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

=== 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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==== 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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==== 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

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| 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).

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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)]] .

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