Editing IPCC:AR6/WGII/Chapter-10 (section)

==== 10.4.6.3 Observed and Projected Impacts ====

<div id="h3-26-siblings" class="h3-siblings"></div>

<div id="10.4.6.3.1" class="h4-container"></div>

<span id="multi-hazard-risk"></span>
===== 10.4.6.3.1 Multi-hazard risk =====

<div id="h4-12-siblings" class="h4-siblings"></div>

Of the multi-hazard global average annual loss (AAL) [[#footnote-009|4]] of 293 billion USD, 170 billion USD (58%) is in the Asia Pacific region ( [[#UNISDR--2017|UNISDR, 2017]] ). Of the top ten highest AALs associated with multi-hazards, six are in Asia (Japan, China, Republic of Korea, India, the Philippines and Taiwan, Province of China) ( [[#UNISDR--2017|UNISDR, 2017]] ). As per [[#Gu--2015|Gu et al. (2015)]] , 56% of cities with populations greater than 300,000 in 2014 are exposed to at least one of the six physical hazards (cyclones, floods, droughts, earthquakes, landslides and volcanic eruptions). Cities in areas highly exposed and vulnerable to multiple hazards were also the ones that grew rapidly in population between 1950 and 2014, implying greater infrastructural investments in climate-sensitive areas. Among 27 cities highly exposed to multiple disasters, 13 cities had a population of 1 million or more in 2014. Among them were three megacities, Tokyo (Japan), Osaka (Japan) and Manila (the Philippines), with more than 10 million inhabitants exposed to three or more hazards. Seven other cities with 1 million inhabitants or more in Asia were at high risk of three or more types of disaster. Manila is highly vulnerable to economic losses and disaster-related mortality from all six types of disasters. Moscow (Russia) is the only megacity not exposed to the risk of any of the six types of physical hazards analysed (cyclones, floods, droughts, earthquakes, landslides and volcano eruptions). Of the eight megacities most vulnerable to disaster-related mortality, seven–Tokyo, Osaka, Karachi, Kolkata, Manila, Tianjin and Jakarta, totalling 143 million people–are in Asia ( [[#Gu--2015|Gu et al., 2015]] ).

<div id="10.4.6.3.2" class="h4-container"></div>

<span id="extreme-temperatures-and-heatwaves"></span>
===== 10.4.6.3.2 Extreme temperatures and heatwaves =====

<div id="h4-13-siblings" class="h4-siblings"></div>

Urbanisation and climate change interact to drive an urban heat island (UHI) effect across Asian cities ( [[#Hauck--2016|Hauck et al., 2016]] ; [[#Chapman--2017|Chapman et al., 2017]] ; also see Figure 6.4 in Chapter 6). Three regions which are expected to see higher maximum wet-bulb temperature than global averages are southwest Asia around the Persian Gulf and Red Sea, South Asia in the Indus and Ganges river valleys, and eastern China ( [[#Im--2017|Im et al., 2017]] ; Perkins-Kirkpatrick et al., 2020).

Impacts of heatwaves at 1.5°C and 2°C in cities are substantially larger than under the present climate ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). In South Asia particularly, more intense heatwaves of longer durations and occurring at a higher frequency are projected with ''medium confidence'' over India ( [[#Murari--2015|Murari et al., 2015]] ) and Pakistan (IPCC AR6, WGI, Table 11.5; [[#Ali--2018|Ali et al., 2018]] ; [[#Nasim--2018|Nasim et al., 2018]] ; [[#Ali--2020|Ali et al., 2020]] ; [[#IPCC--2021|IPCC, 2021]] ). At the city level, these projections could translate into significant impacts: at 1.5°C, on average, every year Kolkata will experience heat equivalent to the 2015 record heatwaves; Karachi about once every 3.6 years; and under 2°C warming, both regions could expect such heat annually ( [[#Matthews--2017|Matthews et al., 2017]] ). In Pakistan, Hyderabad is ''likely'' to be the hottest city by 2100 with the highest average temperature reaching 29.9°C (RCP4.5) to 32°C (RCP8.5) followed by Jacobabad, Bahawalnagar and Bahawalpur cities ( [[#Ali--2020|Ali et al., 2020]] ). The frequency of heatwave days (HWF) will increase by 22.8, 22.3 and 26.5 d yr –1 in northern, eastern and western Japan, respectively, with megacities such as Tokyo, Osaka and Nagoya seeing large increases in HWF and related deaths ( [[#Nakano--2013|Nakano et al., 2013]] ).

In China’s urban agglomerations, an increase in the global warming from 1.5°C to 2°C is ''likely'' to exacerbate the intensity of extreme maximum temperature 4.1 times ( [[#Yu--2018d|Yu et al., 2018d]] ). From 1995 to 2014 China’s urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing–Chengdu and Pearl River Delta) experienced no more than three heat danger d yr –1 , which is projected to increase to 3–13 d by 2041–2060 and 8–67 d by 2081–2100 under high-emissions shared socioeconomic pathways SSP3-7.0 and SSP5-8.5, resulting in approximately 260 million people (19% of the total population of China) and 310 million people (39% of the total population), respectively, facing more than three heat-danger days annually ( [[#Zhang--2021|Zhang et al., 2021]] ). This projected risk exposure is reduced under low-emissions pathways (SSP1-2.6 and SSP2-4.5), where annual heat-danger days will remain similar to current levels or increase slightly ( [[#Zhang--2021|Zhang et al., 2021]] ).

Critically, these projections of higher temperatures will have a significant impact on heat-related morbidity and mortality, labour productivity, mental health, and health and well-being outcomes across all sub-regions of Asia ( ''medium evidence, high confidence'' ) ( [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Im--2017|Im et al., 2017]] ; [[#Arifwidodo--2019|Arifwidodo et al., 2019]] ; [[#Arshad--2020|Arshad et al., 2020]] ). In West Asia and the North China Plain especially, extreme wet-bulb temperatures are expected to approach, and possibly exceed, the physiological threshold for human adaptability (35°C) ( [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Kang--2018|Kang and Eltahir, 2018]] ). By the end of the century, under higher projections (RCP8.5), the daily maximum wet-bulb temperature is expected to exceed the survivability threshold across most of South Asia ( [[#Im--2017|Im et al., 2017]] ). City-specific studies articulate what these regional projections will mean for urban populations. For example, at 1.5°C warming, without adaptation, annual heat-related mortality in 27 major cities across China is projected to increase from 32.1 per million inhabitants annually in 1986–2005 to 48.8–67.1 per million. This number increases to 59.2–81.3 per million for 2°C warming ( [[#Wang--2019a|Wang et al., 2019a]] ). In the Republic of Korea, deaths from heat disorders are expected to increase approximately fivefold under the RCP4.5 and 7.2-fold under RCP8.5 by 2060 compared with the current baseline value of ~23 people per summer ( [[#Kim--2016a|Kim et al., 2016a]] ). Importantly, heat exposure is differentiated within cities: it disproportionally affects the poorest populations (Lohrey et al., 2021) and those with lesser access to green spaces ( [[#Arifwidodo--2020|Arifwidodo and Chandrasiri, 2020]] ).

<div id="10.4.6.3.3" class="h4-container"></div>

<span id="precipitation-extremes-excess-rainfall-drought-and-water-scarcity"></span>
===== 10.4.6.3.3 Precipitation extremes: excess rainfall, drought and water scarcity =====

<div id="h4-14-siblings" class="h4-siblings"></div>

Warming from 1.5°C to 2°C will increase extreme precipitation events across Asia especially over East and South Asia ( ''medium evidence, high agreement'' ) (Zhang et al. 2018; [[#Supari--2020|Supari et al., 2020]] ; [[#Zhang--2020b|Zhang et al., 2020b]] ). In East and Central Asia, under 1.5°C warming, extreme 1- and 5-d precipitation will increase by 28 and 15% relative to 1971–2000 ( [[#Zhang--2020b|Zhang et al., 2020b]] ). In China’s urban agglomerations, an increase in global warming from 1.5°C to 2°C is ''likely'' to increase the intensity of total precipitation of very wet days 1.8 times and double maximum 5-d precipitation ( [[#Yu--2018d|Yu et al., 2018d]] ). Extreme rainfall has direct and increasing consequences on urban flooding risk ( [[#Dasgupta--2013b|Dasgupta et al., 2013b]] ), which is further exacerbated by urbanisation trends that reduce permeability, divert water flow and disrupt watersheds ( [[#Chen--2015b|Chen et al., 2015b]] ; [[#Duan--2016|Duan et al., 2016]] ).

Urban extent in drylands in expected to increase from 2000 to 2030 with large expansions in West Asia, Central Asia, South Asia and China and antecedent impacts on exposure to drought and water scarcity ( [[#Güneralp--2015|Güneralp et al., 2015]] ). Urban dryland extent in West Asia will increase from 19,400 to 67,400 km 2 ( [[#Güneralp--2015|Güneralp et al., 2015]] ). In the Haihe River basin in China, the proportion of people exposed to droughts at 1.5°C (without accounting for population growth) is projected to decrease by 30.4% but increase by 74.8% at 2°C relative to people exposed in 1986–2005 (339.65 million) ( [[#Sun--2017|Sun et al., 2017]] ). About 411 million people living in 330 cities above 300,000 population are exposed to drought risk, which include three Asian megacities Delhi (India), Karachi (Pakistan) and Kolkata (India). Drought-related economic losses are also high in Dhaka (Bangladesh) ( [[#Pervin--2020|Pervin et al., 2020]] ), Istanbul (Turkey), Manila (the Philippines) and Shenzhen (China), and Manila is also highly vulnerable to drought-related mortality ( [[#Gu--2015|Gu et al., 2015]] ).

Increasing urban drought risk will also have cascading impacts on regions from where water is imported, exacerbating drought exposure beyond urban settlements and limiting water availability in certain regions (Chuah et al., 2018; [[#Garrick--2019|Garrick et al., 2019]] ; [[#Zhang--2020c|Zhang et al., 2020c]] ; [[#Zhao--2020|Zhao et al., 2020]] ). There is ''medium evidence'' ( ''high agreement'' ) that urban water insecurity is experienced differentially based on income, risk exposure, and assets, and that urban drought and water scarcity is causing material and non-material losses and damage ( [[#Singh--2021a|Singh et al., 2021a]] ). Importantly, in several Asian cities, flood and drought risk is expected to occur concurrently, especially in South Asia which is projected to see the largest increase in urban land exposed to both floods and droughts (25–32% increase in flood and drought risk between 2000 and 2030).

<div id="10.4.6.3.4" class="h4-container"></div>

<span id="sea-level-rise-and-coastal-flooding"></span>
===== 10.4.6.3.4 Sea level rise and coastal flooding =====

<div id="h4-15-siblings" class="h4-siblings"></div>

Global assessments identify Asia as the most exposed region to SLR (see Section [https://www.ipcc.ch/chapter/10#CCP2.2 CCP2.2.1] ) in terms of the number of people living in low-elevation coastal zones and the number of people exposed to flooding from 1-in-100-years storm surge events ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ; [[#Kulp--2019|Kulp and Strauss, 2019]] ; [[#Abadie--2020|Abadie et al., 2020]] ; [[#Haasnoot--2021|Haasnoot, 2021]] ). Twelve of the top 20 countries exposed to SLR and associated flood events are in Asia, and of these, China, India, Bangladesh, Indonesia and Vietnam are estimated to have the highest total coastal population exposure ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Edmonds--2020|Edmonds et al., 2020]] ). Critically, regardless of the emissions scenario, 70% of the global population exposed to SLR and land subsidence are in eight Asian countries: China, Bangladesh, India, Vietnam, Indonesia, Thailand, the Philippines and Japan ( [[#Kulp--2019|Kulp and Strauss, 2019]] ). This is particularly worrisome since in highly populated low-lying coastal cities across Asia, it is estimated that land subsidence could be as influential as climate-induced SLR over the 21st century ( [[#Cao--2021|Cao et al., 2021]] ; [[#Nicholls--2021|Nicholls et al., 2021]] ). In East Asia and the Asia–Pacific in general (expected to see 0.2–0.5 m SLR), without adaptation, 1 million people (range of 0.3–2.2 million) are projected to be affected by submergence under RCP8.5 by 2095. Limiting warming will reduce this risk, and under RCP4.5, these numbers of people at risk will be reached by 2140. However, continuing on RCP8.5 increases risk exposure to 7 million (estimated range of 2–24 million people) ( [[#Haasnoot--2021|Haasnoot, 2021]] ). Notably, assuming present-day population and adaptation (in the form of existing protection standards), East and South Asia already have a large number of people at risk of a 1-in-100-years flooding event (63 million) because of relatively lower flood protection (except in China and Malaysia).

These global scenarios will have significant impacts on national and subnational populations. For example, in Bangladesh, under 0.44 and 2 m mean SLR, direct inundation is estimated to drive migration of 0.73–2.1 million people by 2100 ( [[#Davis--2018|Davis et al., 2018]] ). Such migration will have direct development implications: for example, destination locations could see additional demands on jobs (594,000), housing (197,000) and food (783 × 10 9 calories) by mid-century as a result of those displaced by SLR ( [[#Davis--2018|Davis et al., 2018]] ).

Among the 20 largest coastal cities with the highest flood losses by 2050, 13 are in Asia [[#footnote-008|5]] , with a regional concentration in South, Southeast and East Asia ( [[#Hallegatte--2013|Hallegatte et al., 2013]] ). Furthermore, 9 of these cities (Guangzhou, Kolkata, Tianjin, Ho Chi Minh City, Jakarta, Zhanjiang, Bangkok, Xiamen, Nagoya) also have an additional risk of subsidence due to SLR and flooding ( [[#Hallegatte--2013|Hallegatte et al., 2013]] ). Guangzhou, China, is estimated to be the most economically vulnerable city in the world to SLR by 2050, with estimated losses of 254 million USD yr –1 under 0.2 m SLR ( [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ). With a 2°C warming, Guangzhou is expected to see SLR of 0.34 m; under 5°C warming, this number would rise to 1.93 m. A more recent estimate calculates expected damage in Guangzhou due to SLR under RCP8.5 to reach 331 billion USD by 2050 and 420 billion USD under the high-end scenario with figures doubling by 2070. By 2100, expected damage could reach 1.4 trillion USD under RCP8.5 and 1.8 trillion USD under the high-end scenario. Similarly, in Mumbai (India) SLR damages amount to US$ 112–162 billion by 2050 and could increase by a factor of 2.8–2.9 by 2070 ( [[#Abadie--2020|Abadie et al., 2020]] ). In coastal cities such as Bangkok and Ho Chi Minh City, projected land subsidence rates, mainly due to excessive groundwater extraction, are comparable to, or exceed, expected rates of SLR, resulting in an additional 0.2 m SLR by 2025 ( [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ). In Shanghai, current annual damage by coastal inundation is estimated at 0.03% of local GDP; under RCP4.5, this increases to 0.8% by 2100 (uncertainty range of 0.4–1.4%) and is further exacerbated by land subsidence and socioeconomic development ( [[#Du--2020|Du et al., 2020]] ). It is important to note that these projections assume (a) no adaptation and (b) that damage repairs are undertaken and completed annually. Given these assumptions, while these estimates communicate the scale of projected impacts, they are indicators of possible damages in the absence of adaptation and ''not'' actual projections.

The SLR affects economic growth, its drivers and welfare outcomes ( [[#Hallegatte--2012|Hallegatte, 2012]] ; [[#Pycroft--2016|Pycroft et al., 2016]] ; [[#Lee--2020|Lee and Asuncion, 2020]] ) through (a) permanent loss of land and natural capital, (b) loss of infrastructure and physical capital, (c) loss of social capital and migration, (d) temporary floods, food insecurity and loss of livelihoods and (e) added expenditure for coastal protection. Without adaptation, direct damage to the GDP by 2080 due to SLR would be highest in Asia ( ''robust evidence, medium agreement'' ), with China losing between 64.2 billion USD (under A1B of 2.4°C by the 2050s and 3.8°C by the 2090s at 0.47 m), 95.8 billion USD (under the RAHM scenario of 1.4 m SLR by 2100 at 1.12 m) and 118.4 billion USD (at a high SLR of 2 m by 2100 at 1.75 m) in direct damages, and an additional 5.7, 4.5 and 4.5 billion USD, respectively, due to migration ( [[#Pycroft--2016|Pycroft et al., 2016]] ). Closely after China will be India, the Republic of Korea, Japan, Indonesia and Russia. Overall, Asia can experience direct losses of about 167.6 billion USD (at 0.47 m), 272.3 billion USD (at 1.12 m) or 338.1 billion USD (at 1.75 m), and an additional 8.5, 24 or 15 billion USD at the respective SLR projections, due to migration [[#footnote-007|6]] .

<div id="10.4.6.3.5" class="h4-container"></div>

<span id="tropical-cyclones"></span>
===== 10.4.6.3.5 Tropical cyclones =====

<div id="h4-16-siblings" class="h4-siblings"></div>

Globally, there is ''high confidence'' that the proportion of intense tropical cyclones is expected to increase despite the total number of tropical cyclones being expected to decrease or remain unchanged ( [[#Arias--2021|Arias et al., 2021]] ), especially in Southeast and East Asia ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Yamamoto--2021|Yamamoto et al., 2021]] ). Historical trends from South Asia indicate that more lives are lost due to storm surge levels than the intensity of the cyclone (Niggol and Bakkensen, 2017). The number of people exposed to 1-in-100-years storm surge events is highest in Asia. China, India, Bangladesh, Indonesia and Vietnam have the highest numbers of coastal populations exposed ( [[#Neumann--2015|Neumann et al., 2015]] ) with Guangzhou, Mumbai, Shenzen, Tianjin, Ho Chi Minh City, Kolkata and Jakarta incurring losses of 1520 million USD due to coastal flooding in 2005 alone ( [[#Dulal--2019|Dulal, 2019]] ), although Jakarta is exposed to monsoonal storm surge. It is projected that by 2050, without adaptation, the annual losses incurred in these cities will increase to approximately 32 billion USD ( [[#Dulal--2019|Dulal, 2019]] ).

Globally, six of the top ten countries/places with the highest AAL associated with tropical cyclones are in Asia (Japan, Republic of Korea, the Philippines, China, Taiwan, Province of China, and India) ( [[#Mori--2021a|Mori et al., 2021a]] ). The AAL associated with storm surge is primarily concentrated in Japan, China, Hong Kong SAR of China, and India. The AAL associated with wind and storm surge relative to the existing capital stock in the country is highest in New Caledonia, Tonga, Vanuatu, Palau, the Philippines, Fiji and the Solomon Islands, indicating less resilience. For example, in Ise Bay, Japan, the current storm surges are estimated to lead to property and business damage of approximately 100.04 billion JPY with current adaptation (protective sea wall), but this can more than double to 236.49 billion JPY under climate-change-induced increases in storm surge intensity ( [[#Jiang--2016|Jiang et al., 2016]] ).

<div id="10.4.6.3.6" class="h4-container"></div>

<span id="riverine-floods"></span>
===== 10.4.6.3.6 Riverine floods =====

<div id="h4-17-siblings" class="h4-siblings"></div>

Over one-third of Asian cities and about 932 million urban dwellers live in areas with high risk of flooding ( [[#Gu--2015|Gu et al., 2015]] ). Of 437 cities at low risk of flood exposure but highly vulnerable to flood-related economic losses, approximately half are in Asia ( [[#Gu--2015|Gu et al., 2015]] ).

Globally, China and India have the highest AALs associated with riverine floods, with a magnitude of 13 and 6 billion USD, respectively. Other countries from Asia among the top ten of absolute AALs are Japan, Bangladesh and Thailand. There is an increased flood risk for habitations on the deltas influenced by both riverine and coastal drivers of flooding ( [[#Szabo--2016a|Szabo et al., 2016a]] ), globally exposing 9.3% more people annually to riverine flooding than otherwise estimated without the compounded influence ( [[#Eilander--2020|Eilander et al., 2020]] ). Simultaneously, SLR and subsidence are also expected to increase the risk due to frequent flood events for these delta regions than the longer-return periods otherwise associated with SLR ( [[#Yin--2020|Yin et al., 2020]] ).

<div id="10.4.6.3.7" class="h4-container"></div>

<span id="permafrost-thawing-and-associated-risks"></span>
===== 10.4.6.3.7 Permafrost thawing and associated risks =====

<div id="h4-18-siblings" class="h4-siblings"></div>

In Northern Eurasia, observed and projected climate-change impacts are especially pronounced. On land, the presence of permafrost, which occupies substantial areas of eastern Russia, Mongolia and mountain regions of China, creates specific challenges for economic development and human activities. By 2050, it is likely that 69% of fundamental human infrastructure in the Pan Arctic will be at risk (RCP 4.5 scenario)( ''medium confidence'' ), including more than 1200 settlements ( [[#Hjort--2018|Hjort et al., 2018]] ). The majority of the population and the absolute majority (85%) of large settlements on permafrost are located in Russia, and 44% of those are expected to be profoundly affected by permafrost thaw by 2050 ( [[#Streletskiy--2019|Streletskiy et al., 2019]] ; Ramage et al., 2021). Under RCP8.5, the climate-induced decrease of bearing capacity and, in regions with ice-rich permafrost, thaw subsidence, is projected to affect 54% of all residential buildings on permafrost with a combined worth of 20.7 billion USD; 20% of commercial and industrial structures and 19% in critical infrastructure with a total worth of 84.4 billion USD (Streletskiy, 2019). Transport infrastructure in Russia and China are impacted by thaw subsidence and, to a lesser degree, from frost heave, which add significant operational costs and limit accessibility to remote settlements (Porfiriev et al., 2019; Ni et al., 2021).

Especially in Russia, significant populations and fixed infrastructure assets are located in urban centres on permafrost that is degrading significantly. Two major risks associated with permafrost degradation are loss of permafrost bearing capacity and ground subsidence ( [[#Streletskiy--2015|Streletskiy et al., 2015]] ). The former determines the ability to support foundations of buildings and structures and is a vital characteristic of sustainability of the economic centres, while the latter impacts the ability of critical infrastructure (roads, railroads) to provide transportation and support accessibility of remote populations and economic centres on permafrost. The proximity of some settlements to the coasts or areas with uneven topography may further increase risks associated with permafrost degradation as ice-rich coasts characterised by high rates of coastal erosion, while settlements located on slopes may experience higher rates of mass wasting processes.

Changes in climate have resulted in permafrost warming and increased thaw depth in undisturbed locations ( [[#Biskaborn--2019|Biskaborn et al., 2019]] ), but in built up areas these transformations have been exacerbated by human activities ( [[#Grebenets--2012|Grebenets et al., 2012]] ). Norilsk, the largest city built on permafrost above the Arctic Circle ( [[#Shiklomanov--2017b|Shiklomanov et al., 2017b]] ), was found to have one of the highest trends of near-surface permafrost warming ( [[#Streletskiy--2012|Streletskiy et al., 2012]] ). Anomalous high temperatures and earlier snowmelt in 2020 may have contributed to oil storage collapse and the resulting spill of 20,000 tons of diesel fuel in Norilsk area (Rajendan et al., 2021). The ability of foundations to support structures has decreased by 10–40% relative to the 1960s in the majority of settlements on permafrost in Russia ( [[#Streletskiy--2012|Streletskiy et al., 2012]] ) and is expected to further decrease by 20–33% by 2050–2059 relative to 2006–2015 ( [[#Streletskiy--2019|Streletskiy et al., 2019]] ).

<div id="10.4.6.3.8" class="h4-container"></div>

<span id="risks-and-impacts-on-infrastructure"></span>
===== 10.4.6.3.8 Risks and impacts on infrastructure =====

<div id="h4-19-siblings" class="h4-siblings"></div>

South Asia and Africa bear the highest losses from unreliable infrastructure, and climate change will increase these losses due to hazards and necessitate additional infrastructure investments to address new risks ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ; [[#Lu--2019|Lu, 2019]] ) [[#footnote-006|7]] . Specifically, power generation and transport infrastructure incur losses of 30 billion USD a year on average from hazards (about 15 billion USD each), with low- and middle-income countries shouldering about 18 billion USD of the total amount ( [[#Koks--2019|Koks et al., 2019]] ; [[#Nicholls--2019|Nicholls et al., 2019]] ).

Among the top 20 countries that are rapidly expanding their infrastructure stock while facing high disaster risk and low infrastructure quality, the Asian countries are Lao PDR, the Philippines, Bangladesh, Cambodia, Kyrgyzstan, Bhutan and Vietnam. ( [[#UNISDR--2017|UNISDR, 2017]] ; [[#WEF--2018|WEF, 2018]] ). The losses are due to direct damage to infrastructure, disruption in services and affected supply chains ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). East Asia and the Pacific and South Asia have the highest adaptation deficits in coastal protection with 75 billion USD in the former and 49 billion USD in the latter ( [[#Nicholls--2019|Nicholls et al., 2019]] ). If overall damages are minimised, low- and middle-income countries may need to invest 0.1–0.5% of their GDP annually up to 2030 for protection against both coastal and river floods, varying based on level of acceptable risks, construction costs, urbanisation and climate uncertainties [[#footnote-005|8]] .

* '''Power disruption:''' Contrasting with high-income, countries such as the USA, where hazards, particularly storms, are responsible for 50% of power outages, this share is much lower in countries like Bangladesh or India, because system failures due to unnatural causes are very frequent. However, outages caused by hazards tend to be longer and geographically more widespread than other outages ( [[#Rentschler--2019|Rentschler et al., 2019]] ). Climate-change-induced SLR is expected to impact power infrastructure, even necessitating power plant relocation ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In Bangladesh, to avoid inundations caused by SLR (SSP2, RCP8.5), approximately one-third of power plants may need to be relocated by 2030. An additional 30% of power plants are ''likely'' to be affected by increased salinity of cooling water and increased frequency of flooding, while power plants in the northern region will probably see a decrease in output because of droughts ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In 2013 in Chittagong ( [[#Pervin--2020|Pervin et al., 2020]] ), users experienced about 16 power outages due to storms alone ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). Furthermore, low-carbon technology diffusion might make certain infrastructures redundant, leading to stranded assets.

Across Asia, infrastructure impacts are mixed: net importers, such as China and India, will see GDP gains, while extreme examples include Russia, a net exporter, which could see steep declines in fossil fuel production ( [[#Mercure--2018|Mercure et al., 2018]] ). In low- and middle-income countries globally, disruption in power supply can impact firms directly (up to 120 billion USD yr –1 ), with coping costs (up to 65 billion USD yr –1 ) and other indirect impacts. Similarly, for households, the direct impact and cost of coping could be between 2.3 and 190 billion USD yr –1 . Although all power outage is not due to natural hazards, there is a significant number that is attributed to disasters. Besides, outages caused by natural hazards tend to be longer and geographically larger than other causes ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ).

<ul>
<li>'''Transportation disruption:''' Of the 20 countries in which the road and railway infrastructure is expected to be most affected in absolute terms due to multi-hazards, half are Asian ( [[#Koks--2019|Koks et al., 2019]] ). In low- and middle-income countries globally, the direct losses to firms on account of transportation disruption are about 107 billion USD yr –1 , excluding the costs due to sales losses or delayed supplies and deliveries alone ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In the transport sector, floods and other hazards disrupt traffic and cause congestion, taking a toll on people and firms in rich and poor countries alike.</li>
<li><p>'''Water supply and disposal infrastructure disruption:''' In low- and middle-income countries, disruption of water supply could lead to direct losses of about 6 billion USD yr –1 for firms, and between 88 and 153 billion USD yr –1 for households (due to willingness to pay to avoid disruption). Additionally, there are second-order costs associated with finding alternate sources of water and also health issues (on the order of 6–9 billion USD yr –1 accounting for medical bills and missed income) ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In China, climate models project that an increasing number of wastewater-treatment-plant assets face climate-induced flood hazards in both the near and distant future, potentially affecting as many as 208 million users by 2050 ( [[#Hu--2019|Hu et al., 2019]] ).</p>
<span id="adaptation-in-cities-across-asia"></span>