2019]] ). Heatwave-induced intense fires can cause property damage, physical injury and death, as well as health and psychological harm of the victims. Heatwaves also create ideal conditions for the prevalence of certain pathogens, increase the risk of temperature-related health problems and exacerbate many pre-existing diseases ( [[#Rossiello--2019|Rossiello and Szema, 2019]] ).

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

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'''Figure 8.3 |''' '''Illustration of the relationship between climate hazards, their impacts (including economic and non-economic losses and damages) and human systems leading to systemic vulnerability.''' We need to understand who is vulnerable, where, at what scale and why. We cannot just look at the climate hazard (e.g., wild fires, floods, droughts, sea level rise, etc.) but must also look at who is being affected by these hazards and factors that make people and groups vulnerable (e.g., poverty, uneven power structures, disadvantage and discrimination due to, for example, social location and the intersectionality or the overlapping and compounding risks from ethnicity or racial discrimination, gender, age, or disability, etc.) (see also Cross-Chapter Box GENDER in Chapter 18; [[IPCC:Wg2:Chapter:Chapter-5#5.12|Section 5.12]] ).

Extreme events (e.g., heatwaves, cold periods, icy conditions) occurring in the Global North illustrate that such events cause disproportionate impacts among ageing populations, due to their immobility, isolation, infrastructure deficiencies and poor health assistance ( [[#Carter--2016|Carter et al., 2016]] ; [[#Reckien--2018|Reckien et al., 2018]] ). A well-known example is the heatwave in 2003 that killed thousands of elderly citizens across Europe ( [[#Poumadere--2005|Poumadere et al., 2005]] ; [[#García-Herrera--2010|García-Herrera et al., 2010]] ; [[#Laaidi--2011|Laaidi et al., 2011]] ). More recently, in the Nordic region, elderly populations have been experiencing distress associated with heatwaves and extreme cold events, with significant increases in morbidity and mortality due to cardiovascular and respiratory failure, showing that both age and underlying health issues intersect with climate change impacts ( [[#Carter--2016|Carter et al., 2016]] ; [[#Li--2016|Li et al., 2016]] ). The elderly also experience severe impacts from extreme winter seasons, such as in Finland, where of the from 3000 deaths associated with extreme winter weather and 50,000 injuries associated with slippery pavement conditions, the majority were people over 65 years old ( [[#Carter--2016|Carter et al., 2016]] ). Adaptation to extreme events including heatwaves, cold periods and icy conditions in the Global South and North will increase energy demand and the individuals’ carbon footprint across all income levels ( [[#van%20Ruijven--2019|van Ruijven et al., 2019]] ).

The 2018 US National Climate Assessment has identified that southeastern USA is already experiencing more frequent and longer summer heatwaves and, by 2050, rising global temperatures are expected to mean that cities in southeastern USA may experience extreme heat ( [[#USGCRP--2018|USGCRP, 2018]] ). This includes disadvantaged African American communities, who are more exposed and hence disproportionately experience the impacts of climate change ( [[#Shepherd--2015|Shepherd and KC, 2015]] ; [[#Marsha--2018|Marsha et al., 2018]] ). The historically discriminated Sami in northern Sweden and Maasai in Africa are examples of Indigenous People who also face climate risks and have limited resources, capacity or power to respond ( [[#Leal%20Filho--2017|Leal Filho et al., 2017]] ; [[#Persson--2017|Persson et al., 2017]] ).

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==== 8.2.1.4 Climate-related Hazards, Livelihood Transitions and Migration ====

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Agricultural livelihoods of the rural poor, especially in Africa, Asia and Latin America, are already in transition due to the forces of industrialisation, urbanisation and economic globalisation ( [[#De%20Brauw--2014|De Brauw et al., 2014]] ; [[#Tacoli--2015|Tacoli et al., 2015]] ). Scientific evidence shows that climate change is accelerating livelihood transitions from rural agricultural production to urban wages ( [[#Cai--2016|Cai et al., 2016]] ; [[#Cattaneo--2016|Cattaneo and Peri, 2016]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ).

There is now ''robust evidence'' from virtually every region on Earth showing that the livelihood impacts from a multitude of climate hazards are driving people to diversify rural income sources (Figure 8.2; Cross-Chapter Box MIGRATE in Chapter 7). Rural households frequently accomplish the goal of livelihood diversification with an increasing reliance on migration, urban wage labour and remittances ( [[#Marchiori--2012|Marchiori et al., 2012]] ; [[#Bohra-Mishra--2014|Bohra-Mishra et al., 2014]] ; [[#Gray--2016|Gray and Wise, 2016]] ; [[#Nawrotzki--2016|Nawrotzki and DeWaard, 2016]] ; [[#Banerjee--2019a|Banerjee et al., 2019a]] ). What is different about rural-to-urban livelihood transitions under climate change impacts is that they accelerate both rural and urban stratification of wealth ( [[#Barrett--2014|Barrett and Santos, 2014]] ; [[#Thiede--2016|Thiede et al., 2016]] ). On the one hand, climate change impacts on rural livelihoods increase the necessity of migration as an income strategy, accelerating migration ( [[#Cai--2016|Cai et al., 2016]] ) even while households that cannot select individuals for migration become more impoverished ( [[#Suckall--2017|Suckall et al., 2017]] ; [[#Nawrotzki--2018|Nawrotzki and DeWaard, 2018]] ).

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

Migration and displacement are directly induced by the impacts of climate change ( ''high confidence'' ) (Cross-Chapter Box MIGRATE in Chapter 7), however, migration responses to climate change are differentiated across the spectrum of households’ wealth. In well-off households, migration can be used as a way to support income diversification through remittances ( [[#Gemenne--2017|Gemenne and Blocher, 2017]] ). High levels of poverty mean that a large part of the African population does not have sufficient resources to be mobile ( [[#Borderon--2019|Borderon et al., 2019]] ; [[#Leal%20Filho--2020c|Leal Filho et al., 2020c]] ). The poorest households, conversely, will typically lack the resources that would allow them to migrate in ways that maintain an acceptable standard of living, and may find themselves unable or unwilling to move in the face of climate change impacts ( [[#Sam--2021|Sam et al., 2021]] ).

There is ''high agreement'' and ''robust evidence'' that climate change impacts also have a major influence on key enabling conditions for migration, such as sociodemographic, economic and political factors ( [[#Abel--2019|Abel et al., 2019]] ; [[#Borderon--2019|Borderon et al., 2019]] ), and that climate change impacts to development and governance may affect how people migrate ( [[#Wrathall--2019|Wrathall et al., 2019]] ; CCB MIGRATE in Chapter 7). Mobility, which was considered the most viable climate change adaptation strategy to poor pastoralists, is restricted due to the political marginalisation of pastoral groups, land privatisation, governments’ decentralisation policies and plantation investment ( [[#Blench--2001|Blench, 2001]] ; [[#Randall--2015|Randall, 2015]] ; [[#Leal%20Filho--2020c|Leal Filho et al., 2020c]] ). While migration can be an adaptation response to climate change impacts ( [[#Black--2011|Black et al., 2011]] ; [[#Gemenne--2017|Gemenne and Blocher, 2017]] ), climate change impacts can also act as a direct driver of forced displacement ( [[#Marchiori--2012|Marchiori et al., 2012]] ). Societal groups that are forced to involuntarily migrate in response to climate change impacts may lack resources to invest in planned relocation mainly due to lack of good governance systems ( [[#Reckien--2018|Reckien et al., 2018]] ). For people displaced by climate change impacts, policy interventions have a determining influence on migration outcomes, such as the numbers of migrants, the timing of migration and destinations ( [[#Gemenne--2017|Gemenne and Blocher, 2017]] ; [[#Wrathall--2019|Wrathall et al., 2019]] ).The process of displacement and forced migration leaves people more exposed to climate change-related extreme weather events, particularly in low-income countries which often host the highest number of displaced people ( [[#Adger--2018|Adger et al., 2018]] ).

Climate change may be accelerating livelihood transitions and migration in ways that accelerate urbanisation ( [[#Adger--2020|Adger et al., 2020]] ). Although a range of climate hazards are noted for accelerating rural-to-urban livelihood transitions (see Cross-Chapter Box MIGRATE in Chapter 7), a key theme to emerge across many case studies is the impact of rising temperatures on agricultural productivity ( [[#Mueller--2014|Mueller et al., 2014]] ; [[#Cattaneo--2016|Cattaneo and Peri, 2016]] ; [[#Call--2017|Call et al., 2017]] ; [[#Wrathall--2018|Wrathall et al., 2018]] ). In other words, when people cannot farm due to rising temperatures (and related stressors), they migrate. In this context, migration as a livelihood diversification strategy may evolve and take multiple forms over time (Bell et al., 2019), such as temporary migration ( [[#Mueller--2020|Mueller et al., 2020]] ), seasonal migration ( [[#Gautam--2017|Gautam, 2017]] ) or permanent migration ( [[#Nawrotzki--2017|Nawrotzki et al., 2017]] ), but generally conforms to existing patterns of migration ( [[#Curtis--2015|Curtis et al., 2015]] ).

A key concern for the poor is climate change impacts that undermine livelihood diversification and resilience, narrowing the set of available livelihood alternatives ( [[#Tanner--2015|Tanner et al., 2015]] ; [[#Bailey--2016|Bailey and Buck, 2016]] ; [[#Perfecto--2019|Perfecto et al., 2019]] ).

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==== 8.2.1.5 The Long-lasting Effects of Climate Change on Poverty and Inequality ====

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New studies document the long-term effects of climate change impacts on people’s livelihoods that persist long after a hazard event. For example, the impact of drought on livelihoods and food security is still recognisable in Mali, 30 years after 1982–1984, the period of most intense drought during the protracted late 20th century drying of the Sahel. The most food secure households associated with persistent drought-induced famine were those that diversified livelihoods away from subsistence agriculture during and after the famine ( [[#Giannini--2017|Giannini et al., 2017]] ). Meanwhile, a larger fraction of households with fewer livelihood activities, lower food security with higher reliance on detrimental nutrition-based coping strategies (such as reducing the quantity or quality of meals) were those unable to diversify livelihoods 30 years previously. Sufficient time has passed to consider the long-term outcomes for the poor in extreme cases featured in previous IPCC assessments, including Hurricane Katrina (2005) (e.g., [[#Fussell--2015|Fussell, 2015]] ; [[#Raker--2019|Raker et al., 2019]] ) and Hurricane Mitch (1998) (e.g., [[#Alaniz--2017|Alaniz, 2017]] ), forewarning that recovery is complex and requires significant sustained long-term investment in ‘soft’ aspects of development, including community organisation and mental health ( [[#O’Neill--2020|O’Neill et al., 2020]] ; [[#Fraser--2021|Fraser et al., 2021]] ).

The IPCC Special Report on 1.5°C concluded that climate change has already increased the probability and intensity of individual extreme weather events occurring ( [[#Roy--2018|Roy et al., 2018]] ), and our new baseline consideration should be that serious climate change impacts are already being experienced by the most vulnerable, with long-term implications for development (Box 8.1; [[#Roy--2018|Roy et al., 2018]] ). In both developing and developed countries the disproportionate impacts of the compounding effects of climate change on development are felt by the most disadvantaged. For example, the residual impacts of storms like Hurricane Maria (see [[#8.2.1.1|Section 8.2.1.1]] ) illustrate how rising temperatures, extreme weather events, coral bleaching and sea level rise come together and create compounding hazard-cascades to leave long-lasting effects on the lives of the poor, as well as their food and water security, health, livelihoods and prospects for sustainable development—not only in developing countries ( [[#Adger--2014|Adger et al., 2014]] ; [[#Olsson--2014|Olsson et al., 2014]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ), but also in highly inequitable industrialised countries within the same region ( [[#Gamble--2016|Gamble et al., 2016]] ). According to the US National Climate Assessment ( [[#USGCRP--2018|USGCRP, 2018]] ), damages caused to communities by Hurricanes Irma and Maria in 2017 sparked unprecedented humanitarian crises. Hurricane Maria, a category 5 hurricane, passed through Dominica, St Croix and Puerto Rico and is considered the worst climate disaster in recorded history to affect those islands ( [[#Rodríguez-Díaz--2018|Rodríguez-Díaz, 2018]] ). Approximately 200,000 people migrated from Puerto Rico to the mainland USA in the weeks following the storm ( [[#Alexander--2019|Alexander et al., 2019]] ). Estimates for direct and indirect casualties in Puerto Rico point out a total of 4645 excess deaths, equivalent to a 62% increase in the mortality rate ( [[#Kishore--2018|Kishore et al., 2018]] ). The example of Hurricane Maria and Puerto Rico illustrates that vulnerability is part of a long history of discrimination and colonial governance, which led to greater impacts on the island ( [[#Moleti--2020|Moleti et al., 2020]] ). In Puerto Rico, the economic costs of the collapse of the island’s energy, water, transport, and communication infrastructures are estimated to range from USD 25 to USD 43 billion (USD in 2017), further indebting the island and putting its long-term development at risk. Meanwhile the economic impacts of Hurricanes Irma and Maria on the Caribbean region are estimated between USD 27 and USD 48 billion, and have long-term implications for state budgets for infrastructure supporting development of the poorest.

New evidence provides little expectation of net positive impacts of climate change for the poor ( [[#Hallegatte--2015|Hallegatte et al., 2015]] ). Nevertheless, some benefits of climate change adaptation include improved disaster preparedness, the accumulation of social assets, economic benefits of agricultural diversification and benefits associated with migration, as well as the political benefits of collective action ( [[#Pelling--2018|Pelling et al., 2018]] ). In contrast, wealthier tiers of society facing climate change impacts are more able to liquidate assets to avoid losses from climate change, to be formally compensated for losses ( [[#Fang--2019|Fang et al., 2019]] ) and employ social positions to leverage gains from adaptation ( [[#Nadiruzzaman--2015|Nadiruzzaman and Wrathall, 2015]] ).

The poor frequently suffer the direct and indirect impacts of climate change, including the cost of adopting adaptive measures ( [[#Atteridge--2018|Atteridge and Remling, 2018]] ; [[#Bro--2020|Bro et al., 2020]] ). Costs to the poor may also include the secondary impacts of first-order adaptation activities, including the livelihood consequences to people migrating due to climate change impacts. The poor frequently bear indirect impacts of adaptation interventions, such as flood protection barriers, which may displace flood waters away from high-income populations toward poorer communities ( [[#Mustafa--2011|Mustafa and Wrathall, 2011]] ). Adaptation programming may also indirectly affect the poor as public resources are drawn into risk reduction interventions, and away from spending on social welfare and safety nets ( [[#Eriksen--2015|Eriksen et al., 2015]] ). Measures to enhance social welfare and safety nets themselves help enhance the poor’s resilience to climate impacts because they focus on non-climatic stressors affecting livelihoods, which interact with climate hazards. Therefore, diverting attention away from safety nets may in fact undermine adaptation efforts ( [[#Leichenko--2019|Leichenko and O’Brien, 2019]] ; [[#Tenzing--2020|Tenzing, 2020]] ).

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<span id="interactions-between-climate-hazards-and-social-ecological-thresholds"></span>
==== 8.2.1.6 Interactions Between Climate Hazards and Social-ecological Thresholds ====

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Climate change threatens to rapidly transform unique and threatened ecosystems (Reasons for Concern RFC1), such as tropical rain forests, coral reefs, arctic and high-mountain ecosystems, as well as the indigenous and forest-dwelling people whose livelihoods, cultures and identities are dependent on these ecosystems. In recent years, the case of Amazonia has illustrated how such systems are transforming, with detrimental consequences for Indigenous Peoples, and the vital role that Indigenous Peoples serve in protecting vulnerable ecosystems ( [[#Ricketts--2010|Ricketts et al., 2010]] ; Box 8.6). Globally, indigenous territories cover the greatest area of remaining tropical forest in comparison to other protected areas. They encompass the bulk of Earth’s biodiversity and are the locus for a number of key ecosystem services across spatial and temporal scales ( [[#Walker--2020|Walker et al., 2020]] ). Specifically, in 2014 indigenous territories and other protected areas represented the equivalent of 58.5% of all the carbon stored in the Brazilian Amazon biome and had the lowest deforestation rate (2.1%) and fire incidences, evidencing the effectiveness in safeguarding important ecosystems services and well-being ( [[#Nogueira--2018|Nogueira et al., 2018]] ). It is estimated that indigenous territories in the Brazilian Amazon contribute at least USD 5 billion each year to the global economy through food and energy production, GHG emissions offsets, and climate regulation and stability ( [[#Siqueira-Gay--2020|Siqueira-Gay et al., 2020]] ). Given the high incidence of poverty of Amazonian countries and high proportion of traditional and Indigenous Peoples, remoteness and neglected governance place these unique ecosystems and indigenous populations as highly vulnerable to climate change impacts ( [[#Pinho--2014|Pinho et al., 2014]] ; [[#Brondízio--2016|Brondízio et al., 2016]] ; [[#Mansur--2016|Mansur et al., 2016]] ; [[#Kasecker--2018|Kasecker et al., 2018]] ). Despite their importance, the survival of Indigenous Peoples in the Amazon is on the brink in the wake of increasing deforestation, land conflicts and invasions, cattle ranching, mining, fire incidence, health problems and human rights violation ( [[#Ferrante--2019|Ferrante and Fearnside, 2019]] ). There is ''robust evidence'' that both economic and non-economic L&amp;Ds are currently, and will be, unevenly experienced by populations in vulnerable conditions, such as children, women, Indigenous Peoples and traditional communities ( [[#Pinho--2016|Pinho, 2016]] ; [[#Lapola--2018|Lapola et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Eloy--2019|Eloy et al., 2019]] ; [[#Machado-Silva--2020|Machado-Silva et al., 2020]] ). Increasing wildfires inside protected areas, in particular, territories of Indigenous Peoples and traditional communities, is worrisome and presents challenges for the future of unique and threatened socio-ecological systems, and the ecosystem services they provide. The Amazonian indigenous territories and protected areas can deliver protection of biodiversity and important ecosystem services if appropriate governance mechanisms are in place and their land tenure rights and livelihoods are secured ( [[#Steege--2015|Steege et al., 2015]] ). The role of enabling environments is discussed in [[#8.5|Section 8.5]] .

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<span id="linkages-between-climate-change-impacts-and-sustainable-development-goals"></span>
==== 8.2.1.7 Linkages Between Climate Change Impacts and Sustainable Development Goals ====

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Many of the observed outcomes of climate change, for example, migration, are also outcomes of multidimensional poverty in low-income countries ( [[#Burrows--2016|Burrows and Kinney, 2016]] ). Future impacts may be better understood if the vulnerability and the capacity for adaptation is understood to be rooted in a sustainable development context (see Box 8.2). The UN Sustainable Development Goals (SDGs), which aim to reduce poverty and inequality, and identify options for achieving development progress, also provide insight on reducing climate vulnerability ( [[#United%20Nations--2015|United Nations, 2015]] ). First, climate change impacts may undermine progress toward various SDGs ( ''medium confidence'' ), primarily poverty reduction (SDG1), zero hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others ( ''medium evidence, high agreement'' ). In both developing and high-income countries, climate change hazards in connection with other non-climatic drivers already accelerate trends of wealth inequality (SDG 1) ( [[#Leal%20Filho--2020b|Leal Filho et al., 2020b]] ). Climate impacts on SDGs illustrate the complex interrelations in development. For example, in regions encountering obstacles to SDGs, characterised by high levels of inequality and poverty, such as in Africa, Central Asia and Central America, climate change is exacerbating water insecurity (SDG 6), which may then also drive food insecurity (SDG 2), impacting the poor directly (e.g., via crop failure), or indirectly (e.g., via rising food prices) ( [[#Conway--2015|Conway et al., 2015]] ; [[#Hertel--2015|Hertel, 2015]] ; [[#Cheeseman--2016|Cheeseman, 2016]] ; [[#Rasul--2016|Rasul and Sharma, 2016]] ). There is a pressing need to address poverty issues, since these may negatively influence the implementation of all SDGs ( [[#Leal%20Filho--2021a|Leal Filho et al., 2021a]] ).

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

<div id="box-8.1" class="h2-container box-container"></div>

'''Box 8.1 | Climate traps: A focus on refugees and internally displaced people'''
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Populations of concern, who are extremely vulnerable to climate change impacts with limited capacity to adapt, are those displaced and resettled in the course of conflict or disaster, either internally or across borders ( [[#Burrows--2016|Burrows and Kinney, 2016]] ). The risk for refugees and internally displaced people (IDPs) is two-fold: on the one hand, refugee and IDP settlements are disproportionately concentrated in regions (e.g., Central Africa and the Near East) that are exposed to higher-than-average warming levels and specific climate hazards, including temperature extremes and drought. On the other, these populations frequently inhabit settlements and legal circumstances that are intended to be temporary but are protracted across generations, and at the same time, face legal and economic barriers on their ability to migrate away from climate impacts. ( [[#Adams--2016|Adams, 2016]] ; [[#Devictor--2016|Devictor and Do, 2016]] ). Large concentrations of these settlements are located in the Sahel, the Near East and Central Asia, where temperatures will rise higher than the global average, and extreme temperatures will exceed thresholds for safe habitation (Figure Box 8.1.1). Already largely dependent on state and humanitarian intervention, these immobile populations will require interventions to safely maintain residence in areas exposed to climate hazards. Adaptation planning should prioritise immobile populations living in an already destabilised development context, on improving their capacities to deal with the further consequences of climate change.

Refugees and IDPs fit into a global category of extremely structurally vulnerable people that are missing from standard poverty assessments, officially uncounted or uncountable using traditional census and survey methods ( [[#Carr-Hill--2013|Carr-Hill, 2013]] ). These include highly mobile populations, internally displaced by war and environmental hazards ( [[#UNHCR--2020|UNHCR, 2020]] ; [[#IDMC--2021|IDMC, 2021]] ); itinerant labourers; urban poor in informal settlements ( [[#Lucci--2018|Lucci et al., 2018]] ); unauthorised migrants living in countries where they do not hold citizenship ( [[#Passel--2006|Passel, 2006]] ); guest workers ( [[#Reichel--2017|Reichel and Morales, 2017]] ); the homeless and institutionalised ( [[#Caton--2007|Caton et al., 2007]] ); rural nomadic, pastoralist or landless populations ( [[#Randall--2015|Randall, 2015]] ); and Indigenous Peoples and forest-dwelling communities ( [[#Galappaththi--2020|Galappaththi et al., 2020]] ). Frequently living without social safety nets, such as health care and formal education, these uncounted or ‘missing millions’ are vulnerable to problems associated with acute and chronic poverty, such as the spread of infectious disease and malnutrition ( [[#Ezeh--2017|Ezeh et al., 2017]] ). Because these ‘missing’ populations are not counted, they are frequently not a part of planning ( [[#Carr-Hill--2013|Carr-Hill, 2013]] ), including adaptation planning. In any particular national context, these missing populations may represent a small fraction of the population (about 5% in South Asian countries), however cumulatively hundreds of millions of people may be missing from official estimates ( [[#Carr-Hill--2013|Carr-Hill, 2013]] ). Over the last decade, techniques for estimating the locations, numbers and socioeconomic status of missing populations have moved beyond census and nationally representative household surveys, leveraging advances in satellite imagery ( [[#Kuffer--2016|Kuffer et al., 2016]] ; [[#Bennett--2017|Bennett and Smith, 2017]] ) and data from mobile digital devices ( [[#Jean--2016|Jean et al., 2016]] ; Xie et al., 2016; [[#Steele--2017|Steele et al., 2017]] ).

[[File:1e6413ff1a8eabb5474f17bb199e2077 IPCC_AR6_WGII_Figure_8_Box_8_1_1.png]]

'''Figure Box 8.1.1 |''' '''The global distribution of the United Nations High Commissioner for Refugees (UNHCR) refugee and internally displaced people (IDP) settlements (as of 2018) overlaid on a gridded map of the days predicted to exceed safe temperature thresholds for human health in the coming decades (2041–2060 under SSP2 8.5).''' Semi-circles indicate the presence of refugee and IDP camps in grid cells, with darker semi-circles depicting increasingly dense concentrations of settlements. Darker background colors indicate increasingly unsafe conditions. Regions of concern include the southern edge of the Sahel, and the northern edge of the Levant

Box 8.1

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'''Box 8.2 | Livelihood strategies of internally displaced atoll communities in Yap'''
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On Yap Island in the Federated States of Micronesia, displaced atoll communities have been under considerable pressure due to climate change. This is because of the island’s vulnerability, as a result of its weak economic status, and the little access it has to technologies that may support adaptation efforts. This trend is seen in many SIDS (see also Chapter 15). On small islands and remote atolls where resources are often limited, recognising the starting point for action is critical to maximising benefits from adaptation. They do not have uniform climate risk profiles, and not all adaptations are equally appropriate in all contexts ( [[#Nurse--2014|Nurse et al., 2014]] ) ( ''high confidence'' ).

The recurrences of natural hazards (e.g., El Niño-driven tropical storms, associated coastal erosion and saltwater or seasonal droughts leading to water scarcity) and crises threaten food and nutrition security through impacts on traditional agriculture, leading to income losses and causing the forced migration of coastal communities to highlands in search of better living conditions. As many of the projected climate change impacts are unavoidable, implementing some degree of adaptation becomes crucial for enhancing food and nutrition security, strengthening livelihoods, preventing poverty traps and increasing the resilience of coastal communities to future climate risks ( [[#Krishnapillai--2018|Krishnapillai, 2018]] ).

With support from the US Department of Agriculture and the US agency for International Development, the Cooperative Research and Extension wing of the College of Micronesia- Federated States of Micronesia Yap Campus has been providing outreach, technical assistance and extension education to regain food and nutrition security and stability. They have done this by improving the soil and cultivating community vegetable gardens, as well as indigenous trees and traditional crops. This programme implemented a three-pronged adaptation model to boost household and community resilience under harsh conditions on a degraded landscape, hence addressing poverty risks and promoting more sustainable livelihoods (Meyer and Jose, 2017).

The following three strategies: (a) gender-focused capacity development on soil health management, (b) good practices in sustainable land management (SLM) and (c) income-generation activities were employed to mitigate crop production losses and increase resilience to climate-influenced hazard events within the 258 ha of degraded lands in Gargey Village.

The project first focused on increasing the capacity development for 1100 residents of Gargey Village, including women and youth, in order to create a base of community knowledge for soil health management. Training on soil health management including the following: use of cover crops and improved fallow, legumes, composting and agroforestry systems, mulching, minimum tillage and contour farming, as well as altering production practices (planting time, spacing, pest and disease treatment, harvesting time), alternative crop production methods (container gardening, raised-bed gardening, small-plot intensive farming), hands-on training on compost preparation and seed germination.

<div id="_idContainer012" class="Box_Header-continued"></div>

Box 8.2

'''Dissemination and use of good practices in sustainable land management'''
Following capacity building, the project trained villagers in the use of SLM practices to further soil resilience during ongoing and acute precipitation events. The SLM practices focused on volcanic soil management and compost preparation and use, along with the planting of native trees and crops. The protective soil cover was improved through cover crops, crop residues or mulch, and crop diversification through rotations. Local salt-tolerant crop varieties were introduced. Seed packets and seedlings were distributed to ensure a continuous supply of resilient traditional plants and to provide for sustainable post-disaster recovery.

'''Income-generation activities'''
The project also included training to increase the incomes of households by training household members in the cultivation of vegetables using various alternative crop production methods. Households were then able to sell their vegetables in the local markets.

Less hunger and more cash from leafy vegetables is a concept adopted at the household level to not only reduce poverty, but also to empower displaced communities to address the issue of malnutrition. Practices include growing a variety of nutritious vegetables as part of a large crop portfolio and using alternative crop production methods, such as small-plot intensive farming using container gardening or raised-bed gardening ( [[#Krishnapillai--2014|Krishnapillai and Gavenda, 2014]] ). In addition, focusing efforts on increasing the sustainable production of staple crops confers significant nutritional benefits.

More households in the settlements are consuming vegetables since home gardeners started harvesting regularly and sharing their produce with extended families or selling them to generate income. The location-specific, community-based adaptation model improved food and nutrition security and livelihoods ( [[#Krishnapillai--2017|Krishnapillai, 2017]] ). People can access more nutritious and reliable food sources, and they are growing their own food and selling their surplus, creating new optimism about their future.

The climate-smart agriculture (CSA) package increased land cover by more than 50% within Gargey Village. This includes the planting of 42 varieties of native trees and crops. Current major crops that are being successfully grown at this location include coconut, breadfruit, mango, noni, chestnut, pineapple, sugarcane, land taro, tapioca and sweet potato. There have been additional benefits in terms of improvement in water availability. These activities have directly benefited the resilience and food security of more than 1000 residents in Gargey Village, and lessons learnt from this project have helped to scale up similar projects at three locations in Yap that have experienced equivalent climate-damaging processes.

Overall, this case study illustrates the benefits of promoting resilient crop production in Gargey Village, as an example of displaced atoll communities. Innovative and sustainable CSA strategies have offered broader insights and lessons for enhancing adaptive capacity and resilience, on a degraded landscape. The coherent strategies and methods employed have strengthened livelihood opportunities by improving access to services, knowledge and resources. By its concurrent focus on enhancing food security through traditional crops, coupled with nutrient-rich vegetables, promoting rainwater harvesting systems and water conservation, and promoting resilient household livelihood opportunities, atoll communities brought together crucial elements needed to reduce vulnerabilities and to better cope with disasters and climate extremes, while embracing the traditional culture. The location-specific yet knowledge-intensive CSA methods deployed, offered opportunities for atoll communities to revitalise themselves, overcoming barriers while adjusting to new landscapes.

<div id="8.2.2 " class="h2-container"></div>

<span id="povertyenvironment-traps-and-observed-responses-to-climate-change-with-implications-for-poverty-livelihoods-and-sustainable-development"></span>
=== 8.2.2 Poverty–Environment Traps and Observed Responses to Climate Change with Implications for Poverty, Livelihoods and Sustainable Development ===

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

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

Responses to observed impacts such as glacier melt, sea level rise and increases in the frequency of extreme weather events such as droughts, hurricanes and floods need to take into account how they influence other policy issues and sectors, including poverty alleviation, human health and well-being ( [[#Orimoloye--2019|Orimoloye et al., 2019]] ), water/energy and the built environment ( [[#Andrić--2018|Andrić et al., 2018]] ), transportation and mobility (Markolf et al., 2019), agriculture ( [[#Hertel--2014|Hertel and Lobell, 2014]] ) and biodiversity/ecosystems (Nogués-Bravo et al., 2019), only to mention a few. Recent literature provides evidence that impacts of climate change together with non-climatic drivers can create poverty–environment traps that may increase the probability of long-term and chronic poverty (Figure 8.4; [[#Hallegatte--2015|Hallegatte et al., 2015]] ; [[#Djalante--2020|Djalante et al., 2020]] ; [[#Malhi--2020|Malhi et al., 2020]] ; [[#McCloskey--2020|McCloskey et al., 2020]] ) ( ''high confidence'' ).

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[[File:d9d347887402890d44633f97eeadea00 IPCC_AR6_WGII_Figure_8_004.png]]

'''Figure 8.4 |''' '''Schematic representation of a poverty–environment trap that can increase chronic poverty.'''

In addition, observed climate change responses, including autonomous and planned adaptation, can exacerbate poverty and vulnerability ( [[#Eriksen--2021|Eriksen et al., 2021]] ). There is ''robust evidence'' that planned responses to climate change, such as large-scale adaptation projects, in some context can also increase vulnerability due to the reinforcement of inequalities and the effects of further marginalisation ( [[#Fritzell--2015|Fritzell et al., 2015]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). There is increasing evidence that the responses to indirect impacts of climate change, such as to shifts in marine or terrestrial ecosystems due to climate change ( [[#Seddon--2016|Seddon et al., 2016]] ) also affect different groups differently and impact poverty and livelihood security. Apart from influences on agriculture trends ( [[#Reichstein--2014|Reichstein et al., 2014]] ) and changes in yields ( [[#Reyes-Fox--2014|Reyes-Fox et al., 2014]] ; [[#Craparo--2015|Craparo et al., 2015]] ), climate change has significant (direct and indirect) impacts on livelihood assets and resources such as forests, livestock production and fisheries, which may undermine the livelihoods security in the medium and long run.

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<span id="characteristics-of-responses"></span>
==== 8.2.2.1 Characteristics of Responses ====

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Many of the observed responses to climate change aim to reduce exposure of people to climate-related hazards, such as flood defences, sea walls and embankments ( [[#Gralepois--2016|Gralepois et al., 2016]] ), rather than aiming specifically to address structural vulnerability to climate change, which means the root causes of vulnerability (e.g., [[#Mikulewicz--2020|Mikulewicz, 2020]] ; [[#McNamara--2021a|McNamara et al., 2021a]] ). Evidence suggests that responses to the impacts of climate change should consider the physical climate event, and also historical and institutional root causes that make people or systems vulnerable. However, addressing structural vulnerability must be balanced with the political context and the range of options available to people, communities or countries (see [[#8.3|Section 8.3]] ). Political frameworks need to consider both types of responses, to revive democratic debate and citizenship ( [[#Pepermans--2016|Pepermans et al., 2016]] ). In addition to reducing poverty and vulnerability, planned climate change responses must also be intersectoral, in order to increase their effectiveness. This requires higher levels of vertical and horizontal coordination and integration ( [[#GIZ--2019|GIZ, 2019]] ). Horizontal coordination encompasses, for example, the integrated coordination of responses to climate change across different sectors, which requires suitable governance structures and processes that allow for such a coordination ( [[#Di%20Gregorio--2017|Di Gregorio et al., 2017]] ; [[#Burch--2019|Burch et al., 2019]] ). Vertical integration is needed in order to ensure that effective responses also include different levels of governance and benefit from knowledge at different scales. The inclusion of local knowledge within national or provincial adaptation strategies requires such linkages and vertical coordination. Overall, there is an increasing body of literature that highlights the importance of improved integration and coordination also in order to promote a higher effectiveness of strategies and an improved consideration of social justice and climate justice when designing and implementing responses ( [[#Levy--2015|Levy and Patz, 2015]] ).

However, evaluating the effectiveness, social impacts and social justice of climate change responses is not uniform across locations, nations and regions for three principal reasons:

* Temporal dimensions of responses: effective and appropriate climate change responses require that strategies and responses are tested in a specific context and that ongoing learning and adaptive management is a necessary to avoid maladaptation or other unintended consequences ( [[#Eriksen--2021|Eriksen et al., 2021]] ),
* Goal of responses: responses may have distinct and locally specific goals, such as reducing vulnerability ( [[#Sarker--2019|Sarker et al., 2019]] ), which is distinct from increasing resilience ( [[#Alam--2018|Alam et al., 2018]] ). Vulnerability reduction and the increase of resilience (i.e., raising the ability to cope) are two different goals and often involve different processes.
* Level of responses: there is a need to ascertain the relevant level at which the responses are needed or expected (e.g., the individual level, community level, regional level). This analysis, however, also needs to consider the differential capacities of people, for example, the limited capacities of poor people or constrained capacities of most vulnerable countries (see also [[#8.3|Section 8.3]] ).

Effective responses to climate change impacts for one group could impose higher costs and negative consequences for other groups, in terms of shifts in exposure and vulnerability. This category of response is known as maladaptation. Maladaptation actions defined in the IPCC SR1.5°C ( [[#IPCC--2018b|IPCC, 2018b]] ) and in the Land Report ( [[#IPCC--2019a|IPCC, 2019a]] ) are the ones that usually have unintended consequences, and can lead to increased negative risk to poor population mostly in the Global South to climate hazards by either increasing GHG emissions or by increasing the vulnerabilities to climate change with diminished welfare, now and in the near future ( [[#Roy--2018|Roy et al., 2018]] ). For example, migration to urban centres can represent a significant adaptation opportunity for the migrants themselves, but can also increase the vulnerability of their community of origin or destination (e.g., through a depletion of the workforce or an addition pressure on environmental resources and infrastructure respectively) ( [[#Gemenne--2017|Gemenne and Blocher, 2017]] ). Some types of observed responses to climate change may not yield long-term benefits. For example, food imports during droughts or adverse climate conditions are not a fully adequate response, since they may alleviate a problem on the one hand (i.e., an imminent food shortage due to crop failure) but, on the other, lead to no long-lasting improvements in physical conditions and create new dependencies that can increase vulnerability in the long run ( [[#Zimmermann--2018|Zimmermann et al., 2018]] ).

In the AR5, the maladaptation outcomes emerge when climate change impacts and risks are disproportionately born by the poorest populations ( [[#Olsson--2014|Olsson et al., 2014]] ). Since then, most maladaptation evidence emerges as a consequence of failure to address root causes of vulnerabilities that emerge under high and multiple forms of inequalities. In fact, the literature shows that adaptation practices can indeed redistribute vulnerabilities and increase risks to already poor and marginalised people with risk to maladaptation outcomes mainly in the Global South countries ( [[#Atteridge--2018|Atteridge and Remling, 2018]] ).

The maladaptation outcomes also emerge when responses are not equitable at the policy level, and exacerbate the precarity of vulnerable populations by excluding them from benefits and support, while attending to the needs of people of the most enfranchised segments of society ( [[#Thomas--2019|Thomas and Warner, 2019]] ; Asplund and Hjerpe 2020). In Tanzania, the political marginalisation of pastoralist access to critical riparian wetlands and increasing expansion of agriculture may result in adaptation pathways that heighten risk for these groups, while reducing risk for others ( [[#Smucker--2015|Smucker et al., 2015]] ). Salim et al. (2019) found that adaptation to flooding in Jakarta privileges political economic elites, while poor infrastructure in poorest neighbourhoods exacerbates loss of assets, housing and displacements ( [[#Salim--2019|Salim et al., 2019]] ). In Bangladesh, intense and consecutive flooding led to national and regional adaptation plans, that resulted in maladaptive trajectories as local poverty context and precarities of properties were not carefully considered and disconnected from local autonomous practice ( [[#Rahman--2019|Rahman and Hickey, 2019]] ).

Overall, the assessment shows that understanding impacts of climate change should not be limited to the analysis of direct impacts or physical changes under different climatic conditions, but needs also account for the distributional effects that responses to climate change may imply. For example, responses implemented in order to benefit one sector or social group (e.g., farmers), should not undermine the well-being of others (e.g., pastoralists). Documented cases of maladaptation (see [[#Eriksen--2021|Eriksen et al., 2021]] ) hint that responses to climate change can exacerbate existing inequality in some cases and may discourage other types of responses (see also Sections 8.5; 8.6). Furthermore, responses to similar climate change impacts and hazards may be extremely differentiated according to various social contexts (see [[#8.3|Section 8.3]] ). In some cases, responses to climate change (e.g., relocation programmes) can even trigger social tipping points when climate change responses lead to major social transformations, such as forced displacement (see [[#8.4|Section 8.4]] ).

Also the influence of new global phenomena, such as urbanisation, issues of urban health (Schmid and [[#Raju--2020|Raju, 2020]] ) and the consequences of the COVID-19 pandemic need to be considered when assessing actual and potential consequences of different responses to climate change. For example, inequalities, vulnerabilities and poverty pockets are expected to change and increase, particularly in urban areas in countries with rapid urbanisation processes and high levels of poverty ( [[#Djalante--2020|Djalante et al., 2020]] ), hence urban and urbanisation trends need more attention. Urbanisation processes add another level of complexity ( [[#Raju--2021|Raju et al., 2021]] ). This is particularly the case in rapidly growing medium-sized cities in Africa that at present do not have sufficient resources to cope and adapt, and to implement climate-sensitive land use planning ( [[#Birkmann--2016|Birkmann et al., 2016]] ).

Tables 8.1 and 8.2 present a summary of a set of common climate change responses observed, classified according to their main approach. All these responses demand a certain level of commitment, the support of adequate policies and enough budget for their implementation ( [[#Archie--2018|Archie et al., 2018]] ). The observed climate change adaptation responses—differentiated along urban and rural settings—underscore the very different nature of various responses and the need for cross-sectoral approaches.

While Table 8.1 shows selected adaptation responses, Table 8.2 shows selected mitigation responses that highlight that some mitigation responses (e.g., increasing energy efficiency) also have a potential benefit for the poor or more vulnerable groups, for example, through the reduction of costs for electricity. Both tables underscore that climate change mitigation and adaptation responses are strongly interlinked with broader development issues (industrial production, land use planning, education, etc.) at different scales.

'''Table 8.1 |''' Selected observed climate change adaptation responses in urban and rural areas commonly associated with positive implications for poverty, livelihoods and sustainable development.

{| class="wikitable"
|-
! Modality of response

! Impacts to urban communities

! Impacts to rural communities (e.g., farmers, pastoralists)

|-
| Integrated natural resource management

(e.g., [[#van%20Noordwijk--2019|van Noordwijk, 2019]] )

| Better conservation of green areas and reduced exposure to floods

| Conservation of natural resources (e.g., water, soil, pasture, forest, wildlife, biodiversity, aquatic life)

|-
| Disaster risk management

(e.g., [[#Mall--2019|Mall et al., 2019]] )

| Pre-disaster risk management and post-disaster risk management measures reduce loss of life and damage to property

| Disaster risk management may play an important role in avoiding or limiting the impacts of floods, droughts and other extreme events

|-
| Physical/structural improvements

(e.g., [[#Vallejo--2017|Vallejo and Mullan, 2017]] )

| Improving physical/structural measures to prevent property damage and foster ecosystems integrity

| Flood defences may help to prevent property losses, planting of trees may stabilise slopes, reduce soil erosion and siltation, rainwater harvesting increases water availability, protection of biotopes supports biodiversity

|-
| Relocation of vulnerable communities

(e.g., [[#McNamara--2015|McNamara and Des Combes, 2015]] )

| Moving vulnerable communities before and during climate-induced hazards may reduce loss of life

| Reduces the exposure of vulnerable communities to climate change and extremes hazards (e.g., floods and droughts), lessens their vulnerability, improves access to better resources and builds their capacity to adjust to a new context

|-
| Education and communication

(e.g., [[#Monroe--2017|Monroe et al., 2017]] )

| Public education and awareness, improved communication may reduce the damages and losses from adverse impacts of climate change and from extreme events

| Fosters awareness creation, reducing the degree of vulnerability to certain climate-induced hazards and help build the capacity to adapt

|}

'''Table 8.2 |''' Selected climate change mitigation responses.

{| class="wikitable"
|-
! Modality of response

! Impacts on urban communities

! Impacts on rural communities (e.g., farmers, pastoralists)

|-
| Land use planning

(e.g., [[#Frose--2019|Frose and Schiling, 2019]] )

| Helps to reduce GHG emissions and support environmental conservation, preventing urban heat islands

| Helps to reduce pressure on the natural resources (deforestation, land filling, damaging wetland) and promotes carbon sequestration

|-
| Improving industrial processes

(e.g., [[#van%20Vuuren--2018|van Vuuren et al., 2018]] )

| Unlocks many opportunities for improvement, including the optimised use of energy, reuse of waste in production, reducing GHG emissions, use of biomass and more efficient equipment

| In rural settings, industrialisation and technological innovation may directly assist vulnerable communities through provision of inputs (e.g., water storage, drip irrigation, forecast information), or reuse of biowaste in agriculture or energy production, hence reducing costs and pollution levels

|-
| Renewable energy

(e.g., [[#Cronin--2018|Cronin et al., 2018]] )

| Reduction of GHG emissions and reduction of the cost of electricity

| Some options (e.g., solar, wind) may help to reduce deforestation, reduce GHG emissions and promote healthier air within households

|-
| Energy efficiency

(e.g., [[#Abrahamse--2018|Abrahamse and Shwom, 2018]] )

| Efficient end-users’ energy utilisation reduces energy wastage, reduces costs and lowers carbon emissions

| Efficient end-users’ energy utilisation leads to natural resource conservation and a reduction of GHG emissions

|-
| Local/individual actions

(e.g., [[#Shaffril--2018|Shaffril et al., 2018]] ; [[#Tvinnereim--2018|Tvinnereim et al., 2018]] )

| Can contribute to reduce carbon footprints

| Fosters personal and community motivation to manage individually and communally owned resources, helps to reduce GHG emissions and foster resources conservation

|}

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

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

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

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

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

Next to the direct differential impacts of climate change on different social groups, the impacts of climate change can also exacerbate inequality due to the lower access and limited ability to benefit from services provided by ecosystems. Marginalised poor people often significantly depend on the access to surrounding environments, natural resources and ecosystem services for their livelihoods, for leisure or cultural practices. Thus shifts in such resources, for example, due to the bleaching of coral reefs or shifts in fish stock, also cause severe challenges and risks to these communities ( [[#Leal%20Filho--2018|Leal Filho, 2018]] ; [[#Le--2019|Le, 2019]] ; [[#UNTTSDCC--2014|UNTTSDCC, 2014]] ).

Overall, the assessed literature highlights that climate change impacts are not emerging in isolation from development context and development pathways. Economic and social ramifications mean that they may exacerbate poverty and marginalisation ( [[#Finkbeiner--2018|Finkbeiner et al., 2018]] ; [[#Dogru--2019|Dogru et al., 2019]] ). Choudhary et al. (2019) and [[#Orimoloye--2019|Orimoloye et al. (2019)]] highlight how the effects of climate change can be even more prejudicial to poor countries, which, in most cases, already suffer from weak governance, high prevalence of informal settlements and lack of resources. Health, livelihood assets and economy are examples of aspects that will worsen as a result of the negative impacts of climate change and failure to provide opportunities for sustainable adaptation ( [[#United%20Nations--2015|United Nations, 2015]] ). These facts highlight the importance of mitigation and adaptation measures especially in these regions characterised by high levels of vulnerability (see also [[#8.3|Section 8.3]] ).

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'''Box 8.3 | COVID-19 pandemic'''
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During the COVID-19 pandemic, countries such as India were affected by hydro-meteorological hazards ( [[#Raju--2020|Raju, 2020]] ) making it extremely difficult to handle a public health crisis in the context of compounding risks and cascading hazards ( [[#Phillips--2020|Phillips et al., 2020]] ). The COVID-19 pandemic can increase the adverse consequences of climate change since it has the potential to delay some key adaptation actions. On the other hand, the pandemic also highlights the importance of better preparedness to the impacts of climate change ( [[#Djalante--2020|Djalante et al., 2020]] ). Overall, the COVID-19 pandemic has worsened the economic situation within many countries and local communities particularly for already marginalised groups ( [[#Gupta--2021|Gupta et al., 2021]] ). The accumulation of crises, such as the COVID-19 pandemic alongside climate change impacts, underscore the fact that stressors do not occur in isolation, but are interlinked, with clear implications for structural vulnerability and adaptation options available to the poorest ( [[#Sultana--2021|Sultana, 2021]] ). Responses to COVID-19 have led to significant economic and social distress within and across societies and local communities, especially in poorer countries. The direct health and economic impacts of the lockdowns have further limited the ability of many people across the developing world to pursue income-generating activities, and sustain livelihoods that are already affected by climate hazards. In addition, poor or most vulnerable groups face further marginalisation due to misinformation that these groups transmit the virus to other wealthier groups and areas. The pandemic has intensified inequalities in both developing countries ( [[#FAO--2020|FAO, 2020]] ) and in industrialised nations ( [[#Anderson--2020|Anderson et al., 2020]] ; [[#McCloskey--2020|McCloskey et al., 2020]] ), whereby vulnerable groups are especially affected ( [[#Raju--2021|Raju et al., 2021]] ). Whereas different models and scenarios contain different data and figures, there is ''high agreement'' that it is likely that socioeconomic impacts are particularly severe within selected global regions and areas that are already characterised by a rather high level of human vulnerability (see also [[#8.3|Section 8.3]] ). This also implies that the capacity of people to prepare for present and future climate change impacts will further decrease within these countries and population groups under the direct and indirect consequences of the COVID-19 pandemic.

Moreover, the COVID-19 pandemic is not only influencing climate change research ( [[#Leal%20Filho--2021b|Leal Filho et al., 2021b]] ) but is also influencing the capacities of governmental institutions and nations to support planned adaptation and poverty reduction favouring the most vulnerable groups, since the crisis also means among other issues a significant reductions in tax revenues ( [[#Clemens--2020|Clemens and Veuger, 2020]] ). COVID-19 may also force people to seek alternative sources of income that can lead to the further erosion of long-term adaptive capacities. In many settings, the pandemic has had significant impact on businesses and SMEs ( [[#Schmid--2021|Schmid et al., 2021]] ). The important role of governmental support for buffering crises and periods of income loss of individual households (e.g., unemployment) and private businesses (e.g., SMEs) has also been demonstrated during the COVID-19 pandemic in Organisation for Economic Co-operation and Development (OECD) countries ( [[#OECD--2020b|OECD, 2020b]] ).

Livelihood disruptions and an increasing probability of higher levels of poverty and of structural vulnerability in various countries have already been observed ( [[#Laborde--2020b|Laborde et al., 2020b]] ). These vulnerabilities and the new layers created by the pandemic must be seen with an intersectional lens ( [[#Raju--2019|Raju, 2019]] ; [[#Sultana--2021|Sultana, 2021]] ).

In addition, the COVID-19 pandemic has also revealed the unequal access to vaccine and the importance of national state institutions to buffer negative impacts, for example, of the lock downs or in terms of unemployment. The COVID-19 pandemic recovery also sets some basis for a stronger narrative towards a green recovery approach ( [[#Djalante--2020|Djalante et al., 2020]] ; [[#Forster--2020|Forster et al., 2020]] ).

<div id="box-8.4" class="h2-container box-container"></div>

'''Box 8.4 | Conflict and governance'''
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Climate change impacts carry the risk of amplifying or aggravating existing tensions within and between communities or countries ( [[#Sakaguchi--2017|Sakaguchi et al., 2017]] ). There is, however, ''limited evidence'' for a universal direct causal linkage between climate change and violent conflicts ( [[#Mach--2019|Mach et al., 2019]] ). The triggering of conflicts related to climate impacts is strongly determined by contextual factors, such as the type of government or the level of development ( [[#Mach--2019|Mach et al., 2019]] ). A study of 156 countries ( [[#Abel--2019|Abel et al., 2019]] ) showed that an increase in periods of drought exacerbate the risk of conflict, especially in democratic countries. This influence was particularly marked during the period 2010–2012 in countries of western Asia and northern Africa that were undergoing political transformations, such as the Arab Spring. Conflict can then represent people’s discontent in governments’ inefficient responses to climate impacts ( [[#Abel--2019|Abel et al., 2019]] ). Research has noted conditions under which climate change can increase the risk of armed conflict, which includes ethnic exclusion, agricultural dependence, large populations, insufficient infrastructure, dysfunctional local institutions and low levels of development ( [[#von%20Uexkull--2016|von Uexkull et al., 2016]] ; [[#Ide--2020|Ide et al., 2020]] ).

Since the AR5, there is ''robust evidence'' of the socially destabilising measures and high-risk income alternatives that the world’s poorest commonly take to cope with the impacts of climate change on livelihoods ( [[#Blattman--2016|Blattman and Annan, 2016]] ). To avoid impoverishment, households often pursue risky livelihood alternatives, with high potential for return on investment ( [[#Sovacool--2018|Sovacool et al., 2018]] ), but which in some cases undermine environmental quality ( [[#Bolognesi--2015|Bolognesi et al., 2015]] ), violate laws ( [[#Ahmed--2019|Ahmed et al., 2019]] ), contradict social norms ( [[#Hagerman--2014|Hagerman and Satterfield, 2014]] ), erode institutions ( [[#Sovacool--2018|Sovacool et al., 2018]] ), or affect intra- and inter-community cooperation ( [[#Nadiruzzaman--2015|Nadiruzzaman and Wrathall, 2015]] ). At the same time, a narrowing of livelihood options carries a strong potential for participation and association with violent non-state organisations and movements, either criminal or ideological ( [[#Nett--2016|Nett and Rüttinger, 2016]] ). In order to reduce the risk of instability and violence associated with climate change, a broadening of livelihood options among the most vulnerable people appears to be an effective policy approach ( [[#Miguel--2004|Miguel et al., 2004]] ).

The determinants of violence in the context of climate shocks are primarily poor institutional planning and response to impacts, such as the capacity of a government to respond to and manage environmental risk ( [[#Selby--2017|Selby et al., 2017]] ). In Latin America, for example, evidence on social conflicts related to disputes over access to water in the context of drought and decreasing water availability point to institutional failures, such as poor, inequitable or corrupt water governance ( [[#Poupeau--2017|Poupeau et al., 2017]] ). Such observations are not confined to low-income countries. In industrialised countries, failure of governments to address climate change is ''likely'' to fuel discontent, a condition in which violent outcomes are possible ( [[#Ide--2020|Ide et al., 2020]] ).

In this regard, specific attention ought to be paid to how responses to climate change exacerbate inequalities within societies and create tensions between different groups—typically between those who are able to protect themselves from climate change impacts and those who do not have sufficient resources or are not prioritised in the responses to climate change. Frequently the possibility of migration from climate change is conflated with conflict outcomes from climate change; however, there is ''limited evidence'' and ''low agreement'' that climate change and migration will result in increased conflict ( [[#Okpara--2016b|Okpara et al., 2016b]] ), while there is ''robust evidence'' and ''medium agreement'' that climate change can exacerbate existing tensions, which can in turn result in political violence and an increase in asylum-seeking ( [[#Marchiori--2012|Marchiori et al., 2012]] ). In the future, conflict in the context of climate change impacts may increase the number of migrants seeking asylum, although at present there is scant empirical evidence for this ( [[#Schutte--2021|Schutte et al., 2021]] ). Recent evidence also provides support for social conflict around inequitable climate mitigation policy as well (e.g., fossil fuel subsidies and emissions reductions targets) ( [[#Rentschler--2016|Rentschler, 2016]] ).

In recent years, research on the climate–security nexus has developed considerably, and has highlighted risks pertaining to conflicts, geo-political rivalries, critical infrastructure, terrorism or human security ( [[#Gemenne--2014|Gemenne et al., 2014]] ). While different studies have identified strong past correlations between climatic variations (of temperature and rainfall in particular) and the occurrence of violent conflicts ( [[#Hsiang--2013|Hsiang et al., 2013]] ), others have stressed the need for stronger explanatory models or the risk of a selection bias ( [[#Benjaminsen--2012|Benjaminsen et al., 2012]] ; [[#Solow--2013|Solow, 2013]] ; [[#Buhaug--2014|Buhaug et al., 2014]] ).

While climate change may increase armed conflict risks in certain contexts ( [[#Mach--2019|Mach et al., 2019]] ), responses to climate change will be crucial to mitigate these risks. Poor institutional responses can directly drive violence, and there is ''robust evidence'' that inequitable responses further exacerbate marginalisation, exclusion or disenfranchisement of some populations, which are commonly recognised drivers of violent conflict.

''Robust evidence'' suggests environmental problems (related to climate change) can be dealt with cooperatively, hence leading to more positive and peaceful relations between groups ( [[#Wolf--2003|Wolf et al., 2003]] ; [[#Ide--2019|Ide, 2019]] ). To avert violent outcomes induced by climate change, stronger local and national climate adaptation institutions within vulnerable societies, and stronger cooperative resource governance mechanisms between vulnerable countries (such as transboundary water governance agreements) are needed.

<div id="8.2.3" class="h2-container"></div>

<span id="observed-impacts-and-responses-and-their-relevance-for-decision-making"></span>
=== 8.2.3 Observed Impacts and Responses and their Relevance for Decision Making ===

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Many countries base their adaptation strategies on National Adaptation Programmes of Action (NAPAs), which often correlate different levels of decision making and governance ( [[#Golrokhian--2016|Golrokhian et al., 2016]] ). Whereas the involvement of national governments is needed for designing appropriate responses to climate change, recent studies underscore the need to also consider IKLK within adaptation and risk reduction strategies, thus fostering stronger linkages with local communities, leading to improved vertical integration between different strategies, programmes and actors ( [[#Ford--2016|Ford et al., 2016]] ; [[#Vij--2017|Vij et al., 2017]] ; [[#Singh--2020|Singh et al., 2020]] ). The relevance of addressing the issue of vulnerability and poverty to reduce the climate change risks has been demonstrated within the assessed literature on the impact of climate change ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ). In this regard, it is noticeable that not many NAPAs explicitly aim to reduce poverty, even though poverty reduction is associated with vulnerability reduction to climate change ( [[#Demski--2017|Demski et al., 2017]] ).

Next to issues of observed impacts and responses to climate change, it is important to assess observed barriers in implementing climate change responses. The discussion of barriers is complemented later in the chapter with an assessment of the enabling environments for adaptation (see [[#8.5.1|Section 8.5.1]] ). Some of the most common barriers outlined in the scientific literature are summarised in Table 8.3.

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

{| class="wikitable"
|-
! Dimensions

! Barriers in implementing effective climate change responses

! Implications

|-
| Governance

| Unfavourable political frameworks ( [[#Gupta--2016|Gupta, 2016]] )

| Governance structures can undermine autonomous adaptation ( [[#8.4|Section 8.4]] ; Table 8.6); inability to include gender differentiated vulnerabilities in governance schemes ( [[#Bryan--2017|Bryan et al., 2017]] )

|-
| Social

| Attitudes to risks and cultural values may hamper responses ( [[#Billi--2019|Billi et al., 2019]] )

| Social norms of reciprocity and cohesion may erode as a consequence of climate change responses ( [[#Volpato--2019|Volpato and King, 2019]] ); socio-cultural conditions as key barriers to gender differentiated support to impact reduction ( [[#Bryan--2017|Bryan et al., 2017]] )

|-
| Institutional

| Limited availability coordination and prioritisation processes ( [[#Patterson--2019|Patterson and Huitema, 2019]] )

| Lack of anticipatory risks undermining local efforts to cope with hazards ( [[#Singh--2019a|Singh et al., 2019a]] )

|-
| Behavioural

| Psychological distress may cause insecurity and behaviour of some groups may increase vulnerability ( [[#Van%20Lange--2018|Van Lange et al., 2018]] )

| Psychological distress associated with loss of attachment to a place has also been observed among vulnerable communities in regions such as South Asia ( [[#Maharjan--2020|Maharjan et al., 2020]] )

|-
| Financial

| Limited financial resources to support adaptation projects ( [[#Khan--2019|Khan et al., 2019]] )

| Lack of financial resources and assets among urban poor increase their exposure and vulnerabilities to the increasing climate hazards ( [[#Salim--2019|Salim et al., 2019]] )

|-
| Structural

| Unsuitable infrastructure may increase exposure ( [[#Chinowsky--2015|Chinowsky et al., 2015]] ; [[#Vallejo--2017|Vallejo and Mullan, 2017]] )

| Structural marginalisation of Indigenous Peoples and their IKLK can exacerbate risks of maladaptation among SIDS countries ( [[#McNamara--2014|McNamara and Prasad, 2014]] ; [[#Aipira--2017|Aipira et al., 2017]] ; [[#Granderson--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 adaptation (e.g., climate services) ( [[#Bel--2018|Bel and Joseph, 2018]] )

| The highest level of illiteracy among women prevent their engagement to access technology and risk reductions in vulnerable communities ( [[#Balehey--2018|Balehey et al., 2018]] )

|}

There are various characteristics of responses to climate change, which aim to protect livelihoods and prevent poverty expansion (i.e., an enlargement of the group of people already affected by poverty). Some of them are:

* Timely: meaning that responses need to take place within a matter of weeks or months and not over years ( [[#Wise--2014|Wise et al., 2014]] ).
* Targeted: with a focus on the affected communities and groups, to help alleviate the pressures they are under (e.g., [[#Aleksandrova--2020|Aleksandrova, 2020]] ).
* Sustainable: with long-lasting results leading to self-sufficiency of the affected communities and their resource base, as opposed to short-term ones relying on external support (e.g., [[#Caetano--2020|Caetano et al., 2020]] ).
* Integrated: the impact of climate change is multifaceted and far reaching and requires the engagement of various actors (e.g., the vulnerable community, government agencies, local and international nongovernmental organisations, civil societies, media) ( [[#Ayal--2020|Ayal et al., 2020]] ).

Finally, responses such as those outlined in Table 8.1 and Table 8.2, need to ensure the active participation of local stakeholders considering their diverse interests, so that they are grounded in reality. In addition, responses need to be complemented with operational procedures and time frames so that they can be more systematically pursued and implemented ( [[#Alves--2020|Alves et al., 2020]] ).

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