Editing IPCC:AR6/WGII/Chapter-12 (section)

== 12.5 Adaptation ==

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Adaptation initiatives across the region have increased since AR5. National Communications (NCs), Nationally Determined Contributions (NDCs) and National Adaptation Plans (NAPs) ( https://unfccc.int ) recently published are providing guidance for adaptation in CSA. There is also a diversity of non-governmental adaptation initiatives, at both the national and sub-national levels. In this context, this section assesses, through a sectoral approach, the main challenges, opportunities, trends and initiatives to adapt to climate change in the region.

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=== 12.5.1 Terrestrial and Freshwater Ecosystems and Their Services ===

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CSA is one of the most biodiverse regions in the world, hosting unique socioecosystems that will be strongly impacted by climate change ( ''high confidence'' ) ( [[#12.3|Section 12.3]] ; Cross-Chapter Paper 1; [[#CAF--2014|CAF, 2014]] ; [[#Camacho%20Guerreiro--2016|Camacho Guerreiro et al., 2016]] ; [[#IPBES--2018a|IPBES, 2018a]] ; [[#Li--2018|Li et al., 2018]] ; [[#Retsa--2020|Retsa et al., 2020]] ). Warming has generated extreme heat events in many parts of CSA ( [[#IPCC--2019a|IPCC, 2019a]] ) that, together with droughts and floods, will seriously affect the integrity of terrestrial and freshwater ecosystems in the entire region ( [[#12.3|Section 12.3]] ; [[#CAF--2014|CAF, 2014]] ). A reduction in net primary productivity in tropical forests and glacier retreat in the Andes, for example, are expected to cause significant negative socioecological impacts ( [[#Feldpausch--2016|Feldpausch et al., 2016]] ; [[#Lyra--2017|Lyra et al., 2017]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ) (Case Study, 12.7.1). Biodiversity hotspots in the region are well assessed in the literature compared to other regions of the world, especially the Atlantic Forest, Mesoamerica and Cerrado (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; [[#Manes--2021|Manes et al., 2021]] ). Up to 85% of evaluated natural systems (species, habitats and communities) in the literature for biodiversity hotspots since AR5 were projected to be negatively impacted by climate change ( ''high confidence'' ), with 26% of projections predicting species extinctions (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; [[#Manes--2021|Manes et al., 2021]] ). IKLK play an important role in adaptation and are vital components of many socioecological systems, while also being threatened by climate change ( ''high confidence'' ) (Box 7.1) ( [[#Valdivia--2010|Valdivia et al., 2010]] ; [[#Tengö--2014|Tengö et al., 2014]] ; [[#Mistry--2016|Mistry et al., 2016]] ; [[#Harvey--2017|Harvey et al., 2017]] ; [[#Diamond--2018|Diamond and Ansharyani, 2018]] ; [[#Camico--2021|Camico et al., 2021]] ).

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==== 12.5.1.1 Challenges and Opportunities ====

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The conversion of natural ecosystems to agriculture, pasture and other land uses in CSA has been identified as a major challenge to climate-change adaptation in the region ( ''high confidence'' ) ( [[#Scarano--2018|Scarano et al., 2018]] ; [[#IPCC--2019a|IPCC, 2019a]] ). In the last three decades, SA has been a significant contributor to the growth of agricultural production worldwide (OECD/Food and Agriculture Organization of the United Nations, 2015), driven partly by increased international demand for commodities, especially soybeans and meat ( [[#IPCC--2019a|IPCC, 2019a]] ). Between 2001 and 2015 about 65% of all forest disturbance in the region was associated with commodity-driven deforestation ( [[#Curtis--2018|Curtis et al., 2018]] ). High rates of native vegetation conversion in Argentina, Bolivia, Brazil, Colombia, Ecuador, Paraguay and Peru threaten important ecosystems (Amazon, Cerrado, Chacos and Llanos savannahs, Atlantic rainforest, Caatinga and Yungas) ( [[#Graesser--2015|Graesser et al., 2015]] ; [[#FAO--2016c|FAO, 2016c]] ). Almost two-thirds of soy consumed in EU+ comes from Brazil, Argentina and Paraguay ( [[#IDH--2020|IDH, 2020]] ), increasing conversion risk in the Amazon, Cerrado and Gran Chaco. Despite growing commodity production traceability, in 2018 only 19% of the soybean meal consumed in EU+ was certified deforestation-free and 38% compliant with the FEFAC Soy Sourcing Guidelines ( [[#IDH--2020|IDH, 2020]] ), which poses a serious challenge at the international level ( [[#Negra--2014|Negra et al., 2014]] ; [[#Curtis--2018|Curtis et al., 2018]] ; [[#Lambin--2018|Lambin et al., 2018]] ; [[#IDH--2020|IDH, 2020]] ).

Investing in actions aimed at protection, restoration and the sustainable use of biodiversity and ecosystems represents a good approach to maintaining critical ecosystem services and constitutes part of a common strategy for adaptation, mitigation and disaster risk reduction in the region ( ''high confidence'' ) ( [[#Kabisch--2016|Kabisch et al., 2016]] ; [[#Scarano--2018|Scarano et al., 2018]] ). These strategies also satisfy international forest and water conservation agendas in terms of optimising resources and solutions ( [[#Strassburg--2019|Strassburg et al., 2019]] ). Global conservation and sustainable development commitments, such as the Aichi Targets (Convention on Biological Diversity [CBD]), Sustainable Development Goals (UN), the NDCs under the Paris Agreement and the New York Declaration on Forests, strongly rely on nature-based solutions (NbS) to achieve their objectives ( [[#Brancalion--2019|Brancalion et al., 2019]] ) (Figure 12.12). The COVID-19 outbreak also brought attention to the need to preserve tropical forests as a means of preventing spillover of viruses from wildlife to humans, with concerns over that risk in the Amazon ( [[#Allen--2017b|Allen et al., 2017b]] ; [[#Dobson--2020|Dobson et al., 2020]] ; [[#IPBES--2020|IPBES, 2020]] ; [[#Ferreira--2021|Ferreira et al., 2021]] ). These represent an important opportunity for ecosystem-based adaptation (EbA) to be at the core of NbS for climate change, access finance and promote climate resilient development pathways in CSA.

The Declaration on Protected Areas and Climate Change, presented by 18 CSA countries during the United Nations Framework Convention on Climate Change (UNFCCC) Conference of the Parties 21 (COP21), highlights the fundamental role of protected areas in providing the so-called GI needed to implement climate-change mitigation and adaptation and safeguard the provision of essential ecosystem services and the livelihoods of Indigenous Peoples and local communities ( [[#Gross--2016|Gross et al., 2016]] ). Protected area in CSA are underfunded ( ''very high confidence'' ). Latin American (including Mexico) governments allocate just about 1% of their national environmental budgets on protected areas (about USD 1.18 ha −1 on average). This figure only covers 54% of their basic needs, resulting in insufficient management. The financing gap to achieve optimal needs for protected areas in CSA is approximately USD 700 million yr −1 ( [[#Bovarnick--2010|Bovarnick et al., 2010]] ). This seriously compromises the management and delivery capacity of protected areas for climate-change adaptation and preparedness for ongoing ecological transformation ( [[#van%20Kerkhoff--2019|van Kerkhoff et al., 2019]] ). Furthermore, to become a relevant mechanism for resilience, protected areas need to be managed for this purpose ( [[#Mansourian--2009|Mansourian et al., 2009]] ). About 40% of protected areas in Latin America and the Caribbean (including Mexico) have undertaken management effectiveness evaluations ( [[#UNEP-WCMC%20and%20IUCN--2020a|UNEP-WCMC and IUCN, 2020a]] ). This is hardly representative of Aichi’s Target 11, although far better than the 11% global average. Collaborations with Indigenous Peoples and local communities are also an important issue to consolidate protected areas ( [[#Gross--2016|Gross et al., 2016]] ). In addition to protected areas as solutions for climate-change adaptation and mitigation, there is also a need to protect or restore ecosystems outside the protected areas, as illustrated by the Mesoamerican Biological Corridor ( [[#Imbach--2013|Imbach et al., 2013]] ).

Despite some local and specific assessments (e.g., [[#Warner--2016|Warner (2016)]] ), there is a significant gap when it comes to identifying barriers to adaptation or maladaptation in the region ( [[#Dow--2013|Dow et al., 2013]] ). In their NCs, NDCs and/or NAPs, most countries identified inadequate financing and access to technology as barriers to adaptation relevant to terrestrial and freshwater socioecosystems ( ''high confidence'' ). Insufficient institutional coordination is also frequently mentioned ( [[#Rangecroft--2013|Rangecroft et al., 2013]] ; [[#Cameron--2015|Cameron et al., 2015]] ). These limitations could be partially addressed through multi-lateral cooperation, incorporation of synergies from local to national scales, local empowerment and poverty alleviation ( [[#Rangecroft--2013|Rangecroft et al., 2013]] ; [[#Harvey--2017|Harvey et al., 2017]] ; [[#Murcia--2017|Murcia et al., 2017]] ; [[#Calispa--2018|Calispa, 2018]] ; [[#Chain-Guadarrama--2018|Chain-Guadarrama et al., 2018]] ).

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==== 12.5.1.2 Governance and Financing ====

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All CSA countries have formulated policies that include measures relevant for socioecosystem adaptation in their NCs, NDCs and NAPs, with an emphasis on protecting and restoring water and forests ( ''high confidence'' ). Existing proposed measures, instruments and programmes, however, do not yet reflect the vision needed to integrate the ecosystem and human dimensions of vulnerability. Administration coordination and the progress in adaptive ecosystem management are still in their early stages, due in part to the lack of stable financial resources and scientific knowledge and IKLK about adapting ecosystems to climate change ( [[#Bustamante--2020|Bustamante et al., 2020]] ). Brazil was an exception, showing dramatic policy-driven reduction in deforestation in the Amazon between 2004–2012, with a concomitant 70% increase in soy production, the most profitable Amazon crop ( [[#Hansen--2013|Hansen et al., 2013]] ; [[#Nepstad--2014|Nepstad et al., 2014]] ). Policies included territorial planning (protected areas, Indigenous territories and land tenure), satellite monitoring and market and credit restrictions on high-deforesting municipalities, plus some incentives to small farmers (Boucher et al., 2013; [[#Hansen--2013|Hansen et al., 2013]] ; [[#Nepstad--2014|Nepstad et al., 2014]] ; [[#Castelo--2015|Castelo, 2015]] ; [[#Cunha--2016a|Cunha et al., 2016a]] ). It is important to highlight the important role of Indigenous territories, in addition to protected areas, in forest conservation in the Amazon ( ''high evidence, medium agreement'' ) ( [[#Schwartzman--2013|Schwartzman et al., 2013]] ; [[#Barber--2014|Barber et al., 2014]] ; [[#Nepstad--2014|Nepstad et al., 2014]] ; [[#Walker--2014b|Walker et al., 2014b]] ). These policies were partially funded by results-based compensation through the Amazon Fund. Since 2012, however, policies and institutions have weakened, and Amazon deforestation rates have started to rise ( [[#Carvalho--2019|Carvalho et al., 2019]] ), becoming more acute in recent years ( [[#Silva%20Junior--2021|Silva Junior et al., 2021]] ). Conservation incentives, a new complementary and allegedly cost-effective approach, is increasingly being implemented in the region ( [[#Magrin--2014|Magrin et al., 2014]] ). They include PES, REDD+, environmental certification and conservation easements, but remain controversial, and more research is needed on their effectiveness, possible negative side effects, participatory management systems and collective decision-making processes ( [[#Larson--2011|Larson and Petkova, 2011]] ; [[#Locatelli--2011|Locatelli et al., 2011]] ; [[#Pinho--2014|Pinho et al., 2014]] ; [[#Strassburg--2014|Strassburg et al., 2014]] ; [[#Mistry--2016|Mistry et al., 2016]] ; [[#Gebara--2017|Gebara and Agrawal, 2017]] ; [[#Scarano--2018|Scarano et al., 2018]] ; [[#Ruggiero--2019|Ruggiero et al., 2019]] ; [[#To--2019|To and Dressler, 2019]] ; [[#Vallet--2019|Vallet et al., 2019]] ).

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==== 12.5.1.3 Adaptation Options to Avert and Reduce Key Risks to Terrestrial and Freshwater Ecosystems ====

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Research, monitoring systems and other initiatives for knowledge management are promoted in the region on terrestrial and freshwater socioecosystem adaptation ( ''high confidence'' ) (NCs, NDCs and NAPs, https://unfccc.int ). In Chile, for example, the Eco-social Observatory of Climate Change Effects for High Altitude Wetlands of Tarapacá has been collecting information on physical, biological and social variables since 2013 ( [[#Uribe%20Rivera--2017|Uribe Rivera et al., 2017]] ). Other examples in the Andes are the GLORIA-Andes network ( [[#Cuesta--2017a|Cuesta et al., 2017a]] ), the Andean Forest Network ( [[#Malizia--2020|Malizia et al., 2020]] ) and the Initiative of Hydrological Monitoring in the Andes (IMHEA), with measures to optimise watershed management and protection and reduce the risk of water insecurity ( [[#Correa--2020|Correa et al., 2020]] ).

Poverty is a driver of climate-change risk, while the sustainable use of ecosystems fosters adaptation ( [[#Kasecker--2018|Kasecker et al., 2018]] ) ( ''high confidence'' ). Most of the 398 ecosystem-based adaptation hotspots identified in Brazil on this premise are located in some of the ecosystems that are most vulnerable to climate change ( [[#Kasecker--2018|Kasecker et al., 2018]] ). Although conservation and restoration are reported as being effective at reducing risk ( ''medium confidence: medium evidence, high agreement'' ) (Anderson et al., 2010; [[#Borsdorf--2013|Borsdorf et al., 2013]] ; [[#Keenan--2015|Keenan, 2015]] ; [[#Pires--2017|Pires et al., 2017]] ; [[#Ramalho--2021|Ramalho et al., 2021]] ), their effectiveness depends on the integration of conservation actions with enhancements of local socioeconomic conditions ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Scarano--2015|Scarano and Ceotto, 2015]] ; [[#Pires--2017|Pires et al., 2017]] ; [[#Kasecker--2018|Kasecker et al., 2018]] ; [[#de%20Siqueira--2021|de Siqueira et al., 2021]] ; [[#Vale--2021|Vale et al., 2021]] ).

Since AR5, there has been an increase in the number of adaptation measures through natural resource and ecosystem service management. The main approaches are EbA and community-based adaptation (CbA) ( ''high confidence'' ) (NCs, NDCs and NAPs, https://unfccc.int ). IKLK can be very detailed and usually relates to people’s priorities as identified by collective decision-making (Box 7.1) ( [[#Hurlbert--2019|Hurlbert et al., 2019]] , SRCCL Section 7.6.4; SRCCL Cross-Chapter Box 13 ILK; [[#de%20Coninck--2018|de Coninck et al., 2018]] , SR1.5 [[IPCC:Wg2:Chapter:Chapter-4#4.3.5|Section 4.3.5.5]] ). In Manaus, central Amazon, fishermen perceive reductions in fish size, diversity and capture levels caused by droughts, while recognising that floods hinder access to fishing grounds ( [[#Keenan--2015|Keenan, 2015]] ; [[#Camacho%20Guerreiro--2016|Camacho Guerreiro et al., 2016]] ). In the Amazon floodplains, small-scale fisher and farmer communities incorporate their knowledge on natural hydrologic and ecological processes into management systems that reduce climate-change risk and impacts ( [[#Oviedo--2016|Oviedo et al., 2016]] ). Smallholder grain farmers in Guatemala and Honduras implement EbA practices based on local knowledge (e.g., live fences, home gardens, shade trees in coffee plantations, dispersed trees in corn fields and other food insecurity risk reduction practices) ( [[#Harvey--2017|Harvey et al., 2017]] ; [[#Chain-Guadarrama--2018|Chain-Guadarrama et al., 2018]] ). There is, therefore, great potential for terrestrial and freshwater ecosystem adaptation to climate change in CSA, provided the right incentives and sociocultural protective measures are in place ( ''high confidence'' ) ( [[#12.5.10.4|Section 12.5.10.4]] ; Table SM12.7).

Disarticulation between policy and implementation is a common problem. Ecuadorian climate public policy points towards a CbA approach, but it is often downsized when implemented ( [[#Calispa--2018|Calispa, 2018]] ). Important adaptation actions have been undertaken in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Paraguay, Peru and Uruguay, both in policymaking and institutional arrangements, but they tend to be poorly coordinated with policies on development, land planning and other sectoral policies ( [[#Ryan--2012|Ryan, 2012]] ). Some type of community participation mechanisms is present in most country strategies, but their levels of implementation vary considerably ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Ryan--2012|Ryan, 2012]] ; [[#Pires--2017|Pires et al., 2017]] ; [[#Calispa--2018|Calispa, 2018]] ).

There is an ecosystem bias in adaptation priorities for research and implementation, hindering the development of comprehensive adaptation programmes. Most scientific research on adaptation in Peru focuses on the highlands and coastal regions, while mitigation research focuses on forests ( [[#Chazarin--2014|Chazarin et al., 2014]] ). Combined adaptation and mitigation strategies can produce positive results, but they are often disconnected ( [[#Locatelli--2015|Locatelli et al., 2015]] ). Most reviewed cases in agriculture and forestry in Latin America (84% of 274 cases) reported positive synergies between adaptation and mitigation. Nevertheless, research on Latin American forests tend to focus on mitigation, while studies on agriculture are usually oriented towards adaptation ( ''high confidence'' ) ( [[#Locatelli--2015|Locatelli et al., 2015]] , 2017).

Rural communities in the Cusco region, Peru, ground their ability to adapt to climate change on four cultural values, known in Quechua as ''ayni'' (reciprocity), ''ayllu'' (collectiveness), ''yanantin'' (equilibrium) and ''chanincha'' (solidarity), but policies oriented towards so-called modernisation undermine these traditional mechanisms. Adaptation strategies could benefit from integrating these and other insights from traditional cultures, fostering risk reduction and transformational adaptation towards intrinsically sustainable systems ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Walshe--2016|Walshe and Argumedo, 2016]] ).

Protected areas have become an important component as enablers of national climate-change adaptation strategies. They increase ecosystems’ adaptive potential, reducing climate risk and delivering numerous ecosystem services and sustainable development benefits while playing an important role in climate-change mitigation ( ''high confidence'' ) ( [[#Mackey--2008|Mackey et al., 2008]] ; [[#Dudley--2010|Dudley et al., 2010]] ; [[#Gross--2016|Gross et al., 2016]] ; [[#Bebber--2017|Bebber and Butt, 2017]] ; [[#Dinerstein--2019|Dinerstein et al., 2019]] ; [[#IPCC--2019a|IPCC, 2019a]] ). CSA already has a greater percentage of land (24.1%) under protected status than the world average (14.7%) ( [[#UNEP-WCMC%20and%20IUCN--2020b|UNEP-WCMC and IUCN, 2020b]] ). Some countries, including Belize, Bolivia, Brazil, Guatemala, Nicaragua and Venezuela, have already met or surpassed the 30% CDB and IUCN goal ( [[#Dinerstein--2019|Dinerstein et al., 2019]] ), and others, like Costa Rica and Honduras, are very close to doing so. In some cases, the establishment of protected areas not accompanied by collective decision-making processes has displaced local people or denied them access to natural resources, increasing their vulnerability to climate change ( [[#Brockington--2015|Brockington and Wilkie, 2015]] ).

In addition to better managing and expanding protected area networks, other effective area-based conservation measures (OECMs), recently defined by the Parties to the Convention on Biological Diversity ( [[#Dudley--2018|Dudley et al., 2018]] ), could also enhance ecosystem resilience ( ''low confidence'' ). Private protected areas in the mountain regions of the Americas (e.g., Andes) play an important role in closing the gaps in fragmented biomes and expanding protection in underrepresented areas ( [[#Hora--2018|Hora et al., 2018]] ). In Brazil, there is also huge potential for conservation and sustainable management in private areas, as roughly 53% of the country’s native vegetation is within private land ( [[#Lapola--2014|Lapola et al., 2014]] ; [[#Soares-Filho--2014|Soares-Filho et al., 2014]] ).

Large-scale restoration is also seen as pivotal to limiting both climate change ( [[#IPCC--2019a|IPCC, 2019a]] ) and species extinction ( [[#IPBES--2018a|IPBES, 2018a]] ) ( ''very high confidence'' ). A new multi-criteria approach to optimising multiple restoration outcomes (for biodiversity, climate-change mitigation and cost), for example, indicate that SA has the greatest extension of converted lands, evenly distributed in the top 50% of global priorities ( [[#Strassburg--2020|Strassburg et al., 2020]] ).

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=== 12.5.2 Ocean and Coastal Ecosystems and Their Services ===

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Ocean and coastal ecosystems provide suitable habitats for a high number of species that support important local fisheries, the tourism sector and the regional economy ( ''high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-3#3.5|Section 3.5]] ; Table 3.9; [[#González--2017|González and Holtmann-Ahumada, 2017]] ; [[#Venerus--2017|Venerus and Cedrola, 2017]] ; [[#CEPAL--2018|CEPAL, 2018]] ; [[#Carvache-Franco--2019|Carvache-Franco et al., 2019]] ; SROCC [[IPCC:Wg2:Chapter:Chapter-5#5.4|Section 5.4]] , [[#Bindoff--2019|Bindoff et al., 2019]] ). There is ''high confidence'' that CSA ocean and coastal ecosystems are already being impacted by climate change (Figure 12.9, 12.10; Table SM12.3; [[IPCC:Wg2:Chapter:Chapter-3#3.4|Section 3.4]] ; [[IPCC:Wg2:Chapter:Chapter-5#5.4|Section 5.4]] in SROCC, [[#Bindoff--2019|Bindoff et al., 2019]] ) and are highly sensitive to non-climatic stressors (Figure 12.8; Table SM12.3; [[IPCC:Wg2:Chapter:Chapter-3#3.4|Section 3.4]] ). Projections for CSA ocean and coastal ecosystems warn about significant and negative impacts ( ''high confidence'' ), which include major loss of ecosystem structure and functionality, changes in the distributional range of several species and ecosystems, major mortality rates and increasing numbers of coral bleaching events (Figure 12.9; Figure 12.10; Table SM12.3; [[IPCC:Wg2:Chapter:Chapter-3#3.4|Section 3.4]] ; SROCC Sections 5.3, 5.4, [[#Bindoff--2019|Bindoff et al., 2019]] ).

CSA sub-regions are highly dependent on ocean and coastal ecosystems and, thus, vulnerable to climate change ( [[#FAO--2018|FAO, 2018]] ). Fisheries and aquaculture contribute significantly to food security and livelihoods by creating employment (more than two million people), income and economic growth for the region ( [[IPCC:Wg2:Chapter:Chapter-3#3.5|Section 3.5]] ; [[#FAO--2018|FAO, 2018]] ). More than 45% of the total fisheries in CSA are based on marine products ( [[#CEPALSTAT--2019|CEPALSTAT, 2019]] ). Peru, Chile, Argentina and Ecuador are among the 15 countries with the largest marine capture production worldwide ( [[#Gutiérrez--2016a|Gutiérrez et al., 2016a]] ; [[#FAO--2018|FAO, 2018]] ; [[#Vannuccini--2018|Vannuccini et al., 2018]] ), while more than 90% of the hydrological resources produced by aquaculture in CSA have a marine origin ( [[#CEPALSTAT--2019|CEPALSTAT, 2019]] ). There is ''high confidence'' about important current and future impacts of climate-change hazards in marine resources used by fisheries; however, there is ''low evidence'' regarding impacts on regional economies (Figure 12.9, 12.10; Table SM12.3).

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==== 12.5.2.1 Adaptation Measures and Strategies Applied to Oceans and Coasts of Central and South America ====

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Similar to those strategies pointed out by WGII AR5 Chapter 27 ( [[#Magrin--2014|Magrin et al., 2014]] ) and [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg2:Chapter:Chapter-3#3.5|Section 3.5]] ; [[IPCC:Wg2:Chapter:Chapter-3#3.6.2|Section 3.6.2]] ; Box SLR in Chapter 3), adaptation strategies in ocean and coastal ecosystems in CSA remain focused on ecosystem protection and restoration and the sustainable use of marine resources ( ''high confidence'' ). There is ''low evidence'' on how coastal urban areas and tourist settlements of CSA countries are adapting to SLR and extreme events ( [[#Calil--2017|Calil et al., 2017]] ; [[#Villamizar--2017|Villamizar et al., 2017]] ). Some of these strategies include planned relocation ( [[#Dannenberg--2019|Dannenberg et al., 2019]] ) and the use of grey infrastructures like seawalls and bulkheads ( [[#Silva--2014|Silva et al., 2014]] ; [[#Isla--2018|Isla et al., 2018]] ).

There is ''medium confidence'' that EbA is the main strategy used in CSA coral reef ecosystems. The set of strategies applied include the protection, restoration (e.g., coral gardening, larval propagation) and conservation of coral reef areas through the application of spatial ocean zoning schemes such as marine protected areas (MPAs), marine managed areas (MMAs), national parks, wildlife refuges, special zones of marine protection, special management zones, responsible fishing areas and the establishment of management plans with some level of participatory processes. These strategies are complemented by actions that promote the development of research and education programmes, recreational and cultural activities, the use of community-based approaches and the creation of national specific laws ( [[#Graham--2017|Graham, 2017]] ) and the adherence to international treaties (e.g., Convention on International Trade in Endangered Species of Wild Fauna and Flora [CITIES], AGENDA 21, United Nations Convention on the Law of the Sea (UNCLOS), Ramsar Convention on Wetlands of International Importance Especially as Waterfowl Habitat) ( [[#Cruz-Garcia--2015|Cruz-Garcia and Peters, 2015]] ; [[#Gopal--2015|Gopal et al., 2015]] ; [[#Graham--2017|Graham, 2017]] ; [[#Bayraktarov--2020|Bayraktarov et al., 2020]] ).

Adaptation measures in mangrove ecosystems are mainly focused on the application of EbA strategies ( ''high confidence'' ). These measures include the application of restoration programmes, the creation of management plans, which also have significant co-benefits with mitigation ( [[IPCC:Wg2:Chapter:Chapter-3#3.6.2.1|Section 3.6.2.1]] ), and the establishment of coastal protected areas, followed by the development of research activities and the creation of specific mangrove policies through new laws and resolutions (e.g., Colombia) ( [[#Cvitanovic--2014|Cvitanovic et al., 2014]] ; [[#Krause--2014|Krause, 2014]] ; [[#Blanco-Libreros--2015|Blanco-Libreros and Estrada-Urrea, 2015]] ; [[#Carter--2015|Carter et al., 2015]] ; [[#Estrada--2015|Estrada et al., 2015]] ; [[#Ferreira--2016|Ferreira and Lacerda, 2016]] ; Oliveira- [[#Filho--2016|Filho et al., 2016]] ; [[#Rodríguez-Rodríguez--2016|Rodríguez-Rodríguez et al., 2016]] ; [[#Alvarado--2017|Alvarado et al., 2017]] ; [[#Álvarez-León--2017|Álvarez-León and Álvarez Puerto, 2017]] ; [[#Baptiste--2017|Baptiste et al., 2017]] ; [[#Borges--2017|Borges et al., 2017]] ; [[#Jaramillo--2018|Jaramillo et al., 2018]] ; [[#Salazar--2018|Salazar et al., 2018]] ; [[#Armenteras--2019|Armenteras et al., 2019]] ; [[#Blanco-Libreros--2019|Blanco-Libreros and Álvarez-León, 2019]] ; [[#Maretti--2019|Maretti et al., 2019]] ; [[#Ellison--2020|Ellison et al., 2020]] ).

The use of territorial planning tools, the promotion of sustainable resource exploitation, the adherence to certification schemes and the implementation of management instruments, such as ecosystem-based management (EbM), followed by the use of an integrated coastal zone management, coastal marine spatial planning and capacity building, ecological risk assessments, have been the main strategies used to ensure the sustainability of marine resources in fisheries across EEZs of CSA ( ''high confidence'' ) ( [[#Hellebrandt--2014|Hellebrandt et al., 2014]] ; [[#Gelcich--2015|Gelcich et al., 2015]] ; [[#Singh-Renton--2015|Singh-Renton and McIvor, 2015]] ; [[#Gutiérrez--2016a|Gutiérrez et al., 2016a]] ; [[#Karlsson--2016|Karlsson and Bryceson, 2016]] ; [[#Oyanedel--2016|Oyanedel et al., 2016]] ; [[#Debels--2017|Debels et al., 2017]] ; [[#Isaac--2017|Isaac and Ferrari, 2017]] ; Mariano [[#Gutiérrez--2017|Gutiérrez et al., 2017]] ; [[#Barragán--2018|Barragán and Lazo, 2018]] ; [[#Bertrand--2018|Bertrand et al., 2018]] ; [[#Lluch-Cota--2018|Lluch-Cota et al., 2018]] ; [[#Guerrero-Gatica--2020|Guerrero-Gatica et al., 2020]] ). Other strategies include the application of local regulations (e.g., closed seasons) ( [[#Fontoura--2016|Fontoura et al., 2016]] ) and the use of participative programmes ( [[#Hellebrandt--2014|Hellebrandt et al., 2014]] ; [[#Arroyo%20Mina--2016|Arroyo Mina et al., 2016]] ; [[#Matera--2016|Matera, 2016]] ).

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==== 12.5.2.2 Adaptation Success in Ocean and Coastal Ecosystems of Central and South America ====

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There is ''low evidence'' about how the strategies and actions taken and implemented in ocean and coastal systems of CSA have contributed to advance in the protection and conservation of ocean and coastal ecosystems. However, some important advances are visible in Colombian Pacific areas with coral reefs (new conservation plans, research monitoring and conservation practices) ( ''low confidence'' ) ( [[#Cruz-Garcia--2015|Cruz-Garcia and Peters, 2015]] ; [[#Alvarado--2017|Alvarado et al., 2017]] ; [[#Bayraktarov--2020|Bayraktarov et al., 2020]] ). In Panama, actions taken have allowed the protection of a high number of marine areas with coral reefs, as well as the incorporation of management approaches that include several sectors such as fisheries, tourism, coral protection and coral conservation ( ''low confidence'' ) ( [[#Alvarado--2017|Alvarado et al., 2017]] ). In the case of Costa Rica, 80% of coral habitats are located inside of MPAs, multiple research coral-related activities have been performed, and several training activities have favoured the engagement of the local community in their protection against climate and non-climate hazards ( ''low confidence'' ) ( [[#Alvarado--2017|Alvarado et al., 2017]] ).

There is ''low evidence'' of how the incorporation of mangroves as Ramsar sites, the reforms of legislations (e.g., fines and stronger regulations), and the creation of reserves and private protection initiatives (e.g., Belize Association of Private Protected Areas BAPPA), and capacity-building projects or new educational programmes have promoted the protection of mangroves in CSA countries such as Honduras, Guatemala and Belize ( [[#Cvitanovic--2014|Cvitanovic et al., 2014]] ; [[#Carter--2015|Carter et al., 2015]] ; [[#Ellison--2020|Ellison et al., 2020]] ). In Brazil, between 75–84% of mangroves are under some level of protection which has improved the forest structures, and multiple research programmes (e.g., Mangrove Dynamics and Management, MADAM, and ‘GEF-Mangle’) have been developed ( ''medium confidence'' ) ( [[#Krause--2014|Krause, 2014]] ; Medeiros et al., 2014; [[#Estrada--2015|Estrada et al., 2015]] ; [[#Ferreira--2016|Ferreira and Lacerda, 2016]] ; Oliveira- [[#Filho--2016|Filho et al., 2016]] ; [[#Borges--2017|Borges et al., 2017]] ; [[#Maretti--2019|Maretti et al., 2019]] ; [[#Strassburg--2019|Strassburg et al., 2019]] ). In Colombia, research projects (e.g., Mangroves of Colombia Projects, MCP), the installation of a geographic information system for mangroves (e.g., SIGMA Sistema de Información para la Gestión de los Manglares en Colombia), surveillance monitoring plans (e.g., EGRETTA Herramientas para el Control y Vigilancia de los Manglares), and the establishment of protected areas have contributed to decrease loss of the mangrove forest ( ''high confidence'' ) ( [[#Blanco-Libreros--2015|Blanco-Libreros and Estrada-Urrea, 2015]] ; [[#Rodríguez-Rodríguez--2016|Rodríguez-Rodríguez et al., 2016]] ; [[#Álvarez-León--2017|Álvarez-León and Álvarez Puerto, 2017]] ; [[#Baptiste--2017|Baptiste et al., 2017]] ; [[#Jaramillo--2018|Jaramillo et al., 2018]] ; [[#Salazar--2018|Salazar et al., 2018]] ; [[#Armenteras--2019|Armenteras et al., 2019]] ; [[#Blanco-Libreros--2019|Blanco-Libreros and Álvarez-León, 2019]] ).

There is ''low evidence'' whether the establishment of MPAs and the creation of legal instruments have allowed the development of new research activities have increased the environmental awareness, decreased the illegal extraction, and improved the local coordination which have promoted the sustainable use of marine resources, and improved the community-government cooperation in marine ecosystems ( [[#Alvarado--2017|Alvarado et al., 2017]] ). The experience in countries like Chile demonstrates the importance of implementing robust management plans that guarantee the protection objectives and the sustainability through the implementation of EbA measures such as MPAs ( [[#Petit--2018|Petit et al., 2018]] ).

There is ''low confidence'' about how measures adopted are ensuring the sustainability of marine resources used by fisheries. In Peru, industrial fisheries follow an adaptive management approach (i.e., stock assessments, catch limits), while in Chile, small-scale fisheries of benthic-demersal resources is managed through the granting of exclusive territorial use rights (called TURFS) with established quotas defined by the central authority ( [[#Bertrand--2018|Bertrand et al., 2018]] ). In addition, MPAs in Chile play a key role in climate-change adaptation for fisheries ( ''medium confidence'' ) ( [[#Gelcich--2015|Gelcich et al., 2015]] ; [[#Petit--2018|Petit et al., 2018]] ), and an increasing amount of funds have been invested in initiatives to reduce the vulnerability of fishery and aquaculture sectors to climate change ( [[#OECD--2017|OECD, 2017]] ). Since 2016, Argentina has been developing a strategy to implement EbM on fisheries with support from the Global Environment Facility (GEF) programme. Also, Argentina and Chile are promoting the local consumption of seafood and the certification of its fishery products ( [[#OECD--2017|OECD, 2017]] ), while Brazil and Chile have advanced in their response to climate change through the development of new research studies and methodologies incorporating research institutions ( [[#Nagy--2015|Nagy et al., 2015]] ). Uruguay is incorporating stakeholders in its climate-change adaptation strategies ( ''low confidence'' ) ( [[#Nagy--2015|Nagy et al., 2015]] ), while Colombia is supporting the capacity building of fishers, promoting livelihood diversification to increase the resilience of the sector ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Hellebrandt--2014|Hellebrandt et al., 2014]] ; [[#Arroyo%20Mina--2016|Arroyo Mina et al., 2016]] ; [[#Matera--2016|Matera, 2016]] ). Chile and Peru have made certain advances in the development of guidelines for the management of the coast line and the implementation of the EbM, which has favoured the collaboration of diverse and multiple stakeholders (fishers, academics, municipal institutions), the development of outreach and educational activities, the creation of networks and the interests of other fishery communities to implement EbM ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Hellebrandt--2014|Hellebrandt et al., 2014]] ; [[#Gelcich--2015|Gelcich et al., 2015]] ; [[#Gutiérrez--2016a|Gutiérrez et al., 2016a]] ; [[#Oyanedel--2016|Oyanedel et al., 2016]] ; [[#Guerrero-Gatica--2020|Guerrero-Gatica et al., 2020]] ). In countries like Peru and Chile, there is an increasing presence of intergovernmental and international cooperation agencies, in addition to new funding (e.g., GEF) and projects (Inter-American Development, SPINCAM) related to change adaptation for the fishery sector ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Galarza--2015|Galarza and Kámiche, 2015]] ; [[#Barragán--2018|Barragán and Lazo, 2018]] ).

<div id="12.5.2.3" class="h3-container"></div>

<span id="national-climate-change-commitments-for-ocean-and-coasts"></span>
==== 12.5.2.3 National Climate Change Commitments for Ocean and Coasts ====

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Beyond the protection, conservation and climate-change adaptation strategies implemented on CSA ocean and coastal areas and their ecosystems, a high number of adaptation goals to address climate-change impacts on ocean and coastal ecosystems and their services are incorporated into most of the national climate-change adaptation commitments of CSA countries (Table 12.7).

<div id="_idContainer021" class="Figure"></div>

[[File:30870558b6410378baba00050cf7ffa9 IPCC_AR6_WGII_Figure_12_007.png]]

'''Figure 12.7 |''' '''Sectoral distribution of vulnerability levels to climate change for sub-regions.''' The vulnerability levels are based on studies that include: (a) databases with climate-change vulnerability indexes by country and sector, (b) studies that apply climate-change vulnerability indexes by sector at the local, national, regional or global scale, and (c) studies that define some vulnerability level based on the authors’ expert judgment.

'''Panel (a)''' shows the vulnerability and confidence levels for each sub-region.

'''Panel (b)''' indicates the references used and the level of vulnerability by sub-region. The numbers within the table indicate the reference used for the assessment in the following order: (1) [[#Aitken--2016|Aitken et al. (2016)]] ; (2) [[#Anderson--2018b|Anderson et al. (2018b)]] ; (3) Bañales-Seguel et al. (2018); (4) [[#Bouroncle--2017|Bouroncle et al. (2017)]] ; (5) [[#CAF--2014|CAF (2014)]] ; (6) Carrão et al. (2016); (7) [[#Donatti--2019|Donatti et al. (2019)]] ; (8) [[#Eguiguren-Velepucha--2016|Eguiguren-Velepucha et al. (2016)]] ; (9) [[#FAO--2020a|FAO (2020a)]] ; (10) [[#FAO--2020b|FAO (2020b)]] ; (11) [[#FAO--2021a|FAO (2021a)]] ; (12) [[#FAO--2021b|FAO (2021b)]] ; (13) [[#FAO--2021c|FAO (2021c)]] ; (14) [[#FAO--2021|FAO et al. (2021)]] ; (15) FAO and [[#ECLAC--2020|ECLAC (2020)]] ; (16) Ferreira Filho and Moraes (2015); (17) [[#Filho--2016|Filho et al. (2016)]] ; (18) Fuentes-Castillo et al. (2020); (19) [[#FSIN%20and%20Global%20Network%20Against%20Food%20Crisis--2021|FSIN and Global Network Against Food Crisis (2021)]] ; (20) [[#Global%20Health%20Security%20Index--2019|Global Health Security Index (2019)]] ; (21) [[#Godber--2014|Godber and Wall (2014)]] ; (22) Handisyde et al. (2017); (23) [[#Hannah--2017|Hannah et al. (2017)]] ; (24) [[#Immerzeel--2020|Immerzeel et al. (2020)]] ; (25) [[#Inform%20Risk%20Index--2021|Inform Risk Index (2021)]] ; (26) [[#Koutroulis--2019|Koutroulis et al. (2019)]] ; (27) Krishnamurthy et al. (2014); (28) [[#Lapola--2019a|Lapola et al. (2019a)]] ; (29) [[#Li--2018|Li et al. (2018)]] ; (30) [[#Lin--2020|Lin et al. (2020)]] ; (31) [[#Mansur--2016|Mansur et al. (2016)]] ; (32) [[#Martins--2017|Martins et al. (2017)]] ; (33) [[#Menezes--2018|Menezes et al. (2018)]] ; (34) [[#Nagy--2018|Nagy et al. (2018)]] ;35) [[#ND-Gain--2020|ND-Gain (2020)]] ; (36) [[#Northey--2017|Northey et al. (2017)]] ; (37) [[#Olivares--2015|Olivares et al. (2015)]] ; (38) [[#Pacifici--2015|Pacifici et al. (2015)]] ; (39) [[#Qin--2020|Qin et al. (2020)]] ; (40) [[#Romeo--2020|Romeo et al. (2020)]] ; (41) [[#Liu--2021|Liu and Chen (2021)]] ; (42) [[#Silva--2019b|Silva et al. (2019b)]] ; (43) [[#Soto%20Winckler--2019|Soto Winckler and Del Castillo Pantoja (2019)]] ; (44) [[#Soto--2019|Soto et al. (2019)]] ; (45) [[#Tomby--2019|Tomby and Zhang (2019)]] ; (46) [[#Venegas-González--2018b|Venegas-González et al. (2018b)]] ; (47) [[#Yeni--2017|Yeni and Alpas (2017)]] ; (48) Marengo et al. (2017); (49) Bedran-Martins et al. (2018); (50) [[#Confalonieri--2014a|Confalonieri et al. (2014a)]] . Detailed methodology can be found in SM12.2.

Current goals in national and sectoral adaptation plans attempt to promote research and monitoring (e.g., new research actions, modelling, knowledge management), the development of new legislative tools and policies (e.g., inter-institutional and territorial coordination, improvement of public policies), the conservation of ocean and coastal ecosystems and their biodiversity (e.g., creation of new MPAs, protection tools), the management of climate risks (e.g., warning systems), the management of productive activities (e.g., diversification of resources), the promotion of the construction of new infrastructure and technology (e.g., grey-green infrastructure [GGI]), the creation of new financial tools (e.g., types of insurance), improved the capacity building (e.g., education, awareness), water and residue management (e.g., sewage and freshwater availability), social inclusion (e.g., strategies to support vulnerable sectors, gender inclusion) and the incorporation of traditional practices (e.g., restoring traditional practices including Indigenous knowledge [IK]). However, the amount and type of adaptation goals differ enormously from country to country (Figure 12.12).

<div id="_idContainer033" class="Figure"></div>

[[File:132cb4a771760696aad3436e9a5196b7 IPCC_AR6_WGII_Figure_12_012.png]]

'''Figure 12.12 |''' '''Type and amount of adaptation goals identified in NAPs for ocean and coastal systems of CSA countries.'''

'''Table 12.7 |''' National plans with adaptation goals for ocean and coasts in CSA.

{| class="wikitable"
|-
! '''CSA co''' '''unt''' '''ry'''

! '''Adaptation initiative'''

! '''Year'''

|-
| Argentina

| Plan Nacional de Adaptación y Mitigación al Cambio Climático 1

| 2019

|-
| Brazil

| National Climate Change Adaptation Plan (Volume 1); General Strategies 2

| 2016

|-
|
| National Climate Change Adaptation Plan (Volume 2); Sectoral and thematic strategies 3

| 2016

|-
| Chile

| Plan Nacional de Adaptación al Cambio Climático 4

| 2014

|-
|
| Plan Sectorial de Adaptación al Cambio Climático en Biodiversidad 5

| 2014

|-
|
| Plan Sectorial de Adaptación al Cambio Climático en Pesca y Acuicultura 6

| 2015

|-
|
| Plan de Adaptación y Mitigación de los Servicios de Infraestructura al Cambio Climático 7

| 2017

|-
|
| Plan de Adaptación al Cambio Climático Sector Salud 8

| 2017

|-
| Colombia

| Plan Nacional de Adaptación al Cambio Climático 9

| 2016

|-
| Costa Rica

| Política Nacional de Adaptación al Cambio Climático 10

| 2018

|-
| Ecuador

| Plan Nacional de Cambio Climático 11

| 2015

|-
| El Salvador

| Plan Nacional de Cambio Climático 12

| 2015

|-
| Guatemala

| Plan de Acción Nacional de Cambio Climático 13

| 2018

|-
| Guyana

| Política de Adaptación y Plan de Implementación 14

| 2001

|-
| Honduras

| Plan Nacional de Adaptación al Cambio 15

| 2018

|-
| Nicaragua

| Plan de Adaptación a la Variabilidad y el Cambio Climático en el Sector Agropecuario, Forestal y Pesca 16

| 2013

|-
| Peru

| Plan Nacional de Adaptación al Cambio Climático del Peru 17

| 2021

|-
| Suriname

| Suriname National Adaptation Plan 18

| 2019

|-
| Uruguay

| Plan Nacional de Respuesta al Cambio Climático 19

| 2010

|-
| Belize

| Not Available

| 2019

|-
| Panamá

| Not Available

|
|-
| Venezuela

| Not Available

|
|}

References: 1 ( [[#Ministerio%20de%20Ambiente%20y%20Desarrollo%20Sostenible%20de%20la%20República%20de%20Argentina--2019|Ministerio de Ambiente y Desarrollo Sostenible de la República de Argentina, 2019]] ) 2 ( [[#Ministry%20of%20Environment%20of%20Brazil--2016a|Ministry of Environment of Brazil, 2016a]] ) 3 ( [[#Ministry%20of%20Environment%20of%20Brazil--2016b|Ministry of Environment of Brazil, 2016b]] ) 4 ( [[#Ministerio%20de%20Medio%20Ambiente%20de%20Chile--2014b|Ministerio de Medio Ambiente de Chile, 2014b]] ) 5 ( [[#Ministerio%20de%20Medio%20Ambiente%20de%20Chile--2014a|Ministerio de Medio Ambiente de Chile, 2014a]] ) 6 ( [[#Ministerio%20de%20Economía%20Fomento%20y%20Turismo%20de%20Chile--2015|Ministerio de Economía Fomento y Turismo de Chile, 2015]] ) 7 ( [[#Ministerio%20de%20Medio%20Ambiente%20de%20Chile--2017|Ministerio de Medio Ambiente de Chile, 2017]] ) 8 ( [[#Ministerio%20de%20Salud%20de%20Chile--2017|Ministerio de Salud de Chile, 2017]] ) 9 ( [[#Ministerio%20de%20Ambiente%20y%20Desarrollo%20Sostenible%20de%20Colombia--2016|Ministerio de Ambiente y Desarrollo Sostenible de Colombia, 2016]] ) 10 ( [[#Ministerio%20de%20Ambiente%20y%20Energía%20de%20la%20República%20de%20Costa%20Rica--2018|Ministerio de Ambiente y Energía de la República de Costa Rica, 2018]] ) 11 ( [[#Gobierno%20Nacional%20de%20la%20República%20del%20Ecuador--2015|Gobierno Nacional de la República del Ecuador, 2015]] ) 12 ( [[#Ministerio%20de%20Medio%20Ambiente%20y%20Recursos%20Naturales%20de%20El%20Salvador--2015|Ministerio de Medio Ambiente y Recursos Naturales de El Salvador, 2015]] ) 13 ( [[#Consejo%20Nacional%20de%20Cambio%20Climático%20y%20la%20Secretaría%20de%20Planificación%20y%20Programación%20de%20la%20Presidencia%20de%20Guatemala--2018|Consejo Nacional de Cambio Climático y la Secretaría de Planificación y Programación de la Presidencia de Guatemala, 2018]] ) 14 ( [[#National%20Ozone%20Action%20Unit%20of%20Guyana--2016|National Ozone Action Unit of Guyana, 2016]] ) 15 ( [[#Secretaría%20de%20Recursos%20Naturales%20y%20Ambiente%20del%20Gobierno%20de%20la%20República%20de%20Honduras--2018|Secretaría de Recursos Naturales y Ambiente del Gobierno de la República de Honduras, 2018]] ) 16 ( [[#Ministerio%20Agropecuario%20y%20Forestal%20de%20Nicaragua--2013|Ministerio Agropecuario y Forestal de Nicaragua, 2013]] ) 17 (Ministerio del Ambiente Gobierno del Perú, 2021) 18 ( [[#Government%20of%20Suriname--2019|Government of Suriname, 2019]] ) 19 ( [[#Ministerio%20de%20Vivienda%20Ordenamiento%20Territorial%20y%20Medio%20Ambiente%20de%20la%20República%20de%20Uruguay--2010|Ministerio de Vivienda Ordenamiento Territorial y Medio Ambiente de la República de Uruguay, 2010]] )

<div id="12.5.2.4" class="h3-container"></div>

<span id="limits-and-barriers-to-adaptation-in-ocean-and-coastal-ecosystems"></span>
==== 12.5.2.4 Limits and Barriers to Adaptation in Ocean and Coastal Ecosystems ====

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Although current NAPs and many other actions and strategies focus on improving the conservation and restoration of ocean and coastal ecosystems, as well as the suitability of marine resources throughout CSA, these measures are still not able to reduce the vulnerability and sensitivity of these ecosystems to climate-change hazards ( ''high confidence'' ) (Figure 12.6; Table SM12.3; [[#Leal%20Filho--2018|Leal Filho, 2018]] ; [[#Nagy--2019|Nagy et al., 2019]] ). There is ''high confidence'' that sandy beach ecosystems of CSA countries have suffered significant losses of dunes as a consequence of the construction of infrastructures that have caused interruptions in the natural dynamic of beaches, reducing protection against tides, waves, extreme events or tsunamis ( ''high confidence'' ) ( [[#Amaral--2016|Amaral et al., 2016]] ; [[#Bernardino--2016|Bernardino et al., 2016]] ; [[#González--2017|González and Holtmann-Ahumada, 2017]] ; [[#Obraczka--2017|Obraczka et al., 2017]] ). Also, adaptation measures to cope with SLR and coastal extreme events sometimes fail because they exacerbate coastal erosion and damage ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Spalding--2014|Spalding et al., 2014]] ; [[#Lins-de-Barros--2018|Lins-de-Barros and Parente-Ribeiro, 2018]] ). There is ''medium evidence'' but ''high agreement'' that the most serious barriers limiting the success of adaptation strategies in ocean and coastal systems in CSA stem from a lack of coordination (e.g., absence of participatory processes, overlapping among fishing and protection activities), lack of knowledge (e.g., poor monitoring, poor control and surveillance, no long-term studies) and lack of adequate metrics for evaluating adaptation actions informing decision makers hinder the continuity and adjustment of measures and lead to weak governance (e.g., perverse incentives, resource overexploitation, conflicts), a lack of financial resources and long-term commitments (e.g., crisis, lack of budgets, market fluctuations), weak policies, cultural constraints, poverty, low flexibility, lack of awareness of climate risks and lack of engagement by stakeholders ( [[#Leal%20Filho--2018|Leal Filho, 2018]] ; [[#Nagy--2019|Nagy et al., 2019]] ; [[#Moreno--2020b|Moreno et al., 2020b]] ; [[#Aburto--2021|Aburto et al., 2021]] ).

Some important limits and barriers have been detected for productive systems such as fisheries and tourism in CSA ( ''medium confidence: medium evidence, high agreement'' ). Major Brazilian fisheries do not follow an ecosystem approach to management, although some small-scale fisheries apply a precautionary approach ( [[#Singh-Renton--2015|Singh-Renton and McIvor, 2015]] ). The management of Peruvian artisanal (medium and small-scale) fisheries is minimal and is governed by a lack of regulations, control and management actions ( [[#Bertrand--2018|Bertrand et al., 2018]] ). In Argentina, recreational marine fisheries have been largely unregulated, and there exists a lack of monitoring programmes, which has contributed to the overexploitation of some key coastal stocks ( [[#Venerus--2017|Venerus and Cedrola, 2017]] ). Moreover, womenfisher in CSA are excluded of the decision-making processes ( [[#FAO--2016b|FAO, 2016b]] ; [[#Bruguere--2017|Bruguere and Williams, 2017]] ). Due to the lack of monitoring programmes, it is unknown how this tourist industry will respond to long-term changes driven by climate change ( [[#Weatherdon--2016|Weatherdon et al., 2016]] ).

<div id="12.5.2.5" class="h3-container"></div>

<span id="challenges-and-opportunities-1"></span>
==== 12.5.2.5 Challenges and Opportunities ====

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There is ''low evidence and high agreement'' that empowering local stakeholders (e.g., multi-lateral fisheries agreements) improves public awareness and simplifies regulations and increases the flexibility and sustainability of marine resources managed in fisheries under future scenarios ( [[#Weatherdon--2016|Weatherdon et al., 2016]] ; [[#Kalikoski--2019|Kalikoski et al., 2019]] ). Ecosystem-based fisheries management (EBFM) has emerged as a suitable tool to minimise the risk to climate change, avoid ecosystem degradation and related services ( [[#Gullestad--2017|Gullestad et al., 2017]] ). Further, when EBFM includes climate complexity and the relationships among species within the ecological systems it contributes to maintain long-term socioeconomic benefits ( [[#Long--2015|Long et al., 2015]] ). There is ''high confidence'' that EbA is more successful and feasible than hard coastal defences for the protection, management and restoration of ocean and coastal ecosystems and their resources ( [[#Spalding--2014|Spalding et al., 2014]] ; [[#González--2017|González and Holtmann-Ahumada, 2017]] ; [[#Scarano--2017|Scarano, 2017]] ).

There is ''high confidence'' that ecological and social resilience is improved by the presence of adequate metrics to evaluate adaptation measures to allow dynamic changes; and by increasing basic research and climate data ( [[#Moreno--2020b|Moreno et al., 2020b]] ). Resilience also increases with the existence of EWSs, improved local institutions, the construction of adequate infrastructure, major funding for capacity building and the enhanced engagement and empowerment of women ( [[#FAO--2016b|FAO, 2016b]] ; [[#Harper--2017|Harper et al., 2017]] ; [[#Frangoudes--2018|Frangoudes and Gerrard, 2018]] ; [[#Gallardo-Fernández--2018|Gallardo-Fernández and Saunders, 2018]] ; [[#Leal%20Filho--2018|Leal Filho, 2018]] ).

<div id="12.5.3" class="h2-container"></div>

<span id="water"></span>
=== 12.5.3 Water ===

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CSA is one of the regions most affected by current and future hydrological risks to water security with an increasing number of vulnerable people depending on water from mountains ( ''high confidence'' ) (Sections 4.3, 4.4, 4.5; [[#Immerzeel--2020|Immerzeel et al., 2020]] ; [[#Viviroli--2020|Viviroli et al., 2020]] ; [[#WWAP--2020|WWAP, 2020]] ). Adaptation to changing water availability is therefore a priority, but most efforts are documented only in the grey literature (e.g., governmental documents, project reports) with highly variable standards of quality and evidence. Most of the documented adaptation initiatives are in an early planning or implementation stage and evidence on successful outcomes is quite limited ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ). However, the growing number of adaptation initiatives across the CSA region has contributed to improved understanding of complex interlinkages of climate change, human vulnerabilities, local policies and feasible adaptation approaches ( [[#McDowell--2019|McDowell et al., 2019]] ).

<div id="12.5.3.1" class="h3-container"></div>

<span id="challenges-and-opportunities-2"></span>
==== 12.5.3.1 Challenges and Opportunities ====

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In several regions of CSA, water scarcity is a serious challenge to local livelihoods and economic activities. Regions that are (seasonally) dry, partly with large populations and increasing water demand, exhibit particularly significant water stress. These include the Dry Corridor in CA, coastal areas of Peru (SWS) and northern Chile (SWS), the Bolivian-Peruvian Altiplano (NWS, SAM), the Dry Andes of Central Chile (SWS), Western Argentina and Chaco in northwestern Paraguay (SES) and Sertão in northeastern Brazil (NES) ( ''high confidence'' ) ( [[#Kummu--2016|Kummu et al., 2016]] ; [[#Mekonnen--2016|Mekonnen and Hoekstra, 2016]] ; [[#Schoolmeester--2018|Schoolmeester et al., 2018]] ). In NWS and SWS, downstream areas are increasingly affected by decreasing and unreliable river runoff due to rapid glacier shrinkage ( ''high confidence'' ) (Table SM12.6; [[#Carey--2014|Carey et al., 2014]] ; [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Buytaert--2017|Buytaert et al., 2017]] ). Many regions in CSA rely heavily on hydroelectric energy, and as a result of rising energy demand, hydropower capacity is constantly being extended ( [[#Schoolmeester--2018|Schoolmeester et al., 2018]] ). Worldwide, SA features the second-fastest growth rate, with about 5.2 GW additional annual capacity installed in 2019 ( [[#IHA--2020|IHA, 2020]] ). This development requires additional water storage options, which entail the construction of large dams and reservoirs with important social-ecological implications. River fragmentation and corresponding loss of habitat connectivity due to dam constructions have been described for, for example, the NSA, SAM, NES and SES ( ''high confidence'' ) ( [[#Grill--2015|Grill et al., 2015]] ; [[#Anderson--2018a|Anderson et al., 2018a]] ), with important implications for freshwater biota, such as fish migration ( ''medium confidence'' ) ( [[#Pelicice--2015|Pelicice et al., 2015]] ; [[#Herrera-R--2020|Herrera-R et al., 2020]] ). Furthermore, examples in, for instance, NWS ( [[#Carey--2012|Carey et al., 2012]] ; [[#Duarte-Abadía--2015|Duarte-Abadía et al., 2015]] ; [[#Hommes--2018|Hommes and Boelens, 2018]] ) and SWS ( [[#Muñoz--2019b|Muñoz et al., 2019b]] ) showcase unresolved water-related conflicts between local villagers, peasant communities, hydropower operators and governmental institutions in a context of distrust and lack of water governance ( ''high confidence'' ).

Increasing water scarcity is also shaped by poor water quality, which has barely been assessed in CSA. Declining water quality can be observed, for example, due to intense agricultural and industrial activities in SWS, SES and SSA ( ''medium confidence'' ) ( [[#Mekonnen--2015|Mekonnen et al., 2015]] ; [[#Gomez--2021|Gomez et al., 2021]] ), mining in Andean headwaters (NWS, SWS and Western SAM) and tropical lowlands (eastern SAM and NSA) ( ''medium confidence'' ) ( [[#Bebbington--2015|Bebbington et al., 2015]] risk and climate resilience; [[#Vuille--2018|Vuille et al., 2018]] ), urban domestic use ( [[#Desbureaux--2019|Desbureaux and Rodella, 2019]] ), decreasing meltwater contribution ( [[#Milner--2017|Milner et al., 2017]] ) and acid rock drainages from recently exposed glacial sediments ( [[#Santofimia--2017|Santofimia et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ). The level of water pollution is often exacerbated by missing water treatment infrastructure and low governance levels ( ''medium confidence'' ) ( [[#Mekonnen--2015|Mekonnen et al., 2015]] ), with considerable negative implications for human health ( [[#Lizarralde%20Oliver--2016|Lizarralde Oliver and Ribeiro, 2016]] ).

Water scarcity risks are projected to affect a growing number of people in the near and mid-term future in view of growing water demand in most regions ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Veldkamp--2017|Veldkamp et al., 2017]] ; [[#Schoolmeester--2018|Schoolmeester et al., 2018]] ; [[#Viviroli--2020|Viviroli et al., 2020]] ), expected precipitation reductions in western and northern SAM and SWS ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Neukom--2015|Neukom et al., 2015]] ; [[#Schoolmeester--2018|Schoolmeester et al., 2018]] ), substantial vanishing of glacier extent in NWS, SAM and SWS (Table SM12.6; [[#Rabatel--2018|Rabatel et al., 2018]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ) and increasing evaporation rates in CA ( ''medium confidence'' ) ( [[#CEPAL--2017|CEPAL, 2017]] ). Furthermore, flood risk is a serious concern ( [[#Arnell--2016|Arnell et al., 2016]] ) and expected to increase, especially in NWS, SAM, SES and SWS in the mid- and long-term future ( ''high confidence'' ) ( [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ).

Risks of water scarcity and flood threaten people unevenly across the region. In CSA, about 26% (130 million people) of the population have no access to safe drinking water, and strong disparities prevail regarding its spatial distribution; for example, in Chile, 99% of the population have access, compared to 50% in Peru, 73% in Colombia, 52% in Nicaragua or 56% in Guatemala ( ''high confidence'' ) ( [[#UNICEF%20and%20WHO--2019|UNICEF and WHO, 2019]] ). Inequalities can be further exacerbated by unregulated or privately owned water rights and allocation systems (e.g., in Chile) ( [[#Muñoz--2020a|Muñoz et al., 2020a]] ). The most vulnerable people belong to low-income groups in rural areas and informal settlements of large urban areas ( ''high confidence'' ) ( [[#WWAP--2020|WWAP, 2020]] ).

Considerable uncertainties remain concerning future hydrological risks that strongly depend on the respective pathways of human intervention, management, adaptation and socioeconomic development. The combination of (seasonally) reduced water supply, growing water demand, declining water quality, ecosystem deterioration and habitat loss and low water governance could lead to increasing competition and conflict associated with high economic losses ( ''high confidence'' ) ( [[#Vergara--2007|Vergara et al., 2007]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Desbureaux--2019|Desbureaux and Rodella, 2019]] ). This situation threatens human water security in the long term and poses an increasing risk to adaptation success in CSA ( ''high confidence'' ) ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Huggel--2015b|Huggel et al., 2015b]] ; [[#Urquiza--2020a|Urquiza and Billi, 2020a]] ).

Important progress has been made on climate change and water management policies in combination with more inclusive stakeholder processes. For instance, the implementation of NDCs in most countries of the region provides an important baseline for improving water efficiency, quality and governance at a multi-sectoral level and, thus, long-term adaptation planning ( [[#UNEP--2015|UNEP, 2015]] ).

<div id="12.5.3.2" class="h3-container"></div>

<span id="main-concepts-and-approaches"></span>
==== 12.5.3.2 Main Concepts and Approaches ====

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Adaptation in the water sector includes a broad set of responses to improve and transform, for example, water infrastructure, ecosystem functions, institutions, capacity building and knowledge production, habits and culture and local-national policies ( [[IPCC:Wg2:Chapter:Chapter-4#4.6|Section 4.6]] ).

Most adaptive water management approaches in CSA centre around extending the water supply side, including large infrastructure projects. However, ‘hard path’ interventions are now strongly contested because negative effects exacerbate local water conflicts ( [[#Carey--2012|Carey et al., 2012]] ; [[#Boelens--2019|Boelens et al., 2019]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ), potentially leading to increasing water demand, vulnerabilities and water shortage risks ( [[#Di%20Baldassarre--2018|Di Baldassarre et al., 2018]] ), thereby limiting adaptive capacity ( ''high confidence'' ) ( [[#Ochoa-Tocachi--2019|Ochoa-Tocachi et al., 2019]] ). More integrated approaches focus on multiple uses of water storage with shared stakeholder vision, responsibilities, rights and costs, as well as risks and benefits, and often integrating water and risk management ( [[#Branche--2017|Branche, 2017]] ; [[#Haeberli--2017|Haeberli et al., 2017]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ). In this chapter, a feasibility assessment was carried out for six major dimensions of multi-use water storage for the entire CSA (Table 12.11). While geophysical and economic aspects allow for the implementation of water storage projects under a multi-use approach, the institutional, social and environmental dimensions pose a major barrier ( [[#12.5.3|Section 12.5.3]] ). Further demand-oriented approaches focus on incentives for the reduction of water use through changes in people’s habits, efficiency increase and smart water management ( [[#Gleick--2002|Gleick, 2002]] ). These are promoted in some regions, such as in CA and NWS (e.g., Colombia, Ecuador and Peru), to foster a sustainable water culture ( [[#Bremer--2016|Bremer et al., 2016]] ; [[#Paerregaard--2016|Paerregaard et al., 2016]] ).

Major emphasis has been placed on NbS, that is, catchment interventions that are inspired and supported by nature and leverage natural processes and ecosystem services to contribute to the improved management of water. NbS potentially enhance water infiltration, groundwater recharge and surface storage, contribute to disaster risk reduction and can replace or complement grey (i.e., conventionally built) infrastructure that is often socioenvironmentally contested ( [[#WWAP--2018|WWAP, 2018]] ). Some examples include the reactivation of ancestral infiltration enhancement systems in the Peruvian Andes (NWS) ( [[#Ochoa-Tocachi--2019|Ochoa-Tocachi et al., 2019]] ), the use of erosion control structures in the Bolivian Altiplano (SAM) ( [[#Hartman--2016|Hartman et al., 2016]] ) and the potential improvement of drinking water quality and flood risk reduction in urban areas of CSA ( [[#Tellman--2018|Tellman et al., 2018]] ) ( [[#12.5.5.3.2|Section 12.5.5.3.2]] ). Additionally, NbS in combination with ecosystem and community-based adaptation potentially generate important co-benefits, including increasing water security and the attenuation of social conflicts in Chile (SWS) ( [[#Reid--2018|Reid et al., 2018]] ), water conservation in coastal Peru (NWS) and flood protection in Guyana (NSA) ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Spencer--2017|Spencer et al., 2017]] ). However, the evaluation of implementation success of NbS is often hampered by limited evidence on actual benefits ( [[#WWAP--2018|WWAP, 2018]] ).

In recent years, the inclusion of IKLK in current adaptation baselines has attracted increasing attention, particularly in regions with a high share of Indigenous Peoples (NWS, SAN, SWS, NSA) ( ''high confidence'' ) ( [[#Reyes-García--2016|Reyes-García et al., 2016]] ; [[#Schoolmeester--2018|Schoolmeester et al., 2018]] ; [[#McDowell--2019|McDowell et al., 2019]] ). One example is the adapted use of agrobiodiversity when dealing with more frequent and intense tidal floods in the Amazon delta (NSA) ( [[#Vogt--2016|Vogt et al., 2016]] ). In another context, IKLK has been considered for the evaluation of water scarcity and GLOF risks in Peru (NWS) ( [[#Motschmann--2020b|Motschmann et al., 2020b]] ). Additionally, local citizen science-based initiatives ( [[#Buytaert--2014|Buytaert et al., 2014]] ; [[#Tellman--2016|Tellman et al., 2016]] ; [[#Njue--2019|Njue et al., 2019]] ) can support the production of multiple forms of knowledge with flexible and extensive data collection. Important questions centre around how to integrate IKLK and other types of knowledge from the early planning stages on, to achieve enhanced or transformational adaptation building on co-produced knowledge ( [[#Kates--2012|Kates et al., 2012]] ; [[#Klenk--2017|Klenk et al., 2017]] ). NbS combined with community engagement and integration of diverse knowledge can foster transformational adaptation of social-ecological systems ( [[#Palomo--2021|Palomo et al., 2021]] ).

<div id="12.5.3.3" class="h3-container"></div>

<span id="policies-governance-and-financing"></span>
==== 12.5.3.3 Policies, Governance and Financing ====

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National policies on climate change, water protection, regulation and management laws are important focal areas of adaptation in the water sector ( [[IPCC:Wg2:Chapter:Chapter-4#4.7|Section 4.7]] ). Notable in the jurisdiction field is the Glacier Protection Law in place in Argentina (2010–2019) and under construction in Chile (since 2005). This first glacier law in the world represents a milestone for high-mountain conservation but is also criticised for hindering effective disaster risk adaptation measures and excluding local socioeconomic needs ( [[#Anacona--2018|Anacona et al., 2018]] ). Furthermore, the first Framework Law on Climate Change was implemented in Peru (2018) and is under way in Colombia, Chile and Venezuela (Figure 12.13; Table SM12.6). Overarching regional institutions (e.g., OAS [2016] ''')''' and most countries in CSA promote a move towards more integrative and sustainable management of water resources through new legislation and financing mechanisms. For instance, new water laws that include principles of integrated water resource management (IWRM) have entered into force, for example, in Nicaragua (2007), Peru (2009), Ecuador (2014) and Costa Rica (2014), or are under way, such as in Colombia (since 2009). However, current realities in all regions show major challenges in implementing IWRM mechanisms and policies, related but not limited to political and institutional instabilities, governance structures, fragmented service provision, lack of economies of scale and scope, corruption and social conflicts ( ''high confidence'' ) ( [[#WWAP--2020|WWAP, 2020]] ).

<div id="_idContainer035" class="Figure"></div>

[[File:708f40e14087168944d643627a1993b1 IPCC_AR6_WGII_Figure_12_013.png]]

'''Figure 12.13 |''' '''Overview map of observed glacier changes, associated impacts, adaptation and policy efforts across the Andes.'''

'''(a)''' Selected impacts from glacier shrinkage.

'''(b)''' Selected adaptation efforts (see upper-right map for the location of each adaptation measure).

'''(c)''' Policies and glacier inventory: NDC = submission year(s) of Nationally Determined Contributions (u = update), CCL = climate change law, GLL = glacier law (i = initialised framework), INV = last national glacier inventory. The explicit mention of glaciers, snow and mountain ecosystems within each law/inventory is highlighted with the corresponding symbols (grey = has not come into force).

'''(d)''' Glacier area (km²) according to last national inventory.

'''(e)''' Glacier area change (%/year) according to baseline of last national inventory.

'''(f)''' Geodetic glacier mass balance in metres water equivalent per year (m w.e./year) and error estimate (±m w.e./year) retrieved from [[#Dussaillant--2019|Dussaillant et al. (2019)]] . nd = no data available. Further details can be found in the appendix in Table SM12.6.

Many water-related conflicts in CSA are rooted in inequitable water governance that excludes water users from decisions on water allocation ( ''high confidence'' ) ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Vuille--2018|Vuille et al., 2018]] ). In turn, inclusive water regimes leverage long-term adaptation planning. These have been addressed in some national strategies, such as in Brazil ( [[#Ministry%20of%20Environment%20of%20Brazil--2016a|Ministry of Environment of Brazil, 2016a]] ). At the local level, a decentralised and participatory bottom-up water governance model was induced by civil society and research institutions to foster rainwater harvesting technologies reducing drought risk in semiarid Brazil (NES) ( [[#Lindoso--2018|Lindoso et al., 2018]] ).

Water fund programmes can generate important co-benefits for sustainable development, contributing to improved governance and conservation of watershed systems in CSA. Nevertheless, only a few experiences have been evaluated as successful due to insufficient implementation, low decision-making ability of some stakeholder groups and poor evidence-based approaches ( ''medium confidence'' ) ( [[#Bremer--2016|Bremer et al., 2016]] ; [[#Leisher--2019|Leisher et al., 2019]] ). Furthermore, financing mechanisms that produce incentives for sustainable water management have been promoted, tested or implemented. PES for water provision represents such an example and such mechanisms have been implemented across CSA since the 1990s ( [[#Grima--2016|Grima et al., 2016]] ).

Only about 50–70% of required financial resources are currently allocated per year to meet the national targets in the water, sanitation and hygiene (WASH) sector for the Sustainable Development Goal (SDG) 6 agenda in several regions of CSA. This share drops down to less than 50% in NSA (Venezuela) and SES (Argentina, Uruguay, Paraguay), except for Panama in CA, which allocates more than 75% of the required financial resources. For the implementation of NbS, evidence suggests that the overall expenditure remains well below 1% of total investment in water resource management infrastructure ( [[#WWAP--2018|WWAP, 2018]] ). These funding deficits set important limitations on future water provision, adaptation to changing water resources and the achievement of the SDGs by 2030 ( ''high confidence'' ) ( [[#WHO--2017|WHO, 2017]] ).

<div id="12.5.3.4" class="h3-container"></div>

<span id="successful-adaptation-and-limitations"></span>
==== 12.5.3.4 Successful Adaptation and Limitations ====

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Although a growing body of adaptation initiatives exists for CSA, evidence on their effectiveness is scarce. In many parts of CSA the level of success of adaptation measures depends largely on the governance of projects and stakeholder-based processes and is closely related to their effectiveness, efficiency, social equity and sociopolitical legitimacy ( ''high confidence'' ) ( [[#Adger--2005|Adger et al., 2005]] ; [[#Rasmussen--2016b|Rasmussen, 2016b]] ; [[#Moulton--2021|Moulton et al., 2021]] ). Several PES experiences across CSA have been described as successful measures for watershed conservation and adaptation ( ''high confidence'' ). An example of success is the Quito water fund in Ecuador, which aims to improve the city’s water quality by integrating public and private stakeholder interests with ecosystem conservation and local community development since the 2000s ( [[#Bremer--2016|Bremer et al., 2016]] ; [[#Grima--2016|Grima et al., 2016]] ) (Case Study 12.6.1). At the same time, in Moyobamba in Peru, the development of a watershed protection programme was leveraged by a multi-stakeholder platform process that enabled deep social learning ( [[#Lindsay--2018|Lindsay, 2018]] ). In turn, initiatives that do not consider the entire set of social-ecological dimensions and dynamics of adaptation or unintentionally increase vulnerabilities of human or natural systems are at risk of leading to reduced outcomes ( [[#McDowell--2021|McDowell et al., 2021]] ) or maladaptation ( [[#Reid--2018|Reid et al., 2018]] ; [[#McDowell--2019|McDowell et al., 2019]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). However, systematic assessments of maladaptation in the water sector have barely been provided for CSA.

In CSA, only limited information on the limits of adaptation in relation to water is available, for instance on the possible path dependency of institutions and associated resistance to change ( [[#Barnett--2015|Barnett et al., 2015]] ). Examples of soft adaptation limits (i.e., options to avoid intolerable risks currently not available) include lack of trust and stakeholder flexibility, associated with unequal power relations that lead to reduced social learning and poor outcomes for improved water management, as reported in, for example, NWS ( [[#Lindsay--2018|Lindsay, 2018]] ). An example of hard adaptation limits (i.e., intolerable risks cannot be avoided) in the region is the loss of livelihoods and cultural values associated with glacier shrinkage in NWS ( [[#Jurt--2015|Jurt et al., 2015]] ).

Most barriers to advance adaptation in CSA correspond to soft limits associated with missing links of science–society–policy processes, institutional fragilities, pronounced hierarchies, unequal power relations and top-down water governance regimes ( ''high confidence'' ). One example is the abandonment of long-term hydrological monitoring sites within tropical Andean ecosystems (paramo) in Venezuela ( [[#Rodríguez-Morales--2019|Rodríguez-Morales et al., 2019]] ) due to the lack of governmental support during the political crisis. In that regard, the collection and availability of consistent hydroclimatic and socioeconomic data at adequate scales represent an important challenge in CSA. Major adaptation barriers are furthermore reported from central Chile in the context of a mega-drought since 2010, related to socioeconomic factors and a deficient bottom-up approach to informing and developing public policy ( [[#Aldunce--2017|Aldunce et al., 2017]] ). These gaps could be bridged by strengthening transdisciplinary approaches at the science–policy interface ( [[#Lillo-Ortega--2019|Lillo-Ortega et al., 2019]] ) with blended bottom-up and top-down adaptation to include scientific knowledge with impact and scenario assessments in local adaptation agendas ( [[#Huggel--2015b|Huggel et al., 2015b]] ). For instance, a new allocation rule for the Laja reservoir in southern Chile (SWS), based on consistent water balance modelling results, could inform policy and water management and potentially improve local water management and reduce water conflicts over the long term ( [[#Muñoz--2019b|Muñoz et al., 2019b]] ).

<div id="12.5.4" class="h2-container"></div>

<span id="food-fibre-and-other-ecosystem-products"></span>
=== 12.5.4 Food, Fibre and Other Ecosystem Products ===

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The CSA region globally has the greatest agricultural land and water availability per capita. With 15% of the world’s land area, it receives 29% of global precipitation and has 33% of globally available renewable resources ( [[#Flachsbarth--2015|Flachsbarth et al., 2015]] ). Agricultural commodities (coffee, bananas, sugar, soybean, corn, sugarcane, beef livestock) are some of the highest users of ecosystem resources such as land, water, nutrients and technology. These exports have gained importance in the past two decades as international trade and globalisation of markets have shaped the global agri-food system. However continuous overuse of the environment might account for resource depletion (deforestation, land degradation, nutrient depletion, pollution), affecting the natural capital base. The effects of climate change on humans, via ecological systems, exacerbate the impact related to the depletion of ecosystem services ( [[#Scholes--2016|Scholes, 2016]] ; [[#IPBES--2018b|IPBES, 2018b]] ; [[#Castaneda%20Sanchez--2019|Castaneda Sanchez et al., 2019]] ; [[#Clerici--2019|Clerici et al., 2019]] ; [[#Tellman--2020|Tellman et al., 2020]] ; [[#Pacheco--2021|Pacheco et al., 2021]] ).

<div id="12.5.4.1" class="h3-container"></div>

<span id="challenges-and-opportunities-3"></span>
==== 12.5.4.1 Challenges and Opportunities ====

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Even though several regions have seen significant improvements in food availability, many countries are also experiencing a declining trend in food self-sufficiency ( [[#Porkka--2013|Porkka et al., 2013]] ; [[#Rolando--2017|Rolando et al., 2017]] ). Drought conditions in CA and the Caribbean increased in line with climate model predictions ( [[#Herrera--2018a|Herrera et al., 2018a]] ). The direct social and economic consequences for the sector are evident in CA’s so-called Dry Corridor, with a growing dependence on food imports ( [[#Porkka--2013|Porkka et al., 2013]] ), and these degrees of dependency make the region more vulnerable to price variability, climatic conditions ( [[#Bren%20d’Amour--2016|Bren d’Amour et al., 2016]] ; [[#ECLAC--2018|ECLAC, 2018]] ) and, therefore, to food insecurity in the absence of adaptation actions ( ''high confidence'' ) ( [[#Porkka--2013|Porkka et al., 2013]] ; [[#Bren%20d’Amour--2016|Bren d’Amour et al., 2016]] ; López Feldman and Hernández [[#Cortés--2016|Cortés, 2016]] ; [[#Eitzinger--2017|Eitzinger et al., 2017]] ; [[#Imbach--2017|Imbach et al., 2017]] ; [[#Lachaud--2017|Lachaud et al., 2017]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#Niles--2018|Niles and Salerno, 2018]] ; [[#del%20Pozo--2019|del Pozo et al., 2019]] ; [[#Alpízar--2020|Alpízar et al., 2020]] ; [[#Anaya--2020|Anaya et al., 2020]] ).

Given these circumstances, some regions in CSA (Andes region and CA) will just meet, or fall below, the critical food supply/demand ratio for their populations ( [[#Bacon--2014|Bacon et al., 2014]] ; [[#Barbier--2018b|Barbier and Hochard, 2018b]] ). Meanwhile, the more temperate part of SA in the south is projected to have agricultural production surpluses ( ''low confidence'' ) ( [[#Webb--2016|Webb et al., 2016]] ; [[#Prager--2020|Prager et al., 2020]] ). The challenge for this region will be to retain the ability to feed and adequately nourish its internal population as well as making food supplies available to the rest of the world.

The access of other markets to the region’s agricultural products might be conditioned on the adoption of low-carbon-agriculture measures. Achieving net-zero emissions while improving standards of living will be possible but will also require developing transition policy frameworks to reach the target ( [[#Frank--2019|Frank et al., 2019]] ; [[#Mahlknecht--2020|Mahlknecht et al., 2020]] ; [[#Cárdenas--2021|Cárdenas et al., 2021]] ).

<div id="12.5.4.2" class="h3-container"></div>

<span id="governance-and-barriers-for-adaptation"></span>
==== 12.5.4.2 Governance and Barriers for Adaptation ====

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The governance of adaptation for CSA implies modifying agricultural, socioeconomic and institutional systems in response to and in preparation for actual or expected impacts of climate variability and change, to reduce harmful effects and exploit beneficial opportunities ( ''high confidence'' ). CSA agriculture has a diversity of systems and segments of producers. While small-scale farmers contribute significantly to food production and food security, especially in developing economies, they face global policies oriented towards global commodity markets ( [[#Knapp--2017|Knapp, 2017]] ; [[#Fernández--2019|Fernández et al., 2019]] ). Climate action initiatives that consider CSA’s high levels of poverty and inequality to reduce these pervasive problems are central for adaptation in the region ( [[#Crumpler--2020|Crumpler et al., 2020]] ; [[#Locatelli--2020|Locatelli et al., 2020]] ).

Since AR5, important advances at the institutional level have occurred based on the development and implementation of NAPs for the agriculture and forestry sector among countries. Adapting to climate change entails the interaction of decision makers, stakeholders and institutions at different scales of government, from local to national. The Climate-Adapted Sustainable Agriculture Strategy for the region of the Central American Integration System (EASAC) of the Central American Agricultural Council of Ministers of Agriculture constitutes a valuable example of how to undertake climate action in the agricultural sector, as a block of countries and in an intersectoral manner, to enhance results and make better use of resources ( [[#IICA--2019|IICA, 2019]] ).

In Brazil, the Low-Carbon Agriculture (LCA) programme (Programa ABC) funds practices for reducing GHG emissions in the sector ( [[#Government%20of%20Brazil--2012|Government of Brazil, 2012]] ), accounting for about 15% of the total agriculture official finance portfolio, although it faces challenges to advance ( [[#Souza%20Piao--2021|Souza Piao et al., 2021]] ). Costa Rica offers an example on how reforestation can help achieve Paris Agreement objectives. Reforestation through natural regeneration on abandoned pastures boosted forest cover from 48% in 2005 to 53.4% in 2010 ( [[#Reid--2019|Reid et al., 2019]] ; [[#Cárdenas--2021|Cárdenas et al., 2021]] ). Some key success factors included a strong institutional context, fiscal and financial incentives for reforestation, conservation measures such as payment for environmental services, cattle ranch subsidy reform and a historically strong enforcement and focus on land titles that favoured the restoration of lands. Uruguay offers another example, with the farm sector contribution of 32.8% of all exports and 73.8% of the country’s emissions, so decarbonisation is not just an environmental issue but an economic competitiveness one as well. In the Intended Nationally Determined Contributions (INDCs) submitted to the UNFCCC in 2015, Uruguay set a specific target for the agriculture sector to reduce enteric methane emissions intensity per kilogram of beef (live weight) by 33% to 46% in 2030 by improving efficiency of beef production by controlling the grazing intensity to increase animal intake, reproductive efficiency and daily weight gain ( [[#Picasso--2014|Picasso et al., 2014]] ).

It is relevant to create conditions for the development of sustainable agricultural practices in a framework where factors associated with climate have become important for producers, given recent experiences of drought and lack of water ( ''high confidence'' ) ( [[#Clarvis--2014|Clarvis and Allan, 2014]] ; [[#Roco--2016|Roco et al., 2016]] ; [[#Hurlbert--2017|Hurlbert and Gupta, 2017]] ; [[#Pérez-Escamilla--2017|Pérez-Escamilla et al., 2017]] ; [[#Cruz--2018|Cruz et al., 2018]] ; [[#Zúñiga--2021|Zúñiga et al., 2021]] ). Solutions that consider relevant drivers that have demonstrated a positive effect in diffusion of adaptation strategies are more efficient (Table 12.8). Some conditions, such as the promotion of education programmes, participation in cooperatives, credit access and land tenure security, can help. In the same line, in CSA some elements, such as technology and information access and local knowledge, reinforce climate-change adaptation ( [[#Khatri-Chhetri--2019|Khatri-Chhetri et al., 2019]] ; [[#Piggott-McKellar--2019|Piggott-McKellar et al., 2019]] ). As stated in Table 12.8, barriers of various origins persist in connection with climate-change adaptation in the region increasing the vulnerability of farming systems and rural livelihoods.

'''Table 12.8 |''' Recent studies related to climate-change adaptation of agricultural systems and its determinants in Central and South America region.

{| class="wikitable"
|-
! '''Reference'''

! '''Countries'''

! '''Sample size (n)'''

! '''Study approach'''

! '''Crop systems'''

! '''Adaptation strategies'''

! '''Main drivers promoting climate-change adaptation'''

! '''Main barriers limiting climate-change adaptation'''

! '''Main barriers detected'''

|-
| [[#de%20Souza%20Filho--2021|de Souza Filho et al. (2021)]]

| Brazil

| 175

| Quant.

| Cattle farmers

| Integrated crop-livestock and livestock-forestry systems

| Credit access, extension services

| Lack of resources

| Lack of agricultural market access strategies

|-
| [[#Magalhães--2021|Magalhães et al. (2021)]]

| Brazil

| 94

| Qual.

| Several crops

| Farm management

| Previous experience with risks

| Inadequate infrastructure, low purchasing power

| Opportunities limited by infrastructure

|-
| [[#Carrer--2020|Carrer et al. (2020)]]

| Brazil

| 175

| Quant.

| Several crops

| Agricultural insurance

| Schooling, technical assistance

| Higher risk propensity

| Limited financial market access

|-
| [[#Quiroga--2020|Quiroga et al. (2020)]]

| Nicaragua

| 212

| Quant.

| Coffee

| Several adaptation measures

| Farm size, awareness of climate change, schooling

| Limited access to rain water

| Absence of climate-change education

|-
| [[#Bro--2019|Bro et al. (2019)]]

| Nicaragua

| 236

| Quant.

| Coffee

| Crop,

soil and water

| Schooling, participation in cooperatives, radio

| Household size

| Institutional framework to promote cooperatives

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

| Venezuela and Colombia

| 73

| Qual.

| Several crops at high altitudes

| Irrigation management

| Perception of water scarcity, local knowledge

| Degradation of fragile areas

| Ineffectiveness of local institutions

|-
| Cherubin et al. (2019)

| Colombia

| 6

| Quant.

| Several crops and pasture

| Agroforestry systems

| Improving soil quality and biota

| Degradation of conventional pasture

| Lack of crop diversification

|-
| [[#Harvey--2018|Harvey et al. (2018)]]

| Costa Rica, Honduras and Guatemala

| 860

| Quant.

| Coffee, beans and maize

| Several adaptation practices

| Awareness of climate change

| Affordability of adaptation practices

| Lack of adaptation involving agroecological and socioeconomic contexts

|-
| [[#Chen--2018|Chen et al. (2018)]]

| Costa Rica and Nicaragua

| 559

| Quant.

| Several crops

| Intensification and diversification

| Access to weather information, participation in organisations, credit access, farming experience

| Land renting

| Lack of crop and practices diversification

|-
| [[#Vidal%20Merino--2019|Vidal Merino et al. (2019)]]

| Peru

| 137

| Quant.

| Several crops

| Water management

| Farm size, capital, irrigated proportion

| Limited access to off-farm activities, small cultivated area

| Lack of site-specific design of interventions

|-
| [[#Meldrum--2018|Meldrum et al. (2018)]]

| Bolivia

| 193

| Quant.

| Potato, quinoa and others

| Diversification of crop portfolio

| Weather information

| Loss to traditional knowledge

| Lack of resilience and actions to expand and maintain variety portfolio

|-
| [[#Lan--2018|Lan et al. (2018)]]

| Nicaragua

| 180

| Quant.

| Cocoa

| Crop management

| Schooling, household size, farm size

| Lack of income

| Income inequality, gaps of profitability of practices, benefits of practices depends on costs

|-
| [[#Kongsager--2017|Kongsager (2017)]]

| Belize

| 125

| Qual.

| Maize

| Alley cropping

| Schooling

| Land tenure, market distance, degradation of fragile areas

| Lack of land tenure, lack of market access, lack of trust

|-
| [[#Schembergue--2017|Schembergue et al. (2017)]]

| Brazil

| 5485 a

| Quant.

| Several crops

| Agroforestry systems

| Financing, presence of associations, credit access

| High potential for agriculture, lack of climate information

| Adaptation conditioned by agricultural, socioeconomic and climatic conditions

|-
| [[#Harvey--2017|Harvey et al. (2017)]]

| Guatemala, Honduras and Costa Rica

| 300

| Quant.

| Coffee and maize

| Ecosystem-based adaptation

| Schooling, age, farming experience, access to technological support

| Lack of land tenure

| Lack of access to training and finance

|-
| [[#Roco--2016|Roco et al. (2016)]]

| Chile

| 665

| Quant.

| Several crops

| Water management

| Farm size, access to weather information

| Locations, age

| Lack of availability and access to climate-change information

|-
| [[#Mussetta--2015|Mussetta and Barrientos (2015)]]

| Argentina

| 41

| Qual.

| Vine and others

| Crop and water management

| Organisation of producers, labour availability, knowledge and information access, technology access

| Water allocation system

| Lack of water management and distribution strategies

|}

Notes:

(a) municipalities; Quant.: mainly quantitative; Qual.: mainly qualitative.

Limited information regarding cost-benefit analyses of adaptation is available in the region and regarding avoiding maladaptation effects and promoting site-specific and dynamic adaptation options considering available technologies ( ''medium confidence'' ) ( [[#Roco--2017|Roco et al., 2017]] ; [[#Zavaleta--2018|Zavaleta et al., 2018]] ; [[#Ponce--2020|Ponce, 2020]] ; [[#Shapiro-Garza--2020|Shapiro-Garza et al., 2020]] ).

Climate information services has an important role in climate-change adaptation and there is a recognised gap between climate science and farmers ( ''high confidence'' ) ( [[#Vaughan--2017|Vaughan et al., 2017]] ; [[#Loboguerrero--2018|Loboguerrero et al., 2018]] ; [[#Tall--2018|Tall et al., 2018]] ; [[#Thornton--2018|Thornton et al., 2018]] ; [[#Ewbank--2019|Ewbank et al., 2019]] ). Such services should address the challenges of ensuring that climate information and advisory services are relevant to the decisions of smallholder and family farmers, providing timely climate service access to remote rural communities with marginal infrastructure and ensuring that farmers own climate services and shape their design and delivery. An interesting case facing this gap is the implementation of local technical agro-climatic committees in Colombia, which make it possible to share and validate climatic and weather forecasts, as well as crop model results for seasonal drought events ( [[#Loboguerrero--2018|Loboguerrero et al., 2018]] ). Another example is the web service AdaptaBrasil-MCTI, which forecasts the risk of climate-change impacts on strategic sectors (e.g., food, energy, water) in Brazil ( [[#Government%20of%20Brazil--2021|Government of Brazil and Ministry of Science Technology and Innovation Secretariat of Policies and Programs, 2021]] ).

Barriers to financial access are present in the region, restricting effective adaptation to extreme weather events ( ''high confidence'' ) ( [[#Chen--2018|Chen et al., 2018]] ; [[#Fisher--2019|Fisher et al., 2019]] ; [[#Piggott-McKellar--2019|Piggott-McKellar et al., 2019]] ; [[#Vidal%20Merino--2019|Vidal Merino et al., 2019]] ; [[#de%20Souza%20Filho--2021|de Souza Filho et al., 2021]] ). In 2014, the penetration rate of this type of insurance in the region averaged 0.03% of GDP, and a few countries dominate the market (Brazil, Argentina). Beyond these countries, some initiatives also exist in Uruguay, Paraguay, Chile and Ecuador. In most Latin American and Caribbean countries, the public sector plays an important role in providing insurance or reinsurance and coexists with private-sector companies ( [[#Cárdenas--2021|Cárdenas et al., 2021]] ). Insurance protections represent a strategy to transfer climate risk to protect the well-being of vulnerable small farmers and accelerate uptake (recovery) after a climate-related extreme weather event. Lack of finance and proper infrastructure is compounded by limited knowledge of sustainable farming practices and high rates of financial illiteracy ( ''high confidence'' ) ( [[#Hurlbert--2017|Hurlbert and Gupta, 2017]] ; [[#Piggott-McKellar--2019|Piggott-McKellar et al., 2019]] ).

Insufficient access to digital services and technologies further widens the gap between the rural poor and more urban populations of Latin America and the Caribbean ( ''medium confidence: insufficient evidence, high agreement'' ). In turn, these factors compromise productivity and competitiveness. Support for the rural poor can be focused on both economic competitiveness and social development. Finally, to align the identified adaptation options as a priority for achieving future food security in the NDCs of CSA countries to mitigation commitments, it will be essential to highlight synergies by generating evidence (national research) in relation to progress towards increasing productivity and resilience and reducing GHG, in addition to demonstrating its added value as a development initiative ( [[#Rudel--2015|Rudel et al., 2015]] sustainable; [[#Loboguerrero--2019|Loboguerrero et al., 2019]] ).

<div id="12.5.4.3" class="h3-container"></div>

<span id="adaptation-options"></span>
==== 12.5.4.3 Adaptation Options ====

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To contextualise the adaptation options at the regional level, the majority of the NDCs of the CSA countries reported observed and/or projected climate-related hazards: occurrence of droughts and floods (80% of countries each), followed by storms (45%) and landslides (30%), as well as extreme heat, wildfire and invasion by pests and non-native species in agriculture (25% each) ( [[#Crumpler--2020|Crumpler et al., 2020]] ).

The main adaptation options for climate change in the region include preventive measures against soil erosion; climate-smart agriculture, which provides a framework for synergies between adaptation, mitigation and improved food security; climate information systems; land use planning; shifting plantations at high altitudes to avoid temperature increases and plagues; and improved varieties of pastures and cattle ( [[#Lee--2014|Lee et al., 2014]] ; [[#Jat--2016|Jat et al., 2016]] ; [[#Crumpler--2020|Crumpler et al., 2020]] ; [[#Moreno--2020a|Moreno et al., 2020a]] ; [[#Aragón--2021|Aragón et al., 2021]] ). Agricultural technologies are not necessarily changing, but the economic activity is shifting to accommodate increasing climate variation and adapt to changes in water availability and ideal growing conditions ( ''high confidence'' ), as is observed in Argentina, Colombia and Brazil ( [[#McMartin--2018|McMartin et al., 2018]] ; [[#Rolla--2018|Rolla et al., 2018]] ; [[#Sloat--2020|Sloat et al., 2020]] ; [[#Gori%20Maia--2021|Gori Maia et al., 2021]] ). Coffee plantations are moving further up mountain regions, with the land at lower elevations converted for other uses. In Brazil, crop modelling suggests the need for the development of new cultivars, with a longer crop cycle and with higher tolerance to high temperatures, a necessary technological advance for maize, an essential staple crop, to be produced in the future. Additionally, irrigation becomes essential for sustaining productivity in adverse climate-change scenarios in several regions of CSA ( [[#McMartin--2018|McMartin et al., 2018]] ; [[#Lyons--2019|Lyons, 2019]] ; [[#Reay--2019|Reay, 2019]] ).

Livestock production is for small farmers one of the main sources of protein and contributes to food security ( [[#Rodríguez--2016|Rodríguez et al., 2016]] ). The importance of this sub-sector in CSA will continue to increase as the demand for meat products does as well in the coming years, driven by growing incomes in the region ( [[#OECD%20and%20FAO--2019|OECD and FAO, 2019]] ). However, the increase in animal production has been associated with land degradation, triggered by the conversion of native vegetation to pastureland and aggravated by overgrazing and abandoning of the degraded pastures ( [[#Baumann--2017|Baumann et al., 2017]] ; [[#ECLAC--2018|ECLAC, 2018]] ; [[#Müller-Hansen--2019|Müller-Hansen et al., 2019]] ). Sá et al. (2017) simulated the adoption of agricultural systems based on LCA strategies towards 2050. According to the simulation, the adoption of LCA strategies in the SA region can alter the growing trend of land use and land use change emissions, and at the same time, it can increase meat production by 55 Mt for the entire period (2016–2050). The restoration of degraded pasture and livestock intensification account for 71.2% and integrated crop–livestock–forestry system contributes 28.8% of total meat production for the entire period. These results indicate that combined actions in agricultural management systems in SA can result in synergistic responses that can be used to make agriculture and livestock production an important part of the solution of global climate change and advance food security ( ''medium confidence: insufficient evidence and high agreement'' ) ( [[#Zu%20Ermgassen--2018|Zu Ermgassen et al., 2018]] ; [[#Pompeu--2021|Pompeu et al., 2021]] ). Crop–livestock–forestry systems are also important for climate-change adaptation as they provide multiple benefits, including the coproduction of food, animal feed, organic fertilizers and soil organic carbon sequestration ( [[#Sharma--2016|Sharma et al., 2016]] ; [[#Rodríguez--2021|Rodríguez et al., 2021]] ), achieving mitigation and adaptation goals ( ''high confidence'' ) ( [[#Picasso--2014|Picasso et al., 2014]] ; [[#Modernel--2016|Modernel et al., 2016]] , 2019; [[#Rolla--2019|Rolla et al., 2019]] ; [[#Locatelli--2020|Locatelli et al., 2020]] ). A recent analysis of agroforestry in Brazil showed positive and relevant impacts on the heads/pasture area rate in livestock production and that the system may have also stimulated a shift towards other production activities with higher gross added value ( [[#Gori%20Maia--2021|Gori Maia et al., 2021]] ). Agroforestry has also proven to have protective benefits to obtain more stable, less fluctuating yields due to climate-related damage in coffee production ( ''high confidence'' ) ( [[#Bacon--2017|Bacon et al., 2017]] ; [[#Durand-Bessart--2020|Durand-Bessart et al., 2020]] ; [[#Ovalle-Rivera--2020|Ovalle-Rivera et al., 2020]] ). In the same way, the production of plant-based fibre can be less vulnerable to economic and climatic variability through farming system diversification. Textile fibre crops for the case of cotton include crop rotation, agroecological intercropping and agroforestry ( [[#Oliveira%20Duarte--2019|Oliveira Duarte et al., 2019]] ).

Adaptation strategies also concern Indigenous agriculture, that is, the vast majority of the 44 million Amerindians ( [[#CEPAL--2014|CEPAL, 2014]] ). IKLK can play an important role in adaptation ( [[#Zavaleta--2018|Zavaleta et al., 2018]] ). On one hand, they ensure the conservation of a very rich agrobiodiversity that is likely to meet the challenges of climate change ( ''high confidence'' ) ( [[#Carneiro%20da%20Cunha--2017|Carneiro da Cunha and Morim de Lima, 2017]] ; [[#Magni--2017|Magni, 2017]] ; [[#Emperaire--2018|Emperaire, 2018]] ; [[#Donatti--2019|Donatti et al., 2019]] ), while on the other hand, the sustainability of large territories that assure their livelihood ( [[#Singh--2017|Singh and Singh, 2017]] ; [[#Mustonen--2021|Mustonen et al., 2021]] ). In the Andes, ancient technologies increased the quantity of crops produced and made it possible to cope with climatic changes and water scarcity, while nutrition conditions were improved ( ''high confidence'' ) (López Feldman and Hernández [[#Cortés--2016|Cortés, 2016]] ; [[#Parraguez-Vergara--2018|Parraguez-Vergara et al., 2018]] ; [[#Carrasco-Torrontegui--2020|Carrasco-Torrontegui et al., 2020]] food). Also, fire prevention management and protection against forest and biodiversity loss are recognised as important elements in IK ( [[#Mistry--2016|Mistry et al., 2016]] ; [[#Bowman--2021|Bowman et al., 2021]] ).

<div id="12.5.5" class="h2-container"></div>

<span id="cities-settlements-and-infrastructure"></span>
=== 12.5.5 Cities, Settlements and Infrastructure ===

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CSA is the second most urbanised region of the world, with 5 megacities and half of the urban population in 129 secondary cities ( [[#UNDESA--2019|UNDESA, 2019]] ), huge metropolitan areas concentrated on the coast and an increasing number of small cities by the sea ( [[#Barragán--2016|Barragán and de Andrés, 2016]] ). Besides the many climatic events threatening urban areas in the region (extreme heat, droughts, heavy storms, floods, landslides), cities by the coast are also exposed to SLR ( [[#12.3|Section 12.3]] ; Figure 12.6; [[#Dawson--2018|Dawson et al., 2018]] ; [[#Leal%20Filho--2018|Leal Filho et al., 2018]] ; [[#Le--2020|Le, 2020]] ). The main determinants of urban vulnerability assessed in the region are poor and unevenly distributed infrastructure, housing deficits and informality, poverty and the occupation of risk areas, including LECZs ( [[#12.3|Section 12.3]] ). Those features of urban systems increase the risks to health, ecosystems and its services, water, food and energy supplies ( [[#12.4|Section 12.4]] ). Impacts of climate events on urban water supply, drainage and sewer infrastructures are the most frequently reported in the region ( [[#12.3|Section 12.3]] ; Figure 12.9).

<div id="12.5.5.1" class="h3-container"></div>

<span id="challenges-and-opportunities-4"></span>
==== 12.5.5.1 Challenges and Opportunities ====

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The inequality, poverty and informality shaping cities in the region increase vulnerability to climate change ( ''high confidence'' ) ( [[#Romero-Lankao--2014|Romero-Lankao et al., 2014]] ; [[#Rasch--2017|Rasch, 2017]] ; [[#Filho--2019|Filho et al., 2019]] ) and can hinder adaptation ( [[#12.5.7.1|Section 12.5.7.1]] ), while interventions addressing these social challenges and the existing development deficits (e.g., build or improve infrastructure and housing applying climate-adapted patterns) can go hand in hand with adaptation and mitigation ( ''medium confidence: high agreement, medium evidence'' ) ( [[#12.5.7.3|Section 12.5.7.3]] ; [[#Creutzig--2016|Creutzig et al., 2016]] ; [[#Le--2020|Le, 2020]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ). Over 20% of the urban population in Latin America and the Caribbean live in slums and many other forms of precarious and segregated neighbourhoods, settled in risk areas and lacking infrastructure ( [[#Rasch--2017|Rasch, 2017]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ; [[#Rojas--2019|Rojas, 2019]] ). This vulnerable condition is boosted by unstable political and governmental institutions, which suffer from ongoing corruption, weak governance and reduced capacity to finance adaptation ( [[#Rasch--2016|Rasch, 2016]] ). Facing governance challenges by including diverse stakeholders and encouraging and learning from community-based experiences has also presented the opportunity to improve adaptation strategies ( [[#Archer--2014|Archer et al., 2014]] ). An example of this is the Regional Climate Change Adaptation Plan of Santiago ( [[#Krellenberg--2014|Krellenberg and Katrin, 2014]] ).

<div id="12.5.5.2" class="h3-container"></div>

<span id="governance-and-financing-1"></span>
==== 12.5.5.2 Governance and Financing ====

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The lack of a high multi-level and intersectoral governance capacity with strong multi-player horizontal and vertical coordination and long-term support limit adaptation in the region ( ''high confidence'' ) ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Bai--2016|Bai et al., 2016]] ; [[#Chu--2016|Chu et al., 2016]] ; [[#Schaller--2016|Schaller et al., 2016]] ; [[#Miranda%20Sara--2017|Miranda Sara et al., 2017]] ). The ability to enrol stakeholders and include community-based initiatives can determine adaptation success, particularly considering their impact in the decision-making arena ( ''high confidence'' ) ( [[#12.5.8.1|Section 12.5.8.1]] ; [[IPCC:Wg2:Chapter:Chapter-6#6.4|Section 6.4]] ; [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Archer--2014|Archer et al., 2014]] ; [[#Chu--2017|Chu et al., 2017]] ; [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ).

Lima’s Climate Action Strategy is an example ( [[#Metropolitan%20Municipality%20of%20Lima--2014|Metropolitan Municipality of Lima, 2014]] ). It was approved following a participatory and consultative process with the technical group on climate change from the Metropolitan Environmental Commission, focusing on the reduction of water vulnerabilities to drought and heavy rain, on the basis of which 10 (out of 51 with Callao) Lima district municipalities are developing and starting to implement adaptation measures ( [[#Foro%20Ciudades%20Para%20la%20Vida--2021|Foro Ciudades Para la Vida, 2021]] ). In 2021 the municipality of Lima also approved its Local Climate Change Plan ( [[#Metropolitan%20Municipality%20of%20Lima--2021|Metropolitan Municipality of Lima, 2021]] ) under a similar process. The engagement of local players was central to spreading and mobilising different types of knowledge and creating networks able to support adaptation ( [[#12.6.3|Section 12.6.3]] ; [[#Miranda%20Sara--2014|Miranda Sara and Baud, 2014]] ; [[#Miranda%20Sara--2017|Miranda Sara et al., 2017]] ). The inclusive process is also a goal based on the example of Chile Municipalities Network Facing Climate Change (RedMuniCC) engaged in developing participatory strategic plans for climate adaptation and mitigation ( [[#RedMuniCC--2021|RedMuniCC, 2021]] ).

New forms of financing and leadership focused on community-based approaches have been developed to overcome the funding challenge and enable adaptation in the region ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Castán%20Broto--2013|Castán Broto and Bulkeley, 2013]] ; [[#Archer--2014|Archer et al., 2014]] ; [[#Paterson--2019|Paterson and Charles, 2019]] ). Systems for measuring, reporting and verifying adaptation financing, as in Colombia ( [[#Guzmán--2018|Guzmán et al., 2018]] ), or a national legislation geared towards adaptation, can also help access funds. The Peruvian Law on the Retribution Mechanism of Eco-Systemic Services and Code ( [[#Miranda%20Sara--2014|Miranda Sara and Baud, 2014]] ; [[#MINAM%20Peru--2016|MINAM Peru, 2016]] ) in addition to the Ley Marco de la Gestión y Prestación de los Servicios de Saneamiento and Its Code (Ministerio de Vivienda, Construcción y Saneamiento de Perú, 2017), allowed potable water companies to add 1% to the bill to guarantee ecosystem services, water treatment and reuse with GI. Another 4% of bill go to developing and implementing adaptation plans and measures ( [[#Government%20of%20Peru--2016|Government of Peru, 2016]] ).

<div id="12.5.5.3" class="h3-container"></div>

<span id="adaptation-options-in-urban-design-and-planning"></span>
==== 12.5.5.3 Adaptation Options in Urban Design and Planning ====

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Both the shape and activities of a city have an impact on carbon emissions, adaptation and mitigation opportunities ( ''high confidence'' ) ( [[#Raven--2018|Raven et al., 2018]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ). Combining urgent measures, strategic action ( [[#Chu--2017|Chu et al., 2017]] ) on long-term planning is central for transformative adaptation and avoiding maladaptation ( [[#Filho--2019|Filho et al., 2019]] ). Urban planning, considering climate risk assessments, and regulation (e.g., land use and building codes), including climate-adapted parameters, are central to coordinating and fostering private and public investments in adaptation, reducing risks related to features of the built environment (infrastructure and buildings) and the occupation of risk areas (e.g., threatened by floods and landslides) ( [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ). A lack of information at the local scale, human resources and clear liability for climate-change response planning can limit adaptation ( [[#Aylett--2015|Aylett, 2015]] ).

Strategic adaptation approaches have been adopted by many cities in dealing with the multi-level and intersectoral complexity of urban systems, with gains in fostering leadership and facing the predominant pattern of uneven urban development in the region ( ''medium confidence: limited evidence, high agreement'' ) ( [[#Chu--2017|Chu et al., 2017]] ). Medellin’s metropolitan green belt, for example, focuses on problems such as irregular settlements, inequality and poor governance, formulating programmes and projects of the municipality of Medellin and the municipalities of the Vale do Aburra in a strategic long-term plan. Places with informal and precarious settlements were slated to be transformed with the belt’s integration areas: eco-parks and eco-gardens ( [[#Alcaldía%20de%20Medellín--2012|Alcaldía de Medellín, 2012]] ; [[#Chu--2017|Chu et al., 2017]] ).

<div id="12.5.5.3.1" class="h4-container"></div>

<span id="housing-informality-and-risk-areas"></span>
===== 12.5.5.3.1 Housing, Informality and Risk Areas =====

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Informality and precariousness in housing is one of the most sensitive issues for adaptation in CSA cities ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ). Housing deficit in 2009, as a regional baseline, estimated that 37% of households suffered from quantitative or qualitative deficiencies due to the high cost of housing and the incidence of poverty ( [[#Blanco%20Blanco--2014|Blanco Blanco et al., 2014]] ; [[#McTarnaghan--2016|McTarnaghan et al., 2016]] ; NU CEPAL et al., 2016; [[#Vargas--2018a|Vargas et al., 2018a]] ; [[#Rojas--2019|Rojas, 2019]] ).

Policies and programmes have been implemented accumulating good practices and reducing the percentage of population in informal and precarious settlements (33.7% in 1990 to 21% in 2014) (NU CEPAL et al., 2016; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ; [[#Teferi--2018|Teferi and Newman, 2018]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ). Slum upgrading and built-environment interventions (housing and infrastructure improvement and provision) in informal settlements can enhance adaptation ( ''high confidence'' ) ( [[#Teferi--2018|Teferi and Newman, 2018]] ; [[#Núñez%20Collado--2020|Núñez Collado and Wang, 2020]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ) while reducing floods, landslides and cascading impacts of storms, floods and epidemics, as observed with the ‘incremental housing approach’ in Quinta Monroy ( [[#Rojas--2019|Rojas, 2019]] ) and the ‘social urbanism’ in Medellin ( [[#Garcia%20Ferrari--2018|Garcia Ferrari et al., 2018]] ).

The climate adaptation plans of several large CSA cities include efficient land use and occupation planning and urban control systems (comprising regulation, monitoring), fostering the articulation with housing and environmental policy (by means of intersectoral and multi-level governance), inhibiting and reducing the occupation of risk areas (mainly flooding and landslides risks); increasing population density in areas already served by infrastructure; expanding slum urbanisation and technical assistance programmes to improve and expand social housing ( ''high confidence'' ) ( [[#Municipio%20del%20Distrito%20Metropolitano%20de%20Quito--2020|Municipio del Distrito Metropolitano de Quito, 2020]] ; [[#Prefeitura%20Municipal%20do%20Salvador--2020|Prefeitura Municipal do Salvador, 2020]] ; [[#Municipalidad%20de%20Lima--2021|Municipalidad de Lima, 2021]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ; [[#Prefeitura%20do%20Município%20de%20São%20Paulo--2021|Prefeitura do Município de São Paulo, 2021]] ).

Housing programmes and initiatives that consider resilient construction and site selection strategies are still in their nascent stages ( [[#Martin--2013|Martin et al., 2013]] ). Initiatives in slum upgrading, social housing improvement and regularising land tenure, associated with infrastructure provision, do not usually focus on adaptation, although they often focus on risk reduction. Those initiatives, associated with a housing policy that guarantees access to land and decent housing, represent a comprehensive intervention in vulnerable neighbourhoods for their adaptation to climate change, and CbA (community-based adaptation) strategies, including housing self-management and the participation of cooperatives, demonstrate the need and opportunity to transition to a transformative urban agenda that encompasses sustainable development, poverty reduction, disaster-risk reduction, climate-change adaptation and climate-change mitigation ( ''high confidence'' ) ( [[#Muntó--2018|Muntó, 2018]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ; [[#Valadares--2018|Valadares and Cunha, 2018]] ; [[#Bárcena--2020b|Bárcena et al., 2020b]] ; [[#Núñez%20Collado--2020|Núñez Collado and Wang, 2020]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

Several large cities are implementing municipal risk management plans and management and restoration plans for hydrologically relevant areas, considering threats of drought and heat waves, integrated watershed management and flood control programmes ( ''high confidence'' ) ( [[#Municipio%20del%20Distrito%20Metropolitano%20de%20Quito--2020|Municipio del Distrito Metropolitano de Quito, 2020]] ; [[#Prefeitura%20Municipal%20do%20Salvador--2020|Prefeitura Municipal do Salvador, 2020]] ; [[#Municipalidad%20de%20Lima--2021|Municipalidad de Lima, 2021]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ; [[#Prefeitura%20do%20Município%20de%20São%20Paulo--2021|Prefeitura do Município de São Paulo, 2021]] ). Quito and Rio de Janeiro are two examples of comprehensive and effective city-level climate action that includes creating environmental protected areas, managing appropriate land use, household relocation and EWSs in areas vulnerable to high levels of precipitation associated with EbA, such as reforestation projects, to address natural hazards ( [[#ELLA--2013|ELLA, 2013]] ; [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Calvello--2015|Calvello et al., 2015]] ; [[#Alcaldía%20de%20Quito--2017|Alcaldía de Quito, 2017]] ; [[#Sandholz--2018|Sandholz et al., 2018]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ) ( [[#12.6.1|Section 12.6.1]] ). EWS and the use of mapping tools as undertaken in La Paz proved to be an effective adaptation measure in the face of increasing hydro-climatic extreme events ( [[#Aparicio-Effen--2018|Aparicio-Effen et al., 2018]] ).

<div id="12.5.5.3.2" class="h4-container"></div>

<span id="green-and-grey-infrastructure"></span>
===== 12.5.5.3.2 Green and Grey Infrastructure =====

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Hybrid solutions, combining green and grey infrastructure (GGI), have been adopted for better efficiency in flood control ( [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Drosou--2019|Drosou et al., 2019]] ; [[#Romero-Duque--2020|Romero-Duque et al., 2020]] ), sanitation, water scarcity, landslide prevention and coastal protection ( ''high confidence'' ) ( [[#12.5.6.4|Section 12.5.6.4]] ; [[#Mangone--2016|Mangone, 2016]] ; [[#Depietri--2017|Depietri and McPhearson, 2017]] ; [[#Leal%20Filho--2018|Leal Filho et al., 2018]] ; [[#McPhearson--2018|McPhearson et al., 2018]] ). The adoption of NbS, which embraces well-known approaches such as GI and EbA ( [[#Pauleit--2017|Pauleit et al., 2017]] ; [[#Le--2020|Le, 2020]] ), has increased (Box 1.3). The Fund for the Protection of Water (FONAG) and the Participative Urban Agriculture (AGRUPAR) are initiatives using NbS in Quito ( [[#12.6.1|Section 12.6.1]] ). An example of GGI is a stormwater detention pond as a water storage solution to flood prevention, allowing multiple uses of an urban space, and adapting and revitalising a degraded area in Mesquita, Rio’s metropolitan region ( [[#Jacob--2019|Jacob et al., 2019]] ). These systemic and holistic solutions still need to overcome governance and sectorial barriers to be more widely adopted ( [[#Herzog--2019|Herzog and Rozado, 2019]] ; [[#Wamsler--2020|Wamsler et al., 2020]] ; [[#Valente%20de%20Macedo--2021|Valente de Macedo et al., 2021]] ).

Managing water in cities in an adaptive way has been central to reducing impacts such as floods and contributes to water security ( ''high confidence'' ) ( [[#Van%20Leeuwen--2016|Van Leeuwen et al., 2016]] ; [[#Okumura--2021|Okumura et al., 2021]] ). Many cities facing frequent heavy storms that impact mostly underprivileged communities, slums and vulnerable areas could benefit from integrated NbS for disaster risk reduction and adaptation ( ''high confidence'' ) ( [[#Sandholz--2018|Sandholz et al., 2018]] ; [[#Ronchi--2019|Ronchi and Arcidiacono, 2019]] ). A study covering 70 Latin American cities estimated that 96 million people would benefit from improving main watersheds with GI ( [[#Tellman--2018|Tellman et al., 2018]] ). In several municipal climate plans, NbSs were introduced mainly to enhance rainwater management, reduce energy consumption and urban heat areas, improve water quality, prevent landslides and set aside green areas ( ''high confidence'' ) ( [[#Gobierno%20de%20la%20Ciudad%20de%20Buenos%20Aires--2015|Gobierno de la Ciudad de Buenos Aires, 2015]] ; [[#Municipio%20del%20Distrito%20Metropolitano%20de%20Quito--2020|Municipio del Distrito Metropolitano de Quito, 2020]] ; [[#Prefeitura%20Municipal%20de%20Curitiba--2020|Prefeitura Municipal de Curitiba, 2020]] ; [[#Alcaldía%20de%20Medellín--2021|Alcaldía de Medellín, 2021]] ; [[#Municipalidad%20de%20Lima--2021|Municipalidad de Lima, 2021]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ; [[#Prefeitura%20do%20Município%20de%20São%20Paulo--2021|Prefeitura do Município de São Paulo, 2021]] ). São Paulo’s project for Jaguaré River proposes a large-scale landscape transformation applying innovative multi-functional NbSs instead of exclusively large, expensive and monofunctional hard-engineered solutions to manage stormwater ( [[#Marques--2018|Marques et al., 2018]] ; [[#Herzog--2019|Herzog and Rozado, 2019]] ). In Bogota, the Humedales Foundation has restored wetlands to enhance areas near the Van Der Hammen reserve to improve water quality and quantity, restore habitat for biodiversity and provide flood protection ( [[#Portugal%20Del%20Pino--2020|Portugal Del Pino et al., 2020]] ). In Petrópolis, a medium-sized city in the hills of Rio de Janeiro state, the water service company has implemented 10 NbS multi-functional micro wastewater treatment plants in low-income areas, helping to reduce cascading impacts of storms, floods and epidemics ( [[#Herzog--2019|Herzog and Rozado, 2019]] ). In Costanera Sur, Buenos Aires, a public initiative to protect an auto-regenerated River Plate bank, which had received demolition material to create land, currently offers numerous ecosystem services for residents and attract visitors, activating the tourist industry and helping reducing riverine floods ( [[#Bertonatti--2021|Bertonatti, 2021]] ; [[#OICS--2021|OICS, 2021]] ).

A hybrid solution to water management that merges traditional interventions in urban areas with sustainable urban drainage systems (SUDSs) ( [[#Davis--2017|Davis and Naumann, 2017]] ), considering small-scale low-impact development (LID) measures scattered over the watershed instead of concentrate huge hydraulic grey structures, can help reduce the risk and damage of flooding ( ''high confidence'' ) ( [[#Miguez--2014|Miguez et al., 2014]] , 2015a; [[#Depietri--2017|Depietri and McPhearson, 2017]] ; [[#Da%20Silva--2018a|Da Silva et al., 2018a]] ; [[#de%20Macedo--2018|de Macedo et al., 2018]] ). Quito’s climate plan explicitly cites the strategy for implementing blue and grey infrastructure to reduce risk due to extreme precipitation and its associated impacts such as flooding and landslides and the possible impact of water scarcity ( [[#Municipio%20del%20Distrito%20Metropolitano%20de%20Quito--2020|Municipio del Distrito Metropolitano de Quito, 2020]] ). The Integrated Iguaçu-Sarapuí River Basin Flood Control Master Plan, in Rio’s metropolitan area, combines different solutions to flood protection, focusing on river restoration by retrofitting levee systems combined with adapting land use to provide a multi-functional landscape as an alternative to bring together green and grey solutions, creating urban parks to prevent further paving and avoid irregular occupation of riverbanks and provide storage capacity for damping flood peaks ( [[#Miguez--2015b|Miguez et al., 2015b]] ).

Many cities are implementing adaptation measures on integrated water and flood management systems ( [[#Sarkodie--2019|Sarkodie and Strezov, 2019]] ), improving basic sanitation services ( ''medium confidence: medium evidence, high agreement'' ). The main strategies are established by NAPs periodically focusing on improving water distribution network and reservoir systems, as in Honduras ( [[#Government%20of%20Honduras--2018|Government of Honduras, 2018]] ) and Ecuador ( [[#Mills-Novoa--2020|Mills-Novoa et al., 2020]] ), sewage and effluent treatment, as in Guatemala, Brazil and Paraguay ( [[#Government%20of%20Brazil--2007|Government of Brazil, 2007]] ; [[#Government%20of%20Guatemala--2016|Government of Guatemala, 2016]] ; [[#Government%20of%20Paraguay--2017|Government of Paraguay, 2017]] ), facing water scarcity and environmental degradation. Local authorities follow this guideline in an effort to maintain and upgrade existing drainage systems in Georgetown ( [[#Mycoo--2014|Mycoo, 2014]] ) or in Medellin, focusing on improving drainage systems to prevent landslides or flooding ( [[#Núñez%20Collado--2020|Núñez Collado and Wang, 2020]] ; [[#Alcaldía%20de%20Medellín--2021|Alcaldía de Medellín, 2021]] ). Rio de Janeiro has constructed three large stormwater detention reservoirs to deal with frequent flood, ( [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2015|Prefeitura da Cidade do Rio de Janeiro, 2015]] ), adopting a set of exclusively grey solutions, not combined into a NbS that could improve urban flood resilience ( [[#Rezende--2019|Rezende et al., 2019]] ). The main proposed actions still consider the traditional approach in improving the hydraulic capacity of urban drainage systems as an adaptive measure ( ''high confidence'' ) (Gobierno de la Ciudad de Buenos Aires, 2020; [[#Prefeitura%20Municipal%20do%20Salvador--2020|Prefeitura Municipal do Salvador, 2020]] ; [[#Municipalidad%20de%20Lima--2021|Municipalidad de Lima, 2021]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ). In addition to this strategy, several local plans propose actions for the retention and storage of rainwater, both in urban drainage networks on a smaller intervention scale ( [[#Prefeitura%20Municipal%20de%20Curitiba--2020|Prefeitura Municipal de Curitiba, 2020]] ) and along rivers and canals with large-scale works ( ''medium confidence: medium evidence, high agreement'' ) (Gobierno de la Ciudad de Buenos Aires, 2020; [[#Prefeitura%20Municipal%20de%20Curitiba--2020|Prefeitura Municipal de Curitiba, 2020]] ; [[#Alcaldía%20de%20Medellín--2021|Alcaldía de Medellín, 2021]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2021|Prefeitura da Cidade do Rio de Janeiro, 2021]] ).

<div id="12.5.5.3.3" class="h4-container"></div>

<span id="mobility-and-transport-system"></span>
===== 12.5.5.3.3 Mobility and Transport System =====

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Mobility and transport systems play a key role in urban resilience ( ''high confidence'' ) ( [[#Walker--2014a|Walker et al., 2014a]] ; [[#Caprì--2016|Caprì et al., 2016]] ; [[#Espinet--2016|Espinet et al., 2016]] ; [[#Lee--2016|Lee and Lee, 2016]] ; [[#Ford--2018|Ford et al., 2018]] ; [[#Mehrotra--2018|Mehrotra et al., 2018]] ; [[#Quinn--2018|Quinn et al., 2018]] ). Examples reported in the scientific literature assessed focus on mitigation strategies, even when they are labelled as adaptation measures ( [[#da%20Silva--2016|da Silva and Buendía, 2016]] ; [[#Di%20Giulio--2018|Di Giulio et al., 2018]] ; [[#Valderrama--2019|Valderrama et al., 2019]] ; [[#Goes--2020|Goes et al., 2020]] ).

The integration of transport and land use planning and the improvement of public transport, also as important mitigation actions, has emerged as a consensus in countries’ adaptation plans; nevertheless, emphasis on mobility and transport systems in the many published NAPs is low ( ''medium confidence: medium evidence, high agreement'' ). The NAPs of Honduras, Costa Rica and El Salvador do not approach adaptation or mitigation in the sector, while those of Peru, Ecuador, Guatemala and Paraguay focus on mitigation only. The NAPs of Chile, Colombia and Brazil focus on both mitigation and adaptation of mobility and transport systems. Chile’s and Colombia’s plans dedicate specific action lines to adapting mobility and transport systems to climate change, while Brazil published a complementary volume to accompany its NAP that is dedicated exclusively to sectoral strategies, although it presents only general guidelines ( [[#Government%20of%20Peru--2010|Government of Peru, 2010]] ; [[#Government%20of%20Chile--2014|Government of Chile, 2014]] ; [[#Government%20of%20Ecuador--2015|Government of Ecuador, 2015]] ; [[#Government%20of%20Brazil--2016|Government of Brazil, 2016]] ; [[#Government%20of%20Colombia--2016|Government of Colombia, 2016]] ; [[#Government%20of%20Guatemala--2016|Government of Guatemala, 2016]] ; [[#Government%20of%20Paraguay--2017|Government of Paraguay, 2017]] ; [[#Government%20of%20Costa%20Rica--2018|Government of Costa Rica, 2018]] ; [[#Government%20of%20Honduras--2018|Government of Honduras, 2018]] ; [[#Government%20of%20El%20Salvador--2019|Government of El Salvador, 2019]] ).

On the municipal scale, among the biggest cities, São Paulo, Rio de Janeiro, Lima and Santiago stand out for including mobility and transport as a strategic axis of its climatic plans, though they prioritise mitigation, while Buenos Aires and Bogota do not delve into the issue in their plans ( [[#Gobierno%20de%20la%20Ciudad%20de%20Buenos%20Aires--2015|Gobierno de la Ciudad de Buenos Aires, 2015]] ; [[#Prefeitura%20da%20Cidade%20do%20Rio%20de%20Janeiro--2016|Prefeitura da Cidade do Rio de Janeiro, 2016]] ; [[#Alcaldía%20Mayor%20de%20Bogotá%20D.C.--2018|Alcaldía Mayor de Bogotá D.C., 2018]] ; [[#Municipalidad%20de%20Lima--2021|Municipalidad de Lima, 2021]] ; [[#Municipalidad%20de%20Santiago--2021|Municipalidad de Santiago, 2021]] ; [[#Prefeitura%20do%20Município%20de%20São%20Paulo--2021|Prefeitura do Município de São Paulo, 2021]] ). Most of those same cities have sectoral mobility plans, which are key tools in urban resilience. Those plans, however, do not focus on adaptation actions, instead emphasising mitigation ( [[#Government%20of%20Peru--2005|Government of Peru, 2005]] ; [[#Gobierno%20de%20la%20Ciudad%20de%20Buenos%20Aires--2011|Gobierno de la Ciudad de Buenos Aires, 2011]] ; [[#Prefeitura%20do%20Município%20de%20São%20Paulo--2015|Prefeitura do Município de São Paulo, 2015]] ; [[#Alcaldía%20Mayor%20de%20Bogotá%20D.C.--2017|Alcaldía Mayor de Bogotá D.C., 2017]] ; [[#Ilustre%20Municipalidad%20de%20Santiago--2019|Ilustre Municipalidad de Santiago, 2019]] ; [[#Município%20de%20Rio%20de%20Janeiro--2019|Município de Rio de Janeiro, 2019]] ).

<div id="12.5.6" class="h2-container"></div>

<span id="health-and-well-being"></span>
=== 12.5.6 Health and Well-being ===

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The most common adaptation strategies include the development of climate services such as epidemic forecast tools, integrated climate-health surveillance and observatories and forecasting climate-related disasters (floods, heat waves). Geographic information system (GIS) technologies are being used to identify locations where vulnerable populations are exposed to climate hazards and associated health risks.

<div id="12.5.6.1" class="h3-container"></div>

<span id="climate-services-for-health"></span>
==== 12.5.6.1 Climate Services for Health ====

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The measures most directly linked to diminishing risk are those related to climate services for health ( ''high confidence'' ). Climate services provide tailored, sector-specific information from climate forecasts to support decision-making ( [[#WHO%20and%20WMO--2016|WHO and WMO, 2016]] ); they allow decision makers and practitioners to plan interventions in anticipation of a weather/climate event ( [[#Mahon--2019|Mahon et al., 2019]] ). More recently, climate services, such as EWSs and forecast models, have been promoted for the health sector ( [[#WHO%20and%20WMO--2012|WHO and WMO, 2012]] , 2016; [[#WMO--2014|WMO, 2014]] ; [[#Thomson--2018|Thomson and Mason, 2018]] ) and are an important adaptation measure to reduce the impacts of climate on health ( ''high confidence'' ). To guide this process, the Global Framework for Climate Services (GFCS) issued a Health Exemplar ( [[#Lowe--2014|Lowe et al., 2014]] ; [[#WMO--2014|WMO, 2014]] ), which aims to foster stakeholder engagement between health and climate actors at all levels to promote the effective use of climate information within health research, policy and practice.

There exist at least 24 EWS in SA to avoid deaths and injuries from floods in the countries such as Argentina, Colombia, Ecuador, Bolivia, Brazil, Peru, Uruguay and Venezuela ( [[#Bravo--2010|Bravo et al., 2010]] ; [[#Bidegain--2014|Bidegain, 2014]] ; [[#Moreno--2014|Moreno et al., 2014]] ; [[#Dávila--2016|Dávila, 2016]] ; [[#del%20Granado--2016|del Granado et al., 2016]] ; [[#López-García--2017|López-García et al., 2017]] ; [[#Carrizo%20Sineiro--2018|Carrizo Sineiro et al., 2018]] ). A total of 149 emergency prevention and response systems are reported in CA ( [[#UNESCO--2012|UNESCO, 2012]] ). In addition, some countries implement programmes for the relocation of families who are in risk condition, like in Bogota and Medellin, Colombia ( [[#World%20Bank--2014|World Bank, 2014]] ; [[#Watanabe--2015|Watanabe, 2015]] ).

Epidemic forecast tools are an example of an adaptation measure being developed and/or implemented in this region ( ''high confidence'' ). Climate-driven forecast models have been developed for dengue in Ecuador, Puerto Rico, Peru, Brazil, Mexico, Dominican Republic, and Colombia ( [[#Lowe--2013|Lowe et al., 2013]] ; [[#Eastin--2014|Eastin et al., 2014]] ; [[#Johansson--2016|Johansson et al., 2016]] ; [[#Lowe--2017|Lowe et al., 2017]] ; [[#Johansson--2019|Johansson et al., 2019]] ); for Zika virus infections across the Americas ( [[#Muñoz--2017|Muñoz et al., 2017]] ); for cutaneous leishmaniasis in Costa Rica and Brazil ( [[#Chaves--2006|Chaves and Pascual, 2006]] ; [[#Lewnard--2014|Lewnard et al., 2014]] ); for Aedes-borne diseases across the Americas ( [[#Muñoz--2020b|Muñoz et al., 2020b]] ); and a nowcast model for chikungunya virus infections across the Americas ( [[#Johansson--2014|Johansson et al., 2014]] ). In Ecuador, a prototype system utilised forecasts of seasonal climate and ENSO forecasts of to predict dengue transmission, providing the health sector with warnings of increased transmission several months ahead of time ( [[#Stewart-Ibarra--2013|Stewart-Ibarra and Lowe, 2013]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Despite these advances, few tools have become operational and mainstreamed in decision making processes. However, Brazil and Panama have been able to operationalise an EWS for the surveillance of dengue fever transmission ( [[#Codeço--2016|Codeço et al., 2016]] ; [[#McDonald--2016|McDonald et al., 2016]] ).

One of the most promising climate services for the health sector are heat and cold early-warning and alert systems ( ''medium confidence'' ). These have been developed by the national meteorological institutes in Peru, Argentina, and Uruguay ( [[#Bidegain--2014|Bidegain, 2014]] ). A heat alert system was implemented in Argentina in 2017 and daily alerts are issued for 57 localities across the country. A stoplight colour scheme is used to issue alerts, identifying specific groups at risk and actions to be taken to reduce the risk ( [[#Herrera--2018b|Herrera et al., 2018b]] ).

The public dissemination of climate–health warnings via bulletins, websites and other outlets can be an adaptation measure to address climate change and weather variability to reduce health risks ( ''high confidence'' ). The information produced is systematised to be communicated to authorities and the general public. The Caribbean Health-Climatic Bulletin has been issued quarterly since 2018 to health ministries across the region, including CA and NSA. Regional climate and health authorities meet to review 3-month climate forecasts and issue statements about the probable impacts on health ( [[#Trotman--2018|Trotman et al., 2018]] ). In Panama, information on dengue is distributed in a monthly bulletin that is used by health authorities to inform vector control activities ( [[#McDonald--2016|McDonald et al., 2016]] ). Another example is the climate-driven forecast of dengue risk that was produced prior to Brazil’s 2014 FIFA World Cup to inform disease prevention interventions ( [[#Lowe--2014|Lowe et al., 2014]] , 2016). In Colombia, the Intersectoral National Technical Commission for Environmental Health publishes a monthly bulletin with regional weather forecasts and potential effects on health ( [[#CONASA--2019|CONASA, 2019]] ). Paraguay improves epidemiological surveillance and trains first-level health staff via information campaigns on the prevention of climate-sensitive diseases and promotes health networks with the participation of civil society ( [[#Environmental%20Secretariat%20of%20Paraguay--2011|Environmental Secretariat of Paraguay, 2011]] ).

<div id="12.5.6.2" class="h3-container"></div>

<span id="integrated-climatehealth-surveillance-and-observatories"></span>
==== 12.5.6.2 Integrated Climate–Health Surveillance and Observatories ====

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Integrated climate–health surveillance systems are another key adaptation strategy ( ''medium confidence'' ). This information can be used by the health sector to inform decision-making about when and where to deploy a public health intervention. It can also feed into an EWS, particularly if the data are compatible in format and spatiotemporal scales. An integrated climate–health surveillance system for vector-borne disease control was developed in southern coastal Ecuador through a partnership among the climate and health sectors and academia ( [[#Borbor-Cordova--2016|Borbor-Cordova et al., 2016]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Additionally, an interdisciplinary multi-national team working at the border of Ecuador and Peru created a cooperation network for climate-informed dengue surveillance ( [[#Quichi--2016|Quichi et al., 2016]] ), and their successful binational collaboration resulted in the local elimination of malaria ( [[#Krisher--2016|Krisher et al., 2016]] ). A similar tool is innovative community-based data collection to understand and find solutions to rainfall-related diarrheal diseases in Ecuador ( [[#Palacios--2016|Palacios et al., 2016]] ).

Climate and health observatories represent a promising strategy that is being developed at sub-national, national (e.g., Brazil, Argentina) and regional levels ( ''high confidence'' ) ( [[#Muñoz--2016|Muñoz et al., 2016]] ; [[#Rusticucci--2020|Rusticucci et al., 2020]] ). The Brazilian Observatory of Climate and Health brings together climate and health information for the Amazon region of Manaus ( [[#Barcellos--2016|Barcellos et al., 2016]] ). At the national level, Brazil has created a climate and health observatory, where information and data visualisations are available for various climate-sensitive health indicators ( [[#Ministério%20da%20Saúde%20and%20FIOCRUZ--2021|Ministério da Saúde and FIOCRUZ, 2021]] ).

<div id="12.5.6.3" class="h3-container"></div>

<span id="vulnerability-and-risk-maps"></span>
==== 12.5.6.3 Vulnerability and Risk Maps ====

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Vulnerability and risk maps have been widely used as an adaptation strategy to understand the potential impacts of climate on health outcomes both directly (e.g., maps of disease risk) and indirectly (e.g., maps of populations vulnerable to climate disasters) ( ''high confidence'' ). There are many examples of where climate services have been used to construct vulnerability maps for health outcomes, including maps in the aforementioned climate–health observatories. Dengue, malaria and Zika vulnerability maps using climate, social and environmental information have been developed in Brazil and Colombia ( [[#Cunha--2016b|Cunha et al., 2016b]] ; [[#López-Álvarez--2016|López-Álvarez, 2016]] ; [[#Pereda--2016|Pereda, 2016]] ; [[#IDEAM--2017|IDEAM, 2017]] ). Argentina focuses on improving its health system using a climate change risk map system as a tool that identifies the risks and allows assessing their management ( [[#OPS%20and%20WHO--2018|OPS and WHO, 2018]] ).

Vulnerability and risk maps for climate disasters have been developed at the city level, for example in Bogota, Cartagena de Indias and Mocoa in Colombia ( [[#Yamin--2013|Yamin et al., 2013]] ; [[#Guzman%20Torres--2014|Guzman Torres and Barrera Arciniegas, 2014]] ; [[#Tehelen--2017|Tehelen and Pacha, 2017]] ; [[#Zamora--2018|Zamora, 2018]] ), and for the metropolitan district of Quito in Ecuador ( [[#Tehelen--2017|Tehelen and Pacha, 2017]] ). In addition, vulnerability maps were created for the primary road network of Colombia ( [[#Tehelen--2017|Tehelen and Pacha, 2017]] ). At the regional level, vulnerability maps using climate-change probability, disaster risk and food insecurity variables have been produced for the Andean region ( [[#WFP--2014|WFP, 2014]] ). In Brazil, vulnerability maps that consider exposure, sensitivity and adaptive capacity, coupled with climate scenarios, were designed to support the NAP on a municipal scale ( [[#Chang--2018|Chang and Garcia, 2018]] ; Duval et al., 2018; [[#Marinho--2018|Marinho and Silva, 2018]] ; [[#Menezes--2018|Menezes, 2018]] ; Santos and Marinho, 2018; Silva et al., 2018). A Climate Change Vulnerability Index was used to generate vulnerability maps for countries of the Latin American and Caribbean region ( [[#Vörösmarty--2013|Vörösmarty et al., 2013]] ; [[#CAF--2014|CAF, 2014]] ).

<div id="12.5.6.4" class="h3-container"></div>

<span id="other-adaptation-actions"></span>
==== 12.5.6.4 Other Adaptation Actions ====

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Diverse adaptation measures are being implemented through public policies, private household responses and communal management that directly or indirectly reduce the impacts of climate change on human health ( ''high confidence'' ) (Table 12.9) ''.'' Private and communal management measures could be considered indirect measures because they might be adopted even in the absence of climate change.

'''Table 12.9 |''' Hazards from climate change that impact human health and examples of adaptation strategies proposed or implemented in CSA. Based on McMichael et al. (2006), Miller et al. (2013a, b, c, d), [[#Hardoy--2014|Hardoy et al. (2014)]] , [[#IPCC--2014|IPCC (2014)]] , Janches et al. (2014), [[#Lee--2014|Lee et al. (2014)]] , [[#Mejia--2014|Mejia (2014)]] , [[#Sosa-Rodriguez--2014|Sosa-Rodriguez (2014)]] , [[#Vergara--2014|Vergara et al. (2014)]] , [[#Lemos--2016|Lemos et al. (2016)]] , [[#Villamizar--2017|Villamizar et al. (2017)]] , [[#Magoni--2018|Magoni and Munoz (2018)]] and [[#Zhao--2019|Zhao et al. (2019)]] .

{| class="wikitable"
|-
! rowspan="2"| '''Hazard and'''

'''impacts on human health'''

! colspan="3"| '''Examples of adaptation strategies'''

|-
! '''Public'''

! '''Private'''

! '''Communal'''

|-
| Extreme heat and cold: deaths/illness by thermal stress

| * Creation of urban green spaces
* Health promotion campaigns
* Shelters during heatwaves
* Technology transfer for home heating

| * Cooling by swamp coolers, air conditioning, open windows, wet floors, shade trees
* Bioclimatic building design

| * Training of community health volunteers to recognise and treat heat strain

|-
| Extreme rainfall, wildfire, wind speed: injuries/deaths from floods, storms, cyclones, bushfires and landslides (Key risk 2, Table 12.6)

| * EWSs for extreme climate events
* Safe housing programmes and relocation
* GGI (e.g., channels, drainage systems)

| * GGI to prevent landslides
* Insurance mechanisms and financing for long-term recovery

| * Communal efforts to clear debris from canals to reduce flood risk
* Cooperative efforts to rebuild following flood events

|-
| Drought and dryness: poor nutrition due to reduced food yields and dehydration due to limited or inadequate management of freshwater (Key risk 1, Table 12.6)

| * Formalising land ownership for small farmers and Indigenous people
* Address emerging water conflicts

| * Water infrastructure and irrigation
* Soil moisture retention techniques
* Insurance mechanisms
* Selection of drought-resistant crops

| * Incorporation of local stakeholders in formulating adaptation responses
* Recognition of Indigenous and local wisdom and knowledge

|-
| Changes in climate that promote microbial proliferation: food poisoning and unsafe drinking water (Key risk 3, Table 12.6).

| * Restoration of watersheds
* Integrated health-climate surveillance
* Improve access to drinking water, drainage, sanitation and waste removal

| * Water disinfection: boiling, chlorination
* Purchasing water or water filters

| * Participatory water management strategies, including protection of drinking water sources

|-
| Changes in climate that affect vector–pathogen host relations and infectious disease geography/seasonality (Key risk 4, Table 12.6)

| * Vector control
* EWS for epidemics
* NbS (e.g., forest conservation)

| * Use of bed nets and screens
* Use of repellent and insecticides
* Elimination of standing water

| * Community volunteers to collect blood smears for malaria diagnosis
* Community-led elimination of vector habitat

|-
| SLR and storm surges: impaired crop, livestock and fisheries yields; unsafe drinking water, leading to impaired nutrition (Key risk 8, Table 12.6)

| * Improve governance of water utilities
* Address emerging water conflicts
* Protection, restoration and soil conservation to recharge aquifers

| * Improve water efficiency in agriculture

| * Incorporation of local stakeholders in formulating adaptation responses
* Recognition of Indigenous and local wisdom and knowledge

|-
| Environmental degradation: loss of livelihoods and displacement leading to poverty and adverse health outcomes (related to Key risk 6, Table 12.6)

| * Long-term risk management planning for cities
* Sustainable forestry programmes
* Protection and restoration of lacustrine areas

| * Identification of alternative livelihoods

| * Community-led efforts to reforest and restore/protect watersheds

|}

Participatory management can be relevant in the case of mosquito-borne disease prevention (e.g., dengue fever or malaria), where the reduction in mosquito habitat in one area or ‘hotspot’ can reduce the risk for all surrounding households. This approach is also relevant when considering new places where vector-borne diseases can emerge because of changes in climate ( [[#Andersson--2015|Andersson et al., 2015]] ).

Adaptation strategies implemented by the public sector include a diverse suite of strategies ranging from the creation of green spaces in urban areas, relocation of families located in disaster-prone areas, ecosystem restoration and improved access to clean water, among many others ( ''high confidence'' ) (Table 12.9). Building GGI has been a popular public adaptation measure to reduce deaths and injuries because of floods ( [[#12.5.5.3.2|Section 12.5.5.3.2]] ). Infrastructure has been improved at schools, public buildings and drainage systems in cities such as Bogota, Colombia ( [[#World%20Bank--2014|World Bank, 2014]] ) and La Paz, Bolivia ( [[#Fernández--2016|Fernández and Buss, 2016]] ). In Brazil, channel works were implemented to reduce the flooding of the Tiete River, which crosses the metropolitan area of São Paulo; these projects were designed based on simulated flood scenarios ( [[#Hori--2017|Hori et al., 2017]] ).

Another example of a public adaptation measure is the protection and restoration of natural areas, which have the potential to decrease the transmission of water- and vector-borne infectious diseases ( ''medium confidence: robust evidence, low agreement'' ). Studies have shown that these measures can diminish the cases of malaria and diarrhoea in Brazil and cases of diarrhoea in children in Colombia ( [[#Bauch--2015|Bauch et al., 2015]] ; [[#Herrera--2017|Herrera et al., 2017]] ; [[#Chaves--2018|Chaves et al., 2018]] ). However, deforestation and malaria have a complex relationship that relies on local context interactions, where land use and land cover changes play an important role due to vector ecology alterations and social conditions of human settlements ( [[#Rubio-Palis--2013|Rubio-Palis et al., 2013]] ). Forest conservation can improve hydrological cycle control and soil erosion that can help to improve water quality and reduce the burden of water-borne diseases. In addition, forest cover can help to diminish the habitat for larval mosquitoes that transmit malaria. These measures can help to design policies at sites where these problems do not currently exist but can emerge as a consequence of climate change and the increase in the frequency of weather extreme events.

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<span id="challenges-and-opportunities-5"></span>
==== 12.5.6.5 Challenges and Opportunities ====

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Despite the proliferation of disaster EWSs in the region, only 37 can be considered operational, because many of these systems do not operate or function properly or do not meet the requirements that would allow them to be considered EWSs ( [[#UNESCO--2012|UNESCO, 2012]] ). Sustainable financing and political support are needed to ensure the functioning of disaster EWSs ( ''high confidence'' ) (Table 12.11). Several studies identified difficulties in implementing disaster EWSs due to a lack of community engagement and response to the alerts that are issued ( [[#del%20Granado--2016|del Granado et al., 2016]] ; [[#López-García--2017|López-García et al., 2017]] ). To address these challenges, the document ‘Developing Early Warning Systems: A Checklist’ provides guidance for the implementation of a ''people-centred approach to early warning systems'' , as proposed in the Hyogo Framework for Action 2005–2015 ( [[#Wiltshire--2006|Wiltshire, 2006]] ).

With respect to the development of climate-driven epidemic forecasts, efforts are needed to improve the utility of such forecasts for the health sector. Few such forecasts have been operationalised to inform health-sector decision-making. A review of 73 studies that predicted and forecasted Zika virus infections (42% from the Americas) identified a high degree of variation in access, reproducibility, timeliness and incorporation of uncertainty ( [[#Kobres--2019|Kobres et al., 2019]] ). A recent systematic review of epidemic forecasting and prediction studies found that no reporting guidelines exist; the development of guidance to improve the transparency, quality and implementation of forecast models in the public health sector was recommended ( [[#Pollett--2020|Pollett et al., 2020]] ). An earlier review of dengue early-warning models found that few models incorporated both spatial and temporal aspects of disease risk ( [[#Racloz--2012|Racloz et al., 2012]] ), limiting their potential application as an adaptation strategy by the health sector. Advances have been made in the last decade with respect to modelling and computing tools, increasing access to digital climate information and health records and the use of Earth observations to forecast climate-sensitive diseases ( [[#Fletcher--2021|Fletcher et al., 2021]] ; [[#Wimberly--2021|Wimberly et al., 2021]] ).

The growing field of implementation science—defined as ‘a discipline focused on systematically examining the gap between knowledge and action’—represents another opportunity to address the challenges and barriers to using climate information for health-sector decision-making ( [[#Boyer--2020|Boyer et al., 2020]] ). Implementation science in the health sector in CSA is nascent; research in this area could help to address barriers to mainstreaming climate information in the health sector as an adaptation strategy (Table 12.11; Table SM12.7).

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<span id="governance-and-financing-2"></span>
==== 12.5.6.6 Governance and Financing ====

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A description of the governance and financing dimensions of the feasibility of implementing EWSs is presented in Table 12.11 and Table SM12.7.

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<span id="national-health-plans"></span>
===== 12.5.6.6.1 National Health Plans =====

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Some countries have developed national plans on health including the role of climate. Chile has a Climate Change Adaptation Plan of the Health Sector that proposes several actions to enhance monitoring, institutions and citizen information and education ( [[#Ministry%20of%20Health%20of%20Chile%20and%20Ministry%20of%20Environment%20of%20Chile--2016|Ministry of Health of Chile and Ministry of Environment of Chile, 2016]] ). Based on the identification of vulnerability to climate change, Colombia has developed 11 regional adaptation plans to strengthen institutional capacities, climate-change education for behavioural changes and cost estimation to promote health resilience ( [[#WHO%20and%20UNFCCC--2015|WHO and UNFCCC, 2015]] ). In addition, El Salvador implemented actions to strengthen health infrastructure using high latrines for housing in flood communities, as well as other measures focused on water supply and quality based on an education and awareness programme ( [[#Ministry%20of%20Environment%20and%20Natural%20Resources%20of%20El%20Salvador--2013|Ministry of Environment and Natural Resources of El Salvador, 2013]] ). Only Brazil and Peru have implemented actions so far in the region derived from national health adaptation plans, and only Brazil completed a national assessment of impacts, vulnerability and adaptation for health ( [[#Watts--2018|Watts et al., 2018]] ). Some countries include health as a priority sector in their NAPs, as in the case of Ecuador and Costa Rica, which has a national plan addressing the prevention and care of climate-sensitive diseases coupled with a National Health Plan (2016–2020) ( [[#Ministry%20of%20Health%20Costa%20Rica--2016|Ministry of Health Costa Rica, 2016]] ; Jiménez, n. d.).

<div id="12.5.6.6.2" class="h4-container"></div>

<span id="national-disaster-management-plans"></span>
===== 12.5.6.6.2 National Disaster Management Plans =====

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National Risk Management Plans or National Disaster Response Plans are tools for adapting to climate change that can help to diminish death and injuries from disasters ( ''high confidence'' ). These plans are generally promoted by governments as national instruments that guide the processes of estimating, preventing and reducing disaster risk. Updated National Risk Management Plans have been found for Guatemala ( [[#CONRED--2014|CONRED, 2014]] ), Honduras ( [[#COPECO--2014|COPECO, 2014]] ), El Salvador ( [[#Ministry%20of%20Health%20of%20El%20Salvador--2017|Ministry of Health of El Salvador, 2017]] ), Costa Rica ( [[#CNE--2016|CNE, 2016]] ), Ecuador ( [[#SGR--2018|SGR, 2018]] ), Peru ( [[#SGRD--2014|SGRD et al., 2014]] ), Argentina ( [[#Ministerio%20de%20Seguridad%20de%20Argentina--2018|Ministerio de Seguridad de Argentina, 2018]] ), Bolivia ( [[#VIDECI--2017|VIDECI, 2017]] ), Chile ( [[#ONEMI--2015|ONEMI, 2015]] ) and Colombia ( [[#UNGRD--2015|UNGRD, 2015]] ). It has been shown in Brazil that information on drought conditions can be used to reduced health impacts of drought using a national disaster risk reduction framework ( [[#Sena--2016|Sena et al., 2016]] ).

<div id="12.5.7" class="h2-container"></div>

<span id="poverty-livelihood-and-sustainable-development"></span>
=== 12.5.7 Poverty, Livelihood and Sustainable Development ===

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Climate-change impacts are increasing and exacerbating poverty and social inequalities, affecting those already vulnerable and disfavoured, generating new and interlinked risk and challenging climate resilient development pathways ( ''high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-8#8.2.1.4|Section 8.2.1.4]] ; [[#Shi--2016|Shi et al., 2016]] ; [[#Otto--2017|Otto et al., 2017]] ; [[#Johnson--2021|Johnson et al., 2021]] ). Poverty, high levels of inequality and pre-existing vulnerabilities can also be worsened by climate-change policies ( [[#Antwi-Agyei--2018|Antwi-Agyei et al., 2018]] ; [[#IPCC--2018|IPCC, 2018]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). Those already suffering are losing their livelihoods and reducing their development options; poor populations and countries are more vulnerable and have lower adaptive capacity to climate change compared to rich ones ( ''very high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-8#8.5.2.1|Section 8.5.2.1]] ; [[#Rao--2017|Rao et al., 2017]] ).

Inequality is growing, a CSA structural characteristic; the Gini index average for Latin American countries (including Mexico) decreased to 0.466 in 2017, where 1% of the richest got 22 times more income than 10% of the poorest ( [[#ECLAC--2019b|ECLAC, 2019b]] ; [[#Busso--2020|Busso and Messina, 2020]] ), but in 2018, 29.6% of Latin American populations were poor (which increased to 182 million) and 10.2% were living in extreme poverty; in 2018 (increased to 63 million) ( [[#ECLAC--2019b|ECLAC, 2019b]] ) and in 2020, due to the COVID crisis, the Gini coefficient projection of increases range from 1.1% to 7.8% ( [[#ECLAC%20and%20PAHO--2020|ECLAC and PAHO, 2020]] ), with poverty increasing to 33.7% (209 millions) and extreme poverty to 12.5% (78 millions) ( [[#ECLAC%20and%20PAHO--2020|ECLAC and PAHO, 2020]] ; [[#ECLAC--2021|ECLAC, 2021]] ). Those poverty and extreme poverty rates are higher among children, young people, women, Indigenous Peoples ( [[#Reckien--2017|Reckien et al., 2017]] ; [[#Busso--2020|Busso and Messina, 2020]] ), migrants ( [[#Dodman--2019|Dodman et al., 2019]] ) and rural populations. Climate change has differential impacts, and even within a household there may be important differences in relation to age, gender, health and disability; these factors may intersect with one another ( ''high confidence'' ) ( [[#Reckien--2017|Reckien et al., 2017]] ; [[#Busso--2020|Busso and Messina, 2020]] ).

In IPCC’s Third Assessment Report (TAR), AR4 and AR5, WGII recognised higher risks associated with poor living conditions, substandard housing, inadequate services, location of hazardous sites stemming from a lack of alternatives and the need to work more seriously on strengthening governance structures involving residents and community organisations, among others ( [[#Wilbanks--2007|Wilbanks et al., 2007]] ; [[#Revi--2014|Revi et al., 2014]] ). The AR5 CSA chapter stated that poverty levels remained high (45% for CA and 30% for SA in 2010) despite years of sustained economic growth. Poor and vulnerable groups are disproportionately affected in negative ways by climate change ( [[IPCC:Wg2:Chapter:Chapter-8#8.2.1.4|Section 8.2.1.4]] ; [[IPCC:Wg2:Chapter:Chapter-8#8.2|Section 8.2.2.3]] ; SR15 [[IPCC:Wg2:Chapter:Chapter-5#5.2|Section 5.2]] and [[IPCC:Wg2:Chapter:Chapter-5#5.2.1|Section 5.2.1]] , [[#Roy--2018|Roy et al., 2018]] ) due to physical exposure derived from their place of residence or work, illiteracy, low income and skills, political and institutional marginalisation tied to a lack of recognition of informal settlements and employment, poor access to good-quality services and infrastructure, resources and information and other factors ( ''very high confidence'' ) ( [[#UN-Habitat--2018|UN-Habitat, 2018]] ; SR15 Sections 5.2.1, 5.6.2, 5.6.3, 5.6.4, [[#Roy--2018|Roy et al., 2018]] ).

International agreements aim for climate resilient development pathways where efforts to eradicate poverty, reduce inequality and promote fair and cross-scalar adaptation and mitigation are strengthened. The first and second objectives of the SDGs aim to reduce poverty, allowing no one to fall through the cracks ( [[#UN%20General%20Assembly--2015|UN General Assembly, 2015]] ). Researchers argue that poverty is mischaracterised and has multiple dimensions ( [[#Castán%20Broto--2013|Castán Broto and Bulkeley, 2013]] ) ( [[IPCC:Wg2:Chapter:Chapter-8#8.1|Section 8.1.1]] ), that biodiversity loss, climate change and pollution will undermine efforts on 80% of assessed SDG targets, that biodiversity and climate change must be tackled together ( [[#Pörtner--2021|Pörtner et al., 2021]] ; [[#United%20Nations%20Environment%20Programme--2021|United Nations Environment Programme, 2021]] ) and due to the COVID crisis LAC countries have made uneven progress in terms of meeting SDGs ( ''high confidence'' ) ( [[#ECLAC--2020|ECLAC, 2020]] ).

<div id="12.5.7.1" class="h3-container"></div>

<span id="challenges-and-opportunities-6"></span>
==== 12.5.7.1 Challenges and Opportunities ====

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Climate change exacerbates pre-existing vulnerability conditions and can drive societies further away from achieving resilience, equity and sustainable development ( [[#Tanner--2015b|Tanner et al., 2015b]] ; [[#Bartlett--2016|Bartlett and Satterthwaite, 2016]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Bárcena--2020a|Bárcena et al., 2020a]] ). Existing inequalities in the provision and consumption of services are bound to be exacerbated by future risks and uncertainties associated with climate-change scenarios ( [[#Miranda%20Sara--2017|Miranda Sara et al., 2017]] ). Climate change will be a major obstacle in reducing poverty ( ''high confidence'' ) ( [[#Bartlett--2016|Bartlett and Satterthwaite, 2016]] ; [[#Allen--2017a|Allen et al., 2017a]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ; [[#United%20Nations%20Environment%20Programme--2021|United Nations Environment Programme, 2021]] ), affecting even wealthier populations that become vulnerable facing climate-change scenarios (WGI AR6 Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ), dragging them into poverty and erasing decades of work and asset accumulation.

CSA is highly urbanised, and the vast majority of the region’s poor live in urban areas (except in CA), while urban extreme poverty is becoming more pronounced ( [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Dodman--2019|Dodman et al., 2019]] ; [[#Almansi--2020|Almansi et al., 2020]] ; [[#Sette%20Whitaker%20Ferreira--2020|Sette Whitaker Ferreira et al., 2020]] ), with those living in informal settlements and working within informal economy being critical factors in each city’s economy ( [[#Satterthwaite--2018|Satterthwaite et al., 2018]] , 2020). Many households in the region’s cities live in precarious neighbourhoods with insufficient infrastructure and substandard housing ( [[#Adler--2018|Adler et al., 2018]] ; [[#Rojas--2019|Rojas, 2019]] ). On average, between 21% and 25% of urban populations live in informal settlements ( [[#Jaitman--2015|Jaitman, 2015]] ; [[#UN-Habitat--2015|UN-Habitat, 2015]] ; [[#Rojas--2019|Rojas, 2019]] ; [[#Sandoval--2019|Sandoval and Sarmiento, 2019]] ). This hides important disparities: Habitat III reports, by individual countries, the percentage of urban population living in informal settlements, which ranged from 5% to 60% and in absolute terms means 105 million people living in precarious conditions (106 million estimated in 1990) ( [[#12.5.5|Section 12.5.5]] ; [[#Sandoval--2019|Sandoval and Sarmiento, 2019]] ).

High levels of inequality and informality remain the biggest challenges in terms of adaptation measures being effective ( [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Dodman--2019|Dodman et al., 2019]] ). The interaction of projected impacts with existing vulnerabilities in the region (such as hunger, malnutrition and health inequalities, arising from the region’s social, economic and demographic profile) affects CSA development and well-being in different ways ( [[#Reyer--2017|Reyer et al., 2017]] ) increasing poverty and inequality and threatening paths to sustainable development ( [[IPCC:Wg2:Chapter:Chapter-18#18.1.1|Section 18.1.1]] ; [[#Reckien--2017|Reckien et al., 2017]] ).

The uneven enforcement of land use regulations, relocations and evictions in connection with environmental risk management and climate adaptation is a contested issue ( [[#Brockington--2015|Brockington and Wilkie, 2015]] ; [[#Lavell--2016|Lavell, 2016]] ; [[#Quimbayo%20Ruiz--2016a|Quimbayo Ruiz and Vásquez Rodríguez, 2016a]] ; [[#Quimbayo%20Ruiz--2016b|Quimbayo Ruiz and Vásquez Rodríguez, 2016b]] ; [[#Anguelovski--2018|Anguelovski et al., 2018]] ; Anguelovski et al., 2019; [[#Shokry--2020|Shokry et al., 2020]] ; [[#Chávez%20Eslava--2021|Chávez Eslava, 2021]] ; [[#Oliver-Smith--2021|Oliver-Smith, 2021]] ). This suggests that caution in framing climate adaptation and resilience related interventions equally benefits everyone ( ''high confidence'' ) ( [[#Brown--2014|Brown, 2014]] ; [[#Chu--2016|Chu et al., 2016]] ; [[#Connolly--2019|Connolly, 2019]] ; [[#Romero-Lankao--2019|Romero-Lankao and Gnatz, 2019]] ; [[#Johnson--2021|Johnson et al., 2021]] ) and that equality and justice dimensions should be incorporated into decision-making ( ''very high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-18#18.1.2|Section 18.1.2.2]] ; [[#Agyeman--2016|Agyeman et al., 2016]] ; [[#Meerow--2016|Meerow and Newell, 2016]] ; [[#Romero-Lankao--2016|Romero-Lankao et al., 2016]] ; [[#Shi--2016|Shi et al., 2016]] ; [[#Reckien--2017|Reckien et al., 2017]] ; [[#Leal%20Filho--2021|Leal Filho et al., 2021]] ).

Poor rural households in marginal territories that have a low productive potential and/or that are far from markets and infrastructure are highly vulnerable to climate-change impacts and could easily fall into poverty-environment traps ( ''high confidence'' ) ( [[#Barbier--2019|Barbier and Hochard, 2019]] ; [[#Heikkinen--2021|Heikkinen, 2021]] ). Climate change is one of the main threats to rural livelihoods in CA, since agriculture is a pillar of rural economies and food security, especially in the poorest sectors, which rely on subsistence crops in areas with low soil fertility and rainfall seasonality ( [[#Bouroncle--2017|Bouroncle et al., 2017]] ).

Impacts are likely to occur simultaneously, exacerbating the challenges faced by the poorer segments of society, but also creating new groups at risk ( [[#Miranda%20Sara--2016|Miranda Sara et al., 2016]] ; [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Dodman--2019|Dodman et al., 2019]] ). The material basis for poor and vulnerable urban and rural populations’ adaptations is in a critical state across the CSA region, magnifying extreme events’ impacts, making CSA less resilient. Consequences in terms of social vulnerability and livelihood will be widely felt, inasmuch as the security and protection of critical assets (housing, infrastructure and water, land and ecosystem services) continue to lag behind. Small businesses are usually located within homes, and if the home is affected, so is the business ( [[#Stein--2015|Stein and Moser, 2015]] ), adding another layer of vulnerability for this population.

As productivity declines, outside sources of income are sought, and people rely on resource extraction for subsistence and for income, further increasing their vulnerability to climate change ( [[#Barbier--2018a|Barbier and Hochard, 2018a]] ). Cycles of declining productivity, environmental degradation, wildlife poaching and trafficking, the search for outside employment, reduced incomes, livelihood opportunities and poverty have been observed in rural El Salvador, Honduras, Amazonia ( [[#López-Feldman--2014|López-Feldman, 2014]] ; [[#Graham--2017|Graham, 2017]] ; [[#Barbier--2018a|Barbier and Hochard, 2018a]] ). The protection of communities that defend and are dependent on wildlife and natural environments requires immediate attention. Latin America is home to eight million forest-dependent people, which represents about 82% of the region’s rural extreme poor ( [[#FAO%20and%20UNEP--2020|FAO and UNEP, 2020]] ).

Poverty and disaster risk reduction interlinked with climate-change adaptation share a focus on identifying and acting on local risks and their root causes, even though they view risk through different lenses ( ''very high confidence'' ) ( [[#IPCC--2014|IPCC, 2014]] ; [[#Allen--2017a|Allen et al., 2017a]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] , 2020; [[#UN-Habitat--2018|UN-Habitat, 2018]] ). Construction of climate knowledge and risk perceptions affect decision-making to define implementation priorities, but the poor are less able to cope with and adapt so as to avoid so-called adaptation injustices ( ''high confidence'' ) ( [[#Mansur--2016|Mansur et al., 2016]] ; [[#Miranda%20Sara--2017|Miranda Sara et al., 2017]] ; [[#Reckien--2017|Reckien et al., 2017]] ; [[#Hardoy--2019|Hardoy et al., 2019]] ).

Adaptation, social policies, poverty reduction and inequality are weakly articulated to daily or chronic risk reduction. Poor residents are often caught in ‘risk traps’, accumulated cycles of everyday risks and small-scale disasters ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Bartlett--2016|Bartlett and Satterthwaite, 2016]] ; [[#Mansur--2016|Mansur et al., 2016]] ; [[#Allen--2017a|Allen et al., 2017a]] ; [[#Leal%20Filho--2021|Leal Filho et al., 2021]] ), which are exacerbated by climate risks and COVID pandemic with the most vulnerable populations suffering. Chronic and everyday risks (poor access to infrastructure, services, incomes, housing, land tenure, education, security, location and poor-quality environment and networks and lack of a voice) are often exacerbated and generate new unknown risks by climate change ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Bartlett--2016|Bartlett and Satterthwaite, 2016]] ; [[#Mansur--2016|Mansur et al., 2016]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ; [[#Leal%20Filho--2021|Leal Filho et al., 2021]] ), extreme events and risks related to ENSO oscillation. All these risks need to be considered simultaneously ( [[#UN-Habitat--2018|UN-Habitat, 2018]] ). Risks are seldom distributed equally, highlighting socioeconomic inequalities and governance failures ( ''high confidence'' ) ( [[#IPCC--2014|IPCC, 2014]] ; [[#Bartlett--2016|Bartlett and Satterthwaite, 2016]] ; [[#Rasch--2016|Rasch, 2016]] ; [[#Romero-Lankao--2018|Romero-Lankao et al., 2018]] ).

Adaptation, disaster risk reduction and social and poverty reduction policies contribute to sustainable development ( [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ) and improve prospects for climate-resilient pathways ( [[IPCC:Wg2:Chapter:Chapter-18#18.1.1|Section 18.1.1]] ). Without pro-poor interventions, adaptation options could reinforce poverty cycles ( [[#Kalikoski--2018|Kalikoski et al., 2018]] ). Secure locations, good-quality infrastructure, services and housing are critical to reducing risks from extreme climate events ( [[#Satterthwaite--2018|Satterthwaite et al., 2018]] ; [[#Dodman--2019|Dodman et al., 2019]] ).

<div id="12.5.7.2" class="h3-container"></div>

<span id="governance-and-finance"></span>
==== 12.5.7.2 Governance and Finance ====

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Poor and most vulnerable groups have limited political influence, fewer capacities and opportunities to participate in decision and policymaking and are less able to leverage government support to invest in adaptation measures linked with poverty, inequality and vulnerability reduction ( ''very high confidence)'' (Chapter 8; [[#Miranda%20Sara--2017|Miranda Sara et al., 2017]] ; [[#Reyer--2017|Reyer et al., 2017]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Dodman--2019|Dodman et al., 2019]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

Existing imbalances in power relations, corruption, historic structural problems and high levels of risk tolerance ( [[#Miranda%20Sara--2016|Miranda Sara et al., 2016]] ) constitute climate governance barriers to implementing more effective adaptation and preventive measures. Corruption, particularly in the construction and infrastructure sectors, has proven to be a barrier to CSA development, even reproducing and reconstructing the same risks ( [[#French--2017|French and Mechler, 2017]] ; [[#Vergara--2018|Vergara, 2018]] ; [[#Durand--2019|Durand, 2019]] ). Critical infrastructure and valuable assets continue to be placed in vulnerable areas ( [[#Calil--2017|Calil et al., 2017]] ; [[#Escalante%20Estrada--2020|Escalante Estrada and Miranda, 2020]] ), demonstrating the persistence of maladaptation and adaptation deficit ( [[#Villamizar--2017|Villamizar et al., 2017]] ).

Social organisation, participation and governance reconfiguration are essential for building climate resilience ( ''very high confidence'' ) ( [[#Stein--2015|Stein and Moser, 2015]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] , 2020; [[#Stein--2018|Stein et al., 2018]] ; [[#Hardoy--2019|Hardoy et al., 2019]] ; [[#Stein--2019|Stein, 2019]] ; [[#Miranda%20Sara--2021|Miranda Sara, 2021]] ). Adaptation measures have trade-offs that need to be acknowledged and acted upon, most importantly by developing the capacity to convene discussions that draw in all key actors and commit them to do things differently ( [[#Almeida--2018|Almeida et al., 2018]] ; [[#Hardoy--2019|Hardoy et al., 2019]] ). Collaborative approaches integrate groups and organisations (e.g., saving, women’s groups, clubs, vendor associations, cooperatives) contributing to the exchange of information to visibilise people’s needs, generate safety networks and negotiate for improvements and enhance adaptive capacity.

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<span id="adaptation-options-1"></span>
==== 12.5.7.3 Adaptation Options ====

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Effective adaptation can be achieved by addressing pre-existing development deficits, particularly the needs and priorities of informal settlements and economies ( [[#Revi--2014|Revi et al., 2014]] ; [[#UN-Habitat--2018|UN-Habitat, 2018]] ). There is urgency in making sure that social systems are better able to respond to climate-related risks and increase their adaptive capacity ( [[#Lemos--2016|Lemos et al., 2016]] ), focusing on path dependency, lock-ins and poor specific needs ( [[#Leal%20Filho--2021|Leal Filho et al., 2021]] ).

The linkages between climate adaptation and poverty are not clearly addressed at the national level ( [[#Kalikoski--2018|Kalikoski et al., 2018]] ). A revision of some NDCs presented by CSA countries ( https://unfccc.int ) shows that NDCs are developed with almost no connection to poverty and livelihoods. Exceptions include Bolivia, whose NDC developed the ‘good life’ concept as an alternative development pathway, supporting sustainable livelihoods as a means to eradicate poverty. Honduras asserts that climate action should improve living conditions. Peru defined a poverty and vulnerability reduction approach. Finally, El Salvador conditioned its NDCs to macroeconomic stability, economic growth and poverty reduction. A sustainable development approach permeates the proposed actions for sectors such as energy, agriculture, transport, water and forestry.

Adaptive capacity is linked to addressing climate-related risks (specific capacity) and structural deficits (generic capacity) and synergies, and a strategic balance between both is necessary ( [[#Eakin--2014|Eakin et al., 2014]] ; [[#Lemos--2016|Lemos et al., 2016]] ). Adaptation institutional context can undermine one form of capacity with repercussions for the other compromising overall adaptation and sustainable development ( [[#Eakin--2014|Eakin et al., 2014]] ).

The literature assessing the effectiveness of pro-poor or community-based adaptation practices and livelihood options continues to be weak, though such practices and options are being increasingly documented, as in AR5 ( [[#Magrin--2014|Magrin et al., 2014]] ). A great variety of measures and financial instruments are being applied to strengthen and protect livelihoods and assets: collective insurance schemes, micro-credit, financial instruments for transferring risks, agricultural insurance and PES ( [[#Dávila--2016|Dávila, 2016]] ; [[#Hardoy--2016|Hardoy and Velásquez, 2016]] ; [[#Lemos--2016|Lemos et al., 2016]] ; [[#Porras--2016|Porras et al., 2016]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ). Small-scale household businesses in poor neighbourhoods develop adaptation strategies to keep operations going, showing how household-level adaptation strategies are multi-purpose ( [[#Stein--2018|Stein et al., 2018]] ; [[#Stein--2019|Stein, 2019]] ). There are emerging interinstitutional communities of practice whose aim is to share practices and lessons learned ( [[#ECLAC--2013|ECLAC, 2013]] , 2015, 2019a).

There is also increasing evidence of human mobility associated with climate change and disaster risk ( [[#IOM--2021|IOM, 2021]] ) and the adoption of sustainable tourism, diversification of livelihood strategies, climate forecasts, appropriate construction techniques, neighbourhood layout, integral urban upgrading initiatives, territorial and urban planning, regulatory frameworks, water harvesting and NbS ( [[#Stein--2014|Stein and Moser, 2014]] ; [[#Hardoy--2016|Hardoy and Mastrangelo, 2016]] ; [[#Almeida--2018|Almeida et al., 2018]] ; [[#Barbier--2018a|Barbier and Hochard, 2018a]] ; [[#Desmaison--2018|Desmaison et al., 2018]] ; [[#Satterthwaite--2018|Satterthwaite et al., 2018]] , 2020; [[#Villafuerte--2018|Villafuerte et al., 2018]] ; [[#Hidalgo--2020|Hidalgo, 2020]] ). Mostly, socioeconomical and sociopolitical factors show that safety and continuity measures are critical enablers of adaptation.

At the municipal level, a study in CA highlighted that adaptive capacity in rural areas is associated with the satisfaction of basic needs (safe drinking water, school, quality dwelling, gender parity index), access to resources for innovation and action (road density, economically active population with non-agricultural employment and rural demographic dependency ratio) and access to credit and technical support ( [[#Bouroncle--2017|Bouroncle et al., 2017]] ).

CSA adaptation initiatives to reduce poverty, improve livelihoods and achieve sustainable development in scale and scope, from planned and collective interventions to autonomous and individual actions. Many of them are bottom-up, community-led initiatives together with civil society organisations; others are government-led, including local governments, or a combination of them ( [[#McNamara--2017|McNamara and Buggy, 2017]] ; [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ). Vulnerable groups are a focus to achieve equity at planning and as a target including mainly rural low-income, Indigenous Peoples and women and migrants in most references. Responses detected were focused on behavioural and cultural followed by ecosystem-based responses, institutional, and technological/infrastructural responses. Out of 55 articles analysed from CSA ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ) about poverty, equity and adaptation options, half covered adaptation planning and early implementation, but only 2% could show evidence of risk reduction associated with adaptation efforts.

Tensions and conflicts may result from differing perceptions and knowledge of vulnerabilities and risk, which can hinder the acceptance of adaptation measures or the implementation of stronger adaptive or preventive actions ( [[#Miranda%20Sara--2016|Miranda Sara et al., 2016]] ). There is a need to better understand complex interactions and community responses to climate change in the Amazonian and Andean regions. Climate-change hotspot impacts have shown that poverty reduction measures alone were not enough to improve adaptive capacity because people will not necessarily invest in their enhancement ( [[#Pinho--2014|Pinho et al., 2014]] ; [[#Filho--2016|Filho et al., 2016]] ; [[#Nelson--2016|Nelson et al., 2016]] ; [[#Lapola--2018|Lapola et al., 2018]] ; [[#Zavaleta--2018|Zavaleta et al., 2018]] ). Current adaptation strategies and methods may be neglecting cultural values, even eroding them, in the Peruvian Andes, indicating that success of adaptation practices is tied to deep cultural values ( [[#Walshe--2016|Walshe and Argumedo, 2016]] ).

Limits to adaptation include access to land, territory and resources ( [[#Mesclier--2015|Mesclier et al., 2015]] ), poor labour opportunities coupled with knowledge gaps, weak multi-actor coordination, and lack of effective policies and supportive frameworks ( [[#Berrang-Ford--2021|Berrang-Ford et al., 2021]] ).

Low participation of women in income-earning opportunities contrasts with their role in unpaid activities ( [[#ECLAC--2019b|ECLAC, 2019b]] ). Despite the progress that has been made, gender differences in labour markets remain an unjustifiable form of inequality ( [[#OIT--2019|OIT, 2019]] ), and women easily fall back on the informal labour market during crisis situations, such as those generated by climate events ( [[#Collodi--2020|Collodi et al., 2020]] ).

Participatory processes are leveraging adaptation measures throughout CSA; they contribute to the prioritisation of specific adaptation measures as well as the strengthening of local capacities. Results of participatory processes show how climate adaptation needs to be part of larger transformation processes to that have vulnerable communities at the center and reduce vulnerability drivers ( [[#Stein--2015|Stein and Moser, 2015]] ; [[#Stein--2018|Stein et al., 2018]] ; [[#Stein--2019|Stein, 2019]] ). Stronger national policies interlinking poverty and inequality reduction to adaptation considering the coupled human-environmental systems to comprehend poor and vulnerable groups’ capacity to adapt are urgently needed.

<div id="12.5.8" class="h2-container"></div>

<span id="cross-cutting-issues-in-the-human-dimension"></span>
=== 12.5.8 Cross-cutting Issues in the Human Dimension ===

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<div id="12.5.8.1" class="h3-container"></div>

<span id="public-policies-social-movements-and-participation"></span>
==== 12.5.8.1 Public Policies, Social Movements and Participation ====

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Public policies related to adaptation must be seen in the wider context of environmental policies and governance, as they usually address climatic processes in synergy with other environmental and socioeconomic drivers ( ''very high confidence'' ) ( [[#Ding--2017|Ding et al., 2017]] ; [[#Aldunce%20Ide--2020|Aldunce Ide et al., 2020]] ; [[#Comisión%20Europea--2020|Comisión Europea, 2020]] ; [[#Lampis--2020|Lampis et al., 2020]] ; [[#Scoville-Simonds--2020|Scoville-Simonds et al., 2020]] ). However, some people point to education, sanitation or social assistance, among other sectors ( [[#Bonatti--2019|Bonatti et al., 2019]] ). In Brazil, for example, it would be difficult to clearly separate climate-change adaptation and urban policies ( ''high confidence'' ) ( [[#PBMC--2016|PBMC, 2016]] ; [[#Barbi--2017|Barbi and da Costa Ferreira, 2017]] ; [[#Marques%20Di%20Giulio--2017|Marques Di Giulio et al., 2017]] ; [[#Empresa%20de%20Pesquisa%20Energética--2018|Empresa de Pesquisa Energética, 2018]] ; [[#Checco--2019|Checco and Caldas, 2019]] ; [[#Canil--2020|Canil et al., 2020]] ).

Many public policies related to climate change have become symbolic, in conflict with prevailing economic policies and practices ( ''medium confidence: low evidence, high agreement'' ). Urban adaptation plans can be in conflict with other policies, and there may exist insufficient support in multiple areas such as social attitudes and behaviour, knowledge, education and human capital, finance, governance, institutions and policy ( [[#Villamizar--2017|Villamizar et al., 2017]] ; [[#Koch--2018|Koch, 2018]] ). Some policies around climate-related displacements and migrants have been considered in NDCs (Priotto and Salvador Aruj, 2017; [[#Yamamoto--2018|Yamamoto et al., 2018]] ; [[#de%20Salles%20Cavedon-Capdeville--2020|de Salles Cavedon-Capdeville et al., 2020]] ).

Because there are asymmetries among populations regarding the vulnerability and benefits of adaptation, along the lines of gender, age, socioeconomic conditions and ethnicity, it has been noticed that adaptation policies and programmes must be adequate to diverse conditions and actors ( ''very high confidence'' ) ( [[#Kaijser--2014|Kaijser and Kronsell, 2014]] ; [[#Walshe--2016|Walshe and Argumedo, 2016]] ; [[#Baucom--2017|Baucom and Omelsky, 2017]] ; [[#Harvey--2018|Harvey et al., 2018]] ).

Effective adaptation and mitigation depend on policies and measures at multiple scales, especially when it comes to the involvement of more exposed and vulnerable people. The participation of experts, communities and citizens has shown to be effective ( [[#FAO%20and%20Fundación%20Futuro%20Latinoamericano--2019|FAO and Fundación Futuro Latinoamericano, 2019]] ), particularly through partnerships between grassroots organisations and impoverished communities, providing valued expertise and capacities to support the implementation of government climate resilience strategies (World Bank Group, 2015). More inclusive planning processes correspond to higher climate equity and justice outcomes in the short term, but an emphasis on building dedicated multi-sector governance institutions may also enhance long-term programmes’ stability while ensuring civil society has a voice in adaptation planning and implementation ( [[#Chu--2016|Chu et al., 2016]] ). Some local organisations and people have succeeded when they were in charge of their own resiliency efforts, where international projects and protocols proved less effective ( [[#Doughty--2016|Doughty, 2016]] ). Some decentralised governmental programmes have tried to increase public responsiveness to the adaptation needs of the people, but such programmes have proven to be only mildly successful and provoke the mobilisation of communities against existing governance structures ( [[#Thompson--2016|Thompson, 2016]] ).

IKLK participation is thought to be more considered in adaptation policies because it yields good results ( ''high confidence'' ) ( [[#Nagy--2014b|Nagy et al., 2014b]] ; [[#Jurt--2015|Jurt et al., 2015]] ; [[#Arias--2016|Arias et al., 2016]] ; [[#Stensrud--2016|Stensrud, 2016]] ). IK has been adaptive for long periods in the Andes ( [[#Cuvi--2018|Cuvi, 2018]] ), but there might be limits to adaptation in the face of present climatic and other environmental and socioeconomic drivers ( [[#Postigo--2019|Postigo, 2019]] ). Approaches integrating IK with more formal sciences, to address research and policies, have improved adaptation processes, but they carry their own complications ( ''high confidence'' ) ( [[#Doswald--2014|Doswald et al., 2014]] ; [[#Metternicht--2014|Metternicht et al., 2014]] ; [[#Tengö--2014|Tengö et al., 2014]] ; [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Keenan--2015|Keenan, 2015]] ; [[#Lasage--2015|Lasage et al., 2015]] ; [[#Camacho%20Guerreiro--2016|Camacho Guerreiro et al., 2016]] ; [[#Hurlbert--2016|Hurlbert and Gupta, 2016]] ; [[#Roco--2016|Roco et al., 2016]] ; [[#Santos--2016|Santos et al., 2016]] ; [[#Walshe--2016|Walshe and Argumedo, 2016]] ; [[#Uribe%20Rivera--2017|Uribe Rivera et al., 2017]] ; [[#Kasecker--2018|Kasecker et al., 2018]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Ulloa--2019|Ulloa, 2019]] ; [[#Ariza-Montobbio--2020|Ariza-Montobbio and Cuvi, 2020]] ). More interdisciplinary and transdisciplinary research will help to better understand and manage the relationships among governance, implementation, management priorities, wealth distribution and trade-offs between adaptation, mitigation and the SDGs.

Representations of climate change can also emerge as critiques and resistances that reveal that climate-change-labelled politics or interventions have posed even greater risks or do not address poverty issues ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Lampis--2013|Lampis, 2013]] ; [[#Pokorny--2013|Pokorny et al., 2013]] ; [[#Ojeda--2014|Ojeda, 2014]] ). Indigenous and social movements have joined with climate justice activists, calling for action to address climate change ( [[#Hicks--2016|Hicks and Fabricant, 2016]] ; [[#Ruiz-Mallén--2017|Ruiz-Mallén et al., 2017]] ; [[#Charles--2021|Charles, 2021]] ). The Bolivian Platform against Climate Change, a coalition of civil society and social movement organisations working to address the effects of global warming in Bolivia and to influence the broader global community, reflects an innovative dimension that, though at times conflictual, has shown how increasing climate variability hinders the right of Indigenous Peoples to the conservation of their culture and practices and illustrates how grassroots movements are increasingly taking over climate-change policy in the region ( [[#Hicks--2016|Hicks and Fabricant, 2016]] ). Social movements have engaged with international networks, such as Blokadia, which surged after COP 23, whose claims try to go beyond the protection of the environment and delve into issues of democracy and resource control ( [[#Martínez-Alier--2018|Martínez-Alier et al., 2018]] ).

Many social movements address adaptation to climate change. Some engage and participate in policy and planning, often producing good results at the local level. In contrast, top-down approaches without citizen or community participation have shown to be less effective ( ''high confidence'' ) ( [[#Krellenberg--2014|Krellenberg and Katrin, 2014]] ; [[#Nagy--2014b|Nagy et al., 2014b]] ; [[#Stein--2014|Stein and Moser, 2014]] ; [[#Ruiz-Mallén--2015|Ruiz-Mallén et al., 2015]] ; [[#Sherman--2015|Sherman et al., 2015]] ; [[#Waylen--2015|Waylen et al., 2015]] ; [[#Bizikova--2016|Bizikova et al., 2016]] ; [[#Chelleri--2016|Chelleri et al., 2016]] ; [[#Merlinsky--2016|Merlinsky, 2016]] ; [[#Villamizar--2017|Villamizar et al., 2017]] ).

Some conflicts in which the direct biophysical impacts of climate change play a major role can unleash social protests and strengthen social movements ( [[#12.6.4|Section 12.6.4]] ). In Cartagena, since 2010, the increase in precipitation has increasingly impacted the ''barrio'' Policarpa, promoprting residents to call for solutions to the problems caused by the coupled effect of flooding and industrial pollution. Also, in El Cambray II, in Guatemala City, in 2015 a nearby hill collapsed, causing the deaths of 280 people, 70 missing and the destruction of hundreds of homes. The affected community entered into a conflict with the municipality demanding resettlement and a reform of land-use planning ( [[#Stein%20Heinemann--2018|Stein Heinemann, 2018]] ).

<div id="12.5.8.2" class="h3-container"></div>

<span id="perceptions"></span>
==== 12.5.8.2 Perceptions ====

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Perception and understanding of climate change can be seen as an adaptive feature. In CSA, the awareness of climate change as a threat is increasing, a situation related to growth in climate justice activism and to the occurrence of extreme weather events of all kinds ( ''high confidence'' ) ( [[#Forero--2014|Forero et al., 2014]] ; [[#Magrin--2014|Magrin et al., 2014]] ; [[#Capstick--2015|Capstick et al., 2015]] ). Perception of climate change is positively associated across countries with the HDI and ND-Gain Readiness Index and negatively associated with the Vulnerability Index and, within countries, with education level, while perception is negatively associated with the degree of political affinity for the market economy ( [[#Azócar--2021|Azócar et al., 2021]] ). However, some communities do not associate their problems with the scientific concept of climate change, so discussions on whether it is human induced and its causes or relationship with other problems can become irrelevant ( [[#Sapiains%20Arrué--2017|Sapiains Arrué and Ugarte Caviedes, 2017]] ). Even communities affected by the same changes do not necessarily perceive them in the same way ( [[#Bonatti--2016|Bonatti et al., 2016]] ). The interpretations of change, as well as its causes and effects, can vary widely ( [[#Paerregaard--2018|Paerregaard, 2018]] ; [[#Scoville-Simonds--2018|Scoville-Simonds, 2018]] ). Rather than adapting to climate change, some people adapt climate change to their social worlds ( [[#Rasmussen--2016a|Rasmussen, 2016a]] ).

Perceptions tend to be different in rural and urban areas ( [[#Sherman--2015|Sherman et al., 2015]] ). In rural areas, it largely relates to temperature rise and changes in rainfall patterns, changes in agriculture (pests, calendars), biodiversity loss, solar radiation or changes in the oceans, and their impacts are sometimes related or even more attributed to socioeconomic and environmental drivers, as well as to negative financial outcomes ( ''high confidence'' ) ( [[#Infante--2013|Infante and Infante, 2013]] ; [[#Postigo--2014|Postigo, 2014]] ; [[#Jacobi--2015|Jacobi et al., 2015]] ; [[#Barrucand--2017|Barrucand et al., 2017]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#Martins--2018|Martins and Gasalla, 2018]] ; [[#Meldrum--2018|Meldrum et al., 2018]] ; [[#Córdoba%20Vargas--2019|Córdoba Vargas et al., 2019]] ; [[#Leroy--2019|Leroy, 2019]] ; [[#Viguera--2019|Viguera et al., 2019]] ; [[#Gutierrez--2020|Gutierrez et al., 2020]] ; [[#Iniguez-Gallardo--2020|Iniguez-Gallardo et al., 2020]] ; [[#Lambert--2020|Lambert and Eise, 2020]] ). In such places as Amazonia, perception increases with age ( [[#Funatsu--2019|Funatsu et al., 2019]] ). In Mediterranean Chile, younger, more educated producers and those who own their land tend to have clearer perceptions than older, less educated or tenant farmers, but they do not have a clear perception or how it may affect their yields and farming operation ( [[#Roco--2015|Roco et al., 2015]] ). In some dry and humid Ecuadorian montane forests, peasantss perceptions are in line with the scientific data, but they have a lot of difficulties to predict the changes and believe that they may not be prepared and can only be reactive ( [[#Herrador-Valencia--2016|Herrador-Valencia and Paredes, 2016]] ). In an Andean community, perceptions of climate change are homogeneous and do not vary according to gender, age or ethnicity ( [[#Cáceres-Arteaga--2020|Cáceres-Arteaga et al., 2020]] ). Among representatives of five municipalities of Lima, it was found that climate change is not well understood and residents have trouble distinguishing it from other environmental issues ( [[#Siña--2016|Siña et al., 2016]] ). In an Amazonian region, farmers provided a more accurate description than regional institutions of how it affects the local livelihood system ( [[#Altea--2020|Altea, 2020]] ). In Cuenca Auqui peasants attribute recently experienced challenges in agricultural production mainly to perceived changes in precipitation patterns, but statistical analyses of daily precipitation records at nearby stations do not corroborate those perceived changes ( [[#Gurgiser--2016|Gurgiser et al., 2016]] ).

<div id="12.5.8.3" class="h3-container"></div>

<span id="gender-and-intersectionality"></span>
==== 12.5.8.3 Gender and Intersectionality ====

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There is ample empirical evidence that the impacts of climate change are not of equal scope for men and women. Women, particularly the poorest, are more vulnerable and are impacted in greater proportion. Often, for several economic and social reasons, women have less capacity to adapt, further widening structural gender gaps ( ''high confidence'' ) (Box 7.4; [[#Arana%20Zegarra--2017|Arana Zegarra, 2017]] ; [[#Casas%20Varez--2017|Casas Varez, 2017]] ; [[#Segnestam--2017|Segnestam, 2017]] ; [[#Acosta--2019|Acosta et al., 2019]] ; [[#Aldunce%20Ide--2020|Aldunce Ide et al., 2020]] ; [[#Olivera--2021|Olivera et al., 2021]] ; [[#Silva%20Rodríguez%20de%20San%20Miguel--2021|Silva Rodríguez de San Miguel et al., 2021]] ). Gender equity is deemed to be central to discussions on climate-change adaptation policies. In issues such as drinking water, energy, disasters, impacts on health and agriculture and capacity to migrate, women (poor women in particular) are affected in greater proportion, further widening structural gender gaps. In a rural community vulnerable to drought, short-term coping was more common among the women, especially among female heads of household, while adaptive actions were more common among the men; there are gendered inequalities in access to and control over different forms of capital that lead to a gender-differentiated capacity to adapt, where men are better able to adapt and women experience a downward spiral in their capacity to adapt and increasing vulnerability to drought ( [[#Segnestam--2017|Segnestam, 2017]] ).

However, women are not always the more vulnerable group. While in a broad sense climate-change impacts women more severely, there are situations where they have reacted, adapted better to or been more resilient. Grassroots women self-help groups can be active agents of change for their communities, designing and delivering gender-responsive adaptation solutions ( [[#Huairou%20Commission--2019|Huairou Commission, 2019]] ). Some studies suggest that women establish friendlier relationships with the environment and towards natural resources; studies on masculinity and environment confirm this tendency ( [[#Brough--2016|Brough et al., 2016]] ). In a multi-country study, some female-headed households tend to be slightly less vulnerable and more resilient than male-headed households, though some exceptions were found among sub-groups ( [[#Andersen--2017|Andersen et al., 2017]] ). In Chile, women are more likely to modernise irrigation and infrastructure, and gender appears to be an important element in drought adaptation ( [[#Roco--2016|Roco et al., 2016]] ). A change to agroecological practices has improved gender equality and adaptive capacity to climate change ( [[#Cáceres-Arteaga--2020|Cáceres-Arteaga et al., 2020]] ).

Recent studies emphasise that a gender approach to social inequalities ought to move beyond just looking at men and women as experiencing impacts in a differentiated manner; rather, an intersectional analysis illuminates how different individuals and groups relate differently to climate change due to their situatedness in power structures based on context-specific and dynamic social categorisations ( ''high confidence'' ) ( [[#Kaijser--2014|Kaijser and Kronsell, 2014]] ; [[#Djoudi--2016|Djoudi et al., 2016]] ; [[#Thompson-Hall--2016|Thompson-Hall et al., 2016]] ; [[#Olivera--2021|Olivera et al., 2021]] ). Thus, the relationship between gender and adaptation demands an analytical framework that connects environmental problems with social inequalities in a complex way ( [[#Godfrey--2012|Godfrey, 2012]] ). An intersectional approach helps to better capture the diversity of adaptive strategies that men and women adopt vis-à-vis climate change. Particular constellations of race, gender, class, age or nationality reveal more complex realities ( ''high confidence'' ).

<div id="12.5.8.4" class="h3-container"></div>

<span id="migrations-and-displacements"></span>
==== 12.5.8.4 Migrations and Displacements ====

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Migration and displacements are multi-causal phenomena, and climate may exacerbate political, social, economic or other environmental drivers ( ''high confidence'' ) ( [[#Kaenzig--2014|Kaenzig and Piguet, 2014]] ; [[#Brandt--2016|Brandt et al., 2016]] ; Priotto and Salvador Aruj, 2017; [[#Sudmeier-Rieux--2017|Sudmeier-Rieux et al., 2017]] ; [[#Radel--2018|Radel et al., 2018]] ; [[#Heslin--2019|Heslin et al., 2019]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ; [[#Silva%20Rodríguez%20de%20San%20Miguel--2021|Silva Rodríguez de San Miguel et al., 2021]] ). Many case studies have been conducted on the region, but data to assess and monitor precisely the effects of climate- and weather-related disasters in migration and displacements from a broad perspective remain inaccurate (Priotto and Salvador Aruj, 2017; [[#Abeldaño%20Zuñiga--2020|Abeldaño Zuñiga and Fanta Garrido, 2020]] ). The most common climatic drivers include tropical storms and hurricanes, heavy rains, floods and droughts ( [[#Kaenzig--2014|Kaenzig and Piguet, 2014]] ). Positive climatic conditions also can facilitate migration ( [[#Gray--2013|Gray and Bilsborrow, 2013]] ). Peru, Colombia and Guatemala are among the countries with the largest average displacements caused by hydro-meteorological causes; Brazil had 295,000 people displaced because of disasters in 2019 (Global Internal Displacement Database, https://www.internal-displacement.org/database/displacement-data ).

These processes can be interpreted as impacts on vulnerable peoples, but also as adaptation strategies to manage risks and reduce exposure when people continue with their lives, temporarily or permanently, in a different but stable situationor when family members send remittances to those that remain in the affected areas ( [[IPCC:Wg2:Chapter:Chapter-7#7.4.3.2|Section 7.4.3.2]] ; Cross-Chapter Box MIGRATE in Chapter 7). The remittances create opportunities for adaptive capacity building because they reduce some vulnerabilities in the form of infrastructures, agricultural supplies, food, education or health, as in northern CA (NU [[#CEPAL--2018|CEPAL, 2018]] ). Anyhow, migration as adaptation is not available to everyone ( [[#Kaenzig--2014|Kaenzig and Piguet, 2014]] ), and the idea has also been contested because it may not help to overcome structural problems or point to in situ options ( [[#Radel--2018|Radel et al., 2018]] ; [[#Ruiz-de-Oña--2019|Ruiz-de-Oña et al., 2019]] ). The causal processes are complex. Surveys of migrants usually find that the main reported reason for migration is to find a job or to increase household income ( [[#Wrathall--2016|Wrathall and Suckall, 2016]] ; [[#OIM--2017|OIM, 2017]] ; [[#Radel--2018|Radel et al., 2018]] ), but the underlying reason for the lack of a job or income is rarely examined and at times may be related to climatic hazards.

Migration most often originates in rural areas, with people moving to other rural or urban areas within their home countries (Table Cross-Chapter Box MIGRATE 1 in Chapter 7). In the Amazon, approximately 80% of the population are concentrated in cities due to rural–urban migrations in search of better income, livelihoods and services, in cases associated with extreme floods and droughts ( [[#Pinho--2015|Pinho et al., 2015]] ). In Ecuador, environmental variables are most likely to enhance international than internal migration ( [[#Gray--2013|Gray and Bilsborrow, 2013]] ). Hurricanes have been seen as positive triggers for international migration in CA ( [[#Spencer--2018|Spencer and Urquhart, 2018]] ). The highlands of Peru see different patterns, including daily circular migration to combine the scarce income from agricultural production with urban income, rather than abandoning farm land ( [[#Milan--2014|Milan and Ho, 2014]] ; [[#Zimmerer--2014|Zimmerer, 2014]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ).

Migration to cities can mean opportunities for migrants and for urban areas, but it can also worsen existing problems, as urban poor people can become even more exposed and vulnerable, and the pressure on urban capacities may not be well absorbed ( ''high confidence'' ) ( [[#Chisari--2016|Chisari and Miller, 2016]] ; [[#Gemenne--2020|Gemenne et al., 2020]] ). Internal migration to cities is likely to exacerbate pre-existing vulnerabilities related to inequality, poverty, indigence and informal activities and housing ( [[#Warn--2014|Warn and Adamo, 2014]] ). Immigration can make cities/residents more vulnerable to climate-change risks (Sections 12.5.5 and 12.5.7). Groups such as children, Indigenous Peoples and the poor are usually among the most vulnerable in migrations and displacements, which poses challenges to national policies and international aid ( [[#Sedeh--2014|Sedeh, 2014]] ; [[#Gamez--2016|Gamez, 2016]] ; [[#Ulla--2016|Ulla, 2016]] ; Priotto and Salvador Aruj, 2017; [[#Ramos--2017|Ramos and de Salles Cavedon-Capdeville, 2017]] ; [[#Amar-Amar--2019|Amar-Amar et al., 2019]] ; [[#Gemenne--2020|Gemenne et al., 2020]] ). In migration or displacement driven by climate effects, women are prone to lose their leadership, autonomy and voice, especially in new organisational structures imposed by authorities. This is especially the case in temporary accommodation camps created after disasters, exacerbating existing differentiated vulnerabilities ( [[#Aldunce%20Ide--2020|Aldunce Ide et al., 2020]] ). International migration has become more dangerous and difficult as border controls have become stricter, but programmes such as one to help temporary agricultural workers from Guatemala to Canada have proven successful ( [[#Gabriel--2018|Gabriel and Macdonald, 2018]] ). At the same time, emigration may lead to the loss of IKLK for adaptation ( [[#Moreno--2020b|Moreno et al., 2020b]] ).

Some areas are more likely to generate climatic migration: the Andes, the dry areas of Amazonia, northern Brazil and northern countries in CA ( ''high confidence'' ). Northeastern Brazil will lose population that will move to the south, deepening existing inequalities ( [[#Oliveira--2020|Oliveira and Pereda, 2020]] ). In a study of eight countries around the world, including Guatemala and Peru, a link was found between rainfall variability and food insecurity, which could lead to migration in areas of high prevalence of rainfed agriculture and low diversification ( [[#Warner--2014|Warner and Afifi, 2014]] ). In CA, younger individuals are more likely to migrate in response to hurricanes and especially to droughts ( [[#Baez--2017|Baez et al., 2017]] ).

The perception of gradual changes lowers the likelihood of internal migration, while sudden-onset events increase movement ( [[#Koubi--2016|Koubi et al., 2016]] ). On the other hand, it has been seen that extreme events like floods or droughts can hinder population mobility, immobilising them in their localities ( [[#Thiede--2016|Thiede et al., 2016]] ). These immobilised populations are supposed to face a double set of risks: they are unable to move away from environmental threats, and their lack of capital makes them especially vulnerable to environmental changes ( [[#Black--2011|Black et al., 2011]] ). In CSA, migrating to the US is becoming dangerous and expensive because that country is restricting entry; these trends expose local populations to the risk of becoming immobile in the near future in a place where they are extremely vulnerable ( [[#Ruano--2014|Ruano and Milan, 2014]] ; [[#McLeman--2019|McLeman, 2019]] ). A survey in Guatemala found no correlation between migration to the US and severe food insecurity in households, but the correlation became significant if the level of food insecurity was moderate, suggesting that families in extreme hardship did not have the resources to migrate ( [[#Aguilar--2019|Aguilar et al., 2019]] ). At the same time, some populations just have chosen not to move, as in Peru, where immobility among dissatisfied people is more likely to be caused by attachment to place than resource constraints ( [[#Adams--2016|Adams, 2016]] ; [[#Correia--2017|Correia and Ojima, 2017]] ). Some populations have chosen to adapt relying on their IKLK ( [[#Boillat--2013|Boillat and Berkes, 2013]] ).

Migration is often the last resort for rural communities facing water stress problems ( [[#Magrin--2014|Magrin et al., 2014]] ; [[#Ruano--2014|Ruano and Milan, 2014]] ). In Bolivia, glacial retreat has not triggered new migration flows and had a limited impact on the existing migratory patterns ( [[#Kaenzig--2015|Kaenzig, 2015]] ). In SA, climatic variability increases the likelihood of interprovince migration, rather than trapping populations. In a study of interprovincial migration motivated by temperature, an exception arose in Bolivia, and even if that could suggest an immobilised population ( [[#Thiede--2016|Thiede et al., 2016]] ), it is not clear whether they want to stay and adapt. In some cases, people want to move but wait for relocation until after the climate-related disasters have subsided (Priotto and Salvador Aruj, 2017).

<div id="12.5.8.5" class="h3-container"></div>

<span id="financing"></span>
==== 12.5.8.5 Financing ====

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Climate-change financing is unequally distributed among CSA countries ( ''high confidence'' ). Financing of climate-change adaptation remains very much delegated to multilateral and bilateral cooperation, and the governments in the region have heavily relied on it. Still, there are some concerns regarding justice in the distribution of these funds ( [[#Khan--2020|Khan et al., 2020]] ). The UNFCCC has created financing mechanisms throughout its functioning years, but a wide range of issues that present challenges for access by recipients ( [[#Hickmann--2019|Hickmann et al., 2019]] ). These include a lack of technical capacity, difficulties in following the procedures established by the various financial entities and low levels of awareness about the need for action, as well as the different sources of funds available. The fiscal policies of various countries have contributed to government financing in the fight against climate change ( [[#World%20Bank--2021|World Bank, 2021]] ). Since the Paris Agreement, countries have pledged NDCs that introduce the need to design and implement carbon budgets with a corresponding consideration of the efficiency and costs and benefits involved in each mitigation or adaptation to climate-change projects ( [[#Fragkos--2020|Fragkos, 2020]] ).

According to UNFCCC, Latin America and the Caribbean, for the period 2015–2016, obtained 22% of climate financing from multilateral climate funds. In this section we use data from https://climatefundsupdate.org/data-dashboard , and most of the reported information for Latin American and the Caribbean includes Mexico, since the scope of this chapter does not include Mexico, so we must rely on the raw data included in the data dashboard mentioned in the link (see also Guzmán et al. [2016]). According to the data, 76% of climate-related financing went to mitigation projects, with the remaining 24% going to adaptation. Of the total financing provided by the multilateral climate funds to the region, 51% took the form of concessional loans, while 47% was provided as grants. For the region, approvals in the 2015–2016 period were concentrated in Argentina, Chile, Brazil and Colombia, where large-scale mitigation projects were launched supported by the Green Climate Fund (GCF) and the Clean Technology Fund (CTF). For the period 2003–2019, the total contribution of climate financing to SA and the Caribbean is about USD 3558 million. The largest contributors to climate financing in the region come from the GCF, which approved USD 824.2 million for 23 projects. Brazil is the top recipient with USD 195 million, followed by Argentina with about USD 162 million. The second provider is the Amazon Fund with USD 717 million allocated to 102 projects in Brazil. In 2018, the CTF became the third largest source of financing, with USD 483 million dollars approved for 24 projects; the main recipient is Chile with USD 16,207 million, followed by Colombia with USD 170 million. The five largest projects approved in the region in 2018 were through the GCF. Brazil (USD 195 million) received support for reducing energy consumption across Brazilian cities, while Argentina (USD 103 million) received support to scale up investments by small and medium-sized enterprises (SMEs) in renewable energy and energy efficiency. In both cases, financing is predominantly provided as concessional loans.

Climate financing in CSA is mainly focused on mitigation actions ( ''high confidence'' ). In SA and the Caribbean, 73% (USD 2579 million) of funding to date has supported mitigation. Only 21% (USD 761 million) of the funding supports adaptation projects, and the remaining 4% (USD 217 million) supports multi-focus projects. Of the 51 new projects in SA and the Caribbean approved in 2018–2019, the GCF financed USD 508 million over ten projects. Amazon Fund was next with USD 81 million for 10 projects. While the GCF focuses on large and transformative projects and programmes, and in connection with broader reform of the policy framework in the region, the Amazon Fund targets smaller project interventions.

Climate financing in the region is concentrated in Brazil, which receives a third of the region’s funding, with 41 mitigation activities receiving more than 6 times that of adaptation from multilateral climate funds. By the size of its GDP, Brazil receives the largest amount of financing; this leaves the poorest countries with little or no financing and therefore reinforces a vicious circle of poverty and vulnerability. Whether this is due to Brazil’s being more successful at presenting eligible projects, a lack of commitment from other developing countries or some other structural factors is an open question. In any case, compensation schemes for the most vulnerable countries appear as needed, given the differences in vulnerability to climate-related damage ( [[#Antimiani--2017|Antimiani et al., 2017]] ). This is aggravated by the fact that fund management is in the hands of supranational entities while inequalities remain in regions within a country, particularly in highly centralised countries, as is the case for countries in the region.

COVID-19 recovery plans can have synergistic effects for climate-change adaptation ( ''medium confidence: low evidence, high agreement'' ). A key decision point for adaptation will be how the world responds to the pandemic. The global recovery can serve as a catalyst to increased and more equitable climate financing. Globally, recovery packages will likely have the power to change the global trajectory towards meeting the targets of the Paris Agreement and building a more just future ( [[#Forster--2020|Forster et al., 2020]] ). Several factors are relevant to the design of economic recovery packages: the long-run economic multiplier, contributions to the productive asset base and national wealth, speed of implementation, affordability, simplicity, impact on inequality and various political considerations ( [[#Hepburn--2020|Hepburn et al., 2020]] ). A key objective of any recovery package is to stabilise expectations, restore confidence and channel desired surplus savings into productive investment. However, ‘business as usual’ implies temperature increases over 3°C, implying great future uncertainty, instability and climate damage. An alternative way to restore confidence is to steer investment towards a productive and balanced portfolio of sustainable physical capital, human capital, social capital, intangible capital and natural capital assets ( [[#Zenghelis--2020|Zenghelis et al., 2020]] ), consistent with global goals on climate change. Finally, any recovery package, including climate-friendly recovery, is unlikely to be implemented unless it also addresses existing societal and political concerns—such as poverty alleviation, inequality and social inclusion—which vary from country to country.

<div id="12.5.9" class="h2-container"></div>

<span id="adaptation-options-to-address-key-risks-in-central-and-south-america"></span>
=== 12.5.9 Adaptation Options to Address Key Risks in Central and South America ===

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This section integrates, in Table 12.10 as follows, the sectoral assessment of adaptation options (Sections 12.5.1–12.5.8) with the eight key risks assessed in the region ( [[#12.4|Section 12.4]] ). Table 12.10 presents a list of the summarised adaptation options, which are detailed in their adaptation sections, from [[#12.5.1|Section 12.5.1]] to [[#12.5.8|Section 12.5.8]] in this chapter.

'''Table 12.10 |''' Adaptation options addressing key risks organised by sector. See the note at the end for descriptions of the sector name abbreviations.

{| class="wikitable"
|-
| colspan="2"| '''''1. Risk of food insecurity due to frequent/extreme droughts'''''

|-
| T&amp;F ecosystems

| EbA: Agroecosystem resilience practices

|-
| O&amp;C ecosystems

| Not assessed (NA)

|-
| Water

| Water infrastructure and irrigation; NbS and PES; participatory water management; multi-purpose water use

|-
| Food

| Climate information services; EWSs; insurance; land use planning; LCA strategies; agroforestry; IKLK

|-
| Cities

| NA

|-
| Health and well-being

| EWS, insurance; participatory water management; water infrastructure and irrigation

|-
| Poverty and SD

| CbA; government and institutional support

|-
| Human Dimension

| Participatory management; incorporation of IKLK in water and crop management; education and communication

|-
| colspan="2"| '''''2. Risk to life and infrastructure due to floods and landslides'''''

|-
| T&amp;F ecosystems

| NA

|-
| O&amp;C ecosystems

| NA

|-
| Water

| NbS; land use regulation; EWSs; integrated risk management

|-
| Food

| NA

|-
| Cities

| Urban planning; climate-adapted parameters in land use and building regulation; intersectoral and multi-level governance; slum upgrading; social housing improvement; urban control systems; CbA; risk management plans; integrated watershed management; flood control programmes; environment protected areas; household relocation; EWS; NbS; mapping tools; GGI; water storage solutions; wetland restoration; SUDSs; LID; river restoration; multi-functional landscapes; improving basic sanitation services

|-
| Health and well-being

| EWS; GGI; community-led and managed relocation; insurance

|-
| Poverty and SD

| Secure location; social housing policies; EWS

|-
| Human dimensions

| Education and communication

|-
| colspan="2"| '''3. Risk of water insecurity'''

|-
| T&amp;F ecosystems

| Monitoring systems; EbA; forest protection and restoration; watershed protection

|-
| O&amp;C ecosystems

| CbA; land use and development regulation

|-
| Water

| Water infrastructure and irrigation; NbS and PES; participatory water management; multi-purpose water use

|-
| Food

| Management and planning; NbS; soil and water conservation

|-
| Cities

| Intersectoral and multi-level governance; CbA; risk management plans; integrated watershed management; environment protected areas; NbS; GGI; wetland restoration; improving basic sanitation services; reservoir system

|-
| Health and well-being

| Protection and restoration; NAPs; participatory water management

|-
| Poverty and SD

| NbS: water harvesting; equitable water distribution

|-
| Human dimensions

| Participatory management; incorporation of IKLK in water management; education and communication

|-
| colspan="2"| '''''4. Risk of severe health effects due to increasing epidemics'''''

|-
| T&amp;F ecosystems

| NA

|-
| O&amp;C ecosystems

| NA

|-
| Water

| Water infrastructure; sanitation improvement

|-
| Food

| NA

|-
| Cities

| NA

|-
| Health and well-being

| EWS; health-climate surveillance systems; national plans on health; communal management; GGI; protection and restoration

|-
| Poverty and SD

| CbA; transparent democratic governance; equitable services; education

|-
| Human dimensions

| Education and communication

|-
| colspan="2"| '''''5. Systemic risks of surpassing infrastructure and public service systems'''''

|-
| T&amp;F ecosystems

| NA

|-
| O&amp;C ecosystems

| EWS; EbA; territorial planning; CbA; land use and development regulation; GGI

|-
| Water

| Water infrastructure; land use regulation; water retention capacity; EWS; capacity building

|-
| Food

| NA

|-
| Cities

| Urban planning; climate-adapted parameters in land use and building regulation; intersectoral and multi-level governance; slum upgrading; social housing improvement; CbA; improving basic sanitation services; micro wastewater treatment plants

|-
| Health and well-being

| EWS; vulnerability and risk maps; NAPs; GGI

|-
| Poverty and SD

| Transparent, democratic governance

|-
| Human dimensions

| NA

|-
| colspan="2"| '''''6. Risk of large-scale changes and biome shifts in Amazon'''''

|-
| T&amp;F ecosystems

| Monitoring systems; EbA; protected areas; forest protection and restoration; watershed protection

|-
| O&amp;C ecosystems

| NA

|-
| Water

| IWRM

|-
| Food

| Territorial planning

|-
| Cities

| NA

|-
| Health and well-being

| Protection and restoration

|-
| Poverty and SD

| Insurance; micro-credits; PES; CbA

|-
| Human dimensions

| Participatory management; incorporation of IK and LK in forest management; education and communication

|-
| colspan="2"| '''''7. Risk to coral reef ecosystems due to coral bleaching'''''

|-
| T&amp;F ecosystems

| NA

|-
| O&amp;C ecosystems

| Zoning schemes; MPAs; EbA; CbA; adherence to international treaties

|-
| Water

| NA

|-
| Food

| NA

|-
| Cities

| NA

|-
| Health and well-being

| Protection and restoration

|-
| Poverty and SD

| NA

|-
| Human dimensions

| NA

|-
| colspan="2"| '''''8. Risks to coastal socioecological systems due to sea level rise, storm surges and coastal erosion'''''

|-
| T&amp;F ecosystems

| NA

|-
| O&amp;C ecosystems

| EbA; planned relocation; GGI

|-
| Water

| NA

|-
| Food

| NA

|-
| Cities

| Urban planning; climate-adapted patterns in land use and building regulation; intersectoral and multi-level governance; CbA; risk management plans; household relocation; NbS; GGI

|-
| Health and well-being

| GGI; communal management; protection and restoration

|-
| Poverty and SD

| Secure location; CbA relocation

|-
| Human dimensions

| Participatory management; education and communication

|}

Notes:

Some sectors are represented by abbreviations: Terrestrial and freshwater ecosystems and their services (T&amp;F ecosystems); ocean and coastal ecosystems and their services (O&amp;C ecosystems); food, fibre and other ecosystem products (food); cities, settlements and key infrastructure (cities); poverty, livelihood and sustainable development (poverty and SD); cross-cutting issues in the human dimension (human dimensions).

<div id="12.5.10" class="h2-container"></div>

<span id="feasibility-assessment-of-adaptation-options"></span>
=== 12.5.10 Feasibility Assessment of Adaptation Options ===

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This section assesses the feasibility of selected adaptations options by sector, relevant for CSA, in five dimensions (economic, technological, institutional, social, environmental and geophysical), according to the methodology developed by [[#Singh--2020a|Singh et al. (2020a)]] . Table 12.11 shows the summary of results and Table SM12.7 the details of the assessment and the supporting literature.

'''Table 12.11 |''' Feasibility assessment of selected adaptation options for CSA region.

{| class="wikitable"
|-
! rowspan="2"| System

! rowspan="2"| Adaptation option

! rowspan="2"| Evidence

! rowspan="2"| Agreement

! colspan="6"| Dimension assessed

|-
! Economic

! Technological

! Institutional

! Social

! Environmental

! Geophysical

|-
| Food, fibre and other ecosystem products

| Agroforestry

| ''Medium''

| ''High''

| Insignificant barriers

| Mixed effect

| Significant barriers

| Mixed effect

| Insignificant barriers

| Mixed effect

|-
| Health and well-being

| EWSs

| ''Robust''

| ''High''

| Insignificant barriers

| Mixed effect

| Significant barriers

| Mixed effect

| Insignificant barriers

| Mixed effect

|-
| Water

| Multi-use of water storage approaches

| ''Robust''

| ''Medium''

| Insignificant barriers

| Mixed effect

| Mixed effect

| Mixed effect

| Mixed effect

| Insignificant barriers

|-
| Freshwater and terrestrial ecosystems

| EbA

| ''Medium''

| ''High''

| Insignificant barriers

| Mixed effect

| Mixed effect

| Insignificant barriers

| Insignificant barriers

| Insignificant barriers

|}

<div id="12.5.10.1" class="h3-container"></div>

<span id="food-fibre-and-other-ecosystem-products-agroforestry"></span>
==== 12.5.10.1 Food, fibre and other ecosystem products: agroforestry ====

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For agrifood systems, the adoption of agroforestry provides for more diverse and sustainable agricultural production, where farmers maintain or improve their current production by incorporating suitable trees that ameliorate climatic conditions. Thus, in the same unit of land, these systems incorporate exotic tree species or managed native forests into farming systems allowing for the simultaneous production of trees, crops and livestock with different spatial arrangements or temporal sequences. On the other hand, it is recognised that the initial investment and time until trees start to produce may create an economic vulnerability. Therefore, there is a need to design adequate programmes and allocate resources for agroforestry system implementation and technical assistance and training ( ''medium confidence'' ). Also, some market schemes such as PES and certification can help to reduce this vulnerability.

<div id="12.5.10.2" class="h3-container"></div>

<span id="health-and-well-being-early-warning-systems"></span>
==== 12.5.10.2 Health and Well-being: Early-warning Systems ====

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For the health sector, we assessed the barriers and facilitators for the implementation of climate-driven EWSs under natural extreme events and epidemic situations. We found institutional dimensions to be potential barriers. These included the legal and regulatory feasibility, institutional capacity and administrative feasibility, transparency and political acceptability ( ''high confidence'' ). The fewest barriers were identified for the economic and environmental dimensions.

One of the main institutional challenges is the lack of policy with climate–health linkages. Opportunities include a national plan for the health sector to address the impacts of climate change by formalising collaborations via agreements memoranda of understanding (MOUs). Another key barrier is that relatively few institutions in the region have the human technical and administrative capacity to implement and operate an EWS. Regional platforms may provide a solution for technical assistance at national levels.

On the other hand, the economic dimensions faced relatively few barriers, although the initial costs of designing, implementing, equipping and maintaining the system are a potential barrier for health-related sectors with reduced budgets. However, the health benefits and economic savings (due to averted epidemics or damage from disasters) may offset these costs. The resilience built into the health sector by these systems may be applicable to other economic sectors that could benefit from the early warning of an immenent extreme event and associated health impacts.

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<span id="watermulti-use-water-storage-approaches"></span>
==== 12.5.10.3 Water—Multi-use Water Storage Approaches ====

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For the water sector, geophysical and economic dimensions do not pose a major barrier thanks to the potential reduction of flood hazard exposure, physical-technical viability of project implementation, different suitable economic mechanisms for joint public-private financing and more efficient water use. However, limited institutional capacities and the social-environmental impacts of large water infrastructure ( [[#12.5.3|Section 12.5.3]] ) reduce the institutional, social, environmental and, to some extent, technological feasibility. This may be a potential barrier to the adaptive approach of multi-use water storage ( ''medium confidence'' ).

<div id="12.5.10.4" class="h3-container"></div>

<span id="freshwater-and-terrestrial-ecosystemsecosystem-based-adaptation"></span>
==== 12.5.10.4 Freshwater and Terrestrial Ecosystems—Ecosystem-based Adaptation ====

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In the terrestrial and freshwater ecosystem sector, we assessed the feasibility of implementing EbA options in the CSA region. Given that EbA encompasses a wide range of projects, techniques and political and socioeconomic arrangements, extreme care should be taken when applying these general findings to particular cases. EbA can enhance food sovereignty and carbon stocks and foster SDGs by protecting and restoring ecosystems’ health and productivity. EbA is a strategy that frequently involves bottom-up decision-making and local communities’ empowerment and usually contributes to inequality reduction. EbA tends to benefit vulnerable groups, but aspects such as the impact on socioeconomic inequalities when implemented should be taken into account.

In general, EbA does not require advanced technologies for local communities. However, limitations in technical assistance and funding for specific key technologies and training may act as a barrier for EbA adoption ( ''medium confidence'' ). EbA practices can reduce risk in several ways by increasing awareness among communities and providing food diversity and production. EbA is recognised as a desirable policy for most stakeholders in CSA, particularly because as a strategy it incorporates environmental and social concerns. Nonetheless, it is important that all stakeholders agree on the goals and methods for EbA to be effective. A lack of institutional coordination, clear goals and strategies were identified as a potential barrier for EbA implementation. EbA is heavily based in local and IK, as well as academic ecological knowledge.

For the adaptation options analysed, significant barriers and mixed effects were observed for the institutional dimension, which indicates the relevance of the design and implementation of public policies and institutional arrangements for effective adaptation in the region. Considering the results, there is a need to advance initiatives, programmes and projects that facilitate adaptation to climate change. In the same way, barriers were apparent in the technological dimension, which indicates the importance of increasing access and diffusion of appropriate techniques and technologies in order to face the challenges of climate change in the region.

<div id="12.6" class="h1-container"></div>

<span id="case-studies"></span>