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

== 10.5 Adaptation Implementation ==

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=== 10.5.1 Governance ===

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==== 10.5.1.1 Points of Departure ====

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Climate-change governance is characterised by a scalar/stakeholder turn which includes: (a) acknowledgement of the importance of both subnational and transnational–regional scales along with the global scale; (b) involvement of diverse stakeholders in decision-making systems; (c) reliance on bottom-up architectures of governance that are supported by the framework given by the SDGs; (d) emphasis on developmental and environmental co-benefits; (e) recognition of diverse experiences of marginalisation and social stratification, and their impacts on participation in governance-related activities; and (f) greater decentralisation and strengthening of local institutions.

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==== 10.5.1.2 Findings ====

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In order to facilitate local adaptation, especially in a context characterised by regional diversity and spatio-temporal variation, climate-adaptive governance invites greater policy attention to institution building (formal and informal) at multiple scales and across sectors ( [[#Mubaya--2017|Mubaya and Mafongoya, 2017]] ). An incremental EbA approach underlines the advantage of drawing upon ecosystem services for reducing vulnerabilities, increasing resilience of communities to adapt to climate change, and minimising threats to social systems and human security, provided climate change remains below 2°C or, better yet, below the 1.5°C of global warming ( [[#Barkdull--2018|Barkdull and Harris, 2018]] ).

Focus on multi-level governance, both below and beyond the state level, is steadily growing ( [[#Jogesh--2015|Jogesh and Dubash, 2015]] ; [[#Jörgensen--2015|Jörgensen et al., 2015]] ; [[#Beermann--2016|Beermann et al., 2016]] ). Discernible diversity across political systems and sectors in Asia notwithstanding, issues relevant to multi-level climate governance includes interplay between top-down national initiatives, which stem from supranational, regional and sub-regional levels. In the case of India, national climate governance has proliferated beyond the National Action Plan on Climate Change to include State Action Plans on Climate Change of over 28 states and union territories, demonstrating graphically the shared ‘co-benefit’ in terms of creating greater space for innovation and experimentation ( [[#Jörgensen--2015|Jörgensen et al., 2015]] ).

In Japan’s Climate Change Adaptation Act, enacted by the Japanese Diet in June 2018, the national government shall formulate a national action plan to promote adaptation in all sectors. This Act recommends that prefectures and municipalities designate a ‘local climate change adaptation centre’ as a local climate-change data collection and provision centre to provide more locally specific information and support for adaptation planning at the level of local municipalities. The Japanese government, in partnership with the private sector, has formulated a new comprehensive strategy, named Society 5.0, which aims at devising a number of technologically innovative solutions ( [[#Mavrodieva--2020|Mavrodieva and Shaw, 2020]] ).

Significantly, the co-benefit concept for international city partnerships along with comparative analysis of the challenges, capabilities and limitations of urban areas in Asia with regard to CCA governance remains under-researched ( [[#Beermann--2016|Beermann et al., 2016]] ).

In the case of Vietnam, especially at district and community levels, where the policy capacities in hierarchical governance systems to deal with climate-change impacts are generally constrained, the value of clear legal institutions, provision of financing for implementing policies and the training opportunities for governmental staff has been well demonstrated ( [[#Phuong--2018b|Phuong et al., 2018b]] ). A key finding is that any effort to support local actors (i.e., smallholder farmers) should ensure augmentation of policy capacity through necessary investments.

In the case of China, a combination of market-based policies, emissions trading systems, a growing number of environmental non-governmental organisations (NGOs) and international networks appear to be serving as an important tool for climate governance ( [[#Ramaswami--2017|Ramaswami et al., 2017]] ; [[#Wang--2017b|Wang et al., 2017b]] ). Public private partnership (PPP) too is receiving increasing focus, especially with regard to climate-related cost-effective and innovative infrastructure projects. In the absence of major investments in resilience, climate change may force up to 77 million people into a poverty trap by 2030 ( [[#World%20Bank--2016|World Bank, 2016]] ). As seen in the case of Japan, most of the countries in Asia face the challenge of contractual allocation of risks associated with natural hazards and climate change between the public and private sectors and its long-term management in the face of uncertainty. Risk sharing, therefore, could be addressed by clear definition and allocation ( [[#World%20Bank--2017|World Bank, 2017]] ). Given that in Asia, especially Singapore, China, Japan and Republic of Korea, where the water sector is a target of industrial and technology policy, PPPs could prove to be mutually beneficial. As a middle ground, key findings of a study on Indonesia ( [[#Yoseph-Paulus--2016|Yoseph-Paulus and Hindmarsh, 2016]] ) underline the importance of building, sustaining and augmenting local capacity by addressing inadequacies with regard to resource endowment and capacity building, public awareness about climate change, government–community partnerships, vulnerability assessment and providing inclusive decision-making spaces to Indigenous knowledge systems and communities.

In the agriculture sector, farmers in Asia are adapting to climate change at the grassroots level ( [[#Tripathi--2017|Tripathi and Mishra, 2017]] ). A recent, comprehensive and systematic review ( [[#Shaffril--2018|Shaffril et al., 2018]] ) shows how farmers in diverse sub-regions of Asia have adopted diverse adaptation strategies through management of crops, irrigation and water, farms, finances, physical infrastructure and social activities. Much more qualitative research on farmers’ perceptions and decision-making processes about adaptation practices is needed in order to capture their location-specific priorities and get a diverse understanding of the risks and threats. A study of Vietnamese smallholder farmers’ perceptions of their current and future capacity to adapt to climate change ( [[#Phuong--2018a|Phuong et al., 2018a]] ) found considerable differences between farmers in crop production and livestock production in terms of their motives behind adopting particular planned adaptation options.

A study on farmers’ awareness of, and adaptation to, climate change in the dry zones of Myanmar, critically dependent on agriculture, indicates how those at the front line of the adverse effects of climate change are steadily abandoning the common sesame/groundnut cropping pattern, and trying to adapt to risks and uncertainties with the aid of conventional agricultural practices such as rainwater collection, water-harvesting techniques and even traditional weather forecasting techniques for weather prediction. Similarly, a case study of the Gandak basin in Nepal showed that incorporation of local knowledge into agricultural practices and weather warning systems works best when coupled with multiple sources of information based on a method of triangulation. This also intersects with gender outcomes, where women frequently receive information from the men of their households rather than directly from state institutional sources ( [[#Acharya--2019|Acharya and Prakash, 2019]] ). Climate-change adaptive governance is facilitated by improved cross-scalar and cross-sectoral cooperation, exchange of information and experiences, and best practices ( [[#Smith--2014|Smith et al., 2014]] ; [[#Watts--2015|Watts et al., 2015]] ; [[#Gamble--2016|Gamble et al., 2016]] ; [[#Gilfillan--2017|Gilfillan et al., 2017]] ).

An integrated approach informed by science, which examines multiple stressors along with Indigenous knowledge, appears to be of immense value ( [[#Elum--2017|Elum et al., 2017]] ). A study on Pakistan concluded that poor agricultural communities are among the worst victims of climate change ( [[#Ali--2017|Ali and Erenstein, 2017]] ) and that farmers who are younger, better educated, belong to joint families and possess more landholdings are ''likely'' to adapt sooner and better. Correspondingly, this category achieved higher levels of income and food security. The climate-development nexus suggests that CCA practices at the farm level can have significant development outcomes, besides reducing risk posed by changing weather patterns. Central to the CCA process is the growing recognition of the role that institutions play in both the hierarchical setting and across different scales to influence implementation of CCA in diverse areas of governance across social and political domains. [[#Cuevas--2018|Cuevas (2018)]] highlights the usefulness of mainstreaming CCA into local land-use planning in Albay, the Philippines, by involving networks of interacting institutions and institutional arrangements for overcoming obstacles that are potentially counterproductive and conflictual.

As noted by AR5 ( [[#IPCC--2014a|IPCC, 2014a]] ), research on issues related to both climate-change impacts on livestock production–demand for which is expected to double by 2050 in a world of 10 billion people–and policy choices with regard to adaptation, especially at the local scale, is still limited but progressing ( [[#Rojas-Downing--2017|Rojas-Downing et al., 2017]] ). The promise of diversification of livestock animals (within species), crop diversification and transition to mixed crop–livestock systems needs to be further explored. A study of livestock farmers in Pakistan showed that risk-coping mechanisms, such as purchasing livestock insurance and increasing land areas for fodder, are far more rewarding policy options in comparison with selling livestock and migrating to another place. Relatedly, the association of migration with adaptation measures is context specific and involves a number of factors pertaining to the socioeconomic circumstances of vulnerable agricultural groups in countries like India and Bangladesh ( [[#Ojha--2014|Ojha et al., 2014]] ). In the 2010 United Nations Framework Convention on Climate Change’s Cancun Adaptation Framework, migration was recognised as a form of adaptation that should be included in a country’s long-term adaptation planning where appropriate (Paragraph 14 f).

Furthermore, agricultural climate-adaptation policy targeting livestock farmers in rural areas is ''very likely'' to benefit from better education and awareness as well as increased access to extension services among livestock farmers on climate risk-coping choices and strategies ( [[#Rahut--2018|Rahut and Ali, 2018]] ). In Myanmar, the lack of adequate agricultural extension strategies has had a negative impact on adaptation outcomes in what is labelled the ‘central dry zone’. Farmers’ perceptions of climate change contribute to a comprehensive understanding of the context where they identify deforestation and related activities as the main culprits. Their adaptive methods include agricultural land preparation and crop rotation practices in addition to rainwater-harvesting techniques ( [[#Swe--2015|Swe et al., 2015]] ). A study of vulnerable areas in Bangladesh ( [[#Alam--2017|Alam et al., 2017]] ) has shown that with policy support, livestock rearing can prove to be a viable substitute for crop production in areas prone to riverbank erosion. Carefully developed partnerships between government organisations and NGOs can come to the rescue of poor farmers and their precarious households by providing information about best practices for local adaptation strategies, including credit options with various institutions and creating an enabling environment for the promotion of agro-based industries. A study in community forestry in the Indian Himalayan region ( [[#Gupta--2019|Gupta and Koontz, 2019]] ) has shown how the synergies and successful partnerships could evolve between government and NGOs in local forest governance, with the former providing technical and financial support, and the latter directing the communities to those resources, and in the making up for each other’s limitations thereby enabling and augmenting community efforts in forest governance.

A study of Pakistan ( [[#Ali--2017|Ali and Erenstein, 2017]] ) shows that factors such as enhanced awareness about various climate risk-coping strategies, better education and agricultural extension services, augmenting farm-household assets, lowering the cost of adaptation, improving access to services and alternative livelihoods, and providing support to poorer households appear to have paid rich dividends. Countries such as Bhutan and Sri Lanka have included provisions for ‘climate-smart agriculture’ in their nationally determined contributions (NDCs) ( [[#Amjath-Babu--2019|Amjath-Babu et al., 2019]] ).

In the domain of forest adaptive governance, ever since the introduction of Reducing Emissions from Deforestation and forest Degradation plus (REDD+) at COP 13 in 2007 in Bali, the Indonesian experience suggests that some of the major challenges include curbing emissions, changes in cross-sectoral land-use as well as practice within forestry and lack of effective, efficient and equitable implementation of diverse forest governance practices. The issue of how forest governance institutions are conceived and managed, both at national and subnational levels, involving state, private sector and civil society, also needs serious attention ( [[#Agung--2014|Agung et al., 2014]] ).

In an example from Nepal, [[#Clement--2018|Clement (2018)]] showed that deliberative governance mechanisms can create the space for alternative framings of climate change to take a hold in ways that are cognisant of both the local and global contexts; this moves beyond a dependence on techno-managerialism in the construction of solutions, where local governance solutions can support institutional changes. The possibilities more incorporating deliberative methods into wider governance architecture are also expanded through an acknowledgment of the role of social learning; this is observable in the multi-stakeholder involvement that this approach fosters in regions of South Asia such as the Brahmaputra River basin ( [[#Varma--2018|Varma and Hazarika, 2018]] ). Additionally, recent studies have reconfirmed the importance of linking Indigenous knowledge with the scientific knowledge of climate change in diverse regions of the globe, including Asia and Africa ( [[#Hiwasaki--2014|Hiwasaki et al., 2014]] ; [[#Etchart--2017|Etchart, 2017]] ; [[#Taremwa--2017|Taremwa et al., 2017]] ; [[#Vadigi--2017|Vadigi, 2017]] ; [[#Apraku--2018|Apraku et al., 2018]] ; [[#Inaotombi--2018|Inaotombi and Mahanta, 2018]] ; [[#Makondo--2018|Makondo and Thomas, 2018]] ) for building farmers’ resilience, enhancing CCA, ensuring cross-cultural communication, promoting local skills, drawing upon Indigenous Peoples’ intuitive thinking processes and geographic knowledge of remote areas.

A study of the Sylhet Division in Bangladesh, deploying a knowledge quality assessment tool, found significant correlation between a narrow technocratic problem framing, divorced from traditional knowledge strongly rooted in local sociocultural histories and relatively low project success due to skewed risk-based calculations disconnected from the ground realities ( [[#Haque--2017|Haque et al., 2017]] ; [[#Wani--2018|Wani and Ariana, 2018]] ). Highlighting the vulnerability of the Bajo tribal communities, who inhabit the coastal areas of Indonesia, to climate change, the study showed how they share several examples of their Indigenous knowledge and traditions of marine resource conservation, and how this wisdom, a valuable asset for climate adaptation governance, has been passed from generation to generation through oral tradition.

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==== 10.5.1.3 Knowledge Gaps and Future directions ====

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One of the major knowledge gaps in the domain of climate adaptation governance relates to implementation by various stakeholders at multiple scales, and sharing of information and experiences in this regard. There is a need to assuage the perceptions of distrust in global information, through governance methods that engage multiple stakeholders in open and lucid channels of communication ( [[#Stott--2014|Stott and Huq, 2014]] ). This is observable in the structure of the New Urban Agenda which formed part of the SDGs pertaining to cities and has been shaped by a bottom-up process marked by diverse participation including communities, experts and activists, rather than the top-down variant that is observable in the Millenium Development Goals ( [[#Barnett--2016|Barnett and Parnell, 2016]] ). This approach could also be evidenced in the Paris Agreement, which placed the onus of a successful global governance regime on the development of efficient systems of regional governance. However, these emerging systems of regional governance could equally pose a challenge to the global governance in a way that can be witnessed through the development of financial groups such as the BRICS (associated economies of Brazil, Russia, India, China and South Africa) and Asian Infrastructure Investment Bank, which resulted from a perception of inadequate institutional transformation at the global level. From another perspective, a comprehensive approach would require simultaneous implementation of both bottom-up and top-down models of governance, retaining flexibility of scale.

Given the concerns surrounding food security, especially in light of the principles of common but differentiated responsibilities, under the NDCs submitted by South Asian nations under the Paris Agreement, emission reduction commitments are ''less likely'' to include the agriculture sector. Prospects for enhancing both adaptive capacity and food security could be improved by strengthening resilience and profitability through the introduction of a basket of policy choices and actions including structural reforms, agriculture value-chain interventions and landscape-level efforts for climate resilience. Correspondingly, the substantial adaptation finance gap could be closed with the help of both private finance (autonomous adaptation) and international financial transfers ( [[#Amjath-Babu--2019|Amjath-Babu et al., 2019]] ).

For nearly five decades, integrated coastal management (ICM), advocated by several international organisations (e.g., IMO, UNEP, WHO, FAO) and adopted by over 100 countries, has been acknowledged as a holistic coastal governance approach aimed at achieving coastal sustainability and reducing the vulnerability of coastal communities in the face of multiple environmental impacts ( ''high confidence'' ). In view of threats posed to coastal ecological integrity by climate-change-induced tropical storm activity, accelerated SLR and littoral erosion and social–ecological impacts on the livelihood security of vulnerable coastal communities, the pressing need for approaches that innovatively combine coastal zone management and CCA measures is widely acknowledged ( [[#Rosendo--2018|Rosendo et al., 2018]] ) yet under researched. A study focusing on the three coastal cities of Xiamen, Quanzhou and Dongying, in China, a country with nearly 12% of its national coastline already covered under the ICM governance framework, suggests that whereas the ICM approach has been found to be effective in promoting the overall sustainability of China’s coastal cities ( [[#Ye--2015|Ye et al., 2015]] ) using accurate and reliable data, in addition, the developing unified standards could usefully reveal changing conditions and parameters related to ICM performance.

Steadily the regional scale of climate adaptive governance is acquiring salience in diverse sub-regions of Asia, and more policy-oriented empirical research is needed on how various regional forums, agencies and multilateral organisations could further contribute by way of in-house expertise and other resources, including financial. A study of climate adaptation in the health sector in Southeast Asia ( [[#Gilfillan--2018|Gilfillan, 2018]] ) highlights the growing role of the Asian Development Bank (ADB) and the Asia-Pacific Regional Forum on Health and Environment, and shows that their mandates and goals could mutually benefit from the institutionalisation of coordination mechanism. An example from the Maldives shows that the 2014 Tsunami, climate change and the risk of extreme weather events have led to the legitimisation of state-led population resettlement programmes. In China, this has occurred through the renaming of previously existing resettlement initiatives as climate adaptation initiatives; however, the efficacy of resettlement as a CCA measure requires further scrutiny ( [[#Arnall--2019|Arnall, 2019]] ). In India, the National Adaptation Fund on Climate Change has been instituted in order to enable states to implement adaptation programmes; however, this does not address the question of mainstreaming CCA into designs for development ( [[#Prasad--2019|Prasad and Sud, 2019]] ). This is closely related to the development of National Adaptation Programmes of Action (NAPAs) where the mainstreaming of adaptation within countries has been an important concern. Insights from developing countries indicate that there is still much ground to cover. The NAPA of the Maldives prioritises food security, coastal resources and public health, while Nepal has prioritised ecosystem management and public health, and food security, among other concerns ( [[#Saito--2013|Saito, 2013]] ). Importantly, Bangladesh’s NAPA has shown that there is potential for ‘reflexivity’ in the integration of adaptation objectives with sectoral objectives ( [[#Vij--2018|Vij et al., 2018]] ). Conspicuous by their absence are the transboundary-scale adaptation policies in South Asia ( [[#Vij--2017|Vij et al., 2017]] ).

A distinguishing feature of the case of Japanese apple growers is the co-existence of both top-down and bottom-up adaptation practices. The former pertains to farmers who rely on the support of the cooperative for agricultural support and follow institutional mechanisms. The latter pertains to non-co-op farmers who have been responsible for innovative practices of cultivation such as the shift to peaches and the sale in the market of apples without leaf-picking. Importantly, the non-co-op group also have access to sales channels that may not be accessible to the former owing to their direct interactions with customers, among other factors ( [[#Fujisawa--2011|Fujisawa and Kobayashi, 2011]] ; [[#Fujisawa--2015|Fujisawa et al., 2015]] ). The significance of this combination of top-down and bottom-up approaches to agricultural adaptation practices may be further sharpened by formulating approaches for Asia and the Pacific region in ways that contribute to the fortification of food security objectives and the idea of co-benefits. This may be carried out by enhancing the ability of farmers to better manage cultivation practices in the context of climatic variability ( [[#FAO--2018d|FAO, 2018d]] ).

There exist numerous barriers to the mainstreaming of CCA measures across Asia. The integration of CCA into the dissemination of localised climatic information and its uptake and implementation through institutional policy arrangements remain areas of concern ( [[#Cuevas--2018|Cuevas, 2018]] ). Institutional incentives to agricultural production, for instance, are frequently compounded by the negative impacts they have on existing bases of natural resources. The disconnected operations of local governmental agencies coupled with inadequacies of cross-sectoral coordination further highlights the prevalent food–water–energy nexus ( [[#Rasul--2016|Rasul, 2016]] ). One possible way of addressing these intersecting sources’ complexity is by locating emerging CCA measures in educational development. The introduction of CCA thinking into land-use planning in the Philippines is an example of the successful role of enhancing public education and awareness through the dissemination of information by institutional channels. The linkages between the strength of local leadership and the inclusion of CCA in localised planning activities are also well illustrated by the case study of [[#Cuevas--2018|Cuevas (2018)]] .

As shown in the case of Pakistan, level of education shares a positive relationship with the implementation of adaptation measures ( [[#Ali--2017|Ali and Erenstein, 2017]] ). However, a closer examination of the educational imperatives that drive CCA in ways that improve the representational architecture of adaptation actions through a focus on gender is needed. Mainstreaming of gender into CCA would involve addressing a host of barriers to education and involvement that are often rooted in the differential structures of households, social norms and roles, and the domestic division of labour ( [[#Rao--2019|Rao et al., 2019]] ). A study from the Indian state of Bihar shows that gender plays a major role in determining intra-household decision making and also inhibits the ability of female-headed households to establish access to agricultural extension services ( [[#Mehar--2016|Mehar et al., 2016]] ). Even within wider female farmer-operated federations, such as the Bangladesh Kishani Sabha (BKS), the barriers to participation stem from social factors that include the limitation of female mobility through the gendered division of labour and a lack of recognition of female agency ( [[#Routledge--2015|Routledge, 2015]] ). Gendered inequalities in educational attainment and outcomes viewed through the lens of social vulnerability thus intersect with environmental vulnerabilities in ways that affect the ability of women to participate in CCA, owing also to a lack of access to health and sanitation facilities. These factors have a direct impact on the ability of adaptation to be effective in the global South, and are especially important in the context of the commitments of the UN Convention on the Elimination of All Forms of Discrimination Against Women countries to the objective of gender equality ( [[#Roy--2018|Roy, 2018]] ).

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'''Box 10.5 | Bangladesh Delta Plan 2100'''
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''The Bangladesh Delta Plan'' ( ''BDP'' ) ''2100 is the plan moving Bangladesh forward for the next 100 years. We have formulated BDP 2100 in the way we want to build Bangladesh.'' ( [[#Commission--2018|Commission, 2018]] ).

The vision of BDP is revealed by the foregoing statement from Sheikh Hasina, the prime minister of Bangladesh. The government approved BDP 2100 in 2018. Achievement of a safe, climate-resilient and prosperous delta is the aspiration of the delta plan. Ensuring water and food security with economic growth, environmental sustainability, climate resilience, vulnerability reduction to natural hazards and minimising different challenges of the delta through robust, adaptive and integrated strategies, and equitable water governance, are the mission of this mega plan. Under this mission, three higher-level goals and six specific goals have been determined. Three higher-level goals include elimination of extreme poverty by 2030, achievement of upper middle-income status by 2030 and becoming a prosperous country beyond 2041. Six specific goals of BDP 2100 are fully linked with SDG Goals 2, 6, 13 and 14 and partially linked with Goals 1, 5, 8, 9, 11 and 15. These specific goals comprise a wide range of issues, including land and water resources, climate change, disaster, wetlands and ecosystems, river systems and estuaries. The vision, mission and goals of BDP 2100 reveal that this mega plan is a holistic and integrated approach considering diversified themes and sectors for the whole country. The implementation of the BDP 2100 requires total spending of an amount of about 2.5% of the GDP per annum. A series of strategies have been formulated for better implementation of the mega plan.

Water is the key and complicated resource of Bangladesh, and therefore BDP 2100 has kept water at the centre of the plan. It aims to promote wise and integrated use of water and other resources through development of effective institutions and equitable governance for in-country and transboundary water resource management.

Along with water, for the first time in any development planning, BDP 2100 has taken the climate-change issue as an exogenous variable in developing the macroeconomic framework of the plan. In a brief, it is stated that the principle of BDP 2100 is ‘Living with Nature’.

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=== 10.5.2 Technology and Innovation ===

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==== 10.5.2.1 Point of Departure ====

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Much like any other field, CCA is greatly facilitated by science, technology and continuous innovation. These range from the application of existing science, to the development of new scientific tools and methods, to the utilisation of Indigenous knowledge and citizen science. Many of the pressing problems in Asia, including water scarcity, rapid urbanisation, loss of natural habitats, biodiversity, rising coastal and river basin hazards, and agricultural loss can be effectively minimised through the adoption of suitable scientific and technological methods. Despite the current challenges in the region, many significant advances in science and technology have been made, and the future prospects look bright. The following sections outline the present status and future prospects of science and technology in scaling up adaptation actions in four key sectors, namely (a) DRR, (b) urbanisation, (c) water and agriculture, and (d) forests and biodiversity.

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==== 10.5.2.2 Findings ====

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===== 10.5.2.2.1 Disaster risk reduction =====

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Technological advances have enhanced the capabilities of Asian countries to monitor and prepare for climate-related hazards. Remote sensing technologies and GIS are widely used for DRR (Kato et al., 2017), for example, to assess and mitigate risks of an area to potential climate-related disasters ( [[#Wu--2018b|Wu et al., 2018b]] ). The potential impacts of different types of hazards can be visualised using interactive maps ( [[#Lee--2017|Lee, 2017]] ), which help local communities to understand risks and find appropriate evacuation areas ( [[#Cadiz--2018|Cadiz, 2018]] ). These maps provide a situational overview and instant risk assessment ( [[#Yang--2012b|Yang et al., 2012b]] ). As an emerging technology, artificial intelligence (AI) can identify conditioning factors of a landslide disaster ( [[#Hong--2019b|Hong et al., 2019b]] ). Mobile virtual reality is used for disaster mitigation training through a three-dimensional visualisation of a past disaster ( [[#Ghosh--2018|Ghosh et al., 2018]] ).

A community-based DRR system provides risk investigation, training and information analysis ( [[#Liu--2016b|Liu et al., 2016b]] ). Sharing information enhances establishment of such a system and contributes to disaster prevention ( [[#Nakamura--2017|Nakamura et al., 2017]] ). One example is an online mapping tool, which has been developed by volunteers ( [[#Sakurai--2017|Sakurai and Thapa, 2017]] ). Social media enables the population to access real-time information on a disaster ( [[#Ghosh--2018|Ghosh et al., 2018]] ), raises situation awareness ( [[#Yin--2012|Yin et al., 2012]] ) and empowers communities towards appropriate emergency actions ( [[#Leong--2015|Leong et al., 2015]] ). Among the various forms of social media, Twitter is widely used as a social sensor to detect what is happening in a disaster event ( [[#Sakaki--2013|Sakaki et al., 2013]] ). Accuracy of information on Twitter has been proved in collecting local details about floods ( [[#Shi--2019b|Shi et al., 2019b]] ); however, it has been noticed that Twitter generates rumours as well ( [[#Ogasahara--2019|Ogasahara et al., 2019]] ). Artificial intelligence is expected to reduce human error when they operate a decision-making system ( [[#Lin--2018|Lin et al., 2018]] ). Since technologies supporting DRR completely depend on electricity, the loss of power supply and communication constrains the recovery work in disaster-affected areas ( [[#Sakurai--2014|Sakurai et al., 2014]] ).

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===== 10.5.2.2.2 Urban sector =====

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In the urban sector, a wide variety of sensor technologies are being used to monitor urban land-use and climate changes over time, and to better understand the potential impacts of future changes. These sensors range from large optical–thermal–radar satellite instruments with (near) global coverage–for example, Landsat (US Geological Service), Sentinel (European Space Agency), ALOS (Japan Aerospace Exploration Agency) and MethaneSAT–to portable sensors embedded in mobile phones (e.g., phone cameras or temperature sensors) whose data are collected into centralised databases through crowdsourcing ( [[#Fenner--2017|Fenner et al., 2017]] ; [[#Meier--2017|Meier et al., 2017]] ). To combine and extract useful information from these heterogeneous sensor data–for example, for conducting climate risk assessments ( [[#Perera--2018|Perera and Emmanuel, 2018]] ; [[#Bechtel--2019|Bechtel et al., 2019]] ) and/or simulations of future land-use or climate changes in urban areas ( [[#Bateman--2016|Bateman et al., 2016]] ; [[#Iizuka--2017|Iizuka et al., 2017]] ; [[#Liu--2017c|Liu et al., 2017c]] )–AI technologies (e.g., machine-learning algorithms) are now being widely adopted ( [[#Johnson--2016|Johnson and Iizuka, 2016]] ; [[#Joshi--2016|Joshi et al., 2016]] ; [[#Mao--2017|Mao et al., 2017]] ). Thanks to advances in cloud-computing technology, which allows for online processing of massive volumes of remote sensing data, high-resolution (~30 m) global urban-area maps from the late 1990s to 2018 are now available from several different sources ( [[#Gong--2020|Gong et al., 2020]] ). Using these historical maps, researchers have been able to generate maps of future urban land-use changes at the global level to 2100 ( [[#Chen--2020a|Chen et al., 2020a]] ), which can help to elucidate the potential impacts of this future urban expansion and identify adaptation needs. Technology also plays a major role in urban planning and design in the context of adaptation. To mitigate rising urban temperatures and reduce the impacts of climate-related hazards, many new ‘grey’ infrastructure and ‘green’ infrastructure technologies are being adopted in urban areas in Asia, for example, cool (i.e., high solar reflectance) rooftops and pavements as well as green (i.e., vegetated) rooftops to mitigate high temperatures; and porous pavements to mitigate flooding ( [[#Akbari--2016|Akbari and Kolokotsa, 2016]] ).

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===== 10.5.2.2.3 Water and agriculture =====

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The majority of the Asian region is experiencing water stress in terms of both quantity and quality, due to poor management systems and governance. This has dire consequences for the national GDP as the majority of the population belongs to agrarian communities and their water-dependent agriculture system. Despite a substantial investment and progress in research and development, and capacity building in the recent past, the majority of developing countries in Asia are struggling to manage both water resources and agriculture sectors heavily ''dependent'' on water resources, in response to rapid global changes. Considering the frequent extreme weather conditions, progress in management tasks is even more consequential; hence, critically important for these countries, to achieve better CCA, are advanced science and technology viz. smart agriculture, robust EWS using downscaled meteorological information, a participatory approach, IWRM and so forth.

Having scientific knowledge relevant at the local scale through placed knowledge is important to identify climate-change risk and vulnerability. Moreover, once integrated with socioeconomic attributes, it can be useful for natural resource management, agriculture and so forth ( [[#Leith--2017|Leith and Vanclay, 2017]] ). Role of big data and data mining is undeniably very huge to get reliable climatic information and hence for designing appropriate adaptation measures for natural resource measurement. For example, use of big data in terms of EWS and real-time observation data provides more accurate information on hydro-meteorological extreme weather conditions or hazards like drought and flood, and will help farmers and local governments to improve their perception and hence preparedness for better adaptation ( [[#Hou--2017|Hou et al., 2017]] ; Ong and G.L.B.L., 2017). Using big data, different adaptation measures, such as new cultivar breeding, cropping-region adjustment, irrigation-pattern change, crop rotation and cropping-practice optimisation, are being designed in the agriculture sector, and these practices have greatly increased crop yield, leading to higher resource-use efficiency as well as greatly increased soil organic carbon content with reduced GHG emissions. It results in a win–win situation in terms of enhancing food security and mitigating climate warming ( [[#Deng--2017|Deng et al., 2017]] ). However, usability and application of this technology are still not common, especially in data-scarce regions. Integrated numerical simulations are efficient tools for estimating the current status and predicting the risk and efficiency of the adaptive capabilities of different countermeasures for sustainable natural resource management such as with water ( [[#Kumar--2019|Kumar, 2019]] ). Similarly, the agent-based model is commonly used to estimate risk of food-borne diseases due to climate change, using tunable parameters such as hygiene level, the microorganism’s growth rate and the number of consumers, and hence it has the potential to be a useful tool for optimising decision making and urban planning strategies related to health and climate change (Gay [[#Garcia--2017|Garcia et al., 2017]] ). The integrated assessment model under the shared-socioeconomic pathway (SSP) framework is effectively used to estimate future energy development and possible mitigation strategies to reduce GHG emissions related to the energy sector ( [[#Bauer--2017|Bauer et al., 2017]] ). Sound understanding of different drivers, pressures and stress factors, such as abnormal temperature, rainfall, insect pests or pathogens and their interaction pattern with the genetic makeup of crops, is the key to produce high-yielding varieties of wheat with better nutritional quality and resistance to major diseases ( [[#Goel--2017|Goel et al., 2017]] ). Another critical point to address this water security is inclusive, polycentric and adaptive governance. Polycentric governance is a means by which water management plans and policies should be framed and agreed by all relevant stakeholders. For adaptive governance, more emphasis will be on finding the best pathways to make robust water management plans amid rapid global changes. The benefit of such plans should reach the end users in terms of providing clean water, protection from hydrological hazards and maintaining the health of the ecosystem. In addition, there is urgent need for co-management, which includes the cycle of co-design, co-implementation and co-delivery throughout the whole water cycle. The best suitable example is using the circulating and ecological sphere (CES) approach. The CES is a concept that complements and supports regional resources by building broader networks, which are composed of natural connections (connections among forests; city and countryside; groundwater, rivers and the sea) and economic connections (human resources, funds and others), thus complementing each other and generating synergy ( [[#Mavrodieva--2020|Mavrodieva and Shaw, 2020]] ). Another suitable example for managing water resources is the participatory watershed land-use management (PWLM) approach. The PWLM is another very innovative and successful approach for more robust water resource management as explained by [[#Kumar--2020|Kumar et al. (2020)]] . It helps to make land-use and CCA policies more effective at the local scale. This is an integrative method using both participating tactics and computer-simulation modelling for water resource management at the regional scale.

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<span id="forests-and-biodiversity"></span>
===== 10.5.2.2.4 Forests and biodiversity =====

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Technologies and their applications to identify habitat degradation, ecosystem functions and biodiversity conservation are increasing in Asia, with many countries looking to new and improved means for forest and biodiversity monitoring and conservation. In particular, there has been an impressive use of temporal satellite data, particularly from the Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) series for widespread monitoring of forests and ecological resources. These series have provided reliable information on forests and ecosystem services at the country level, in difficult terrains, such as the mountains, cross-boundaries and otherwise inaccessible areas. For instance, [[#Yin--2017|Yin et al. (2017)]] estimated cross-boundary forest resources in Central Asia, a region which traditionally suffered from lack of reliable forest data, using remote sensing techniques. In a separate study, [[#Reddy--2020|Reddy et al. (2020)]] used long-term MODIS forest fire data from 2003 to 2017 to characterise fire frequency, density and hotspots in South Asia. Archives of scientific data, backed by state-of-the art modelling techniques, advanced-computing methods and innovations in big-data analysis, particularly helped the provisioning of scientific research. A number of studies simulated forest futures from the local to the continent scale under different socioeconomic and climate scenarios. For instance, at the local scale, Dasgupta et al. (2018) projected the future extent of mangroves in the Sundarban Delta under four local scenarios, while [[#Estoque--2019|Estoque et al. (2019)]] modelled and developed spatial maps of regional forest futures in Southeast Asia using the five SSP scenarios. Science and technology also helped the monitoring of species diversity and abundance, pivotal for sustaining an ecosystem and ecosystem-based adaptation. Digital camera traps and radio-collaring methods have largely replaced old film cameras and labour-intensive methods of photo screening to count target species ( [[#Pimm--2015|Pimm et al., 2015]] ). This enhanced scientific capacities to monitor biodiversity and facilitate better conservation in difficult terrains, control poachers and maintain steady ecological balance. [[#Umapathy--2016|Umapathy et al. (2016)]] , for example, used VHF radio collars and satellite-based tracking tools to monitor the movement of Bengal tigers over hostile island terrain. Photo recognition and other non-invasive techniques for individual identification have been rising in Asia. For example, a study by [[#Gray--2014|Gray et al. (2014)]] used faecal-DNA samples to estimate the population density of the Asian elephant in Cambodia. Furthermore, the advancement of citizen science programmes has greatly facilitated better monitoring of forest resources, including invasive floral and faunal species ( [[#Chandler--2017|Chandler et al., 2017]] ; [[#Johnson--2020|Johnson et al., 2020]] ). In Asia, citizen science has been used effectively in India ( [[#Chandler--2017|Chandler et al., 2017]] ), and also in Malaysia for the monitoring urban bird abundance ( [[#Puan--2019|Puan et al., 2019]] ).

<div id="10.5.2.3" class="h3-container"></div>

<span id="knowledge-gaps-and-future-directions-1"></span>
==== 10.5.2.3 Knowledge Gaps and Future Directions ====

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With rapid advances of technologies, the use of appropriate technologies generates some degree of management problems. To resolve such problems, the enhancement of information science is essential to understand design, implementation and adoption of digital tools under crisis ( [[#Xie--2020|Xie et al., 2020]] ). For example, social media research reveals a way of controlling malicious information ( [[#Tanaka--2014|Tanaka et al., 2014]] ), and its characteristics under COVID-19–showing a plain text message–can be more powerful in the context of citizen engagement than media-rich communications ( [[#Chen--2020b|Chen et al., 2020b]] ). Information behaviour needs more investigation to understand how people survive and connect in the era of information overload ( [[#Pan--2020|Pan et al., 2020]] ). Moreover, a new set of data (e.g., travel history record, personal health data, etc.) becomes an important base of DRR ( [[#Xie--2020|Xie et al., 2020]] ). Analysis of these personal data requires careful consideration, as it generates ethical issues ( [[#Sakurai--2020|Sakurai and Chughtai, 2020]] ). Indicators or measurements of technology-enabled crisis response needs to be developed for further risk reduction ( [[#Akbari--2016|Akbari and Kolokotsa, 2016]] ; [[#Wong--2020|Wong et al., 2020]] ). On the other hand, adopting infrastructure technologies requires investment, and due to the inherent uncertainties of climate projections, the future payoffs of these investments are also somewhat uncertain ( [[#Ginbo--2020|Ginbo et al., 2020]] ). In the water and infrastructure sectors, for example, various options exist for conducting cost–benefit analysis considering future uncertainty (i.e., so-called robust approaches, which are able to identify adaptation projects and infrastructure that can achieve their intended purpose(s) across a wide range of climate scenarios) ( [[#Dittrich--2016|Dittrich et al., 2016]] ). Despite substantial investment and progress in research, development and capacity building in the recent past, the majority of the developing countries in Asia are struggling to manage both water resources and the agriculture sector. Considering the frequent extreme weather conditions, progress in management tasks has become even more mammoth and, hence, we need a holistic solution, which is currently missing in field implementation. This solution should be based on advanced science and technology in association with other attributes like social, economic and political dynamics, which play a pivotal role in sustainable management of water resources and agriculture, as a way forward.

<div id="10.5.3" class="h2-container"></div>

<span id="lifestyle-changes-and-behavioural-factors"></span>
=== 10.5.3 Lifestyle Changes and Behavioural Factors ===

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<div id="10.5.3.1" class="h3-container"></div>

<span id="point-of-departure-1"></span>
==== 10.5.3.1 Point of Departure ====

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Understanding the motivations and processes underpinning decisions to adapt or not is key to enabling adaptation (see [[IPCC:Wg2:Chapter:Chapter-17#17.2.2.1|Section 17.2.2.1]] ; [[#Clayton--2015|Clayton et al., 2015]] ; [[#de%20Coninck--2018|de Coninck et al., 2018]] ; [[#Taylor--2019|Taylor, 2019]] ; [[#van%20Valkengoed--2019a|van Valkengoed and Steg, 2019a]] ; [[#van%20Valkengoed--2019b|van Valkengoed and Steg, 2019b]] ), because how and why certain people adapt is shaped by sociocultural factors, ways of making sense of risks and uncertainty, and personal motivations to undertake action ( [[#Nguyen--2016|Nguyen et al., 2016]] ; [[#Mortreux--2017|Mortreux and Barnett, 2017]] ; [[#Singh--2018b|Singh et al., 2018b]] ; [[#van%20Valkengoed--2019b|van Valkengoed and Steg, 2019b]] ). The IPCC’s Assessment Report 5 was critiqued for silences on how perceptions shape climate action and the behavioural drivers of adaptation responses ( [[#Lorenzoni--2014|Lorenzoni and Whitmarsh, 2014]] ). Addressing this gap and assessing the growing literature from social sciences, notably psychology, behavioural economics and risk perception studies, the IPCC Special Report on 1.5°C ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ) comprehensively assessed behavioural dimensions of CCA for the first time; however, compared with studies on mitigation behaviour, the literature on what motivates adaptation remains incomplete ( [[#Lorenzoni--2014|Lorenzoni and Whitmarsh, 2014]] ; [[#Clayton--2015|Clayton et al., 2015]] ).

<div id="10.5.3.2" class="h3-container"></div>

<span id="findings-2"></span>
==== 10.5.3.2 Findings ====

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There are three key aspects of adaptation to which psychology and behavioural science contribute: understanding perceptions of climate risk, identifying the behavioural drivers of adaptation actions and analysing the impacts of climate change on human well-being ( [[#Clayton--2015|Clayton et al., 2015]] ). Overall, there is growing acknowledgement that individual adaptation is significantly shaped by perceptions of risk, perceived self-efficacy (i.e., beliefs about which options are effective and one’s ability to implement specific adaptation interventions), sociocultural norms and beliefs within which adaptation decisions are taken, past experiences of risk management and the nature of the intervention itself ( [[#Grothmann--2005|Grothmann and Patt, 2005]] ; [[#Werg--2013|Werg et al., 2013]] ; [[#Clayton--2015|Clayton et al., 2015]] ; [[#Truelove--2015|Truelove et al., 2015]] ; [[#Pyhälä--2016|Pyhälä et al., 2016]] ; [[#Deng--2017|Deng et al., 2017]] ; [[#Sullivan-Wiley--2017|Sullivan-Wiley and Short Gianotti, 2017]] ; [[#Taylor--2019|Taylor, 2019]] ; [[#van%20Valkengoed--2019a|van Valkengoed and Steg, 2019a]] ). This is in addition to more commonly understood factors shaping adaptation behaviour such as technical know-how and the cost and benefits associated with an option.

Across Asia, behavioural aspects of adaptation have been studied to a lesser extent: a global meta-analysis of 106 studies found that most research focused on North America and Europe with only 12% of papers from Asia ( [[#van%20Valkengoed--2019a|van Valkengoed and Steg, 2019a]] ). Within Asia, behavioural drivers of adaptation decision making have been studied primarily in agriculture (in South, East and Southeast Asia) and disaster risk management (from Southeast and East Asia) (Table 10.4) and tend to focus on technical adaptation interventions rather than how and why people adapt ( [[#Sun--2018|Sun and Han, 2018]] ).

'''Table 10.4 |''' Sectors and sub-regions where behavioural aspects of adaptation have been assessed

{| class="wikitable"
|-
! Sub-region

! Sector

! Adaptation interventions

! Behavioural aspects affecting adaptation

! Supporting references

|-
| '''West Asia'''

| '''Agriculture'''

| '''Soil and water conservation activities to mitigate drought impacts'''

| '''Response efficacy and perceived severity shape water conservation.'''

| '''Iran ( [[#Keshavarz--2016|Keshavarz and Karami, 2016]] )'''

|-
| '''Central Asia'''

| '''NE''' ''a''

| '''NE''' ''a''

| '''NE''' ''a''

| '''NE''' ''a''

|-
| rowspan="8"| '''South Asia'''

| rowspan="4"| '''Agriculture'''

| '''Conservation agriculture, adjusting agricultural practices'''

| '''Risk perceptions shape adoption of adaptation strategy (e.g., perceptions of decreasing rainfall motivate building water storage tanks).'''

| '''Nepal ( [[#Piya--2013|Piya et al., 2013]] ; [[#Halbrendt--2014|Halbrendt et al., 2014]] )'''

|-
| '''Sustainable water management practices, adjusting agricultural practices'''

| '''Risk perception is shaped by sociocultural context, memories, experiences and expectations (of future change).'''

| '''India ( [[#Singh--2016|Singh et al., 2016]] )'''

|-
| '''Alternate wetting and drying irrigation, alternative crop selection, using drought-resistant seeds'''

| '''A combination of attitudes, self-efficacy, outcome efficacy, and community efficacy predict intent to adapt strongly.'''

| '''Sri Lanka ( [[#Truelove--2015|Truelove et al., 2015]] )'''

|-
| '''Adjustment in farm management including growing short duration or drought-tolerant varieties, pest-resistant varieties, changing planting distance, increasing weeding, soil conservation techniques, cultivation of direct-seeded rice, switching to non-rice crops'''

| '''Farmers’ education, access to credit and extension services, experience with climate-change impacts such as drought and flood, information on climate-change issues, belief in climate change and the need to adapt all variously determine their decision making.'''

| '''Nepal ( [[#Khanal--2018|Khanal et al., 2018]] )'''

|-
| rowspan="4"| '''Disaster management'''

| '''Flood and cyclone preparedness measures such as using durable building materials, raising plinth levels, storing food and water'''

| '''Disaster management behaviour is intuitive: low evidence to suggest outcome expectancy, self-efficacy, and preparedness intention follow linear patterns.'''

| '''India ( [[#Samaddar--2014|Samaddar et al., 2014]] ); Bangladesh ( [[#Dasgupta--2014b|Dasgupta et al., 2014b]] )'''

|-
| '''Use of emergency toolkits and evacuation plans'''

| '''Risk perception and knowledge of adaptation options shape uptake and perceived benefits.'''

| '''Pakistan, Bangladesh ( [[#Alvi--2020|Alvi and Khayyam, 2020]] )'''

|-
| '''Insurance to deal mitigate financial losses from floods and droughts'''

| '''Frequency, the severity of previous extreme events, socioeconomic settings and ability to pay shape decisions to take crop insurance.'''

'''Acceptability of flood insurance depends on the perceived efficacy of the insurance (among other factors such as age of household head, land ownership and off-farm income sources).'''

| '''Pakistan (Arshad et al., 2016; [[#Abbas--2015|Abbas et al., 2015]] )'''

|-
| '''Embankments and dikes for flood risk mitigation'''

| '''Willingness to contribute manual labour to flood protection measures is positively influenced by the number of adult family members, livestock damage, compensation received and expected effectiveness of the intervention, but is negatively influenced by age and education of the household head, farm income and the distance of the farm from the river.'''

| '''Pakistan ( [[#Abbas--2019|Abbas et al., 2019]] )'''

|-
| rowspan="5"| '''Southeast Asia'''

| '''Agriculture'''

| '''Changing agricultural practices, diversifying livelihoods'''

| '''Values along with personal and social beliefs of risk shape adaptation.'''

| '''Vietnam ( [[#Le%20Dang--2014|Le Dang et al., 2014]] ; [[#Cullen--2016|Cullen and Anderson, 2016]] ; [[#Nguyen--2016|Nguyen et al., 2016]] ; [[#Arunrat--2017|Arunrat et al., 2017]] )'''

|-
| rowspan="4"| '''Disaster management'''

| '''Raising floor height to avoid flooding, retrofitting houses'''

| '''Perceived probabilities and perceived consequences of flood shape preparedness.'''

| '''Vietnam ( [[#Reynaud--2013|Reynaud et al., 2013]] ; [[#Ling--2015|Ling et al., 2015]] )'''

|-
| '''Flood insurance'''

| '''Likelihood of purchasing flood insurance increased with higher physical exposure and subjective perceptions of vulnerability.'''

| '''Malaysia (Aliagha, 2013; [[#Aliagha--2014|Aliagha et al., 2014]] )'''

|-
| '''Evacuation'''

| '''Individual risk perceptions lead to learning, but only where previous disaster experiences are traumatic.'''

| '''Philippines, India ( [[#Walch--2018|Walch, 2018]] )'''

|-
| '''Disaster preparedness measures such as having kits, undertaking precautionary measures'''

| '''Perceived self-efficacy was the most significant measure affecting reactive adaptation; education had the highest effect size on anticipatory adaptation.'''

| '''Cambodia ( [[#Ung--2015|Ung et al., 2015]] )'''

|-
| rowspan="3"| '''East Asia'''

| '''Agriculture'''

| '''Changing agricultural practices, diversifying incomes, adopting water-saving technology, purchasing weather insurance'''

| '''Perceived self-efficacy strongly predicts adaptive intent.'''

| '''China ( [[#Jianjun--2015|Jianjun et al., 2015]] ; [[#Zhang--2016a|Zhang et al., 2016a]] ; [[#Burnham--2017|Burnham and Ma, 2017]] ; [[#Feng--2017|Feng et al., 2017]] )'''

|-
| rowspan="2"| '''Disaster management'''

| '''General'''

| '''Higher education and being in environments where climate is discussed leads to stronger risk perceptions.'''

| '''Taiwan, Province of China ( [[#Sun--2018|Sun and Han, 2018]] )'''

|-
| '''Drought management through early warnings, prevention information'''

| '''Policies can positively shape adaptation decision making depending on how information is given and what support is provided.'''

| '''China ( [[#Wang--2015|Wang et al., 2015]] )'''

|}

(a) No evidence

In agriculture, studies demonstrate how perceptions of risk (e.g., climate variability) ( [[#Singh--2016|Singh et al., 2016]] ; [[#Zheng--2016|Zheng and Dallimer, 2016]] ; [[#Burnham--2017|Burnham and Ma, 2017]] ; [[#Feng--2017|Feng et al., 2017]] ), sociocultural norms and personal experiences (Masud, 2016; [[#Nguyen--2016|Nguyen et al., 2016]] ; [[#Singh--2016|Singh et al., 2016]] ), and perceived efficacy of adaptation interventions in having a positive and desirable impact ( [[#Halbrendt--2014|Halbrendt et al., 2014]] ; [[#Truelove--2015|Truelove et al., 2015]] ; [[#Feng--2017|Feng et al., 2017]] ), affect adaptation decisions. Policies on providing early warnings of drought or information on prevention techniques shape farmer decisions to undertake adaptation interventions ( [[#Wang--2015|Wang et al., 2015]] ).

In disaster risk management, risk appraisal ( [[#Samaddar--2014|Samaddar et al., 2014]] ; [[#Rauf--2017|Rauf et al., 2017]] ; [[#Hung--2018|Hung et al., 2018]] ), previous experience and losses ( [[#Said--2015|Said et al., 2015]] ; [[#Hung--2018|Hung et al., 2018]] ; [[#Walch--2018|Walch, 2018]] ) 12 , [[#footnote-002|11]] perceived probabilities and consequences ( [[#Reynaud--2013|Reynaud et al., 2013]] ), perceived self-efficacy ( [[#Ung--2015|Ung et al., 2015]] ; [[#Hung--2018|Hung et al., 2018]] ) and awareness ( [[#Hung--2018|Hung et al., 2018]] ; [[#Wu--2018a|Wu et al., 2018a]] ; [[#Alvi--2020|Alvi and Khayyam, 2020]] ) shape preparedness. Individual risk management is nested within public policies, such as those on flood management, which shape individual flood risk perception and protective behaviours ( [[#Reynaud--2013|Reynaud et al., 2013]] ) as well as personal factors such as religious beliefs ( [[#Alshehri--2013|Alshehri et al., 2013]] ). For example, communities often perceive disasters as ‘acts of God’ ( [[#Birkmann--2019|Birkmann et al., 2019]] ) or punishment for wrongdoings ( [[#Alshehri--2013|Alshehri et al., 2013]] ; [[#Iqbal--2018|Iqbal et al., 2018]] ), which might constrain adaptive action. However, religious faith can also motivate people to prepare for extreme events, as Alshehri et al. (2013) showed in Saudi Arabia demonstrating how ‘Islam urges that it is most important to prepare the people to escape from disaster’ (p.1825).

Trust in public action as a mediator of risk management has had conflicting evidence: some studies have discussed trust as being critical to effective preparedness ( [[#Kittipongvises--2015|Kittipongvises and Mino, 2015]] ; [[#Walch--2018|Walch, 2018]] ), while others have found that trust in public actions, such as structural interventions to mitigate flood impacts, can lower individual motivations to act since they feel protected ( [[#Hung--2018|Hung et al., 2018]] ).

Belief in climate variability and change significantly shapes adaptation decision making ( [[#Le%20Dang--2014|Le Dang et al., 2014]] ; [[#Singh--2016|Singh et al., 2016]] ; [[#Khanal--2018|Khanal et al., 2018]] ; [[#Liu--2018a|Liu et al., 2018a]] ) with those believing in climate change and associated impacts tending to engage in adaptation. Crucially, those who do not believe in climate change can be influenced by social norms ( [[#Arunrat--2017|Arunrat et al., 2017]] ) thereby incentivising adaptation behaviour. While risk perception is a critical step in adaptation decision making, higher risk perception does not necessarily signal better capacity to cope: in Taiwan, Province of China, [[#Sun--2018|Sun and Han (2018)]] highlight how perceptions of climate risk as a global problem tends reduce its urgency as an individual issue. Providing information on climate risks, impacts and possible adaptation options enables adaptation behaviour ( [[#Piya--2013|Piya et al., 2013]] ; [[#Zheng--2016|Zheng and Dallimer, 2016]] ; [[#Rauf--2017|Rauf et al., 2017]] ), but information alone is not sufficient to motivate adaptive behaviour. Specifically, awareness building on concrete measures and outcomes, such as the amount of water saved or number of deaths averted, rather than abstract notions of climate change, motivate adaptation ( [[#Deng--2017|Deng et al., 2017]] ; [[#Rauf--2017|Rauf et al., 2017]] ).

<div id="10.5.3.3" class="h3-container"></div>

<span id="lifestyle-changes"></span>
==== 10.5.3.3 Lifestyle changes ====

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Changes in current lifestyles and consumption patterns are acknowledged as critical to climate action ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ; IGES, 2019). With rapidly changing diets and increasing purchasing power, lifestyle changes in countries across Asia, especially those with large populations such as China and India, will be critical to contributing to global climate solutions (IGES, 2019). Lifestyle shifts that can contribute towards adaptation include:

* Engaging in urban agriculture through rooftop gardening, community gardens in urban and peri-urban areas and so forth (with implications for food-associated footprints but also nutritional, livelihood and well-being benefits) ( [[#Mohanty--2012|Mohanty et al., 2012]] ; [[#Ackerman--2014|Ackerman et al., 2014]] ; [[#Padgham--2015|Padgham and Dietrich, 2015]] )
* Shifts towards organic farming and creating demand for organically sourced food and other materials
* Shifts towards water-saving behaviour such as rainwater harvesting, water conservation, reducing water usage and so forth

<div id="10.5.3.4" class="h3-container"></div>

<span id="knowledge-gaps"></span>
==== 10.5.3.4 Knowledge gaps ====

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Overall, understanding behavioural factors shaping adaptation implementation and uptake is important ( ''medium evidence, high agreement'' ). While there is a growing literature on behavioural drivers of adaptation at the individual and household levels, gaps remain in understanding how socio-cognitive factors affect adaptation behaviour at higher scales (e.g., at local or subnational government, in the private sector, etc.) 13 . [[#footnote-001|12]] More empirical evidence is needed in sectors beyond agriculture and disaster risk management (e.g., factors motivating urban adaptation) and better coverage across Asia’s sub-regions. Importantly, there are no studies on the behavioural aspects of adaptation from Central Asia.

<div id="10.5.4" class="h2-container"></div>

<span id="costs-and-finance"></span>
=== 10.5.4 Costs and Finance ===

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<div id="10.5.4.1" class="h3-container"></div>

<span id="point-of-departure-2"></span>
==== 10.5.4.1 Point of Departure ====

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Estimates of adaptation costs and financial needs have evolved significantly since the previous IPCC assessments. These developments are based on improvements in the understanding of how the hazard interacts with the physical and socioeconomic elements, and how to capture these interactions in systematic modelling frameworks. The developments are also clearly reported especially in the area of addressing the underestimates in adaptation costs that the previous studies suffered from as the previous studies tended to rely on data from wealthy economies ( [[#Hochrainer-Stigler--2014|Hochrainer-Stigler et al., 2014]] ; [[#Carleton--2020|Carleton et al., 2020]] ). The adaptation cost estimates have also improved since the previous IPCC reports due to constant improvements in capturing the loss and damages of disaster events ( [[#Hochrainer-Stigler--2014|Hochrainer-Stigler et al., 2014]] ). The reliance of earlier studies on correlations to derive adaptation costs was addressed to some extent by addressing the endogeneity of disaster measures ( [[#Kousky--2014|Kousky, 2014]] ), especially by relying upon the physical measures of disasters such as wind speed, although more work is needed in this area.

<div id="10.5.4.2" class="h3-container"></div>

<span id="findings-3"></span>
==== 10.5.4.2 Findings ====

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Climate change can cause significant impacts and as a result can impose considerable adaptation costs on countries and people. Despite the importance, the research on adaptation costs is limited in Asia, especially on the economy-wide costs, while fragmented literature is available on sector-level adaptation costs. Most of the available literature on adaptation costs at the regional level originate from the work carried out by development finance institutions such as ADB.

Estimates suggest that climate-change impacts could result in a loss of 2% of the GDP of South Asian countries by 2050 and 9% by 2100 ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ). These impacts will be felt in major vulnerable sectors, including agriculture, water, coastal, marine, health and energy, and will have significant impact on the economic growth and poverty reduction in the region. Countries could differ widely in terms of the economic costs they face. In South Asia, the economic costs were projected to be 12.6% of the GDP for the Maldives, which is the highest among the South Asian countries, and 6.6% for Sri Lanka, the least among the South Asian countries. The resultant adaptation costs for countries were projected to range from 0.36% (Copenhagen Cancun Scenario for 2050) to 1.32% (business-as-usual scenario) of the GDP in various scenarios during 2010–2050 ( ''Medium agreement'' , ''limited evidence'' ) ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ).

[[#Arto--2019|Arto et al. (2019)]] have reported the adaptation costs of the Mahanadi Delta in India for agriculture, fisheries and infrastructure sectors ( [[#Arto--2019|Arto et al., 2019]] ). The cumulative adaptation costs for 2015–2016 were reported to be 276 million USD for agriculture and 0.163 million USD for fisheries. In comparison, the modelled cumulative agricultural GDP loss due to climate-change impacts was reported to be 5% up to 2050, and 8% for infrastructure. Adaptation interventions, such as embankments, were found to provide an avoided losses (adaptation benefits) to the tune of 2.2% of the delta’s GDP by 2050. Similarly, input subsidies in seeds, fertilisers and biofertilisers were found to buffer the shocks in agriculture by 10%, and buffer the GDP per capita by 3% ( [[#Arto--2019|Arto et al., 2019]] ).

[[#Markandya--2019|Markandya and González-Eguino (2019)]] have estimated the adaptation costs and residual adaptation costs accrued due to insufficient adaptation using integrated assessment models. Using the residual damages as a measure of loss and damage, the authors have estimated adaptation costs and residual costs under scenarios of high damages–low discount rate and low damages–high discount rate. The estimates suggested adaptation costs of 182 and 193 billion USD by 2050, and 737 and 783 billion USD by 2100 for South Asia and East Asia, respectively, under the scenario of high damages–low discount rate. The residual costs for the same scenario were 289 and 76% for 2050 and 238 and 62% for 2100 for South Asia and East Asia, respectively. Estimates for low damages–high discount rate were significantly lower adaptation costs and residual costs for both of these sub-regions of Asia.

The CCA efforts can be characterised as fragmented, incoherent and lacking perspective ( [[#Ahmed--2019a|Ahmed et al., 2019a]] ), and the picture on adaptation financing can be stated as similarly fragmented with very limited literature published in peer-reviewed journals. Adaptation financing is crucial for supporting vulnerable countries and enhances adaptation, as it is evident that the enhanced adaptation finance support has positively affected the pace of adaptation in low-income countries ( [[#Ford--2015|Ford et al., 2015]] ). At the organisational level, adaptation financing has provided multiple functions that include risk assessment functions, valuation functions and risk disclosure functions ( [[#Linnenluecke--2016|Linnenluecke et al., 2016]] ).

Of the total global public adaptation finance of 28 billion USD, East Asia and the Asia–Pacific attracted 46% of the total funding, while South Asian countries attracted only 9% of the total funding ( [[#UNEP--2016|UNEP, 2016]] ). These differences reflect the capacity of countries to attract adaptation finance. Some of the important adaptation-targeted climate funds are Pilot Programmes for Climate Resilience, Green Climate Fund, and Least-Developed Countries Fund, and South Asian countries have significantly benefited from these dedicated climate funds. Due to the disaster implications of climate change, there is a need to allocate adaptation finances for DRR. Estimates suggest that East Asia and the Asia–Pacific in general allocated 27% of the total adaptation funds to DRR, while South Asia allocated 25% ( [[#Caravani--2016|Caravani, 2016]] ). Low-income economies tend to allocate more adaptation funds to DRR (46%), while lower-middle-income economies allocated 22%.

The least developed countries lack the capacity to adapt to climate change and the Least Developed Country Fund (LDCF) has made significant contributions to adaptation in these countries ( ''High agreement, limited evidence'' ). Based on the interview-based field research in four least developed countries, Sovacool et al. (2017) opined that the LDCF projects are contributing to the adaptive capacity of these countries ( [[#Sovacool--2017|Sovacool et al., 2017]] ). They also found that these projects are taking a marginal approach, rather than a radical or transformational one, to adaptation.

[[#Kissinger--2019|Kissinger et al. (2019)]] have estimated the climate financing needs in the land sector under the Paris Agreement. The estimates suggested adaptation needs of 2.5 billion USD for Bangladesh, 40.5 million USD for Lao PDR and 31 million USD for Mongolia, for the forest sector alone ( ''Low agreement, limited evidence'' ).

Financing green growth and low-carbon development can provide resilience benefits ( ''high agreement'' , ''limited evidence'' ). Kameyama et al. (2016) have estimated the cost of low-carbon investments that can provide resilience benefits in Asia and reported that such low-carbon development will cost in the range of 125–149 billion USD annually. A combination of public, private, bilateral and multilateral funding sources, and carbon-market offsets, were suggested to achieve this level of funding. In terms of the total resources available, a combination of public, private and bi- and multi-lateral funding could help the region to raise as much as 222.3–412.5 billion USD annually, with a possibility to reach higher amounts depending on the future economic growth of countries in the region. Soil carbon sequestration in agricultural soils was found to be a win–win solution for both mitigation and adaptation as it can help improve soils while increasing farm yields and incomes of smallholders ( [[#Aryal--2020a|Aryal et al., 2020a]] ).

New adaptation financing sources have been emerging which could provide country-specific adaptation financing suiting local-level adaptation needs in Asia. The newly established Asia Infrastructure Investment Bank (AIDB), and newly emerging developing-country finance institutions, are known to provide an additional adaptation finance ( [[#Neufeldt--2018|Neufeldt et al., 2018]] ); however, despite these emerging financial sources, the region will fall short of the adaptation target in the Paris Agreement ( [[#Neufeldt--2018|Neufeldt et al., 2018]] ).

<div id="10.5.4.3" class="h3-container"></div>

<span id="knowledge-gaps-1"></span>
==== 10.5.4.3 Knowledge Gaps ====

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Adaptation cost estimates can vary between various studies due to the differences in methodologies they adopt. Some studies have conducted cost assessments using a combination of stakeholder consultations and quantitative modelling of climate-change impacts and adaptation ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ), while others depended solely on the quantitative modelling. Studies also differ in the coverage of sectors too: they either have focused on the multiple vulnerable sectors ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ) or on a single sector ( [[#Hossain--2019|Hossain et al., 2019]] ). Studies have differed in their estimates depending on their ability to take into consideration the transition costs of sudden adaptation ( [[#Hossain--2019|Hossain et al., 2019]] ), the nature of social cost and/or damage functions employed ( [[#Arto--2019|Arto et al., 2019]] ), the discount rates applied ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ) and consideration for the effects of GHG mitigation on adaptation needs ( [[#Duan--2019a|Duan et al., 2019a]] ). In addition, the assumptions made on the pace of adaptation in estimating adaptation costs can make a difference in adaptation cost estimates. Adaptation at a slow or normal pace could require more adaptation finance, as large amounts of damage are not eliminated, than when adaptation is implemented at a faster rate ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ). Although there have been improvements in adaptation cost estimates, there is a need to address the issue of endogeneity ( [[#Kousky--2014|Kousky, 2014]] ; [[#Samuel--2019|Samuel et al., 2019]] ). The vast majority of studies that rely on databases, such as EM-DAT, tend to suffer from such endogeneity problems due to their inability to control the causality between GDP and damages ( [[#Kousky--2014|Kousky, 2014]] ). Costs attributable to non-economic losses and damages are the least reported and least quantified in the adaptation costs literature due to lack of sufficient, robust and accessible methodologies ( [[#Chiba--2017|Chiba et al., 2017]] ; [[#Chiba--2019|Chiba et al., 2019]] ; [[#Serdeczny--2019|Serdeczny, 2019]] ). This is a major limitation in assessing adaptation costs and financial needs, and it can lead to gross underestimation of adaptation costs. A detailed description of issues related to non-economic losses and damages, and its importance in strengthening adaptation, is provided in Box 10.6 and Table 10.5.

<div id="box-10.6" class="h2-container box-container"></div>

'''Box 10.6 | Loss and Damage Across Asia: Mapping the Evidence and Knowledge Gaps'''
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Losses and damages are climate impacts after implementing adaptation and mitigation actions, signifying the presence of residual risks (Chapter 1; [[#Kugler--2016|Kugler and Sariego, 2016]] ; [[#Mechler--2019|Mechler et al., 2019]] ). These residual risks indicate that despite adaptation, there are soft and hard adaptation limits ( [[#Mechler--2019|Mechler et al., 2019]] ). This box reviews the adaptation literature across 51 countries in Asia on loss and damage (L&amp;D), and adaptation barriers and limits, and identifies knowledge and regional gaps. The key messages are that (a) climate-induced L&amp;D is already occurring across Asia ( ''medium evidence, high agreement'' ), (b) these L&amp;D are ''very likely'' to increase at higher warming levels ( ''medium evidence, high agreement'' ) and (c) measuring and attributing non-economic and intangible L&amp;D remains a challenge ( ''low evidence, high agreement'' ).

'''Findings on losses and damages in Asia:''' Evidence on climate-related L&amp;D highlights tangible or material losses and damages such as loss to life, property, infrastructure and livelihoods ( ''medium evidence, high agreement'' ); and intangible or non-material losses and damages such as increasing conflict and civil unrest, erosion of sociocultural practices and decreased well-being ( ''low evidence, high agreement'' ). The main constraint in assessing past and future L&amp;D is that this terminology is not used prominently or consistently in the disaster management and climate risk literature in Asia, which potentially leads to under-reporting. In contrast, there is ''robust evidence'' ( ''high agreement'' ) on adaptation constraints, notably on governance, informational and physical constraints, to adapting, but regional evidence is very uneven with gaps in Central, North and West Asia. Table 10.5 presents a summary of L&amp;D but draws on national and subnational studies.

The knowledge gaps are as follows:

* Attribution studies linking anthropogenic climate change and L&amp;D remain focused on rapid-onset extreme events, and evidence on L&amp;D from slow-onset events, such as drought and water scarcity, is low ( [[#Pereira--2019|Pereira et al., 2019]] ; [[#Singh--2021a|Singh et al., 2021a]] ).
* Regional evidence gaps in Central, North and West Asia; and ''low evidence'' of national-level projected L&amp;D ( [[#Uchiyama--2020|Uchiyama et al., 2020]] ; [[#Singh--2021a|Singh et al., 2021a]] ).
* Disproportionate emphasis on economic L&amp;D while intangible, non-economic L&amp;D are relatively less measured and reported ( [[#Chiba--2017|Chiba et al., 2017]] ; [[#Bahinipati--2020|Bahinipati, 2020]] ). Economic loss estimates are largely approximations and therefore suffer from various methodological, assumption and data-related uncertainties.
* Insufficient literature differentiating L&amp;D under future adaptation scenarios, which makes assessment of residual damages and future L&amp;D difficult. The L&amp;D projections are constrained by limited understanding on how vulnerabilities will evolve with economic and demographic changes. Most projected L&amp;D are based on the population and GDP projections. More future projections are based on the RCP scenarios, and the least number of studies were conducted on the combination of RCP and SSPs.
* Mitigation will have L&amp;D and adaptation co-benefits ( [[#Kugler--2016|Kugler and Sariego, 2016]] ; [[#Toussaint--2020|Toussaint, 2020]] ), especially at the lower temperature stabilisation 1.5°C ( [[#Nishiura--2020|Nishiura et al., 2020]] ), but the literature is currently insufficient to assess these L&amp;D co-benefits of mitigation efforts.
* Negligible regional evidence on limits to adaptation.

'''Way forward:''' Developing robust metrics and institutions for measuring and reporting L&amp;D at national and regional scales, especially non-economic damages and L&amp;D due to slow-onset events, is critical. In addition to vulnerability assessments, assessing L&amp;D and limits to adaptation can inform adaptation prioritisation and enhance adaptation effectiveness (e.g., [[#Craft--2016|Craft and Fisher, 2016]] ; [[#Leiter--2019|Leiter et al., 2019]] ). Lessons are available from biodiversity and ecosystem services monitoring frameworks that have well-developed metrics and processes (e.g., [[#Díaz--2020|Díaz et al., 2020]] ).

'''Table 10.5 |''' Tangible and intangible losses and damages across Asia a

{| class="wikitable"
|-
! rowspan="3"| Sub-region (no. of papers)

! rowspan="3"| Key risks reported in L&amp;D papers

! colspan="5"| Losses and damages

! colspan="8"| Adaptation constraints (bold ticks denote strong barrier)

! colspan="2"| Adaptation limits

|-
! colspan="4"| Tangible

! rowspan="2"| Intangible

! rowspan="2"| E

! rowspan="2"| S

! rowspan="2"| H

! rowspan="2"| G

! rowspan="2"| F

! rowspan="2"| I

! rowspan="2"| P

! rowspan="2"| B

! rowspan="2"| Soft

! rowspan="2"| Hard

|-
! Past

! RCP2.5

! RCP4.5

! RCP8.5

|-
| East Asia (32)

| Coastal flooding, heatwaves, SLR

| \***

| \*

| \**

| \**

| \*

| ✓

|
| ✓

| ✓

| ✓

| ✓

|
| NE

| NE

|-
| Southeast Asia (4)

| Coastal flooding, SLR

| \*

|
| \*

|
| ✓

| ✓

|
| ✓

|
| NE

| NE

|-
| South Asia (18)

| Coastal flooding, drought, SLR, heatwaves

| \***

| \*

| \**

| \**

| \**

| ✓

|
| ✓

| ✓

| ✓

|
| \*

| \**

|-
| Central Asia (3)

| Snowmelt, heatwaves, drought

| \*

|
| \*

| \*

|
| ✓

| ✓

|
| NE

| NE

|-
| North Asia (2)

| Permafrost thaw

|
| \*

| \*

| \*

|
| ✓

|
| NE

| NE

|-
| West Asia (9)

| Heatwaves, drought

| \**

|
| \*

| \*

| \*

|
| ✓

|
| \*

| \**

|-
| colspan="2"| Magnitude of losses and damages

| colspan="5"| Evidence

| colspan="10"| Adaptation constraints

|-
| rowspan="2"|
| rowspan="2"| High (&gt;50% sector/population affected relative to reported baseline)

| rowspan="2" colspan="2"| \***

| rowspan="2" colspan="3"| High (≥10 papers)

| colspan="4"| E

| colspan="6"| Economic

|-
| colspan="4"| S

| colspan="6"| Sociocultural

|-
| rowspan="2"|
| rowspan="2"| Medium (25–50% sector/population affected)

| rowspan="2" colspan="2"| \**

| rowspan="2" colspan="3"| Medium (5–9 papers)

| colspan="4"| H

| colspan="6"| Human capacity

|-
| colspan="4"| G

| colspan="6"| Governance

|-
| rowspan="2"|
| rowspan="2"| Low (&lt;25% sector/population affected)

| rowspan="2" colspan="2"| \*

| rowspan="2" colspan="3"| Low (≤4 papers)

| colspan="4"| F

| colspan="6"| Financial

|-
| colspan="4"| I

| colspan="6"| Informational/technological

|-
| rowspan="2"|
| rowspan="2"| Not assessed due to inadequate evidence

| rowspan="2" colspan="2"| NE

| rowspan="2" colspan="3"| No evidence

| colspan="4"| P

| colspan="6"| Physical

|-
| colspan="4"| B

| colspan="6"| Biological

|}

Notes:

'''East Asia:''' [[#Tezuka--2014|Tezuka et al. (2014)]] ; Elliott et al. (2015); [[#Lei--2015|Lei et al. (2015)]] ; [[#Li--2015a|Li et al. (2015a)]] ; [[#Li--2015b|Li et al. (2015b)]] ; [[#Kim--2016a|Kim et al. (2016a)]] ; [[#Lee--2016|Lee and Kim (2016)]] ; [[#Yu--2016|Yu (2016)]] ; [[#Zhao--2016b|Zhao et al. (2016b)]] ; Abadie et al. (2017); [[#Chen--2017a|Chen et al. (2017a)]] ; Chen et al. (2017b); [[#Chung--2017b|Chung et al. (2017b)]] ; [[#Lee--2017|Lee et al. (2017)]] ; [[#Feng--2018a|Feng et al. (2018a)]] ; [[#Lee--2018b|Lee et al. (2018b)]] ; Lee et al. (2018c); [[#Udo--2018|Udo and Takeda (2018)]] ; [[#Yu--2018a|Yu et al. (2018a)]] ; [[#Yu--2018c|Yu et al. (2018c)]] ; [[#Lee--2019|Lee et al. (2019)]] ; [[#Liu--2019c|Liu et al. (2019c)]] ; [[#Liu--2019d|Liu et al. (2019d)]] ; [[#Wang--2019b|Wang et al. (2019b)]] ; [[#Wu--2019d|Wu et al. (2019d)]] ; [[#Kim--2020|Kim and Lee (2020)]] ; [[#Liu--2020|Liu (2020)]] ; [[#Liu--2020|Liu and Chen (2020)]] ; [[#Yu--2020|Yu et al. (2020)]] . '''Southeast Asia:''' [[#Giuliani--2016|Giuliani et al. (2016)]] ; Dau et al. (2017); [[#Vu--2017|Vu and Ranzi (2017)]] ; [[#Mehvar--2018|Mehvar et al. (2018)]] . '''South Asia:''' Wijetunge (2014); [[#Ahmed--2016b|Ahmed et al. (2016b)]] ; [[#Jevrejeva--2016|Jevrejeva et al. (2016)]] ; [[#Patankar--2016|Patankar and Patwardhan (2016)]] ; Abadie et al. (2017); [[#Aslam--2017|Aslam et al. (2017)]] ; Chiba et al. (2017); [[#Mishra--2017|Mishra et al. (2017)]] ; [[#van%20der%20Geest--2017|van der Geest (2017)]] ; [[#Chhogyel--2018|Chhogyel and Kumar (2018)]] ; [[#Jevrejeva--2018|Jevrejeva et al. (2018)]] ; [[#Leng--2019|Leng and Hall (2019)]] ; [[#Bahinipati--2020|Bahinipati (2020)]] ; [[#Bahinipati--2020|Bahinipati and Patnaik (2020)]] ; [[#Khan--2020|Khan et al. (2020)]] ; Bhowmik et al. (2021). '''Central Asia:''' [[#Groll--2015|Groll et al. (2015)]] ; [[#Babagaliyeva--2017|Babagaliyeva et al. (2017)]] ; [[#Otto--2017|Otto et al. (2017)]] . '''North Asia:''' [[#Gleick--2014|Gleick (2014)]] ; [[#Hjort--2018|Hjort et al. (2018)]] ; [[#Tschakert--2019|Tschakert et al. (2019)]] . '''West Asia:''' [[#Mantyka-Pringle--2015|Mantyka-Pringle et al. (2015)]] ; [[#Pal--2016|Pal and Eltahir (2016)]] ; [[#Ghomian--2017|Ghomian and Yousefian (2017)]] ; Gohari et al. (2017); [[#Ashrafzadeh--2019b|Ashrafzadeh et al. (2019b)]] ; [[#Bierkens--2019|Bierkens and Wada (2019)]] ; [[#Houmsi--2019|Houmsi et al. (2019)]] ; Mosavi et al. (2020).

(a) For definitions on losses and damages and limits, see Cross-Chapter Box LOSS in Chapter 1.

<div id="10.5.5" class="h2-container"></div>

<span id="risk-insurance"></span>
=== 10.5.5 Risk Insurance ===

<div id="h2-15-siblings" class="h2-siblings"></div>

<div id="10.5.5.1" class="h3-container"></div>

<span id="point-of-departure-3"></span>
==== 10.5.5.1 Point of Departure ====

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Risk insurance approaches and tools have significantly evolved during recent years. The emphasis has been mainly on mitigating the adverse selection and moral hazard that have been the limitations of traditional area-based crop insurance approaches ( [[#He--2019|He et al., 2019]] ). This has been achieved by shifting the indemnity calculations on to the specific weather parameters and developing a weather index ( [[#Greatrex--2015|Greatrex et al., 2015]] ; Fischer, 2019). Technological applications in the development of insurance products have seen significant progress, including that of the blockchain and smart contracts ( [[#Gatteschi--2018|Gatteschi et al., 2018]] ). There are technological developments in loss estimation, which has been a major limitation in the traditional insurance approaches in the past that either delayed the indemnity payment or misjudged the losses. Application of multi-model and multi-stage decision support systems has begun to make crop loss assessments for insurance more efficient ( [[#Aggarwal--2020|Aggarwal et al., 2020]] ). Technological applications also include remote sensing ( [[#Di--2017|Di et al., 2017]] ) and mobile phone app technologies ( [[#Meena--2018|Meena et al., 2018]] ) to provide accurate and quick damage assessments, and application of Internet-based indemnity approvals have enabled quick payment of indemnities ( [[#OECD--2017b|OECD, 2017b]] ).

<div id="10.5.5.2" class="h3-container"></div>

<span id="findings-4"></span>
==== 10.5.5.2 Findings ====

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As against financing post-disaster relief and reconstruction, which has been the norm of disaster management for decades in Asia, the evolution of ex-ante risk financing in the form of risk insurance has seen a steady rise globally and in Asia. The rise in popularity for risk financing in general and insurance in specific stem from the observation that governments have recognised the burden of mainly financing the post-disaster relief and reconstruction only ( [[#Juswanto--2017|Juswanto and Nugroho, 2017]] ; [[#UNESCAP--2018c|UNESCAP, 2018c]] ; [[#ADB--2019|ADB, 2019]] ), and from the realisation of cost savings and efficiency that risk financing for risk mitigation brings overall risk reduction ( ''high agreement'' , ''medium evidence'' ). As a result, a gamut of risk-financing instruments have been introduced to finance DRR and CCA initiatives in Asia among which risk insurance has gained prominence for it provides a low-cost and easy option for individuals, provides an opportunity for the governments to effectively engage the private sector in implementation and has the ability to inculcate risk-aware decision making at various levels ( ''high agreement'' , ''medium evidence'' ) ( [[#Hazell--2017|Hazell and Hess, 2017]] ; [[#UNESCAP--2018c|UNESCAP, 2018c]] ).

Several Asian countries, including India, the Philippines and China, have a significant experience of offering agricultural insurance against typhoons, droughts and floods ( [[#Yang--2018|Yang, 2018]] ). For the most part, these insurance systems have followed a traditional indemnity-based insurance which faces several challenges in implementation including moral hazard and adverse selection, disagreements and delays in crop-damage assessments that relied upon crop-cutting experiments, often leading to a delay in processing indemnity payments, costly insurance premiums and poor insurance expansion ( ''high agreement'' , ''robust evidence'' ) ( [[#Patnaik--2017|Patnaik and Swain, 2017]] ; [[#Ghosh--2019|Ghosh et al., 2019]] ). Other factors contributing to poor penetration of insurance include limited awareness on the importance of insurance, and poor access.

To tackle the problem of costly insurance premiums, governments have subsidised the premiums ( [[#Ghosh--2019|Ghosh et al., 2019]] ). Premium subsidies have been reported to undermine the ability to convey the real cost of risks by the insured (price distortion), and have encouraged adverse selection and moral hazard ( [[#Nguyen--2019|Nguyen and Jolly, 2019]] ). On the contrary, subsidies have been suggested to address the issue of adverse selection associated with the insurance ( [[#Zhao--2017c|Zhao et al., 2017c]] ).

Despite the fact that the insurance programmes are able to obtain high participation rates due to subsidised premiums, their impact on farmers’ income seems to be insignificant especially under the conditions of low indemnities, low guarantee and wide coverage ( [[#Zhao--2016a|Zhao et al., 2016a]] ). The subsidy burden of insurance on national governments is found to be significant with an estimated equivalent of 6 billion USD spent by China alone on insurance ( [[#Hazell--2017|Hazell et al., 2017]] ). In addition, the insurance programmes in Asian countries are reporting higher producer claim ratios, and often governments have to spend more than the money being transferred to the insured through the insurance programmes ( [[#Hazell--2017|Hazell et al., 2017]] ).

To address the issues associated with traditional indemnity insurance, efforts have been made to develop weather-index insurance in Asia that bases the payouts on the rainfall or temperature index, rather than on the direct damage measurements. The parametric insurance products help avoid the delays in insurance payouts as they are based on modelled risks, rather than actual damage measurements, and control the adverse selection and moral hazard, although basis risks could be increased due to improper matching of payouts with the index ( [[#De%20Leeuw--2014|De Leeuw et al., 2014]] ). Index insurance is known to promote public–private partnerships that in turn will enhance the efficiency of overall programme delivery ( [[#Hazell--2017|Hazell and Hess, 2017]] ). Several countries, including India, Bangladesh, Thailand, Indonesia, Myanmar and the Philippines, either are currently piloting or expanding the weather-index insurance ( [[#Surminski--2014|Surminski and Oramas-Dorta, 2014]] ; [[#Tyagi--2019|Tyagi and Joshi, 2019]] ). Index insurance is constantly expanding with an estimated 194 million farmers already enrolled in China and India, which is much lower than the potential number of farmers it can reach ( [[#Hazell--2017|Hazell et al., 2017]] ).

Few significant bottlenecks that are limiting the scaling up of weather-index insurance include lack of reliable weather data, low density of weather stations leading to high basis risk, and limited data on damage and hazard for parametric modelling of the insurance ( [[#Shirsath--2019|Shirsath et al., 2019]] ). Several innovations are being tried and tested to overcome the limitations associated with the index insurance which include developing multiscale index insurance, application of remote sensing, smartphone-based near-surface remote sensing and building insurance based on vegetation indices instead of relying on weather data alone ( [[#Hufkens--2019|Hufkens et al., 2019]] ). Alternative indices, such as the NDVI, are being tested for their applications in designing index-based insurance in India ( [[#IFAD--2017|IFAD, 2017]] ). Agro-meteorology-based statistical analysis and crop growth modelling have been suggested to calibrate and rectify faulty weather indices ( [[#Shirsath--2019|Shirsath et al., 2019]] ; [[#Zhu--2019|Zhu et al., 2019]] ). Establishing automatic weather stations can improve data accuracy while preventing the delay in acquiring the weather data ( [[#Sinha--2016|Sinha and Tripathi, 2016]] ). These technological applications have already started finding space within insurance programmes designed by national governments in Asia. For example, the government of India has released new operational guidelines for the application of new technologies such as drones, remote sensing and mobile phone apps in implementation of the national agricultural insurance, which is the third largest insurance in the world ( [[#Department%20of%20Agriculture--2019|Department of Agriculture, 2019]] ).

<div id="10.5.5.3" class="h3-container"></div>

<span id="knowledge-gaps-2"></span>
==== 10.5.5.3 Knowledge Gaps ====

<div id="h3-45-siblings" class="h3-siblings"></div>

Despite these developments, several issues still seem to hinder the penetration of insurance in Asia. Issues such as lack of sufficient choices, lack of clear model, lack of legal support, limited or absence of proper monitoring and evaluation, and limited data for underwriters to properly evaluate claims have been suggested ( [[#Nguyen--2019|Nguyen and Jolly, 2019]] ). Low interest among the potential buyers due to unaffordable insurance premiums, lack of provision for partial-loss claim settlement, big hassles in the claim settlement process and lack of timely settlement of claims are reported ( [[#Parappurathu--2017|Parappurathu et al., 2017]] ). In addition, insurance has been reported to have expanded the coverage of cash crops at the expense of drought-resistant subsistence crops with effects on natural capital and a potential increase in farmer’s vulnerability to market price fluctuations ( [[#Müller--2017|Müller et al., 2017]] ).

Regional catastrophic insurance pools have also received attention in Asia. With the formation of Southeast Asia Disaster Risk Insurance Facility ( [[#Haraguchi--2019|Haraguchi and Lall, 2019]] ), the regional insurance pool has been introduced in Southeast Asia, initially being piloted in Lao PDR and Myanmar and to be expanded to the rest of the ASEAN region. Regional catastrophic insurance allows vulnerable countries to buffer climatic shocks by diversifying the risks beyond country boundaries.

<div id="10.5.6" class="h2-container"></div>

<span id="social-protection"></span>
=== 10.5.6 Social Protection ===

<div id="h2-16-siblings" class="h2-siblings"></div>

<div id="10.5.6.1" class="h3-container"></div>

<span id="point-of-departure-4"></span>
==== 10.5.6.1 Point of Departure ====

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Social protection (SP) encompasses initiatives that involve transfer income or assets to the poor, protect the vulnerable against risks to their livelihood, and enhance the social status and rights of the marginalised ( [[#Béné--2014|Béné et al., 2014]] ; [[#Kothari--2014|Kothari, 2014]] ). Social protection offers a wide range of instruments (e.g., cash transfers, insurance products, pension schemes and employment guarantee schemes) that can be used to support households that are exposed to climate changes ( [[#Bank--2015|Bank, 2015]] ). It also presents an opportunity to develop inclusive comprehensive risk management strategies to address L&amp;D from climate change as well as a means to CCA ( [[#Aleksandrova--2019|Aleksandrova, 2019]] ).

Social protection programmes assist individuals and families, especially the poor and vulnerable, cope with crises and shocks, finds jobs, improve productivity, invest in the health and education of their children, and protect the ageing population ( [[#Bank--2018b|Bank, 2018b]] ). Social protection that is well designed and implemented in a more long-term approach can enhance human capital and productivity, reduce inequalities, build resilience and empowerment, and end the inter-generational cycle of poverty ( ''medium evidence, medium agreement'' ) as indicated from various experiences in the region such as (a) cash transfer programmes in Indonesia ( [[#Kwon--2015|Kwon and Kim, 2015]] ), (b) the Benazir Income Support Programme in Pakistan ( [[#Watson--2017|Watson et al., 2017]] ), (c) the Chars Livelihoods Programme in Bangladesh ( [[#Pritchard--2015|Pritchard et al., 2015]] ) and (d) Minsei-in designated volunteer social workers in Japan ( [[#Boeckmann--2016|Boeckmann, 2016]] ). A key consideration in strengthening resilience through SP programmes is to design with climate and disaster risk considerations in mind and implement in close synergy with existing programmes, such as on sustainable livelihoods, EWS and financial inclusion ( [[#Coirolo--2013|Coirolo et al., 2013]] ; [[#Bank--2018a|Bank, 2018a]] ).

<div id="10.5.6.2" class="h3-container"></div>

<span id="findings-5"></span>
==== 10.5.6.2 Findings ====

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Asia is already the most disaster-prone region in the world, with over 200,000 lives lost and almost 1 billion people affected by storms and floods alone between 2005 and 2014, while a heatwave in North and Central Asia in 2010 killed 56,000 people ( [[#United%20Nations--2015|United Nations, 2015]] ). Climate change is increasing the frequency and intensity of these sudden and slow-onset disasters, among them, hydrological changes in major river basins where 1.5 billion people live (such as the Indus, Ganges, Brahmaputra, Mekong, Yellow, Yangtze, Tarim, Amu and Syr Darya rivers) ( [[#Bank--2017a|Bank, 2017a]] ). According to the latest estimates of the International Labour Organisation (ILO), 55% of the global population (around 4 billion people) remain without any SP benefits, and the SP coverage gap is highest in Africa (82.2%) and the Asia–Pacific (61%) ( [[#ILO--2017b|ILO, 2017b]] ).

Risks are generally amplified for people without SP or essential infrastructure and services, and for people with limited access to land and quality housing, especially those in exposed areas and informal settlements without secure tenure ( [[#ESCAP--2017|ESCAP, 2017]] ). Stateless people are disproportionately affected by climate change and disasters as they tend to reside in hazard-prone areas and their statues as non-citizens often limits access to assistance ( [[#Connell--2015|Connell, 2015]] ). The three main types of SP are: (a) social safety nets (also known as social assistance), which include conditional and unconditional cash transfers, public work programmes, subsidies and food stamps; (b) social insurance, which consists of contributory pensions and contributory health insurance; and (c) labour market measures, which include instruments such as unemployment compensation ( [[#Bank--2018b|Bank, 2018b]] ). The potential for an integrated adaptive SP is not yet harnessed by policymakers in tackling the structural causes of vulnerability to climate change ( [[#Tenzing--2019|Tenzing, 2019]] ). Public works programmes (i.e., India’s Mahatma Gandhi National Rural Employment Guarantee Act, MGNREGA) should take into account climate risk in planning and support development of community assets to increase collective resilience.

Aligning SP with climate-change interventions is an attempt to develop more durable pathways out of poverty and climate vulnerability; examples from MGNREGA depicting the attempt to align through a mainstreaming approach has helped women and their households ( [[#Adam--2015|Adam, 2015]] ; [[#Steinbach--2016|Steinbach et al., 2016]] ). On another note, the Catastrophe Insurance Framework, the first model introduced in Shenzen, China, provides timely relief for citizens and operates as a safety net, particularly for the poorest residents who do not have disposable income to cover the costs associated with bodily injuries arising from disasters ( [[#Telesetsky--2016|Telesetsky and He, 2016]] ). The Department of Labour and Employment’s Integrated Livelihood and Emergency Employment Programme in the Philippines is part of the recovery efforts after Typhoon Haiyan, providing short-term wage employment, and facilitates entrepreneurship for people affected by natural calamities and economic shocks ( [[#Bank--2018b|Bank, 2018b]] ).

In each of these instances, governments are using SP to protect populations suffering from climate change or are adversely affected by structural, pro-climate economic reforms ( [[#Hallegatte--2015|Hallegatte et al., 2015]] ). However, additional research is still needed and new tools need to be developed to inform policy design and support the implementation of ‘green’ SP, as well as to measure the net-welfare impacts of such policies (Canonge, 2016). In order to enhance SP programmes, one of the cross-cutting issues is to discuss the linkages between gender roles and responsibilities, food security, agricultural productivity and the mediating role that SP programmes can have ( [[#Jones--2017|Jones et al., 2017]] ). Social protection has a potentially important role to play in contributing to food security and agricultural productivity in a gender-responsive way ( [[#Holmes--2013|Holmes and Jones, 2013]] ). As such, experience from Challenging the Frontiers of Poverty Reduction: Targeting the Ultra Poor programme in Bangladesh has promoted social innovation by creating social and economic values, fostering microenterprises, increasing food security and fostering inclusive growth, all while empowering ultra-poor women ( [[#Emran--2014|Emran et al., 2014]] ; [[#Mahmuda--2014|Mahmuda et al., 2014]] ). Although there is increasing evidence that SP programmes are having a positive impact in terms of reducing vulnerability in women’s everyday lives ( [[#Jones--2017|Jones et al., 2017]] ), the transformative impact of these programmes is rare due to limitations in recognising women’s access to productive inputs and resources ( [[#Tanjeela--2018|Tanjeela and Rutherford, 2018]] ; Cameron, 2019).

On the other hand, poor governance practices affect delivery of SP programmes and the ability of beneficiary households to reap the benefits from such support ( [[#Sijapati--2017|Sijapati, 2017]] ). In Nepal, a closer look at public expenditure shows that about 60% of the SP budget is used by social insurance programmes that predominantly consist of public-sector pensions (Babken Babajanian, 2014; [[#Koehler--2014|Koehler, 2014]] ). Towards this end, more effort is needed to improve its existing programmes so that there is an equality of opportunities, along with secured human rights. The example from Nepal’s Child Grant is an indicative of an incremental approach to social policy ( [[#Garde--2017|Garde et al., 2017]] ). Meanwhile, in the Philippines, despite the existence of flagship national interventions that cover a significant number of people in need and have clear and robust implementation rules, there are still many programmes with overlapping mandates and target population, and several gaps in their monitoring systems ( [[#Bank--2018b|Bank, 2018b]] ).

Having an integrated SP information system would allow policymakers to better monitor inputs, outputs and outcomes (e.g., who the beneficiaries are, what they are receiving, at what frequency, what the existing gaps are) ( [[#OECD--2017a|OECD, 2017a]] ; [[#Samad--2018|Samad and Shahid, 2018]] ). Based on evidence from the assessments of three countries (Mongolia, Nepal and Vietnam), the political and institutional arrangements (i.e., the software) is as important as the technical fixes (i.e., the hardware) in the success of using information and communication technology for delivering SP programmes ( [[#ADB--2016|ADB, 2016]] ). By 2050, climate-induced migration will ''likely'' be a major policy aspect of the rural–urban nexus as slow-onset impacts of climate change in sub-Saharan Africa, South Asia and Latin America will ''likely'' force over 143 million people to migrate within their national borders (Kumari Rigaud, 2018). This will have major implications for SP systems, and therefore national SP strategies should be designed to anticipate and address climate-induced internal mobility ( [[#Schwan--2017|Schwan and Yu, 2017]] ). For instance, it does not offer a solution for maintaining Indigenous cultures which are often strongly affected by, or even disrupted by, climate change (Olsson, 2014). Hence, an effective approach needs to combine different policy instruments to support protection, adaptation and migration ( [[#O’Brien--2018|O’Brien et al., 2018]] ).

Evidently, SP has been financed typically through the combination of government tax revenues and official development assistance, and the challenges of the increasing frequency and intensity of natural and economic crises are straining these traditional financial sources (Durán-Valverde, 2020). In this context, innovative financing schemes are seen as critical to achieve the sustainable financing of SP ( [[#Asher--2015|Asher, 2015]] ; [[#UNICEF--2019|UNICEF, 2019]] ) via social and solidarity economy, as seen in women’s autonomous adaptation measures in precautionary savings and flood preparedness in Nepal ( [[#Banerjee--2019|Banerjee et al., 2019]] ), and self-help groups as development intermediaries ( [[#Anderson--2019|Anderson, 2019]] ). Still, there are constraints of SP to reach those who are most vulnerable to climate change and other hazards due to their legal status, such as the fact that attention to forcibly displaced populations within the SP field has been limited ( [[#Sabates-Wheeler--2019|Sabates-Wheeler, 2019]] ).

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==== 10.5.6.3 Knowledge Gaps ====

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Government SP can attenuate the negative impacts in facing disasters, depending on the differences in political systems and the focus put on sociopolitical measures ( ''medium evidence, medium agreement'' ), not only in restoring livelihoods but also in easing mental burdens faced by rural households in developing countries ( [[#Dalton--2016|Dalton et al., 2016]] ; [[#Kosec--2017|Kosec and Mo, 2017]] ; [[#Liebenehm--2018|Liebenehm, 2018]] ). However, limited government capacities and fiscal feasibility may impede the expansion and effective implementation of SP as developing countries need further support to design, adjust and implement SP schemes effectively ( [[#Klonner--2014|Klonner, 2014]] ; [[#Schwan--2017|Schwan and Yu, 2017]] ). Most countries have comprehensive strategies for both SP and climate change, but few have attempted to align them, as in practice they remain in separate institutional homes, governed by their own intra-sector coordination groups and funding channels ( [[#Steinbach--2016|Steinbach et al., 2016]] ; [[#Bank--2018b|Bank, 2018b]] ). Thus, significant knowledge gaps remain in terms of understanding the potential of SP to build long-term resilience to climate change ( [[#Ulrichs--2019|Ulrichs et al., 2019]] ). Future efforts should be geared to develop climate-responsive SP policies that consider a broad range of issues including urbanisation and migration, the impact of green policies on the poor, access to essential health care and risks to socially marginalised groups ( [[#Aleksandrova--2019|Aleksandrova, 2019]] ). Along with strengthening links to climate information and EWS, finance for enabling SP systems to address climate-related shocks and stresses dynamically needs to be scaled up ( [[#Kuriakose--2013|Kuriakose et al., 2013]] ; [[#Ulrichs--2019|Ulrichs et al., 2019]] ).

<div id="10.5.7" class="h2-container"></div>

<span id="education-and-capacity-development"></span>
=== 10.5.7 Education and Capacity Development ===

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==== 10.5.7.1 Point of Departure ====

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Asian areas with the least capacity to respond, such as the Himalayan region and densely populated deltas, are hit first and hardest by climate impacts ( [[#De%20Souza--2015|De Souza et al., 2015]] ; [[#Khan--2017|Khan, 2017]] ). Acknowledging the limitations in terms of capacity and coping mechanisms towards climate change, education, training and awareness building is central to sustaining long-term capacity building ( [[#Clemens--2016|Clemens et al., 2016]] ). Education has a lot more to offer in terms of improvements in addressing climate change, particularly in the climate hotspots of Asia where mostly poor, disadvantaged communities vulnerable to climate change reside ( [[#Mani--2018|Mani et al., 2018]] ). In particular, when disseminated, climate-change awareness and information need more explanation ( [[#Steg--2014|Steg et al., 2014]] ; [[#Wi--2018|Wi and Chang, 2018]] ; [[#Cho--2020|Cho, 2020]] ). In addition, international and national support through institutions and financing is critical for successful capacity building ( [[#Hemachandra--2019|Hemachandra, 2019]] ), which must be designed for the long term and be self-sustaining ( [[#Gustafson--2018|Gustafson et al., 2018]] ). National ownership by recipient countries and members of communities of capacity-building efforts is key to ensuring their success ( [[#Roberts--2016|Roberts and Pelling, 2016]] ; [[#Mikulewicz--2017|Mikulewicz, 2017]] ).

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<span id="findings-6"></span>
==== 10.5.7.2 Findings ====

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The need to develop tailored climate communication and education strategies for individual nations as public awareness and risk perceptions towards climate change vary greatly ( ''medium evidence, high agreement'' ) ( [[#Lee--2015a|Lee et al., 2015a]] ). Improving on, and investing in, basic education, climate literacy and location-based strategies of climate change are vital to enhance public engagement, societies’ adaptive capacity and support for climate action ( [[#Lutz--2014c|Lutz et al., 2014c]] ; [[#Hu--2016|Hu and Chen, 2016]] ). As stated in the IPCC Special Report 1.5°C, sustainable development has the potential to significantly reduce systemic vulnerability, enhance adaptive capacity and promote livelihood security for poor and disadvantaged populations ( [[#Roy--2018|Roy et al., 2018]] ). Hence, various concepts are introduced to foster awareness, understanding, knowledge, participation as well as commitment towards managing climate change in a sustainable manner. One such concept is education for sustainable development (ESD), which is aimed at integrating the principles and practices of sustainable development in all aspects of education, and training individuals who will contribute to the realisation of a more sustainable society ( [[#Kitamura--2017|Kitamura, 2017]] ).

Climate-change education (CCE) is also now addressed in the context of ESD and allows for learners to understand the causes and consequences of climate change, and teaches them how to take action ( [[#Mochizuki--2015|Mochizuki and Bryan, 2015]] ). Both ESD and CCE are gaining broader attention, for instance, in China ( [[#Han--2015|Han, 2015]] ) and the Republic of Korea ( [[#Sung--2015|Sung, 2015]] ); however, development of policies and implementation of initiatives regarding ESD and CCE still face a handful of challenges which require a strong political will and consensus of key stakeholders ( [[#Læssøe--2015|Læssøe and Mochizuki, 2015]] ). Effective communication on CCE particularly for younger-generation engagement is also essential, as they are our future leaders as climate change is an inter-generational equity issue ( [[#Corner--2015|Corner et al., 2015]] ). Action for Climate Empowerment of Article 6 of the UNFCCC target youth as a major group for effective engagement in the formulation and implementation of decisions on climate change ( [[#UNFCCC--2015|UNFCCC, 2015]] ). Increasing attention from countries in Asia, such as Thailand and India, will encourage innovative ways to provide adequately in educating and engaging youth in climate-change issues ( [[#Narksompong--2015|Narksompong and Limjirakan, 2015]] ; [[#Dür--2018|Dür and Keller, 2018]] ).

An integrated approach to knowledge about climate change embraces both the importance in bridging knowledge of climate science and respecting IKLK, and should be at the heart of any effort to educate citizens to have a deeper understanding of the causes and consequence of climate change in a holistic manner ( [[#Aswani--2018|Aswani et al., 2018]] ). Indigenous Peoples, comprising about 6% of the global population, play a crucial role in the fight against climate change for two interlinked reasons. First, they have a particular physical and spiritual relationship with land, water and associated ecosystems, and tend to be among the most vulnerable group to climate change ( [[#Magni--2017|Magni, 2017]] ). Second, they have a specialised ecological and traditional knowledge relevant to finding the best solutions to climate change ( [[#Rautela--2015|Rautela and Karki, 2015]] ). Indigenous knowledge systems and resource management practices are important tools for both mitigating and adapting to climate change ( [[#Fernandez-Llamazares--2015|Fernandez-Llamazares et al., 2015]] ). Indigenous knowledge is increasingly recognised as a powerful tool for compiling evidence of climate change over time ( [[#Ahmed--2016a|Ahmed et al., 2016a]] ). Knowledge of CCA and DRR provide a range of complementary approaches in building resilience and reducing the vulnerability of natural and human systems to the impacts of climate change and environmental hazards ( [[#Mall--2019|Mall et al., 2019]] ). The adaptation dimension involves developing knowledge and utilising existing IKLK, skills and dispositions to better cope with already evident and looming climate impacts ( [[#Aghaei--2018|Aghaei et al., 2018]] ). It is also important to ensure inclusive efforts in DRR across different nations and communities as well as increasing skills and capacities of women towards DRR efforts ( [[#Alam--2014|Alam and Rahman, 2014]] ; [[#Drolet--2015|Drolet et al., 2015]] ; [[#Islam--2016b|Islam et al., 2016b]] ; [[#Reyes--2016|Reyes and Lu, 2016]] ; [[#Hemachandra--2018|Hemachandra et al., 2018]] ). More effective and efficient teaching and learning strategies, as well as collaborative networks, are needed to increase preparedness and DRR activities across various levels of community ( [[#Oktari--2015|Oktari et al., 2015]] ; [[#Takahashi--2015|Takahashi et al., 2015]] ; [[#Tuladhar--2015b|Tuladhar et al., 2015b]] ; [[#Shiwaku--2016|Shiwaku et al., 2016]] ; [[#Gampell--2017|Gampell et al., 2017]] ).

Table 10.6 shows education and capacity-building aspects affecting adaptation by sub-region examples.

'''Table 10.6 |''' Education and capacity-building aspects affecting adaptation by sub-regions examples

{| class="wikitable"
|-
! Sub-region

! Sectors

! Adaptation interventions

! Education and capacity-building factors affecting adaptation

! Supporting references

|-
| North Asia

| Human well-being

| PEEX (Pan Eurasian Experiment) originating from a bottom-up approach by the science communities aiming at resolving major uncertainties in Earth system science and global sustainability issues concerning the Arctic and boreal pan-Eurasian regions, as well as China

| Educating the next generation of multidisciplinary experts and scientists capable of finding tools in solving future environmental, socioeconomic and demographic development problems of the Arctic and boreal regions, as well as China

| Pan Eurasian regions, as well as China ( [[#Kulmala--2015|Kulmala et al., 2015]] )

|-
| West Asia

| Agriculture

| Smallholder farmers’ vulnerability assessment

| High level of education, more human capacity and adaptive capacity, less vulnerability

| Iran ( [[#Jamshidi--2019|Jamshidi et al., 2019]] )

|-
| Central Asia

| Agriculture, water resources and energy

| Carrying out the selection and cultivation of drought-tolerant, salt-tolerant crops, preservation of the upper watershed of the rivers, improving climate resilience of hydro-facilities

| Placing the focal point for the preparation and implementation of programmes for climate change at the regional level, increasing capacity of professionals in targeted areas and networking between them, and strengthening institutional, technical and human resources to promote adaptation and research in fields of climate and hydrological investigations, geographic information systems, environmental impact assessment, and protection and re-cultivation of lands

| Kazakhstan, Tajikistan and Kyrgyzstan mountain societies in Central Asia ( [[#Schmidt-Vogt--2016|Schmidt-Vogt et al., 2016]] ; [[#Xenarios--2019|Xenarios et al., 2019]] )

|-
| South Asia

| Agriculture

| Productivity, net crop income, improvement in livelihoods and food security

| Farmers’ education, easy access to farm advisory services, weather forecasting and marketing information

| Pakistan ( [[#Abid--2016|Abid et al., 2016]] )

|-
|
| Passive adaptation in agricultural and farming practices implicitly to cope with climate change

| Increasing knowledge on climate change so that concrete steps can be taken in dealing with perceived climate changes

| India ( [[#Tripathi--2017|Tripathi and Mishra, 2017]] )

|-
|
| Having farmers’ perceptions shape knowledge, and vice versa on climate change

| Age, education, occupation, farming experience, knowledge about coping strategies (all significantly related to farmers’ perceptions about climate change)

| India (Aslam [[#Ansari--2018|Ansari, 2018]] )

|-
|
| Disaster risk reduction

| Local institutions’ preparedness and capacity for managing disaster at the local scale

| Capacity building, technical support and financial capacity, as well as adopting a proactive approach, to achieve a higher level of disaster preparedness

| Pakistan ( [[#Shah--2019|Shah et al., 2019]] )

|-
| Southeast Asia

| Agriculture

| Farming cultural practices adopted to minimise production losses due to extreme weather

| Small-scale farmers’ attendance at climate-change training to enhance adaptive capacity

| Vietnam ( [[#Trinh--2018|Trinh et al., 2018]] )

|-
|
| Removing farmers’ barriers to adopting adaptation measures, and provide funds and timely information

| Knowledge of crop variety, increasing educational outreach and communicating climate-change-related information to increase the likelihood of employing adaptive strategies

| China ( [[#Zhai--2018|Zhai et al., 2018]] )

|-
|
| Making farmers in rural, under-resourced communities in the Lower Mekong basin aware of how climate change will affect them

| Scientific findings which can be merged with local knowledge at a community level to help raise awareness, and knowledge gaps on both which can be filled for better understanding and adaptation planning

| Cambodia, Lao PDR, Vietnam and Thailand ( [[#USAID--2015|USAID, 2015]] ; [[#Gustafson--2018|Gustafson et al., 2018]] )

|-
|
| Coastal areas

| Reducing households’ vulnerability due to variation in socioeconomic and livelihoods assets

| Increasing resilience by establishing effective an communication system, improving knowledge on climate change

| Vietnam and Indonesia ( [[#Nanlohy--2015|Nanlohy et al., 2015]] ; [[#Huynh--2018|Huynh and Stringer, 2018]] )

|-
|
| Disaster risk reduction

| Capacity building through learning labs on disaster risk management for sustainable development (DRM-SD)

| Transfer of learning initiatives to provide approach guidelines and innovative mechanisms for DRM practitioners who will have the know-how and potential for leadership in DRM-SD

| Four ASEAN countries: Malaysia, Vietnam, Lao PDR and Cambodia (Ahmad [[#Shabudin--2017|Shabudin et al., 2017]] )

|-
| East Asia

| Disaster risk reduction

| Conducting community participation and disaster education so that people can take action in disaster management

| Educational-resilience system tested and revised through experiences from past disasters; recognising and integrating gender perspectives into mainstream disaster management; ‘school-based recovery concept’ facilitating short-term recovery and the longer-term community building needs, which can also help communities in building new networks and solving chronic social problems

| Japan ( [[#Matsuura--2014|Matsuura and Shaw, 2014]] ; [[#Saito--2014|Saito, 2014]] ; [[#Shiwaku--2016|Shiwaku et al., 2016]] )

|-
|
| Bridging Indigenous knowledge and scientific knowledge

| In efforts to solve real-world problems, engaging first with those local communities that are most affected, beginning with the perspective of Indigenous knowledge and then seeking relevant scientific knowledge

| Paying attention to the Indigenous perception of a hazard and risk with the aim of increasing the effectiveness of projects implemented by practitioners who might need to communicate risks in the future; empowering the younger generation to ensure continuity of Indigenous cultures and their linked ecosystems

| ( [[#Mistry--2016|Mistry and Berardi, 2016]] ; [[#Roder--2016|Roder et al., 2016]] )

|}

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<span id="knowledge-gaps-4"></span>
==== 10.5.7.3 Knowledge gaps ====

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Capacity building at national and local levels still needs to address gaps in research and practice, such as impacts and results of different preparedness measures ( [[#Alcayna--2016|Alcayna et al., 2016]] ). Ad-hoc and localised documentations and monitoring of efforts to build adaptive capacities has rendered it difficult to assess success ( [[#Cinner--2018|Cinner et al., 2018]] ). Recommendations for strengthened capacity building are sometimes made or understood in isolation from the underlying structural issues shaping vulnerability, or without adequately recognising the political relationships that mediate the ways in which particular technical interventions result in differentiated outcomes for different groups ( [[#Archer--2015|Archer and Dodman, 2015]] ). Thus, design- and decision-based tools, such as rapid assessment for community resilience to climate change as well as rapid approach to monitor the effectiveness of aid projects, support the community-based adaptation to climate change to analyse using a multi-dimensional approach, procedural, distributional, rights and responsibilities ( [[#Nkoana--2018|Nkoana et al., 2018]] ; [[#Jacobson--2019|Jacobson et al., 2019]] ).

As a model of communication and engagement, citizen science has the potential to promote individual and collective climate-change action ( [[#Groulx--2017|Groulx et al., 2017]] ). More than information provision is needed to mobilise public action on climate change ( [[#Kyburz-Graber--2013|Kyburz-Graber, 2013]] ). Citizen science links communication and engagement in a manner that holds important lessons on ways to promote collective responses to climate ( [[#Wals--2014|Wals et al., 2014]] ; [[#Bonney--2016|Bonney et al., 2016]] ). The power of science-based citizen engagement lies in citizen group contribution in drawing upon their local knowledge to enrich the knowledge base required for management decisions ( [[#Sayer--2015|Sayer et al., 2015]] ). Scientific evidence may be less attuned to the complexity of local realities in managing climate change; thus, citizen science has the potential in bridging this gap and has many advantages for climate mitigation and adaptation practice and policy ( [[#Ford--2016|Ford et al., 2016]] ). While citizen science uses citizens as policy-passive objects for research in conducting measurements for big datasets, citizen social science is gaining momentum where it repositions citizens as central co-learners who can widen the climate-science evidence base to achieve a more holistic understanding for the benefit of all ( [[#Kythreotis--2019|Kythreotis et al., 2019]] ).

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<span id="climate-resilient-development-pathways"></span>