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

=== 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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==== 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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==== 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]] ).

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'''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.

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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.

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