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

=== 10.4.6 Cities, Settlements and Key Infrastructures ===

<div id="h2-10a-siblings" class="h2-siblings"></div>

Cities across Asia have large populations exposed to climate risks but also present an opportunity for concerted climate action ( [[#Revi--2014|Revi et al., 2014]] ; [[#Chu--2017|Chu et al., 2017]] ; [[#Revi--2017|Revi, 2017]] ; [[#Khosla--2019|Khosla and Bhardwaj, 2019]] ) and report numerous examples of adaptation actions at various stages of planning and implementation ( [[#Dulal--2019|Dulal, 2019]] ; [[#Singh--2021b|Singh et al., 2021b]] ). However, challenges specific (though not exclusive) to Asian cities such as uneven economic development, rapid land-use changes, increasing inequality, growing exposure to extreme events and environmental change, such as land subsidence (with antecedent impacts on people and infrastructure), and large, socially differentiated vulnerable populations, remain key concerns as Asian cities simultaneously tackle challenges of sustainable development and equitable climate action.

<div id="10.4.6.1" class="h3-container"></div>

<span id="sub-regional-diversity-1"></span>
==== 10.4.6.1 Sub-regional Diversity ====

<div id="h3-24-siblings" class="h3-siblings"></div>

By 2050, urban areas are expected to add 2.5 billion people, 90% of whom will be in Asia and Africa ( [[#UNDESA--2018|UNDESA, 2018]] ). Critically, this urban population increase will be concentrated in India, China and Nigeria, with India and China adding 416 million and 255 million urban dwellers, respectively, between 2018 and 2050 ( [[#UNDESA--2018|UNDESA, 2018]] ).

Asia is home to 54% of the world’s urban population, and by 2050, 64% of Asia’s 3.3 billion people will be living in cities. Asia is also home to the world’s largest urban agglomerations: Tokyo (37 million inhabitants), New Delhi (29 million) and Shanghai (26 million) are the top three with Cairo, Mumbai, Beijing and Dhaka home to nearly 20 million people each ( [[#UNDESA--2018|UNDESA, 2018]] ). By 2028, New Delhi is projected to become the most populous city in the world. In certain parts of Asia (e.g., some cities in Japan and the Republic of Korea), a steep decline in urban population is projected, mainly due to declining birth rates ( [[#Hori--2020|Hori et al., 2020]] ). Within Asia, rates of urbanisation differ sub-regionally. Eastern Asia has seen the most rapid urban growth with the percentage of urban population having more than tripled from 18 to 60% between 1950 and 2015, while rates of urbanisation have decreased in West Asia and remained steady in Central Asia ( [[#UNDESA--2018|UNDESA, 2018]] ).

Asian cities are seeing growing income inequality, with rural poverty being replaced by urban poverty ( [[#ADB--2013|ADB, 2013]] ). Regional studies show high and growing inequality within Indian and Chinese urban areas and decreasing rural–urban income gaps in Thailand and Vietnam ( [[#Baker--2017|Baker and Gadgil, 2017]] ; [[#Imai--2018|Imai and Malaeb, 2018]] ). Critically, East Asia and the Asia–Pacific in general continue to house the world’s largest population of slum dwellers at 250 million, with most of them in China, Indonesia and the Philippines, and the highest rates of urban poverty in Papua New Guinea, Vanuatu, Indonesia and the Lao PDR ( [[#McIlreavy--2015|McIlreavy, 2015]] ; [[#Baker--2017|Baker and Gadgil, 2017]] ). A lot of urbanisation, especially in South Asia, is also ‘hidden’ due to poor, competing definitions of what is ‘urban’ and limited data ( [[#Ellis--2016|Ellis and Roberts, 2016]] ).

<div id="10.4.6.2" class="h3-container"></div>

<span id="key-drivers-of-vulnerabilities"></span>
==== 10.4.6.2 Key Drivers of Vulnerabilities ====

<div id="h3-25-siblings" class="h3-siblings"></div>

In Asian cities, climatic hazards such as changes in precipitation and, during the Asian monsoon, SLR, cyclones, flooding, dust storms, heatwaves and permafrost thawing ( [[#Byers--2018|Byers et al., 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; Shiklomanov, 2019), as well as non-climatic vulnerabilities such as non-climatic hazards (e.g., seismic hazards), inadequate infrastructure and services, unplanned urbanisation, socioeconomic inequalities and existing adaptation deficits ( [[#Johnson--2013|Johnson et al., 2013]] ; [[#Araos--2016|Araos et al., 2016]] ; [[#de%20Leon--2017|de Leon and Pittock, 2017]] ; [[#Meerow--2017|Meerow, 2017]] ; [[#Dulal--2019|Dulal, 2019]] ) interact to shape overall urban risk ( [[#Shaw--2016a|Shaw et al., 2016a]] ; [[#Rumbach--2017|Rumbach and Shirgaokar, 2017]] ; [[#Dodman--2019|Dodman et al., 2019]] ). Caught at the intersection of high exposure, socioeconomic vulnerability and low adaptive capacities, informal settlements in urban and peri-urban areas are particularly at risk ( ''robust evidence, high agreement'' ) ( [[#Meerow--2017|Meerow, 2017]] ; [[#Rumbach--2017|Rumbach and Shirgaokar, 2017]] ; [[#Byers--2018|Byers et al., 2018]] ).

<div id="10.4.6.3" class="h3-container"></div>

<span id="observed-and-projected-impacts"></span>
==== 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>
==== 10.4.6.4 Adaptation in Cities Across Asia ====
</li></ul>

A review of urban adaptation in South, East and Central Asia found examples of 180 adaptation activities across 74 cities ( [[#Dulal--2019|Dulal, 2019]] ). Most adaptation actions in Asia are in the initial stages ( [[#Araos--2016|Araos et al., 2016]] ) with 57% focused on preparatory actions, such as capacity building and vulnerability assessment, and 43% focused on implemented adaptation (see also SM10.4). Most adaptation actions were focused on disaster risk management ( [[#Dulal--2019|Dulal, 2019]] ), although the proportion of climate finance spent on disaster preparedness is not very high (as [[#Georgeson--2016|Georgeson et al., 2016]] , show in the megacities of Beijing, Mumbai and Jakarta). Although key port cities across Asia are at high risk of climate impacts, it is estimated that adaptation interventions constitute only a small proportion of cities’ climate efforts ( [[#Blok--2015|Blok and Tschötschel, 2015]] ). Figure 10.8 shows risks and key adaptation options in select cities across Asia.

<div id="_idContainer024" class="Figure"></div>

[[File:d1da2e524a9884942611e779d1a12b91 IPCC_AR6_WGII_Figure_10_008.png]]

'''Figure 10.8 |''' '''Risks and key adaptation options in select cities across Asia.''' Cities were chosen to ensure coverage of different sub-regions of Asia, represent different risk profiles, different city sizes (based on current population and projected growth) and reported progress on different adaptation strategies (infrastructural, institutional, ecosystem based and behavioural). There is a full line of sight in SM10.4.

Critically, most urban adaptation in South, East and Central Asia is reactive in nature ( [[#Dulal--2019|Dulal, 2019]] ; [[#Singh--2021b|Singh et al., 2021b]] ), raising questions on preparedness, proactive building of adaptive capacities and whether present actions can lock certain cities or sectors into maladaptive pathways ( [[#Friend--2014|Friend et al., 2014]] ; [[#Gajjar--2018|Gajjar et al., 2018]] ; [[#Salim--2019|Salim et al., 2019]] ; [[#Chi--2020|Chi et al., 2020]] ). China, India, Thailand and the Republic of Korea record the most number of urban adaptation initiatives, driven mainly by supportive government policies ( [[#Lee--2015|Lee and Painter, 2015]] ; [[#Dulal--2019|Dulal, 2019]] ). The number of actors working on urban adaptation is growing: in addition to national governments and local municipalities, civil society, private-sector actors ( [[#Shaw--2019|Shaw, 2019]] ) and transnational municipal networks ( [[#Fünfgeld--2015|Fünfgeld, 2015]] ) are emerging as important for knowledge brokering, capacity building and financing urban adaptation ( [[#Karanth--2014|Karanth and Archer, 2014]] ; [[#Chu--2017|Chu et al., 2017]] ; [[#Bazaz--2018|Bazaz et al., 2018]] ).

Adaptation options include: (a) infrastructural measures such as building flood protection measures and sea walls, and climate-resilient highways and power infrastructure ( [[#Shaw--2016b|Shaw et al., 2016b]] ; [[#Ho--2017|Ho et al., 2017]] ); (b) sustainable land-use planning through zoning, developing building codes ( [[#Knowlton--2014|Knowlton et al., 2014]] ; [[#Nahiduzzaman--2015|Nahiduzzaman et al., 2015]] ; [[#Rahman--2016|Rahman et al., 2016]] ; [[#Ahmed--2019b|Ahmed et al., 2019b]] ); (c) ecosystem-based adaptation measures such as protecting urban green spaces, improving permeability, mangrove restoration in coastal cities, etc. ( [[#Brink--2016|Brink et al., 2016]] ; [[#Fink--2016|Fink, 2016]] ; [[#Yu--2018d|Yu et al., 2018d]] ); (d) relocation and migration out of risk-prone areas ( [[#McLeman--2019|McLeman, 2019]] ; [[#Hauer--2020|Hauer et al., 2020]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ); and (e) disaster management and contingency planning such as through Early warning systems (EWS), improved awareness and preparedness measures ( [[#Shaw--2016a|Shaw et al., 2016a]] ). Asian cities are also focusing on institutional adaptation measures which cut across the five categories mentioned above such as through building capacity and local networks ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Friend--2014|Friend et al., 2014]] ; [[#Knowlton--2014|Knowlton et al., 2014]] ), improving awareness ( [[#Knowlton--2014|Knowlton et al., 2014]] ), and putting local research and monitoring mechanisms in place ( [[#Lee--2015|Lee and Painter, 2015]] ) to enable adaptation. Figure 10.9 shows the effectiveness of select adaptation options in cities across Asia.

<div id="_idContainer028x" class="Figure"></div>

[[File:31408e3b142daa24a2a2ceaffe41ad00 IPCC_AR6_WGII_Figure_10_009.png]]

'''Figure 10.9 |''' '''Effectiveness of select adaptation options in cities across Asia.''' Effectiveness is assessed based on the option’s ability to reduce risk as reported in the literature.

<div id="10.4.6.4.1" class="h4-container"></div>

<span id="infrastructural-adaptation-options"></span>
===== 10.4.6.4.1 Infrastructural adaptation options =====

<div id="h4-20-siblings" class="h4-siblings"></div>

The challenge of adapting infrastructure to climate change across Asia is twofold: there are significant infrastructure deficiencies, especially in low-income countries, and key infrastructures are at high risk due to climate change ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ; [[#Lu--2019|Lu, 2019]] ). Infrastructural adaptation options in cities attempt to enable networked energy, water, waste and transportation systems to prepare for, and deal with, climate risks better ( [[#Meerow--2017|Meerow, 2017]] ) through interventions such as improved highways and power plants, climate-resilient housing, improved water infrastructure and so forth ( [[#ADB--2014|ADB, 2014]] ).

* '''Power infrastructure:''' Adaptations in electricity systems include climate-resilient power infrastructure, particularly essential for coastal megacities such as Manila, Mumbai, Bangkok and Ho Chi Minh City ( [[#Meerow--2017|Meerow, 2017]] ; [[#Duy--2019|Duy et al., 2019]] ), which double as regional economic hubs and are home to tens of millions of people. In the Philippines, solar panels at water pumping stations are installed to operate and maintain a minimal capacity to pump water if the electricity grid were to break down ( [[#Stip--2019|Stip et al., 2019]] ).
* '''Water infrastructure:''' Sustainable water supply and resource management are key to urban adaptation through improved water service delivery, wastewater recycling and storm-water diversion ( [[#Deng--2015|Deng and Zhao, 2015]] ; [[#Xie--2017|Xie et al., 2017]] ; [[#Yu--2018d|Yu et al., 2018d]] ). Infrastructure-based adaptation options in urban water management include building water storage facilities, storm-water management and enhancing water quality improving permeability, managing runoff and enabling groundwater recharge. One example is of Shanghai (China), where infrastructural and policy incentives come together to enable adaptation: the city has been divided into 14 water conservancy zones, including 348 polder areas with 2517 km of dykes, 1499 pump stations and 2203 sluices ( [[#Yu--2018d|Yu et al., 2018d]] ). It also depends on a regional inundation control system, flood Early warning system and an emergency plan to deal with flood risk and mitigate waterlogging ( [[#Chen--2018e|Chen et al., 2018e]] ; [[#Yu--2018d|Yu et al., 2018d]] ). Another example is Ho Chi Minh City (Vietnam), where, given significant increases in area at risk of flooding under climate change, the city has invested in storm sewer upgradation, dike works, improving drainage and increasing the height of road embankments and minor bridges ( [[#Storch--2011|Storch and Downes, 2011]] ; [[#ADB--2014|ADB, 2014]] ; [[#Ho--2017|Ho et al., 2017]] ). These infrastructural interventions were complemented by designing an Early warning system to initiate flood mitigation procedures, such as isolating critical electrical and mechanical operating systems from water.
* '''Built infrastructure:''' Current built-infrastructure adaptation interventions are mostly reactive (e.g., strengthening housing units, using sandbags during flooding, storing of food, evacuation) rather than preventive (e.g., relocation, building multi-storey and stronger housing units), mainly due to limited resources within most vulnerable households for investing in proactive measures ( [[#Francisco--2019|Francisco and Zakaria, 2019]] ). For cities in North Asia seeing permafrost thawing, adequate land-use practices, permafrost monitoring, maintenance of infrastructure and engineering solutions (e.g., using thermosiphons) may temporarily offset the negative effects of permafrost degradation in small, economically vital areas, but are unlikely to have an effect beyond the immediate areas ( [[#Shiklomanov--2017b|Shiklomanov et al., 2017b]] ; Streletskiy, 2019). Importantly, thawing permafrost and GHG emissions create feedbacks where emissions amplify warming and drive additional thaw. Reducing these impacts through mitigation will reduce the need for adaptation significantly ( [[#Schaefer--2014|Schaefer et al., 2014]] ).
* '''Infrastructure and technology:''' Several infrastructural options employ technology, such as smart meters, to monitor water usage and service delivery, but these are differentially adopted across Asian sub-regions with higher adoption across East Asia. Examples include: the Yokohama smart city project in Japan, which has been smart eco-urbanism interventions since 2011 (e.g., energy saving and storage infrastructure, wastewater management, behavioural change towards renewable energy and low-carbon transportation) (IUC, 2019); the Tianjin Eco-city mega-project in China, which is testing a range of measures to meet urban sustainability goals in partnership with Singapore ( [[#ICLEI--2014b|ICLEI, 2014b]] ; [[#Blok--2015|Blok and Tschötschel, 2015]] ); development in New Songdo (Republic of Korea), which is experimenting with interventions, such as embedded smart waste management ( [[#Anthopoulos--2017|Anthopoulos, 2017]] ), and national policy initiatives such as the Smart Cities Mission covering 100 cities in India (e.g. technology-enabled water, energy and land management for urban agriculture in Nashik city) ( [[#ICLEI--2014a|ICLEI, 2014a]] ). However, the efficacy of such measures, especially for larger sustainability and climate-change goals, remains to be seen ( [[#ICLEI--2014a|ICLEI, 2014a]] ; [[#ICLEI--2014b|ICLEI, 2014b]] ; [[#Caprotti--2015|Caprotti et al., 2015]] ; [[#Anthopoulos--2017|Anthopoulos, 2017]] ).

Infrastructural measures alone are seldom effective in building urban resilience as seen in the examples of the 2011 floods in Bangkok and the 2005 typhoon in Manila ( [[#Duy--2019|Duy et al., 2019]] ), or projected estimates by [[#Pervin--2020|Pervin et al. (2020)]] who found that structural interventions in existing drainage systems reduce flooding risk by 7–19% in Sylhet (Bangladesh) and Bharatpur (Nepal); however, without proper solid waste management, areas under flood risk could increase to 18.5% in Sylhet and 7.6% in Bharatpur in five years, rendering the infrastructural interventions ineffective over time. While in some cities it is estimated that infrastructural adaptation through ‘hard’ flood protection strategies (e.g., storm surge barriers and floodwalls) is more effective than institutional or ecosystem-based adaptation by 2100, for example, Shanghai. A hybrid approach where hard strategies protect from flood risk, and soft strategies reduce residual risk from hard strategies, is suggested ( [[#Du--2020|Du et al., 2020]] ). In Japan, without adaptation, estimated damage costs of floods (caused by tropical cyclones and altered precipitation) by 2081–2100 under RCP2.6 will be 28% higher (compared with 1981–2000), rising to 57% higher under RCP8.5 ( [[#Yamamoto--2021|Yamamoto et al., 2021]] ). With a combination of adaptation measures (such as land-use control, piloti building and flood control measures), estimated damage costs can be reduced even below the 1981–2000 levels, and with a combination of mitigation and these adaptation measures, an estimated 69% reduction in flood damage costs are expected–demonstrating the importance of concerted and immediate climate action in reducing damage.

Infrastructural interventions can sometimes be maladaptive when assessed over longer time periods: for example, the Mumbai Coastal Road (MCR) project aimed at reducing flood risk and protecting against SLR will potentially cause damages to intertidal fauna and flora and local fishing livelihoods ( [[#Senapati--2017|Senapati and Gupta, 2017]] ); and Jakarta’s Great Garuda project aimed at reducing flood risk is expected to ''increase'' flood risk for the poorest urban dwellers ( [[#Salim--2019|Salim et al., 2019]] ).

<div id="10.4.6.4.2" class="h4-container"></div>

<span id="sustainable-land-use-planning-and-regulation"></span>
===== 10.4.6.4.2 Sustainable land-use planning and regulation =====

<div id="h4-21-siblings" class="h4-siblings"></div>

Land use in cities impacts resource use (e.g., water, energy), risk (a function of population density, service provision and hazard exposure) and adaptive capacity, all of which influence the efficacy of urban adaptation ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). Locally suited land-use planning and regulation (such as appropriate zoning or building codes and safeguarding land rights) can have adaptation co-benefits ( [[#Mitchell--2015|Mitchell et al., 2015]] ; [[#Dhar--2016|Dhar and Khirfan, 2016]] ): for example, strict building regulations can protect urban wetlands and associated ecosystem services ( [[#Jiang--2015|Jiang et al., 2015]] ); appropriate land zoning can safeguard green spaces, ensure improvements in permeability and obviate new development in risk-prone locations ( [[#Duy--2019|Duy et al., 2019]] ); and ensuring tenurial security or regularising informal settlements can incentivise improvements to housing quality, thereby alleviating vulnerability of the most marginal people ( [[#Mitchell--2015|Mitchell et al., 2015]] ).

Land tenure arrangements strongly shape urban dwellers’ vulnerability and their adaptive capacities ( [[#Roy--2013|Roy et al., 2013]] ; [[#Michael--2018|Michael et al., 2018]] ). For example, in Khulna (Bangladesh), Roy et al. (2013) found significant differences between the adaptive strategies of homeowners and renters in low-income settlements, a finding echoed in Bangalore (India) ( [[#Deshpande--2018|Deshpande et al., 2018]] ) and Phnom Penh (Cambodia) ( [[#Mitchell--2015|Mitchell et al., 2015]] ). In Riyadh (Saudi Arabia), land-based adaptation strategies include land zoning to control population and building density, demarcating environmental protection zones, and sub-urbanisation ( [[#Nahiduzzaman--2015|Nahiduzzaman et al., 2015]] ; [[#Rahman--2016|Rahman et al., 2016]] ). In many Asian cities, land subsidence control can serve as an adaptation strategy since it is estimated to significantly reduce relative SLR ( ''high confidence'' ). This has an important implication in that subsidence control would be a good and complementary measure to climate mitigation and climate adaptation in many coastal urban settings in Asia ( [[#Cao--2021|Cao et al., 2021]] ; [[#Nicholls--2021|Nicholls et al., 2021]] ). Urban land-use planning, if used proactively, can incentivise adaptation–mitigation synergies and obviate unintended negative consequences of urbanisation as [[#Xu--2019|Xu et al. (2019)]] have shown in Xiamen.

<div id="10.4.6.4.3" class="h4-container"></div>

<span id="ecosystem-based-adaptation"></span>
===== 10.4.6.4.3 Ecosystem-based adaptation =====

<div id="h4-22-siblings" class="h4-siblings"></div>

The literature on urban ecosystem-based adaptation (EbA) [[#footnote-004|9]] , especially across Asia, has grown significantly since AR5 (Demuzere and al., 2014; [[#Yao--2015|Yao et al., 2015]] ; [[#Brink--2016|Brink et al., 2016]] ; [[#Bazaz--2018|Bazaz et al., 2018]] ; [[#de%20Coninck--2018|de Coninck et al., 2018]] ; Ren, 2018). This growing literature reflects the wide recognition that infrastructural adaptation can often have ecological and social trade-offs ( [[#Palmer--2015|Palmer et al., 2015]] ) and need to be complemented by ecosystem-based actions to manage risk more effectively ( [[#Du--2020|Du et al., 2020]] ), build adaptive capacity, and in some cases, meet mitigation and SDGs ( [[#Huang--2020|Huang et al., 2020]] ).

Illustrative examples of EbA in Asian cities include sponge cities in China for sustainable water management, flood mitigation and minimising heatwave impact ( [[#Jiang--2018|Jiang et al., 2018]] ; [[#Yu--2018d|Yu et al., 2018d]] ; [[#Wang--2019a|Wang et al., 2019a]] ; [[#Zhanqiang--2019|Zhanqiang et al., 2019]] ), Singapore’s Active, Beautiful, Clean Waters (ABC Waters) Programme, which uses bio-engineering approaches to protect river channels and prevent localised flooding, improve water quality and create community spaces, and Dhaka’s green roofs and urban agriculture ( [[#Zinia--2018|Zinia and McShane, 2018]] ).

The EbA approaches to manage floods, capture and store rainwater, restore urban lakes and rivers, and reduce surface runoff often blend infrastructural and ecosystem-based approaches. For example, in Tokyo, stormwater management is done by sophisticated underground infrastructure and an artificial infiltration stormwater system ( [[#Saraswat--2016|Saraswat, 2016]] ; [[#Mishra--2019|]] [[#Mishra--2019|Mishra et al., 2019]] ). China’s Sponge City Programme aims to reduce the impacts of flooding through low-impact development measures, urban greenery and drainage infrastructure, such that 80% of urban areas reuse 70% of rainwater by 2020, which would help ensure the resilience of these cities to floods ( [[#Li--2016b|Li et al., 2016b]] ; [[#Stip--2019|Stip et al., 2019]] ).

Case studies on urban EbA also raise equity concerns ( ''medium evidence, medium agreement'' ) such as interventions biased towards suburban areas in Haizhu District, Guangzhou (China) ( [[#Zhu--2019|Zhu et al., 2019]] ); inadequate consideration of low-income, vulnerable populations ( [[#Blok--2015|Blok and Tschötschel, 2015]] ; [[#Meerow--2017|Meerow, 2017]] ; [[#Mabon--2021|Mabon and Shih, 2021]] ); and low familiarity with interventions such as artificial wetlands, water retention ponds as well as green façades and walls can restrict inclusiveness ( [[#Zinia--2018|Zinia and McShane, 2018]] ). Furthermore, urban EbA is constrained by a range of factors such as inadequate institutional structures and processes for connecting different remits and knowledge systems along with trade-offs in land use for different purposes ( [[#Mabon--2021|Mabon and Shih, 2021]] ; [[#Singh--2021b|Singh et al., 2021b]] ).

The EbA interventions are not uniform across Asian cities: in a global study on urban EbA, [[#Brink--2016|Brink et al. (2016)]] found that Eastern Asia, India and Israel report most EbA interventions and that there is variable and ''limited evidence'' on effectiveness and scalability (SM10.5). Using a risk framing (i.e., the extent to which an option reduces risk), urban EbA options in Asian cities score as being ‘low to medium’ effective (see SM10.5); however, when the assessment is expanded to include the ecosystem benefits, economic impacts and human well-being co-benefits of EbA, effectiveness increases. Figure 10.10 shows evidence of the effectiveness of EbA.

<div id="_idContainer028" class="Figure"></div>

[[File:68388bfd6f5d105824b1a71661789e47 IPCC_AR6_WGII_Figure_10_010.png]]

'''Figure 10.10 |''' '''Evidence of the effectiveness of ecosystem-based adaptation (EbA) using examples of four commonly used EbA options''' '''[[#footnote-000|10]]''' '''.''' Effectiveness is assessed qualitatively based on the evidence (for a full line of sight see SM10.5) and is examined through four framings: potential to reduce risk (e.g., reduced exposure to hazard means to reduce risk); benefits to ecosystems (through improved ecosystem health and high biodiversity); economic benefits (e.g., improved incomes, fewer man-days lost, better livelihoods); and human well-being outcomes (e.g., health, quality of life, etc.). The darker shading signifies high effectiveness and the lightest shade signifies low effectiveness of an EbA option (i.e., the option scores low on the indicator).

-----

<div id="footnote-000" class="_idFootnote"></div>

[[#footnote-000-backlink|1]] 10 Assessing the effectiveness of adaptation actions is challenging because of the lack of a clear goal that signifies effective adaptation, varied conceptual framings and metrics used to assess effectiveness, and low empirical evidence on the effectiveness of implemented adaptation actions ( [[#Owen--2020|Owen, 2020]] ; [[#Singh--2021a|Singh et al., 2021a]] ).

For example, urban agriculture is identified as offering multiple benefits such as mitigating emissions associated with food transportation from rural to urban areas, improving food and nutritional security, strengthening local livelihoods and economic development, improved microclimate, soil conservation, improved water and nutrient recycling, and efficient water management ( [[#Padgham--2015|Padgham and Dietrich, 2015]] ; [[#Patil--2019|Patil et al., 2019]] ). However, it can potentially undermine ecosystem services through land-use changes, water overextraction or applying chemical fertilisers ( [[#Ackerman--2014|Ackerman et al., 2014]] ), exposure of smallholders to volatile markets and crops that are not consumed by farming households themselves (thus undermining food security) or increasing the work burdens on women, as well as health externalities (e.g. through use of untreated wastewater, or rearing poultry and livestock in unsanitary conditions). There remain gaps in understanding the differential impacts of urban agriculture at different scales as well as its effectiveness in improving adaptive capacity at scale.

<div id="10.4.6.4.4" class="h4-container"></div>

<span id="migration-and-planned-relocation"></span>
===== 10.4.6.4.4 Migration and planned relocation =====

<div id="h4-23-siblings" class="h4-siblings"></div>

There is ''medium evidence'' with ''high agreement'' that climatic risks are exacerbating internal and international migration across Asia (see Box 10.2; [[#IDMC--2019|IDMC, 2019]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ). In coastal cities, formal ‘retreat’ measures, such as forced displacement and planned relocation ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ), are commonly considered ‘last resort’ adaptation strategies once other infrastructural and ecosystem-based protect-and-accommodate strategies are exhausted ( [https://www.ipcc.ch/chapter/10#CCP2.3 CCP2.3] ) ( [[#Haasnoot--2019|Haasnoot et al., 2019]] ). In contrast, migration (which can take various forms from seasonal, temporary mobility to circular or permanent movement) is a regular feature across Asian urban settlements (Box 10.2, CCB MIGRATE, [[#Maharjan--2020|Maharjan et al., 2020]] ).

There is ''robust evidence'' ( ''medium agreement'' ) that across Asia, migration (and increasingly planned relocation) will continue to be a key risk management strategy, especially in low-lying flood-prone cities (e.g., in Southeast and South Asia) and across drylands (e.g., in South and Central Asia) ( [[#Davis--2018|Davis et al., 2018]] ; [[#Ajibade--2019|Ajibade, 2019]] ; [[#Lincke--2021|Lincke and Hinkel, 2021]] ). While there is insufficient evidence to project migration numbers under different warming levels, it is well established that migration as an adaptation strategy is not equally available to all ( [[#Ayeb-Karlsson--2020|Ayeb-Karlsson, 2020]] ), and climatic risks might reduce vulnerable populations’ ability to move due to loss of assets, thus reinforcing existing inequalities and differential adaptive capacities ( [[#Blondin--2019|Blondin, 2019]] ; [[#Zickgraf--2019|Zickgraf, 2019]] ; [[#Singh--2020|Singh and Basu, 2020]] ; [[#Cundill--2021|Cundill et al., 2021]] ; [[#Gavonel--2021|Gavonel et al., 2021]] ).

There is ''medium evidence'' ( ''low agreement'' ) about the effectiveness of migration and planned relocation in reducing risk exposure. Evidence on climate-driven internal migration shows that moving has mixed outcomes on risk reduction and adaptive capacity. On one hand, migration can improve adaptive capacity by increasing incomes and remittances as well as diversifying livelihoods ( [[#Maharjan--2020|Maharjan et al., 2020]] ); on the other, migration can expose migrants to new risks. For example, in Bangalore (India), migrants often face high exposure to localised flooding, insecure and unsafe livelihoods, and social exclusion, which collectively shape their vulnerability ( [[#Michael--2018|Michael et al., 2018]] ; [[#Singh--2020|Singh and Basu, 2020]] ). In greater Manila (the Philippines) and Chennai (India), planned relocations to reduce disaster risk have often exacerbated vulnerability, due to relocation sites being in environmentally sensitive areas, inadequate livelihood opportunities and exposure to new risks ( [[#Meerow--2017|Meerow, 2017]] ; [[#Ajibade--2019|Ajibade, 2019]] ; [[#Jain--2021|Jain et al., 2021]] ).

<div id="10.4.6.4.5" class="h4-container"></div>

<span id="disaster-management-and-contingency-planning"></span>
===== 10.4.6.4.5 Disaster management and contingency planning =====

<div id="h4-24-siblings" class="h4-siblings"></div>

There is rich case-based evidence across Asia on urban adaptation to extreme events with relatively more evidence on rapid-onset events such as cyclones and flooding than slow-onset disasters such as drought (see Box 10.6; [[#Ray--2019|Ray and]] [[#Shaw--2019|Shaw, 2019]] ; [[#UNESCAP--2019|UNESCAP, 2019]] ; [[#Singh--2021a|Singh et al., 2021a]] ). Overall, there has been a growing emphasis on ‘build back better’ interventions ( [[#Mannakkara--2013|Mannakkara and Wilkinson, 2013]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ) that approach disaster management holistically through infrastructural solutions such as climate-resilient housing or sea walls and soft approaches such as strengthening livelihoods, developing EWS 11 , [[#footnote-003|10]] increasing awareness about disaster risks and impacts, and building local capacities to deal with them ( [[#Bhowmik--2021|Bhowmik et al., 2021]] ). Notably, urban disaster management is effective when land-use planning processes, including greenfield development, zoning and building codes, and urban redevelopment, are leveraged to reduce and/or obviate risk, thereby averting potential maladaptation ( [[#Kuhl--2021|Kuhl et al., 2021]] ).

There is relatively lower empirical evidence on how microenterprises and businesses are adapting to increased risk, but recent examples in Mumbai, India ( [[#Schaer--2018|Schaer and Pantakar, 2018]] ), and Kratie, Cambodia ( [[#Ngin--2020|Ngin et al., 2020]] ), suggest that businesses primarily adopt temporary and reactive responses rather than long-term, anticipatory adaptation measures.

A review of innovative DRR approaches notes the use of geographic information system (GIS) and drone-based technologies for mapping risk exposure and impacts, mobile-based payments for post-disaster compensation, and transnational initiatives and learning networks to promote urban resilience ( [[#Izumi--2019|Izumi et al., 2019]] ). Furthermore, technology-based innovations, such as using big data ( [[#Yu--2018b|Yu et al., 2018b]] ), improved warnings through mobile phones or mobilising relief through social media ( [[#Carley--2016|Carley et al., 2016]] ), are proving effective for disaster preparedness, relief and recovery. Community-based DRR is consistently ranked as most effective for its role in transforming DRR towards being more context relevant and inclusive. Ecosystem-based DRR (EbDRR) is also gaining prominence and includes strategies such as mangrove plantation and rejuvenation in vulnerable coastal areas. Nature-based solutions for flood protection and reducing drought incidence have emerged as an alternative to costlier ‘hard’ infrastructure ( [[#UN-Water--2018|UN-Water, 2018]] ; [[#Zevenbergen--2018|Zevenbergen et al., 2018]] ; [[#Rozenberg--2019|Rozenberg and Fay, 2019]] ). Some cities are also reporting adaptation to heat risk. For example, Ahmedabad (India) has pioneered preparedness for extreme temperatures and heatwaves by developing annual Heat Action Plans, building regulations to minimise trapping heat, advisories about managing heat stress and instituting a cool-roofs policy ( [[#Ahmedabad%20Municipal--2018|Ahmedabad Municipal, 2018]] ).

Financing, regulations and institutional processes play a significant role in incentivising DRR and resilience in large-scale, city-level built infrastructure by the private sector and other actors. Currently there are gaps in these mechanisms, leading to infrastructure development in disaster-prone areas, increasing exposure to people, property, economy and systems ( [[#Jain--2013|Jain, 2013]] ). Both firms and governments need to take disaster risks into consideration in supply-chain management to avoid disruptions and subsequent negative effects ( [[#Abe--2013|Abe and Ye, 2013]] ). There are several institutional challenges faced during DRR and CCA implementation including overlapping efforts and inefficient use of scarce resources due to inappropriate funding mechanisms, a lack of coordination and collaboration, a lack of implementation and mainstreaming, scale mismatches, poor governance, the social–political–cultural structure, competing actors and institutions, and lack of information, communication, knowledge sharing, and community involvement, as well as policy gaps ( [[#Seidler--2018|Seidler et al., 2018]] ; [[#Islam--2020|Islam et al., 2020]] ).

<div id="10.4.6.5" class="h3-container"></div>

<span id="enabling-urban-adaptation-across-asia"></span>
==== 10.4.6.5 Enabling Urban Adaptation Across Asia ====

<div id="h3-27-siblings" class="h3-siblings"></div>

There is growing empirical evidence of conditions enabling and constraining urban adaptation (Table 10.3) with relatively more literature from South, Southeast and East Asia. Governance and capacity-related deficits are repeatedly identified as significant barriers to urban adaptation ( ''robust evidence, high agreement'' ) and interact with financial and informational constraints to mediate adaptation action.

'''Table 10.3 |''' Barriers and enablers to climate adaptation across Asian cities

{| class="wikitable"
|-
! Indicator

! As an enabler

! As a barrier

|-
| Governance and planning

| National policy directives to adapt: for example, strong national climate commitments in China, India and Thailand ( [[#Dulal--2019|Dulal, 2019]] ); and dedicated public–private councils on climate change in Seoul, Republic of Korea ( [[#Lee--2015|Lee and Painter, 2015]] )

Participatory planning, co-producing solutions and engaging multiple stakeholders: for example, Surat ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Karanth--2014|Karanth and Archer, 2014]] ; [[#Chu--2017|Chu et al., 2017]] ) and Guwahati ( [[#Archer--2014|Archer et al., 2014]] ) in India; Bandar Lampung and Semarang in Indonesia ( [[#Archer--2014|Archer et al., 2014]] ); and Seoul, Republic of Korea ( [[#Lee--2015|Lee and Painter, 2015]] )

Devolving decision making to city governments ( [[#ADB--2013|ADB, 2013]] ) and strong political leadership helps to institutionalise adaptation programmes ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Friend--2014|Friend et al., 2014]] ; [[#Lee--2015|Lee and Painter, 2015]] ): for example, in Moscow where the city mayor has spearheaded climate action ( [[#van%20der%20Heijden--2019|van der Heijden et al., 2019]] ).

Mainstreaming climate adaptation in city plans ( [[#UN-HABITAT%20and%20UNESCAP--2018|UN-HABITAT and UNESCAP, 2018]] )

| Low accountability and transparency in planning processes with inadequate spaces for public dialogue ( [[#Friend--2014|Friend et al., 2014]] ) and limited accountability to the most economically and politically marginalised people within cities ( [[#Garschagen--2019|Garschagen and Marks, 2019]] )

Of 180 urban adaptation interventions across Asia, 65% are reactive in nature ( [[#Dulal--2019|Dulal, 2019]] ), thus missing opportunities for risk prevention and preparedness ( [[#Francisco--2019|Francisco and Zakaria, 2019]] ).

Lack of forward-looking, learning-oriented processes constrain adaptation with short-term development priorities often overshadowing long-term climate-action needs ( [[#Friend--2014|Friend et al., 2014]] ; [[#de%20Leon--2017|de Leon and Pittock, 2017]] ; [[#Gajjar--2018|Gajjar et al., 2018]] ; [[#Khaling--2018|Khaling et al., 2018]] ; [[#Garschagen--2019|Garschagen and Marks, 2019]] ; [[#Jain--2021|Jain et al., 2021]] ).

Fragmented governance, lack of mainstreaming between CCA and DRR ( [[#Fuhr--2018|Fuhr et al., 2018]] ; [[#Khaling--2018|Khaling et al., 2018]] ): for example, in Vietnam, Thailand, Indonesia ( [[#Friend--2014|Friend et al., 2014]] ) and greater Manila ( [[#Meerow--2017|Meerow, 2017]] )

|-
| Information

| Knowledge sharing through transnational municipal networks such as C40, ACCRN and A-PLAT ( [[#Fünfgeld--2015|Fünfgeld, 2015]] )

City-level knowledge creation and knowledge-transfer institutions ( [[#Lee--2015|Lee and Painter, 2015]] )

| Data gaps on projected climate risks and impacts in certain sub-regions and small settlements ( [[#Revi--2014|Revi et al., 2014]] )

Numerous tools for assessing vulnerability and adaptation planning ( [[#Nordgren--2016|Nordgren et al., 2016]] )

|-
| Technology and infrastructure

| Early warning systems, climate information and modelling studies inform adaptation decision making ( [[#Reed--2015|Reed et al., 2015]] ; [[#Singh--2018a|Singh et al., 2018a]] )

| Inadequate regional downscaled data at the city scale ( [[#ADB--2013|ADB, 2013]] ; [[#Khaling--2018|Khaling et al., 2018]] );

inadequate cost–benefit analyses of different adaptation strategies (Khaling et al. 2018)

|-
| Capacity and awareness

| A focus on learning, experimentation, awareness and capacity building leads to more sustained, legitimate and inclusive adaptation ( [[#ADB--2013|ADB, 2013]] ; [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Reed--2015|Reed et al. (2015)]] .

| Limited access to, and capacity to use, risk assessment tools ( [[#ADB--2013|ADB, 2013]] ; [[#Shaw--2016b|Shaw et al., 2016b]] )

|-
| Finance

| Dedicated adaptation financing (e.g., in Beijing, adaptation spending is 0.33% of the city’s GDP) ( [[#Georgeson--2016|Georgeson et al., 2016]] ); steering international and local funding to leverage adaptation benefits in urban development programmes such as in Surat (India) ( [[#Cook--2019|Cook and Chu, 2019]] ); mainstreaming climate adaptation into development programming to leverage developmental finance for adaptation action ( [[#Cuevas--2016|Cuevas et al., 2016]] ; [[#Narender--2018|Narender and Sethi, 2018]] )

| Inadequate adaptation funding and lack of financial devolution to city governments ( [[#Fuhr--2018|Fuhr et al., 2018]] ; [[#Garschagen--2019|Garschagen and Marks, 2019]] )

|}

<div id="10.4.7" class="h2-container"></div>

<span id="health-and-well-being"></span>