Editing IPCC:AR6/SR15/Chapter-3 (section)

== Cross-Chapter Box 6: Food Security ==

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<span id="lead-authors-1"></span>
====== Lead Authors ======

* Ove Hoegh-Guldberg (Australia)
* Sharina Abdul Halim (Malaysia)
* Marco Bindi (Italy)
* Marcos Buckeridge (Brazil)
* Arona Diedhiou (Senegal)
* Kristie L. Ebi (United States)
* Debora Ley (Guatemala, Mexico)
* Diana Liverman (United States)
* Chandni Singh (India)
* Rachel Warren (United Kingdom)
* Guangsheng Zhou (China)

<span id="contributing-authors-1"></span>
====== Contributing Authors ======

* Lorenzo Brilli (Italy)

<div id="section-3-4-6-3-block-1"></div>

Climate change influences food and nutritional security through its effects on food availability, quality, access and distribution (Paterson and Lima, 2010; Thornton et al., 2014; FAO, 2016) <sup>[[#fn:r940|940]]</sup> . More than 815 million people were undernourished in 2016, and 11% of the world’s population has experienced recent decreases in food security, with higher percentages in Africa (20%), southern Asia (14.4%) and the Caribbean (17.7%) (FAO et al., 2017) <sup>[[#fn:r941|941]]</sup> . Overall, food security is expected to be reduced at 2°C of global warming compared to 1.5°C, owing to projected impacts of climate change and extreme weather on yields, crop nutrient content, livestock, fisheries and aquaculture and land use (cover type and management) (Sections 3.4.3.6, 3.4.4.12 and 3.4.6), ( ''high confidence'' ). The effects of climate change on crop yield, cultivation area, presence of pests, food price and supplies are projected to have major implications for sustainable development, poverty eradication, inequality and the ability of the international community to meet the United Nations sustainable development goals (SDGs; Cross-Chapter Box 4 in Chapter 1).

Goal 2 of the SDGs is to end hunger, achieve food security, improve nutrition and promote sustainable agriculture by 2030. This goal builds on the first millennium development goal (MDG 1); which focused on eradicating extreme poverty and hunger, through efforts that reduced the proportion of undernourished people in low-and middle-income countries from 23.3% in 1990 to 12.9% in 2015. Climate change threatens the capacity to achieve SDG 2 and could reverse the progress made already. Food security and agriculture are also critical to other aspects of sustainable development, including poverty eradication (SDG 1), health and well-being (SDG 3), clean water (SDG 6), decent work (SDG 8), and the protection of ecosystems on land (SDG 14) and in water (SDG 15) (UN, 2015, 2017; Pérez-Escamilla, 2017) <sup>[[#fn:r942|942]]</sup> .

Increasing global temperature poses large risks to food security globally and regionally, especially in low-latitude areas ( ''medium confidence'' ) (Cheung et al., 2010; Rosenzweig et al., 2013; Porter et al., 2014; Rosenzweig and Hillel, 2015; Lam et al., 2016) <sup>[[#fn:r943|943]]</sup> , with warming of 2°C projected to result in a greater reduction in global crop yields and global nutrition than warming of 1.5°C ( ''high confidence'' ) (Section 3.4.6), owing to the combined effects of changes in temperature, precipitation and extreme weather events, as well as increasing CO <sub>2</sub> concentrations. Climate change can exacerbate malnutrition by reducing nutrient availability and the quality of food products ( ''medium confidence'' ) (Cramer et al., 2014; Zhu et al., 2018) <sup>[[#fn:r944|944]]</sup> . Generally, vulnerability to decreases in water and food availability is projected to be reduced at 1.5°C versus 2°C (Cheung et al., 2016a; Betts et al., 2018) <sup>[[#fn:r945|945]]</sup> , especially in regions such as the African Sahel, the Mediterranean, central Europe, the Amazon, and western and southern Africa ( ''medium confidence'' ) (Sultan and Gaetani, 2016; Lehner et al., 2017; Betts et al., 2018; Byers et al., 2018; Rosenzweig et al., 2018) <sup>[[#fn:r946|946]]</sup> .

Rosenzweig et al. (2018) <sup>[[#fn:r947|947]]</sup> and Ruane et al. (2018) <sup>[[#fn:r948|948]]</sup> reported that the higher CO <sub>2</sub> concentrations associated with 2°C as compared to those at 1.5°C of global warming are projected to drive positive effects in some regions. Production can also benefit from warming in higher latitudes, with more fertile soils, favouring crops, and grassland production, in contrast to the situation at low latitudes (Section 3.4.6), and similar benefits could arise for high-latitude fisheries production ( ''high confidence'' ) (Section 3.4.6.3). Studies exploring regional climate change risks on crop production are strongly influenced by the use of different regional climate change projections and by the assumed strength of CO <sub>2</sub> fertilization effects (Section 3.6), which are uncertain. For C3 crops, theoretically advantageous CO <sub>2</sub> fertilization effects may not be realized in the field; further, they are often accompanied by losses in protein and nutrient content of crops (Section 3.6), and hence these projected benefits may not be realized. In addition, some micronutrients such as iron and zinc will accumulate less and be less available in food (Myers et al., 2014) <sup>[[#fn:r949|949]]</sup> . Together, the impacts on protein availability may bring as many as 150 million people into protein deficiency by 2050 (Medek et al., 2017) <sup>[[#fn:r950|950]]</sup> . However, short-term benefits could arise for high-latitude fisheries production as waters warm, sea ice contracts and primary productivity increases under climate change ( ''high confidence'' ) (Section 3.4.6.3; Cheung et al., 2010; Hollowed and Sundby, 2014; Lam et al., 2016; Sundby et al., 2016; Weatherdon et al., 2016) <sup>[[#fn:r951|951]]</sup> .

Factors affecting the projections of food security include variability in regional climate projections, climate change mitigation (where land use is involved; see Section 3.6 and Cross-Chapter Box 7 in this chapter) and biological responses ( ''medium confidence'' ) (Section 3.4.6.1; McGrath and Lobell, 2013; Elliott et al., 2014; Pörtner et al., 2014; Durand et al., 2018) <sup>[[#fn:r952|952]]</sup> , extreme events such as droughts and floods ( ''high confidence'' ) (Sections 3.4.6.1, 3.4.6.2; Rosenzweig et al., 2014; Wei et al., 2017) <sup>[[#fn:r953|953]]</sup> , financial volatility (Kannan et al., 2000; Ghosh, 2010; Naylor and Falcon, 2010; HLPE, 2011) <sup>[[#fn:r954|954]]</sup> , and the distributions of pests and disease (Jiao et al., 2014; van Bruggen et al., 2015) <sup>[[#fn:r955|955]]</sup> . Changes in temperature and precipitation are projected to increase global food prices by 3–84% by 2050 (IPCC, 2013) <sup>[[#fn:r956|956]]</sup> . Differences in price impacts of climate change are accompanied by differences in land-use change (Nelson et al., 2014b) <sup>[[#fn:r957|957]]</sup> , energy policies and food trade (Mueller et al., 2011; Wright, 2011; Roberts and Schlenker, 2013) <sup>[[#fn:r958|958]]</sup> . Fisheries and aquatic production systems (aquaculture) face similar challenges to those of crop and livestock sectors (Section 3.4.6.3; Asiedu et al., 2017a, b; Utete et al., 2018) <sup>[[#fn:r959|959]]</sup> . Human influences on food security include demography, patterns of food waste, diet shifts, incomes and prices, storage, health status, trade patterns, conflict, and access to land and governmental or other assistance (Chapters 4 and 5). Across all these systems, the efficiency of adaptation strategies is uncertain because it is strongly linked with future economic and trade environments and their response to changing food availability ( ''medium confidence'' ) (Lobell et al., 2011; von Lampe et al., 2014; d’Amour et al., 2016 <sup>[[#fn:r960|960]]</sup> ; Wei et al., 2017) <sup>[[#fn:r961|961]]</sup> .

Climate change impacts on food security can be reduced through adaptation (Hasegawa et al., 2014) <sup>[[#fn:r962|962]]</sup> . While climate change is projected to decrease agricultural yield, the consequences could be reduced substantially at 1.5°C versus 2°C with appropriate investment ( ''high confidence'' ) (Neumann et al., 2010; Muller, 2011; Roudier et al., 2011) <sup>[[#fn:r963|963]]</sup> , awareness-raising to help inform farmers of new technologies for maintaining yield, and strong adaptation strategies and policies that develop sustainable agricultural choices (Sections 4.3.2 and 4.5.3). In this regard, initiatives such as ‘climate-smart’ food production and distribution systems may assist via technologies and adaptation strategies for food systems (Lipper et al., 2014; Martinez-Baron et al., 2018; Whitfield et al., 2018) <sup>[[#fn:r964|964]]</sup> , as well as helping meet mitigation goals (Harvey et al., 2014) <sup>[[#fn:r965|965]]</sup> .

K.R. Smith et al. (2014) <sup>[[#fn:r966|966]]</sup> concluded that climate change will exacerbate current levels of childhood undernutrition and stunting through reduced food availability. As well, climate change can drive undernutrition-related childhood mortality, and increase disability-adjusted life years lost, with the largest risks in Asia and Africa (Supplementary Material 3.SM, Table 3.SM.12; Ishida et al., 2014; Hasegawa et al., 2016; Springmann et al., 2016 <sup>[[#fn:r967|967]]</sup> ). Studies comparing the health risks associated with reduced food security at 1.5°C and 2°C concluded that risks would be higher and the globally undernourished population larger at 2°C (Hales et al., 2014; Ishida et al., 2014; Hasegawa et al., 2016) <sup>[[#fn:r968|968]]</sup> . Climate change impacts on dietary and weight-related risk factors are projected to increase mortality, owing to global reductions in food availability and consumption of fruit, vegetables and red meat (Springmann et al., 2016) <sup>[[#fn:r969|969]]</sup> . Further, temperature increases are projected to reduce the protein and micronutrient content of major cereal crops, which is expected to further affect food and nutritional security (Myers et al., 2017; Zhu et al., 2018) <sup>[[#fn:r970|970]]</sup> .

Strategies for improving food security often do so in complex settings such as the Mekong River basin in Southeast Asia. The Mekong is a major food bowl (Smajgl et al., 2015) <sup>[[#fn:r971|971]]</sup> but is also a climate change hotspot (de Sherbinin, 2014; Lebel et al., 2014) <sup>[[#fn:r972|972]]</sup> . This area is also a useful illustration of the complexity of adaptation choices and actions in a 1.5°C warmer world. Climate projections include increased annual average temperatures and precipitation in the Mekong (Zhang et al., 2017) <sup>[[#fn:r973|973]]</sup> , as well as increased flooding and related disaster risks (T.F. Smith et al., 2013; Ling et al., 2015; Zhang et al., 2016) <sup>[[#fn:r974|974]]</sup> . Sea level rise and saline intrusion are ongoing risks to agricultural systems in this area by reducing soil fertility and limiting the crop productivity (Renaud et al., 2015) <sup>[[#fn:r975|975]]</sup> . The main climate impacts in the Mekong are expected to be on ecosystem health, through salinity intrusion, biomass reduction and biodiversity losses (Le Dang et al., 2013; Smajgl et al., 2015) <sup>[[#fn:r976|976]]</sup> ; agricultural productivity and food security (Smajgl et al., 2015) <sup>[[#fn:r977|977]]</sup> ; livelihoods such as fishing and farming (D. Wu et al., 2013) <sup>[[#fn:r978|978]]</sup> ; and disaster risk (D. Wu et al., 2013; Hoang et al., 2016) <sup>[[#fn:r979|979]]</sup> , with implications for human mortality and economic and infrastructure losses.

Adaptation imperatives and costs in the Mekong will be higher under higher temperatures and associated impacts on agriculture and aquaculture, hazard exposure, and infrastructure. Adaptation measures to meet food security include greater investment in crop diversification and integrated agriculture–aquaculture practices (Renaud et al., 2015) <sup>[[#fn:r980|980]]</sup> , improvement of water-use technologies (e.g., irrigation, pond capacity improvement and rainwater harvesting), soil management, crop diversification, and strengthening allied sectors such as livestock rearing and aquaculture (ICEM, 2013) <sup>[[#fn:r981|981]]</sup> . Ecosystem-based approaches, such as integrated water resources management, demonstrate successes in mainstreaming adaptation into existing strategies (Sebesvari et al., 2017) <sup>[[#fn:r982|982]]</sup> . However, some of these adaptive strategies can have negative impacts that deepen the divide between land-wealthy and land-poor farmers (Chapman et al., 2016) <sup>[[#fn:r983|983]]</sup> . Construction of high dikes, for example, has enabled triple-cropping, which benefits land-wealthy farmers but leads to increasing debt for land-poor farmers (Chapman and Darby, 2016) <sup>[[#fn:r984|984]]</sup> .

Institutional innovation has happened through the Mekong River Commission (MRC), which is an intergovernmental body between Cambodia, Lao PDR, Thailand and Viet Nam that was established in 1995. The MRC has facilitated impact assessment studies, regional capacity building and local project implementation (Schipper et al., 2010) <sup>[[#fn:r985|985]]</sup> , although the mainstreaming of adaptation into development policies has lagged behind needs (Gass et al., 2011) <sup>[[#fn:r986|986]]</sup> . Existing adaptation interventions can be strengthened through greater flexibility of institutions dealing with land-use planning and agricultural production, improved monitoring of saline intrusion, and the installation of early warning systems that can be accessed by the local authorities or farmers (Renaud et al., 2015; Hoang et al., 2016; Tran et al., 2018) <sup>[[#fn:r987|987]]</sup> . It is critical to identify and invest in synergistic strategies from an ensemble of infrastructural options (e.g., building dikes); soft adaptation measures (e.g., land-use change) (Smajgl et al., 2015; Hoang et al., 2018) <sup>[[#fn:r988|988]]</sup> ; combinations of top-down government-led (e.g., relocation) and bottom-up household strategies (e.g., increasing house height) (Ling et al., 2015) <sup>[[#fn:r989|989]]</sup> ; and community-based adaptation initiatives that merge scientific knowledge with local solutions (Gustafson et al., 2016, 2018; Tran et al., 2018) <sup>[[#fn:r990|990]]</sup> . Special attention needs to be given to strengthening social safety nets and livelihood assets whilst ensuring that adaptation plans are mainstreamed into broader development goals (Sok and Yu, 2015; Kim et al., 2017) <sup>[[#fn:r991|991]]</sup> . The combination of environmental, social and economic pressures on people in the Mekong River basin highlights the complexity of climate change impacts and adaptation in this region, as well as the fact that costs are projected to be much lower at 1.5°C than 2°C of global warming.

<span id="human-health"></span>
=== 3.4.7 Human Health ===

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Climate change adversely affects human health by increasing exposure and vulnerability to climate-related stresses, and decreasing the capacity of health systems to manage changes in the magnitude and pattern of climate-sensitive health outcomes (Cramer et al., 2014; Hales et al., 2014) <sup>[[#fn:r992|992]]</sup> . Changing weather patterns are associated with shifts in the geographic range, seasonality and transmission intensity of selected climate-sensitive infectious diseases (e.g., Semenza and Menne, 2009) <sup>[[#fn:r993|993]]</sup> , and increasing morbidity and mortality are associated with extreme weather and climate events (e.g., K.R. Smith et al., 2014) <sup>[[#fn:r994|994]]</sup> . Health detection and attribution studies conducted since AR5 have provided evidence, using multistep attribution, that climate change is negatively affecting adverse health outcomes associated with heatwaves, Lyme disease in Canada, and ''Vibrio'' emergence in northern Europe (Mitchell, 2016; Mitchell et al., 2016; Ebi et al., 2017) <sup>[[#fn:r995|995]]</sup> . The IPCC AR5 concluded there is ''high'' to ''very high'' ''confidence'' that climate change will lead to greater risks of injuries, disease and death, owing to more intense heatwaves and fires, increased risks of undernutrition, and consequences of reduced labour productivity in vulnerable populations (K.R. Smith et al., 2014) <sup>[[#fn:r996|996]]</sup> .

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<span id="projected-risk-at-1.5c-and-2c-of-global-warming"></span>
==== 3.4.7.1 Projected risk at 1.5°C and 2°C of global warming ====

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The projected risks to human health of warming of 1.5°C and 2°C, based on studies of temperature-related morbidity and mortality, air quality and vector borne diseases assessed in and since AR5, are summarized in Supplementary Material 3.SM, Tables 3.SM.8, 3.SM.9 and 3.SM.10 (based on Ebi et al., 2018) <sup>[[#fn:r997|997]]</sup> . Other climate-sensitive health outcomes, such as diarrheal diseases, mental health issues and the full range of sources of poor air quality, were not considered because of the lack of projections of how risks could change at 1.5°C and 2°C. Few projections were available for specific temperatures above pre-industrial levels; Supplementary Material 3.SM, Table 3.SM.7 provides the conversions used to translate risks projected for particular time slices to those for specific temperature changes (Ebi et al., 2018) <sup>[[#fn:r998|998]]</sup> .

'''Temperature-related morbidity and mortality''' : The magnitude of projected heat-related morbidity and mortality is greater at 2°C than at 1.5°C of global warming ( ''very'' ''high confidence'' )(Doyon et al., 2008; Jackson et al., 2010; Hanna et al., 2011; Huang et al., 2012; Petkova et al., 2013; Hajat et al., 2014; Hales et al., 2014; Honda et al., 2014; Vardoulakis et al., 2014; Garland et al., 2015; Huynen and Martens, 2015; Li et al., 2015; Schwartz et al., 2015; L. Wang et al., 2015; Guo et al., 2016; T. Li et al., 2016; Chung et al., 2017; Kendrovski et al., 2017; Mishra et al., 2017; Arnell et al., 2018; Mitchell et al., 2018b) <sup>[[#fn:r999|999]]</sup> . The number of people exposed to heat events is projected to be greater at 2°C than at 1.5°C (Russo et al., 2016; Mora et al., 2017; Byers et al., 2018; Harrington and Otto, 2018; King et al., 2018) <sup>[[#fn:r1000|1000]]</sup> . The extent to which morbidity and mortality are projected to increase varies by region, presumably because of differences in acclimatization, population vulnerability, the built environment, access to air conditioning and other factors (Russo et al., 2016; Mora et al., 2017; Byers et al., 2018; Harrington and Otto, 2018; King et al., 2018) <sup>[[#fn:r1001|1001]]</sup> . Populations at highest risk include older adults, children, women, those with chronic diseases, and people taking certain medications ( ''very'' ''high confidence'' ). Assuming adaptation takes place reduces the projected magnitude of risks (Hales et al., 2014; Huynen and Martens, 2015; T. Li et al., 2016) <sup>[[#fn:r1002|1002]]</sup> .

In some regions, cold-related mortality is projected to decrease with increasing temperatures, although increases in heat-related mortality generally are projected to outweigh any reductions in cold-related mortality with warmer winters, with the heat-related risks increasing with greater degrees of warming (Huang et al., 2012; Hajat et al., 2014; Vardoulakis et al., 2014; Gasparrini et al., 2015; Huynen and Martens, 2015; Schwartz et al., 2015) <sup>[[#fn:r1003|1003]]</sup> .

'''Occupational health:''' Higher ambient temperatures and humidity levels place additional stress on individuals engaging in physical activity. Safe work activity and worker productivity during the hottest months of the year would be increasingly compromised with additional climate change ( ''medium confidence'' ) (Dunne et al., 2013; Kjellstrom et al., 2013, 2018; Sheffield et al., 2013; Habibi Mohraz et al., 2016) <sup>[[#fn:r1004|1004]]</sup> . Patterns of change may be complex; for example, at 1.5°C, there could be about a 20% reduction in areas experiencing severe heat stress in East Asia, compared to significant increases in low latitudes at 2°C (Lee and Min, 2018) <sup>[[#fn:r1005|1005]]</sup> . The costs of preventing workplace heat-related illnesses through worker breaks suggest that the difference in economic loss between 1.5°C and 2°C could be approximately 0.3% of global gross domestic product (GDP) in 2100 (Takakura et al., 2017) <sup>[[#fn:r1006|1006]]</sup> . In China, taking into account population growth and employment structure, high temperature subsidies for employees working on extremely hot days are projected to increase from 38.6 billion yuan yr <sup>–1</sup> in 1979–2005 to 250 billion yuan yr <sup>–1</sup> in the 2030s (about 1.5°C) (Zhao et al., 2016) <sup>[[#fn:r1007|1007]]</sup> .

'''Air quality:''' Because ozone formation is temperature dependent, projections focusing only on temperature increase generally conclude that ozone-related mortality will increase with additional warming, with the risks higher at 2°C than at 1.5°C ( ''high confidence'' ) (Supplementary Material 3.SM, Table 3.SM.9; Heal et al., 2013; Tainio et al., 2013; Likhvar et al., 2015; Silva et al., 2016; Dionisio et al., 2017; J.Y. Lee et al., 2017) <sup>[[#fn:r1008|1008]]</sup> . Reductions in precursor emissions would reduce future ozone concentrations and associated mortality. Mortality associated with exposure to particulate matter could increase or decrease in the future, depending on climate projections and emissions assumptions (Supplementary Material 3.SM, Table 3.SM.8; Tainio et al., 2013; Likhvar et al., 2015; Silva et al., 2016) <sup>[[#fn:r1009|1009]]</sup> .

'''Malaria:''' Recent projections of the potential impacts of climate change on malaria globally and for Asia, Africa, and South America (Supplementary Material 3.SM, Table 3.SM.10) confirm that weather and climate are among the drivers of the geographic range, intensity of transmission, and seasonality of malaria, and that the relationships are not necessarily linear, resulting in complex patterns of changes in risk with additional warming ( ''very high confidence'' ) (Ren et al., 2016; Song et al., 2016; Semakula et al., 2017) <sup>[[#fn:r1010|1010]]</sup> . Projections suggest that the burden of malaria could increase with climate change because of a greater geographic range of the ''Anopheles'' vector, longer season, and/or increase in the number of people at risk, with larger burdens at higher levels of warming, but with regionally variable patterns ( ''medium to high confidence'' ). Vector populations are projected to shift with climate change, with expansions and reductions depending on the degree of local warming, the ecology of the mosquito vector, and other factors (Ren et al., 2016) <sup>[[#fn:r1011|1011]]</sup> .

'''''Aedes'' (mosquito vector for dengue fever, chikungunya, yellow fever and Zika virus):''' Projections of the geographic distribution of ''Aedes aegypti'' and ''Ae'' . ''albopictus'' (principal vectors) or of the prevalence of dengue fever generally conclude that there will be an increase in the number of mosquitos and a larger geographic range at 2°C than at 1.5°C, and they suggest that more individuals will be at risk of dengue fever, with regional differences ( ''high confidence'' ) (Fischer et al., 2011, 2013; Colón-González et al., 2013, 2018; Bouzid et al., 2014; Ogden et al., 2014a; Mweya et al., 2016) <sup>[[#fn:r1012|1012]]</sup> . The risks increase with greater warming. Projections suggest that climate change is projected to expand the geographic range of chikungunya, with greater expansions occurring at higher degrees of warming (Tjaden et al., 2017) <sup>[[#fn:r1013|1013]]</sup> .

'''Other vector-borne diseases:''' Increased warming in North America and Europe could result in geographic expansions of regions (latitudinally and altitudinally) climatically suitable for West Nile virus transmission, particularly along the current edges of its transmission areas, and extension of the transmission season, with the magnitude and pattern of changes varying by location and level of warming (Semenza et al., 2016) <sup>[[#fn:r1014|1014]]</sup> . Most projections conclude that climate change could expand the geographic range and seasonality of Lyme and other tick-borne diseases in parts of North America and Europe (Ogden et al., 2014b; Levi et al., 2015) <sup>[[#fn:r1015|1015]]</sup> . The projected changes are larger with greater warming and under higher greenhouse gas emissions pathways. Projections of the impacts of climate change on leishmaniosis and Chagas disease indicate that climate change could increase or decrease future health burdens, with greater impacts occurring at higher degrees of warming (González et al., 2014; Ceccarelli and Rabinovich, 2015) <sup>[[#fn:r1016|1016]]</sup> .

In summary, warming of 2°C poses greater risks to human health than warming of 1.5°C, often with the risks varying regionally, with a few exceptions ( ''high confidence'' ). There is ''very'' ''high confidence'' that each additional unit of warming could increase heat-related morbidity and mortality, and that adaptation would reduce the magnitude of impacts. There is ''high confidence'' that ozone-related mortality could increase if precursor emissions remain the same, and that higher temperatures could affect the transmission of some infectious diseases, with increases and decreases projected depending on the disease (e.g., malaria, dengue fever, West Nile virus and Lyme disease), region and degree of temperature change.

<span id="urban-areas"></span>
=== 3.4.8 Urban Areas ===

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There is new literature on urban climate change and its differential impacts on and risks for infrastructure sectors – energy, water, transport and buildings – and vulnerable populations, including those living in informal settlements (UCCRN, 2018) <sup>[[#fn:r1017|1017]]</sup> . However, there is limited literature on the risks of warming of 1.5°C and 2°C in urban areas. Heat-related extreme events (Matthews et al., 2017) <sup>[[#fn:r1018|1018]]</sup> , variability in precipitation (Yu et al., 2018) <sup>[[#fn:r1019|1019]]</sup> and sea level rise can directly affect urban areas (Section 3.4.5, Bader et al., 2018; Dawson et al., 2018) <sup>[[#fn:r1020|1020]]</sup> . Indirect risks may arise from interactions between urban and natural systems.

Future warming and urban expansion could lead to more extreme heat stress (Argüeso et al., 2015; Suzuki-Parker et al., 2015) <sup>[[#fn:r1021|1021]]</sup> . At 1.5°C of warming, twice as many megacities (such as Lagos, Nigeria and Shanghai, China) could become heat stressed, exposing more than 350 million more people to deadly heat by 2050 under midrange population growth. Without considering adaptation options, such as cooling from more reflective roofs, and overall characteristics of urban agglomerations in terms of land use, zoning and building codes (UCCRN, 2018) <sup>[[#fn:r1022|1022]]</sup> , Karachi (Pakistan) and Kolkata (India) could experience conditions equivalent to the deadly 2015 heatwaves on an annual basis under 2°C of warming (Akbari et al., 2009; Oleson et al., 2010; Matthews et al., 2017) <sup>[[#fn:r1023|1023]]</sup> . Warming of 2°C is expected to increase the risks of heatwaves in China’s urban agglomerations (Yu et al., 2018) <sup>[[#fn:r1024|1024]]</sup> . Stabilizing at 1.5°C of warming instead of 2°C could decrease mortality related to extreme temperatures in key European cities, assuming no adaptation and constant vulnerability (Jacob et al., 2018; Mitchell et al., 2018a) <sup>[[#fn:r1025|1025]]</sup> . Holding temperature change to below 2°C but taking urban heat islands (UHI) into consideration, projections indicate that there could be a substantial increase in the occurrence of deadly heatwaves in cities. The urban impacts of these heatwaves are expected to be similar at 1.5°C and 2°C and substantially larger than under the present climate (Matthews et al., 2017; Yu et al., 2018) <sup>[[#fn:r1026|1026]]</sup> . Increases in the intensity of UHI could exacerbate warming of urban areas, with projections ranging from a 6% decrease to a 30% increase for a doubling of CO <sub>2</sub> (McCarthy et al., 2010) <sup>[[#fn:r1027|1027]]</sup> . Increases in population and city size, in the context of a warmer climate, are projected to increase UHI (Georgescu et al., 2012; Argüeso et al., 2014; Conlon et al., 2016; Kusaka et al., 2016; Grossman-Clarke et al., 2017) <sup>[[#fn:r1028|1028]]</sup> .

For extreme heat events, an additional 0.5°C of warming implies a shift from the upper bounds of observed natural variability to a new global climate regime (Schleussner et al., 2016b) <sup>[[#fn:r1029|1029]]</sup> , with distinct implications for the urban poor (Revi et al., 2014; Jean-Baptiste et al., 2018; UCCRN, 2018) <sup>[[#fn:r1030|1030]]</sup> . Adverse impacts of extreme events could arise in tropical coastal areas of Africa, South America and Southeast Asia (Schleussner et al., 2016b) <sup>[[#fn:r1031|1031]]</sup> . These urban coastal areas in the tropics are particularly at risk given their large informal settlements and other vulnerable urban populations, as well as vulnerable assets, including businesses and critical urban infrastructure (energy, water, transport and buildings) (McGranahan et al., 2007; Hallegatte et al., 2013; Revi et al., 2014; UCCRN, 2018) <sup>[[#fn:r1032|1032]]</sup> . Mediterranean water stress is projected to increase from 9% at 1.5°C to 17% at 2°C compared to values in 1986–2005 period. Regional dry spells are projected to expand from 7% at 1.5°C to 11% at 2°C for the same reference period. Sea level rise is expected to be lower at 1.5°C than 2°C, lowering risks for coastal metropolitan agglomerations (Schleussner et al., 2016b) <sup>[[#fn:r1033|1033]]</sup> .

Climate models are better at projecting implications of greenhouse gas forcing on physical systems than at assessing differential risks associated with achieving a specific temperature target (James et al., 2017) <sup>[[#fn:r1034|1034]]</sup> . These challenges in managing risks are amplified when combined with the scale of urban areas and assumptions about socio-economic pathways (Krey et al., 2012; Kamei et al., 2016; Yu et al., 2016; Jiang and Neill, 2017) <sup>[[#fn:r1035|1035]]</sup> .

In summary, in the absence of adaptation, in most cases, warming of 2°C poses greater risks to urban areas than warming of 1.5°C, depending on the vulnerability of the location (coastal or non-coastal) ( ''high confidence'' ), businesses, infrastructure sectors (energy, water and transport), levels of poverty, and the mix of formal and informal settlements.

<span id="key-economic-sectors-and-services"></span>
=== 3.4.9 Key Economic Sectors and Services ===

<div id="section-3-4-9-block-1"></div>

Climate change could affect tourism, energy systems and transportation through direct impacts on operations (e.g., sea level rise) and through impacts on supply and demand, with the risks varying significantly with geographic region, season and time. Projected risks also depend on assumptions with respect to population growth, the rate and pattern of urbanization, and investments in infrastructure. Table 3.SM.11 in Supplementary Material 3.SM summarizes the cited publications.

<div id="section-3-4-9-1"></div>

<span id="tourism"></span>
==== 3.4.9.1 Tourism ====

<div id="section-3-4-9-1-block-1"></div>

The implications of climate change for the global tourism sector are far-reaching and are impacting sector investments, destination assets (environment and cultural), operational and transportation costs, and tourist demand patterns (Scott et al., 2016a; Scott and Gössling, 2018) <sup>[[#fn:r1036|1036]]</sup> . Since AR5, observed impacts on tourism markets and destination communities continue to be not well analysed, despite the many analogue conditions (e.g., heatwaves, major hurricanes, wild fires, reduced snow pack, coastal erosion and coral reef bleaching) that are anticipated to occur more frequently with climate change. There is some evidence that observed impacts on tourism assets, such as environmental and cultural heritage, are leading to the development of ‘last chance to see’ tourism markets, where travellers visit destinations before they are substantially degraded by climate change impacts or to view the impacts of climate change on landscapes (Lemelin et al., 2012; Stewart et al., 2016; Piggott-McKellar and McNamara, 2017) <sup>[[#fn:r1037|1037]]</sup> .

There is limited research on the differential risks of a 1.5° versus 2°C temperature increase and resultant environmental and socio-economic impacts in the tourism sector. The translation of these changes in climate resources for tourism into projections of tourism demand remains geographically limited to Europe. Based on analyses of tourist comfort, summer and spring /autumn tourism in much of western Europe may be favoured by 1.5°C of warming, but with negative effects projected for Spain and Cyprus (decreases of 8% and 2%, respectively, in overnight stays) and most coastal regions of the Mediterranean (Jacob et al., 2018) <sup>[[#fn:r1038|1038]]</sup> . Similar geographic patterns of potential tourism gains (central and northern Europe) and reduced summer favourability (Mediterranean countries) are projected under 2°C (Grillakis et al., 2016) <sup>[[#fn:r1039|1039]]</sup> . Considering potential changes in natural snow only, winter overnight stays at 1.5°C are projected to decline by 1–2% in Austria, Italy and Slovakia, with an additional 1.9 million overnight stays lost under 2°C of warming (Jacob et al., 2018) <sup>[[#fn:r1040|1040]]</sup> . Using an econometric analysis of the relationship between regional tourism demand and climate conditions, Ciscar et al. (2014) <sup>[[#fn:r1041|1041]]</sup> projected that a 2°C warmer world would reduce European tourism by 5% (€15 billion yr <sup>–1</sup> ), with losses of up to 11% (€6 billion yr <sup>–1</sup> ) for southern Europe and a potential gain of €0.5 billion yr <sup>–1</sup> in the UK.

There is growing evidence that the magnitude of projected impacts is temperature dependent and that sector risks could be much greater with higher temperature increases and resultant environmental and socio-economic impacts (Markham et al., 2016; Scott et al., 2016a; Jones, 2017; Steiger et al., 2017) <sup>[[#fn:r1042|1042]]</sup> . Studies from 27 countries consistently project substantially decreased reliability of ski areas that are dependent on natural snow, increased snowmaking requirements and investment in snowmaking systems, shortened and more variable ski seasons, a contraction in the number of operating ski areas, altered competitiveness among and within regional ski markets, and subsequent impacts on employment and the value of vacation properties (Steiger et al., 2017) <sup>[[#fn:r1043|1043]]</sup> . Studies that omit snowmaking do not reflect the operating realities of most ski areas and overestimate impacts at 1.5°C–2°C. In all regional markets, the extent and timing of these impacts depend on the magnitude of climate change and the types of adaptive responses by the ski industry, skiers and destination communities. The decline in the number of former Olympic Winter Games host locations that could remain climatically reliable for future Olympic and Paralympic Winter Games has been projected to be much greater under scenarios warmer than 2°C (Scott et al., 2015; Jacob et al., 2018) <sup>[[#fn:r1044|1044]]</sup> .

The tourism sector is also affected by climate-induced changes in environmental assets critical for tourism, including biodiversity, beaches, glaciers and other features important for environmental and cultural heritage. Limited analyses of projected risks associated with 1.5°C versus 2°C are available (Section 3.4.4.12). A global analysis of sea level rise (SLR) risk to 720 UNESCO Cultural World Heritage sites projected that about 47 sites might be affected under 1°C of warming, with this number increasing to 110 and 136 sites under 2°C and 3°C, respectively (Marzeion and Levermann, 2014) <sup>[[#fn:r1045|1045]]</sup> . Similar risks to vast worldwide coastal tourism infrastructure and beach assets remain unquantified for most major tourism destinations and small island developing states (SIDS) that economically depend on coastal tourism. One exception is the projection that an eventual 1 m SLR could partially or fully inundate 29% of 900 coastal resorts in 19 Caribbean countries, with a substantially higher proportion (49–60%) vulnerable to associated coastal erosion (Scott and Verkoeyen, 2017) <sup>[[#fn:r1046|1046]]</sup> .

A major barrier to understanding the risks of climate change for tourism, from the destination community scale to the global scale, has been the lack of integrated sectoral assessments that analyse the full range of potential compounding impacts and their interactions with other major drivers of tourism (Rosselló-Nadal, 2014; Scott et al., 2016b) <sup>[[#fn:r1047|1047]]</sup> . When applied to 181 countries, a global vulnerability index including 27 indicators found that countries with the lowest risk are located in western and northern Europe, central Asia, Canada and New Zealand, while the highest sector risks are projected for Africa, the Middle East, South Asia and SIDS in the Caribbean, Indian and Pacific Oceans (Scott and Gössling, 2018) <sup>[[#fn:r1048|1048]]</sup> . Countries with the highest risks and where tourism represents a significant proportion of the national economy (i.e., more than 15% of GDP) include many SIDS and least developed countries. Sectoral climate change risk also aligns strongly with regions where tourism growth is projected to be the strongest over the coming decades, including sub-Saharan Africa and South Asia, pointing to an important potential barrier to tourism development. The transnational implications of these impacts on the highly interconnected global tourism sector and the contribution of tourism to achieving the 2030 sustainable development goals (SDGs) remain important uncertainties.

In summary, climate is an important factor influencing the geography and seasonality of tourism demand and spending globally ( ''very high confidence'' ). Increasing temperatures are projected to directly impact climate-dependent tourism markets, including sun, beach and snow sports tourism, with lesser risks for other tourism markets that are less climate sensitive ( ''high confidence'' ). The degradation or loss of beach and coral reef assets is expected to increase risks for coastal tourism, particularly in subtropical and tropical regions ( ''high confidence'' ).

<div id="section-3-4-9-2"></div>

<span id="energy-systems"></span>
==== 3.4.9.2 Energy systems ====

<div id="section-3-4-9-2-block-1"></div>

Climate change is projected to lead to an increased demand for air conditioning in most tropical and subtropical regions (Arent et al., 2014; Hong and Kim, 2015) <sup>[[#fn:r1049|1049]]</sup> ( ''high confidence'' ). Increasing temperatures will decrease the thermal efficiency of fossil, nuclear, biomass and solar power generation technologies, as well as buildings and other infrastructure (Arent et al., 2014) <sup>[[#fn:r1050|1050]]</sup> . For example, in Ethiopia, capital expenditures through 2050 might either decrease by approximately 3% under extreme wet scenarios or increase by up to 4% under a severe dry scenario (Block and Strzepek, 2012) <sup>[[#fn:r1051|1051]]</sup> .

Impacts on energy systems can affect gross domestic product (GDP). The economic damage in the United States from climate change is estimated to be, on average, roughly 1.2% cost of GDP per year per 1°C increase under RCP8.5 (Hsiang et al., 2017) <sup>[[#fn:r1052|1052]]</sup> . Projections of GDP indicate that negative impacts of energy demand associated with space heating and cooling in 2100 will be greatest (median: –0.94% change in GDP) under 4°C (RCP8.5) compared with under 1.5°C (median: –0.05%), depending on the socio-economic conditions (Park et al., 2018) <sup>[[#fn:r1053|1053]]</sup> . Additionally, projected total energy demands for heating and cooling at the global scale do not change much with increases in global mean surface temperature (GMST) of up to 2°C. A high degree of variability is projected between regions (Arnell et al., 2018) <sup>[[#fn:r1054|1054]]</sup> .

Evidence for the impact of climate change on energy systems since AR5 is limited. Globally, gross hydropower potential is projected to increase (by 2.4% under RCP2.6 and by 6.3% under RCP8.5 for the 2080s), with the most growth expected in Central Africa, Asia, India and northern high latitudes (van Vliet et al., 2016) <sup>[[#fn:r1055|1055]]</sup> . Byers et al. (2018) <sup>[[#fn:r1056|1056]]</sup> found that energy impacts at 2°C increase, including more cooling degree days, especially in tropical regions, as well as increased hydro-climatic risk to thermal and hydropower plants predominantly in Europe, North America, South and Southeast Asia and southeast Brazil. Donk et al. (2018) <sup>[[#fn:r1057|1057]]</sup> assessed future climate impacts on hydropower in Suriname and projected a decrease of approximately 40% in power capacity for a global temperature increase in the range of 1.5°C. At minimum and maximum increases in global mean temperature of 1.35°C and 2°C, the overall stream flow in Florida, USA is projected to increase by an average of 21%, with pronounced seasonal variations, resulting in increases in power generation in winter (+72%) and autumn (+15%) and decreases in summer (–14%; Chilkoti et al., 2017) <sup>[[#fn:r1058|1058]]</sup> . Greater changes are projected at higher temperature increases. In a reference scenario with global mean temperatures rising by 1.7°C from 2005 to 2050, U.S. electricity demand in 2050 was 1.6–6.5% higher than in a control scenario with constant temperatures (McFarland et al., 2015) <sup>[[#fn:r1059|1059]]</sup> . Decreased electricity generation of –15% is projected for Brazil starting in 2040, with values expected to decline to –28% later in the century (de Queiroz et al., 2016) <sup>[[#fn:r1060|1060]]</sup> . In large parts of Europe, electricity demand is projected to decrease, mainly owing to reduced heating demand (Jacob et al., 2018) <sup>[[#fn:r1061|1061]]</sup> .

In Europe, no major differences in large-scale wind energy resources or in inter-or intra-annual variability are projected for 2016–2035 under RCP8.5 and RCP4.5 (Carvalho et al., 2017) <sup>[[#fn:r1062|1062]]</sup> . However, in 2046–2100, wind energy density is projected to decrease in eastern Europe (–30%) and increase in Baltic regions (+30%). Intra-annual variability is expected to increase in northern Europe and decrease in southern Europe. Under RCP4.5 and RCP8.5, the annual energy yield of European wind farms as a whole, as projected to be installed by 2050, will remain stable (±5 yield for all climate models). However, wind farm yields are projected to undergo changes of up to 15% in magnitude at country and local scales and of 5% at the regional scale (Tobin et al., 2015, 2016) <sup>[[#fn:r1063|1063]]</sup> . Hosking et al. (2018) <sup>[[#fn:r1064|1064]]</sup> assessed wind power generation over Europe for 1.5°C of warming and found the potential for wind energy to be greater than previously assumed in northern Europe. Additionally, Tobin et al. (2018) <sup>[[#fn:r1065|1065]]</sup> assessed impacts under 1.5°C and 2°C of warming on wind, solar photovoltaic and thermoelectric power generation across Europe. These authors found that photovoltaic and wind power might be reduced by up to 10%, and hydropower and thermoelectric generation might decrease by up to 20%, with impacts being limited at 1.5°C of warming but increasing as temperature increases (Tobin et al., 2018) <sup>[[#fn:r1066|1066]]</sup> .

<div id="section-3-4-9-3"></div>

<span id="transportation"></span>
==== 3.4.9.3 Transportation ====

<div id="section-3-4-9-3-block-1"></div>

Road, air, rail, shipping and pipeline transportation can be impacted directly or indirectly by weather and climate, including increases in precipitation and temperature; extreme weather events (flooding and storms); SLR; and incidence of freeze–thaw cycles (Arent et al., 2014) <sup>[[#fn:r1067|1067]]</sup> . Much of the published research on the risks of climate change for the transportation sector has been qualitative.

The limited new research since AR5 supports the notion that increases in global temperatures will impact the transportation sector. Warming is projected to result in increased numbers of days of ice-free navigation and a longer shipping season in cold regions, thus affecting shipping and reducing transportation costs (Arent et al., 2014) <sup>[[#fn:r1068|1068]]</sup> . In the North Sea Route, large-scale commercial shipping might not be possible until 2030 for bulk shipping and until 2050 for container shipping under RCP8.5. A 0.05% increase in mean temperature is projected from an increase in short-lived pollutants, as well as elevated CO <sub>2</sub> and non-CO <sub>2</sub> emissions, associated with additional economic growth enabled by the North Sea Route. (Yumashev et al., 2017) <sup>[[#fn:r1069|1069]]</sup> . Open water vessel transit has the potential to double by mid-century, with a two to four month longer season (Melia et al., 2016) <sup>[[#fn:r1070|1070]]</sup> .

<span id="livelihoods-and-poverty-and-the-changing-structure-of-communities"></span>
=== 3.4.10 Livelihoods and Poverty, and the Changing Structure of Communities ===

<div id="section-3-4-10-block-1"></div>

Multiple drivers and embedded social processes influence the magnitude and pattern of livelihoods and poverty, as well as the changing structure of communities related to migration, displacement and conflict (Adger et al., 2014) <sup>[[#fn:r1071|1071]]</sup> . In AR5, evidence of a climate change signal was limited, with more evidence of impacts of climate change on the places where indigenous people live and use traditional ecological knowledge (Olsson et al., 2014) <sup>[[#fn:r1072|1072]]</sup> .

<div id="section-3-4-10-1"></div>

<span id="livelihoods-and-poverty"></span>
===== 3.4.10.1 Livelihoods and poverty =====

<div id="section-3-4-10-1-block-1"></div>

At approximately 1.5°C of global warming (2030), climate change is expected to be a poverty multiplier that makes poor people poorer and increases the poverty head count (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017) <sup>[[#fn:r1073|1073]]</sup> . Poor people might be heavily affected by climate change even when impacts on the rest of population are limited. Climate change alone could force more than 3 million to 16 million people into extreme poverty, mostly through impacts on agriculture and food prices (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017) <sup>[[#fn:r1074|1074]]</sup> . Unmitigated warming could reshape the global economy later in the century by reducing average global incomes and widening global income inequality (Burke et al., 2015b) <sup>[[#fn:r1075|1075]]</sup> . The most severe impacts are projected for urban areas and some rural regions in sub-Saharan Africa and Southeast Asia.

<div id="section-3-4-10-2"></div>

<span id="the-changing-structure-of-communities-migration-displacement-and-conflict"></span>
===== 3.4.10.2 The changing structure of communities: migration, displacement and conflict =====

<div id="section-3-4-10-2-block-1"></div>

'''Migration:''' In AR5, the potential impacts of climate change on migration and displacement were identified as an emerging risk (Oppenheimer et al., 2014) <sup>[[#fn:r1076|1076]]</sup> . The social, economic and environmental factors underlying migration are complex and varied; therefore, detecting the effect of observed climate change or assessing its possible magnitude with any degree of confidence is challenging (Cramer et al., 2014) <sup>[[#fn:r1077|1077]]</sup> .

No studies have specifically explored the difference in risks between 1.5°C and 2°C of warming on human migration. The literature consistently highlights the complexity of migration decisions and the difficulties in attributing causation (e.g., Nicholson, 2014; Baldwin and Fornalé, 2017; Bettini, 2017; Constable, 2017; Islam and Shamsuddoha, 2017; Suckall et al., 2017) <sup>[[#fn:r1078|1078]]</sup> . The studies on migration that have most closely explored the probable impacts of 1.5°C and 2°C have mainly focused on the direct effects of temperature and precipitation anomalies on migration or the indirect effects of these climatic changes through changing agriculture yield and livelihood sources (Mueller et al., 2014; Piguet and Laczko, 2014; Mastrorillo et al., 2016; Sudmeier-Rieux et al., 2017) <sup>[[#fn:r1079|1079]]</sup> .

Temperature has had a positive and statistically significant effect on outmigration over recent decades in 163 countries, but only for agriculture-dependent countries (R. Cai et al., 2016) <sup>[[#fn:r1080|1080]]</sup> . A 1°C increase in average temperature in the International Migration Database of the Organisation for Economic Co-operation and Development (OECD) was associated with a 1.9% increase in bilateral migration flows from 142 sending countries and 19 receiving countries, and an additional millimetre of average annual precipitation was associated with an increase in migration by 0.5% (Backhaus et al., 2015) <sup>[[#fn:r1081|1081]]</sup> . In another study, an increase in precipitation anomalies from the long-term mean, was strongly associated with an increase in outmigration, whereas no significant effects of temperature anomalies were reported (Coniglio and Pesce, 2015) <sup>[[#fn:r1082|1082]]</sup> .

Internal and international migration have always been important for small islands (Farbotko and Lazrus, 2012; Weir et al., 2017) <sup>[[#fn:r1083|1083]]</sup> . There is rarely a single cause for migration (Constable, 2017) <sup>[[#fn:r1084|1084]]</sup> . Numerous factors are important, including work, education, quality of life, family ties, access to resources, and development (Bedarff and Jakobeit, 2017; Speelman et al., 2017; Nicholls et al., 2018) <sup>[[#fn:r1085|1085]]</sup> . Depending on the situation, changing weather, climate or environmental conditions might each be a factor in the choice to migrate (Campbell and Warrick, 2014) <sup>[[#fn:r1086|1086]]</sup> .

'''Displacement:''' At 2°C of warming, there is a potential for significant population displacement concentrated in the tropics (Hsiang and Sobel, 2016) <sup>[[#fn:r1087|1087]]</sup> . Tropical populations may have to move distances greater than 1000 km if global mean temperature rises by 2°C from 2011–2030 to the end of the century. A disproportionately rapid evacuation from the tropics could lead to a concentration of population in tropical margins and the subtropics, where population densities could increase by 300% or more (Hsiang and Sobel, 2016) <sup>[[#fn:r1088|1088]]</sup> .

'''Conflict:''' A recent study has called for caution in relating conflict to climate change, owing to sampling bias (Adams et al., 2018) <sup>[[#fn:r1089|1089]]</sup> . Insufficient consideration of the multiple drivers of conflict often leads to inconsistent associations being reported between climate change and conflict (e.g., Hsiang et al., 2013; Hsiang and Burke, 2014; Buhaug, 2015, 2016; Carleton and Hsiang, 2016; Carleton et al., 2016) <sup>[[#fn:r1090|1090]]</sup> . There also are inconsistent relationships between climate change, migration and conflict (e.g., Theisen et al., 2013; Buhaug et al., 2014; Selby, 2014; Christiansen, 2016; Brzoska and Fröhlich, 2016; Burrows and Kinney, 2016; Reyer et al., 2017c; Waha et al., 2017) <sup>[[#fn:r1091|1091]]</sup> . Across world regions and from the international to micro level, the relationship between drought and conflict is weak under most circumstances (Buhaug, 2016; von Uexkull et al., 2016) <sup>[[#fn:r1092|1092]]</sup> . However, drought significantly increases the likelihood of sustained conflict for particularly vulnerable nations or groups, owing to the dependence of their livelihood on agriculture. This is particularly relevant for groups in the least developed countries (von Uexkull et al., 2016) <sup>[[#fn:r1093|1093]]</sup> , in sub-Saharan Africa (Serdeczny et al., 2016; Almer et al., 2017) <sup>[[#fn:r1094|1094]]</sup> and in the Middle East (Waha et al., 2017) <sup>[[#fn:r1095|1095]]</sup> . Hsiang et al. (2013) <sup>[[#fn:r1096|1096]]</sup> reported causal evidence and convergence across studies that climate change is linked to human conflicts across all major regions of the world, and across a range of spatial and temporal scales. A 1°C increase in temperature or more extreme rainfall increases the frequency of intergroup conflicts by 14% (Hsiang et al., 2013) <sup>[[#fn:r1097|1097]]</sup> . If the world warms by 2°C–4°C by 2050, rates of human conflict could increase. Some causal associations between violent conflict and socio-political instability were reported from local to global scales and from hour to millennium time frames (Hsiang and Burke, 2014) <sup>[[#fn:r1098|1098]]</sup> . A temperature increase of one standard deviation increased the risk of interpersonal conflict by 2.4% and intergroup conflict by 11.3% (Burke et al., 2015a) <sup>[[#fn:r1099|1099]]</sup> . Armed-conflict risks and climate-related disasters are both relatively common in ethnically fractionalized countries, indicating that there is no clear signal that environmental disasters directly trigger armed conflicts (Schleussner et al., 2016a) <sup>[[#fn:r1100|1100]]</sup> .

In summary, average global temperatures that extend beyond 1.5°C are projected to increase poverty and disadvantage in many populations globally ( ''medium confidence'' ). By the mid-to late 21st century, climate change is projected to be a poverty multiplier that makes poor people poorer and increases poverty head count, and the association between temperature and economic productivity is not linear ( ''high confidence'' ). Temperature has a positive and statistically significant effect on outmigration for agriculture-dependent communities ( ''medium confidence'' ).

<span id="interacting-and-cascading-risks"></span>
=== 3.4.11 Interacting and Cascading Risks ===

<div id="section-3-4-11-block-1"></div>

The literature on compound as well as interacting and cascading risks at warming of 1.5°C and 2°C is limited. Spatially compound risks, often referred to as hotspots, involve multiple hazards from different sectors overlapping in location (Piontek et al., 2014) <sup>[[#fn:r1101|1101]]</sup> . Global exposures were assessed for 14 impact indicators, covering water, energy and land sectors, from changes including drought intensity and water stress index, cooling demand change and heatwave exposure, habitat degradation, and crop yields using an ensemble of climate and impact models (Byers et al., 2018) <sup>[[#fn:r1102|1102]]</sup> . Exposures are projected to approximately double between 1.5°C and 2°C, and the land area affected by climate risks is expected to increase as warming progresses. For populations vulnerable to poverty, the exposure to climate risks in multiple sectors could be an order of magnitude greater (8–32 fold) in the high poverty and inequality scenarios (SSP3; 765–1,220 million) compared to under sustainable socio-economic development (SSP1; 23–85 million). Asian and African regions are projected to experience 85–95% of global exposure, with 91–98% of the exposed and vulnerable population (depending on SSP/GMT combination), approximately half of which are in South Asia. Figure 3.19 shows that moderate and large multi-sector impacts are prevalent at 1.5°C where vulnerable people live, predominantly in South Asia (mostly Pakistan, India and China), but that impacts spread to sub-Saharan Africa, the Middle East and East Asia at higher levels of warming. Beyond 2°C and at higher risk thresholds, the world’s poorest populations are expected to be disproportionately impacted, particularly in cases (SSP3) of great inequality in Africa and southern Asia. Table 3.4 shows the number of exposed and vulnerable people at 1.5°C and 2°C of warming, with 3°C shown for context, for selected multi-sector risks.

<div id="section-3-4-11-block-2"></div>

<span id="figure-3.19"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 3.19'''

<span id="multi-sector-risk-maps-for-1.5c-top-2c-middle-and-locations-where-2c-brings-impacts-not-experienced-at-1.5c-2c1.5c-bottom."></span>
<!-- IMG CAPTION -->
'''Multi-sector risk maps for 1.5°C (top), 2°C (middle), and locations where 2°C brings impacts not experienced at 1.5°C (2°C–1.5°C; bottom).'''
<!-- IMG FILE -->
[[File:189374852e698025fe15fd90217cabbf Figure_3.18-1024x853.jpg]]

The maps in the left column show the full range of the multi-sector risk (MSR) score (0–9), with scores ≤5.0 shown with a transparency gradient and scores &gt;5.0 shown with a colour gradient. Score must be &gt;4.0 to be considered ‘multi-sector’. The maps in the right column overlay the 2050 vulnerable populations (low income) under Shared Socio-Economic Pathway (SSP)2 (greyscale) with the multi-sector risk score &gt;5.0 (colour gradient), thus indicating the concentrations of exposed and vulnerable populations to risks in multiple sectors. Source: Byers et al. (2018) <sup>[[#fn:r1103|1103]]</sup> .

<!-- END IMG -->
<div id="section-3-4-11-block-3"></div>

<span id="table-3.4"></span>
<!-- START TABLE -->
'''Table 3.4'''

<span id="number-of-exposed-and-vulnerable-people-at-1.5c-2c-and-3c-for-selected-multi-sector-risks-under-shared-socioeconomic-pathways-ssps."></span>
'''Number of exposed and vulnerable people at 1.5°C, 2°C, and 3°C for selected multi-sector risks under shared socioeconomic pathways (SSPs).'''

Source: Byers et al. (2018) <sup>[[#fn:r1104|1104]]</sup>

<!-- TABLE -->
{| class="wikitable"
|-
! SSP2<br />
(SSP1 to SSP3 range), millions
! colspan="2"| 1.5°C
! colspan="2"| 2°C
! colspan="2"| 3°C
|-
| ''Indicator''
| Exposed
| Exposed<br />
and vulnerable
| Exposed
| Exposed<br />
and vulnerable
| Exposed
| Exposed<br />
and vulnerable
|-
| Water stress index
| 3340
(3032–3584)

| 496
(103–1159)

| 3658
(3080–3969)

| 586
(115–1347)

| 3920
(3202–4271)

| 662
(146–1480)

|-
| Heatwave event exposure
| 3960
(3546–4508)

| 1187
(410–2372)

| 5986
(5417–6710)

| 1581
(506–3218)

| 7909
(7286–8640)

| 1707
(537–3575)

|-
| Hydroclimate risk to power production
| 334
(326–337)

| 30
(6–76)

| 385
(374–389)

| 38
(9–94)

| 742
(725–739)

| 72
(16–177)

|-
| Crop yield change
| 35
(32–36)

| 8
(2–20)

| 362
(330–396)

| 81
(24–178)

| 1817
(1666–1992)

| 406
(118–854)

|-
| Habitat degradation
| 91
(92–112)

| 10
(4–31)

| 680
(314–706)

| 102
(23–234)

| 1357
(809–1501)

| 248
(75–572)

|-
| Multi-sector exposure
|
|-
| Two indicators
| 1129
(1019–1250)

| 203
(42–487)

| 2726
(2132–2945)

| 562
(117–1220)

| 3500
(3212–3864)

| 707
(212–1545)

|-
| Three indicators
| 66
(66–68)

| 7
(0.9–19)

| 422
(297–447)

| 54
(8–138)

| 1472
(1177–1574)

| 237
(48–538)

|-
| Four indicators
| 5
(0.3–5.7)

| 0.3
(0–1.2)

| 11
(5–14)

| 0.5
(0–2)

| 258
(104–280)

| 33
(4–86)

|}
<!-- END TABLE -->

<span id="summary-of-projected-risks-at-1.5c-and-2c-of-global-warming"></span>
=== 3.4.12 Summary of Projected Risks at 1.5°C and 2°C of Global Warming ===

<div id="section-3-4-12-block-1"></div>

The information presented in Section 3.4 is summarised below in Table 3.5, which illustrates the growing evidence of increasing risks across a broad range of natural and human systems at 1.5°C and 2°C of global warming.

<div id="section-3-4-12-block-2"></div>

<span id="table-3.5"></span>
<!-- START TABLE -->
'''Table 3.5'''

<span id="summary-of-projected-risks-to-natural-and-human-systems-at-1.5c-and-2c-of-global-warming-and-of-the-potential-to-adapt-to-these-risks"></span>
'''Summary of projected risks to natural and human systems at 1.5°C and 2°C of global warming, and of the potential to adapt to these risks'''

Table summarizes the chapter text and with references supporting table entries found in the main chapter text. Risk magnitude is provided either as assessed levels of risk (very high: vh, high: h, medium: m, or low: l) or as quantitative examples of risk levels taken from the literature. Further compilations of quantified levels of risk taken from the literature may be found Tables 3.SM1-5 in the Supplementary Material. Similarly, potential to adapt is assessed from the literature by expert judgement as either high (h), medium (m), or low (l). Confidence in each assessed level/quantification of risk, or in each assessed adaptation potential, is indicated as very high (VH), high (H), medium (M), or low (L). Note that the use of l, m, h and vh here is distinct from the use of L, M, H and VH in Figures 3.18, 3.20 and 3.21.

<!-- TABLE -->
{| class="wikitable"
|-
! Sector
! Physical climate change drivers
! Nature of risk
! Global risks at 1.5°C of global warming above pre-industrial
! Global risks at 2°C of global warming above pre-industrial
! Change in risk when moving from 1.5°C to 2°C of warming
! Confidence in risk statements
! Regions where risks are particularly high with 2°C of global warming
! Regions where the change in risk when moving from 1.5°C to 2°C are particularly high
! Regions with little or no information
! RFC*
! Adaptation potential at 1.5°C
! Adaptation potential at 2°C
! Confidence in assigning adaptation potential
|-
| rowspan="3"| Freshwater
| rowspan="3"| Precipitation, temperature, snowmelt
| Water Stress
| Around half compared to the risks at 2°C <sup>1</sup>
| Additional 8% of the world population in 2000 exposed to new or aggravated water scarcity <sup>1</sup>
| Up to 100% increase
| M
|
| Europe, Australia, southern Africa
|
| 3
| l
| M
|-
| Fluvial flood
| 100% increase in the population affected compared to the impact simulated over the baseline period 1976–2005 <sup>2</sup>
| 170% increase in the population affected compared to the impact simulated over the baseline period 1976–2005 <sup>2</sup>
| 70% increase
| M
| USA, Asia, Europe
|
| Africa,<br />
Oceania
| 2
| l/m
| M
|-
| Drought
| 350.2 ± 158.8 million, changes in urban population exposure to severe drought at the globe scale <sup>3</sup>
| 410.7 ± 213.5 million, changes in urban population exposure to severe drought at the globe scale <sup>3</sup>
| 60.5 ± 84.1 million (±84.1 based on the SSP1 scenario)<br />
(based on PDSI estimate)
| M
| Central Europe, southern Europe, Mediterranean, West Africa, East and West Asia, Southeast Asia (based on PDSI estimate#)
|
| 2
| l/m
| L
|-
| rowspan="4"| Terrestrial ecosystems
| rowspan="3"| Temperature, precipitation
| Species range loss
| 6% insects, 4% vertebrates, 8% plants, lose &gt;50% range <sup>4</sup>
| 18% insects, 8% vertebrates, 16% plants lose &gt;50% range <sup>4</sup>
| Double or triple
| M
|
| Amazon, Europe, southern Africa
|
| 1,4
| m
| l
| H
|-
| Loss of ecosystem functioning and services
| m
| h
|
| M
|
| 4
|
|-
| Shifts of biomes (major ecosystem types)
| About 7% transformed <sup>5</sup>
| 13% (range 8–20%) transformed <sup>5</sup>
| About double
| M
|
| Arctic, Tibet, Himalayas, South Africa, Australia
|
| 4
|
|-
| Heat and cold stress, warming, precipitation drought
| Wildfire
| h
| Increased risk
| M
| Canada, USA and Mediterranean
| Mediterranean
| Central and South America, Australia, Russia, China, Africa
| 1, 2,<br />
4, 5
| l
| M
|-
| rowspan="15"| Ocean
| rowspan="6"| Warming and stratification of the surface ocean
| Loss of framework species (coral reefs)
| vh
| Greater rate of loss: from 70–90% loss at 1.5°C to 99% loss at 2°C and above
| H/very H
| Tropical/subtropical countries
| Southern Red Sea, Somalia, Yemen, deep water coral reefs
| 1,2
| h
| l
| H
|-
| Loss of framework species (seagrass)
| m
| h
| Increase in risk
| M
| Tropical/subtropical countries
| Southern Red Sea, Somalia, Yemen, Myanmar
| 1,2
| m
| l
| M/H
|-
| Loss of framework species (mangroves)
| m
| Uncertain and depends on other human activities
| M/H
| Tropical/subtropical countries
| Southern Red Sea, Somalia, Yemen, Myanmar
| 1,3
| m
| l
| L/M
|-
| Disruption of marine foodwebs
| h
| vh
| Large increase in risk
| M
| Global
| Deep sea
| 4
| m
| l
| M/H
|-
| Range migration of marine species and ecosystems
| m
| h
| Large increase in risk
| H
| Global
| Deep sea
| 1
| m
| l
| H
|-
| Loss of fin fish and fisheries
| h
| h/vh
| Large increase in risk
| H
| Global
| Deep sea, up-welling systems
| 4
| m
| m/l
| M/H
|-
| rowspan="3"| Ocean acidification and elevated sea temperatures
| Loss of coastal ecosystems and protection
| m
| h
| Increase in risk
| M
| Low-latitude tropical/subtropical countries
| Most regions – risks not well defined
| 1
| m
| m/l
| M
|-
| Loss of bivalves and bivalve fisheries
| m/h
| h/vh
| Large increase in risk
| H
| Temperate countries with upwelling
| Most regions – risks not well defined
| 4
| m/h
| l/m
| M/H
|-
| Changes to physiology and ecology of marine species
| l/m
| m
| Increase in risk
| H
| Global
| Most regions – risks not well defined
| 4
| l
| M/H
|-
| rowspan="2"| Reduced bulk ocean circulation and de-oxygenation
| Increased hypoxic dead zones
| l
| l/m
| Large increase in risk
| L/M
| Temperate countries with upwelling
| Deep sea
| 4
| m
| l
| M
|-
| Changes to upwelling productivity
| l
| m
| Increase in risk
| L/M
| Most upwelling regions
| Some upwelling systems
| 4
| l
| M
|-
| rowspan="2"| Intensified storms, precipitation plus sea level rise
| Loss of coastal ecosystems
| h
| h/vh
| Large increase in risk
| H
| Tropical/subtropical countries
|
| 1, 4
| m
| l
| M
|-
| Inundation and destruction of human/coastal infrastructure and livelihoods
| h
| h/vh
| Large increase in risk
| H
| Global
|
| 1, 5
| m/h
| m
| M/L
|-
| rowspan="2"| Loss of sea ice
| Loss of habitat
| h
| vh
| Large increase in risk
| H
| Polar regions
|
| 1
| l
| very l
| H
|-
| Increased<br />
productivity<br />
but changing fisheries
| l/m
| m/h
| Large increase in risk
| very H
| Polar regions
|
| 1, 4
| l
| m/l
| H
|-
| rowspan="3"| Coastal
| rowspan="3"| Sea level rise, increased storminess
| Area exposed (assuming no defences)
| 562–575th km <sup>2</sup> when 1.5°C first reached <sup>6,7,8</sup>
| 590–613th km <sup>2</sup> when 2°C first reached <sup>6,7,8</sup>
| Increasing; 25–38th km <sup>2</sup> when temperatures are first reached, 10–17th km <sup>2</sup> in 2100 increasing to 16–230th km <sup>2</sup> in 2300 <sup>6,7,8</sup>
| M/H (dependent on population datasets)
| Asia, small islands
| Small islands
| 2, 3
| m
| M
|-
| Population exposed (assuming no defences)
| 128–143 million when 1.5°C first reached
| 141–151 million when 2°C first reached
| Increasing; 8–13 million when temperatures are first reached, 0–6 million people in 2100, increasing to 35–95 million people in 2300 <sup>6</sup>
| M/H (dependent on population datasets)
| Asia, small islands
| Small islands
| 2, 3
| m
| M
|-
| People at risk accounting for defences (modelled in 1995)
| 2–28 million people yr <sup>–1</sup> if defences are not upgraded from the modelled 1995 baseline <sup>9</sup>
| 15–53 million people yr <sup>–1</sup> if defences are not upgraded from the modelled 1995 baseline <sup>9</sup>
| Increasing with time, but highly dependent on adaptation <sup>9</sup>
| M/H (dependent on adaptation)
| Asia, small islands, potentially African nations
| Asia, small islands
| Small islands
| 2, 3, 4
| m
| M
|-
| rowspan="2"| Food security and food production systems
| Heat and cold stress, warming, precipitation, drought
| Changes in ecosystem production
| m/h
| h
| Large increase
| M/H
| Global
| North America, Central and South America, Mediterranean basin, South Africa, Australia, Asia
|
| 2, 4, 5
| h
| m/h
| M/H
|-
| Heat and cold stress, warming, precipitation drought
| Shift and composition change of biomes (major ecosystem types)
| m/h
| h
| Moderate increase
| L/M
| Global
| Global, tropical areas, Mediterranean
| Africa, Asia
| 1, 2, 3, 4
| l/m
| l
| L/M
|-
| rowspan="4"| Human health
| rowspan="2"| Temperature
| Heat-related morbidity and mortality
| m
| m/h
| Risk increased
| VH
| All regions at risk
| All regions
| Africa
| 2, 3, 4
| h
| H
|-
| Occupational heat stress
| m
| m/h
| Risk increased
| M
| Tropical regions
| Africa
| 2, 3, 4
| h
| m
| M
|-
| Air quality
| Ozone-related mortality
| m (if precursor emissions remain the same)
| m/h (if precursor emissions remain the same)
| Risk increased
| H
| High income and emerging economies
| Africa, parts of Asia
| 2, 3, 4
| l
| M
|-
| Temperature, precipitation
| Undernutrition
| m
| m/h
| Risk increased
| H
| Low-income countries in Africa and Asia
| Small islands
| 2, 3, 4
| m
| l
| M
|-
| Key economic sectors
| Temperature
| Tourism (sun, beach, and snow sports)
| m/h
| h
| Risk increased
| VH
| Coastal tourism, particularly in subtropical and tropical regions
| Africa
| 1, 2, 3
| m
| l
| H
|}
<!-- END TABLE -->

\*RFC: 1 = unique and threatened systems, 2 = extreme events, 3 = unequal distribution of impacts, 4 = global aggregate impacts (economic + biodiversity), 5 = large-scale singular events.

\# PDSI-based drought estimates tend to overestimate drought impacts (see Section 3.3.4); hence projections with other drought indices may differ. Further quantifications may be found in Table 3.SM.1

1 Gerten et al., 2013; 2 Alfieri et al., 2017; 3 Liu et al., 2018; 4 Warren et al., 2018a; 5 Warzawski et al., 2013; 6 Brown et al., 2018a; 7 Rasmussen et al., (2018); 8 Yokoki et al., (2018); 9 Nicholls et al., 2018 <sup>[[#fn:r1401|1401]]</sup>

<span id="synthesis-of-key-elements-of-risk"></span>
=== 3.4.13 Synthesis of Key Elements of Risk ===

<div id="section-3-4-13-block-1"></div>

Some elements of the assessment in Section 3.4 were synthesized into Figure 3.18 and 3.20, indicating the overall risk for a representative set of natural and human systems from increases in global mean surface temperature (GMST) and anthropogenic climate change. The elements included are supported by a substantive enough body of literature providing at least ''medium confidence'' in the assessment. The format for Figures 3.18 and 3.20 match that of Figure 19.4 of WGII AR5 Chapter 19 (Oppenheimer et al., 2014) <sup>[[#fn:r1105|1105]]</sup> indicating the levels of additional risk as colours: undetectable (white) to moderate (detected and attributed; yellow), from moderate to high (severe and widespread; red), and from high to very high (purple), the last of which indicates significant irreversibility or persistence of climate-related hazards combined with a much reduced capacity to adapt. Regarding the transition from undetectable to moderate, the impact literature assessed in AR5 focused on describing and quantifying linkages between weather and climate patterns and impact outcomes, with limited detection and attribution to anthropogenic climate change (Cramer et al., 2014) <sup>[[#fn:r1106|1106]]</sup> . A more recent analysis of attribution to greenhouse gas forcing at the global scale (Hansen and Stone, 2016) <sup>[[#fn:r1107|1107]]</sup> confirmed that the impacts related to changes in regional atmospheric and ocean temperature can be confidently attributed to anthropogenic forcing, while attribution to anthropogenic forcing of those impacts related to precipitation is only weakly evident or absent. Moreover, there is no strong direct relationship between the robustness of climate attribution and that of impact attribution (Hansen and Stone, 2016) <sup>[[#fn:r1108|1108]]</sup> .

The current synthesis is complementary to the synthesis in Section 3.5.2 that categorizes risks into ‘Reasons for Concern’ (RFCs), as described in Oppenheimer et al. (2014) <sup>[[#fn:r1109|1109]]</sup> . Each element, or burning ember, presented here (Figures 3.18, 3.20) maps to one or more RFCs (Figure 3.21). It should be emphasized that risks to the elements assessed here are only a subset of the full range of risks that contribute to the RFCs. Figures 3.18 and 3.20 are not intended to replace the RFCs but rather to indicate how risks to particular elements of the Earth system accrue with global warming, through the visual burning embers format, with a focus on levels of warming of 1.5°C and 2°C. Key evidence assessed in earlier parts of this chapter is summarized to indicate the transition points between the levels of risk. In this regard, the assessed confidence in assigning the transitions between risk levels are as follows: L=Low, M=Medium, H=High, and VH=Very high levels of confidence. A detailed account of the procedures involved is provided in the Supplementary Material (3.SM.3.2 and 3.SM.3.3).

In terrestrial ecosystems (feeding into RFC1 and RFC4), detection and attribution studies show that impacts of climate change on terrestrial ecosystems began to take place over the past few decades, indicating a transition from no risk (white areas in Figure 3.20) to moderate risk below recent temperatures ( ''high confidence'' ) (Section 3.4.3). Risks to unique and threatened terrestrial ecosystems are generally projected to be higher under warming of 2°C compared to 1.5°C (Section 3.5.2.1), while at the global scale severe and widespread risks are projected to occur by 2°C of warming. These risks are associated with biome shifts and species range losses (Sections 3.4.3 and 3.5.2.4); however, because many systems and species are projected to be unable to adapt to levels of warming below 2°C, the transition to high risk (red areas in Figure 3.20) is located below 2°C ( ''high confidence'' ). With 3°C of warming, however, biome shifts and species range losses are expected to escalate to very high levels, and the systems are projected to have very little capacity to adapt (Figure 3.20) ( ''high confidence'' ) (Section 3.4.3).

In the Arctic (related to RFC1), the increased rate of summer sea ice melt was detected and attributed to climate change by the year 2000 (corresponding to warming of 0.7°C), indicating moderate risk. At 1.5°C of warming an ice-free Arctic Ocean is considered ''unlikely'' , whilst by 2°C of warming it is considered ''likely'' and this unique ecosystem is projected to be unable to adapt. Hence, a transition from high to very high risk is expected between 1.5°C and 2°C of warming.

For warm-water coral reefs, there is ''high confidence'' in the transitions between risk levels, especially in the growing impacts in the transition of warming from non-detectable (0.2°C to 0.4°C), and then successively higher levels risk until high and very high levels of risks by 1.2°C (Section 3.4.4 and Box 3.4). This assessment considered the heatwave-related loss of 50% of shallow water corals across hundreds of kilometres of the world’s largest continuous coral reef system, the Great Barrier Reef, as well as losses at other sites globally. The major increase in the size and loss of coral reefs over the past three years, plus sequential mass coral bleaching and mortality events on the Great Barrier Reef, (Hoegh-Guldberg, 1999; Hughes et al., 2017b, 2018) <sup>[[#fn:r1110|1110]]</sup> , have reinforced the scale of climate-change related risks to coral reefs. General assessments of climate-related risks for mangroves prior to this special report concluded that they face greater risks from deforestation and unsustainable coastal development than from climate change (Alongi, 2008; Hoegh-Guldberg et al., 2014; Gattuso et al., 2015) <sup>[[#fn:r1111|1111]]</sup> . Recent climate-related die-offs (Duke et al., 2017; Lovelock et al., 2017) <sup>[[#fn:r1112|1112]]</sup> , however, suggest that climate change risks may have been underestimated for mangroves as well, and risks have thus been assessed as undetectable to moderate, with the transition now starting at 1.3°C as opposed to 1.8°C as assessed in 2015 (Gattuso et al., 2015) <sup>[[#fn:r1113|1113]]</sup> . Risks of impacts related to climate change on small-scale fisheries at low latitudes, many of which are dependent on ecosystems such as coral reefs and mangroves, are moderate today but are expected to reach high levels of risk around 0.9°C– 1.1°C ( ''high confidence'' ) (Section 3.4.4.10).

The transition from undetectable to moderate risk (related to RFCs 3 and 4), shown as white to yellow in Figure 3.20, is based on AR5 WGII Chapter 7, which indicated with ''high confidence'' that climate change impacts on crop yields have been detected and attributed to climate change, and the current assessment has provided further evidence to confirm this (Section 3.4.6). Impacts have been detected in the tropics (AR5 WGII Chapters 7 and 18), and regional risks are projected to become high in some regions by 1.5°C of warming, and in many regions by 2.5°C, indicating a transition from moderate to high risk between 1.5°C and 2.5°C of warming ( ''medium confidence'' ).

Impacts from fluvial flooding (related to RFCs 2, 3 and 4) depend on the frequency and intensity of the events, as well as the extent of exposure and vulnerability of society (i.e., socio-economic conditions and the effect of non-climate stressors). Moderate risks posed by 1.5°C of warming are expected to continue to increase with higher levels of warming (Sections 3.3.5 and 3.4.2), with projected risks being threefold the current risk in economic damages due to flooding in 19 countries for warming of 2°C, indicating a transition to high risk at this level ( ''medium confidence'' ). Because few studies have assessed the potential to adapt to these risks, there was insufficient evidence to locate a transition to very high risk (purple).

Climate-change induced sea level rise (SLR) and associated coastal flooding (related to RFCs 2, 3 and 4) have been detectable and attributable since approximately 1970 (Slangen et al., 2016) <sup>[[#fn:r1114|1114]]</sup> , during which time temperatures have risen by 0.3°C ( ''medium confidence'' ) (Section 3.3.9). Analysis suggests that impacts could be more widespread in sensitive systems such as small islands ( ''high confidence'' ) (Section 3.4.5.3) and increasingly widespread by the 2070s (Brown et al., 2018a) as temperatures rise from 1.5°C to 2°C <sup>[[#fn:r1115|1115]]</sup> , even when adaptation measures are considered, suggesting a transition to high risk (Section 3.4.5). With 2.5°C of warming, adaptation limits are expected to be exceeded in sensitive areas, and hence a transition to very high risk is projected. Additionally, at this temperature, sea level rise could have adverse effects for centuries, posing significant risk to low-lying areas ( ''high confidence'' ) (Sections 3.4.5.7 and 3.5.2.5).

For heat-related morbidity and mortality (related to RFCs 2, 3 and 4), detection and attribution studies show heat-related mortality in some locations increasing with climate change ( ''high confidence'' ) (Section 3.4.7; Ebi et al., 2017) <sup>[[#fn:r1116|1116]]</sup> . The projected risks of heat-related morbidity and mortality are generally higher under warming of 2°C than 1.5°C ( ''high confidence'' ), with projections of greater exposure to high ambient temperatures and increased morbidity and mortality (Section 3.4.7). Risk levels will depend on the rate of warming and the (related) level of adaptation, so a transition in risk from moderate (yellow) to high (red) is located between 1°C and 3°C ( ''medium confidence'' ).

For tourism (related to RFCs 3 and 4), changing weather patterns, extreme weather and climate events, and sea level rise are affecting many – but not all – global tourism investments, as well as environmental and cultural destination assets (Section 3.4.4.12), with ‘last chance to see’ tourism markets developing based on observed impacts on environmental and cultural heritage (Section 3.4.9.1), indicating a transition from undetectable to moderate risk between 0°C and 1.5°C of warming ( ''high confidence'' ). Based on limited analyses, risks to the tourism sector are projected to be larger at 2°C than at 1.5°C, with impacts on climate-sensitive sun, beach and snow sports tourism markets being greatest. The degradation or loss of coral reef systems is expected to increase the risks to coastal tourism in subtropical and tropical regions. A transition in risk from moderate to high levels of added risk from climate change is projcted to occur between 1.5°C and 3°C ( ''medium confidence'' ).

Climate change is already having large scale impacts on ecosystems, human health and agriculture, which is making it much more difficult to reach goals to eradicate poverty and hunger, and to protect health and life on land (Sections 5.1 and 5.2.1 in Chapter 5), suggesting a transition from undetectable to moderate risk for recent temperatures at 0.5°C of warming ( ''medium confidence'' ) ''.'' Based on the limited analyses available, there is evidence and agreement that the risks to sustainable development are considerably less at 1.5°C than 2°C (Section 5.2.2), including impacts on poverty and food security. It is easier to achieve many of the sustainable development goals (SDGs) at 1.5°C, suggesting that a transition to higher risk will not begin yet at this level. At 2°C and higher levels of warming (e.g., RCP8.5), however, there are high risks of failure to meet SDGs such as eradicating poverty and hunger, providing safe water, reducing inequality and protecting ecosystems, and these risks are projected to become severe and widespread if warming increases further to about 3°C ( ''medium confidence'' ) (Section 5.2.3).

'''Disclosure statement:''' The selection of elements depicted in Figures 3.18 and 3.20 is not intended to be fully comprehensive and does not necessarily include all elements for which there is a substantive body of literature, nor does it necessarily include all elements which are of particular interest to decision-makers.

<div id="section-3-4-13-block-2"></div>

<span id="figure-3.20"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 3.20'''

<span id="the-dependence-of-risks-andor-impacts-associated-with-selected-elements-of-human-and-natural-systems-on-the-level-of-climate-change-adapted-from-figure-3.21-and-from-ar5-wgii-chapter-19-figure-19.4-and-highlighting-the-nature-of-this-dependence-between-0c-and-2ºc-warming-above-pre-industrial-levels."></span>
<!-- IMG CAPTION -->
'''The dependence of risks and/or impacts associated with selected elements of human and natural systems on the level of climate change, adapted from Figure 3.21 and from AR5 WGII Chapter 19, Figure 19.4, and highlighting the nature of this dependence between 0°C and 2ºC warming above pre-industrial levels.'''
<!-- IMG FILE -->
[[File:aa5ea5f33ab432c0ae398aac0d2f07f2 figure-3.20-1024x725.jpg]]

The selection of impacts and risks to natural, managed and human systems is illustrative and is not intended to be fully comprehensive. Following the approach used in AR5, literature was used to make expert judgements to assess the levels of global warming at which levels of impact and/or risk are undetectable (white), moderate (yellow), high (red) or very high (purple). The colour scheme thus indicates the additional risks due to climate change. The transition from red to purple, introduced for the first time in AR4, is defined by a very high risk of severe impacts and the presence of significant irreversibility or persistence of climate-related hazards combined with limited ability to adapt due to the nature of the hazard or impact. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GMST. As was done previously, this assessment takes autonomous adaptation into account, as well as limits to adaptation independently of development pathway. The levels of risk illustrated reflect the judgements of the authors of Chapter 3 and Gattuso et al. (2015 <sup>[[#fn:r1117|1117]]</sup> ; for three marine elements). The grey bar represents the range of GMST for the most recent decade: 2006–2015.

<!-- END IMG -->
<span id="avoided-impacts-and-reduced-risks-at-1.5c-compared-with-2c-of-global-warming"></span>