Editing IPCC:AR6/WGII/Chapter-6 (section)

== 6.2 Impacts and Risks ==

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This section assesses the impacts of hazards associated with climate change that will affect cities, settlements and key infrastructure, particularly how climate systems and urban systems interact to produce patterns of risk and loss. The conclusions of the IPCC Special Report on Global Warming of 1.5°C noted that ‘Global warming of 2°C is expected to pose greater risks to urban areas than global warming of 1.5°C ( ''medium confidence'' )’.

This section commences with a review of scenarios and pathways linking urban and infrastructural development with climate change; then assesses the key risks (with a focus on those for which there is a greater degree of evidence or confidence since AR5) and how these risks are created in urban settings. It then assesses evidence on the differentiated nature of human vulnerability and the risks affecting key infrastructure. Finally, this discussion reviews compound and cascading risks, and risks created by adaptation actions.

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=== 6.2.1 Risk Creation in Cities, Settlements and Infrastructure ===

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In addition to direct climate impacts, interactions among changing urban form, exposure and vulnerability can create climate change-induced risks and losses for cities and settlements. Climate change already interacts with ongoing global trends in urbanisation to create regionally specific impacts and risk profiles. Through demographic change and encroachment into natural and agricultural lands and coastal zones, rapidly expanding urban settlements can place new physical assets and people in locations with high exposure (Tessler et al., 2015; [[#Arnell--2016|Arnell and Gosling, 2016]] ; Kundzewicz et al., 2014). Increasing rates of global urbanisation will pose additional challenges to areas that have high levels of poverty, unemployment, informality, and housing and service backlogs ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ; Williams et al., 2019). There is some evidence to suggest that climate change impacts themselves are increasing urbanisation rates, generating a challenging feedback loop. In sub-Saharan Africa, for example, manufacturing towns have experienced growth because of population movement following droughts in agricultural hinterlands (Henderson, Storeygard and Deichmann, 2017). The rapid rate of urbanisation therefore presents a time-limited opportunity to work toward risk reduction and transformational adaptation in towns and cities. The following sections explore these dynamic interactions between urban systems and climate change, and how these shape risk for people and for key infrastructures.

Examining projected climate change impacts and resulting risks in cities, settlements and key infrastructures requires the prerequisite development of scenarios which are plausible descriptions of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces, (e.g., rate of technological change, prices and relationships) and pathways or the temporal evolution of natural and/or human systems, such as demographic and urban land cover change, toward a future state or states ( [[#Gao--2020|Gao and O’Neill, 2020]] ; [[#Gao--2019|Gao and O’Neill, 2019]] ; see also [[#6.1.5|Section 6.1.5]] ).

Climate change research creates scenarios integrating emissions and development pathways dimensions (Ebi et al., 2014; van Vuuren et al., 2017b; van Vuuren et al., 2017a) and Representative Concentration Pathways (RCPs) (Riahi et al., 2017). For risk reduction at regional scales, scenarios require urban-relevant climate projections, for example, downscaling from global and regional climate models of variables such as temperature, precipitation, air pollutants and sea level rise that are analysed usually for mid- or end-21st Century timeframes (e.g., Mika et al., 2018; Kusaka et al., 2016; Masson et al., 2014b). These data are needed to ascertain likely ranges of climate change impacts within city and settlement boundaries, and to quantify physical exposure when developing pathways for risk reduction. Consideration of current and projected future growth pathways of multiple urban sectors and key infrastructure, for example, transport, energy and buildings, are also needed to estimate probabilities of risk outcomes and damages within and across urban systems (O’Neill et al., 2015)(WGIII AR6 Section 8).

The challenges of managing these risks are amplified by the complex interactions between climate and urban scenarios, owing to the smaller spatial–temporal scales of urban areas in climate change modelling relative to global climate models (GCM) and shared socioeconomic pathways (SSP); geographical or geomorphological variations in city location; uncertainties arising from incomplete assumptions about socio-economic pathways at urban scales affecting urban demographics, for example, fertility rates and life expectancies or increased rural–urban migration; and challenges in modelling the urban climate and in developing urban climate observational networks in cities (WGI Box 10.3; Kamei, Hanaki and Kurisu, 2016; Yu, Jiang and Zhai, 2016; [[#Jiang--2017|Jiang and O’Neill, 2017]] ; Baklanov et al., 2018). Additionally, carbon-intensive economic growth, increasing inequalities, global pandemics, and uncontrolled or unmanaged urbanisation will exacerbate the exposure and vulnerability of urban systems modelled in existing climate scenarios and pathways ( ''high confidence'' ) (Phillips et al., 2020; [[#Jackson--2021|Jackson, 2021]] ; [[#Raworth--2017|Raworth, 2017]] ; Moraci et al., 2020). Mitigating these outcomes requires new forms of urban governance for climate adaptation, disaster risk reduction and building resilience (see [[#6.4|Section 6.4]] ).

Strong connections exist between climate change scenarios and urban climate-related risks. In some cases, the linkage is direct as climate change is associated with more frequent and more intense extreme weather and climate events, as assessed in [[#6.2.3|Section 6.2.3]] . In other contexts, the connection is mediated by urban developmental pathways arising from local-scale environmental stresses and degradation, and access to adaptation options, as reviewed in [[#6.2.4|Section 6.2.4]] .

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=== 6.2.2 Dynamic Interaction of Urban Systems with Climate ===

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Urban systems interact with climate systems in multiple, dynamic and complex ways ( [[#6.1.1|Section 6.1.1]] , Doblas-Reyes et al., 2021 Box 10.3). Climate change can have direct impacts on the functioning of urban systems, while the nature of those systems plays a substantial role in modifying the effects of climate change ( ''high confidence'' ) (Frank, Delano and Caniglia, 2017; [[#Smid--2018|Smid and Costa, 2018]] ). An example of this urban system climate nexus is the urban heat island effect (discussed in [[#6.2.3.1|Section 6.2.3.1]] ) ( [[#Susca--2020|Susca and Pomponi, 2020]] ). Assessing the inter-relationships between multiple systems and a range of hazards is particularly important as many cities are presently exposed to multiple climate-related hazards: more than 100 cities analysed as part of a 571 city study in Europe were deemed vulnerable to two or more climate impacts (Guerreiro et al., 2018). Rapid expansion of urban areas increases the exposure of urban populations to various hazards independent of global climate change. [[#Huang--2019|Huang et al. (2019)]] project that urban land areas will expand by 0.6–1.3 million km 2 between 2015 and 2050, an increase of 78–171% over the urban footprint in 2015. Specifically in relation to floods and droughts, Güneralp et al. (2015) calculate that even without accounting for climate change, the extent of urban areas exposed to flood hazards will increase 2.7 times between 2000 and 2030, the extent exposed to drought hazards will approximately double during this period, and urban land exposed to both floods and droughts will increase more than 2.5 times.

This section assesses observed and expected impacts from the main hazards identified for cities, settlements and infrastructure; temperature extremes (and the urban heat island), flooding (including sea level rise), water scarcity and security, as well as other hazards that are either less well-studied and/or likely to affect only a limited number of locations. The data assessed in this section are limited by uneven coverage. Despite improvements since AR5, data continue to be more complete for extreme events than for chronic hazards and everyday risks, which may have high aggregate impacts and disproportionately erode the well-being of urban poor households, especially for the most vulnerable, including women, children, the aged, disabled and homeless (van Wesenbeeck, Sonneveld and Voortman, 2016; Kinay et al., 2019; Connelly et al., 2018). Data coverage is also less comprehensive for smaller settlements in poorer countries, the locations where urban growth is often high and adaptive capacities are often low (e.g., Rufat et al., 2015). Thus, data gaps frequently coincide with highly vulnerable populations (Rufat et al., 2015; [[#Satterthwaite--2017|Satterthwaite and Bartlett, 2017]] ). Here, even small changes in livelihoods, health, or representation and voice can rapidly bring households into positions of risk, even when hazard conditions are relatively stable (Ziervogel et al., 2017). These structural limits in available data are discussed also in Section 7 (Health, Well-being and the Changing Structure of Communities) and Section 8 (Poverty, Livelihoods and Sustainable Development), and in Doblas-Reyes et al. (2021) Box 10.3. There are implications also for adaptation ( [[#6.3|Section 6.3]] ), where the greater availability of evidence on exposure-driven risk can limit resilience-building interventions that focus on the reduction of vulnerability.

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==== 6.2.2.1 Temperatures and the Urban Heat Island ====

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Higher temperatures associated with climate change, through warmer global average temperatures and regional heatwave episodes, will interact with urban systems in a variety of ways (Doblas-Reyes et al., 2021 Box 10.3). Future urbanisation will amplify projected local air temperature increase, particularly by strong influence on minimum temperatures, which is approximately comparable in magnitude to global warming ( ''high confidence'' ) (Arias et al. In Press Box TS14). Within cities, exposure to heat island effects is uneven, with some populations disproportionately exposed to risk including low income communities, children, the elderly, disabled, and ethnic minorities (Quintana-Talvac et al., 2021; Sabrin et al., 2020; [[#Chambers--2020|Chambers, 2020]] ; and see later in this section).

The risks to cities, settlements and infrastructure from heatwaves will worsen ( ''high confidence'' ) (Leal Filho et al., 2021; see also Sections 6.2.5; 6.3.3.1, Arias et al. In Press Box TS14). Depending on the RCP, between half (RCP2.6) to three-quarters (RCP8.5) of the human population could be exposed to periods of life-threatening climatic conditions arising from coupled impacts of extreme heat and humidity by 2100 (Figure 6.3; Mora et al., 2017; Zhao et al., 2021). Cities in mid-latitudes are potentially subject to twice the levels of heat stress compared with their rural surroundings under all RCP scenarios by 2050, for example Belgian cities (Wouters et al., 2017). A disproportionate level of exposure exists in subtropical cities subject to year-round warm temperatures and higher humidity, requiring less warming to exceed ‘dangerous’ thresholds, for example Nairobi (Scott et al., 2017) and São Paulo (Diniz, Gonçalves and Sheridan, 2020). It is expected that more than 90% of the 300 million people who will be exposed to super- and ultra-extreme heatwaves in the Middle East and North Africa will live in urban centres (Zittis et al., 2021), while the major driver for increased heat exposure is the combination of global warming and population growth in already-warm cities in regions including Africa, India and the Middle East ( [[#Klein--2021|Klein and Anderegg, 2021]] ).

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[[File:95ebfce822c99664f5ae5dda0a9e14f0 IPCC_AR6_WGII_Figure_6_003.png]]

'''Figure 6.3 |''' '''Global distribution of population exposed to hyperthermia from extreme heat for (a) the present, and projections from selected Representative Concentration Pathways in (b) the mid-21st century and (c) the end of the 21st century.''' Shading indicates projected number of days in a year in which conditions of air temperature and humidity surpass a common threshold beyond which climate conditions turned deadly and pose a risk of death (Mora et al ''.'' , 2017). Named cities are the top 15 urban areas by population size during 2020, 2050 and 2100, respectively, as projected by [[#Hoornweg--2017|Hoornweg and Pope (2017)]]

Locally, the urban heat island also elevates temperatures within cities relative to their surroundings. It is caused by physical changes to the surface energy balance of the pre-urban site from urbanisation, resulting from the thermal characteristics and spatial arrangement of the built environment, and anthropogenic heat release (Oke et al., 2017; Chow et al., 2014; [[#Susca--2020|Susca and Pomponi, 2020]] ; Doblas-Reyes et al., 2021 FAQ10.1). A considerable body of evidence exists on how the multi-scale impacts and consequent risks arise when local elevated temperatures within settlements are enhanced by climate change, with specific elements of this affecting megacities (Darmanto et al., 2019). The urban heat island itself is amplified during heatwaves ( [[#Founda--2017|Founda and Santamouris, 2017]] ), but the extent to which varies regionally and by time of day (Ward et al., 2016a; Zhao et al., 2018b; Eunice Lo et al., 2020). When combined with warming induced by urban growth, extreme heat risks are expected to affect half of the future urban population, with a particular impact in the tropical Global South and in coastal cities and settlements (Huang et al., 2019; Section [https://www.ipcc.ch/chapter/6#CCP2.2 CCP2.2.2] ; Table CCP2.A.1).

Heat risk is associated with a range of health issues for urban residents, with the consequences of higher urban temperatures being unevenly distributed across urban populations ( ''high confidence'' ). Clear evidence exists of increased health risks to elderly populations in settlements, especially higher levels of mortality in elderly populations from urban heat islands during heatwave events ( [[#Fernandez%20Milan--2015|Fernandez Milan and Creutzig, 2015]] ; Taylor et al., 2015; Ward et al., 2016a; Heaviside, Macintyre and Vardoulakis, 2017; Gough et al., 2019; Xu et al., 2020a), while health and fitness variables are also major determinants of the effects of heat stress (Schuster et al., 2017) (see also Table 7.2). Heat stress and dehydration are also related to behavioural and learning concerns, with dehydration impairing concentration and cognition for both adults and children ( [[#Merhej--2019|Merhej, 2019]] ). Literature on paediatric heat exposure is associated with increases in emergency department visits for heat-related illnesses, electrolyte imbalances, fever, renal disease and respiratory disease in young children (Winquist et al., 2016), with less severe outcomes such as lethargy, headaches, rashes, cramps and exhaustion negatively affecting children in school and play environments ( [[#Vanos--2015|Vanos, 2015]] ; [[#Hyndman--2017|Hyndman, 2017]] ). Young children in cities are particularly sensitive to heatwaves, and may have little experience or capacity to cope with heat extremes ( [[#Norwegian%20Red%20Cross--2019|Norwegian Red Cross, 2019]] ). Such vulnerability of young children to heat is compounded with projected urbanisation rates and poor infrastructure, particularly in South Asian and in African cities ( [[#Smith--2019|Smith, 2019]] ). There is evidence that socioeconomically disadvantaged populations are more ''likely'' to live in hotter parts of cities associated with higher-density residential land use in dwellings with less effective insulation built with poorer or older construction materials (Inostroza, Palme and de la Barrera, 2016; Tomlinson et al., 2011). Specific emerging risks for occupational and related heat illnesses are found in urban tropical or subtropical low- and middle-income countries (Andrews et al., 2018; Green et al., 2019).

There is an emerging risk of diminished indoor thermal comfort due to climate change, evidenced by research into negatively affected thermal comfort indices and/or increased number of overheating hours under future emissions scenarios ( ''medium confidence'' ) (e.g., [[#Liu--2015|Liu and Coley, 2015]] ; van Hooff et al., 2014; Vardoulakis et al., 2015; [[#Dodoo--2016|Dodoo and Gustavsson, 2016]] ; [[#Invidiata--2016|Invidiata and Ghisi, 2016]] ; [[#Makantasi--2016|Makantasi and Mavrogianni, 2016]] ; [[#Mulville--2016|Mulville and Stravoravdis, 2016]] ; Taylor et al., 2016; Hamdy et al., 2017; Pérez-Andreu et al., 2018; Salthammer et al., 2018; Dino and Meral Akgül, 2019; [[#Osman--2019|Osman and Sevinc, 2019]] ; Roshan, Oji and Attia, 2019). Decreases in thermal comfort and increases in overheating risks depend on building characteristics, such as thermal resistance, presence of solar shading, thermal mass, ventilation, orientation and geographical location (e.g., [[#Liu--2015|Liu and Coley, 2015]] ; van Hooff et al., 2014; Vardoulakis et al., 2015; [[#Dodoo--2016|Dodoo and Gustavsson, 2016]] ; [[#Invidiata--2016|Invidiata and Ghisi, 2016]] ; [[#Makantasi--2016|Makantasi and Mavrogianni, 2016]] ; [[#Mulville--2016|Mulville and Stravoravdis, 2016]] ; Taylor et al., 2016; Hamdy et al., 2017; Pérez-Andreu et al., 2018; Salthammer et al., 2018; Dino and Meral Akgül, 2019; [[#Osman--2019|Osman and Sevinc, 2019]] ; Roshan, Oji and Attia, 2019; Alves, Gonçalves and Duarte, 2021). Most of these studies employed numerical simulations in which different climate scenarios were used to construct future climate data. In hot climates, energy-efficient buildings with high insulation values and high airtightness, which have insufficient protection from solar heat gains and/or limited ventilation capabilities, are generally more vulnerable to overheating than older buildings with lower insulation levels (e.g., van Hooff et al., 2014; Vardoulakis et al., 2015; [[#Makantasi--2016|Makantasi and Mavrogianni, 2016]] ; [[#Mulville--2016|Mulville and Stravoravdis, 2016]] ; Salthammer et al., 2018; [[#Fisk--2015|Fisk, 2015]] ; Hamdy et al., 2017; Fosas et al., 2018; [[#Ozarisoy--2019|Ozarisoy and Elsharkawy, 2019]] ; see also Fox-Kemper et al., 2021 9.7 for building heat mitigation/adaptation links).

Higher urban temperatures result in lower labour productivity levels and economic outputs ( ''medium confidence'' ) ( [[#Graff%20Zivin--2014|Graff Zivin and Neidell, 2014]] ; [[#Yi--2017|Yi and Chan, 2017]] ; Houser et al., 2015; [[#Stevens--2017|Stevens, 2017]] ; see [[IPCC:Wg2:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). Globally, urban heat stress is projected to reduce labour capacity by 20% in hot months by 2050 compared with a current 10% reduction (Dunne, Stouffer and John, 2013). Burke et al. (2015) demonstrate a nonlinear relationship between temperature and global economic productivity, with potential global losses of 23% by 2100 due to climate change alone. In specific cases, [[#Zander--2015|Zander et al. (2015)]] estimate heat-related reductions in urban labour productivity in Australia to cost USD 3.6–5.1 billion yr −1 , based on self-reported performance reduction and absenteeism among 1726 workers in 2013–14 [[#footnote-002|2]] ; while the high-temperature subsidies given in China at outdoor air temperatures above 35°C are projected to increase to USD 35.7 billion yr −1 after 2030 (compared with USD 5.5 billion yr −1 for 1979–2005) (Zhao et al., 2016) [[#footnote-001|3]] .

Higher urban temperatures place unequal economic stresses on residents and households through higher utilities demand during warm periods, for example, electricity in regions where air conditioning is predicted to become more prevalent, and due to medical costs associated with care for heat illnesses and related health effects, missed work and other related impacts ( ''medium confidence'' ) (Jovanović et al., 2015; Liu et al., 2019; Schmeltz, Petkova and Gamble, 2016; [[#Soebarto--2014|Soebarto and Bennetts, 2014]] ; [[#Zander--2019|Zander and Mathew, 2019]] ; Zander et al., 2015). Such stresses are projected to increase in many regions associated with continuing global-scale climate change and urbanisation (e.g., Véliz et al., 2017; Ang, Wang and Ma, 2017; Bezerra et al., 2021), although some of these effects in cold-climate cities are offset by reduced stresses in winter associated with urban heat island or rising temperatures more generally (see [[#6.2.2.4|Section 6.2.2.4]] ).

Thermal inequity can also be seen as a distributive justice risk ( [[#Mitchell--2018|Mitchell and Chakraborty, 2018]] ). There are often disproportionate increases of risk for individuals of lower socioeconomic status, especially migrants, from exposure to urban heat. These arise from inadequate housing, less access to air-conditioning, and occupations, such as manual labour and waste picking, that exacerbate heat exposure ( [[#Chu--2018|Chu and Michael, 2018]] ; Santha et al., 2016). Research from South Africa has shown that housing occupied by poor communities regularly experience indoor temperature fluctuations that are between 4°C and 5°C warmer compared with outdoor temperatures (Naicker et al., 2017); while evidence from the USA indicates that historical housing policies, particularly the ‘redlining’ of neighbourhoods based on racially motivated perceptions, are associated with areas that are exposed to elevated land surface temperatures (Hoffman, Shandas and Pendleton, 2020).

Social surveys from temperate and tropical cities highlight the risk of reduced quality of life during heat events, including increased incidence of personal discomfort in indoor and outdoor settings, elevated anxiety, depression and other indicators of adverse psychological health, and reductions in physical activity, social interactions, work attendance, tourism and recreation ( ''high confidence'' ) (Chow et al., 2016; Elnabawi, Hamza and Dudek, 2016; [[#Obradovich--2017|Obradovich and Fowler, 2017]] ; Wang et al., 2017; Wong et al., 2017; Lam, Loughnan and Tapper, 2018; Alves, Duarte and Gonçalves, 2016). Extreme heat may also have a cultural impact, for example affecting major sporting events, with negative impacts on the athletic performance (Brocherie, Girard and Millet, 2015; Casa et al., 2015) and the experience and health of spectators (Hosokawa, Grundstein and Casa, 2018; Kosaka et al., 2018; Matzarakis et al., 2018; Vanos et al., 2019).

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==== 6.2.2.2 Urban Flooding ====

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Flood risks in settlements arise from hydrometeorological events interacting with the urban system which exposes settlements to river (fluvial) floods, flash floods, pluvial (precipitation-driven) floods, sewer floods, coastal floods and glacial lake outburst floods ( [[#IPCC--2012|IPCC, 2012]] ). Sea level increase and increases in tropical cyclone storm surge and rainfall intensity will increase the probability of coastal city flooding ( ''high confidence'' ) (WGI Box TS14). Globally, the increase in frequencies and intensities of extreme precipitation from global warming will ''likely'' [[#footnote-000|4]] expand the global land area affected by flood hazards ( ''medium confidence'' ) ((Alfieri et al., 2018; Alfieri et al., 2017; Hoegh-Guldberg et al., 2018); [[IPCC:Wg2:Chapter:Chapter-4#4.2.4|Section 4.2.4.2]] ). [[#Mishra--2015|Mishra et al. (2015)]] noted that out of 241 urban areas, only 17% of cities experienced statistically significant increases in frequencies of extreme precipitation events from 1973 to 2012. In the future, there is some evidence that changes in high-intensity short duration (sub-daily) rainfall in urban areas will increase ( ''limited evidence'' , ''medium agreement'' ) (Kendon et al., 2014; Ban, Schmidli and Schär, 2015; Abiodun et al., 2017).

Flooding associated with sea level rise is addressed in more detail in Cross-Chapter Paper 2, with detailed regional examples from Africa discussed in [[IPCC:Wg2:Chapter:Chapter-9#9.3|Section 9.3]] . Coastal flooding associated with sea level rise is exacerbated due to the significant number of people living in subsiding areas. As a result of this, the average coastal resident is experiencing (over the last two decades) rates of relative sea level rise three to four times higher than typical estimates due to climate-induced changes (Nicholls et al., 2021). This process can also result in release of coastal waste into urban areas (Beaven et al., 2020).

Urban flooding risks are also increased by urban expansion and land use and land cover change which enlarges impermeable surface areas through soil sealing, impacting drainage of floodwaters with consequent sewer overflows ( ''high confidence'' ) (Arnbjerg-Nielsen et al., 2013; Ziervogel et al., 2016; [[#Aroua--2016|Aroua, 2016]] ; Kundzewicz et al., 2014). These risks are also driven by increasing societal complexity, urban developmental policy on flood control and long-term economic growth (Berndtsson et al., 2019), including in mega-cities (Januriyadi et al., 2018). The increase in flood risk from urban development can be considerable; based on modelling of two RCP (4.5 and 8.5) scenarios, Kaspersen et al. (2017) noted flooding in four European cities could increase by up to 10% for every 1% increase in impervious surface area. Risks are also compounded by the location of settlements, with greater risks within cities located in low elevation coastal zones subject to sea level rise, potential land subsidence and exposure to tropical cyclones (( [[#Koop--2017|Koop and van Leeuwen, 2017]] ; Hoegh-Guldberg et al., 2018; see also Section [https://www.ipcc.ch/chapter/6#CCP2.2 CCP2.2] ) and within informal settlements, where generally little investment in drainage solutions exists and flooding regularly disrupts livelihoods and disproportionately undermines local food safety and security for the urban poor (Dodman, Colenbrander and Archer, 2017; Dodman et al., 2017; Kundzewicz et al., 2014; Sections 5.4 and 5.8).

Future risks of urban flooding is increasing in conjunction with continued increases in global surface temperature ( ''high confidence'' ) ( [[#IPCC--2019b|IPCC, 2019b]] ; Winsemius et al., 2015; [[#Kulp--2019|Kulp and Strauss, 2019]] ; Hoegh-Guldberg et al., 2018). In particular, Asian cities are highly exposed to future flood risks arising from urbanisation processes. Between 2000 and 2030, rapid urbanisation in Indonesia will elevate flood risks by 76–120% for river and coastal floods, while sea level rise will further increase the exposure by 19–37% (Muis et al., 2015). In Can Tho, Vietnam, current urban development patterns put new assets and infrastructure at risk due to sea level rise and river flooding in the Mekong Delta (Chinh et al., 2017; Chinh et al., 2016). Flooding in urban areas is exacerbated both by the encroachment of urban areas into areas that retain water and by the lack of infrastructure such as embankments and flood walls, as is the case for large areas of Dhaka East (Haque, Bithell and Richards, 2020). [[#Zhou--2019|Zhou et al. (2019)]] have also shown that for the city of Hohhot, China, the increase in impervious surfaces contributes between 2–4 times more to modelled annual flood risk compared with risk induced by climate change.

Global trends in surface water flooding are increasing, which poses risks to vulnerable urban systems depending on current adaptation measures to manage flooding impacts, for example, stormwater management, green infrastructure and sustainable urban drainage systems (Molenaar et al., 2015). The economic risks associated with future surface water flooding in towns and cities are considerable. For example in the UK, expected annual damages from surface water flooding may increase by £60–200 million for projected 2–4°C warming scenarios; enhanced adaptation actions could manage flooding up to a 2°C scenario but will be insufficient beyond that (Sayers et al., 2015). Analyses conducted in South Korea suggests that future flood levels could exceed current flood protection design standards by as much as 70% by 2100, considerably increasing urban flood risk (Kang et al., 2016). Modelling of urban flood damage in the Kelani River Basin in Sri Lanka showed increased frequency of flooding by 2030 could increase potential urban property damage by up to 10.2% (Komolafe Akinola, Herath and Avtar, 2018). Urban flood impacts may also exacerbate health burdens (including disease outbreaks of malaria, typhoid and cholera), which are compounded by damage to medical facilities (e.g., damage to hospitals and disruption of medicinal supply chains), as observed in urban areas of Ghana (Gough et al., 2019). In addition, emerging research shows the cascading consequences of hazard events, in this case urban flooding, on other risks to well-being in ways that are particularly severe for the urban poor, including mental ill-health, incidents of domestic violence impacting children and women, chronic diseases and salinity of drinking water ((Matsuyama, Khan and Khalequzzaman, 2020); [[IPCC:Wg2:Chapter:Chapter-4#4.2.4|Section 4.2.4.5]] ; [[#6.2.4.2|Section 6.2.4.2]] ; Box 7.2; [[IPCC:Wg2:Chapter:Chapter-8#8.4.5.2|Section 8.4.5.2]] ).

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==== 6.2.2.3 Urban Water Scarcity and Security ====

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Urban water scarcity occurs when gaps exist between supply and demand of available freshwater resources (Zhang et al., 2019). Urban water security requires a sustainable quantity and quality of water to meet community and ecosystem needs in a changing climate ( [[#Romero-Lankao--2019|Romero-Lankao and Gnatz, 2019]] ; Allan, Kenway and Head, 2018; Huang, Xu and Yin, 2015; [[#Chen--2016|Chen and Shi, 2016]] ). Risks arising from urban water scarcity worldwide are ''very likely'' increasing due to climate drivers (e.g., warmer temperatures and droughts) and urbanisation processes (e.g., land use changes, migration to cities and changing patterns of water use including over extraction of surface and groundwater resources) affecting supply and demand ( ''high confidence'' ) (Allan, Kenway and Head, 2018; Crausbay et al., 2020; Haddeland et al., 2014; Pickard et al., 2017; De Stefano et al., 2015; Sun et al., 2019; Van Loon et al., 2016; Zhang et al., 2019; [[IPCC:Wg2:Chapter:Chapter-4#4.2.4|Section 4.2.4.4]] ; See Box 8.6 for case study on 2018 Cape Town drought). Flörke et al. (2018) estimates that nearly a third of all major cities worldwide may exhaust their current water resources by 2050. Globally, projections suggest that 350 million (± 158.8 million) more people living in urban areas will be exposed to water scarcity from severe droughts at 1.5°C warming and 410.7 million (± 213.5 million) at 2°C warming (Liu et al., 2018).

Decreased regional precipitation and associated changes in runoff and storage from droughts is exacerbating urban scarcity by impairing the quality of water available for its resource management in cities ( ''high confidence'' ). For example, less runoff to freshwater rivers can increase salinity and concentrate pathogens and pollutants that increases risks of urban water scarcity (Hellwig, Stahl and Lange, 2017; [[#Jones--2018|Jones and van Vliet, 2018]] ; [[#Leddin--2020|Leddin and Macrae, 2020]] ; [[#Lorenzo--2020|Lorenzo and Kinzig, 2020]] ; Ma et al., 2020; [[#Mosley--2015|Mosley, 2015]] ; Zhang et al., 2019; van Vliet, Flörke and Wada, 2017; see also Box 6.2). Drought also changes the dynamics of groundwater pollution, leading to increased environmental health risks when those sources are used for urban water supplies (Kubicz et al., 2021; Moreira et al., 2020; Pincetl et al., 2019). Changes in the nature of droughts, for example, hotter droughts ( [[#Herrera--2017|Herrera and Ault, 2017]] ), snow droughts (Cooper, Nolin and Safeeq, 2016; Mote et al., 2016) or ‘flash’ droughts (Otkin et al., 2016; Otkin et al., 2018; Pendergrass et al., 2020) can exacerbate urban water scarcity, exposing the limitations of engineered water infrastructure designed to accommodate historical patterns of supply and demand (Gober et al., 2016; [[#Ulibarri--2019|Ulibarri and Scott, 2019]] ; Zhao et al., 2018a).

Risks of urban water scarcity and security are compounded by vulnerabilities such as service availability and quality of infrastructure to supply water for increased urban demand from in-migration to cities ( ''medium confidence'' ) (Ahmadalipour et al., 2019; Dong et al., 2020; Reynolds et al., 2019; Thomas et al., 2017; [[#Mullin--2020|Mullin, 2020]] ). Risks to local water security in cities are also exacerbated by drivers such as dependence on imported water resources from distant locales that may be exposed to additional drought risks ( ''high confidence'' ) (Ahams et al., 2017; Li et al., 2019b; Marston et al., 2015; Zhao et al., 2020; Zhang et al., 2020); from considerable projected urban expansion in drought-stressed areas, for example, across drylands of Western Asia and North Africa (Güneralp et al., (2015); and by export of virtual water (i.e., export of water embedded in food and energy) from local sources to distant trading partners (Djehdian et al., 2019; D’Odorico et al., 2018; [[#Fulton--2015|Fulton and Cooley, 2015]] ; [[#Rushforth--2016|Rushforth and Ruddell, 2016]] ; Verdon-Kidd et al., 2017; Vora et al., 2017).

Droughts interact and manifest in complex ways in interconnected urban areas that ''likely'' increase risks of urban water scarcity (Tapia et al., 2017; [[#Rushforth--2015|Rushforth and Ruddell, 2015]] ). Urban interdependencies mean droughts in one region can limit water resources availability in another (e.g., Macao and Zhuhai, Hong Kong, Shenzhen in China, Singapore and Johor, in cities in Pakistan and India, and in the west and southwest USA) (Chuah, Ho and [[#Chow--2018|Chow, 2018]] ; Gober et al., 2016; Srinivasan, Konar and Sivapalan, 2017; Zhang et al., 2019; Zhao et al., 2020). Likewise, physical and social teleconnections mean decisions made about water resources in one region or location may impact another in unexpected ways ( [[#Moser--2015|Moser and Hart, 2015]] ; Liu et al., 2015).

Urban water security risks are confounded by inequities in economic opportunity, risk exposure and human well-being ( ''medium evidence'' ) (Sena et al., 2017; Stanke et al., 2013; [[IPCC:Wg2:Chapter:Chapter-4#4.2.4|Section 4.2.4.5]] ). Water scarcity is felt more acutely among low-income compared with high-income populations (Nerkar et al., 2016), and scarcity on top of inequities and political instability can lead to security issues, for example, conflict between different water users (Cosic et al., 2019; von Uexkull et al., 2016; Ahmadalipour et al., 2019; [[#Döring--2020|]] [[#Döring--2020|Döring, 2020]] ; Ide et al., 2021), particularly when road infrastructures and access to water are limited ( [[#Detges--2016|Detges, 2016]] ; Sena et al., 2017). Scarcity risks may also be exacerbated by human and ecosystem needs in water-short years (Srinivasan, Konar and Sivapalan, 2017). Finally, growing populations along with migration into water scarce regions can exacerbate water security issues ( [[#Akhtar--2020|Akhtar and Shah, 2020]] ; [[#Singh--2019|Singh and Sharma, 2019]] ).

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<span id="other-dynamic-interactions"></span>
==== 6.2.2.4 Other Dynamic Interactions ====

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A range of other dynamic climate interactions are relevant for cities, settlements and infrastructure: cold spells, landslides, wind, fire and air pollution.

'''Cold spells.''' Although frequencies and intensities of cold spells/cold waves are ''virtually certain'' to have decreased globally, and are projected to consistently decrease for most warming levels ( ''high confidence'' ; WGI Table 11.2), cold weather events can periodically occur and impact urban areas and their connected infrastructures. For cities in eastern Canada, the intra-annual distribution of freezing rain events may become more frequent from December to February, and less frequent in other months by 2100 (Cheng, Li and Auld, 2011). Freezing rain is also a risk to urban populations and infrastructure. In general, higher population mortality rates ''likely'' occur during the winter season, while more temperature-attributable deaths are caused by cold than by heat in cities located in temperate climates (Gasparrini et al., 2015; Chen et al., 2017; Ryti, Guo and Jaakkola, 2016). Winter mortality is ''unlikely'' to significantly decrease due to warming trends, partly because a range of other medical factors (e.g., influenza seasons and elevations in cardiac risk factors) also drive this winter-excess mortality (Kinney et al., 2015). However, the evidence is unclear whether mortality related to cold waves will decrease in coming decades in European (Smid et al., 2019) or US cities (Wang et al., 2016). While projected global cold extremes are expected to decrease in frequency and intensity, the higher regional variability of future climates means that cold waves may remain locally important threats, including in milder regions where there are larger temperature differences between ‘normal’ winter days and extreme cold events, and where there is less capacity to adapt (Ma, Chen and Kan, 2014; Ho et al., 2019). This will be accentuated in many cities, particularly in Europe, by anticipated demographic changes that result in a more elderly population susceptible to cold wave health risks (Smid et al., 2019).

The effects of cold waves on the energy sector include breakdowns in power plants and reduced oil and gas production ( [[#Jendritzky--1999|Jendritzky, 1999]] ), as well as failures in overhead power lines and towers leading to outages in Moscow and Bucharest ( [[#Panteli--2015|Panteli and Mancarella, 2015]] ; Andrei et al., 2019). Six major power outages associated with cold shocks and ice storms have been recorded since 2010, the majority recorded from large cities in the USA (Añel et al., 2017). Cold waves can also significantly increase energy demand. A cold wave that affected the Iberian Peninsula in January 2017 caused electricity prices to peak at a mean price of 112.8 €/MWh, the highest ever recorded in Spain ( [[#AEMET--2017|AEMET, 2017]] ).

'''Landslides.''' While geomorphological events (e.g., land subsidence from permafrost thaw at high latitudes or from groundwater extraction) and factors associated with the built environment (e.g., settlement location adjacent to steep slopes and zonation laws for building construction) are major factors determining urban landslide risk, these can also be influenced by a range of climatic variables, namely precipitation (frequency, intensity and duration), snow melt and temperature change. Some 48 million people are exposed to landslide risk in Europe alone, with the majority in smaller urban centres (Mateos et al., 2020). [[#Travassos--2020|Travassos et al. (2020)]] also documented all landslide deaths in the São Paulo Macro Metropolis Region from 2016 to 2019 that occurred from extreme rainfall events in vulnerable areas prone to landslides. An increase in the number of people exposed to urban landslide risks is projected for landslide-prone settlements lying within regions projected to experience a corresponding increase in extreme rainfall ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ). In addition, human factors such as expansion of towns onto unstable land and land use changes within settlements (e.g., road building, deforestation) are increasing human exposure to landslides and the likelihood of landslides occurring (Kirschbaum, Stanley and Zhou, 2015). Rainfall triggered landslides kill at least 5000 people per year, and at least 11.7% of these landslides occurred on road networks ( [[#Froude--2018|Froude and Petley, 2018]] ). Although the spatial footprint of an individual landslide might be small (i.e., &lt; 1 km 2 ), the ‘vulnerability shadow’ cast over an area in terms of regional transport network disruptions can be a significant proportion of a region, and cascade to other infrastructures (Winter et al., 2016).

Landslides tend to occur on moderate to steep slopes, and are thus particularly prevalent in mountainous regions which are also characterised by low infrastructure redundancy (i.e., few alternative routes) and increased impacts from climate change (Schlögl et al., 2019). More robust forecasts of landslides driven by climate risk requires (a) more complete long-term records of previous landslides and (b) baseline studies of the Global South which are currently missing from the literature (Gariano et al., 2017).

'''Wind''' . Urban morphology alters wind conditions at multiple spatial scales; generally, increased surface roughness in settlements have resulted in declining trends in both measured wind speed and frequency of extremely windy days (Mishra et al., 2015; Peng et al., 2018; [[#Ahmed--2014|Ahmed and Bharat, 2014]] ; WGI Box 10.3).

Urban wind risks can also be affected by location adjacent to mountains, lakes or coasts with localised wind systems (WGI 10.3.3.4.2; WGI 10.3.3.4.3). In large cities with significant urban heat island, an urban-driven thermal circulation can enhance pollution dispersion under calm conditions (Fan, Li and Yin, 2018) or advect heat to areas downwind of the city (Bassett et al., 2016). Microscale wind conditions within urban canyons also strongly affect ventilation of air pollution dispersion and thermal comfort at pedestrian level, especially in cities located in warm climates (Rajagopalan, Lim and Jamei, 2014; Middel et al., 2014; [[#Lin--2016|Lin and Ho, 2016]] ).

In cities, wind risks from climate change hazards can arise from increased exposure from the expanding built environment. Very high wind speeds associated with severe weather systems, for example, tropical cyclones or derechos can cause significant structural damage to buildings and key infrastructure with insufficient wind load, as well as causing human injury through flying debris (Burgess et al., 2014). In particular, there is evidence from North American cities that tornado damage are ''likely'' fundamentally driven by growing built-environment exposure ( ''medium confidence'' ) (Ashley et al., 2014; [[#Rosencrants--2015|Rosencrants and Ashley, 2015]] ; [[#Ashley--2016|Ashley and Strader, 2016]] ).

Extreme winds in urban areas can have particularly damaging effects on poorly constructed buildings, including low-income houses in African cities ( [[#Okunola--2019|Okunola, 2019]] ), and on urban trees that may be uprooted by strong wind gusts from downbursts ( [[#Ordóñez--2015|Ordóñez and Duinker, 2015]] ; [[#Pita--2016|Pita and de Schwarzkopf, 2016]] ; Brandt et al., 2016), as well as on disrupting transportation along urban road and railway networks (Koks et al., 2019; Pregnolato et al., 2016).

'''Fire.''' Hotter and drier climates in several regions, for example Australia, the Western USA, the Mediterranean and Russia ( [[#IPCC--2018|IPCC, 2018]] ), ''likely'' enable weather conditions driving fire events impacting cities within these regions ( [[IPCC:Wg2:Chapter:Chapter-2#2.4.4.2|Section 2.4.4.2]] , 2.5.5.2). These include wildfires along the margins where cities are adjacent to wildlands, that is, the wildland-urban interface (WUI) ( [[#Bento-Gonçalves--2020|Bento-Gonçalves and Vieira, 2020]] ; Radeloff et al., 2018), or fires in cities with a high degree of informal settlements having greater vulnerability to fire hazards (Kahanji, Walls and Cicione, 2019; [[#Walls--2017|Walls and Zweig, 2017]] ; Sections 8.3.3.2). This vulnerability is considerable; over 95% of urban fire related deaths and injuries occur within informal settlements in low- and middle-income countries (Rush et al., 2020).

For wildfires at the WUI, anthropogenic climate change, natural weather variability, expansion of human settlement and a legacy of fire suppression are key factors in determining fire risk ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ; Knorr, Arneth and Jiang, 2016; van Oldenborgh et al., 2020). Recent wildfires in Australia and California both occurred under hot and dry weather conditions exacerbated by climate change, and resulted in substantial property damage along the WUI, ecosystem destruction and lives lost (Brown et al., 2020; Lewis et al., 2020; Yu et al., 2020). Future climate risk of fires at the WUI are ''likely'' ( ''medium confidence'' ), and are compounded by projected urban development along the WUI within several regions, such as in the Western USA (Syphard et al., 2019), Australia (Dowdy et al., 2019) and the Bolivian Chiquitania (Devisscher et al., 2016).

'''Air Pollution.''' Despite recent observed improvements in air quality arising from COVID-19 restrictions (Krecl et al., 2020; Naik et al., 2021 Cross-Chapter Box 6.1), significant risks to human health in cities leading to premature mortality ''very likely'' arise from exposure to decreased outdoor air quality from a combination of biogenic (e.g., wildfires at the WUI that advect into the urban atmosphere [Reddington et al., 2014; Naik et al., 2021 [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] Box 12.1]) and anthropogenic sources that are influenced by climate change (e.g., fine particulate matter such as PM 2.5 , tropospheric ozone, oxides of nitrogen and volatile organic compounds [Burnett et al., 2018; Knight et al., 2016; Turner et al., 2016; West et al., 2016; Chang et al., 2019b; Li et al., 2019a; Alexader, Luisa and Molina, 2016; Naik et al., 2021 Sections 6.5.1, 6.7.1.1, 6.7.1.2]). Risks of premature mortality from indoor air pollution in cities, arising from biomass burning for heating in winter or cooking, indoor pesticide use or exposure to volatile organic compounds from poor thermal insulation in buildings, are also ''likely'' to occur ( [[#Leung--2015|Leung, 2015]] ; Peduzzi et al., 2020; Cross-Chapter Box HEALTH in Chapter 7).

The mortality risk for several pollutants, for example PM 2.5, is considerable ( ''high confidence'' ). Current estimates indicate that 95% of global population live in areas where ambient PM 2.5 exceeds the WHO guideline of annual average exposure of 10 µg m −3 (Shaddick et al., 2018a; Shaddick et al., 2018b; Chang et al., 2019b). Among the 250 most populous urban areas, estimated PM 2.5 concentrations are generally highest in cities in Africa, South Asia, the Middle East and East Asia; PM 2.5 in many cities in North Africa and the Middle East is ''likely'' due mainly to wind-blown dust, whereas that in South Asia and East Asia are mainly anthropogenic in origin (Anenberg et al., 2019). However, data on PM 2.5 concentrations are unavailable in many cities in low- and middle-income countries owing to a lack of measurements (Martin et al., 2019).

For some air pollutants, for example concentrations of PM 2.5 in several US, Western European and Chinese cities have recently decreased as a result of clean air regulations that have controlled emissions from sources such as motor vehicles, fossil fuel power plants and major industries (Zheng et al., 2018a; Fleming et al., 2018). These decreases have brought substantial improvements in public health in settlements within these regions (Ciarelli et al., 2019; Zhang et al., 2018). In South Asia, Southeast Asia and Africa, however, concentrations of other air pollutants, for example tropospheric ozone, oxides of nitrogen and volatile organic compounds are ''likely'' to continue to grow and peak by mid-century before they subside due to global urbanisation assumptions embedded in the SSPs (Naik et al., 2021 Sections 6.2.1; 6.7.1.2). Broadly, future air pollutant emissions are projected to decline globally by 2050 as societies become wealthier and more willing to invest in air pollution controls, but the trajectories vary among pollutants, world regions and scenarios (Silva et al., 2016b; Rao et al., 2017; Silva et al., 2016c). Whereas cities in East Asia and South Asia currently have large exposure to anthropogenic air pollution, African cities may emerge by 2050 as the most polluted because of growing populations and demand for energy, increased urbanisation and relatively weak regulations to control emissions (Liousse et al., 2014).

Studies modelling climate change impacts on air quality find that the spatiotemporal patterns of concentration changes vary strongly at urban scales, and that often those patterns differ among the different years modelled due to internal variability (Saari et al., 2019) and different models used (Weaver et al., 2009). Changes in PM 2.5 due to climate change are less clear than for ozone and may be relatively smaller (Westervelt et al., 2019) as climate change can affect PM 2.5 species differently (Fiore, Naik and Leibensperger, 2015). For Beijing, climate change is expected to cause a 50% increase in the frequency of meteorological conditions conducive to high PM 2.5 concentrations (Cai et al., 2017). The impacts of future climate change on air quality and consequent risks on human health have been studied at urban (Knowlton et al., 2004; Physick, Cope and Lee, 2014) and national scales (Fann et al., 2015; Orru et al., 2013; Doherty, Heal and O’Connor, 2017); globally, these studies have found a ''likely'' net increased risk of climate change on air pollution-related health ( ''low confidence'' ). They have focused mainly on the USA and Europe, with few studies elsewhere (Orru, Ebi and Forsberg, 2017), although the relationship between climate and air quality in megacities is particularly complex (Baklanov, Luisa and Molina, 2016). [[#Silva--2017|Silva et al. (2017)]] found that global premature mortality attributable to climate change (and not from urbanisation) from ozone and PM 2.5 will increase by about 260,000 deaths per year in 2100 under RCP8.5, but substantial variance in results exists between individual models.

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<span id="differentiated-human-vulnerability"></span>
=== 6.2.3 Differentiated Human Vulnerability ===

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Evidence from urban and rural settlements is unambiguous; climate impacts are felt unevenly, with differentiated human vulnerability leading to uneven social, spatial and temporal loss, risk and experiences of resilience, including capacity for transformation ( ''high confidence'' ) ( [[#Woroniecki--2019|Woroniecki et al., 2019]] ; Tan, Xuchun and Graeme, 2015; [[#Simon--2015|Simon and Leck, 2015]] ; [[#Long--2019|Long and Rice, 2019]] ; Chu, Anguelovski and Roberts, 2017; [[#Borie--2019|Borie et al., 2019]] ). The evidence is also clear that for those with fewest resources and already constrained life chances, losses from climate change associated events reduce well-being and exacerbate vulnerability ( ''high confidence'' ) ( [[#van%20den%20Berg--2019|van den Berg and Keenan, 2019]] ; Kashem, Wilson and Van Zandt, 2016; Michael, Deshpande and Ziervogel, 2018). Human vulnerability is influenced by the adaptive capacity of physical (built) structures, social processes (economic, well-being and health) and institutional structures (organisations, laws, cultural and political systems/norms) (see [[#6.4|Section 6.4]] ). This section should be read in conjunction with [[IPCC:Wg2:Chapter:Chapter-8|Chapter 8]] (Poverty, Livelihoods and Sustainable Development) and will emphasise urban processes that lead to the creation of differential vulnerability, risks and impacts.

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==== 6.2.3.1 Urban Poverty and Vulnerability ====

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In both developed and less-developed regions, poverty in urban areas is frequently associated with higher levels of vulnerability (Huq et al., 2020b). This is evident in both rural and urban settlements in a wide range of contexts, including the Philippines (Porio et al., 2019; Valenzuela, Esteban and Onuki, 2020); Bangladesh (Matsuyama, Khan and Khalequzzaman, 2020); Brazil (Lemos et al., 2016), Santiago, Chile (Inostroza, Palme and de la Barrera, 2016); and New York City (Madrigano et al., 2015).

For individuals in urban communities, new literature highlights how differences in vulnerability established by social and economic processes are further differentiated by household and individual variability and intersectionality ( [[#Kaijser--2014|Kaijser and Kronsell, 2014]] ; Kuran et al., 2020).This includes differences in wealth and capacity (Romero-Lankao, Gnatz and Sperling, 2016); gender and non-binary gender ( [[#Michael--2016|Michael and Vakulabharanam, 2016]] ; [[#Sauer--2021|Sauer and Stieß, 2021]] ; [[#Mersha--2018|Mersha and van Laerhoven, 2018]] ); education, health, political power and social capital (Lemos et al., 2016); age, including young and elderly, low physical fitness, pre-existing disability, length of residence and social and ethnic marginalisation (Inostroza, Palme and de la Barrera, 2016; Schuster et al., 2017; [[#Malakar--2017|Malakar and Mishra, 2017]] ). An increasing proportion of refugees and displaced people now live in urban centres, and their characteristics also make them vulnerable to a range of shocks and stresses ( [[#Earle--2016|Earle, 2016]] ). While some individuals, including children, may be able to exercise agency to reduce their risk ( [[#Treichel--2020|Treichel, 2020]] ), and some indicators are culturally specific, overall, poor, marginalised, socially isolated and informal urban households are particularly at risk ( ''high confidence'' ) ( [[#Brown--2016|Brown and McGranahan, 2016]] ; Kim et al., 2020b; Huq et al., 2020a; Huq et al., 2020b).

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==== 6.2.3.2 Informality, Planning and Vulnerability ====

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Particularly in low- and middle-income countries, much urban building occurs outside formal parameters and entails a high degree of urban informality. According to the United Nations statistics, the proportion of urban populations living in slums and informal settlements increased from 23% in 2014 to 23.5% in 2018 ( [[#United%20Nations--2018|United Nations, 2018]] ). Informality is one pathway through which urbanisation generates differentiated vulnerability, tending to increase exposure and susceptibility of physical structures and their occupants to climate-related risks (Dodman et al., 2017; [[#Dobson--2017|Dobson, 2017]] ) in contexts including Guadalajara, Mexico ( [[#Gran%20Castro--2019|Gran Castro and Ramos De Robles, 2019]] ), Kampala, Uganda (Richmond, Myers and Namuli, 2018), Bengaluru, India (Kumar, Geneletti and [[#Nagendra--2016|Nagendra, 2016]] ), and Dar es Salaam, Tanzania (Yahia et al., 2018). In addition to facing emerging water- and heat-related risks, such areas are also more vulnerable to the health impacts of climate change (Scovronick, Lloyd and Kovats, 2015).

Even where formal planning is the norm, this has often remained oriented toward enabling value by adding construction or the protection of existing high-value physical assets, for example infrastructure and built cultural heritage, private residential) rather than enabling disaster risk reduction for all ( [[#Long--2019|Long and Rice, 2019]] ). This tendency has been widely documented, including from cases in Australia, Thailand, Indonesia (King et al., 2016), Canada ( [[#Stevens--2017|Stevens and Senbel, 2017]] ), Amman, Moscow and Delhi ( [[#Jabareen--2015|Jabareen, 2015]] ), and South Africa (Arfvidsson et al., 2017). Such inconsistencies between the delivery of land use planning and the aims of the SDGs combine with other social structures, economic pathways and governance systems to shape city risk profiles (Dodman et al., 2017).

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==== 6.2.3.3 Migration and Differentiated Vulnerability ====

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Migration, displacement and resettlement each play a foundational role in differentiated vulnerability (see Cross-Chapter Box MIGRATE in Chapter 7). The relationship between migration and vulnerability is complex ( ''robust evidence'' , ''high agreement'' ), and is the first of the three components discussed within this section. Climate change, as a push factor, is only one among multiple drivers (political, economic and social) related to environmental migration (Heslin et al., 2019; [[#Plänitz--2019|Plänitz, 2019]] ; [[#Luetz--2019|Luetz and Merson, 2019]] ). There is consensus that it is difficult to pin climate change as the sole driver of internal (within national boundaries) rural to urban migration decisions owing to, among other factors, the disconnect between national and international policies (Wilkinson et al., 2016), the lack of unifying theoretical frameworks and the complex interactions between climatic and other drivers (social, demographic, economic and political) at multiple scales (Cattaneo et al., 2019; Borderon et al., 2019). Environmental migration, including rural to urban migration, triggered by climate change may ensue from either slow- or rapid-onset climatic events and could be either temporary, cyclical or permanent movement that occurs within or beyond national boundaries (Heslin et al., 2019; [[#Silja--2017|Silja, 2017]] ).

A range of specific studies highlight certain elements of vulnerability and migration, including the ways in which slow-onset events affect precarious, resource-dependent livelihoods (such as farming and fisheries) (Cai et al., 2016). In small town Pakistan and Colombia, heat stress increases long-term migration of men, driven by a negative effect on farm income (Mueller, Gray and Kosec, 2014; [[#Tovar-Restrepo--2013|Tovar-Restrepo and Irazábal, 2013]] ). A study from Mexico reveals that an increase in drought months led to increased rural to urban migration, while increased heat (temperature) led to a ‘nonlinear’ pattern of rural to urban migration that occurred only after extended periods of heat (nearly 34 months) (Nawrotzki et al., 2017). This aligns with other findings that a consistent increase in temperature between 2°C and 4°C in some parts of the world renders involuntary, forced migration inevitable (Otto et al., 2017).

The complexity of migration drivers (as push or pull factors) explains why there is little agreement around quantitative estimates on migration (especially international) triggered by climate change ( [[#Silja--2017|Silja, 2017]] ; Otto et al., 2017), and why estimates of future displacement attributed to climate change and other environmental causes vary between 25 million and 1 billion in 2050 (Heslin et al., 2019). Many authors are critical of existing perspectives on climate-related migration, and argue for more nuanced research on the topic (Boas et al., 2019; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Silja--2017|Silja, 2017]] ; Sakdapolrak et al., 2016; [[#Singh--2020|Singh and Basu, 2020]] ; [[#Luetz--2018|Luetz and Havea, 2018]] ).

Climate-induced migration is not necessarily higher among poorer households whose mobility is more likely to be limited due to the poverty trap (i.e., lack of financial resources) ( ''high confidence'' ) (Cattaneo et al., 2019; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Silja--2017|Silja, 2017]] ). For example, in Bangladesh, vulnerability of rural populations is increasing, so many of the poorest employ migration as a strategy of last resort ( [[#Paprocki--2018|Paprocki, 2018]] ; Penning-Rowsell, Sultana and Thompson, 2013; [[#Adri--2018|Adri and Simon, 2018]] ) that occurs as soil salinity (as opposed to inundation alone) increases and is paralleled by economic diversification (i.e., aquaculture) ( [[#Chen--2018|Chen and Mueller, 2018]] ). There is ''robust evidence'' and ''high agreement'' that rapid-onset climatic events trigger involuntary migration and short-term, short-distance mobilities (Cattaneo et al., 2019). There is also ''robust evidence'' and ''high agreement'' that slow-onset climatic events (such as droughts and sea level rise) lead to long-distance internal displacement, more so than local or international migration ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Silja--2017|Silja, 2017]] ), while sea level rise is expected to lead to the displacement of communities along coastal zones, such as in Florida in the USA ( [[#Hauer--2017|Hauer, 2017]] ; Butler, Deyle and Mutnansky, 2016).

Migration, including rural–urban migration, is also recognised as an adaptation strategy in some circumstances, whether this is voluntary or planned (Jamero et al., 2019; Esteban et al., 2020a; [[#Bettini--2014|Bettini, 2014]] ). Voluntary migration can be an element of household strategies to diversify risk, depending on the nature of the climatic stress, and interacts with household composition, individual characteristics, social networks, and historical, political and economic contexts (Hunter, Luna and Norton, 2015; Carmin et al., 2015; Hayward et al., 2020). For example, in Colombia, rural to urban migration is differentiated across gender depending on the climatic stress whereby men migrate due to droughts, while women migrate due to excessive rain triggers ( [[#Tovar-Restrepo--2013|Tovar-Restrepo and Irazábal, 2013]] ). Especially in Pacific small island developing states, migration can be a strategy for urban settlements or tribal communities to relocate in customary areas, as in the case of Vunidogoloa in Fiji (McMichael, Katonivualiku and Powell, 2019; Hayward et al., 2020); it can be a livelihood strategy as shown in the Cataret Islands in Papua New Guinea ( [[#Connell--2016|Connell, 2016]] ); or it can be used to enhance education and international networks (i.e., voluntary ‘migration with dignity’) as is the case in Kiribati (Heslin et al., 2019; [[#Voigt-Graf--2017|Voigt-Graf and Kagan, 2017]] ).

The second component, displacement, also plays a crucial role in differentiated vulnerability. The lack of resources and capacities to support mobility limits the effectiveness of migration as an adaptation strategy, therefore leading to both displacement and trapped populations in the future (Adger et al., 2015; [[#Faist--2018|Faist, 2018]] ). For example, studies from Colombia ( [[#Tovar-Restrepo--2013|Tovar-Restrepo and Irazábal, 2013]] ), India ( [[#Singh--2020|Singh and Basu, 2020]] ), Mekong Delta in Vietnam ( [[#Miller--2019|Miller, 2019]] ) and Pakistan ( [[#Islam--2018|Islam and Khan, 2018]] ) showed that migration as an adaptation strategy can be constrained due to resource barriers and low mobility potential, and also, to high levels of place attachment such as in the Peruvian Highlands ( [[#Adams--2016|Adams, 2016]] ), Vanuatu ( [[#Perumal--2018|Perumal, 2018]] ) and the Tulun and Nissan Atolls of Bougainville, Papua New Guinea ( [[#Luetz--2018|Luetz and Havea, 2018]] ). Migration can also be maladaptive for the receiving contexts, whether due to the pressure on and/or conflict over land and/or the urban resources ( ''high confidence'' ) ( [[#Faist--2018|Faist, 2018]] ; [[#Singh--2020|Singh and Basu, 2020]] ; [[#Luetz--2018|Luetz and Havea, 2018]] ). Other views maintain that migration as adaptation overlooks the agency of people and their resilience, that is the nuances of ‘translocal social resilience’ ( [[#Kelman--2018|Kelman, 2018]] ; [[#Silja--2017|Silja, 2017]] ; Sakdapolrak et al., 2016). For example, the ni-Vanuatu prioritise ''in situ'' adaptation measures and leave migration as a last resort ( [[#Perumal--2018|Perumal, 2018]] ).

Regardless of the reasons and the initiators for migration, community control over resettlement both at the origin and destination leads to more positive outcomes for both the communities being resettled and the receiving communities ( ''high confidence'' ) ( [[#Perumal--2018|Perumal, 2018]] ; [[#Ferris--2015|Ferris, 2015]] ; [[#Price--2019|Price, 2019]] ; [[#Mortreux--2015|Mortreux and Adams, 2015]] ; Tadgell, Doberstein and Mortsch, 2018; [[#Luetz--2018|Luetz and Havea, 2018]] ). The protection of livelihoods contributes to ensuring the well-being (physical and mental) and the protection of the rights of communities ( ''high confidence'' ) ( [[#Ferris--2015|Ferris, 2015]] ; [[#Price--2019|Price, 2019]] ). There is ''limited evidence'' but ''high agreement'' that the outcomes of resettlement initiatives are complex and multi-faceted ( [[#Ferris--2015|Ferris, 2015]] ). For example, in Shangnan County, northwest China, the Massive Southern Shaanxi Migration Program, based on voluntary participation, reduced risk exposure and improved the quality of life in general, but also disproportionately increased the vulnerability of disadvantaged groups (the poor, migrants, and those left behind) (Lei et al., 2017). Similarly, vulnerability increased due to the loss of connection to place and community bonds in Mekong Delta, Vietnam ( [[#Miller--2019|Miller, 2019]] ), and due to unsafe construction, poor infrastructure, institutional incapacity and general neglect in resettlement initiatives in Malawi, sub-Saharan Africa ( [[#Kita--2017|Kita, 2017]] ).

<div id="6.2.4" class="h2-container"></div>

<span id="risks-to-key-infrastructures"></span>
=== 6.2.4 Risks to Key Infrastructures ===

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Projected climatic changes, such as changing precipitation patterns, temperatures and sea levels, contribute to pressures on human well-being and the functioning of infrastructure systems ( ''high confidence'' ). Furthermore, risks evolve due to macro-scale drivers of change such as urbanisation, economic development, land use changes and other emergent factors (Adger, Brown and Surminski, 2018). Infrastructure networks are rapidly growing around the world (see Table 6.3). Since the quality and accessibility of infrastructure services are varied, it is important to understand how climate change poses different kinds of risk on them. Infrastructure can be broadly understood to include social infrastructure (housing, health, education, livelihoods and social safety nets, security, cultural heritage/institutions, disaster risk management and urban planning), ecological infrastructure (clean air, flood protection, urban agriculture, temperature, green corridors, watercourses and riverways) and physical infrastructure (energy, transport, communications [including digital], built form, water and sanitation and solid waste management) ( [[#Thacker--2019|Thacker et al., 2019]] ). This section focuses especially on physical infrastructure where the literature provides discrete risk and impact assessments. Physical infrastructure systems are often immobile, indivisible, involve high fixed costs and have longer lifecycles. Social and ecological infrastructure elements are rarely assessed alone and instead tend to be included in wider assessments of event impacts.

'''Table 6.3 |''' Selected indicators of global proliferation of infrastructure networks and their annual usage.

{| class="wikitable"
|-
! Infrastructure

! Scale

! Usage on annual basis

! Coverage/equity of access

! References

|-
| Electricity networks

| &gt; 20 million km of power lines in Europe and USA

| 25,721 TWh (2017)

| Global: 3130 kWh per person

Haiti: 39 kWh per person

Iceland: 53,832 kWh per person

| [[#IEA--2019|IEA (2019)]] ;

[[#World%20Bank--2019|World Bank (2019)]] ;

[[#ETSAP--2014|ETSAP (2014)]]

|-
| Gas and LPG pipelines

| Worldwide:

&gt; 2.5 million km w −1

| 40,531 TWh (2017)

| Global: 4.96 MWh per person (2015)

South Africa: 0.96 MWh per person (2015)

Saudi Arabia: 34.65 MWh per person (2015)

| [[#CIA--2015|CIA (2015)]] ;

[[#OWID--2020|OWID (2020)]]

|-
| Railways

| 2.69 million km

| 3835 billion passengers km −1 (2019)

9279.81 billion tonnes km −1 (2019)

| Switzerland: 0.7 m per person; 141 m km −2

Canada: 2.2 m per person; 8.6 m km −2

India: 0.06 m per person; 23 m km −2

| Koks et al. (2019);

[[#Statista--2020|Statista (2020)]]

|-
| Roads

| 63.46 million km

| 12,148 billion passengers km −1 private vehicles (2015)

5713 billion passengers km −1 public vehicles, e.g., buses (2015)

302.5 billion passenger km −1 active modes, e.g., walking and bicycles (2015)

| Belgium: 15 m per person; 5 km km −2

Malawi: 1 m per person; 164 m km −2

Canada: 31 m per person; 115 m km −2

| Koks et al. (2019);

[[#WorldByMap--2017|WorldByMap (2017)]] ;

[[#ITF--2019|ITF (2019)]]

|-
| Information and Communication Technology

| Worldwide:

91 million mobile phones in 1995;

8.2 billion in 2018 worldwide

| Worldwide:

43,000 PB in 2014

242,000 PB in 2018

(*1PB = 1 million GB)

| Europe: 85% of population are unique mobile subscribers; Asia Pacific: 66%; Sub-Saharan Africa: 45%

| [[#ITU--2019|ITU (2019)]] ;

[[#Vodafone--2019|Vodafone (2019)]] ;

[[#GSMA--2019|GSMA (2019)]]

|-
| Water

| 3.3 million km 2 land equipped for irrigation

The Global Reservoir and Dam Database (conservatively records) at least 7100 dams

| This irrigated land accounts for about 70% of total water withdrawals

These dams can retain over 7800 km 3 water.

| Sub-Saharan Africa: 24% coverage of safely managed drinking water services, 28% safely managed sanitation services,

Europe and North America: 94% and 78%, respectively.

| [[#Grigg--2019|Grigg (2019)]] ;

Lehner et al. (2011);

Lehner et al. (2019);

[[#UN%20Water--2018|UN Water (2018)]]

|}

Current climate variability is already causing impacts on infrastructure systems around the world ( ''high confidence'' ). For global physical infrastructure with a present value of USD 143 trillion, The [[#Economist%20Intelligence%20Unit--2015|Economist Intelligence Unit (2015)]] estimates present value losses of USD 4.2 trillion by 2100 under a 2°C scenario. This estimation rises to USD 13.8 trillion under a 6°C scenario. Extreme events are associated with disruption or complete loss of these infrastructure services, whilst gradual changes in mean conditions are altering physical infrastructure performance. Physical infrastructure is usually costly to repair and also have significant impacts on people’s health and well-being.

This section synthesises and assesses the emerging literature on climate change risks to key physical infrastructure domains as listed in Table 6.3: energy/electricity infrastructure, transportation infrastructure and information and communication technology (ICT) (water infrastructure is discussed in [[#6.2.2|Section 6.2.2]] ). It draws on evidence from around the world, but the specific risks to infrastructure in different contexts are explained in more detail in the regional chapters (especially [[IPCC:Wg2:Chapter:Chapter-9#9.8.4|Section 9.8.4.1]] for Africa, [[IPCC:Wg2:Chapter:Chapter-10#10.4.6.3.8|Section 10.4.6.3.8]] for Asia and [[IPCC:Wg2:Chapter:Chapter-13#13.6.1|Section 13.6.1]] for Europe). For cities and settlements, such risks are of particular concern owing to a lack of adaptive capacity across many economically important sectors and low levels of resource and capacity support to enhance adaptive capacity. Recent literature also illustrates the interconnected and interdependent nature of infrastructure systems (see Box 6.2), which lead to uncertainties over how risks in one sector lead to cascading, compounding or knock-on effects across other sectors ( [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ) (see [[#6.2.6|Section 6.2.6]] for elaboration). Therefore, adaptation options should address climate risks to infrastructure in an integrated and co-beneficial manner ( ''medium evidence'' , ''high confidence'' ) (see Sections 6.3 and 6.4).

<div id="6.2.4.1" class="h3-container"></div>

<span id="energy-infrastructure"></span>
==== 6.2.4.1 Energy Infrastructure ====

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Energy infrastructure underpins modern economies and quality of life. Disruption to power or fuel supplies impacts upon all other infrastructure sectors, and affects businesses, industry, healthcare and other critical services both within and across jurisdictional boundaries ( [[#Groundstroem--2019|Groundstroem and Juhola, 2019]] ). The economic impacts of climate change risks are significant, for example in the EU, the expected annual damages to energy infrastructure, currently €0.5 billion yr −1 , are projected to increase 1612% by the 2080s ( [[#Forzieri--2018|Forzieri et al., 2018]] ). In China, 33.9% of the population are vulnerable to electricity supply disruptions from a flood or drought ( [[#Hu--2016|Hu et al., 2016]] ), whilst in the USA, higher temperatures are projected to increase power system costs by about USD 50 billion by the year 2050 ( [[#Jaglom--2014|Jaglom et al., 2014]] ). In a study of 11 Central and Eastern European countries, researchers found that energy poverty is exacerbated by existing infrastructure deficits and energy efficient building stock, as well as income inequality, which can lead to reduced economic productivity ( [[#Karpinska--2020|Karpinska and Śmiech, 2020]] ). Climate change is expected to alter energy demand ( [[#Viguié--2021|Viguié et al., 2021]] ), for example heatwaves increase spot market prices ( [[#Pechan--2014|Pechan and Eisenack, 2014]] ), with a disproportionate impact on the poorest and most vulnerable populations. Energy infrastructure are susceptible to a range of climate risks (Cronin, Anandarajah and Dessens, 2018), whilst issues pertaining to energy demand are considered by Working Group III.

Climate change can, for example, influence energy consumption patterns by changing how household and industrial consumers respond to short-term weather shocks, as well as how they adapt to long-term changes ( [[#Auffhammer--2014|Auffhammer and Mansur, 2014]] ). Recent studies from Stockholm, Sweden, show that future heating demand will decrease while cooling demand will increase (Nik and Sasic Kalagasidis, 2013). A study from the USA showed that climate change will impact buildings by affecting peak and annual building energy consumption ( [[#Fri--2014|Fri and Savitz, 2014]] ). From an infrastructure standpoint, the vulnerability of current hydropower and thermoelectric power generation systems may change due to changes in climate and water systems and projected reduction of usable capacities ( [[#van%20Vliet--2016|van Vliet et al., 2016]] ; [[#Byers--2016|Byers et al., 2016]] ). These examples show how energy infrastructure planning under climate change must take into account a greater number of scenarios and investigate impacts on particular energy segments ( [[#Sharifi--2016|Sharifi and Yamagata, 2016]] ).

'''Electricity generation.''' Electricity generation infrastructure can be directly damaged by floods, storm and other severe weather events. Furthermore, the performance of renewables (solar, hydro-electric, wind) is affected by changes in climate.

Most thermoelectric plants require water for cooling, many are therefore situated near rivers and coasts and thus vulnerable to flooding. Increases in water temperature or restrictions on cooling water availability affect hydroelectric and thermoelectric plants. A 1°C increase in the temperature of water used as coolant yields a decrease of 0.12–0.7% in power output ( [[#Mima--2015|Mima and Criqui, 2015]] ; Ibrahim, [[#Ibrahim--2014|Ibrahim and Attia, 2014]] ). Excess biological growth, accelerated by warmer water, increases risk of clogging water intakes ( [[#Cruz--2013|Cruz and Krausmann, 2013]] ). While some regions are expected to experience increased capacity under climate change (namely India and Russia), global annual thermal power plant capacity is ''likely'' to be reduced by between 7% in a mid-century RCP2.6 scenario and 12% in a mid-century RCP8.5 scenario ( [[#van%20Vliet--2016|van Vliet et al., 2016]] ). Worldwide, hydroelectric capacity reductions are projected at 0.4–6.1% ( [[#van%20Vliet--2016|van Vliet et al., 2016]] ). Analysis of the UK’s water for energy generation abstractions showed that an energy mix of high nuclear or carbon capture technologies could require as much as six times the current cooling water demands (Byers, Hall and Amezaga, 2014; [[#Byers--2016|Byers et al., 2016]] ).

Increasing temperatures improve the efficiency of solar heating but decrease the efficiency of photovoltaic panels, and deposition and abrasive effects of wind-blown sand and dust on solar energy plants can further reduce power output, and the need for cleaning (Patt, Pfenninger and Lilliestam, 2013). Projected changes in wind and solar potential are uncertain; the trends vary by region and season (Burnett, Barbour and Harrison, 2014; [[#Cradden--2015|Cradden et al., 2015]] ; Fant, Schlosser and Strzepek, 2016). In an RCP8.5 scenario, [[#Wild--2015|Wild et al. (2015)]] conservatively calculate a global reduction of 1% per decade between 2005 and 2049 for future solar power production changes due to changing solar resources as a result of global warming and decreasing all-sky radiation over the coming decades. However, positive trends are projected in large parts of Europe, the south-east of North America and the south-east of China.

'''Electricity Transmission and Distribution.''' Electricity transmission and distribution networks span large distances, with overhead power lines often traversing exposed areas. Power lines and other assets, such as substations, are often located near population centres, including those in floodplains. Structural damage to overhead distribution lines will increase in areas projected to see more ice or freezing rain (e.g., most of Canada), snowfall (e.g., Japan) or wildfires (e.g., California, USA) ( [[#Bompard--2013|Bompard et al., 2013]] ; [[#Mitchell--2013|Mitchell, 2013]] ; [[#Sathaye--2013|Sathaye et al., 2013]] ; [[#Jeong--2018|Jeong et al., 2018]] ; [[#Ohba--2020|Ohba and Sugimoto, 2020]] ). Electricity outages may last for prolonged periods of time and across vast areas, in addition to potentially disproportionately affecting poorer or more vulnerable communities. Increases in windstorm frequency and intensity increase the risk of direct damage to overhead lines and pylons, in many locations this is limited but [[#Tyusov--2017|Tyusov et al. (2017)]] calculate an increase as high as 30% in parts of Russia. Where the mode of failure is recorded, transmission pylons are seen to be more susceptible to wind damage, whilst distribution pylons are more ''likely'' to be affected by treefall and debris (Karagiannis et al., 2019). Increased temperatures can lead to the de-rating (lower performance) of power lines, whose resistance increases with temperature with efficiency reductions of 2–14% being projected by 2100 ( [[#Cradden--2013|Cradden and Harrison, 2013]] ; [[#Bartos--2016|Bartos et al., 2016]] ).

'''Fuels Extraction and Distribution.''' Non-electric energy infrastructure is susceptible to many of the same impacts as electric infrastructure. Extreme weather events impact extraction (onshore and offshore) and refining operations of petroleum, oil, coal, gas and biofuels. Disruption of road, rail and shipping routes (see [[#6.2.5|Section 6.2.5.2]] ) interrupts fuel supply chains. However, there are a number of risks that are specific to these sectors. Heat can lead to expansion in oil and gas pipes, increasing the risk of rupture ( [[#Sieber--2013|Sieber, 2013]] ), whilst heatwaves and droughts can reduce the availability of biofuel (Moiseyev et al., 2011; Schaeffer et al., 2012). Subsidence and shrinkage of soils damages underground assets such as pipes intakes ( [[#Cruz--2013|Cruz and Krausmann, 2013]] ), while additional human activity such as extractive drilling may induce earthquakes, as observed in the northern Dutch province of Groningen ( [[#Van%20der%20Voort--2015|Van der Voort and Vanclay, 2015]] ). In Alaska, USA, the thaw of permafrost and subsequent ground instability is estimated to lead to USD 33 million damages to fuel pipelines in an end-of-century RCP8.5 scenario (Melvin et al., 2017), with low-lying coastal deltas particularly vulnerable ( [[#Schmidt--2015|Schmidt, 2015]] ).

<div id="6.2.4.2" class="h3-container"></div>

<span id="transport"></span>
==== 6.2.4.2 Transport ====

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Since AR5, research has highlighted the implications for disruption to global supply chains (Becker et al., 2018; [[#Shughrue--2018|Shughrue and Seto, 2018]] ; [[#Pató--2015|Pató, 2015]] ), and has made advancements in quantifying costs of climate risks to transportation infrastructure. Climate risks to transport infrastructure (from heat- and cold waves, droughts, wildfires, river and coastal floods, and windstorms) in Europe could rise from €0.5 billion to over €10 billion by the 2080s (Forzieri et al., 2018). Across the Arctic, nearly four million people and 70% of all current infrastructure, including resource extraction and transportation routes, will be at risk by 2050 (Hjort et al., 2018), although the design of specific infrastructure may also affect the degree of infrastructure damage, depending on local geological and ecological conditions. Globally, [[#Koks--2019|Koks et al. (2019)]] calculated that approximately 7.5% of road and railway assets are exposed to a 1-in-100 year flood events, and total global expected annual damages (EAD) of USD 3.1–22 billion (mean USD 14.6 billion) due to direct damage from cyclone winds, surface and river flooding, and coastal flooding. The majority of this is caused by surface water and fluvial flooding (mean USD 10.7 billion). Although twice as much infrastructure is exposed to cyclone winds compared with flooding, a mean EAD of USD 0.5 billion is significantly less than for coastal flooding (USD 2.3 billion), as cyclone damages are largely limited to bridge damage and the cost of removing trees fallen on road carriageways and railway tracks. This is small relative to global gross domestic product (GDP; ~0.02%). However, in some countries EAD equates to 0.5–1% of GDP, which is the same order of magnitude as typical national transport infrastructure budgets, but especially significant for countries such as Fiji that already spend 30% of their government budget on transport (World Bank Group, 2017). [[#Koks--2019|Koks et al. (2019)]] did not assess future climate change impacts, but comparable studies calculating changes in EAD from flooding based upon land use show increases of 170–1370%, depending on global greenhouse gas emissions levels (Alfieri et al., 2017; Winsemius et al., 2015). Moreover, Schweikert et al., (2014) report that climate risks to transport infrastructure could cost as much as 5% of annual road infrastructure budgets by 2100, with disproportionate impacts in some low and lower middle-income countries.

Changes in rainfall and temperature patterns are expected to increase geotechnical failures of embankments and earthworks (Briggs, Loveridge and Glendinning, 2017; Tang et al., 2018; [[#Powrie--2018|Powrie and Smethurst, 2018]] ) from landslides, subsidence, sinkholes, desiccation and freeze-thaw action. For instance, Pk et al. (2018) show this could lead to a 30% reduction in the engineering factor of safety of earth embankments in Southern Ontario (Canada). Increased river flows in many catchments will also increase failures from bridge scours (Forzieri et al., 2018). [[#HR%20Wallingford--2014|HR Wallingford (2014)]] calculate that the projected 8% increase in scouring from high river flows in the UK will lead to 1 in 20 bridges being at high risk of failure by the 2080s, whilst in the USA the 129,000 bridges currently deficient could increase by 100,000 (Wright et al., 2012). With respect to temperature, analysis by [[#Forzieri--2018|Forzieri et al. (2018)]] concludes that heatwaves will be the most significant risk to EU transport infrastructure in the 2080s, as a result of buckling of roads and railways due to thermal expansion, melting of road asphalt and softening of pavement material. In the USA, over 50% more roads will require rehabilitation (Mallick et al., 2018), whilst USD 596 million will be required through 2050 to maintain and repair roads in Malawi, Mozambique and Zambia (Chinowsky, Price and Neumann, 2013).

In addition to direct damages from flooding and heatwaves, disruption caused by road blockages will be increased by more frequent flood events. For example, in the city of Newcastle upon Tyne (UK), road travel disruption across the city from a 1-in-50 year surface water flood event could increase by 66% by the 2080s (Pregnolato et al., 2017), whilst heatwaves could treble railway speed restrictions in parts of the UK (Palin et al., 2013). [[#Knott--2017|Knott et al. (2017)]] highlighted risks to coastal infrastructure where ~30 cm sea level rise sea level rise would also push up groundwater and reduce design life by 5–17% in New Hampshire (USA). Heavy rain and flooding can also inundate underground transport systems (Forero-Ortiz, Martínez-Gomariz and Canas Porcuna, 2020).

Many airports, and by their nature ports, are in the low elevation coastal zone, making them especially vulnerable to flooding and sea level rise. Under a 2 o C scenario, the number of airports at risk of storm surge flooding increases from 269 to 338 or as many as 572 in an RCP8.5 scenario; these airports are disproportionately busy and account for up to 20% of the world’s passenger routes ( [[#Yesudian--2021|Yesudian and Dawson, 2021]] ). Airport and port operations could be disrupted by icing of aircraft wings, vessels, decks, riggings and docks (Doll, Klug and Enei, 2014; Chhetri et al., 2015). Warming will increase microbiological corrosion of steel marine structures (Chaves et al., 2016). Fog, high winds and waves can disrupt port and airport activity, but changes are uncertain and with regional variation (Mosvold [[#Larsen--2015|Larsen, 2015]] ; Izaguirre et al., 2021; [[#Becker--2020|Becker, 2020]] ; León-Mateos et al., 2021; Taszarek, Kendzierski and Pilguj, 2020; Danielson, Zhang and Perrie, 2020; Kawai et al., 2016).

Waterways are still important transport routes for goods in many parts of the world, although they are mostly expected to benefit from reduced closure from ice (Jonkeren et al., 2014; [[#Schweighofer--2014|Schweighofer, 2014]] ), low flows will ''likely'' lead to reduced navigability and increased closures; [[#van%20Slobbe--2016|van Slobbe et al. (2016)]] estimate the Rhine may reach a turning point for waterway transportation between 2070–2095. Obstruction due to debris and fallen vegetation of roads and rails and to inland and marine shipping from high winds are expected to increase (Koks et al., 2019; Kawai et al., 2018; Karagiannis et al., 2019)..

<div id="6.2.4.3" class="h3-container"></div>

<span id="information-and-communication-technology"></span>
==== 6.2.4.3 Information and Communication Technology ====

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Information and communication technology (ICT) comprises the integrated networks, systems and components enabling the transmission, receipt, capture, storage and manipulation of information by users on and across electronic devices (Fu, Horrocks and Winne, 2016). ICT infrastructure faces a number of climate risks. Increased frequency of coastal, fluvial or pluvial flooding will damage key ICT assets such as cables, masts, pylons, data centres, telephone exchanges, base stations or switching centres (Fu, Horrocks and Winne, 2016). This leads to loss of voice communications, inability to process financial transactions and interruption to control and clock synchronisation signals. Insufficient information about the location and nature of many ICT assets limits detailed quantitative assessment of climate change risks.

Fixed-line ICT networks that sprawl over large areas are especially susceptible to increases in the frequency or intensity of storms that would increase the risk of wind, ice and snow damage to overhead cables and damage from wind-blown debris. More intense or longer droughts and heatwaves can cause ground shrinkage and damage underground ICT infrastructure (Fu, Horrocks and Winne, 2016). In mountain and northern permafrost regions, communications and other infrastructure networks are subject to subsidence because of warming of ice-rich permafrost (Shiklomanov et al., 2017; Li et al., 2016; Melvin et al., 2017).

<div id="6.2.4.4" class="h3-container"></div>

<span id="housing"></span>
==== 6.2.4.4 Housing ====

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For the urban housing sector, climate impacts such as flooding, heat, fire and wind assessed in [[#6.2.3|Section 6.2.3]] will ''likely'' have detrimental effects on housing stock (including physical damage and loss of property value) and on residents exposed to climate risks ( ''robust evidence, high agreement'' ).

In the USA, for example, 15.4 million housing units fall within a 1-in-100-year floodplain (Wing et al., 2018). Assessment of the Miami-Dade area in Florida noted that coastal inundation caused by tidal flooding (and to a lesser extent sea level rise) resulted in over USD 465 million in lost real-estate market value between 2005 and 2016 ( [[#McAlpine--2018|McAlpine and Porter, 2018]] ), although property values have increased from high-end housing construction and climate adaptation measures ( [[#Kim--2020|Kim, 2020]] ). Emergent risk reflecting novel research include aggravated moisture problems in buildings from wind driven rain (Nik et al., 2015). Future risks from future sea level rise are elaborated in Section [https://www.ipcc.ch/chapter/6#CCP2.2 CCP2.2.1] . Housing infrastructure are also susceptible to extreme heat and wind events (Stewart et al., 2018). These risks are further elaborated on in [[#6.2.3|Section 6.2.3]] , although it is important to note that heat risks, in particular, tend to be concentrated within communities with a higher proportion of social housing (Mavrogianni et al., 2015; Sameni et al., 2015) or low-cost government-built houses and informal settlements.

<div id="6.2.4.5" class="h3-container"></div>

<span id="water-and-sanitation"></span>
==== 6.2.4.5 Water and Sanitation ====

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Apart from land subsidence from urbanisation (e.g., Case Study 6.2), substantial climate risks to urban sanitation arise from droughts, flooding and storm surges. Low flows from drought can lead to sedimentation, increase pollutant concentration and block sewer infrastructure networks ( [[#Campos--2015|Campos and Darch, 2015]] ). Flooding poses a greater risk for urban sanitation in low- and middle-income settings (Burgin et al., 2019) where onsite systems are more common. Floodwater may wash out pits and tanks, mobilising faecal sludges and other hazardous materials leading to both direct and indirect exposure via food and contaminated objects and surfaces, and pollute streams and waterbodies (Howard et al., 2016; [[#Braks--2013|Braks and de Roda Husman, 2013]] ; Bornemann et al., 2019). Floods also damage infrastructure; toilets, pits, tanks and treatment systems are all vulnerable (Sherpa et al., 2014; UNICEF and WHO 2019).

Sanitation systems coupled with floodwater management are at risk of damage and capacity exceedance from high rainfall (Thakali, Kalra and Ahmad, 2016; Kirshen et al., 2015; Dong, Guo and Zeng, 2017). In England, the number of water and wastewater treatment plants at risk of flooding is projected to increase by 33% under a 4 o C scenario (Sayers et al., 2015), but risks are generally increasing for both formal and informal urban sanitation systems (Howard et al., 2016).

<div id="6.2.4.6" class="h3-container"></div>

<span id="natural-and-ecological-infrastructure"></span>
==== 6.2.4.6 Natural and Ecological Infrastructure ====

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Urban ecological infrastructure includes green (i.e., vegetated), blue (i.e., water-based) and grey (i.e., non-living) components of urban ecosystems (Li et al., 2017). While land cover change from urbanisation directly reduces the extent of natural and ecological infrastructure (e.g., Lin, Meyers and Barnett, 2015), notable risks arise from climate drivers. Recent research particularly highlights future climate impacts on coastal natural infrastructure, including beaches, wetlands and mangroves, which cause significant economic losses from property damage and decreasing tourism income, as well as loss of natural capital and ecosystem services. Research on climate risks to urban trees and forests is comparatively limited. Instead, urban vegetation and green infrastructure are most often cast as adaptation strategies to reduce urban heat, mitigate drought and provide other ecosystem benefits (see [[#6.3.2|Section 6.3.2]] ).

Coastal natural infrastructure is exposed to sea level rise, wave action and inundation from increasing storm events (See also Section CCP 2.2.1). Beaches, in particular, are highly exposed to climate-induced coastal erosion (Toimil et al., 2018; Section CCP2). Research from settlements across coastal Southern California, USA, show that 67% of all beaches may completely erode by 2100 (Vitousek, Barnard and Limber, 2017). Coastal zones across Cancún, Mexico, are exposed to a combination of sea level rise and tropical hurricanes, further exacerbated by urban development patterns blocking natural sediment replenishment to beaches (Escudero-Castillo et al., 2018). In another case, beach erosion along the heavily urbanised Valparaíso Bay, Chile, is heightened by El Niño Southern Oscillation (ENSO) events, which in the past have caused an additional 15–20 cm in mean sea level rise (Martínez et al., 2018).

Wetlands, mangroves and estuaries, which tend to be heavily urbanised areas, are highly at risk from sea level rise and changing precipitation (Green et al., 2017; Feller et al., 2017; [[#Alongi--2015|Alongi, 2015]] ; Osland et al., 2017; [[#Chow--2018|Chow, 2018]] ; [[#Godoy--2015|Godoy and Lacerda, 2015]] ). Sea level rise is a concern for wetlands and mangroves across coastal urban Asia, the Mississippi Delta (US) and low lying small island states (Ward et al., 2016b). Research on the highly urbanised Yangtze River estuary in China shows that soil submersion and erosion from sea level rise, compounded by land conversation to agriculture and urban development, will cause all tidal flats to disappear by 2100 (Wu, Zhou and Tian, 2017). In another example, sea level rise and high rates of tidal inundation have increased overall salinity in the San Francisco Bay-Delta estuary, threatening the ecosystem’s ability to support biodiversity ( [[#Parker--2019|Parker and Boyer, 2019]] ).

Research on climate risks to urban trees and forests highlight direct impacts from extreme temperatures, precipitation, wind events and sea level rise, as well as exposure to other hazards such as air pollution, fires, invasive species and disease ( [[#Ordóñez--2014|Ordóñez and Duinker, 2014]] ). Since the 1960s, climate change has enabled growth of urban trees, supported by longer growing seasons, higher atmospheric CO 2 concentrations and reduced diurnal temperature range (Pretzsch et al., 2017), as well as increased fertilisation through urban-enhanced nitrogen deposition (Decina, Hutyra and Templer, 2020). However, these trends may change in the future as further warming and decreasing water supply may depress tree fitness, thus enabling more pests ( [[#Dale--2017|Dale and Frank, 2017]] ).

Climate risks to urban natural and ecosystem infrastructure entail significant economic costs. For example, in 2012, Hurricane Sandy led to total losses of up to USD 6.5 million to the New York City region’s low-lying salt marshes and beaches ( [[#Meixler--2017|Meixler, 2017]] ). Research from coastal settlements across Catalonia, Spain, shows significant levels of tourism loss (which contribute to 11.1% of the region’s GDP), infrastructure damage and natural capital loss attributed to inundation and erosion of beaches, which are projected to retreat by −0.7 m yr −1 given current sea level rise projections of 0.53–1.75 m by 2100 (Jiménez et al., 2017).

<div id="6.2.4.7" class="h3-container"></div>

<span id="health-systems-infrastructure"></span>
==== 6.2.4.7 Health Systems Infrastructure ====

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Healthcare facilities (hospitals, clinics, residential homes) will suffer increasing shocks and stresses related to climate variability and change (Corvalan et al., 2020). Some may be sudden shocks from extreme weather events, which both threaten the facility, staff and patients and increase the number of people seeking health care. There are extensive reports of health facilities being damaged after major floods and windstorms (e.g., 2010 floods in Pakistan, Hurricane Sandy in the USA) which can be further exacerbated by power and water supply failures (Powell, Hanfling and Gostin, 2012). Disruption to services may persist for many months because of damage to buildings, loss of drugs and equipment, and damaged transport infrastructure significantly increasing travel time for patients (Hierink et al., 2020). The impacts of climate change on the health of residents of ‘slum’ settlements will also compound the existing health burdens faced by these individuals, including infectious disease and other environmental public health concerns (Lilford et al., 2016; Mberu et al., 2016).

<div id="box-6.2" class="h2-container box-container"></div>

'''Box 6.2 | Infrastructure Interdependencies'''
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Infrastructure networks are increasingly dependent on each other—for power, control (via ICT) and access for deliveries or servicing (Figure 6.2). Moreover, a range of other mechanisms can create interdependencies that impact upon climate risks by creating pathways for cascading failure (Undorf et al., 2020; [[#Barabási--2013|Barabási, 2013]] ). In the UK, for example, all infrastructures utilities identify failure of components in another utility as a risk to their systems (Dawson et al., 2018).

Key interdependencies include:

# The use of ICT for data transfer, remote control of other systems, and clock synchronisation. Pant et al. (2016) show that ICT is crucial for the successful operation of the UK’s rail infrastructure. The study shows that flooding of the ICT assets in the1-in-200 year floodplain would disrupt 46% of passenger journeys across the whole network.
# Water to generate hydroelectricity and for cooling thermal power stations. Reductions in usable capacity for 61–74% of the hydropower plants and 81–86% of the thermoelectric power plants worldwide for 2040–2069 (van Vliet et al., 2016), with some power generation technologies, including carbon capture and storage, requiring far higher volumes of water for cooling (Byers et al., 2016).
# Energy to power other infrastructure systems. Failure of urban energy supply disrupts other infrastructure services, with disproportionate impacts on the urban poor ( [[#Silver--2015|Silver, 2015]] ).
# Transport systems that ensure access for resources such as fuel, personnel and emergency response. [[#Pregnolato--2017|Pregnolato et al. (2017)]] show disruption across the city from a 1-in-10 year storm event could increase by 43% by the 2080s.
# Green infrastructure can provide multiple services, creating interdependencies between multiple physical infrastructure systems. For example, green space can support sustainable urban drainage, ''in situ'' wastewater treatment and urban cooling (Demuzere et al., 2014).
# Geographical proximity of assets leads to multiple infrastructures being simultaneously exposed to the same climate hazard. Disruption is disproportionately larger for interconnected networks (Fu et al., 2014).

There is usually limited information on the risks between infrastructure sectors. Without frameworks for collaboration, and coupled with commercial and security sensitivities, this remains a barrier to routine sharing and cooperation between operators. Despite this, methods to tackle interdependence in climate risk analysis are emerging ( [[#Dawson--2015|Dawson, 2015]] ). For example, [[#Thacker--2017|Thacker et al. (2017)]] analysed the criticality of the UK’s infrastructure networks by integrating data on infrastructure location, connectivity, interdependence and usage. The analysis showed that criticality hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location. As infrastructure systems become increasingly interconnected, associated risks from climate change will increase and require a cross-sectoral approach to adaptation ( [[#Dawson--2018|Dawson et al., 2018]] ).

<div id="6.2.5" class="h2-container"></div>

<span id="compound-and-cascading-risks-in-urban-areas"></span>
=== 6.2.5 Compound and Cascading Risks in Urban Areas ===

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Compound events can be initiated via hazards such as single extreme events, or multiple coincident events overlapping and interacting with exposed urban systems or sectors as compound climate risks (Leonard et al., 2014; [[#IPCC--2019b|IPCC, 2019b]] ; Piontek et al., 2014). Hydrometeorological hazards, such as extreme precipitation from tropical cyclones, fronts and thunderstorms, often combine with storm surges and freshwater discharge leading to high compound risks at exposed settlements (Zheng, Westra and Sisson, 2013; [[#Chen--2014|Chen and Liu, 2014]] ; [[#Ourbak--2018|Ourbak and Magnan, 2018]] ; [[#Dowdy--2017|Dowdy and Catto, 2017]] ). The compounding effect between these hydrometeorological hazards suggest that the combined impact of these events are greater than each of these variables on its own, and can amplify risks in affected settlements (Kew et al., 2013; Vitousek et al., 2017). These risks are concentrated in coastal cities exposed to sea level rise and severe storms (van den Hurk et al., 2015; Wahl et al., 2015; Paprotny et al., 2018b; Lagmay et al., 2015), or in settlements located in valleys prone to slope failure, such as the 2013 Uttarakhand floods and landslides arising from extreme precipitation and glacial lake outbursts along the Mandakini river in India (Ziegler et al., 2016; Barata et al., 2018).

Cascading climate events occur when an extreme event triggers a sequence of secondary events within natural and human systems that causes additional physical, natural, social or economic disruption. The resulting impact can be significantly larger than the initial hazard ( [[#IPCC--2019b|IPCC, 2019b]] ). Each step in a risk cascade can generate direct (immediate impacts) and secondary (consequential impacts) losses. Risks from these cascading impacts are complex and multi-dimensional (Hao, Singh and Hao, 2018; [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ). For instance, combined droughts and heatwaves increases risks of urban water scarcity (Miralles et al., 2019; Gillner, Bräuning and Roloff, 2014; Gill et al., 2013), as well as increasing wildfire extent and lowering snowpack conditions that affected peri-urban settlements adjacent to forested areas, as observed in California during the 2014 drought (AghaKouchak et al., 2014). Similarly, heatwaves can increase the risk of mortality associated with air pollution (see [[IPCC:Wg2:Chapter:Chapter-7#7.2.2.5|Section 7.2.2.5]] ).

Urban areas and their infrastructure are susceptible to both compounding and cascading risks arising from interactions between severe weather from climate change and increasing urbanisation ( ''medium evidence'' , ''high agreement'' ) ( [[#Moretti--2018|Moretti and Loprencipe, 2018]] ; Markolf et al., 2019). Risks are complex and multi-dimensional, and can significantly amplify the impact of single events across space, scale and time. Impacts are determined by the magnitude of urban vulnerability and/or the interdependence of urban critical infrastructure ( [[#Pescaroli--2018|Pescaroli and Alexander, 2018]] ; Zuccaro, De Gregorio and Leone, 2018). Poorer and wealthier settlements and cities are then both at risk from compound and cascading risks though potentially through contrasting mechanisms. For richer and poorer cities, managing climate risk as part of compound and cascading risks that can also include technological, biological and political risks places renewed emphasis on investment in generic capabilities that reduce vulnerability and on risk monitoring capability to track and respond to impacts across infrastructures and places ( ''limited evidence'' , ''high agreement'' ). Considering climate risk and managing such risk as part of complex, compounding and/or cascading risks is in its infancy but rapidly being accepted as necessary, especially when considering the wider poverty and justice implications of climate change arising from differentiated vulnerability in cities.

Compound risks to key infrastructure in cities have increased from extreme weather ( ''medium evidence'' , ''high agreement'' ), such as from urban flooding from extreme precipitation and storm surges disrupting transport infrastructure and networks, for example Mehrotra et al. (2018), see also San Juan case study in this chapter), ICT networks, for example underground cables or transmission towers (Schwarze et al., 2018), and energy generation from power plants (Marcotullio et al., 2018).

The increased risk arises not just from greater exposure from climate events impacting cities, but is also magnified by low adaptive capacity that can arise from intra-urban variations in infrastructure quality. For instance, infrastructure within expanding informal settlements is associated with deficiency in materials, structural safety and a lack of accessibility. These areas are often located in the most risk-prone urban areas in developing nations that are vulnerable to compound hazards (Dawson et al., 2018). Further, these risks can be exacerbated from complications arising from local versus national governance and/or regulations related to hazard management ( [[#Garschagen--2016|Garschagen, 2016]] ; [[#Castán%20Broto--2017|Castán Broto, 2017]] ).

Projected global compound risks will increase in the future, with significant risks across energy, food and water sectors that likely overlap spatially and temporally while affecting increasing numbers of people and regions particularly in Africa and Asia ( ''high confidence'' ) (Hoegh-Guldberg et al., 2018). In cities, the prevalence of compounding risks therefore necessitates methodologies accounting for non-stationary risk factors.

Secondary impacts occurring sequentially after an extreme hazard can severely affect disaster management, especially in complex urban systems ( ''robust evidence'' , ''high agreement'' ). Over time, relatively small perturbations can cascade outward from a primary failure, triggering further failures in other dependent parts of the network some distance away from the primary failure (Penny et al., 2018). In some cities, such as those prone to compound flood hazards, these dependent network parts can be dams, levees or other critical flood protection infrastructure that are essential for managing these cascading risks ( [[#Serre--2018|Serre and Heinzlef, 2018]] ; [[#Fekete--2019|Fekete, 2019]] ). Failure of these infrastructure systems can result in sequential failures in urban transport ( [[#Zaidi--2018|Zaidi, 2018]] ), energy networks ( [[#Sharifi--2016|Sharifi and Yamagata, 2016]] ), urban biodiversity ( [[#Solecki--2013|Solecki and Marcotullio, 2013]] ) and so-called na-tech disasters; when natural hazards trigger technological disasters (Girgin, Necci and Krausmann, 2019). This risk cascade can propagate more widely by stopping flows of people, goods and services, with economic consequences beyond urban areas ( [[#Wilbanks--2014|Wilbanks and Fernandez, 2014]] ).

Compound and cascading climate risks require a different way of accounting for cumulative hazard impacts in urban areas ( ''medium evidence'' , ''high agreement'' ). There is emerging literature calling for analysis on interactions between individual and inter-related climate extremes with complex urban systems, so as to ascertain how urban and key infrastructural vulnerabilities can be identified and managed in a warming world (Butler, Deyle and Mutnansky, 2016; Gallina et al., 2016; Moftakhari et al., 2017; Zscheischler et al., 2018; Baldwin et al., 2019; [[#Pescaroli--2018|Pescaroli and Alexander, 2018]] ; Yin et al., 2017; AghaKouchak et al., 2020), as well as in managing adaptation for present and future pandemics, for example COVID-19 (Pelling et al., 2021; Phillips et al., 2020).

In terms of policy, case studies from London’s resilience planning process stressed the need for intermodal coordination, hazard risk and infrastructure mapping, clarifying tipping points and acceptable levels of risk, training citizens, strengthening emergency preparedness, identifying relevant data sources, and developing scenarios and contingency plans ( [[#Pescaroli--2018|Pescaroli, 2018]] ). Others also note the utility of a systems approach to analysing risks and benefits, including considerations of potential cascading ecological effects, full lifecycle environmental impacts, and unintended consequences, as well as possible co-benefits of responses (Ingwersen et al., 2014). Lowering these risks requires urban stakeholders to reduce urban vulnerability by going beyond linear approaches to risk management ( ''medium evidence'' , ''high agreement'' ).

<div id="box-6.3" class="h2-container box-container"></div>

'''Box 6.3 | Climate Change Adaptation for Cities in Fragile and Conflict Affected States'''
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Larger cities may be the most stable administrative entities in states affected by conflict. Even here, ability to plan and deliver adaptation can be hampered. Extending into urban areas within stable states, alienation and loss of trust between local populations and the state can be exacerbated by top-down adaptation planning and delivery; socially and spatially uneven adaptation investment; and in the economic and administrative limits of government that can lead to some places being excluded from formal planned investment ( ''high confidence'' ) (see Sections 6.3 and 6.4). These pathways for exclusion can combine among already marginalised and low-income populations where trust in government agencies may already be low (Rodrigues, 2021).

Climate change can be a threat multiplier in cities and urban regions, exacerbating existing human security tension ( ''limited evidence'' , ''medium agreement'' ) ( [[#Froese--2019|Froese and Schilling, 2019]] ; Flörke, Schneider and McDonald, 2018; [[#Rajsekhar--2017|Rajsekhar and Gorelick, 2017]] ). Where conflict or administrative tensions extend beyond cities, adapting regional infrastructure systems that underpin urban life is challenging, for example where elements of networked infrastructure are under the control of conflicting political interests. This has been noted for the water sector (Tänzler, Maas and Carius, 2010). Coordinating political processes is a major challenge even for industrialised countries with adequate administrative capacity. In post-conflict societies, the difficulties of coordination for urban planning are disproportionately greater (Sovacool, Tan-Mullins and Abrahamse, 2018).

In planning adaptation measures in cities, conflict-sensitive approaches to ensure participatory methods (Bobylev et al., 2021) can avoid adaptation being a polarising activity (Tänzler, Maas and Carius, 2010; [[#Tänzler--2017|Tänzler, 2017]] ). Adaptation can provide a common goal reaching across political differences and be a part of building political trust and local cooperation between alienated communities (Tänzler, Maas and Carius, 2010). Peacebuilding programmes led by government or civil society are typically concerned with the short-term and framed by socioeconomic policy, integrating the longer-term view and engineering-technical expertise for adaptation is a challenge ( ''limited evidence'' , ''medium agreement'' ) ( [[#Ishiwatari--2021|Ishiwatari, 2021]] ).

<div id="6.2.6" class="h2-container"></div>

<span id="impacts-and-risks-of-urban-adaptation-actions"></span>
=== 6.2.6 Impacts and Risks of Urban Adaptation Actions ===

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Planning and implementing climate adaptation in cities and settlements can be hampered by incomplete scientific knowledge, a lack of awareness of cascading impacts (and residual risks), mismanagement of actions, human capacity and financing deficits, as well as opportunities for eroding long-term sustainable development priorities (Juhola et al., 2016). These tensions can become acute in fragile and conflict affected states (see Box 6.3). It is important to differentiate between the climatic drivers of risk and social drivers that may compound risk exposures and experiences ( [[#Brown--2014|Brown, 2014]] ; Nightingale et al., 2020), especially since technically- and scientifically-informed adaptation actions can be redirected depending on socioeconomic, political or cultural conditions on the ground (Eriksen, Nightingale and Eakin, 2015). The implementation of adaptation, whether by government, private sector or civil society actors, can therefore lead to unanticipated and unintended amplification of political, economic and ecological risks (Swatuk et al., 2020). Many cities are still in the phase of piloting or testing out appropriate adaptation actions, although there is emerging consensus that adaptation plans and projects should acknowledge trade-offs, intentionally avoid past development mistakes, not lock-in detrimental impacts or further risks arising from implementation and explicitly anticipate the risks of maladaptation in decision making (Magnan et al., 2016; Gajjar, Singh and Deshpande, 2019). Maladaptation describes actions that lead to increased vulnerability or risk to climate impacts or diminish welfare. Urban examples include green gentrification which offers nature-based solutions to the few, social safety nets that promote risk inducing subsidies. Whether an action is maladapted can depend on context, for example air conditioning can reduce risk for the individual but is maladaptive at a societal level (see [[#6.3.4.2|Section 6.3.4.2]] ). It is informed by process; corruption can distort processes and generate maladaptation (see [[#6.4.5.2|Section 6.4.5.2]] ). Climate resilient development raises the ambition for adaptation actions so that it is also possible to describe actions that do not also enhance climate mitigation and sustainable development outcomes as maladaptive (see [[#6.4.3.1|Section 6.4.3.1]] ). This section assesses three broad categories of risk arising from downstream adaptation actions, including interventions that transfer vulnerability across space and time, plans that yield socioeconomically exclusionary outcomes, and actions that undermine long-term sustainable and resilient development priorities.

Downstream impacts occur because adaptive capacity is often unequally distributed across sectors and communities (Matin, Forrester and Ensor, 2018; [[#Makondo--2018|Makondo and Thomas, 2018]] ). In cities and settlements, adaptation interventions can displace ecological impacts to more vulnerable areas or directly lead to socioeconomically exclusionary outcomes (Anguelovski et al., 2016), particularly when adaptation plans and actions are primarily assessed through the prism of economic and/or financial viability (Shi et al., 2016; Klein, Juhola and Landauer, 2017). As a result, adaptation actions make only minimal contributions to the reduction of vulnerability, as the increased vulnerability of excluded communities more than offsets the decreased vulnerability of more well-off communities. Numerous examples, ranging from the mega coastal planning in Jakarta, Indonesia (Salim, Bettinger and Fisher, 2019; [[#Goh--2019|Goh, 2019]] ), fragmentation of urban infrastructure intended to promote climate resilience in Manila, Philippines ( [[#Meerow--2017|Meerow, 2017]] ), exclusionary modes of flood control in São Paulo, Brazil ( [[#Henrique--2019|Henrique and Tschakert, 2019]] ), strategies to reduce risks in the event of mudslides in Sarno, Italy ( [[#D’Alisa--2016|D’Alisa and Kallis, 2016]] ), and involuntary community relocations in Vietnam ( [[#Lindegaard--2020|Lindegaard, 2020]] ) and Mozambique ( [[#Arnall--2019|Arnall, 2019]] ) all point to how an economic logic to adaptation can lead to exclusion of lower income, informal or minority communities in adaptation.

A specific form of maladaptation is so-called green gentrification, this privileges wealthy urban residents in urban greening projects (Rice et al., 2020; Shokry, Connolly and Anguelovski, 2020; Anguelovski, Irazábal-Zurita and Connolly, 2019; [[#Blok--2020|Blok, 2020]] ). For example, in Miami-Dade County, Florida, USA, researchers found that adaptation functionality had a positive effect on property values (Keenan, Hill and Gumber, 2018). In New York City and Atlanta, Georgia, USA, research has shown that adaptation investments can increase property values and lead to neighbourhood change ( [[#Immergluck--2018|Immergluck and Balan, 2018]] ; [[#Gould--2018|Gould and Lewis, 2018]] ). In the Gold Coast and Sunshine Coast, South East Queensland, Australia, where local communities have a strong preference for waterfront living, local governments are pressured by property developers to protect these coastal zones (Torabi, Dedekorkut-Howes and Howes, 2018). In Lagos, Nigeria, efforts to achieve climate resilience and sustainability through future city practices risk perpetuating the enclosure and commodification of land ( [[#Ajibade--2017|Ajibade, 2017]] ). The exclusionary outcomes of some adaptation interventions can therefore further heighten the risk to communities that are socioeconomically more vulnerable. See [[#6.3|Section 6.3]] for further discussion of equity and justice considerations in local climate adaptation.

Human behaviour can exacerbate climate impacts, for example in the emergence of ‘last chance tourism’, Lemieux et al. (2018) focused on built cultural heritage at risk from climate change associated events, including from decay or even total loss generated by increased flooding and sea level rise (Camuffo, Bertolin and Schenal, 2017) and water infiltration from post-flood standing water ( [[#Camuffo--2019|Camuffo, 2019]] ). Last chance tourism can lead to increased touristic interest over a short time horizon and to precarious economic conditions, which can lead to further accelerated degradation cultural heritage sites already at risk from climate change.

Finally, some adaptation policies or actions can erode the preconditions for sustainable and resilient development by indirectly increasing society’s vulnerability (Neset et al., 2019; Juhola et al., 2016). Mandates to mainstream adaptation into existing development logics and structures perpetuates development-as-usual, reinforcing technocratic forms of local governance and locking in structural causes of marginalisation and differential vulnerability (Scoville-Simonds, Jamali and Hufty, 2020). Adaptation policy examples include: Australia’s adaptation policy focus on financial strategies, preference for business-as-usual scenarios and incremental change will not contribute to transformative change ( [[#Granberg--2014|Granberg and Glover, 2014]] ); Surat, India, where a focus on adapting industries and economically important assets in the city can divert policy attention away from general social equity and urban sustainability priorities ( [[#Chu--2016|Chu, 2016]] ; [[#Blok--2020|Blok, 2020]] ); Cambodia, where conflict between adaptation practitioners and local communities and non-compliance with regulatory safeguards led to conflict and potential for maladaptation (Work et al., 2018). Finally, although insurance has the potential to incentivise practices to reduce risks, including through measures to reduce premiums (see [[#6.4.5|Section 6.4.5]] for additional details), researchers of insurance-led adaptation actions have argued that, since insurance regimes privilege normality, they tend to structurally embed risky behaviour and inhibit change (O’Hare, White and Connelly, 2016). All of these examples illustrate how incremental strategies that rely on business-as-usual actions can further entrench unequal and unsustainable development patterns in the long term. There are also significant limits to urban adaptation (see [[#6.4|Section 6.4]] ) with consequential impacts on human well-being.

Table 6.4 lists a selection of key risks (broadly defined as have severe outcomes common to a majority of cities) identified in our assessment of urban impacts and risks in this section. It provides a description of the consequences of the risk that would constitute a severe outcome, as well as the hazard, exposure and vulnerability conditions contributing to its severity. It also provides adaptation options identified and elaborated on in [[#6.3|Section 6.3]] as having the highest potential for reducing the risk, and an assessment of the confidence in the judgement that this risk could become severe. This table is also reflected in [[IPCC:Wg2:Chapter:Chapter-16#16.5.1|Section 16.5.1]] , and the methodology is described in Table SM16.5.1.

'''Table 6.4 |''' Key Risks to cities, settlements and infrastructure

{| class="wikitable"
|-
! colspan="7"| Synthesis of key risks for cities, settlements and key infrastructure

!
|-
! Key risk

! Geographic region

! Consequence that would be considered severe, and to whom.

! Hazard conditions that would contribute to this risk being severe.

! Exposure conditions that would contribute to this risk being severe.

! Vulnerability conditions that would contribute to this risk being severe.

! Adaptation options with highest potential for reducing risk.

! Confidence in key risk identification.

! Chapter and section

|-
| Risk to population from increased heat

| Global but higher risk in temperate and tropical cities. (6.2.3.1)

| Increased heat stress, mortality and morbidity events from urbanisation and climate change.

Increased health risks and mortality in elderly population;

vulnerability of the young to heat. (6.2.3.1)

| Substantial increase in frequency and duration of extreme heat events, exacerbated by urban heat island effects. (6.2.3.1)

Concentration of a mixture of extreme heat and humidity. (6.2.3.1)

| Large increases in exposure, particularly in urban areas, (6.2.3) driven by population growth, changing demographics, and projected urbanisation patterns.

Urbanisation increases annual mean surface air temperature by more than 1°C

Correlation between rising temperatures and increased heat capacity of urban structures, anthropogenic heat release and reduced urban evaporation. (6.2.3.1)

| Changing demographics from aging populations, potential for persistent poverty, slow penetration and increasing cost of air conditioning, and inadequate improvements in public health systems.

(6.2.3.1)

Inadequate housing and occupations with exposure to heat. (6.2.3.1)

| Nature-based solutions e.g., urban greenery at multiple spatial scales; vegetation; shading; lower energy costs; green roofs; community gardens; (6.3.3.1) enhanced space conditioning in buildings; broader access to public health systems for most vulnerable populations.

Less economic stress on residents through utilities, especially electricity. (6.2.3.1)

Tree planting in communities that lack urban greening. (6.3.3.1)

| ''High confidence'' , robust ''evidence'' and high ''agreement'' .

| 6.2, 6.3

|-
| Urban infrastructure at risk of damage from flooding and severe storms

| Global, but higher risk in coastal cities.

| Damage to key urban infrastructure (e.g., buildings, transport networks, and power plants) and services from flood events, particularly high risk within coastal cities, especially those located in low elevation coastal zones. (6.2.3.2)

| Substantial increase in frequency and intensity of extreme precipitation (6.2.3.2) from severe weather events and tropical cyclones contributing to pluvial and fluvial floods, which are exacerbated by long-term sea level rise and potential land subsidence. (6.2.3.2)

| Large increases in exposure, particularly in urban areas, driven by population growth, changing demographics, and projected urbanisation patterns with a geographical focus in coastal regions. Flooding is exacerbated both by encroachment of urban areas into areas that retain water, and lack of infrastructure such as embankments and flood walls. (6.2.3.2)

| Costly maintenance of protective infrastructure, downstream levee effects, and increased concentrations of coastal urban population. Little investment in drainage solutions. (6.2.3.2)

| Early warning systems, Adaptive Social Protection (ASP) to reduce vulnerable populations, nature-based solutions e.g., in sponge cities to enhance flood protection and regulate storm- and floodwaters; this can be improved through reduced risk unto vulnerable urban systems such as stormwater management, sustainable urban drainage system, etc. (6.2.3.2)

Green infrastructure can be more flexible and cost effective for providing flood risk reduction. (6.3.3)

| ''High confidence'' , ''robust evidence'' and high agreement

| 6.2, 6.3, CCP2

|-
| Population at risk from exposure to urban droughts

| Cities located in regions with high drought exposure (e.g., Europe, South Africa, Australia).

| Water shortages in urban areas, and restricted access to water resources to vulnerable populations and low-income settlements. People living in urban areas will be exposed to water scarcity from severe droughts. (6.2.3.3) Increased environmental health risks when using polluted groundwater. (6.2.3.3)

| Projections of more frequent and prolonged drought events potentially compounded with heatwave hazards, and land subsidence from coastal cities that extract groundwater. Climate drivers (warmer temperatures and droughts) along with urbanisation processes (land use changes, migration to cities and changing patterns of water use) contribute to additional risks. (6.2.3.3)

| Large increases in exposure, particularly in urban areas, driven by population growth, changing demographics, and projected urbanisation patterns. Limitations of engineered water infrastructure is also exposed by flash droughts. (6.2.3.3) Settlements are increasingly dependent on imported water resources by locales that may also be exposed to drought risk. (6.2.3.3)

| Greater water demand from urban populations from in-migration and key economic sectors, and inefficient or ineffective water resource management. (6.2.3.3)

| Demand and supply side management strategies that include incorporation of indigenous/local knowledge and practices, equitable access to water. Better water resource management will increase quality of water available. More beneficial physical and social teleconnections to bring mutual benefit of water resources between regions. (6.2.3.3)

| ''High confidence'' , ''robust evidence'' and high agreement

| 6.2, 6.3

|-
| Health risks from air pollution exposure in cities

| Global, in cities located in Africa, South Asia, the Middle East and East Asia

| Increased mortality and morbidity events from respiratory-related illnesses and co-morbidities toward vulnerable urban populations, arising from PM2.5 and tropospheric ozone exposure.

| Increased emissions of pollutants from anthropogenic (e.g., transportation, electric power generation, large industries, indoor burning of fuel, and commercial and residential sources) and biogenic (e.g., forests, windblown dust, and biomass burning) emissions.

Potential for severe compound risks arising from droughts and wildfire.

Projections for frequency of meteorological conditions are expected to severe PM2.5 concentrations. (6.2.3.4)

| Large increases in exposure, particularly in urban areas, driven by population growth, changing demographics, projected urbanisation patterns and demand for energy combined with weak regulations for emissions control. (6.2.2.4)

| High proportion of young or aging populations vulnerable to respiratory illness, potential for persistent poverty, advection of pollutants from upwind, ex-urban areas, and stay in shelter policies from COVID-19. (Box 6.4; 6.2.5)

| Enhanced monitoring of air quality in rapidly developing cities,

investment in air pollution controls, e.g., stricter emissions. regulations, and

increased GHG emissions controls resulting in co-benefits with air quality improvements.

Increase in trees or vegetated barriers with low VOC emissions, low allergen emissions, and high pollutant deposition potential to reduce particulate matter and maximise adaptation benefits. (6.3.3.2)

| ''High confidence'' , ''medium evidence'' and high ''agreement''

| 6.2, 6.3

|-
| Health risks from water pollution exposure and sanitation in cities

| Cities located in regions with high drought exposure resulting in polluted water.

| Increased environmental health risks when using polluted groundwater. (6.2.3.3)

Vulnerability of users such as women; children; the elderly; ill or disabled. (6.3.4.6)

| Decreased regional precipitation and changes in runoff and storage from droughts impairs the quality of water available. Less runoff to freshwater rivers can increase salinity, concentrate pathogens, and pollutants. (6.2.2.3)

| Large increases in exposure, particularly in urban areas, driven by population growth, changing demographics, and projected urbanisation patterns. Low flows from drought can lead to sedimentation, increase pollutant concentration and blocking of sewer infrastructure networks. (6.2.4.8).

| Costly maintenance of protective infrastructure. Sanitation systems coupled with flood water management are at risk of damage and capacity exceedance from high rainfall. (6.2.4.8)

| Investment in well-regulated water sections; wastewater treatment plants; pumping stations. Reducing impacts of floods on sanitation infrastructure through active management such as reducing blockage in sewer infrastructure (6.3.4.6)

Adaptive planning; integration of measures of climate resilience; improved accounting and management of water resources. (6.3.4.6)

| ''High confidence'' , ''medium evidence'' and High agreement

| 6.2, 6.3

|}

Following Chapter 16, the severity of a risk or impact is a subjective judgment based on a number of criteria. Key risks are ‘potentially’ severe because, while some could already be severe now, more typically they may become so over time because of changes in the nature of the climate-related hazards and/or of the exposure and/or vulnerability of societies or ecosystems to those hazards. They also may become severe owing to the adverse consequences of adaptation or mitigation responses to the risk.

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