Editing IPCC:AR6/WGII/Chapter-7 (section)

== 7.2. Observed Impacts of Climate Change on Health, Well-Being, Migration and Conflict ==

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<span id="observed-impacts-on-health-and-well-being"></span>
=== 7.2.1 Observed Impacts on Health and Well-Being ===

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''Eleven categories of diseases and health outcomes have been identified in this assessment as being climate-sensitive through direct pathways (e.g., heat and floods) and indirect pathways mediated through natural and human systems and economic and social disruptions (e.g., disease vectors, allergens, air and water pollution, and food system disruption) (high confidence)'' . A key challenge in quantifying the specific relationship between climate and health outcomes is distinguishing the extent to which observed changes in prevalence of a climate-sensitive disease or condition are attributable directly or indirectly to climatic factors as opposed to other non-climatic causal factors ( [[#Ebi--2020|Ebi et al., 2020]] ). A subsequent challenge is then determining the extent to which those observed changes in health outcomes associated with climate are attributable to events or conditions associated with natural climate variability compared to persistent human induced shifts in the mean and/or the variability characteristics of climate (i.e., anthropogenic climate change). The context within which the impacts of climate change affect health outcomes and health systems is described in this chapter as being a function of risk, which is in turn a product of interactions between hazard, exposure and vulnerability (Chapter 1), with the impacts in turn having the potential to reinforce vulnerability and/or exposure to risk (Figure 7.4).

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[[File:ccbe467c63489f5bbcd9fd274136e8e6 IPCC_AR6_WGII_Figure_7_004.png]]

'''Figure 7.4 |''' '''Interactions between hazard, exposure and vulnerability that generate impacts on health systems and outcomes, with selected examples.''' WBD: waterborne disease, VBD: Vector-borne disease, and FBD: Food-borne disease.

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'''Box 7.2 | The Global Burden of Climate-Sensitive Health Outcomes Assessed in this Chapter'''
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Global statistics for death and loss of health are increasingly described in terms of ''burden'' , which describes gaps between a population’s actual health status and what its status would be if its members lived free of disease and disability to their collective life expectancy ( [[#Shaffer--2019|Shaffer et al., 2019]] ). Burden for each disease/health outcome is estimated by adding together the number of years of life lost (YLL) by a person because of early death and the number of years of life lived with disability (YLD) from the considered outcome. The resulting statistic, the disability-adjusted life year (DALY) represents the loss of one year of life lived in full health. The total global burden of disease (Vos et al., 2020), expressed in DALYs, is what the world’s health systems must manage and is reported annually in Global Burden of Disease Study (Vos et al., 2020). The estimated current global burden of climate-sensitive diseases and conditions described in this chapter, and the geographical regions most affected, are summarised in Table Box 7.2.1. As was observed in [[IPCC:Wg2:Chapter:Chapter-11|Chapter 11]] (‘Human Health’) of AR5, the ‘background climate-related disease burden of a population is often the best single indicator of vulnerability to climate change – doubling of risk of disease in a low disease population has much less absolute impact than doubling of the disease when the background rate is high.’

The global magnitude of climate-sensitive diseases was estimated in 2019 to be 39,503,684 deaths (69.9% of total annual deaths) and 1,530,630,442 DALYs (Vos et al., 2020). Of these, cardiovascular diseases (CVDs) comprised the largest proportion of climate-sensitive diseases (32.8% of deaths and 15.5% DALYs). The next largest category consists of respiratory diseases – with chronic respiratory disease contributing to 7% of deaths and 4.1% of DALYs and respiratory infection and tuberculosis contributing to 6.5% of deaths and 6% of DALYs. The observed trend of climate-sensitive disease deaths since 1990 is marked by increasing cardiovascular mortality and decreasing mortality from respiratory infections, enteric diseases and other infectious diseases (Vos et al., 2020). Figure Box 7.2.1 illustrates specific global trends between 1990 and 2017 of selected health outcomes estimated by GBDs (Ahmad Kiadaliri et al. 2018).

[[File:58b7a32815254cc1672186732a478901 IPCC_AR6_WGII_Figure_7_Box_7_2_1.png]]

'''Figure Box 7.2.1 |''' '''Global trends of selected health outcomes estimated by GBDs.''' Source: Ahmad Kiadaliri et al. (2018a).

'''Table Box 7.2.1 |''' Global burden of climate-sensitive health risks assessed in this chapter (in order of assessment) (Vos et al., 2020) and synthesis of major observed and projected impacts in most affected regions. Blue represents an increase in positive health impacts, green represents an increase in negative health impacts and yellow represents an increase in both positive and negative impacts, but not necessarily in equal proportions. The confidence level refers to both the attributed observed and projected changes to climate change. No assessment means the evidence is insufficient for assessment.

{| class="wikitable"
|-
!
! colspan="2"| '''Data from Global Burden of Disease 2019 (Vos et al. 2020)'''

! colspan="3"| '''[https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-7 Chapter 7] Assessment'''

|-
| '''Health outcome (disease/condition)'''

| '''Global annual deaths'''

| '''Regions most affected (deaths)'''

| '''Climate change observed impacts'''

| '''Climate change projected impacts in most affected regions'''

| '''Selected key references of the Assessment'''

|-
| '''Malaria'''

| 643,381

| Africa (92%)

| \****

| \***

| [[#M’Bra--2018|M’Bra et al. (2018)]] ; Caminade et al. (2019); [[#Gibb--2020|Gibb et al. (2020)]] ; [[#Tompkins--2016b|Tompkins and Caporaso (2016b)]] ; [[#Ebi--2021a|Ebi et al. (2021a)]]

|-
| '''Dengue'''

| 36,055

| Asia (96%)

| \***

| \***

| [[#Bhatt--2013|Bhatt et al. (2013)]] ; Rocklöv &amp; Dubrow (2020); [[#Messina--2019|Messina et al. (2019)]] ; [[#Monaghan--2018|Monaghan et al. (2018)]]

|-
| '''Diarrhoeal diseases'''

| 1,534,443

| Asia (56%)

| \***

| \**

| Cissé (2019); Levy et al. (2018); [[#Lo%20Iacono--2017|Lo Iacono et al. (2017)]] ; [[#Carlton--2016|Carlton et al. (2016)]]

|-
| '''Salmonella'''

| 79,046

| Africa (89%)

| \***

| \**

| Cissé (2019); [[#Smith--2019|Smith and Fazil (2019)]] ; [[#Lake--2017|Lake (2017)]]

|-
| '''RTIs'''

| 2,493,200

| Asia (47%)

| \**

|
| Geier et al. (2018); [[#Oluwole--2017|Oluwole (2017)]]

|-
| '''Non-communicable respiratory illness'''

| 3,741,705

| Asia (74%)

| \***

| \**

| [[#Schweitzer--2018|Schweitzer et al. (2018)]] ; Hansel et al. (2016); [[#Collaco--2018|Collaco et al. (2018)]] ; [[#D’Amato--2020|D’Amato et al. (2020)]] ; [[#Silva--2017|Silva et al. (2017)]] ; Doherty et al. (2017); [[#Beggs--2021|Beggs (2021)]]

|-
| '''CVD'''

| 18,562,510

| Asia (58%)

| \**

| \***

| [[#Stewart--2017|Stewart et al. (2017)]] ; Phung (2016); Sun (2018); Wang (2016); Tian (2019); Chen (2019); Zhang (2018)

|-
| '''Death from malignant neoplasms'''

| 10,079,637

| Asia (55%)

| \***

|
| [[#Ahmed--2014|Ahmed et al. (2014)]] ; Modenese et al. (2018); [[#Prueksapanich--2018|Prueksapanich et al. (2018)]]

|-
| '''Diabetes'''

| 1,551,170

| Asia (56%)

| \**

| \**

| [[#Hajat--2017|Hajat et al. (2017)]] ; [[#Xu--2019b|Xu et al. (2019b)]] ; [[#Li--2014|Li et al. (2014)]] ; [[#Yang--2016|Yang et al. (2016)]] ; Velez-Valle et al. (2016); [[#Quast--2019|Quast and Feng (2019)]]

|-
| '''Environmental heat and cold exposure'''

| 47,461

| Asia (46%)

| \***

| \****

| [[#Zhang--2019b|Zhang et al. (2019b)]] ; [[#Green--2019|Green et al. (2019)]] ; [[#Murray--2020|Murray et al. (2020)]] ; [[#Ma--2021|Ma and Yuan (2021)]] ; [[#Jones--2018|Jones et al. (2018)]] ; [[#Russo--2019|Russo et al. (2019)]] ; [[#Gosling--2017|Gosling et al. (2017)]]

|-
| '''Nutritional deficiencies'''

| 251,577

| Africa (43%)

| \***

| \***

| [[#Mbow--2019|Mbow et al. (2019)]] ; [[#Lloyd--2018|Lloyd (2018)]] ; [[#Springmann--2016b|Springmann et al. (2016b)]] ; [[#Zhu--2018|Zhu et al. (2018)]] ; [[#Weyant--2018|Weyant et al. (2018)]]

|-
| '''Mental health''' a

| n.a.

| n.a.

| \****

| \****

| Cianconi et al. (2020); [[#Charlson--2021|Charlson et al. (2021)]] ; [[#Hayes--2018|Hayes and Poland (2018)]] ; Hrabok et al. (2020); [[#Obradovich--2018|Obradovich et al. (2018)]]

|-
| colspan="6"| '''Legend'''

|-
| colspan="2"| '''Climate change impacts'''

|
| colspan="2"| '''Confidence'''

|
|-
| colspan="2"| ''Positive health impacts''

|
| colspan="2"| ''Very high''

| \****

|-
| colspan="2"| ''Negative health impacts''

|
| colspan="2"| ''High''

| \***

|-
| colspan="2"| ''Positive and negative impacts''

|
| colspan="2"| ''Medium''

| \**

|-
| colspan="2"| ''No assessment''

|
| colspan="2"| ''Low''

| \*

|}

Notes:

(a) Mental health data were not available (n.a.) due to lack of information in GBD 2019 related to annual deaths and the most affected regions.

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<span id="observed-impacts-on-communicable-diseases"></span>
=== 7.2.2 Observed Impacts on Communicable Diseases ===

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<span id="observed-impacts-on-vector-borne-diseases"></span>
==== 7.2.2.1 Observed Impacts on Vector-Borne Diseases ====

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Climate-sensitive VBDs include mosquito-borne diseases, rodent-borne diseases and tick-borne diseases. Many infectious agents, vectors, non-human reservoir hosts, and pathogen replication rates can be sensitive to ambient climatic conditions. Elevated proliferation and reproduction rates at higher temperatures, longer transmission season, changes in ecology and climate-related migration of vectors, reservoir hosts or human populations contribute to this climate sensitivity (Rocklöv &amp; Dubrow, 2020; [[#Semenza--2021|Semenza and Paz, 2021]] ). Age-standardised DALY rates for many VBDs have decreased over the last decade due to factors unrelated to climate. Vulnerability to VBD is strongly determined by sociodemographic factors (e.g., children, the elderly and pregnant women are at greater risk) with exposure to vectors being strongly influenced by various factors including socioeconomic status, housing quality, healthcare access, susceptibility, occupational setting, recreational activity, conflicts and displacement ( Rocklöv &amp; Dubrow, 2020; [[#Semenza--2021|Semenza and Paz, 2021]] ). Figure 7.5 illustrates how climatic and non-climatic drivers and responses determine VBD outcomes.

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[[File:6e685e9b9a80d5ef88beb6de20e7e1dc IPCC_AR6_WGII_Figure_7_005.png]]

'''Figure 7.5 |''' '''Analysis of the underlying drivers of infectious disease threat events (IDTEs) detected in Europe from 2008 to 2013 by epidemic intelligence at the European Centre of Disease Prevention and Control.''' Seventeen drivers were identified and categorised into three groups: globalisation and environment (green), sociodemographic (red) and public health system (blue). The drivers are illustrated as diamond shapes and arranged in the top and bottom row; the sizes are proportional to the overall frequency of the driver. Here IDTEs (epidemics or first autochthonous cases) of VBDs are illustrated as a horizontal row of dots in the middle. These empirical data include the IDTEs of VBDs such as West Nile fever, malaria, dengue fever, chikungunya and Hantavirus infection. Source: [[#Semenza--2016|Semenza et al. (2016)]] .

''Evidence has increased since AR5 that the vectorial capacity has increased for dengue fever, malaria and other mosquito-borne diseases and that higher global average temperatures are making wider geographic areas more suitable for transmission (very high confidence).'' Transmission rates of malaria are directly influenced by climatic and weather variables such as temperature, with non-climatic socioeconomic factors and health system responses counteracting the climatic drivers ''(very high confidence).'' The burden of malaria is greatest in Africa, where more than 90% of all malaria-related deaths occur ( [[#M’Bra--2018|M’Bra et al., 2018]] ; Caminade et al., 2019). Between 2007 and 2017, DALYs for malaria have decreased by 39% globally. Malaria is mainly caused by five distinct species of plasmodium parasite ( ''Plasmodium falciparum'' , ''Plasmodium vivax'' , ''Plasmodium malariae'' , ''Plasmodium ovale'' and ''Plasmodium knowlesi'' ) and is transmitted by Anopheline mosquitoes. Evidence suggests that in highland areas of Colombia and Ethiopia, malaria has shifted in warmer years towards higher altitudes, indicating that, without intervention, malaria will increase at higher elevations as the climate warms ( [[#Siraj--2014|Siraj et al., 2014]] ; [[#Midekisa--2015|Midekisa et al., 2015]] ). Each year, local outbreaks of malaria occur due to importation in areas from which it was once eradicated, such as Europe, but the risk of re-establishment is considered low.

''The transmission of dengue fever is linked to climatic and weather variables such as temperature, relative humidity and rainfall (high confidence).'' The dengue virus is carried and spread by ''Aedes'' mosquitoes, primarily ''Aedes aegypti'' . Dengue has the second highest burden of VBDs, with the majority of deaths occurring in Asia ( [[#Bhatt--2013|Bhatt et al., 2013]] ). Since 1950, global dengue burden has grown and is attributable to a combination of climate-associated expansion in the geographic range of the vector species and non-climatic factors such as globalised air traffic, urbanisation and ineffective vector abatement measures. Temperature, relative humidity and rainfall variables are significantly and positively associated with increased dengue case incidence and/or transmission rates globally, including in Vietnam ( [[#Phung--2015|Phung et al., 2015]] ; Xuan le et al., 2014), Thailand ( [[#Xu--2019a|Xu et al., 2019a]] ), India ( [[#Mutheneni--2017|Mutheneni et al., 2017]] ; [[#Rao--2018|Rao et al., 2018]] ; [[#Mala--2019|Mala and Jat, 2019]] ), Indonesia ( [[#Kesetyaningsih--2018|Kesetyaningsih et al., 2018]] ), the Philippines ( [[#Carvajal--2018|Carvajal et al., 2018]] ), the USA ( [[#Lopez--2018|Lopez et al., 2018]] ; [[#Pena-Garcia--2017|Pena-Garcia et al., 2017]] ; [[#Duarte--2019|Duarte et al., 2019]] ; [[#Rivas--2018|Rivas et al., 2018]] ; [[#Silva--2016a|Silva et al., 2016a]] ), Jordan ( [[#Obaidat--2018|Obaidat and Roess, 2018]] ) and Timor-Leste ( [[#Wangdi--2018|Wangdi et al., 2018]] ). Variation in winds, sea surface temperatures and rain over the tropical eastern Pacific Ocean (El Niño-Southern Oscillation; ENSO) have been linked to increased dengue incidence in Colombia ( [[#Quintero-Herrera--2015|Quintero-Herrera et al., 2015]] ; [[#McGregor--2018|McGregor and Ebi, 2018]] ; [[#Pramanik--2020|Pramanik et al., 2020]] ) and its interannual variation successfully forecasted in Ecuador using ENSO indices as predictors ( [[#Petrova--2019|Petrova et al., 2019]] ). The observed time lag between climate exposures and increased dengue incidence is approximately 1–2 months ( [[#Chuang--2017|Chuang et al., 2017]] ; [[#Lai--2018|Lai, 2018]] ; [[#Chang--2018|Chang et al., 2018]] ).

''Changing climatic patterns are facilitating the spread of CHIKV, Zika, Japanese encephalitis and Rift Valley Fever in Asia, Latin America, North America and Europe'' ( ''high confidence).'' Climate change may have facilitated the emergence of CHIKV as a significant public health challenge in some Latin American and Caribbean countries ( [[#Yactayo--2016|Yactayo et al., 2016]] ; [[#Pineda--2016|Pineda et al., 2016]] ) and contributed to chikungunya outbreaks in Europe (Rocklöv et al., 2019; [[#Mascarenhas--2018|Mascarenhas et al., 2018]] ; [[#Morens--2014|Morens and Fauci, 2014]] ). The Zika virus outbreak in South America in 2016 was preceded by 2007 outbreaks on Pacific islands and followed a period of record high temperatures and severe drought conditions in 2015 ( [[#Paz--2016|Paz and Semenza, 2016]] ; [[#Tesla--2018|Tesla et al., 2018]] ). Increased use of household water storage containers during the drought is correlated with a range expansion of ''Aedes aegypti'' during this period, increasing household exposure to the vector ( [[#Paz--2016|Paz and Semenza, 2016]] ). Changing climate also appears to be a risk factor for the spread of Japanese encephalitis to higher altitudes in Nepal ( [[#Ghimire--2015|Ghimire and Dhakal, 2015]] ) and in southwest China ( [[#Zhao--2014|Zhao et al., 2014]] ). In eastern Africa, climate change may be a risk factor in the spread of Rift Valley Fever ( [[#Taylor--2016a|Taylor et al., 2016a]] ).

''Changes in temperature, precipitation, and relative humidity have been implicated as drivers of West Nile fever in southeastern Europe (medium confidence'' '').'' The average temperature and precipitation prior to the exceptional 2018 West Nile outbreak in Europe was above the 1981–2010 period average, which may have contributed to an early upsurge of the vector population ( [[#Marini--2020|Marini et al., 2020]] ; [[#Haussig--2018|Haussig et al., 2018]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ). In 2019 and 2020, West Nile fever was first detected in birds and subsequently in humans in Germany and the Netherlands ( [[#Ziegler--2020|Ziegler et al., 2020]] ; [[#Vlaskamp--2020|Vlaskamp et al., 2020]] ).

''Climate change has contributed to the spread of the Lyme disease vector'' Ixodes scapularis '', a corresponding increase in cases of Lyme disease in North America (high confidence) and the spread of the Lyme disease and tick-borne encephalitis vector'' Ixodes ricinus ''in Europe (medium confidence).'' In Canada, there has been a geographic range expansion of the black-legged tick ''I. scapularis,'' the main vector of ''Borrelia burgdorferi'' , the agent of Lyme disease. Vector surveillance of ''I. scapularis'' has identified strong correlation between temperatures and the emergence of tick populations, their range and recent geographic spread, with recent climate warming coinciding with a rapid increase in human Lyme disease cases ( [[#Clow--2017|Clow et al., 2017]] ; [[#Cheng--2017|Cheng et al., 2017]] ; [[#Gasmi--2017|Gasmi et al., 2017]] ; [[#Ebi--2017|Ebi et al., 2017]] ). ''Ixodes ricinus'' , the primary vector in Europe for both Lyme borreliosis and tick-borne encephalitis is sensitive to humidity and temperature ( [[#Daniel--2018|Daniel et al., 2018]] ; [[#Estrada-Peña--2020|Estrada-Peña and Fernández-Ruiz, 2020]] ) ( ''high confidence'' ). There has been an observed range expansion to higher latitudes in Sweden and to higher elevations in Austria and the Czech Republic.

Rodent-borne disease outbreaks have been linked to weather and climate conditions in a small number of studies published since AR5, but more research is needed in this area ''.'' In Kenya, a positive association exists between precipitation patterns and ''Theileria'' -infected rodents, but for ''Anaplasma'' , ''Theileria'' and ''Hepatozoon'' , the association between rainfall and pathogen varies according to rural land use types ( [[#Young--2017|Young et al., 2017]] ). Weather variability plays a significant role in transmission rates of haemorrhagic fever with renal syndrome (HFRS) ( [[#Hansen--2015|Hansen et al., 2015]] ; [[#Xiang--2018|Xiang et al., 2018]] ; [[#Liang--2018|Liang et al., 2018]] ; [[#Fei--2015|Fei et al., 2015]] ; [[#Xiao--2014|Xiao et al., 2014]] ; [[#Vratnica--2017|Vratnica et al., 2017]] ; [[#Roda%20Gracia--2015|Roda Gracia et al., 2015]] ; [[#Monchatre-Leroy--2017|Monchatre-Leroy et al., 2017]] ; [[#Bai--2019|Bai et al., 2019]] ). In Chongqing, HFRS incidence has been positively associated with rodent density and rainfall ( [[#Bai--2015|Bai et al., 2015]] ).

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<span id="observed-impacts-on-waterborne-diseases"></span>
==== 7.2.2.2 Observed Impacts on Waterborne Diseases ====

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Important waterborne diseases (WBDs) include diarrhoeal diseases (such as cholera, shigella, cryptosporidiosis and typhoid), schistosomiasis, leptospirosis, hepatitis A and E and poliomyelitis ( [[#Cisse--2019|Cisse, 2019]] ; [[#Houéménou--2021|Houéménou et al., 2021]] ; [[#Hassan--2021|Hassan et al., 2021]] ; [[#Archer--2020|Archer et al., 2020]] ; [[#Mbereko--2020|Mbereko et al., 2020]] ; [[#Fan--2021|Fan et al., 2021]] ). The number of cases of WBDs is considerable, and even in high-income countries WBDs continue to be a concern ( [[#Cissé--2018|Cissé et al., 2018]] ; [[#Kirtman--2014|Kirtman et al., 2014]] ; [[#Levy--2018|Levy et al., 2018]] ; [[#Murphy--2014|Murphy et al., 2014]] ; [[#Brubacher--2020|Brubacher et al., 2020]] ; [[#Lee--2021|Lee et al., 2021]] ). Nevertheless, diarrhoea mortality has declined substantially since 1990, although there are variations by country, and the global burden of WBDs has decreased in line with vaccination coverage of some WBDs (such as polio and cholera), poverty reduction and improved sanitation and hygiene ( [[#Jacob--2021|Jacob and Kazaura, 2021]] ; [[#Mutono--2020|Mutono et al., 2020]] ; [[#Lee--2019|Lee et al., 2019]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ; [[#Jacob--2021|Jacob and Kazaura, 2021]] ; [[#Mutono--2020|Mutono et al., 2020]] ).

Drinking water containing pathogenic microorganisms is the main driver of the burden of WBDs ( [[#Murphy--2014|Murphy et al., 2014]] ; [[#Lee--2021|Lee et al., 2021]] ; [[#Chen--2021b|Chen et al., 2021b]] ; [[#Musacchio--2021|Musacchio et al., 2021]] ). WBD outbreaks, particularly intestinal diseases, are attributable to a combination of the presence of particular pathogens (bacteria, protozoa, viruses or parasites) and the characteristics of drinking water systems in a given location ( [[#Bless--2016|Bless et al., 2016]] ; [[#Ligon--2016|Ligon and Bartram, 2016]] ; [[#Mutono--2021|Mutono et al., 2021]] ; [[#Ferreira--2021|Ferreira et al., 2021]] ).

''Since AR5 there is a growing body of evidence that increases in temperature (very high confidence), heavy rainfall (high confidence), flooding (medium confidence) and drought (low confidence) are associated with an increase of diarrhoeal diseases.'' In the majority of studies there is a significant positive association observed between WBDs and elevated temperatures, especially in areas where water, sanitation and hygiene (WASH) deficiencies are significant ( [[#Levy--2018|Levy et al., 2018]] ; [[#Carlton--2016|Carlton et al., 2016]] ; [[#Levy--2018|Levy et al., 2018]] ; [[#Sherpa--2014|Sherpa et al., 2014]] ; [[#Guzman%20Herrador--2015|Guzman Herrador et al., 2015]] ; [[#Levy--2016|Levy et al., 2016]] ; [[#Lo%20Iacono--2017|Lo Iacono et al., 2017]] ). In Ethiopia, South Africa and Senegal, increases in temperatures are associated with increases in diarrhoea, while in Ethiopia, Senegal and Mozambique, increases in monthly rainfall are associated with an increase in cases of childhood diarrhoea ( [[#Azage--2015|Azage et al., 2015]] ; [[#Thiam--2017|Thiam et al., 2017]] ; [[#Horn--2018|Horn et al., 2018]] ). Similar associations between weather and diarrhoea have been observed in Cambodia, China, Bangladesh, Pacific Island countries and the Philippines ( [[#McIver--2016a|McIver et al., 2016a]] ; [[#McIver--2016b|McIver et al., 2016b]] ; [[#Liu--2018|Liu et al., 2018]] ; [[#Wu--2014|Wu et al., 2014]] ; [[#Matsushita--2018|Matsushita et al., 2018]] ). Heavy precipitation events have been consistently associated with outbreaks of WBDs in Europe, USA, UK and Canada ( [[#Guzman%20Herrador--2015|Guzman Herrador et al., 2015]] ; [[#Levy--2016|Levy et al., 2016]] ; [[#Lo%20Iacono--2017|Lo Iacono et al., 2017]] ; [[#Curriero--2001|Curriero et al., 2001]] ; [[#Guzman%20Herrador--2016|Guzman Herrador et al., 2016]] ; [[#Levy--2018|Levy et al., 2018]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ).

Impacts of floods include outbreaks of WBDs, with such events disproportionately affecting the young, elderly and immunocompromised ( [[#Suk--2020|Suk et al., 2020]] ; [[#Guzman%20Herrador--2015|Guzman Herrador et al., 2015]] ; [[#Levy--2016|Levy et al., 2016]] ; [[#Lo%20Iacono--2017|Lo Iacono et al., 2017]] ; [[#Zhang--2019a|Zhang et al., 2019a]] ). Water shortage and drought have been found associated with diarrhoeal disease peaks ( [[#Epstein--2020b|Epstein et al., 2020b]] ; [[#Subiros--2019|Subiros et al., 2019]] ; [[#Boithias--2016|Boithias et al., 2016]] ), while some reviews found insufficient evidence of the effects of drought on diarrhoea (Levy et al, 2016 ; [[#Asmall--2021|Asmall et al., 2021]] ; [[#Epstein--2020b|Epstein et al., 2020b]] ; [[#Subiros--2019|Subiros et al., 2019]] ; [[#Boithias--2016|Boithias et al., 2016]] ; [[#Ramesh--2016|Ramesh et al., 2016]] ).

''Heavy rainfall and higher than normal temperatures are associated with increased cholera risk in affected regions (very high confidence'' '').'' Cholera is an acute diarrhoeal disease typically caused by the bacterium ''Vibrio cholerae'' that can result in severe morbidity and mortality. Maximum and minimum temperatures and precipitation have been negatively associated with cholera cases. Cholera outbreaks have occurred in several regions after natural disasters, including cholera incidence increasing three-fold in El Niño-sensitive regions of Africa ( [[#Mpandeli--2018|Mpandeli et al., 2018]] ; [[#Amegah--2016|Amegah et al., 2016]] ; [[#Escobar--2015|Escobar et al., 2015]] ; [[#Jutla--2017|Jutla et al., 2017]] ; [[#Asadgol--2019|Asadgol et al., 2019]] ; [[#Moore--2018|Moore et al., 2018]] ; [[#Moore--2017|Moore et al., 2017]] ; [[#Camacho--2018|Camacho et al., 2018]] ; [[#IPCC--2019a|IPCC, 2019a]] ; Cross-Chapter Box ILLNESS in Chapter 2; Box 3.3).

''Heavy rainfall, warmer weather and drought are linked to increased risks for other GI infections (high confidence).'' As temperature increases, bacterial causes of GI infection also appear to increase, and this association is variably influenced by humidity and rainfall ( [[#Ghazani--2018|Ghazani et al., 2018]] ; [[#Levy--2016|Levy, 2016]] ). In New York it has been found that every 1°C increase in temperature was correlated with a 0.70–0.96% increase in daily hospitalisation for GI infections ( [[#Lin--2016|Lin et al., 2016]] ). In the Philippines, leptospirosis and typhoid fever showed an increase in incidence following heavy rainfall and flooding events ( [[#Matsushita--2018|Matsushita et al., 2018]] ).

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<span id="observed-impacts-on-food-borne-diseases"></span>
==== 7.2.2.3 Observed Impacts on Food-Borne Diseases ====

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FBDs refer to any illness resulting from ingesting food that is spoiled or contaminated by pathogenic bacteria, viruses, parasites, toxins, pesticides and/or medicines ( [[#WHO--2018b|WHO, 2018b]] ). FBD risks are present throughout the food chain, from production to consumption, and most often arise due to contamination at source and from improper food handling, preparation and/or storage ( [[#Smith--2019|Smith and Fazil, 2019]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ). As with WBDs, FBD outbreaks can follow multiple causal pathways as climatic risk factors interact with food production and distribution systems, urbanisation and population growth, resource and energy scarcity, decreasing agricultural productivity, price volatility, modification of diet trends, new technologies and the emergence of antimicrobial resistance ( [[#Lake--2018|Lake, 2018]] ; [[#Yeni--2017|Yeni and Alpas, 2017]] ). The burden of FBDs is also linked to malnutrition as reduced immunity increases susceptibility to various food-borne pathogens and toxins ( [[#FAO--2020|FAO, 2020]] ).

''A strong association exists between increases in FBDs and high air and water temperatures and longer summer seasons (very high confidence).'' The risks occur through complex transmission pathways throughout the food chain and the wide range of food-borne pathogens ( [[#Cisse--2019|Cisse, 2019]] ; [[#Hellberg--2016|Hellberg and Chu, 2016]] ; [[#Lake--2018|Lake and Barker, 2018]] ; [[#Park--2018b|Park et al., 2018b]] ; [[#Smith--2019|Smith and Fazil, 2019]] ). The food-borne pathogens of most concern are those having low infective doses, a significant persistence in the environment and high stress tolerance to temperature change (e.g., enteric viruses, ''Campylobacter'' spp., Shiga toxin-producing ''E. coli'' strains, ''Mycobacterium avium,'' tuberculosis complexes, parasitic protozoa and ''Salmonella'' ) ( [[#Lake--2018|Lake, 2018]] ; [[#Lake--2017|Lake, 2017]] ; [[#Lake--2018|Lake and Barker, 2018]] ; [[#Smith--2019|Smith and Fazil, 2019]] ; European Food Safety Authority 2020; [[#Semenza--2021|Semenza and Paz, 2021]] ). Priority risks include marine biotoxins, mycotoxins, salmonellosis, vibriosis, transfer of contaminants due to extreme precipitation, floods, increased use of chemicals in the food chain (plant protection products, fertilizers, veterinary drugs) and potential residues in food (European Food Safety Authority 2020; World Health Organization 2018b).

''There is a strong association observed between the increase in average ambient temperature and increases in'' Salmonella ''infections (high confidence).'' Most types of ''Salmonella'' infections lead to salmonellosis, while some other types ( ''Salmonella'' Typhi and ''Salmonella'' Paratyphi) can lead to typhoid fever or paratyphoid fever. The transmission to humans of the non-typhoidal ''Salmonella'' infection, one of the most widespread FBDs, usually occurs through eating foods contaminated with animal faeces. Studies conducted in Australia ( [[#Milazzo--2016|Milazzo et al., 2016]] ), New Zealand ( [[#Lal--2016|Lal et al., 2016]] ), the UK ( [[#Lake--2017|Lake, 2017]] ), South Korea ( [[#Park--2018a|Park et al., 2018a]] ; [[#Park--2018c|Park et al., 2018c]] ; [[#Park--2018a|Park et al., 2018a]] ), Singapore ( [[#Aik--2018|Aik et al., 2018]] ) and Hong Kong, SAR of China ( [[#Wang--2018a|Wang et al., 2018a]] ; [[#Wang--2018b|Wang et al., 2018b]] ), have shown that ''Salmonella'' outbreaks are strongly associated with temperature increases.

''Significant associations exist between FBDs due to'' Campylobacter '', precipitation and temperature (medium confidence).'' The timing of heat-associated Campylobacteriosis events varies across countries, with infection rates in the UK appearing to decline immediately after periods of high rainfall ( [[#Djennad--2019|Djennad et al., 2019]] ; [[#Lake--2019|Lake et al., 2019]] ; [[#Rosenberg--2018|Rosenberg et al., 2018]] ; [[#Yun--2016|Yun et al., 2016]] ; [[#Weisent--2014|Weisent et al., 2014]] ). This suggests the association with climate may be indirect and due to weather conditions that encourage outdoor food preparation and recreational activities ( [[#Lake--2017|Lake, 2017]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ).

Outbreaks of human and animal ''Cryptococcus'' have been reported as being associated with a combination of climatic factors and shifts in host and vector populations ( [[#Chang--2015|Chang and]] [[#Chen--2015|Chen, 2015]] ; [[#Rickerts--2019|Rickerts, 2019]] ). The prevalence of childhood cryptosporidiosis, which is the second leading cause of moderate to severe diarrhoea among infants in the tropics and subtropics, shows associations with population density and rainfall, with contamination due to ''Cryptosporidium'' spp. being 2.61 times higher during and after heavy rain ( [[#Lal--2019|Lal et al., 2019]] ; [[#Young--2015|Young et al., 2015]] ; [[#Khalil--2018|Khalil et al., 2018]] ). Studies from Ghana, Guinea Bissau, Tanzania, Kenya and Zambia show a higher prevalence of ''Cryptosporidium'' during high rainfall seasons, with some peaks observed before, at the onset or at the end of the rainy season ( [[#Squire--2017|Squire and Ryan, 2017]] ).

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==== 7.2.2.4 Respiratory Tract Infections ====

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Climatic risk factors for respiratory tract infections (RTIs) due to multiple pathogens (bacteria, viruses and fungi) include temperature and humidity extremes, dust storms, extreme precipitation events and increased climate variability. Amongst a range of RTIs, pneumonia and influenza represent a significant disease burden ( [[#Ferreira-Coimbra--2020|Ferreira-Coimbra et al., 2020]] ; [[#Lafond--2021|Lafond et al., 2021]] ; [[#McAllister--2019|McAllister et al., 2019]] ; [[#Wang--2020|Wang et al., 2020]] c). The drivers of pneumonia incidence are complex and include a range of possible non-climate as well as climate factors. For example, chronic diseases (e.g., lung disease, chronic obstructive pulmonary disease (COPD) and asthma), other comorbidities, a weak immune system, age, gender, community, passive smoking, air pollution and childhood immunisation may confound the climate pneumonia relationship ( [[#Miyayo--2021|Miyayo et al., 2021]] ).

In temperate regions, the incidence of pneumonia is higher in winter months, but the exact causes of this seasonality remain debated ( [[#Mirsaeidi--2016|Mirsaeidi et al., 2016]] ). With regards to temperature, various J-shaped, U-shaped or V-shaped temperature–pneumonia relationships have been reported in the literature ( [[#Huang--2018|]] [[#Huang--2018|Huang et al., 2018]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Liu--2014|Liu et al., 2014]] ; [[#Qiu--2016|Qiu et al., 2016]] ; [[#Sohn--2019|Sohn et al., 2019]] ) with such relationships dependent on location. Humidity also appears important but, like temperature, its effect is not consistent across studies – low temperatures and low humidity ( [[#Davis--2016|Davis et al., 2016]] ), high temperatures and high humidity ( [[#Lam--2020|Lam et al., 2020]] ) and low temperatures and high humidity ( [[#Miyayo--2021|Miyayo et al., 2021]] ) have all been found to be associated with an increased incidence of pneumonia.

Day-to-day variations in temperature also appear important. For Australia, increases in emergency room visits for childhood pneumonia are associated with sharp temperature drops ( [[#Xu--2014|Xu et al., 2014]] ). Large inter-daily changes in temperature are important for respiratory disease incidence in Guangzhou, China ( [[#Lin--2013|Lin et al., 2013]] ) and Shanghai ( [[#Lei--2021|Lei et al., 2021]] ) while rapidly changing and extreme temperatures during pregnancy have been linked to childhood pneumonia ( [[#Miao--2017|Miao et al., 2017]] ; [[#Zeng--2017|Zeng et al., 2017]] ; [[#Zheng--2021|Zheng et al., 2021]] ). In tropical and subtropical areas of Africa and Asia, pneumonia incidence has been reported to be higher during the rainy season, pointing to a positive association between pneumonia patterns and temperature and precipitation ( [[#Chowdhury--2018a|Chowdhury et al., 2018a]] ; [[#Lim--2018|Lim and Siow, 2018]] ; [[#Paynter--2010|Paynter et al., 2010]] ).

The degree to which the timing, duration and magnitude of local influenza virus epidemics is dependent on climate factors is poorly understood ( [[#Lam--2020|Lam et al., 2020]] ). Further, a host of non-climate confounders are ''likely'' to influence the incidence of seasonal influenza ( [[#Caini--2018|Caini et al., 2018]] ). This poses a number of challenges for making reliable climate-based epidemiological forecasts for influenza ( [[#Gandon--2016|Gandon et al., 2016]] ). Although no association between anomalous climate conditions and influenza have been reported in some locations ( [[#Lam--2020|Lam et al., 2020]] ), generally, low winter temperatures and humidity in temperate regions and periods of high humidity and precipitation in the tropical and subtropical regions have been linked to outbreaks of influenza ( [[#Deyle--2016|Deyle et al., 2016]] ; [[#Soebiyanto--2015|Soebiyanto et al., 2015]] ; [[#Tamerius--2013|Tamerius et al., 2013]] ). However, the climate sensitivity of influenza may be more complex than this, with both high and low humidity; the amount and intensity of precipitation; solar activity and/or sunshine; and latitude also being important ( [[#Axelsen--2014|Axelsen et al., 2014]] ; [[#Chong--2020b|Chong et al., 2020b]] ; [[#Geier--2018|Geier et al., 2018]] ; [[#Park--2019|Park et al., 2019]] ; [[#Qu--2016|Qu, 2016]] ; [[#Smith--2017|Smith et al., 2017]] ; [[#Wang--2017|Wang et al., 2017]] c; [[#Zhao--2018a|Zhao et al., 2018a]] ). Moreover, the shape of the climate variable influenza relationship may be conditioned on influenza type ( [[#Chong--2020a|Chong et al., 2020a]] ). Further, distinct periods of weather variability characterised by rapid inter-daily changes in temperature may act as precursors to influenza epidemics as has been demonstrated for the marked 2017–2018 influenza season and others across the USA ( [[#Liu--2020a|Liu et al., 2020a]] ; [[#Zhao--2018a|Zhao et al., 2018a]] ). For the eastern Mediterranean, such rapid weather changes are associated with the ‘Cyprus Low’, with the timing and magnitude of seasonal influenza related to the interannual frequency of this particular weather regime ( [[#Hochman--2021|Hochman et al., 2021]] ). Potentially, large-scale modes of climatic variability such as ENSO and the Indian Ocean Dipole, which strongly moderate the frequency of weather regimes in some parts of the world, could affect influenza pandemic dynamics. However, studies conducted to date report inconsistent results. Some point to an increased (decreased) severity of seasonal influenza during El Niño (La Niña) ( [[#Oluwole--2015|Oluwole, 2015]] ; [[#Oluwole--2017|Oluwole, 2017]] ), while others find influenza to be more severe and frequent when coinciding with La Niña events ( [[#Chun--2019|Chun et al., 2019]] ; [[#Flahault--2016|Flahault et al., 2016]] ; [[#Shaman--2013|Shaman and Lipsitch, 2013]] ). This raises the possibly of non-stationary associations between large-scale modes of climatic variability and influenza dynamics ( [[#Onozuka--2015|Onozuka and Hagihara, 2015]] ) as found for other diseases ( [[#Kreppel--2014|Kreppel et al., 2014]] ), something that might be expected given El Niño’s time-varying impact on global precipitation and temperature fields and associated impacts on health outcomes ( [[#McGregor--2018|McGregor and Ebi, 2018]] ).

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==== 7.2.2.5 Other Water Shortage and Drought-Associated Diseases and Health Outcomes ====

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Water shortage and drought are associated with skin diseases ( [[#Schachtel--2021|Schachtel et al., 2021]] ; [[#Lundgren--2018|Lundgren, 2018]] ; [[#Andersen--2017|Andersen and Davis, 2017]] ; [[#Kaffenberger--2017|Kaffenberger et al., 2017]] ; [[#Andersen--2017|Andersen and Davis, 2017]] ), trachoma ( [[#Ramesh--2016|Ramesh et al., 2016]] ) and violence ( [[#Epstein--2020a|Epstein et al., 2020a]] ); more research is warranted in these areas for future assessment.

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'''Box 7.3 | Cascading Risk Pathways Linking Waterborne Disease to Climate Hazards'''
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The causal linkages between climate variability and change and incidence of WBDs follow multiple direct and indirect pathways, often as part of a cascading series of risks ( [[#Semenza--2020|Semenza, 2020]] ). For example, extreme precipitation can result in a cascading hazard or disease event with implications of greater magnitude than the initial hazard, especially if there are pre-existing vulnerabilities in critical infrastructure and human populations ( [[#Semenza--2021|Semenza and Paz, 2021]] ). Intense or prolonged precipitation can flush pathogens in the environment from pastures and fields to groundwater, rivers and lakes, consequently infiltrating water treatment and distribution systems ( [[#Howard--2016|Howard et al., 2016]] ; [[#Khan--2015|Khan et al., 2015]] ; [[#Sherpa--2014|Sherpa et al., 2014]] ; [[#Cissé--2016|Cissé et al., 2016]] ; [[#Kostyla--2015|Kostyla et al., 2015]] ; Chapter 4). Table Box 7.3.1 shows the variety and complexity of pathways between climate hazard and WBD outcomes ( [[#Semenza--2020|Semenza, 2020]] ).

'''Table Box 7.3.1 |''' Pathways between climate hazard and waterborne disease (WBD) outcomes. Source: [[#Semenza--2020|Semenza (2020)]] .

{| class="wikitable"
|-
| Cascading risk pathways from heavy rain and flooding

|-
| Storm runoff yields water turbidity, which compromises water treatment efficiency

Storm runoff and floods mobilise and transport pathogens

Overwhelmed or damaged infrastructure compromises water treatment efficiency

Floods overwhelm containment system and discharge untreated wastewater

Floods damage critical water supply and sanitation infrastructure

Floods displace populations towards inadequate sanitation infrastructure

|-
| Cascading risk pathways from drought

|-
| Low water availability augments travel distance to alternate (contaminated) sources

Intensified demand for and sharing (e.g., with livestock) of limited water resources decreases water availability and quality

Intermittent drinking water supply results in cross-connections with sewer lines and water contamination

Uncovered household water containers are a source of vector breeding

Poor hygiene due to decreased volume of source water and increased concentration of pathogens

Exposure to accumulated human excrements and animal manure

|-
| Cascading risk pathways from increasing temperature

|-
| Extended transmission season for opportunistic pathogens

Permissive temperature for the replication of marine bacteria

Enhanced pathogen load in animal reservoirs (e.g., chicken)

Pathogen survival and proliferation outside of host

Wildfires during heatwaves degrade water quality

Exposure to contaminated water due to higher water consumption

Behaviour change due to extended season (e.g., food spoilage during barbeque)

|-
| Cascading risk pathways from sea level rise

|-
| Population displacement due to powerful storm surges

Disruption of drinking water supply and sanitation infrastructure due to inundation

Decline in soil and water quality due to saline intrusion into coastal aquifers

Seawater infiltration into drinking water distribution and sewage lines

|}

Notes:

Examples are purposely not exhaustive and should be considered illustrative.

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'''Cross-Chapter Box COVID | COVID-19'''
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Authors: Maarten van Aalst (Netherlands, Chapter 16), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Ayansina Ayanlade (Nigeria, Chapter 9), Lea Berrang-Ford (United Kingdom/Canada, Chapter 16), Rachel Bezner Kerr (Canada/USA, Chapter 5), Robbert Biesbroek (Netherlands, Chapter 13), Kathryn Bowen (Australia, Chapter 7), Martina Angela Caretta (Sweden, Chapter 4), So-Min Cheong (Republic of Korea, Chapter 17), Winston Chow (Singapore, Chapter 6), Mark John Costello (New Zealand/Norway/Ireland, Chapter 11, CCP1), Kristie Ebi (USA, Chapter 7), Elisabeth Gilmore (USA/Canada, Chapter 14), Bruce Glavovic (South Africa/New Zealand, Chapter 18, CCP2), Walter Leal (Germany, Chapter 8), Stefanie Langsdorf (Germany, TSU), Elena Lopez-Gunn (Spain/United Kingdom, Chapter 4), Ruth Morgan (Australia, Chapter 4), Aditi Mukherji (India, Chapter 4), Camille Parmesan (France/ United Kingdom /USA, 2), Mark Pelling (United Kingdom, Chapter 6), Elvira Poloczanska (United Kingdom, TSU), Marie-Fanny Racault (United Kingdom/France, Chapter 3), Diana Reckien (Germany/Netherlands, Chapter 17), Jan C. Semenza (Sweden, Chapter 7), Pramod Kumar Singh (India, Chapter 18), Stavana E. Strutz (USA), Maria Cristina Tirado von der Pahlen (Spain/USA, Chapter 7), Corinne Schuster-Wallace (Canada), Alistair Woodward (New Zealand, Chapter 11), Zinta Zommers (Latvia, Chapter 17)

'''Introduction'''
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, emerged in late 2019, halfway through the preparation of the IPCC WGII Sixth Assessment Report. This Cross-Chapter Box assesses how the massive shock of the pandemic and response measures interact with climate-related impacts and risks as well as its significant implications for risk management and climate resilient development.

'''COVID-19 and environmental connections'''
''Infectious diseases may emerge and spread through multiple climate-related avenues, including direct effects of climatic conditions on disease reproduction and transmission and various indirect effects, often interlinked with ecosystem degradation (high confidence)'' . Climate change is affecting the risk of emerging infectious diseases by contributing to factors that drive the movements of species, including vectors and reservoirs of diseases, into novel human populations and vice versa ( ''high confidence'' ) (Sections 2.4.2.7, 5.2.2.3; Cross-Chapter Box Illness in Chapter 2; [[#IPCC--2019b|IPCC, 2019b]] ; IPBES 2020). The spillover of some emerging infectious diseases from wildlife into humans is associated with live animal–human markets, intensified livestock production and climate-related movements of humans and wild animals into new areas that alter human–animal interactions ( [[IPCC:Wg2:Chapter:Chapter-2#2.4.2.7|Section 2.4.2.7]] ; Chapter 3; Sections 5.2.2.3, 7.2; Cross-Chapter Box ILLNESS in Chapter 2; Cross-Chapter Box MOVING PLATE in Chapter 5).

''Human-to-human transmission is the prominent driver in the spread of the COVID-19 pandemic, rather than climatic drivers (high confidence)'' . There is emerging literature on the environmental determinants of COVID-19 transmission, incidence and mortality rates, with initial evidence suggesting that temperature, humidity and air pollution contribute to these patterns ( [[#Brunekreef--2021|Brunekreef et al., 2021]] ; [[#Xiong--2020|Xiong et al., 2020]] ; [[#Zhang--2020b|Zhang et al., 2020b]] ; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and climate). Climate change is altering environmental factors like temperature and seasonality that affect COVID-19 transmission ( [[#Choi--2021|Choi et al., 2021]] ).

The impact of COVID-19 containment measures resulted in a temporary reduction in greenhouse gas (GHG) emissions and reduced air pollution ''(high confidence)'' (IPCC WGI TS; [[#Arias--2021|Arias et al., 2021]] ; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and climate). However, global and regional climate responses to the radiative effect were undetectable above internal climate variability due to the temporary nature of emission reductions. They therefore do not result in detectable changes in impacts or risks due to changes in climate hazards (Arias et al 2021; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and climate; [[#Naik--2021|Naik et al., 2021]] ).

'''Cascading and compounding risks and impacts'''
''The COVID-19 pandemic posed a severe shock to many socioeconomic systems, resulting in substantial changes in vulnerability and exposure of people to climate risks'' ( ''high confidence'' ). The disease and response measures significantly affected human health, economic activity, food production and availability, health services, poverty, social and gender inequality, education, supply chains, infrastructure maintenance and the environment. These COVID-19 impacts interact with many risks associated with climate change ( [[#IMF--2020|IMF, 2020]] ), often through a cascade of impacts across numerous sectors ( [[#van%20den%20Hurk--2020|van den Hurk et al., 2020]] ). Beyond COVID-19-related mortality and long-term COVID, mortality from other diseases (some of which may also have a climate-related component), as well as maternal and neonatal mortality, increased because of disruption in health services ( [[#Barach--2020|Barach et al., 2020]] ; [[#Maringe--2020|Maringe et al., 2020]] ; [[#Zadnik--2020|Zadnik et al., 2020]] ; [[#Goyal--2021|Goyal et al., 2021]] ). In addition, a rapid rise in poverty has disproportionately affected poorer countries and people ( [[#Ferreira--2021|Ferreira et al., 2021]] ), and thus increased their vulnerability. After many years of steady declines, extreme poverty increased by about 100 million people in 2020 (World Bank, 2021). The effects of the pandemic increased food insecurity and malnourishment, which increased by 1.5 percentage points to around 9.9% in 2020 after being virtually unchanged for the previous five years ( [[#FAO--2021|FAO et al., 2021]] ).

''During the pandemic, extreme weather and climate events such as droughts, storms, floods, wildfires and heatwaves continued, resulting in disastrous compounding impacts'' ( ''high confidence'' ). Between March and September 2020, 92 extreme weather events coincided with the COVID-19 pandemic, affecting an estimated 51.6 million people; additionally, 431.7 million people were exposed to extreme heat, and 2.3 million people were affected by wildfires ( [[#Walton--2020|Walton and van Aalst, 2020]] ). The COVID-19 pandemic, in combination with extreme events, affected disaster preparedness, disaster response and safe evacuations, while physical distancing regulations reduced the capacity of temporary shelters (UN DRR Asia-Pacific, 2020; [[#Tozier%20de%20la%20Poterie--2020|Tozier de la Poterie et al., 2020]] ; Shumake-Guillemot, J, et al, 2020; [[#Bose-O’Reilly--2021|Bose-O’Reilly et al., 2021]] ). Complex humanitarian emergencies were aggravated, with vulnerable populations facing the combined risks of conflict, displacement, COVID-19 and climate impacts ( [[#FSIN--2020|FSIN, 2020]] ). Compounding events are not only found in low-income countries but also in medium- and high-income countries, for instance in the case of COVID-19 and heatwaves ( [[#Shumake-Guillemot--2020|Shumake-Guillemot et al., 2020]] ; [[#Bose-O’Reilly--2021|Bose-O’Reilly et al., 2021]] ).

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Cross-Chapter Box COVID

'''Responses and implications for adaptation and climate resilient development'''
''The pandemic emphasises the inter-connected and compound nature of risks, vulnerabilities and responses to emergencies that are simultaneously local and global (high confidence)'' . COVID-19 is often considered a more ‘explosive’ risk than the more gradual anthropogenic climate change. However, many climate-related risks do already appear as severe shocks at smaller scales, and infrequent or unprecedented extreme weather-related events often warrant similar rapid responses ( [[#Dodds--2020|Dodds et al., 2020]] ; [[#Gebreslassie--2020|Gebreslassie, 2020]] ; [[#Hynes--2020|Hynes et al., 2020]] ; [[#Phillips--2020|Phillips et al., 2020]] ; [[#Schipper--2020|Schipper, 2020]] ; [[#Semenza--2021|Semenza et al., 2021]] ; illustrated in Figure COVID.1). Individuals, households, sub-national and national entities, and international organisations had generally delayed responses or denied the pandemic’s severity before responding at the scale and urgency required, a pattern that resembles the international action required on climate change ( [[#Polyakova--2020|Polyakova et al., 2020]] ; [[#Shrestha--2020|Shrestha et al., 2020]] ).

Improved contingency and recovery planning, including disease mitigation measures, were crucial in responding to the pandemic in similar ways to those seen in the aftermath of climate-related disasters ( [[#Guo--2020|Guo et al., 2020]] ; [[#Ebrahim--2020|Ebrahim et al., 2020]] ; [[#Baidya--2020|Baidya et al., 2020]] ; [[#Shultz--2020|Shultz et al., 2020]] ; [[#Mukherjee--2020|Mukherjee et al., 2020]] ). The pandemic highlighted the lack of global and country-specific capacity to respond to an unexpected and unplanned event and the need to implement more flexible detection and response systems ( [[#Ebi--2021b|Ebi et al., 2021b]] ).

It also exposed underlying vulnerabilities, such as the lack of water access and healthcare in select low- and middle-income countries and among indigenous and marginalised groups in high-income countries ( [[IPCC:Wg2:Chapter:Chapter-4#4.4.3|Section 4.4.3]] ; Box 4.3; 5.12.1). Increased risks of COVID-19 transmission emerged in crowded areas such as urban settings, refugee camps, detention centres and some workplaces, including in rural settings ( [[#Brauer--2020|Brauer et al., 2020]] ; [[#Ramos--2020|Ramos et al., 2020]] ; [[#Staddon--2020|Staddon et al., 2020]] ; [[#Haddout--2020|Haddout et al., 2020]] ). Public health responses to the COVID-19 pandemic, such as mandates for social distancing and advice for frequent handwashing, underlined the need for access to water and sanitation facilities and wastewater management. However, they also sometimes interfered with access, for example, in evacuation and shelter infrastructure during climate-related disasters ( [[#Armitage--2020|Armitage and Nellums, 2020]] ; [[#Adelodun--2020|Adelodun et al., 2020]] ; [[#Poch--2020|Poch et al., 2020]] ; [[#Hallema--2020|Hallema et al., 2020]] ; [[#Patel--2020|Patel et al., 2020]] ; [[#Espejo--2020|Espejo et al., 2020]] ).

The experience of COVID-19 demonstrates that many warnings about the risks of the emergence of zoonotic transmission (‘delay is costly’, ‘adapt early’ and ‘prevention pays’) did not result in sufficient political attention, funding and pandemic prevention. In some countries, there has been an increased awareness of the risks and the real or perceived trade-offs associated with risk management (e.g., economy compared with health and impacts compared with adaptation). Building trust and participatory processes and establishing stronger relationships with communities and other civic institutions may enable a recalibration of how the government responds to crises and society–government relationships more generally ( [[#Amat--2020|Amat et al., 2020]] ; [[#Deslatte--2020|Deslatte, 2020]] ).

''The management of the COVID-19 pandemic has highlighted the value of scientific (including medical and epidemiological) expertise and the importance of fast, accurate and comprehensive data to inform policy decisions and to anticipate and manage risk (high confidence)'' . It emphasises the importance of effective communication of scientific knowledge ( [[#Semenza--2021|Semenza et al., 2021]] ), decision-making under uncertainty and decision frameworks that navigate different values and priorities. Successful policy responses were based on the emerging data, medical advice and collaboration with a wider set of societal stakeholders beyond public health experts. For instance, experience in Aotearoa, New Zealand, highlights the importance of pandemic responses attuned to the needs of different sociocultural groups and Indigenous Peoples in particular. Their strengths-based COVID-19 response goes beyond identifying vulnerabilities to unlocking the resources, capabilities and potential that might otherwise be latent in communities ( [[#McMeeking--2020|McMeeking and Savage, 2020]] ). As far as the value of information for risk management is concerned, compared to the initial uncertainties regarding COVID-19, data about near- and longer-term climate-related hazards is generally very good; however, high-quality and dense meteorological data are often still lacking in lower income countries ( [[#Otto--2020|Otto et al., 2020]] ). Health data are particularly difficult to obtain in real time, as is the case for biodiversity data, which has a time lag of years before being made available and for which there is no coordinated monitoring, hampering effective risk management ( [[#Navarro--2017|Navarro et al., 2017]] ). Therefore, both epidemiological and meteorological forecasts would benefit from more focus on (a) decision support, (b) conveying uncertainty and (c) capturing vulnerability ( [[#Coughlan%20de%20Perez--2021|Coughlan de Perez et al., 2021]] ).

''There is a considerable evidence base of specific actions that have co-benefits for reducing pandemic and climate change risks while enhancing social justice and biodiversity conservation (high confidence)'' . The pandemic highlighted aspects of risk management that have long been recognised but are often not reflected in national and international climate policy: the value of addressing structural vulnerability rather than taking specific measures to control single hazards and drivers of risk and the importance of decision-making capacities and transparency, the rule of law, accountability and addressing inequities (or social exclusion) (reviewed by [[#Pelling--2021|Pelling et al. (2021)]] ; see also Figure COVID.1).

<div id="_idContainer024" class="Box_Header-continued"></div>

Cross-Chapter Box COVID

Comprehensive and integrated risk management strategies can enable countries to address both the current pandemic and increase resilience against climate change and other risks ( [[#Reckien--2021|Reckien, 2021]] ; [[#Semenza--2021|Semenza et al., 2021]] ; [[#Ebi--2021b|Ebi et al., 2021b]] ). In particular, given their immense scale, COVID-19 recovery investments may offer an opportunity to contribute to climate resilient development pathways (CRDPs) through a green, resilient, healthy and inclusive recovery ( ''high confidence'' ) ( [[#Sovacool--2020|Sovacool et al., 2020]] ; [[#Rosenbloom--2020|Rosenbloom and Markard, 2020]] ; [[#Lambert--2020|Lambert et al., 2020]] ; [[#Boyle--2020|Boyle et al., 2020]] ; [[#Bouman--2020|Bouman et al., 2020]] ; UN DRR Asia-Pacific, 2020; [[#Brosemer--2020|Brosemer et al., 2020]] ; [[#Dodds--2020|Dodds et al., 2020]] ; [[#Hynes--2020|Hynes et al., 2020]] ; [[#Markard--2020|Markard and Rosenbloom, 2020]] ; [[#Phillips--2020|Phillips et al., 2020]] ; [[#Schipper--2020|Schipper, 2020]] ; [[#Willi--2020|Willi et al., 2020]] ; [[#Semenza--2021|Semenza et al., 2021]] ; [[#Pasini--2020|Pasini and Mazzocchi, 2020]] ; [[#Meige--2020|Meige et al., 2020]] ; [[#Pelling--2021|Pelling et al., 2021]] ). However, windows of opportunity to enable such transitions are only open for a limited period and need to be swiftly acted upon to effect change ( ''high confidence'' ) (Chapter 18; [[#Weible--2020|Weible et al., 2020]] ; [[#Reckien--2021|Reckien, 2021]] ). Initial indications suggest that only USD 1.8 trillion of the greater than USD 17 trillion COVID-19-related stimulus financing by G20 countries and other major economies that was committed up until mid-2021 contributed to climate action and biodiversity objectives, with significant differences between countries and sectors (Vivideconomics, 2021). Moreover, responses to previous crises (e.g., the 2008–2011 global financial crisis) demonstrate that despite high ambitions during the response phase, opportunities for reform do not necessarily materialise ( [[#Bol--2020|Bol et al., 2020]] ; [[#Boin--2005|Boin et al., 2005]] ). In addition, heightened societal and political attention to one crisis often comes at the cost of other policy priorities ( ''high confidence'' ) ( [[#Maor--2018|Maor, 2018]] ; [[#Tosun--2017|Tosun et al., 2017]] ), which could affect investments for climate resilient development ( [[#Hepburn--2020|Hepburn et al., 2020]] ; [[#WHO--2020a|WHO, 2020a]] ; [[#Bateman--2020|Bateman et al., 2020]] ; [[#Meige--2020|Meige et al., 2020]] ; [[#Semenza--2021|Semenza et al., 2021]] ).

In summary, the emerging literature suggests that the COVID-19 pandemic has aggravated climate-related health risks, demonstrated the global and local vulnerability to cascading shocks and illustrated the importance of integrated solutions that tackle ecosystem degradation and structural vulnerabilities in human societies. This highlights the potential and urgency of interventions that reduce pandemic and climate change risks while enhancing compound resilience, social justice and biodiversity conservation (see Figure COVID.1).

[[File:a7970ececffaaf5ea5301b8da3688bca IPCC_AR6_WGII_Figure_7_Covid_1.png]]

'''Figure COVID.1 |''' '''Compound risk and compound resilience to pandemic and climate change.''' Source: [[#Pelling--2021|Pelling et al. (2021)]] .

<div id="7.2.3" class="h2-container"></div>

<span id="observed-impacts-on-non-communicable-diseases"></span>
=== 7.2.3 Observed Impacts on Non-communicable Diseases ===

<div id="h2-11-siblings" class="h2-siblings"></div>

NCDs are those that are not directly transmitted from one person to another person; they impose the largest disease burden globally. NCDs constitute approximately 80% of the burden of disease in high-income countries; the NCD burden is lower in low- and middle-income countries but are expected to rise ( [[#Bollyky--2017|Bollyky et al., 2017]] ). NCDs constitute a large group of diseases driven principally by environmental, lifestyle and other factors; those identified as being climate sensitive include non-infectious respiratory disease, cardiovascular disease (CVD), cancer and endocrine diseases including diabetes. Additionally, there are potential interactions between multiple climate-sensitive NCDs and food security, nutrition and mental health. The literature on climate change and NCDs continues to develop. More recently, scientists have identified key gaps in the calculation of the global burden of disease due to environmental health factors ( [[#Shaffer--2019|Shaffer et al., 2019]] ).

<div id="7.2.3.1" class="h3-container"></div>

<span id="cardiovascular-diseases"></span>
==== 7.2.3.1 Cardiovascular Diseases ====

<div id="h3-11-siblings" class="h3-siblings"></div>

CVDs are a group of disorders of the heart and blood vessels that include coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis and pulmonary embolism. CVDs are the leading cause of death globally and over three quarters of the world’s CVD deaths now occur in low- and middle-income countries ( [[#Roth--2020|Roth et al., 2020]] ).

''Climate change affects the risk of CVD through high temperatures and extreme heat (assessed in [[#7.2.4.1|Section 7.2.4.1]] ) and through other mechanisms (medium confidence), though the degree to which non-temperature risks may increase remains unclear.'' For example, exposure to air pollutants including PM, ozone (via its precursors), black carbon, oxides of nitrogen, oxides of sulphur, hydrocarbons and metals can invoke pro-inflammatory and prothrombotic states, endothelial dysfunction and hypertensive responses ( [[#Giorgini--2017|Giorgini et al., 2017]] ; [[#Stewart--2017|Stewart et al., 2017]] ). Winter peaks in CVD events, associated with greater concentrations of air pollutants, have been reported in a range of countries and climates ( [[#Claeys--2017|Claeys et al., 2017]] ; [[#Stewart--2017|Stewart et al., 2017]] ); however, the association between air pollution, weather and CVD events is complex and seems to differ between cold and warm months, particularly for gaseous pollutants such as ozone ( [[#Shi--2020|Shi et al., 2020]] ).

Climate change is projected to increase the number and severity of wildfires ( [[#Liu--2015b|Liu et al., 2015b]] ; [[#Youssouf--2014|Youssouf et al., 2014]] ) and the evidence for wildfire smoke-related CVD morbidity and mortality is suggestive of increased CVD morbidity and mortality risk ( [[#Chen--2021a|Chen et al., 2021a]] ) including significant increases in certain cardiovascular outcomes (e.g., cardiac arrests) ( [[#Dennekamp--2015|Dennekamp et al., 2015]] ). CVD risks to highly exposed populations, such as firefighters, are clearer ( [[#Navarro--2019|Navarro et al., 2019]] ) and could increase with additional exposure driven by climate change.

Other climate-related mechanisms that may increase CVD risk include reductions in physical activity related to hot weather ( [[#Obradovich--2017|Obradovich et al., 2017]] ), sleep disturbance ( [[#Obradovich--2017|Obradovich et al., 2017]] ) and dehydration ( [[#Lim--2015|Lim et al., 2015]] ; [[#Frumkin--2019|Frumkin and Haines, 2019]] ). There is little literature on how changes in winter weather may affect these risks. Saline intrusion of groundwater related to sea level rise ( [[#Taylor--2012|Taylor et al., 2012]] ) may increase the salt intake of affected populations, a risk factor for hypertension that has been observed to increase blood pressure in exposed populations ( [[#Talukder--2017|Talukder et al., 2017]] ; Al [[#Nahian--2018|Nahian et al., 2018]] ).

<div id="7.2.3.2" class="h3-container"></div>

<span id="non-communicable-respiratory-diseases"></span>
==== 7.2.3.2 Non-communicable Respiratory Diseases ====

<div id="h3-12-siblings" class="h3-siblings"></div>

Lung diseases, including asthma, COPD and lung cancer, comprise the largest subsets of non-communicable pulmonary disease ( [[#Ferkol--2014|Ferkol and Schraufnagel, 2014]] ). Overall, the global burden of non-communicable lung disease including all chronic lung disease and lung cancer is substantial, being responsible for 10.6% of deaths and 5.9% of DALYs globally in 2019 (Vos et al., 2020).

''Several non-communicable respiratory diseases are climate sensitive based on their exposure pathways (very high confidence).'' Multiple exposure pathways contribute to non-communicable respiratory disease ( [[#Deng--2020|Deng et al., 2020]] ), some of which are climate-related ( [[#Rice--2014|Rice et al., 2014]] ), including mobilisation and transport of dust ( [[#Schweitzer--2018|Schweitzer et al., 2018]] ); changes in concentrations of air pollutants such as small particulates (PM2.5) and ozone formed by photochemical reactions sensitive to temperature ( [[#Hansel--2016|Hansel et al., 2016]] ); increased wildland fires and related smoke exposure ( [[#Johnston--2002|Johnston et al., 2002]] ; [[#Reid--2016|Reid et al., 2016]] ); increased exposure to ambient heat driving reduced lung function and exacerbations of chronic lung disease ( [[#Collaco--2018|Collaco et al., 2018]] ; [[#Jehn--2013|Jehn et al., 2013]] ; [[#McCormack--2016|McCormack et al., 2016]] ; [[#Witt--2015|Witt et al., 2015]] ) and modification of aeroallergen production and duration of exposure ( [[#Ziska--2019|Ziska et al., 2019]] ).

''Burdens of allergic disease, particularly allergic rhinitis and allergic asthma may be changing in response to climate change (medium confidence)'' ( [[#D’Amato--2020|D’Amato et al., 2020]] ; [[#Eguiluz-Gracia--2020|Eguiluz-Gracia et al., 2020]] ; [[#Deng--2020|Deng et al., 2020]] ; [[#Demain--2018|Demain, 2018]] ). This is supported by evidence showing an increase in the length of the North American pollen season attributable to climate change ( [[#Ziska--2019|Ziska et al., 2019]] ), an association between timing of spring onset and higher asthma hospitalisations presumed to be due to higher pollen exposure ( [[#Sapkota--2020|Sapkota et al., 2020]] ) and other evidence linking aeroallergen exposure with a worsening burden of allergic disease ( [[#Demain--2018|Demain, 2018]] ; [[#Poole--2019|Poole et al., 2019]] ).

<div id="7.2.3.3" class="h3-container"></div>

<span id="cancer"></span>
==== 7.2.3.3 Cancer ====

<div id="h3-13-siblings" class="h3-siblings"></div>

''Climate change is'' likely ''to increase the risk of several malignancies (high confidence), though the degree to which risks may increase remains unclear.'' Cancers, also known as malignant neoplasms, include a heterogeneous collection of diseases with various causal pathways, many with environmental influences. Malignant neoplasms impose a substantial burden of disease globally and are responsible for slightly more than 10 million deaths and 251 million DALYs globally in 2019 (Vos et al., 2020). Climatic hazards affect exposure pathways for several different chemical hazards associated with carcinogenesis ( [[#Portier--2010|Portier et al., 2010]] ). Most relevant literature has focused on elaborating potential pathways and producing qualitative or quantitative estimates of effect, though there is limited literature on current and projected impacts.

The vast majority of elaborated pathways point to increased risk; for example, there is concern that climate change may alter the fate and transport of carcinogenic polyaromatic hydrocarbons ( [[#Domínguez-Morueco--2019|Domínguez-Morueco et al., 2019]] ) and increase mobilisation of carcinogens such as bromide ( [[#Regli--2015|Regli et al., 2015]] ), persistent organic pollutants (POPs) including polychlorinated-biphenyls that have accumulated in areas contaminated by industrial runoff ( [[#Miner--2018|Miner et al., 2018]] ) and radioactive material ( [[#Evangeliou--2014|Evangeliou et al., 2014]] ). Exposure to these known carcinogens can occur through multiple environmental media and can be increased by climate change, for example through increased flooding related to extreme precipitation events and mobilisation of sediment where carcinogens have accumulated ( [[#León--2017|León et al., 2017]] ; [[#Santiago--2012|Santiago and Rivas, 2012]] ). In addition, there is concern that changes in ultraviolet light exposure related to shifts in precipitation may increase the incidence of malignant melanoma, particularly for outdoor workers ( [[#Modenese--2018|Modenese et al., 2018]] ). Other harmful pathways include migration of and increased exposure to liver flukes, which cause hepatobiliary cancer ( [[#Prueksapanich--2018|Prueksapanich et al., 2018]] ) and the introduction of infectious diseases such as schistosomiasis that increase cancer risk due to climate-related migration ( [[#Ahmed--2014|Ahmed et al., 2014]] ). Increased exposure to carcinogenic toxins via multiple pathways is also a concern. Aflatoxin exposure, for example, is expected to increase in Europe ( [[#Moretti--2019|Moretti et al., 2019]] ), India ( [[#Shekhar--2018|Shekhar et al., 2018]] ), Africa ( [[#Gnonlonfin--2013|Gnonlonfin et al., 2013]] ; [[#Bandyopadhyay--2016|Bandyopadhyay et al., 2016]] ) and North America ( [[#Wu--2011|Wu et al., 2011]] ). Other carcinogenic toxins originate from cyanobacteria blooms ( [[#Lee--2017a|Lee et al., 2017a]] ), which are projected to increase in frequency and distribution with climate change ( [[#Wells--2015|Wells et al., 2015]] ; [[#Paerl--2016|Paerl et al., 2016]] ; [[#Chapra--2017|Chapra et al., 2017]] ).

<div id="7.2.3.4" class="h3-container"></div>

<span id="diabetes"></span>
==== 7.2.3.4 Diabetes ====

<div id="h3-14-siblings" class="h3-siblings"></div>

''Individuals suffering from diabetes are at higher risk of heat-related illness and death (medium confidence).'' Extreme weather events and rising temperatures have been found to increase morbidity and mortality in patients living with diabetes, especially in those with cardiovascular complications ( [[#Méndez-Lázaro--2018|Méndez-Lázaro et al., 2018]] ; [[#Zilbermint--2020|Zilbermint, 2020]] ; [[#Hajat--2017|Hajat et al., 2017]] ). Evidence suggests that the local heat loss response of skin blood flow is affected by diabetes-related impairments, resulting in lower elevations in skin blood flow in response to a heat or pharmacological stimulus. Thermoregulatory sweating may also be diminished by type-2 diabetes, impairing the body’s ability to transfer heat from its core to the environment ( [[#Xu--2019b|Xu et al., 2019b]] ). Higher rates of doctor consultations by patients with type-2 diabetes and diabetics with cardiovascular comorbidities have been observed during hot days, but without evidence of heightened risk of renal failure or neuropathy comorbidities ( [[#Xu--2019b|Xu et al., 2019b]] ).

''People with chronic illnesses are at particular risk during and after extreme weather events due to treatment interruptions and lack of access to medication (medium confidence).'' The impacts of extreme weather events on the health of chronically ill people are due to a range of factors including disruption of transport, weakened health systems including drug supply chains, loss of power and evacuations of populations ( [[#Ryan--2015a|Ryan et al., 2015a]] ). Evacuations also pose specific health risks to older adults (especially those who are frail, medically incapacitated or residing in nursing or assisted living facilities) and may be complicated by the need for concurrent transfer of medical records, medications and medical equipment ( [[#Becquart--2018|Becquart et al., 2018]] ; [[#Quast--2019|Quast and Feng, 2019]] ; US Global Change Research Program, 2016). Emergency room visits after Hurricane Sandy rose among individuals with type-2 diabetes ( [[#Velez-Valle--2016|Velez-Valle et al., 2016]] ).

<div id="7.2.4" class="h2-container"></div>

<span id="observed-impacts-on-other-climate-sensitive-health-outcomes"></span>
=== 7.2.4 Observed Impacts on Other Climate-Sensitive Health Outcomes ===

<div id="h2-12-siblings" class="h2-siblings"></div>

<div id="7.2.4.1" class="h3-container"></div>

<span id="heat--and-cold-related-mortality-and-morbidity"></span>
==== 7.2.4.1 Heat- and Cold-Related Mortality and Morbidity ====

<div id="h3-15-siblings" class="h3-siblings"></div>

E ''xtreme heat events and extreme temperature have well-documented, observed impacts on health, mortality (very high confidence'' '') and morbidity (high confidence).'' AR5 described the thermoregulatory mechanisms and responses, including acclimatisation, linking heat, cold and health, and these have been further confirmed by recent studies and reviews (e.g., [[#Giorgini--2017|Giorgini et al., 2017]] ; [[#Ikaheimo--2018|Ikaheimo, 2018]] ; [[#McGregor--2015|McGregor et al., 2015]] ; [[#Stewart--2017|Stewart et al., 2017]] ; [[#Schuster--2017|Schuster et al., 2017]] ; [[#Zhang--2018b|Zhang et al., 2018b]] ). The health impacts of heat manifest clearly in periods of extreme heat often codified as heatwaves. For example, heatwaves across Europe (2003), Russia (2010), India (2015) and Japan (2018) resulted in significant death tolls and hospitalisations ( [[#McGregor--2017|McGregor et al., 2017]] ; [[#Hayashida--2019|Hayashida et al., 2019]] ). Heat continues to pose a significant health risk due to increases in exposure, changes in the size and spatial distribution of the human population, mounting vulnerability and an increase in extreme heat events ( ''high confidence'' ) ( [[#Harrington--2017|Harrington et al., 2017]] ; [[#Liu--2017|Liu et al., 2017]] ; [[#Mishra--2017|Mishra et al., 2017]] ; [[#Rohat--2019a|Rohat et al., 2019a]] ; [[#Rohat--2019b|Rohat et al., 2019b]] ; [[#Rohat--2019c|Rohat et al., 2019c]] ; [[#Watts--2019|Watts et al., 2019]] ). Some regions are already experiencing heat stress conditions approaching the upper limits of labour productivity and human survivability ( ''high confidence'' ). These include the Persian Gulf and adjacent land areas, parts of the Indus River Valley, eastern coastal India, Pakistan, northwestern India, the shores of the Red Sea, the Gulf of California, the southern Gulf of Mexico and coastal Venezuela and Guyana ( [[#Krakauer--2020|Krakauer et al., 2020]] ; [[#Li--2020|Li et al., 2020]] ; [[#Raymond--2020|Raymond et al., 2020]] ; [[#Saeed--2021|Saeed et al., 2021]] ; [[#Xu--2020|Xu et al., 2020]] ).

Under a variety of methods, estimates of the world’s population exposed to extreme heat indicate very large and growing numbers and an increase since pre-industrial times. For example, Li et al. (2020) estimate that globally, 1.28 billion people each year experience heatwave conditions similar to that of the lethal Chicago 1995 event, compared with 0.99 billion people that would be similarly exposed under a pre-industrial climate. Further, for the 150 most populated cities of the world, a 500% increase in the exposure to extreme heat events occurred over the 1980–2017 period ( [[#Li--2021|Li et al., 2021]] ), while for the 1986–2005 period, the total exposure to dangerous heat in Africa’s 173 largest cities was 4.2 billion person-days yr –1 ( [[#Rohat--2019a|Rohat et al., 2019a]] ). Globally the present exposure to heatwave events is estimated to be 14.8 billion person-days yr –1 , with the greatest cumulative exposures measured in person-days occurring across southern Asia (7.19 billion), sub-Saharan Africa (1.43 billion), and north Africa and the Middle East (1.33 billion) ( [[#Jones--2018|Jones et al., 2018]] ).

The country level percentage of mortality attributable to non-optimum temperature (heat and cold) has been found to range from 3.4 to 11% ( [[#Gasparrini--2015|Gasparrini et al., 2015]] ; [[#Zhang--2019b|Zhang et al., 2019b]] ). Heat as a health risk factor has largely been overlooked in low- and middle-income countries ( [[#Campbell--2018|Campbell et al., 2018]] ; [[#Green--2019|Green et al., 2019]] ; [[#Dimitrova--2021|Dimitrova et al., 2021]] ). For 2019, the GBD report estimates the burden of DALYs attributable to low temperature was 2.2 times greater than the burden attributable to high temperature. However, this global figure obscures important regional variations. Countries with a high sociodemographic index—mainly mid-latitude high-income temperate to cool climate countries— were found to have a cold-related burden 15.4 times greater than the heat-related burden, while for warm lower-income regions, such as south Asia and sub-Saharan Africa, the heat-related burden was estimated to be 1.7 times and 3.6 times greater, respectively ( [[#Murray--2020|Murray et al., 2020]] ). For countries where data availability permits, there is evidence that extreme heat (and extreme cold) leads to higher rates of premature deaths ( [[#Armstrong--2017|Armstrong et al., 2017]] ; [[#Cheng--2018|Cheng et al., 2018]] ; [[#Costa--2017|Costa et al., 2017]] ).

Rapid changes and variability in temperatures are observed to increase heat-related health and mortality risks, the outcomes varying across temperate and tropical regions ( [[#Guo--2016|Guo et al., 2016]] ; [[#Cheng--2019|Cheng et al., 2019]] ; [[#Kim--2019a|Kim et al., 2019a]] ; [[#Tian--2019|Tian et al., 2019]] ; [[#Zhang--2018b|Zhang et al., 2018b]] ; [[#Zhao--2019|Zhao et al., 2019]] ).

''Several lines of evidence point to a possible decrease in population sensitivity to heat, albeit mainly for high-income countries (high confidence), arising from the implementation of heat warning systems, increased awareness and improved quality of life.'' ( [[#Sheridan--2018|Sheridan and Allen, 2018]] ). Evidence suggests a general decrease in the impact of heat on daily mortality ( [[#Diaz--2018|Diaz et al., 2018]] ; [[#Kinney--2018|Kinney, 2018]] ; [[#Miron--2015|Miron et al., 2015]] ), a decline in the relative risk attributable to heat ( [[#Åström--2018|Åström et al., 2018]] ; [[#Barreca--2016|Barreca et al., 2016]] ; [[#Petkova--2014|Petkova et al., 2014]] ) and an increase in the minimum mortality temperature (MMT) ( [[#Åström--2018|Åström et al., 2018]] ; [[#Folkerts--2020|Folkerts et al., 2020]] ; [[#Follos--2021|Follos et al., 2021]] ; [[#Chung--2018|Chung et al., 2018]] ; [[#Todd--2015|Todd and Valleron, 2015]] ; [[#Yin--2019|Yin et al., 2019]] ). It is difficult to draw conclusions regarding trends in heat sensitivity for low- to middle-income countries and specific vulnerable groups as these are under-represented in the literature ( [[#Sheridan--2018|Sheridan and Allen, 2018]] ). Trends in heat sensitivity are generally believed to be scale and situation dependent, but there is considerable variability in changes in heat sensitivity as measured by trends in heat-related mortality or MMT ( [[#Follos--2021|Follos et al., 2021]] ; [[#Kim--2019a|Kim et al., 2019a]] ; [[#Lee--2021|Lee et al., 2021]] ), with notable variability across different population groups ( [[#Lu--2021|Lu et al., 2021]] ).

Temperature interacts with heat-sensitive physiological mechanisms via multiple pathways to affect health. In the worst cases, these lead to organ failure and death ( [[#Mora--2017a|Mora et al., 2017a]] ; [[#Mora--2017b|Mora et al., 2017b]] ). Excess deaths during extreme heat events occur predominantly in older individuals and are overwhelmingly cardiovascular in origin ''(very high confidence)'' . A higher occurrence of CVD mortality in association with prolonged period of low temperatures has been well documented globally ( [[#Giorgini--2017|Giorgini et al., 2017]] ; [[#Stewart--2017|Stewart et al., 2017]] ); however, there is growing evidence that cardiovascular deaths are more related to heat events than cold spells ( [[#Chen--2019|Chen et al., 2019]] ; [[#Liu--2015a|Liu et al., 2015a]] ; [[#Bunker--2016|Bunker et al., 2016]] ). While there is strong association between ambient temperature and cardiovascular events globally, there are complex interactions and modulators of individual response ( [[#Wang--2017|Wang et al., 2017]] b). Further, some CVD morbidity sub-groups such as myocardial infarction (MI) and stroke hospitalisation display temperature sensitivity while others do not ( [[#Bao--2019|Bao et al., 2019]] ; [[#Sun--2018|Sun et al., 2018]] ; [[#Wang--2016|Wang et al., 2016]] ). Although older adults have inherent sensitivities to temperature-related health impacts ( [[#Bunker--2016|Bunker et al., 2016]] ; [[#Phung--2016|Phung et al., 2016]] ), children can also be affected by extreme heat ( [[#Xu--2014|Xu et al., 2014]] ). Cardiovascular capacity or health is also a critical determinant of individual health outcomes ( [[#Schuster--2017|Schuster et al., 2017]] ). Medications to treat CVDs, such as diuretics and beta-blockers, may impair resilience to heat stress ( [[#Stewart--2017|Stewart et al., 2017]] ). Other mediating factors in the causal pathway range from alcohol consumption ( [[#Cusack--2011|Cusack et al., 2011]] ; [[#Epstein--2019|Epstein and Yanovich, 2019]] ) and obesity ( [[#Speakman--2018|Speakman, 2018]] ) to pre-existing conditions, such as diabetes and hyperlipidaemia, and urban characteristics ( [[#Chen--2019|Chen et al., 2019]] ; [[#Sera--2019|Sera et al., 2019]] ).

Under extreme heat conditions, increases in hospitalisations have been observed for fluid disorders, renal failure, urinary tract infections, septicaemia, general heat stroke as well as unintentional injuries ( [[#Borg--2017|Borg et al., 2017]] ; [[#Phung--2017|Phung et al., 2017]] ; [[#Goggins--2017|Goggins and Chan, 2017]] ; [[#Hayashida--2019|Hayashida et al., 2019]] ; [[#Hopp--2018|Hopp et al., 2018]] ; [[#Ito--2018|Ito et al., 2018]] ; [[#Kampe--2016|Kampe et al., 2016]] ; [[#McTavish--2018|McTavish et al., 2018]] ; [[#Ponjoan--2017|Ponjoan et al., 2017]] ; [[#van%20Loenhout--2018|van Loenhout et al., 2018]] ). Hospitalisations and mortality due to respiratory disorders also occur during heat events with the interactive role of air quality being important for some locations but not others ( [[#Krug--2019|Krug et al., 2019]] ; [[#Pascal--2021|Pascal et al., 2021]] ; [[#Patel--2019|Patel et al., 2019]] ). Increased levels of heat-related hospitalisation also manifest in elevated levels of emergency service calls ( [[#Cheng--2016|Cheng et al., 2016]] ; [[#Guo--2017|Guo, 2017]] ; [[#Papadakis--2018|Papadakis et al., 2018]] ; [[#Williams--2020|Williams et al., 2020]] ).

Heat- and cold-related health outcomes vary by location ( [[#Dialesandro--2021|Dialesandro et al., 2021]] ; [[#Hu--2019|Hu et al., 2019]] ; [[#Phung--2016|Phung et al., 2016]] ), suggesting outcomes are highly moderated by socioeconomic, occupational and other non-climatic determinants of individual health and socioeconomic vulnerability ( [[#Åström--2020|Åström et al., 2020]] ; [[#McGregor--2017|McGregor et al., 2017]] ; [[#McGregor--2017|McGregor et al., 2017]] ; [[#Schuster--2017|Schuster et al., 2017]] ; [[#Benmarhnia--2015|Benmarhnia et al., 2015]] ; [[#Watts--2019|Watts et al., 2019]] ) ( ''high confidence'' ). For example, access to air conditioning is an important determinant of heat-related health outcomes for some locations ( [[#Guirguis--2018|Guirguis et al., 2018]] ; [[#Ostro--2010|Ostro et al., 2010]] ). Although there is a paucity of global level studies of the effectiveness of air conditioning for reducing heat-related mortality, a recent assessment indicates increases in air conditioning explains only part of the observed reduction in heat-related excess deaths, amounting to 16.7% in Canada, 20.0% in Japan, 14.3% in Spain and 16.7% in the US ( [[#Sera--2020|Sera et al., 2020]] ).

Significant effects of heat exposure are evident in sport and work settings with exertional heat illness leading to death and injury ( [[#Adams--2020|Adams and Jardine, 2020]] ). Although most studies of heat-related sports injuries refer to high-income countries, these point to an increasing number of heat injuries with widening participation in sport and an increasing frequency of extreme heat events. The highest rates of exertional heat illness are reported for endurance type events (running, cycling and adventure races), American football and athletics ( [[#Gamage--2020|Gamage et al., 2020]] ; [[#Grundstein--2017|Grundstein et al., 2017]] ; [[#Kerr--2020|Kerr et al., 2020]] ; [[#McMahon--2021|McMahon et al., 2021]] ; [[#Yeargin--2019|Yeargin et al., 2019]] ). The health, safety and productivity consequences of working in extreme heat are widespread ( [[#Ma--2019|Ma et al., 2019]] ; [[#Morabito--2021|Morabito et al., 2021]] ; [[#Kjellstrom--2019|Kjellstrom et al., 2019]] ; [[#Orlov--2020|Orlov et al., 2020]] ; [[#Smith--2021|Smith et al., 2021]] ; [[#Vanos--2019|Vanos et al., 2019]] ; [[#Varghese--2020|Varghese et al., 2020]] ; [[#Williams--2020|Williams et al., 2020]] ). Occupational heat strain in outdoor workers manifests as dehydration, mild reduction in kidney function, fatigue, dizziness, confusion, reduced brain function, loss of concentration and discomfort ( [[#Al-Bouwarthan--2020|Al-Bouwarthan et al., 2020]] ; [[#Boonruksa--2020|Boonruksa et al., 2020]] ; [[#Habibi--2021|Habibi et al., 2021]] ; [[#Levi--2018|Levi et al., 2018]] ; [[#Venugopal--2021|Venugopal et al., 2021]] ; [[#Xiang--2014|Xiang et al., 2014]] ). In the case of armed forces, a global review of the available literature points to a slightly higher incidence of heat stroke in men compared to women but a higher proportion of heat intolerance and greater risk of exertional heat illness amongst women ( [[#Alele--2020|Alele et al., 2020]] ). There is also some evidence that for healthcare workers, the risk of occupational heat stress grew during the COVID-19 pandemic due to the need to wear personal protective equipment ( [[#Foster--2020|Foster et al., 2020]] ; [[#Lee--2020|Lee et al., 2020]] ; [[#Messeri--2021|Messeri et al., 2021]] ). Based on a systematic review of the literature, one study estimates global costs from heat-related lost work time were USD 280 billion in 1995 and USD 311 billion in 2010, with low- and middle-income countries and countries with warmer climates experiencing greater losses as a proportion of gross domestic project (GDP) ( [[#Borg--2021|Borg et al., 2021]] ). Other global level assessments note an increase in the potential hours of work lost due to heat over the 2000–2018 period; in 2018, 133.6 billion potential work hours were lost, amounting to 45 billion hours more than in 2000 ( [[#Watts--2019|Watts et al., 2019]] ). For China, heat-related productivity losses have been estimated at 9.9 billion hours in 2019, equivalent to 0.5% of the total national work hours for that year, with Guangdong province, one of the warmest regions in China, accounting for almost a quarter of the losses ( [[#Cai--2021|Cai et al., 2021]] ).

Wide ranging knowledge regarding the specific detection of heat- and cold-related mortality/morbidity and its attribution to observed climate change is lacking ''.'' Although there has been an observed increase in winter-season temperatures for a number of regions, to date there is variable evidence for a consequential reduction in winter mortality and susceptibility to cold over time due to milder winters; some countries demonstrate decreasing trends, while other countries show stable or even increasing trends in cold-attributable mortality fractions over time (e.g., [[#Arbuthnott--2020|Arbuthnott et al. (2020)]] ; [[#Åström--2013|Åström et al. (2013)]] ; [[#Diaz--2019|Diaz et al. (2019)]] ; [[#Hajat--2017|Hajat (2017)]] ; Hanigan et al. (2021); [[#Lee--2018b|Lee et al. (2018b)]] ). While there is a burgeoning literature on the attribution of extreme heat events to climate change (e.g., [[#Vautard--2020|Vautard et al. (2020)]] ), the number of studies that assess the extent to which observed changes in heat-related mortality may be attributable to climate change is small ( [[#Ebi--2020|Ebi et al., 2020]] ). During the 2003 European heatwave, anthropogenic climate change increased the risk of heat-related mortality by approximately 70% and 20% for London and Paris, respectively ( [[#Mitchell--2016|Mitchell et al., 2016]] ). For the severe heat event across Egypt in 2015, the impact on human discomfort was 69% (±17%) more likely due to anthropogenic climate change ( [[#Mitchell--2016|Mitchell, 2016]] ), and for Stockholm, Sweden, it has been estimated that mortality due to temperature extremes for 1980 to 2009 was double what would have occurred without climate change ( [[#Åström--2013|Åström et al., 2013]] ). To date there has only been one multi-country attempt to quantify the heat-related human health impacts that have already occurred due to climate change. Based on an analysis of 732 locations spanning 43 countries for the 1991–2018 period, the study found that on average 37.0% (inter-quartile range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change, equivalent to an average mortality rate of 2.2/100,000 (median: 1.67/100,000; interquartile range: 1.08–2.34/100,000). Regions with a high attributed percentage (&gt; 50%) include southern and western Asia (Iran and Kuwait), Southeast Asia (Philippines and Thailand) and several countries in Central and South America. Those with lower values (&lt; 35%) include Western Europe (the Netherlands, Germany and Switzerland), eastern Europe (Moldova, the Czech Republic and Romania), southern Europe (Greece, Italy, Portugal and Spain), North America (USA) and eastern Asia (China, Japan and South Korea) ( [[#Vicedo-Cabrera--2021|Vicedo-Cabrera et al., 2021]] ). Due to data restrictions, some of the poorest and most susceptible regions to climate change and increases in heat exposure, such as west and east Africa ( [[#Asefi-Najafabady--2018|Asefi-Najafabady et al., 2018]] ; [[#Sylla--2018|Sylla et al., 2018]] ) and south Asia, could not be included in the analysis ( [[#Mitchell--2021|Mitchell, 2021]] ).

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<span id="injuries-arising-from-extreme-weather-events-other-than-heat-and-cold"></span>
==== 7.2.4.2 Injuries Arising from Extreme Weather Events Other than Heat and Cold ====

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Injuries comprise a substantial portion of the global burden of disease. In 2019, injuries comprised 9.82% of total global DALYs and 7.61% of deaths (Vos et al., 2020). The causal pathways for many injuries, particularly those from heat and extreme weather events, flooding and fires, exhibit clear climate sensitivity ( [[#Roberts--2007|Roberts and Arnold, 2007]] ; [[#Roberts--2005|Roberts and Hillman, 2005]] ), as do some injuries occurring in occupational settings ( [[#Marinaccio--2019|Marinaccio et al., 2019]] ; [[#Sheng--2018|Sheng et al., 2018]] ), but a comprehensive assessment of climate sensitivity in injury causal pathways has not been done. Certain groups, including Indigenous Peoples, children and the elderly ( [[#Ahmed--2020|Ahmed et al., 2020]] ) are at greater risk for a wide range of injuries. Extreme events impose substantial disease burden directly as a result of traumatic injuries, drowning and burns and large mental health burdens associated with displacement ( [[#Fullilove--1996|Fullilove, 1996]] ), depression and post-traumatic stress disorder (PTSD), but the overall injury burden associated with extreme weather is not known. It is known that the Asia-Pacific region has experienced the highest relative burden of injuries from extreme weather in recent decades ( [[#Hashim--2016|Hashim and Hashim, 2016]] ).

Extreme weather imposes a substantial morbidity and mortality burden that is quite variable by location and hazard. The proportion of this burden related specifically to injuries is not established. From 1998 to 2017 there were 526,000 deaths from 11,500 extreme weather events, and the average annual attributable all-cause mortality incidence in the ten most affected countries was 3.5 per 100,000 population ( [[#Eckstein--2017|Eckstein et al., 2017]] ). Rates can be much higher; mortality incidence in Puerto Rico and Dominica from extreme weather were 90.2 and 43.7 per 100,000 population in 2017, respectively ( [[#Eckstein--2017|Eckstein et al., 2017]] ). Not all of these deaths are from injuries, and the proportion of mortality and morbidity associated with injuries varies by location and hazard. One review found that one-year post-event prevalence rates for injuries associated with extreme events (floods, droughts, heatwaves and storms) in developing countries ranged from 1.4% to 37.9% ( [[#Rataj--2016|Rataj et al., 2016]] ). Other literature has documented an increase in the risk of motor vehicle accidents in association with extreme precipitation ( [[#Liu--2017|Liu et al., 2017]] ; [[#Stevens--2019|Stevens et al., 2019]] ), temperature ( [[#Leard--2019|Leard and Roth, 2019]] ) and sandstorms ( [[#Islam--2019|Islam et al., 2019]] ) and an increased risk of traumatic occupational injuries associated with temperature extremes, particularly extreme heat, likely from fatigue and decreased psychomotor performance ( [[#Varghese--2019|Varghese et al., 2019]] ).

There is clear evidence of climate sensitivity for multiple injuries from floods, fires and storms, but there is a need for additional evidence regarding the current injury burden attributable to climate change. It is ''as likely as not'' that climate change has increased the current burden of disease from injuries related to extreme weather, particularly in low-income settings ''(low confidence)'' . Approximately 120 million people are exposed to coastal flooding annually ( [[#Nicholls--2007|Nicholls et al., 2007]] ), causing an estimated 12,000 deaths ( [[#Shultz--2005|Shultz et al., 2005]] ), and there is significant concern for worsening flooding associated with climate change ( [[#Shultz--2018a|Shultz et al., 2018a]] ; [[#Shultz--2018b|Shultz et al., 2018b]] ; [[#Woodward--2018|Woodward and Samet, 2018]] ) but very limited quantification of attributable burden. A range of adverse health outcomes has been identified in a study of fires in sub-zero temperatures that are thought to be increasing in frequency due to climate change ( [[#Metallinou--2017|Metallinou and Log, 2017]] ).

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<span id="observed-impacts-on-maternal-foetal-and-neonatal-health"></span>
==== 7.2.4.3 Observed Impacts on Maternal, Foetal and Neonatal Health ====

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Maternal and neonatal disorders accounted for 3.7% of total global deaths and 7.8% of global DALYs in 2019 (Vos et al., 2020). Children and pregnant women have potentially higher rates of vulnerability and/or exposure to climatic hazards, extreme weather events and undernutrition ( [[#Garcia--2016|Garcia and Sheehan, 2016]] ; [[#Sorensen--2018|Sorensen et al., 2018]] ; [[#Chersich--2018|Chersich et al., 2018]] ). Available evidence suggests that heat is associated with higher rates of pre-term birth ( [[#Wang--2020|Wang et al., 2020]] ), low birthweight, stillbirth, neonatal stress ( [[#Cil--2017|Cil and Cameron, 2017]] ; [[#Kuehn--2017|Kuehn and McCormick, 2017]] ) and adverse child health ( [[#Kuehn--2017|Kuehn and McCormick, 2017]] ). Extreme weather events are associated with reduced access to prenatal care, unattended deliveries ( [[#Abdullah--2019|Abdullah et al., 2019]] ) and decreased paediatric healthcare access ( [[#Haque--2019|Haque et al., 2019]] ).

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<span id="observed-impacts-on-malnutrition"></span>
==== 7.2.4.4 Observed Impacts on Malnutrition ====

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''Climate variability and change contribute to food insecurity that can lead to malnutrition, including undernutrition, overweight and obesity, and to disease susceptibility, particularly in low- and middle-income countries'' ( ''high confidence'' ) ''.'' Since AR5, analyses of the links between climate change and food expanded beyond undernutrition to include the impacts of climate change on a wider set of diet- and weight-related risk factors and their impacts on NCDs, along with the role of dietary choices for GHG emissions ( [[#IPCC--2019b|IPCC, 2019b]] ) including dietary inadequacy (deficiencies, excesses or imbalances in energy, protein and micronutrients), infections and sociocultural factors (Global Nutrition Report 2020). Undernutrition exists when a combination of insufficient food intake, health, and care conditions results in one or more of the following: underweight for age, short for age (stunted), thin for height (wasted), or functionally deficient in vitamins and/or minerals (micronutrient malnutrition or ‘hidden hunger’). Food insecurity and poor access to nutrient-dense food contribute not only to undernutrition but also to obesity and susceptibility to NCDs in low- and middle-income countries ( [[#FAO--2018|FAO et al., 2018]] ; [[#Swinburn--2019|Swinburn et al., 2019]] ).

Globally, more than 690 million people are undernourished, 144 million children are stunted (chronic undernutrition), 47 million children are wasted (acute undernutrition), and more than 2 billion people have micronutrient deficiencies ( [[#FAO--2020|FAO, 2020]] ). More than 135 million people across 55 countries experienced acute hunger requiring urgent food, nutrition and livelihood assistance in 2019 (FSIN/GNAFC, 2020). The COVID-19 pandemic is projected to increase the number of acutely food insecure people to 270 million people ( [[#FSIN--2020|FSIN, 2020]] ) and worsen malnutrition levels ( [[#FAO--2020|FAO et al., 2020]] ; [[#Rippin--2020|Rippin et al., 2020]] ). The relationships between climate change and obesity vary based on geography, population sub-groups and/or stages of economic growth and population growth (An et al., 2017). Increasing temperatures could contribute to obesity through reduced physical activity, increased prices of produce or shifts in eating patterns of populations towards more processed foods ( [[#An--2018|An et al., 2018]] ). In the largest global study to date exploring the connections between child diet diversity and recent climate, data from 19 countries in six regions (Asia, Central America, South America, north Africa, southeast Africa and west Africa) indicated significant reductions in diet diversity associated with higher temperatures and significant increases in diet diversity associated with higher precipitation ( [[#Niles--2021|Niles et al., 2021]] ).

Climate change can affect the four aspects of food security: food production and availability, stability of food supplies, access to food and food utilisation ( [[#IPCC--2019b|IPCC, 2019b]] ). Access to sufficient food does not guarantee nutrition security. Extreme weather and climate events can result in inadequate or insufficient food consumption, increasing susceptibility to infectious diseases ( [[#Rodriguez-Llanes--2016|Rodriguez-Llanes et al., 2016]] ; [[#Gari--2017|Gari et al., 2017]] ; [[#Kumar--2016|Kumar et al., 2016]] ; [[#Lazzaroni--2016|Lazzaroni and Wagner, 2016]] ) but also to being overweight or obese and increasing susceptibility to non-communicable diseases in low- and middle-income countries (FAO, 2018; [[#Swinburn--2019|Swinburn et al., 2019]] ).

Nearly half of all deaths in children under five years of age are attributable to undernutrition, putting children at greater risk of dying from common infections. Undernutrition in the first 1,000 days of a child’s life can lead to stunted growth, which can result in impaired cognitive ability and reduced future school and work performance and the associated costs of stunting in terms of lost economic growth can be of the order of 10% of GDP yr –1 in Africa (UNICEF/WHO/WBG, 2019).

At the same time, diseases associated with high-calorie, unhealthy diets are increasing globally, with 38.3 million overweight children under five years of age (Global Nutrition Report, 2018), 2.1 billion overweight or obese adults and the global prevalence of diabetes almost doubling in the past 30 years ( [[#Swinburn--2019|Swinburn et al., 2019]] ). Unbalanced diets, such as diets low in fruits and vegetables and high in red and processed meat, are the number one risk factor for mortality globally and in most regions (Gakidou et al., 2018; [[#Afshin--2019|Afshin et al., 2019]] ).

Socioeconomic factors that mediate the influence of climate change on nutrition include cultural and societal norms; governance, institutions, policies and fragility; human capital and potential; and social position and access to healthcare, education and food aid ( [[#Rozenberg--2017|Rozenberg, 2017]] ; Alkerwi et al. 2015; [[#Tirado--2017|Tirado, 2017]] ; [[#FAO--2018|FAO et al., 2018]] ; Global Nutrition Report 2020). Extreme events may affect access to adequate diets, leading to malnutrition and increasing the risk of disease ( [[#Beveridge--2019|Beveridge et al., 2019]] ; [[#Rodriguez-Llanes--2016|Rodriguez-Llanes et al., 2016]] ; [[#Gari--2017|Gari et al., 2017]] ; [[#Kumar--2016|Kumar et al., 2016]] ; [[#Lazzaroni--2016|Lazzaroni and Wagner, 2016]] ; [[#Thiede--2020|Thiede and Gray, 2020]] ).

<div id="7.2.4.5" class="h3-container"></div>

<span id="observed-impacts-on-exposure-to-chemical-contaminants"></span>
==== 7.2.4.5 Observed Impacts on Exposure to Chemical Contaminants ====

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''Climate change in northern regions, including Arctic ecosystems, is causing permafrost to thaw, creating the potential for mercury (Hg) to enter the food chain'' ( ''medium agreement, low evidence'' ) ''as methyl mercury (MeHg), which is highly neurotoxic and nephrotoxic and bioaccumulates and biomagnifies throughout the food chain via dietary uptake of fish, seafood and mammals.'' Mercury methylation processes in aquatic environments have been found to be exacerbated by ocean warming, coupled with more acidic and anoxic sediments ( [[#FAO--2020|FAO, 2020]] ). Consumption of mercury-contaminated fish has been found to be linked to neurological disorders due to methyl mercury poisoning (i.e., Minamata disease) that is associated with climate change-contaminant interactions that alter the bioaccumulation and biomagnification of toxic and fat-soluble persistent organic pollutants and polychlorinated biphenyls (PCBs) ( [[#Alava--2017|Alava et al., 2017]] ) in seafood and marine mammals ( ''medium confidence)'' . Indigenous Peoples have a higher exposure to such risks because of the accumulation of such toxins in traditional foods (J.J. et al., 2017). Contamination of food with PCBs and dioxins has a range of adverse health impacts ( [[#Lake--2015|Lake et al., 2015]] ).

[[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] (Sections 5.4.3, 5.5.2.3, 5.8.1, 5.8.2, 5.8.3, 5.9.1, 5.11.1, 5.11.3, 5.12.3) discusses the possible impacts of climate change on food safety, including exposure to toxigenic fungi, PCBs and other POPs, mercury and harmful algal blooms.

''Climate change may affect animal health management practices, potentially leading to an increased use of pesticides or veterinary drugs (such as preventive antimicrobials) that could result in increased levels of residues in foods'' ( ''high agreement, medium/low evidence'' ) ( [[#Beyene--2015|Beyene et al., 2015]] ; [[#FAO%20and%20WHO--2018|FAO and WHO, 2018]] ; European Food Safety Authority, 2020; [[#MacFadden--2018|MacFadden et al., 2018]] ).

<div id="7.2.5" class="h2-container"></div>

<span id="observed-impacts-on-mental-health-and-well-being"></span>
=== 7.2.5 Observed Impacts on Mental Health and Well-Being ===

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<div id="7.2.5.1" class="h3-container"></div>

<span id="observed-impacts-on-mental-disorders"></span>
==== 7.2.5.1 Observed Impacts on Mental Disorders ====

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''A wide range of climatic events and conditions have observed and detrimental impacts on mental health'' ( ''very high confidence'' ) ''.'' The pathways through which climatic events affect mental health are varied, complex and inter-connected with other non-climatic influences that create vulnerability (Figure 7.6). The climatic exposure may be direct, such as experiencing an extreme weather event or prolonged high temperatures, or indirect, such as mental health consequences of undernutrition or displacement. Exposure may also be vicarious, with people experiencing decreased mental health associated with observing the impact of climate change on others or simply by learning about climate change. Non-climatic moderating influences range from an individual’s personality and pre-existing conditions, to social support, and to structural inequities ( [[#Gariepy--2016|Gariepy et al., 2016]] ; [[#Hrabok--2020|Hrabok et al., 2020]] ; [[#Nagy--2018|Nagy et al., 2018]] ; [[#Silva--2016b|Silva et al., 2016b]] ). Depending on these background and contextual factors, similar climatic events may result in a range of potential mental health outcomes, including anxiety, depression, acute traumatic stress, PTSD, suicide, substance abuse and sleep problems, with conditions ranging from being mild in nature to those that require hospitalisation ( [[#Berry--2010|Berry et al., 2010]] ; [[#Cianconi--2020|Cianconi et al., 2020]] ; [[#Clayton--2017|Clayton et al., 2017]] ; [[#Ruszkiewicz--2019|Ruszkiewicz et al., 2019]] ; [[#Bromet--2017|Bromet et al., 2017]] ; Lowe, 2019). The line between mental health and more general well-being is permeable, but in this section we refer to diagnosable mental disorders—conditions that disrupt or impair normal functioning through impacts on mood, thinking or behaviour.

<div id="_idContainer029" class="Figure"></div>

[[File:b9680ca351db5c67f3df6a80181b0f78 IPCC_AR6_WGII_Figure_7_006.png]]

'''Figure 7.6 |''' '''Climate change impacts on mental health and key adaptation responses.''' PTSD: Post traumatic stress disorder.

''There is an observable association between high temperatures and mental health decrements'' ( ''high confidence'' ) '', with an additional possible influence of increased precipitation'' ( ''medium agreement, medium evidence'' ). Heat-associated mental health outcomes include suicide ( [[#Williams--2015a|Williams et al., 2015a]] ; [[#Carleton--2017|Carleton, 2017]] ; [[#Burke--2018|Burke et al., 2018]] ; [[#Kim--2019b|Kim et al., 2019b]] ; [[#Thompson--2018|Thompson et al., 2018]] ; [[#Schneider--2020|Schneider et al., 2020]] ; [[#Cheng--2021|Cheng et al., 2021]] ; [[#Baylis--2018|Baylis et al., 2018]] ; [[#Obradovich--2018|Obradovich et al., 2018]] ); psychiatric hospital admissions and emergency room visits for mental disorders ( [[#Hansen--2008|Hansen et al., 2008]] ; [[#Wang--2014|Wang et al., 2014]] ; [[#Chan--2018|Chan et al., 2018]] ; [[#Mullins--2019|Mullins and White, 2019]] ; [[#Yoo--2021|Yoo et al., 2021]] ); experiences of anxiety, depression and acute stress ( [[#Obradovich--2018|Obradovich et al., 2018]] ; [[#Mullins--2019|Mullins and White, 2019]] ); and self-reported mental health ( [[#Li--2020|Li et al., 2020]] ). In Canada, [[#Wang--2014|Wang et al. (2014)]] found an association between mean heat exposure of 28°C and greater hospital admissions within 0 to 4 days for mood and behavioural disorders (including schizophrenia, mood and neurotic disorders). A US study found mental health problems increased by 0.5% when average temperatures exceed 30°C, compared to averages between 25°C and 30°C; a 1°C warming over five years was associated with a 2% increase in mental health problems ( [[#Obradovich--2018|Obradovich et al., 2018]] ). Another study found a 1°C rise in monthly average temperatures over several decades was associated with a 2.1% rise in suicide rates in Mexico and a 0.7% rise in suicide rates in the USA ( [[#Burke--2018|Burke et al., 2018]] ). A systematic review of published research using a variety of methodologies from 19 countries ( [[#Thompson--2018|Thompson et al., 2018]] ) found an increased risk of suicide associated with a 1°C rise in ambient temperature.

''Discrete climate hazards including storms ( [[#Kessler--2008|Kessler et al., 2008]] ; [[#Boscarino--2013|Boscarino et al., 2013]] ; [[#Boscarino--2017|Boscarino et al., 2017]] ; [[#Obradovich--2018|Obradovich et al., 2018]] ), floods ( [[#Baryshnikova--2019|Baryshnikova and Pham, 2019]] ), heatwaves, wildfires and drought ( [[#Hanigan--2012|Hanigan et al., 2012]] ; [[#Carleton--2017|Carleton, 2017]] ; [[#Zhong--2018|Zhong et al., 2018]] ; [[#Charlson--2021|Charlson et al., 2021]] ) have significant negative consequences for mental health'' ( ''very high confidence'' ). A large body of research identifies the impacts of extreme weather events on PTSD, anxiety and depression; much of the research has been done in the USA and the UK, but a growing number of studies find evidence for similar impacts on mental health in other countries, including Spain ( [[#Foudi--2017|Foudi et al., 2017]] ), Brazil ( [[#Alpino--2016|Alpino et al., 2016]] ), Chile ( [[#Navarro--2016|Navarro et al., 2016]] ), Small Island Developing States (Kelman et al., 2021) and Vietnam ( [[#Pollack--2016|Pollack et al., 2016]] ). Approximately 20–30% of those who live through a hurricane develop depression and/or PTSD within the first few months following the event ( [[#Obradovich--2018|Obradovich et al., 2018]] ; [[#Schwartz--2015|Schwartz et al., 2015]] ; [[#Whaley--2009|Whaley, 2009]] ), with similar rates for people who have experienced flooding ( [[#Waite--2017|Waite et al., 2017]] ; [[#Fernandez--2015|Fernandez et al., 2015]] ). Studies conducted in South America and Asia indicate an increase in PTSD and depressive disorders after extreme weather events ( [[#Rataj--2016|Rataj et al., 2016]] ). Evidence is lacking for African countries ( [[#Otto--2017|Otto et al., 2017]] ). Children and adolescents are particularly vulnerable to post-traumatic stress after extreme weather events ( [[#Brown--2017|Brown et al., 2017]] ; [[#Hellden--2021|Hellden et al., 2021]] ; [[#Kousky--2016|Kousky, 2016]] ), and increased susceptibility to mental health problems may linger into adulthood ( [[#Maclean--2016|Maclean et al., 2016]] ).

''Wildfires have observed negative impacts on mental health'' ( ''high confidence'' ) ''.'' This is due to the trauma of the immediate experience and/or subsequent displacement and evacuation ( [[#Dodd--2018|Dodd et al., 2018]] ; [[#Brown--2019|Brown et al., 2019]] ; [[#Psarros--2017|Psarros et al., 2017]] ; [[#Silveira--2021b|Silveira et al., 2021b]] ). Sub-clinical outcomes, such as increases in anxiety, sleeplessness or substance abuse are reported in response to wildfires and extreme weather events, with impacts being pronounced among those who experience greater losses or are more directly exposed to the event; this may include first responders.

''Mental health impacts can emerge as result of climate impacts on economic, social and food systems'' ( ''high confidence'' ) ''.'' For example, malnutrition among children has been associated with a variety of mental health problems ( [[#Adhvaryu--2019|Adhvaryu et al., 2019]] ; [[#Hock--2018|Hock et al., 2018]] ; [[#Yan--2018|Yan et al., 2018]] ), as has food insecurity among adults ( [[#Lund--2018|Lund et al., 2018]] ). The economic impacts of droughts have been associated with increases in suicide, particularly among farmers ( [[#Carleton--2017|Carleton, 2017]] ; [[#Edwards--2015|Edwards et al., 2015]] ; [[#Vins--2015|Vins et al., 2015]] ); those whose occupations are ''likely'' to be affected by climate change report that it is a source of stress that is linked to substance abuse and suicidal ideation ( [[#Kabir--2018|Kabir, 2018]] ). Studies of Indigenous Peoples often describe food insecurity or reduced access to traditional foods as a link between climate change and reduced mental health ( [[#Middleton--2020b|Middleton et al., 2020b]] ). The loss of family members, for example due to an extreme weather event, increases the risk of mental illness ( [[#Keyes--2014|Keyes et al., 2014]] ). Individuals in low- and middle-income countries may be more severely impacted due to lesser access to mental health services and lower financial resources to help cope with impacts compared with high-income countries ( [[#Abramson--2015|Abramson et al., 2015]] ).

''Anxiety about the potential risks of climate change and awareness of climate change itself can affect mental health even in the absence of direct impacts'' ( ''low confidence'' ) ''.'' There is a need for more evidence about the prevalence or severity of climate change-related anxiety, sometimes called ecoanxiety, but national surveys in the USA, Europe and Australia show that people express high levels of concern and perceived harm associated with climate change ( [[#Steentjes--2017|Steentjes et al., 2017]] ; [[#Clayton--2020|Clayton and Karazsia, 2020]] ; [[#Cunsolo--2018|Cunsolo and Ellis, 2018]] ; [[#Helm--2018|Helm et al., 2018]] ; [[#Leiserowitz--2017|Leiserowitz et al., 2017]] ; [[#Reser--2012|Reser et al., 2012]] ; [[#Steentjes--2017|Steentjes et al., 2017]] ). In a US sample, perceived ecological stress, defined as personal stress associated with environmental problems, predicted depressive symptoms ( [[#Helm--2018|Helm et al., 2018]] ); in a sample of Filipinos, climate anxiety was correlated with lower mental health ( [[#Reyes--2021|Reyes et al., 2021]] ) and a non-random study in 25 countries showed positive correlations between negative emotions about climate change and self-rated mental health ( [[#Ogunbode--2021|Ogunbode et al., 2021]] ). However, an earlier study found no correlation between climate change worry and mental health issues ( [[#Berry--2015|Berry and Peel, 2015]] ). Because the perceived threat of climate change is based on subjective perceptions of risk and coping ability as well as on experiences and knowledge ( [[#Bradley--2014|Bradley et al., 2014]] ), even people who have not been directly affected may be stressed by a perception of looming danger ( [[#Clayton--2020|Clayton and Karazsia, 2020]] ). Not surprisingly, those who have directly experienced some of the effects of climate change may be more likely to show such responses. Indigenous Peoples, whose culture and well-being tend to be strongly linked to local environments, may experience mental health effects associated with changes in environmental risks; studies suggest connections to an increase in depression, substance abuse or suicide in some Indigenous Peoples ( [[#Canu--2017|Canu et al., 2017]] ; [[#Cunsolo%20Willox--2013|Cunsolo Willox et al., 2013]] ; [[#Middleton--2020b|Middleton et al., 2020b]] ; [[#Jaakkola--2018|Jaakkola et al., 2018]] ).

<div id="7.2.5.2" class="h3-container"></div>

<span id="observed-impacts-on-well-being"></span>
==== 7.2.5.2 Observed Impacts on Well-Being ====

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''Overall, research suggests that climate change has already had negative effects on subjective well-being'' ( ''medium confidence'' ) ''. C'' limate change can affect well-being through a number of pathways, including loss of access to green and blue spaces due to damage from storms, coastal erosion, drought or wildfires; heat; decreased air quality; and disruptions to one’s normal pattern of behaviour, residence, occupation or social interactions ( [[#Hayward--2021|Hayward and Ayeb-Karlsson, 2021]] ). For example, substantial evidence shows a negative correlation between air pollution and subjective well-being or happiness ( [[#Apergis--2018|Apergis, 2018]] ; [[#Cunado--2013|Cunado and de Gracia, 2013]] ; [[#Lu--2020|Lu, 2020]] ; [[#Luechinger--2010|Luechinger, 2010]] ; [[#Menz--2010|Menz and Welsch, 2010]] ; [[#Orru--2016|Orru et al., 2016]] ; [[#Yuan--2018|Yuan et al., 2018]] ; [[#Zhang--2017a|Zhang et al., 2017a]] ); in the reverse direction, there is evidence not only that time in nature but more specifically a feeling of connectedness to nature are both associated with well-being ( [[#Martin--2020|Martin et al., 2020]] ) and healthy ecosystems offer opportunities for health improvements ( [[#Pretty--2020|Pretty and Barton, 2020]] ). Negative emotions such as grief—often termed ‘solastalgia’ ( [[#Albrecht--2007|Albrecht et al., 2007]] )—are associated with the degradation of local or valued landscapes ( [[#Eisenman--2015|Eisenman et al., 2015]] ; [[#Ellis--2017|Ellis and Albrecht, 2017]] ; [[#Polain--2011|Polain et al., 2011]] ; [[#Tschakert--2017|Tschakert et al., 2017]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ), which may threaten cultural rituals, especially among Indigenous Peoples ( [[#Cunsolo--2018|Cunsolo and Ellis, 2018]] ; [[#Cunsolo--2020|Cunsolo et al., 2020]] ). Studies conducted in the Solomon Islands and Tuvalu found qualitative and quantitative evidence of experiences of climate change and worry about the future, with negative impacts on respondents’ well-being ( [[#Asugeni--2015|Asugeni et al., 2015]] ; [[#Gibson--2020|Gibson et al., 2020]] ).

''Heat is one of the best-studied aspects of climate change observed to reduce well-being'' ( ''high confidence'' ) ''.'' Higher summer temperatures are associated with decreased happiness and ratings of well-being ( [[#Carleton--2016|Carleton and Hsiang, 2016]] ; [[#Miles-Novelo--2019|Miles-Novelo and Anderson, 2019]] ; [[#Connolly--2013|Connolly, 2013]] ; [[#Noelke--2016|Noelke et al., 2016]] ; [[#Baylis--2018|Baylis et al., 2018]] ; [[#Moore--2019|Moore et al., 2019]] ; [[#Wang--2020|Wang et al., 2020]] b). A study of 1.9 million Americans ( [[#Noelke--2016|Noelke et al., 2016]] ) found that exposure to one day averaging 21°C–27°C was associated with reduced well-being by 1.6% of a standard deviation and days above 32°C were associated with reduced well-being by 4.4% of a standard deviation relative to a reference interval of 10°C–16°C. A similar relationship between heat and mood has been observed in China, where expressed mood began to decrease when the average daily temperature was over 20°C ( [[#Wang--2020|Wang et al., 2020]] b). The causal mechanism is unclear but could be due to impacts on health, economic costs or social interactions ( [[#Belkin--2017|Belkin and Kouchaki, 2017]] ; [[#Osberghaus--2016|Osberghaus and Kühling, 2016]] ) or reduced quality or quantity of sleep ( [[#Fujii--2015|Fujii et al., 2015]] ; [[#Obradovich--2017|Obradovich et al., 2017]] ; [[#Obradovich--2018|Obradovich and Migliorini, 2018]] ). Heat has also been associated with inter-personal and inter-group aggression and increases in violent crime ( [[#Heilmann--2021|Heilmann et al., 2021]] ; [[#Mapou--2017|Mapou et al., 2017]] ; [[#Tiihonen--2017|Tiihonen et al., 2017]] ). For the most part, studies have measured daily response to average daily temperatures and are unable to predict whether the effect is cumulative in response to a sequence of unusually warm days. However, there is no evidence that adaptation occurs over time to eliminate the negative response to very warm temperatures ( [[#Moore--2019|Moore et al., 2019]] ). Some research has found a negative effect of extreme cold on well-being ( [[#Yoo--2021|Yoo et al., 2021]] ); increasing winter temperatures associated with climate change could serve to compensate for the impact of increased summer temperatures. However, the effect of high temperatures is typically found to be stronger than the effect of low temperatures, and in some cases no detrimental impacts of cold weather are found ( [[#Almendra--2019|Almendra et al., 2019]] ; [[#Mullins--2019|Mullins and White, 2019]] ).

Climate change also threatens well-being defined in terms of capabilities or the capacity to fulfil one’s potential and fully participate in society. Heat can limit labour capacity; one study estimated that 45 billion hours of labour productivity were lost in 2018 compared to 2000 due to high temperatures ( [[#Watts--2019|Watts et al., 2019]] ). Both heat and air pollution also impair human capabilities through a negative effect on cognitive performance ( [[#Taylor--2016b|Taylor et al., 2016b]] ) and even impair skills acquisition, reducing the ability to learn ( [[#Park--2021|Park et al., 2021]] ) and affecting marginalised groups more strongly ( [[#Park--2020|Park et al., 2020]] ), although findings are inconsistent and depend in part on the nature of the task ''(low confidence).''

Systematic reviews have found an association between higher ambient levels of fine airborne particles with cognitive impairment in the elderly and with behavioural problems (related to impulsivity and attention problems) in children ( [[#Power--2016|Power et al., 2016]] ; [[#Yorifuji--2017|Yorifuji et al., 2017]] ; [[#Younan--2018|Younan et al., 2018]] ; [[#Zhao--2018b|Zhao et al., 2018b]] ) ( ''medium confidence'' ). Malnutrition has also been associated with reduced educational achievement and long-term decrements in cognitive function ( [[#Acharya--2019|Acharya et al., 2019]] ; [[#Asmare--2018|Asmare et al., 2018]] ; [[#Na--2020|Na et al., 2020]] ; [[#Kim--2017|Kim et al., 2017]] ; [[#Talhaoui--2019|Talhaoui et al., 2019]] ).

<div id="7.2.6" class="h2-container"></div>

<span id="observed-impacts-on-migration"></span>
=== 7.2.6 Observed Impacts on Migration ===

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Consistent with peer-reviewed scholarship and with the United Nations Framework Convention on Climate Change (UNFCCC) Cancun Adaptation Framework section 14(f) and the Paris Agreement, this Chapter assesses the impacts of climate change on four types of migration: (a) adaptive migration (i.e., where migration is an outcome of individual or household choice), (b) involuntary migration and displacement (i.e., where people have few or no options except to move), (c) organised relocation of populations from sites highly exposed to climatic hazards and (d) immobility (i.e., an inability or unwillingness to move from areas of high exposure for cultural, economic or social reasons) (Cross-Chapter Box MIGRATE in Chapter 7).

''A general theme across studies from all regions is that climate-related migration outcomes are diverse'' ( ''high confidence'' ) ''and may be manifest as decreases or increases in migration flows, and may lead to changes in the timing or duration of migration and to changes in migration source locations and destinations.'' Multi-country studies of climatic impacts on migration patterns in Africa have found that migration exhibits weak, inconsistent associations with variations in temperature and precipitation and that migration responses differ significantly between countries and between rural and urban areas ( [[#Gray--2016|Gray and Wise, 2016]] ; [[#Mueller--2020|Mueller et al., 2020]] ). Multi-directional findings such as these are also common in single-country studies from multiple regions ( [[#Call--2017|Call et al., 2017]] ; [[#Nawrotzki--2017|Nawrotzki et al., 2017]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ). The diversity of potential migration and displacement outcomes reflects (a) the variable nature of climate hazards in terms of the rate of onset, intensity, duration, spatial extent and severity of damage caused to housing, infrastructure and livelihoods and (b) the wide range of social, economic, cultural, political and other non-climatic factors that influence exposure, vulnerability, adaptation options and the contexts in which migration decisions are made ( [[#Neumann--2015|Neumann and Hermans, 2015]] ; [[#McLeman--2017|McLeman, 2017]] ; [[#Barnett--2018|Barnett and McMichael, 2018]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ) ( ''high confidence'' ).

''Weather events and climate conditions can act as direct drivers of migration and displacement (e.g., destruction of homes by tropical cyclones) and as indirect drivers (e.g., rural income losses and/or food insecurity due to heat- or drought-related crop failures that in turn generate new population movements)'' ( ''high confidence'' ). Extreme storms, floods and wildfires are strongly associated with high levels of short- and long-term displacement, while droughts, extreme heat and precipitation anomalies are more likely to stimulate longer-term changes in migration patterns ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ). Longer-term environmental changes attributable to anthropogenic climate change—such as higher average temperatures, desertification, land degradation, biodiversity loss and sea level rise—have had observed effects on migration and displacement in a limited number of locations in recent decades but are projected to have wider-scale impacts on future population patterns and migration, and are therefore assessed in [[#7.3.2|Section 7.3.2]] (Projected Risks).

''The diversity of potential migration and displacement outcomes reflects the scale and physical impacts of specific climate hazard events and the wide range of social, economic, cultural, political and other non-climatic factors that influence exposure, vulnerability, adaptation options and the contexts in which migration decisions are made'' ( ''high confidence'' ) ''.'' The diversity in drivers, contexts and outcomes makes it difficult to offer simple generalisations about the relationship between climate change and migration. The characteristics of climatic drivers vary in terms of the rate of onset, intensity, duration, spatial extent and severity of damage caused to housing, infrastructure and livelihoods; the potential migration responses to these are further mediated by cultural, demographic, economic, political, social and other non-climatic factors operating across multiple scales ( [[#Neumann--2015|Neumann and Hermans, 2015]] ; [[#McLeman--2017|McLeman, 2017]] ; [[#Barnett--2018|Barnett and McMichael, 2018]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ).

''Climate-related migration and displacement outcomes display high variability in terms of migrant success, often reflecting pre-existing socioeconomic conditions and household wealth'' ( ''high confidence'' ) ''.'' The decision to migrate or remain in place when confronted by climatic hazards is strongly influenced by the range and accessibility of alternative, ''in situ'' (i.e., non-migration) adaptation options that may be less costly or disruptive ( [[#Cattaneo--2019|Cattaneo et al., 2019]] ). Migration decisions (whether climate-related or not) are typically made at the individual or household level and are influenced by a household’s perceptions of risk, social networks, wealth, age structure, health and livelihood choices ( [[#Koubi--2016b|Koubi et al., 2016b]] ; [[#Gemenne--2017|Gemenne and Blocher, 2017]] ). Households with greater financial resources and higher levels of educational attainment have greater capacity to adapt ''in situ'' ( [[#Cattaneo--2019|Cattaneo and Massetti, 2019]] ; [[#Ocello--2015|Ocello et al., 2015]] ) but are also better able to migrate and with greater agency once such a decision is made ( [[#Kubik--2016|Kubik and Maurel, 2016]] ; [[#Koubi--2016b|Koubi et al., 2016b]] ; [[#Riosmena--2018|Riosmena et al., 2018]] ; [[#Adams--2019|Adams and Kay, 2019]] ). By contrast, poor households with limited physical, social and financial resources have less capacity to adapt ''in situ'' and are often limited in their migration options ( [[#Nawrotzki--2018|Nawrotzki and DeWaard, 2018]] ; [[#Suckall--2017|Suckall et al., 2017]] ; [[#Zickgraf--2016|Zickgraf et al., 2016]] ). Thus, when poorer households do migrate after an extreme climate event, it is often in reaction to lost income or livelihood and occurs with low voluntarity ( [[#Mallick--2017|Mallick et al., 2017]] ; [[#Bhatta--2015|Bhatta et al., 2015]] ) and may perpetuate or amplify migrants’ socioeconomic precarity and/or their exposure to environmental hazards ( [[#Natarajan--2019|Natarajan et al., 2019]] ; see also [[IPCC:Wg2:Chapter:Chapter-8#8.3.1|Section 8.3.1]] ).

''Climate-related migration originates most often in rural areas in low- and middle-income countries, with migrant destinations usually being other rural areas or urban centres within their home countries (i.e., internal migration)'' ( ''medium confidence'' ) ''.'' Rural livelihoods and incomes based on farming, livestock rearing and/or natural resource collection are inherently sensitive to climate variability and change, creating greater potential for migration as a response ( [[#Bohra-Mishra--2017|Bohra-]] [[#Mishra--2017|Mishra et al., 2017]] ; [[#Viswanathan--2015|Viswanathan and Kumar, 2015]] ). Drought events have been associated with periods of higher rural to urban migration within Mexico ( [[#Chort--2016|Chort and de la Rupelle, 2016]] ; [[#Leyk--2017|Leyk et al., 2017]] ; [[#Nawrotzki--2017|Nawrotzki et al., 2017]] ; [[#Murray-Tortarolo--2021|Murray-Tortarolo and Martnez, 2021]] ) and Senegal ( [[#Nawrotzki--2017|Nawrotzki and Bakhtsiyarava, 2017]] ). Extreme temperatures are associated with higher rates of temporary rural out-migration in South Africa and in Bangladesh ( [[#Mastrorillo--2016|Mastrorillo et al., 2016]] ; [[#Call--2017|Call et al., 2017]] ). In rural Tanzania, weather-related shocks to crop production have been observed to increase the likelihood of migration but typically only for households in the middle of community wealth distribution ( [[#Kubik--2016|Kubik and Maurel, 2016]] ). Weather-related losses in rice production have been associated with small-percentage increases in internal migration in India ( [[#Viswanathan--2015|Viswanathan and Kumar, 2015]] ) and the Philippines ( [[#Bohra-Mishra--2017|Bohra-]] [[#Mishra--2017|Mishra et al., 2017]] ). In east Africa, temporary rural–urban labour migration does not show a strong response to climatic drivers ( [[#Mueller--2020|Mueller et al., 2020]] ). There is limited literature on mobility as adaptation in urban populations, with the focus being on resettlement of flood-prone informal settlements within cities ( [[#Kita--2017|Kita, 2017]] ; [[#Tadgell--2017|Tadgell et al., 2017]] ).

''Most documented examples of international climate-related migration are intra-regional movements of people between countries with shared borders'' ( ''high agreement, medium evidence'' ) ''.'' Systematic reviews find few documented examples of long-distance, inter-regional migration driven by climate events ( [[#Veronis--2018|Veronis et al., 2018]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ). One macro-economic analysis found a correlation between migrant flows from low- to high-income countries and adverse climatic events in the source country ( [[#Coniglio--2015|Coniglio and Pesce, 2015]] ). Another study found that high heat stimulates higher rates of international migration from middle-income countries but typically not from low-income countries (Cattaneo and Peri., 2016), while other studies found international climate-related migration originates primarily from agriculture-dependent countries ( [[#Cai--2016|Cai et al., 2016]] ; [[#Nawrotzki--2017|Nawrotzki and Bakhtsiyarava, 2017]] ). Small-sample studies of migrants to Canada from Bangladesh, Haiti and sub-Saharan Africa suggest environmental factors in the source country can be a primary or secondary motivation for some migrants within larger flows of economic and family-reunification migrants ( [[#Veronis--2014|Veronis and McLeman, 2014]] ; [[#Mezdour--2015|Mezdour et al., 2015]] ; [[#McLeman--2017|McLeman et al., 2017]] ). Research on the links between climate hazards and international movements of refugees and/or asylum seekers shows differing results. One study found that asylum applications in Europe increase during climate fluctuations due to interactions with conflict ( [[#Missirian--2017|Missirian and Schlenker, 2017]] ), and another found links between heat, drought, conflict and asylum-seeking migration originating in the Middle East between 2011 and 2015 ( [[#Abel--2019|Abel et al., 2019]] ). Other studies have found that asylum claims in Europe correspond minimally with climatic hazards in source countries ( [[#Schutte--2021|Schutte et al., 2021]] ), with choices in baseline data, timeframes for analysis and methodological approaches potentially explaining the inconsistent results across studies ( [[#Boas--2019|Boas et al., 2019]] ). Media reports and other studies in recent years suggest that climate change has driven large numbers of migrants to the US from Central America and to Europe from the Middle East and Africa, but empirical studies were not identified for this assessment.

<div id="7.2.6.1" class="h3-container"></div>

<span id="relative-importance-of-specific-climatic-drivers-of-migration-and-displacement"></span>
==== 7.2.6.1 Relative Importance of Specific Climatic Drivers of Migration and Displacement ====

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Reliable global estimates of voluntary climate-related migration within and between countries are not available due to a general absence of data of this specific nature, with existing national and global datasets often lacking information on migration causation or motivation. Better data are available for involuntary displacements within countries for reasons associated with weather-related hazards. Data collected annually since 2008 on internal displacements attributed to extreme weather events by the IDMC indicate that extreme storms and floods are the two most significant weather-related drivers of population displacements globally. Because of improvements in collection sources and methods since it first began reporting data in 2008, upward trends since that year in the total reported annual number of people displaced should be treated cautiously. However, it is reasonable to conclude that the average annual rate currently exceeds 20 million people globally, with considerable interannual variation due to the frequency and severity of extreme events in heavily populated areas. Regional distribution of displacement events has been consistent throughout the IDMC data collection period ( ''high confidence'' ), with displacement events occurring most often in East, Southeast and south Asia; sub-Saharan Africa; the USA; and the Caribbean region (Figure 7.7). Relative to their absolute population size, small island states experience a disproportionate risk of climate-related population displacements ( [[#Desai--2021|Desai et al., 2021]] ) ( ''high confidence'' ).

<div id="_idContainer036" class="Figure"></div>

[[File:dced250ee7e0192c3947a11687d10b79 IPCC_AR6_WGII_Figure_7_007.png]]

'''Figure 7.7 |''' '''Average number of people displaced annually by selected weather-related events from 2010 to 2020 by region.''' See text for important notes regarding data collection and trends. Source statistics provided by the Internal Displacement Monitoring Centre ( https://www.internal-displacement.org/ ).

''Tropical cyclones and extreme storms are a particularly significant displacement risk in East and Southeast Asia, the Caribbean region, the Bay of Bengal region and southeast Africa (IDMC 2020)'' ( ''high confidence'' ) ''.'' The scale of immediate displacement from any given storm and potential for post-event migration depend heavily on the extent of damage to housing and livelihood assets and the responsive capacity of governments and humanitarian relief agencies ( [[#Saha--2016|Saha, 2016]] ; [[#Islam--2018|Islam et al., 2018]] ; Mahajan, 2020; [[#Spencer--2018|Spencer and Urquhart, 2018]] ). In Bangladesh, the rural poor are most often displaced, with initial increases in short-term, labour-seeking migration followed by more permanent migration by some groups ( [[#Saha--2016|Saha, 2016]] ; [[#Islam--2016|Islam and Hasan, 2016]] ; [[#Islam--2017|Islam and Shamsuddoha, 2017]] ). Past hurricanes in the Caribbean basin have generated internal and inter-state migration within the region, typically along pre-existing social networks, and to the USA ( [[#Loebach--2016|Loebach, 2016]] ; [[#Chort--2016|Chort and de la Rupelle, 2016]] ). In 2017, Hurricanes Irma and Maria caused widespread damage to infrastructure and health services, and a slow recovery response by authorities was followed by the migration of tens of thousands of Puerto Ricans to Florida and New York ( [[#Zorrilla--2017|Zorrilla, 2017]] ; [[#Echenique--2018|Echenique and Melgar, 2018]] ). In the US, coastal counties experience increased out-migration after hurricanes that flows along existing social networks ( [[#Hauer--2017|Hauer, 2017]] ), with post-disaster reconstruction employment opportunities potentially attracting new labour migrants to affected areas ( [[#Ouattara--2014|Ouattara and Strobl, 2014]] ; [[#Curtis--2015|Curtis et al., 2015]] ; [[#DeWaard--2016|DeWaard et al., 2016]] ; [[#Fussell--2018|Fussell et al., 2018]] ).

''Riverine flood displacement can lead to increases or decreases in temporary or short-distance migration flows, depending on the local context'' ( ''medium confidence'' ) ''( [[#Robalino--2015|Robalino et al., 2015]] ; [[#Ocello--2015|Ocello et al., 2015]] ; [[#Afifi--2016|Afifi et al., 2016]] ; [[#Koubi--2016b|Koubi et al., 2016b]] )'' . Floods are a particularly important driver of displacement in river valleys and deltas in Asia and Africa, although large flood-related displacements have been recorded by the IDMC in all regions. In Africa, populations exposed to low flood risks, as compared with other regions, are observed to have a greater vulnerability to displacement due to limited economic resources and adaptive capacity ( [[#Kakinuma--2020|Kakinuma et al., 2020]] ). In areas where flooding is especially frequent, ''in situ'' adaptations may be more common, and out-migration may temporarily decline after a flood ( [[#Afifi--2016|Afifi et al., 2016]] ; [[#Chen--2017|Chen et al., 2017]] ; [[#Call--2017|Call et al., 2017]] ). Rates of indefinite or permanent migration tend not to change following riverine floods unless damage to homes and livelihood assets is especially severe and widespread, with household perceptions of short- and longer-term risks playing an important role ( [[#Koubi--2016a|Koubi et al., 2016a]] ).

Displacements due to droughts, extreme heat and associated impacts on food and water security are most frequent in east Africa and, to a lesser extent, south Asia and west and southern Africa (IDMC, 2020). Since droughts unfold progressively and typically do not cause permanent damage to housing or livelihood assets, there is greater opportunity for government and non-governmental organisation (NGO) interventions and greater use of ''in situ'' adaptation options ( [[#Koubi--2016b|Koubi et al., 2016b]] ; [[#Koubi--2016a|Koubi et al., 2016a]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ). Drought-related population movements are most common in dryland rural areas of low-income countries and occur after a threshold is crossed and ''in situ'' adaptation options are exhausted ( [[#Gautier--2016|Gautier et al., 2016]] ; [[#Wiederkehr--2018|Wiederkehr et al., 2018]] ; [[#McLeman--2017|McLeman, 2017]] ). Observed population movements may occur for an extended period after the event; one study of Mexican data found this lag to be up to 36 months ( [[#Nawrotzki--2017|Nawrotzki et al., 2017]] ). The most common response to drought is an increase in short-distance, rural–urban migration ( ''medium confidence'' ), with examples being documented in Bangladesh, Ethiopia, Pakistan, sub-Saharan Africa, Latin America and Brazil ( [[#Neumann--2015|Neumann and Hermans, 2015]] ; [[#Gautier--2016|Gautier et al., 2016]] ; [[#Gautier--2016|Gautier et al., 2016]] ; [[#Mastrorillo--2016|Mastrorillo et al., 2016]] ; [[#Baez--2017|Baez et al., 2017]] ; [[#Call--2017|Call et al., 2017]] ; [[#Nawrotzki--2017|Nawrotzki et al., 2017]] ; [[#Jessoe--2018|Jessoe et al., 2018]] '';'' [[#Carrico--2019|Carrico and Donato, 2019]] ; [[#Hermans--2019|Hermans and Garbe, 2019]] ).

Few assessable studies were identified that examine links between wildfires and migration. Wildfire events are often associated with urgent evacuations and temporary relocations, which place significant stress on receiving communities (Spearing and Faust., 2020), but research in the USA suggests fires have only a modest influence on future migration patterns in exposed areas (Winkler and Rouleau., 2021). More research, particularly in other regions, is needed.

<div id="7.2.6.2" class="h3-container"></div>

<span id="immobility-and-resettlement-in-the-context-of-climatic-risks"></span>
==== 7.2.6.2 Immobility and Resettlement in the Context of Climatic Risks ====

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''Immobility in the context of climatic risks can reflect vulnerability and lack of agency (an inability to migrate), but can also be a deliberate choice'' ( ''high confidence'' ) ''.'' Research since AR5 shows that immobility is best described as a continuum from people who are financially or physically unable to move away from hazards (involuntary immobility) to people who choose not to move (voluntary immobility) because of strong attachments to place, culture and people ( [[#Nawrotzki--2018|Nawrotzki and DeWaard, 2018]] ; [[#Adams--2016|Adams, 2016]] ; [[#Farbotko--2019|Farbotko and McMichael, 2019]] ; [[#Zickgraf--2019|Zickgraf, 2019]] ; [[#Neef--2018|Neef et al., 2018]] ; [[#Suckall--2017|Suckall et al., 2017]] ; [[#Ayeb-Karlsson--2018|Ayeb-Karlsson et al., 2018]] ; [[#Zickgraf--2018|Zickgraf, 2018]] ; [[#Mallick--2020|Mallick and Schanze, 2020]] ). Involuntary immobility is associated with individuals and households with low adaptive capacity and high exposure to hazard, and can exacerbate inequality and future vulnerability to climate change ( [[#Sheller--2018|Sheller, 2018]] ), including through impacts on health ( [[#Schwerdtle--2018|Schwerdtle et al., 2018]] ). Voluntary immobility represents an assertion of the importance of culture, livelihoods and people to well-being, and is of particular relevance for Indigenous Peoples ( [[#Suliman--2019|Suliman et al., 2019]] ).

Planned relocations by governments of settlements and populations exposed to climatic hazards are not presently commonplace, although the need is expected to grow in coming decades (Hino et al 2017). Examples include relocations of coastal settlements exposed to storm and erosion hazards as well as smaller numbers of cases of flood-prone settlements in river valleys; these examples suggest that organised relocations are expensive, contentious, create multiple challenges for governments and generate short- and longer-term disruptions for the people involved ( ''high agreement, medium evidence'' ) ( [[#Ajibade--2020|Ajibade et al., 2020]] ; [[#Henrique--2020|Henrique and Tschakert, 2020]] ; [[#Desai--2021|Desai et al., 2021]] ).

Examples of relocations of small indigenous communities in coastal Alaska and villages in the Solomon Islands and Fiji suggest that relocated people experience significant financial and emotional distress as cultural and spiritual bonds to place and livelihoods are disrupted ( [[#Albert--2018|Albert et al., 2018]] ; [[#Neef--2018|Neef et al., 2018]] ; [[#McMichael--2020|McMichael and Katonivualiku, 2020]] ; [[#McMichael--2020|McMichael and Katonivualiku, 2020]] ; [[#McMichael--2021|McMichael et al., 2021]] ; [[#Piggott-McKellar--2019|Piggott-McKellar et al., 2019]] ; [[#Bertana--2020|Bertana, 2020]] ). Voluntary relocation programmes offered by US state governments in communities damaged by Hurricane Sandy in 2012 have been subject to multiple studies, and these show longer-term economic outcomes, social connections and mental well-being vary for a range of reasons unrelated to the impacts of the hazard event itself ( [[#Bukvic--2017|Bukvic and Owen, 2017]] ; [[#Binder--2019|Binder et al., 2019]] ; Koslov and Merdjanoff, 2021).

<div id="7.2.6.3 " class="h3-container"></div>

<span id="connections-between-climate-related-migration-and-health"></span>
==== 7.2.6.3 Connections Between Climate-Related Migration and Health ====

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The number of assessable peer-reviewed studies that make connections between climate-related migration and health and well-being is small. The health outcomes of migrants generally, and of climate-migrants in particular, vary according to geographical context, country and the particular circumstances of migration or immobility ( [[#Hunter--2017|Hunter and Simon, 2017]] ; [[#Hunter--2021|Hunter et al., 2021]] ; [[#Schwerdtle--2020|Schwerdtle et al., 2020]] ). Such linkages are ‘multi-directional’, with studies suggesting that healthy individuals may be more likely to migrate internationally in search of economic opportunities than people in poorer health, except during adverse climatic conditions when migration rates may change across all groups, and that migrants may have different long-term health outcomes than people born in destination areas, potentially displaying a range of positive and negative health outcomes compared to non-migrants ( [[#Kennedy--2015|Kennedy et al., 2015]] ; [[#Dodd--2017|Dodd et al., 2017]] ; [[#Hunter--2017|Hunter and Simon, 2017]] ; [[#Riosmena--2017|Riosmena et al., 2017]] ). Refugees and other involuntary migrants often experience higher exposure to disease and malnutrition, adverse indirect health effects of changes in diet or activity and increased rates of mental health concerns. These latter may be attributable to a sense of loss or fear ( [[#Schwerdtle--2018|Schwerdtle et al., 2018]] ; [[#Torres--2017|Torres and Casey, 2017]] ) as well as due to the interruption of healthcare; occupational injuries; sleep deprivation; non-hygienic lodgings and insufficient sanitary facilities; heightened exposure to vector- and WBDs; vulnerability to psychosocial, sexual and reproductive issues; behavioural disorders; substance abuse; and violence ( [[#Farhat--2018|Farhat et al., 2018]] ; [[#Wickramage--2018|Wickramage et al., 2018]] ) ''(high confidence)'' . Linkages between climate migration and the spread of infectious disease are bidirectional; migrants may be exposed to diseases at the destination to which they have lower immunity than the host community; in other cases, migrants could introduce diseases to the receiving community ( [[#McMichael--2015|McMichael, 2015]] ). Thus, receiving areas may have to pay greater attention to building migrant sensitive health systems and services ( [[#Hunter--2017|Hunter and Simon, 2017]] ). The risk of migration leading to disease transmission is exacerbated by weak governance and lack of policy to support public health measures and access to medicines ( [[#Pottie--2015|Pottie et al., 2015]] ).

<div id="cross-chapter-box-migrate" class="h2-container box-container"></div>

'''Cross-Chapter Box MIGRATE | Climate-Related Migration'''
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Authors: David Wrathall (USA, Chapter 8), Robert McLeman (Canada, Chapter 7), Helen Adams (United Kingdom, Chapter 7), Ibidun Adelekan (Nigeria, Chapter 9), Elisabeth Gilmore (USA/Canada, Chapter 14), François Gemenne (Belgium, Chapter 8), Nathalie Hilmi (Monaco, Chapter 18), Ben Orlove (USA, Chapter 17), Ritwika Basu (India/United Kingdom, Chapter 18), Halvard Buhaug (Norway, Chapter 16), Edwin Castellanos (Guatemala, Chapter 12), David Dodman (United Kingdom, Chapter 6), Felix Kanungwe Kalaba (Zambia, Chapter 9), Rupa Mukerji (Switzerland/India, Chapter 18), Karishma Patel (USA, Chapter 1), Chandni Singh (India, Chapter 10), Philip Thornton (United Kingdom, Chapter 5), Christopher Trisos (South Africa, Chapter 9), Olivia Warrick (New Zealand, Chapter 15), Vishnu Pandey (Nepal, Chapter 4)

'''Key messages on migration in this report'''
Migration is a universal strategy that individuals and households undertake to improve well-being and livelihoods in response to economic uncertainty, political instability and environmental change ( ''high confidence'' ). Migration, displacement and immobility that occur in response to climate hazards are assessed in general in Chapter 7, with specific sectoral and regional dimensions of climate-related migration assessed in sectoral and regional Chapters 5 to 15 (Table MIGRATE.1 in Chapter 7) and involuntary immobility and displacement being identified as representative key risks in [[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] (Sections 16.2.3.8, 16.5.2.3.8). Since AR5 there has been a considerable expansion in research on climate–migration linkages, with five key messages from the present assessment report warranting emphasis.

''Climatic conditions, events and variability are important drivers of migration and displacement'' ( ''high confidence'' ) ''(Table MIGRATE.1 in Chapter 7), with migration responses to specific climate hazards being strongly influenced by economic, social, political and demographic processes'' ( ''high confidence'' ) ''(Sections 7.2.6, 8.2.1.3).'' Migration is among a wider set of possible adaptation alternatives and often emerges when other forms of adaptation are insufficient (Sections 5.5.1.1, 5.5.3.5, 7.2.6, 8.2.1.3, 9.7.2). Involuntary displacement occurs when adaptation alternatives are exhausted or not viable and reflects non-climatic factors that constrain adaptive capacity and create high levels of exposure and vulnerability ( ''high confidence'' ) (Cross-Chapter Box SLR in Chapter 3; Sections 4.3.7, 7.2.6; Box 8.1; [[IPCC:Wg2:Chapter:Chapter-10#10.3|Section 10.3]] ; Box 14.7). There is strong evidence that climatic disruptions to agricultural and other rural livelihoods can generate migration ( ''high confidence'' ) (Sections 5.5.4, 8.2.1.3, 9.8.3; Box 9.8).

''Specific climate events and conditions may cause migration to increase, decrease or flow in new directions (high confidence), and the more agency migrants have (i.e., the degree of voluntarity and freedom of movement), the greater the potential benefits for sending and receiving areas'' ( ''high agreement, medium evidence'' ) ''(Sections 5.5.3.5, 7.2.6, 8.2.1.3; Box 12.2)'' . Conversely, displacement or low-agency migration is associated with poor outcomes in terms of health, well-being and socioeconomic security for migrants and returns fewer benefits to sending or receiving communities ( ''high agreement, medium evidence'' ) (Sections 4.3.7, 4.5.7; Box 8.1; Sections 9.7.2, 10.3; Box 14.7).

''Most climate-related migration and displacement observed currently takes place within countries'' ( ''high confidence'' ) ''(Sections 4.3.7, 4.5.7, 5.12.2, 7.2.6).'' The climate hazards most commonly associated with displacement are tropical cyclones and flooding in most regions, with droughts being an important driver in sub-Saharan Africa, parts of south Asia and South America ( ''high confidence'' ) (Sections 7.2.6.1, 9.7.2, 10.4.6.3, 11.4.1, 12.5.8.4, 13.8.1.3, 14.4.7.3). Currently, observed international migration associated with climatic hazards is considerably smaller relative to internal migration and is most often observed as flowing between states that are contiguous and have labour-migration agreements and/or longstanding cultural ties ( ''high agreement, robust evidence'' ) (Sections 4.3.7, 4.5.7, 5.12.2, 7.2.6).

''In many regions, the frequency and/or severity of floods, extreme storms and droughts is projected to increase in coming decades, especially under high-emissions scenarios (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] (Ranasinghe et al. 2021)), raising future risk of displacement in the most exposed areas'' ( ''high confidence'' ) ''( [[#7.3.2.1|Section 7.3.2.1]] ).'' The additional impacts of climate change anticipated to generate future migration and displacement include mean sea level rise that increases flooding and saltwater contamination of soil and/or groundwater in low-lying coastal areas and small islands ( ''high confidence'' ) ( [[#7.3.2.1|Section 7.3.2.1]] ; Cross-Chapter Box SLR in Chapter 3) and more frequent extreme heat events that threaten the habitability of urban centres in the tropics and arid/semiarid regions ( ''medium confidence'' ), although the causal links between heat and migration are less clear ( [[#7.3.2.1|Section 7.3.2.1]] ).

''There is growing evidence about the future prospects of immobile populations: groups and individuals that are unable or unwilling to move away from areas highly exposed to climatic hazards'' ( ''high confidence'' ) ''(Sections 4.6.9, 7.2.6.2; Box 8.1; Box 10.2).'' Involuntarily immobile populations may be anticipated to require government interventions to continue living in exposed locations or to relocate elsewhere ( ''high agreement, medium evidence)'' (Box 8.1). Managed retreat and organised relocations of people from hazardous areas in recent years have proven to be politically and emotionally charged, socially disruptive and costly ( ''high confidence'' ) ( [[#7.4.5|Section 7.4.5.4]] ).

'''Climate-migration interactions and outcomes'''
Figure MIGRATE.1 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-7 Chapter 7] presents a simplified framework for understanding how migration and displacement may emerge from the interactions of climatic and non-climatic factors, based on the characteristic risk framework introduced in [[IPCC:Wg2:Chapter:Chapter-1|Chapter 1]] ( [[IPCC:Wg2:Chapter:Chapter-1#1.3|Section 1.3]] ). Voluntary migration can be used by households when adapting to climate hazards, while less voluntary forms of migration and displacement emerge when other forms of adaptation (referred to in Figure MIGRATE.1 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-7 Chapter 7] as ''in situ'' adaptation) are inadequate. Migration outcomes—expressed in Figure MIGRATE.1 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-7 Chapter 7] as changes in future risks to the well-being of migrants, sending communities and destination communities—are heavily influenced by the political, legal, cultural and socioeconomic conditions under which migration occurs. Groups and individuals that are involuntarily immobile may find that their exposure, vulnerability and risk increase over time. Table MIGRATE.1 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-7 Chapter 7] summarises the range of potential migration outcomes that may emerge from this dynamic and indicates specific sections in sectoral and regional chapters of the report that describe examples of each.

[[File:16246073a934a256ab78d557dfdba8c5 IPCC_AR6_WGII_Figure_7_Migrate_1.png]]

'''Figure MIGRATE.1 |''' '''General interactions between climatic and non-climatic processes, adaptation, potential migration outcomes and implications for future risk.''' Adapted from [[#McLeman--2021|McLeman et al. (2021)]] .

'''Table MIGRATE.1 |''' Typology of climate-related migration and examples in sectoral and regional chapters of AR6.

{| class="wikitable"
|-
! '''Type of climate-related migration'''

! '''Characteristics'''

! '''Recent or current examples'''

! '''Examples in the literature'''

! '''References in AR6 WGII'''

|-
| Temporary and/or seasonal migration

| Frequently used as a risk-reduction strategy by rural households in less-developed regions with highly seasonal precipitation; includes transhumance

| Pastoralists in sub-Saharan Africa; seasonal farm workers in south Asia; rural–urban labour migration in Central America

| [[#Afifi--2016|Afifi et al. (2016)]] ; [[#Call--2017|Call et al. (2017)]] ; Piguet et al. (2018); [[#Borderon--2019|Borderon et al. (2019)]] ; [[#Cattaneo--2019|Cattaneo et al. (2019)]] ; [[#Hoffmann--2020|Hoffmann et al. (2020)]] ; [[#Lopez-i-Gelats--2015|Lopez-i-Gelats et al. (2015)]] ; [[#Lu--2016|Lu et al. (2016)]] [[#Kaczan--2020|Kaczan and Orgill-Meyer (2020)]]

| Sections 5.5.1.1, 5.5.3.5; [[#7.2.6|Section 7.2.6]] ; [[IPCC:Wg2:Chapter:Chapter-8#8.2.1|Section 8.2.1.3]] ; [[IPCC:Wg2:Chapter:Chapter-9#9.8.3|Section 9.8.3]] ; Box 13.2

|-
| Indefinite or permanent migration

| Less common than temporary or seasonal migration, particularly when the whole household permanently relocates

| Numerous examples in all regions

| See reviews listed in cell above

| [[#7.2.6|Section 7.2.6]] ; [[IPCC:Wg2:Chapter:Chapter-8#8.2.1|Section 8.2.1.3]] ; Box 10.2

|-
| Internal migration

| Movements within state borders; most common form of climate-related migration

| Numerous examples in all regions

| See reviews listed in cell above

| [[IPCC:Wg2:Chapter:Chapter-4#4.3.7|Section 4.3.7]] ; Sections 5.5.4, 5.10.1.1; [[#7.2.6|Section 7.2.6]] ; Sections 9.7.2, 9.11; Box 9.8; Sections 10.3.3, 10.4.6.3, Box 10.2; [[IPCC:Wg2:Chapter:Chapter-11#11.4.1|Section 11.4.1]] ; [[IPCC:Wg2:Chapter:Chapter-12#12.5.8.4|Section 12.5.8.4]] ; [[IPCC:Wg2:Chapter:Chapter-13#13.8.1.3|Section 13.8.1.3]] ; [[IPCC:Wg2:Chapter:Chapter-14#14.4|Section 14.4.7.3]] ; [[IPCC:Wg2:Chapter:Chapter-15#15.3.4.6|Section 15.3.4.6]]

|-
| International migration

| Less common than internal migration; most often occurs between contiguous countries within the same region; often undertaken for purpose of earning wages to remit home

| Cross-border migration within south and Southeast Asia, sub-Saharan Africa

| See reviews listed in cell above; also [[#Veronis--2018|Veronis et al. (2018)]] ; [[#McLeman--2019|McLeman (2019)]] ; Cattaneo and G. (2016); [[#Missirian--2017|Missirian and Schlenker (2017)]] ; [[#Schutte--2021|Schutte et al. (2021)]]

| Sections 4.3.7, 4.5.7; [[IPCC:Wg2:Chapter:Chapter-5#5.12.2|Section 5.12.2]] ; [[#7.2.6|Section 7.2.6]]

|-
| Rural–urban or rural–rural

| Typically internal but may also flow between contiguous states; may be for temporary or indefinite periods; migration may be undertaken by an individual household member or the entire household; may be followed by remittances

| Drought migration in Mexico, east Africa and south Asia

| See reviews in the cell above; also [[#Adger--2015|Adger et al. (2015)]] ; Gautier et al. (2016); [[#Nawrotzki--2017|Nawrotzki et al. (2017)]] ; Wiederkehr et al. (2018); Robalino et al. (2015); [[#Borderon--2019|Borderon et al. (2019)]] ; [[#Murray-Tortarolo--2021|Murray-Tortarolo and Martnez (2021)]]

| [[IPCC:Wg2:Chapter:Chapter-5#5.13.4|Section 5.13.4]] ; [[#7.2.6|Section 7.2.6]] ; [[IPCC:Wg2:Chapter:Chapter-6#6.2.4.3|Section 6.2.4.3]] ; [[IPCC:Wg2:Chapter:Chapter-8#8.2.1|Section 8.2.1.3]] ; [[IPCC:Wg2:Chapter:Chapter-9#9.8.1|Section 9.8.1.2]] ; [[IPCC:Wg2:Chapter:Chapter-12#12.5.8.4|Section 12.5.8.4]] ; [[IPCC:Wg2:Chapter:Chapter-14#14.4|Section 14.4.7.1]]

|-
| Displacement

| Households are forced to leave homes for temporary or indefinite period; typically occurs as a result of extreme events and starts with seemingly temporary evacuation; risk is expected to rise in most regions due to sea level rise and changes in associated coastal hazards

| Tropical cyclones in the Caribbean, Southeast Asia and Bay of Bengal region

| [[#Islam--2017|Islam and Shamsuddoha (2017)]] ; [[#Desai--2021|Desai et al. (2021)]] ; see Internal Displacement Monitoring Centre annual reports for global statistics

| Cross-Chapter Box SLR in Chapter 3; [[IPCC:Wg2:Chapter:Chapter-4#4.3.7|Section 4.3.7]] ; 4.5.7; Cross-Chapter Box MOVING PLATE in Chapter 5; [[#7.2.6.1|Section 7.2.6.1]] ; Box 8.1; [[IPCC:Wg2:Chapter:Chapter-9#9.7.2|Section 9.7.2]] ; [[IPCC:Wg2:Chapter:Chapter-9#9.9.2|Section 9.9.2]] ; [[IPCC:Wg2:Chapter:Chapter-10#10.3|Section 10.3]] ; Box 14.7; Sections 15.3.4.6;

[https://www.ipcc.ch/chapter/7#CCP2.2 CCP2.2.2]

|-
| Planned and/or organised resettlement

| Initiated in areas where settlements become permanently uninhabitable; requires assistance from governments and/or institutions; government-sponsored sedentarisation of pastoral populations

| Fiji, Carteret Islands, Papua New Guinea, Gulf of Mexico coast and coastal Alaska, USA

| [[#Marino--2015|Marino and Lazrus (2015)]] ; Hino et al. (2017); [[#McNamara--2018|McNamara et al. (2018)]] ; [[#McMichael--2020|McMichael and Katonivualiku (2020)]] ; Tadgell et al. (2017); [[#Arnall--2014|Arnall (2014)]] ; [[#Wilmsen--2015|Wilmsen and Webber (2015)]]

| [[IPCC:Wg2:Chapter:Chapter-4#4.6.9|Section 4.6.9]] ; Sections 5.14.1, 5.14.2; [[#7.4.4.4|Section 7.4.4.4]] ; [[IPCC:Wg2:Chapter:Chapter-10#10.4|Section 10.4.6]] ; [[IPCC:Wg2:Chapter:Chapter-15#15.5.3|Section 15.5.3]] ; [https://www.ipcc.ch/chapter/7#CCP2.2 CCP2.2.2] ; [https://www.ipcc.ch/chapter/7#CCP6.3.2 CCP6.3.2]

|-
| Immobility

| Adverse weather or climatic conditions warrant moving, but households are unable to relocate because of lack of resources or choose to remain because of strong social, economic or cultural attachments to place

| Examples in most regions

| [[#Adams--2016|Adams (2016)]] ; [[#Zickgraf--2018|Zickgraf (2018)]] ; [[#Nawrotzki--2018|Nawrotzki and DeWaard (2018)]] ; [[#Farbotko--2020|Farbotko et al. (2020)]]

| [[IPCC:Wg2:Chapter:Chapter-4#4.6.9|Section 4.6.9]] ; [[#7.2.6.2|Section 7.2.6.2]] ; Box 8.1; Box 10.2

|}

'''Policy implications'''
''Future migration and displacement patterns in a changing climate will depend not only on the physical impacts of climate change, but also on future policies and planning at all scales of governance'' ( ''high confidence'' ) ''(4.6.9, 5.14.1, 5.14.1.2, 7.3.2, 7.4.4, 8.2.1.3; Box 8.1; [https://www.ipcc.ch/chapter/7#CCP6.3.2 CCP6.3.2] ).'' Policy interventions can remove barriers to and expand the alternatives for safe, orderly and regular migration that allows vulnerable people to adapt to climate change ( ''high confidence'' ) ( [[#7.2.6|Section 7.2.6]] ). With adequate policy support, migration in the context of climate change can result in synergies for both adaptation and development (Sections 5.12.2, 7.4.4, 8.2.1.3). Migration governance at local, national and international levels will influence the outcomes of climate-related migration for the migrants themselves as well as for receiving and origin communities (Sections 5.13.4, 7.4.4, 8.2.1.3). At the international level, a number of relevant policy initiatives and agreements, including Global Compacts for Safe, Orderly and Regular Migration and for the protection of Refugees; the Warsaw International Mechanism of the UNFCCC; the Sustainable Development Goals; the Sendai Framework for Disaster Risk Reduction; and the Platform on Disaster Displacement, have already been established, merit continued pursuit and provide potential migration governance pathways ( [[#7.4.4|Section 7.4.4]] ). Policy and planning decisions at regional, national and local scales that relate to housing, infrastructure, water provisioning, schools and healthcare are relevant for successful integration of migrants into receiving communities (Sections 5.5.4, 5.10.1.1, 5.12.2, 9.8.3). Policies and practices on movements of people across international borders are also relevant to climate-related migration, with restrictions on movement having implications for the adaptive capacity of communities exposed to climate hazards ( [[#7.4.4.2|Section 7.4.4.2]] ; Box 8.1). Perceptions of migrants and the framing of policy discussions in receiving communities and nations are important determinants of the future success of migration as an adaptive response to climate change ( [[#7.4.4.3|Section 7.4.4.3]] ) ( ''high agreement, medium evidence'' ).

<div id="_idContainer033" class="Box_Header-continued"></div>

Cross-Chapter Box MIGRATE

''Reducing the future risk of large-scale population displacements, including those requiring active humanitarian interventions and organised relocations of people, requires the international community to meet the requirements of the Paris Agreement and take further action to control future warming'' ( ''high confidence'' ) ''(Cross-Chapter Box SLR in Chapter 3; [[#7.3.1|Section 7.3.1]] ; Box 8.1).'' Current emissions pathways lead to scenarios for the period between 2050 and 2100 in which hundreds of millions of people will be at risk of displacement due to rising sea levels, floods, tropical cyclones, droughts, extreme heat, wildfires and other hazards, with land degradation exacerbating these risks in many regions ( [[#7.3.2|Section 7.3.2]] ; IPCC 2019b; Cross-Chapter Box SLR in Chapter 3). At high levels of warming, tipping points may exist, particularly related to sea level rise, that, if crossed, would further increase the global population potentially at risk of displacement (Ranasinghe et al. 2021). Populations in low-income countries and small-island states that have historically had low greenhouse gas (GHG) emissions are at particular risk of involuntary migration and displacement due to climate change, reinforcing the urgency for industrialised countries to continue lowering GHG emissions, to support adaptive capacity-building initiatives under the UNFCCC and to meet objectives expressed in the Global Compacts regarding safe, orderly and regular migration and the support and accommodation of displaced people (Sections 4.3.7, 4.5.7, 5.12.2, 7.4.5.5, 8.4.2; Box 8.1; Cross-Chapter Box SLR in Chapter 3).

<div id="box-7.4" class="h2-container box-container"></div>

'''Box 7.4 | Gender Dimensions of Climate-Related Migration'''
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Migration decision-making and outcomes—in both general terms and in response to climatic risks—are strongly mediated by gender, social context, power dynamics and human capital ( [[#Bhagat--2017|Bhagat, 2017]] ; [[#Singh--2020|Singh and Basu, 2020]] ; [[#Rao--2019a|Rao et al., 2019a]] ; [[#Ravera--2016|Ravera et al., 2016]] ). Women tend to suffer disproportionately from the negative impacts of extreme climate events for reasons ranging from caregiving responsibilities to lack of control over household resources to cultural norms for attire ( [[#Belay--2017|Belay et al., 2017]] ; [[#Jost--2016|Jost et al., 2016]] ). In many cultures, migrants are most often able-bodied, young men ( [[#Call--2017|Call et al., 2017]] ; [[#Heaney--2016|Heaney and Winter, 2016]] ). Women wait longer to migrate because of higher social costs and risks ( [[#Evertsen--2019|Evertsen and Van Der Geest, 2019]] ) and barriers such as social structures, cultural practices, lack of education and reproductive roles ( [[#Belay--2017|Belay et al., 2017]] ; [[#Afriyie--2018|Afriyie et al., 2018]] ; [[#Evertsen--2019|Evertsen and Van Der Geest, 2019]] ).

Research critiques the tendency to portray women as victims of climate hazards rather than recognising differences between women and the potential for women to use their agency and informal networks to negotiate their situations ( [[#Eriksen--2015|Eriksen et al., 2015]] ; [[#Ngigi--2017|Ngigi et al., 2017]] ; [[#Pollard--2015|Pollard et al., 2015]] ; [[#Rao--2019b|Rao et al., 2019b]] ; [[#Ravera--2016|Ravera et al., 2016]] ). Migration can change household composition and structure, which in turn affects the adaptive capacity and choices of those who do not move ( [[#Rao--2019a|Rao et al., 2019a]] ; [[#Rao--2019b|Rao et al., 2019b]] ; [[#Singh--2019|Singh, 2019]] ). For example, when only male household members move, the remaining members of the now female-headed household must take on greater workloads ( [[#Goodrich--2019|Goodrich et al., 2019]] ; [[#Rao--2019b|Rao et al., 2019b]] ; [[#Rigg--2015|Rigg and Salamanca, 2015]] ), leading to increased workload and greater vulnerability for those left behind ( [[#Arora--2017|Arora et al., 2017]] ; [[#Bhagat--2017|Bhagat, 2017]] ; [[#Flatø--2017|Flatø et al., 2017]] ; [[#Lawson--2019|Lawson et al., 2019]] ). It can, however, also increase women’s economic freedom and decision-making capacity, enhance their agency ( [[#Djoudi--2016|Djoudi et al., 2016]] ; [[#Rao--2019|Rao, 2019]] ) and alter the gendered division of paid work, care and intra-household relations ( [[#Rigg--2018|Rigg et al., 2018]] ; [[#Singh--2020|Singh and Basu, 2020]] ), a process that may reduce household vulnerability to extreme climate events ( [[#Banerjee--2019b|Banerjee et al., 2019b]] ).

<div id="7.2.7" class="h2-container"></div>

<span id="observed-impacts-of-climate-on-conflict"></span>
=== 7.2.7 Observed Impacts of Climate on Conflict ===

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<div id="7.2.7.1" class="h3-container"></div>

<span id="introduction-1"></span>
==== 7.2.7.1 Introduction ====

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''In AR5, conflict was addressed in WGII [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] on human security. The chapter concluded that some of the factors that increase the risk of violent conflict within states are sensitive to climate change'' ( ''medium evidence, medium agreement'' ) '', that people living in places affected by violent conflict are particularly vulnerable to climate change'' ( ''medium confidence'' ) ''and that climate change will lead to new challenges to states and will increasingly shape both conditions of security and national security policies'' ( ''medium evidence, medium agreement'' ) ''.'' AR5 characterised a major debate within the field as: authors supporting an association between climate anomalies and conflict that can be extrapolated into the future (e.g., Hsiang et al. (2013); [[#Hsiang--2014|Hsiang and Marshall (2014)]] ; Burke et al. (2015a)) and authors arguing that these associations are not universal and break down when contextual, scale and political factors are introduced (e.g., [[#Buhaug--2014|Buhaug et al. (2014)]] ; Buhaug (2016)).

''Consistent with AR5 findings, there continues to be little observed evidence that climatic variability or change cause violent inter-state conflict. In intra-state settings, climate change has been associated with the onset of conflict, civil unrest or riots in urban settings'' ( ''high agreement, medium evidence'' ) ( [[#Ide--2020|Ide (2020)]] , ''and changes in the duration and severity of existing violent conflicts'' ( [[#Koubi--2019|Koubi, 2019]] ). Climate change is conceptualised as one of many factors that interact to raise tensions ( [[#Boas--2016|Boas and Rothe, 2016]] ) through diverse causal mechanisms ( [[#Mach--2019|Mach et al., 2019]] ; [[#Ide--2020|Ide et al., 2020]] ) and as part of the peace-vulnerability-development nexus ( [[#Barnett--2019|Barnett, 2019]] ; [[#Abrahams--2020|Abrahams, 2020]] ; [[#Buhaug--2021|Buhaug and von Uexkull, 2021]] ). New areas of the literature assessed in this report include the security implications of responses to climate change, the gendered dynamics of conflict and exposure to violence under climate change and civil unrest in urban settings. The impact of violent conflict on vulnerability is not addressed in this chapter but does arise in other chapters (Sections 8.3.2.3, 17.2.2.2). Other chapters address non-violent conflict over changing availability and distribution of resources, for example, competing land uses and fish stocks migrating to different territories (Sections 5.8.2.3; 5.8.3, 5.9.3, 5.13, 9.8.1.1, 9.8.5.1). A commonly used definition of armed conflict is conflicts involving greater than 25 battle-related deaths in a year; this number represents the Uppsala Conflict Data Program threshold for inclusion in their database, a core resource in this field.

''Climatic conditions have affected armed conflict within countries, but their influence has been small compared to socioeconomic, political and cultural factors ( [[#Mach--2019|Mach et al., 2019]] )'' ( ''high agreement, medium evidence'' ) ''.'' Inter-group inequality, and consequent relative deprivation can lead to conflict and the negative impacts of climate change lower the opportunity cost of involvement in conflict ( [[#Buhaug--2020|Buhaug et al., 2020]] ; [[#Vestby--2019|Vestby, 2019]] ). Potential pathways linking climate and conflict include direct impacts on physiology from heat or resource scarcity; indirect impacts of climatic variability on economic output, agricultural incomes, higher food prices and increasing migration flows; and the unintended effects of climate mitigation and adaptation policies ( [[#Koubi--2019|Koubi, 2019]] ; [[#Busby--2018|Busby, 2018]] ; [[#Sawas--2018|Sawas et al., 2018]] ). Relative deprivation, political exclusion and ethnic fractionalisation and ethnic grievances are other key variables ( [[#Schleussner--2016|Schleussner et al., 2016]] ; [[#Theisen--2017|Theisen, 2017]] ). Research shows that factors such as land tenure and competing land uses interacting with market-driven pressures and existing ethnic divisions produce conflict over land resources rather than a scarcity of natural resources caused by climate impacts such as drought ( ''high agreement, medium evidence'' ) ( [[#Theisen--2017|Theisen, 2017]] ; [[#Balestri--2017|Balestri and Maggioni, 2017]] ; [[#Kuusaana--2015|Kuusaana and Bukari, 2015]] ; Box 8.3).

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<span id="impacts-of-climate-change-and-violent-conflict"></span>
==== 7.2.7.2 Impacts of Climate Change and Violent Conflict ====

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''Positive temperature anomalies and average increases in temperature over time have been associated with collective violent conflict in certain settings'' ( ''medium agreement, low evidence'' ) ''.'' Helman and Zaitchik (2020) find statistical associations between temperature and violent conflict in Africa and the Middle East that are stronger in warmer places and identify seasonal temperature effects on violence. However, they are unable to detect the impact of regional temperature increases on violence. For Africa, Van Weezel (2019) found associations between average increases in temperature and conflict risk. Caruso et al. (2016) found an association between rises in minimum temperature and violence through the impact of temperature on rice yields (Box 9.4). However, the associations between temperature and violence are weak compared to those with political and social factors (e.g., Owain and Maslin (2018)) and research focuses on areas where conflict is already present and, as such, is sensitive to selection bias ( [[#Adams--2018|Adams et al., 2018]] ). There is a body of literature that finds statistical associations between temperature anomalies and inter-personal violence, crime and aggression in the Global North, predominantly in the USA (e.g., [[#Ranson--2014|Ranson (2014)]] ; [[#Mares--2019|Mares and Moffett (2019)]] ; [[#Tiihonen--2017|Tiihonen et al. (2017)]] ; [[#Parks--2020|Parks et al. (2020)]] ; [[IPCC:Wg2:Chapter:Chapter-14#14.4|Section 14.4.8]] ). However, authors have cautioned against extrapolating seasonal associations into long-term trends and against focusing on individual crimes rather than wider social injustices associated with climate change and its impacts ( [[#Lynch--2020|Lynch et al., 2020]] ).

''Variation in availability of water has been associated with international political tension and intra-national collective violence'' ( ''low agreement, medium evidence'' ) ''.'' Drought conditions have been associated with violence due to impacts on income from agriculture and water and food security, with studies focusing predominantly on sub-Saharan Africa and the Middle East ( [[#Ide--2015|Ide and Frohlich, 2015]] ; [[#De%20Juan--2015|De Juan, 2015]] ; [[#Von%20Uexkull--2016|Von Uexkull et al., 2016]] ; [[#Waha--2017|Waha et al., 2017]] ; [[#Abbott--2017|Abbott et al., 2017]] ; [[#D’Odorico--2018|D’Odorico et al., 2018]] ). A small set of published studies has argued inconclusively over the role of drought in causing the Syrian civil war ( [[#Gleick--2014|Gleick, 2014]] ; [[#Kelley--2015|Kelley et al., 2015]] ; [[#Selby--2017|Selby et al., 2017]] ; 16.2.3.9). In general, research stresses the underlying economic, social and political drivers of conflict. For example, research on conflict in the Lake Chad region has demonstrated that the lake drying was only one of many factors including lack of development and infrastructure ( [[#Okpara--2016|Okpara et al., 2016]] ; [[#Nagarajan--2018|Nagarajan et al., 2018]] ; [[#Tayimlong--2020|Tayimlong, 2020]] ). Fewer studies examine the relationship between flooding (excess water) and violence and often rely on migration as the causal factor (see below). However, some studies have shown an association between flooding and civil unrest ( [[#Ide--2020|Ide et al., 2020]] ; [[IPCC:Wg2:Chapter:Chapter-4#4.3.6|Section 4.3.6]] ; [[IPCC:Wg2:Chapter:Chapter-12#12.5.3|Section 12.5.3]] ; Box 9.4).

''Extreme weather events can be associated with increased conflict risk'' ( ''low agreement, medium evidence'' ) ''.'' There is the potential for extreme weather events and disasters to cause political instability and increase the risk of violent conflict, although not conclusively ( [[#Brzoska--2018|Brzoska, 2018]] ). Post-disaster settings can be used to intensify state repression ( [[#Wood--2016|Wood and Wright, 2016]] ) and to alter insurgent groups’ behaviour ( [[#Walch--2018|Walch, 2018]] ). Different stakeholders use disasters to establish new narratives and alter public opinion ( [[#Venugopal--2017|Venugopal and Yasir, 2017]] ). Some research has demonstrated how post-disaster activities have had positive impacts on the social contract between people and the state, reducing the risk of conflict by strengthening relations between government and citizens and strengthening the citizenship of marginalised communities (Siddiqi, 2018; [[#Pelling--2010|Pelling and Dill, 2010]] ; [[#Siddiqi--2019|Siddiqi, 2019]] ). However, post-disaster and disaster risk-related activities themselves have limited capacity to support diplomatic efforts to build peace ( [[#Kelman--2018|Kelman et al., 2018]] ).

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<span id="causal-pathways-between-climate-change-impacts-and-violent-conflict"></span>
==== 7.2.7.3 Causal Pathways Between Climate Change Impacts and Violent Conflict ====

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''Increases in food prices due to reduced agricultural production and global food price shocks are associated with conflict risk and represent a key pathway linking climate variability and conflict'' ( ''medium confidence'' ) ''.'' Increases in food prices are associated with civil unrest in urban areas among populations unable to afford or produce their own food and in rural populations due to changes in availability of agricultural employment with shifting commodity prices ( [[#Martin-Shields--2019|Martin-Shields and Stojetz, 2019]] ). Under such conditions, locally specific grievances, hunger and social inequalities can initiate or exacerbate conflicts. Food price volatility in general is not associated with violence, but sudden food price hikes have been linked to civil unrest in some circumstances ( [[#Bellemare--2015|Bellemare, 2015]] ; [[#McGuirk--2020|McGuirk and Burke, 2020]] ; [[#Winne--2019|Winne and Peersman, 2019]] ). In urban settings in Kenya, Koren et al. (2021) found an association between food and water insecurity that is mutually reinforcing and associated with social unrest (although insecurity in either food or water on its own was not). Analysing the global food riots in 2007/2008 and 2011, [[#Heslin--2021|Heslin (2021)]] stresses the role of local politics and pre-existing grievances in determining whether people mobilise around food insecurity (Chapter 5).

''Climate-related internal migration has been associated with the experience of violence by migrants, the prolongation of conflicts in migrant receiving areas and civil unrest in urban areas'' ( ''medium agreement, low evidence'' ). Research points to the potential for conflict to serve as an intervening factor between climate and migration. However, the nature of the relationship is diverse and context specific. For example, displaced people and migrants may be associated with heightened social tensions in receiving areas through mechanisms such as ecological degradation, reduced access to services and a disturbed demographic balance in the host area ( [[#Rüegger--2020|Rüegger and Bohnet, 2020]] ). Ghimire et al. (2015) observed that an influx of flood-displaced people prolonged conflict by causing a lack of access to services for some of the host population and feelings of grievance. Further, migration from drought-stricken areas to local urban centres has been used to suggest a climate trigger for the Syrian conflict (e.g., [[#Ash--2020|Ash and Obradovich (2020)]] ). However, this link has been strongly contested by research that contextualises the drought in wider political economic approaches and existing migration patterns ( [[#De%20Châtel--2014|De Châtel, 2014]] ; [[#Fröhlich--2016|Fröhlich, 2016]] ; [[#Selby--2019|Selby, 2019]] ; 16.2.3.9).

''There is some evidence of an association between climate-related rural-to-urban migration and the risk of civil unrest'' ( ''medium agreement, low evidence'' ) ''.'' [[#Petrova--2021|Petrova (2021)]] found that while migration in general was associated with increased protests in urban receiving areas, the relationship did not hold for hazard-related migration. In other settings, the association of civil unrest with in-migration was found to depend on the political alignment of the host state with the capital ( [[#Bhavnani--2015|Bhavnani and Lacina, 2015]] ), previous experience of extreme climate hazards ( [[#Koubi--2021|Koubi et al., 2021]] ) and previous experience of violence among migrants ( [[#Linke--2018|Linke et al., 2018]] ). Climate-related migrants have reported higher levels of perception and experience of violence in their destination ( [[#Linke--2018|Linke et al., 2018]] ; [[#Koubi--2018|Koubi et al., 2018]] ). There has been no association established between international migration and conflict. The literature highlights how unjust racial logics may generate spurious links between climate migration and security ( [[#Fröhlich--2016|Fröhlich, 2016]] ; [[#Telford--2018|Telford, 2018]] ).

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<span id="gendered-dimensions-of-climate-related-conflict"></span>
==== 7.2.7.4 Gendered Dimensions of Climate-Related Conflict ====

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''Structural inequalities play out at an individual level to create gendered experiences of violence'' ( ''high agreement, medium evidence'' ) ''.'' Violent conflict is experienced differently by men and women because of gender norms that already exist in society and shape vulnerabilities. For example, conflict deepens gendered vulnerabilities to climate change related to unequal access to land and livelihood opportunities ( [[#Chandra--2017|Chandra et al., 2017]] ). Motivations for inter-group violence may be influenced by constructions of masculinity, for example the responsibility to secure their family’s survival or pay dowries ( [[#Myrttinen--2017|Myrttinen et al., 2017]] ), and gendered roles may incentivise young men to protest or to join non-state armed groups during periods of adverse climate ( [[#Myrttinen--2015|Myrttinen et al., 2015]] ; [[#Myrttinen--2017|Myrttinen et al., 2017]] ; [[#Anwar--2019|Anwar et al., 2019]] ; [[#Hendrix--2015|Hendrix and Haggard, 2015]] ; [[#Koren--2017|Koren and Bagozzi, 2017]] ). Research has found a positive correlation between crop failures and suicides by male farmers who could not adapt their livelihoods to rising temperatures (Bryant and Garnham 2015; [[#Kennedy--2014|Kennedy and King, 2014]] ; [[#Carleton--2017|Carleton, 2017]] ).

''Extreme weather and climate impacts are associated with increased violence against women, girls and vulnerable groups'' ( ''high agreement, medium evidence'' ) ''.'' During and after extreme weather events, women, girls and LGBTQI people are at increased risk of domestic violence, harassment, sexual violence and trafficking ( [[#Le%20Masson--2019|Le Masson et al., 2019]] ; [[#Nguyen--2019|Nguyen, 2019]] ; [[#Myrttinen--2015|Myrttinen et al., 2015]] ; [[#Chindarkar--2012|Chindarkar, 2012]] ). For example, early marriage is used as a coping strategy for managing the effects of extreme weather events ( [[#Ahmed--2019|Ahmed et al., 2019]] ) and women are exposed to increase risk of harassment and sexual assault as scarcity and gender-based roles cause them to walk longer distances to fetch water and fuel ( [[#Le%20Masson--2019|Le Masson et al., 2019]] ). Within the household, violent backlash or heightened tensions may arise from changing gender norms as men migrate to find work in post-disaster settings ( [[#Stork--2015|Stork et al., 2015]] ) and men’s use of negative coping mechanisms, such as alcoholism, when unable to meet norms of providing for the household ( [[#Anwar--2019|Anwar et al., 2019]] ; [[#Stork--2015|Stork et al., 2015]] ). Rates of intimate partner violence have been found to increase with higher temperatures ( [[#Sanz-Barbero--2018|Sanz-Barbero et al., 2018]] ).

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<span id="observed-impacts-on-non-violent-conflict-and-geopolitics"></span>
==== 7.2.7.5 Observed Impacts on Non-violent Conflict and Geopolitics ====

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''Climate adaptation and mitigation projects implemented without taking local interests and dynamics into account have the potential to cause conflict'' ( ''high agreement, medium evidence'' ) ''.'' Reforestation or forest management programmes driven by reducing emissions through deforestation, land zoning and managed retreat due to sea level rise have been identified as having the potential to cause friction and conflict within and between groups and communities ( [[#de%20la%20Vega-Leinert--2018|de la Vega-Leinert et al., 2018]] ; [[#Froese--2019|Froese and Schilling, 2019]] ). Conflict may arise when there is resistance to a proposed project, where interventions favour one group over another, or when projects undermine livelihoods or displace populations (e.g., [[#Nightingale--2017|Nightingale (2017)]] ; Sovacool et al. (2015); [[#Sovacool--2018|Sovacool (2018)]] ; Corbera (2017); Hunsberger (2018); Sections 4.6.8, 5.13.4, 14.4.7.3). In addition to conflict generated by the poor implementation of land-based climate mitigation and adaptation projects, [[#Gilmore--2021|Gilmore and Buhaug (2021)]] highlight the links between climate policy and conflict through the potential effects of unequal distribution of economic burdens and fossil fuel markets on economic growth. There is a small literature that draws attention to the potential security of nuclear proliferation, if nuclear energy is increasingly employed as a low-carbon energy source (e.g., Parthemore et al. (2018); Bunn, (2019)).

''Economic and social changes due to changes in sea ice extent in the Arctic are anticipated to be managed as part of existing governance structures'' ( ''high agreement, medium evidence'' ) ''.'' The opening-up of the Arctic and associated geopolitical manoeuvring for access to shipping routes and sub-sea hydrocarbons is often highlighted as a potential source of climate conflict (e.g., Koivurova (2009); [[#Åtland--2013|Åtland (2013)]] ; [[#Tamnes--2014|Tamnes and Offerdal (2014)]] ). Research assessed in AR5 focused on the potential for resource wars and Arctic land grabs. However, research since AR5 is less sensationalist in its approach to Arctic security, focusing instead on the practicalities of polycentric Arctic governance under climate change, the economic impacts of climate change, protecting the human security of Arctic populations whose autonomy is at risk ( [[#Heininen--2020|Heininen and Exner-Pirot, 2020]] ), understanding how different regions (e.g., the EU) are positioning themselves more prominently in the Arctic space (Raspotnik and Østhagen, 2019) and Arctic Indigenous Peoples’ understanding of security ( [[#Hossain--2016|Hossain, 2016]] ; Chapter 3; Chapter 14; CCP6).

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