Latin America and the Caribbean has been at the forefront of efforts to apply policy tools that simultaneously address both climate change and biodiversity loss (Alpízar et al. 2020). Some of the most important are discussed next. Among the common themes that emerge are a scarcity of rigorous evidence on the efficacy of all manner of conservation policy tools and the constraints imposed on the efficacy of these tools by various governance issues (Baylis et al. 2016; Börner et al. 2016).

## Protected Areas

The most frequently applied, highest profile interventions for biodiversity conservation worldwide are protected areas, defined as “geographical spaces that are recognized, dedicated, and managed to achieve the long-term conservation of nature” (UNEP-WCMC, IUCN, and NGS 2021). Protected areas typically entail regulatory restrictions on such activities as changes in land use and land cover and the extraction of natural resources. Latin America and the Caribbean has been at the forefront of their use, with 23 percent of its land area and 12 percent of marine area protected in 2020—considerably more than worldwide totals amounting to 15 percent of land area and 7 percent of marine area (UNEP-WCMC, IUCN, and NGS 2021).

The track record of protected areas in stemming biodiversity loss is mixed. Some are so-called paper parks—polygons drawn on maps that receive few financial, human, or political resources and consequently have little or no impact on conservation (Blackman, Pfaff, and Robalino 2015; Wade et al. 2020). That said, counterfactual studies suggest that, on average, protected areas are associated with modest reductions in deforestation (Busch and Ferretti-Gallon 2023; Börner et al. 2020). They have proved particularly effective in South America and tropical countries in upper-middle-income brackets (Shah et al. 2021).

Two broad strategies have been used to manage protected areas: strict protection that prohibits all economic activities within them and mixed-use protection that allows sustainably managed activities. Although evidence on the benefits and costs of each approach varies, a recent multi-country study finds that, in South America, strict protected areas have been more effective in stemming forest loss than mixed-use ones (Shah et al. 2021).

A longstanding concern about protected areas, particularly strictly protected ones, is that because they restrict economic activity, they can have adverse socioeconomic effects on local communities. In principle, mixed-use protected areas could not only avoid such effects but could even generate socioeconomic benefits related to ecotourism and sustainable resource use and, ultimately, result in more effective long-term conservation outcomes
(Andam et al. 2008; Sims 2010; Robalino and Villalobos 2010). Guatemala's Maya Biosphere Reserve is often held up as a model of successful mixed-use protection. Regulators there granted local communities rights to extract timber sustainably within the reserve. In at least some cases, counterfactual evidence suggests this policy both reduced forest loss and had positive local socioeconomic effects (Bocci et al. 2018; Blackman 2015).

A closely related issue is shared governance or co-management of protected areas, whereby governments and nongovernmental stakeholders sign formal agreements to share rights, responsibilities, and participation in the decision-making process. Co-management can even include an agreement to implement more stringent conservation measures (Oldekop et al. 2016). Actors that co-manage protected areas in the region include Indigenous communities, community associations, municipalities, and nongovernmental organizations (NGOs; Alpízar et al. 2020). Herrera, Pfaff, and Robalino (2019) and Nolte et al. (2013) find shared governance to be an important factor in avoiding deforestation within protected areas in the Brazilian Amazon.

## Payments for Ecosystem Services and REDD+

Payments for ecosystem services (PESs) are voluntary payments made by users of these services to their providers, conditional on the providers' abiding by agreed-upon rules of natural resource management. Payments to providers are made either by the actual private users of the services or by public entities that represent them (Wunder et al. 2020; Persson and Alpizar 2013; Pattanayak, Wunder, and Ferraro 2010). An example of a PES scheme would be one in which downstream users pay upstream land managers to ensure the quality of river water by conserving forests in the river's catchment. PES initiatives increasingly are used to promote climate change mitigation in low- and middle-income countries (Snilsveit et al. 2019).

Reducing Emissions from Deforestation and Forest Degradation (REDD+) is a PES-like mechanism developed under the United Nations Framework Convention on Climate Change starting in the early 2000s to mitigate forest carbon emissions (Wunder et al. 2020; Lima et al. 2017). REDD+ projects are voluntary arrangements wherein a country or subnational unit within a country pays another country or subnational unit to conserve and/or restore forests. REDD+ projects often aim simultaneously to spur climate mitigation, benefit local communities, and support biodiversity. Roughly 350 REDD+ projects are being implemented in fifty countries (Guizar-Coutiño et al. 2022; Lima et al. 2017; Bayrak and Marafa 2016; Magnago et al. 2015).

As with protected areas, Latin America and the Caribbean has been a leader in the adoption of PES schemes since the late 1990s and of REDD+
projects since the early 2000s (Lima et al. 2017; Pattanayak, Wunder, and Ferraro 2010; Alpízar et al. 2020). Of the close to 550 PES schemes implemented worldwide, roughly half are in the region (Alpízar et al. 2020), including two out of three of the world's oldest and best-known national programs: the Payments for Hydrological Services Program in Mexico and the Programa de Pagos por Servicios Ambientales in Costa Rica (Snilsveit et al. 2019; Pattanayak, Wunder, and Ferraro 2010) (Box 4.3). As for REDD+ projects, Brazil, Ecuador, and Mexico have pioneered them, implementing projects at both regional and national scales (Lima et al. 2017).

Although some countries in the region, such as Costa Rica, Ecuador, and Mexico, have created PES programs with explicit biodiversity conservation goals, most have focused on the protection of easily identified ecosystems—mainly watersheds and forests—as opposed to biodiversity and habitat conservation per se (Alpízar et al. 2020). The two sets of goals are typically complementary, however, especially if forests and watersheds at high risk of biodiversity loss receive payments (Alpízar et al. 2020; Barlow et al. 2018; Lewis, Edwards, and Galbraith 2015).

Several challenges limit the effectiveness of PES initiatives. Perhaps most important, land volunteered into a PES program is often at low risk of being cleared or degraded, with or without payment; as a result, the benefit generated by the payments is minimal (Box 4.3). Also, payments are often not targeted to ensure the provision of ecosystem services. PES programs that provide hydrological services, for example, often don't target the upland forests near surface water that are best positioned to provide these services. Finally, monitoring and enforcement of the conditions specified by the agreements is often weak (Wunder et al. 2020).

Because of these and other challenges, the track record of PES programs is mixed. In general, metanalyses suggest that, like protected areas, they have positive but modest environmental impacts (Busch and Ferretti-Gallon 2023; Wunder et al. 2020; Snilsveit et al. 2019; Börner et al. 2016). Evidence on REDD+ is also mixed. While some argue these projects have reduced deforestation and forest degradation (Guizar-Coutiño et al. 2022; Magnago et al. 2015), recent rigorous counterfactual analyses of cross-country data are unable to discern significant impacts. They suggest most of the reductions claimed by REDD+ advocates are not additional—that is, they would have occurred without the interventions (West et al. 2023; Correa et al. 2020; West et al. 2020). Moreover, some evidence indicates that REDD+ projects can disrupt the livelihoods, institutions, and sociocultural systems of local peoples because forest-dependent communities are not sufficiently involved in their design and implementation (Bayrak and Marafa 2016).
Box 4.3. Costa Rica's Payments for Environmental Services Program

Costa Rica's 1996 Forest Law (Law No. 7575) created a national PES program, along with a semi-autonomous body called the National Forestry Finance Fund (FONAFIFO). This PES program compensates landowners for forest conservation and has become one of the best known in the world.

The program is voluntary. Each year, FONAFIFO issues a call for landowners to participate. Applications are scored based on predefined criteria that focus mostly on the size and location of the property; large properties and those situated in conservation areas or officially recognized biological corridors receive a higher score. Properties with scores above a defined threshold are accepted into the program. Participants sign five-year contracts with FONAFIFO that define the area to be protected and the annual payments they will receive. The payments are conditional—FONAFIFO only makes them after verifying that tree cover on the property has not been cleared.

A forthcoming IDB study uses rigorous impact evaluation methods (staggered difference-in-differences) to evaluate the effects of the program on deforestation between 2016 and 2020 (Ordóñez et al., forthcoming). For each year, it estimates the effect of the program by comparing deforestation on properties enrolled in the program during that year with those that enroll in later years. The study finds that the program has had a significant albeit modest environmental benefit. On average, participation reduced deforestation on enrolled properties 81-87 percent compared to the baseline deforestation rate. However, the absolute magnitude of the avoided deforestation was small (0.09 hectares per property), given the low baseline deforestation rate on sample properties.

One of the main criticisms of PES programs is that they have limited additional effects because they mostly enroll properties where deforestation risk is low, as tree cover would not have been cleared absent participation in the program. One of the main strategies for boosting additionality is to improve targeting, that is, to do a better job of enrolling high-risk properties.

To assess whether targeting could improve the efficacy of Costa Rica's PES program, Ordóñez et al. (forthcoming) use a machine learning model to classify properties in the sample as either high or low risk for deforestation at the time of their application to the program. The study finds that the share of high-risk properties in treatment and control groups is similar for almost all cohorts, which in turn suggests that the program is not successfully targeting high-risk properties. Furthermore, when the treatment group is restricted to include only high-risk properties, the estimated treatment effects are 27-73 percent higher, although they are not statistically significant. The implication is that the program would be more effective at reducing deforestation if it targeted high-risk properties.
While the main goal of PES and REDD+ programs is typically to conserve ecosystems, a complementary objective is often to boost local livelihoods. Unfortunately, the rigorous evidence on such effects is meager and suggests that although net effects on livelihoods tend to be positive, they also tend to be quite small (Wunder et al. 2020).

## Ecosystem Restoration

Ideally, ecosystems that have been degraded or destroyed would regenerate without human intervention. When that cannot happen, however, some help is needed in the recovery process by, for example, planting seedlings, removing invasive species, reintroducing lost species, and changing hydrology. Ecosystem restoration can generate all the same benefits as conservation, including climate mitigation, biodiversity conservation, improvement of local livelihoods, and provision of various ecosystem services. These benefits are maximized if restoration is carefully targeted to priority locations (Strassburg et al. 2020).

Although evidence on the benefits of restoration in Latin America and the Caribbean is thin (Barral et al. 2015), global studies are encouraging. In addition to the findings by Strassburg et al. (2020) noted above, global meta-analysis of the impacts of ecological restoration of agroecosystems concludes it has increased overall biodiversity of all organism types by an average of 68 percent, boosted levels of supporting services by an average of 42 percent, and raised levels of regulating services by an average of 120 percent (Barral et al. 2015).

In Latin America and the Caribbean, restored areas still represent an extremely small proportion of the total lands and waters in the region (Rice et al. 2018). Brazil is the country where the most monitoring of ecological restoration is taking place (Bustamante et al. 2019; Mazón et al. 2019). Tropical rainforest and tropical dry forest are the ecosystem types most frequently monitored (Mazón et al. 2019). Restoration activities in highly vulnerable biomes, such as wetlands and grasslands, are, however, underrepresented (Mazón et al. 2019; Rice et al. 2018). Furthermore, the amount of evidence on socioeconomic indicators is even smaller (Mazón et al. 2019).

Ecosystem restoration faces several challenges. It entails high upfront costs and long lags between investment and the generation of benefits (Bustamante et al. 2019; Rice et al. 2018). These challenges can be overcome by strengthening legal frameworks for restoration, providing funding for restoration initiatives, and creating more realistic expectations about the time and funding needed to meet conservation goals (Bustamante et al. 2019; Rice et al. 2018).
## Eco-Certification

Eco-certification programs, also known as eco-labeling and sustainability certification programs, accredit goods and services that have met process standards meant to protect the environment (Blackman and Rivera 2011). Eco-certifications have been developed for a range of commodities, including timber, coffee, cocoa, bananas, soy, and tourism, and are provided by both governments and nonstate actors, such as private companies and NGOs. The criteria for certification vary, depending on which entity awards it, and range from voluntary compliance with vague process standards to third-party verification of more specific ones (Lambin et al. 2018; Tscharntke et al. 2015). According to advocates, eco-certification can create meaningful economic incentives for improved environmental performance because certified producers can obtain price premiums and/or access to new markets (Cashore, Auld, and Newsom 2004). Eco-certification is widespread in Latin America and the Caribbean. Leading initiatives include Forest Stewardship Council timber certification and certifications from the Round Table on Responsible Soy, the Round Table on Sustainable Palm Oil, and the Marine Stewardship Council.

Eco-certification initiatives are difficult to design, implement, and evaluate and, partly as a result, evidence on their efficacy in conserving biodiversity is unclear (Van der Ven, Rothacker, and Cashore 2018; Cohn and O'Rourke 2011). Generally, certification initiatives rely on indirect rather than direct evidence of efficacy. Agricultural certification standards, for example, rarely specify the level of biodiversity conservation that must be achieved but, rather, require sets of improved practices that are hypothesized to promote conservation (Tscharntke et al. 2015). In extreme cases, eco-certification amounts to “greenwashing,” which seeks to convey the impression of sustainability without actually verifying or even providing incentive for it (Lambin et al. 2018; Cohn and O’Rourke 2011). Other problems have to do with self-selection—the tendency of even well-managed programs disproportionately to attract already green producers, which in turn limits the programs’ impact (Blackman et al. 2014)—and the prohibitive expense of enrollment for smallholder farmers (Lambin et al. 2018; Cohn and O’Rourke 2011).

## Policy Coherence and Perverse Incentives

The foregoing discussion reflects the widespread implementation of various biodiversity conservation policies in Latin America and the Caribbean. Yet it also makes clear that biodiversity loss and degradation continue. What
is the reason for this apparent discrepancy? The answer includes, in part, weak policy coherence, design, and implementation—in particular, a failure to tailor policies to local contexts—along with competing perverse incentives (Wunder et al. 2020; Börner et al. 2020; Van der Ven, Rothacker, and Cashore 2018; Baylis et al. 2016; Börner et al. 2016; Cohn and O'Rourke 2011).

Regarding local contexts, conservation policies are often undermined by a failure to consider and accommodate local political, institutional, and socioeconomic conditions (Van der Ven, Rothacker, and Cashore 2018; Börner et al. 2017). These conditions are particularly important in developing countries, where institutional and governance challenges abound (Pattanayak, Wunder, and Ferraro 2010) and state environmental regulatory agencies often face severe shortages of political, financial, and human resources. It is not surprising, then, that evaluations of PES schemes, REDD+ projects, and protected areas generally find strong governance an important determinant of efficacy (Shah et al. 2021; Snilsveit et al. 2019; Herrera, Pfaff, and Robalino 2019; Nolte et al. 2013). Studies show, for instance, that the availability of management and funding resources drives the effectiveness of protected areas (Nolte et al. 2013; Bruner et al. 2001). Moreover, the handling of many conservation policies exclusively by forest or environmental departments, without meaningful participation from either state or nonstate actors involved in agriculture, can limit their efficacy (Lima et al. 2017).

Governance aside, the Latin American and Caribbean region is enormously heterogeneous with respect to the benefits and costs of biodiversity conservation. That variation implies that effective and efficient conservation policy requires first identifying and then targeting certain geographical areas for interventions. Failure to do that consistently partly explains slow progress on biodiversity conservation (Börner et al. 2017). Protected areas, for example, are normally sited in remote regions where deforestation risk is relatively low. As a result, they typically do not have large effects on forest loss (Shah et al. 2021; Börner et al. 2020; Miranda et al. 2016).

With regard to policy design, many biodiversity conservation measures seek additional co-benefits or development goals, such as the creation of new jobs or income sources, which may involve tradeoffs in terms of conservation benefits (Wauchope et al. 2022; Börner et al. 2020; Cohn and O'Rourke 2011). Since ecosystems are complex and have internal dynamics that vary in a nonlinear fashion across space and over time, the pursuit of multiple objectives at multiple scales adds practical hurdles that can limit conservation outcomes (Baylis et al. 2016).

The choices of policymakers and researchers as to which outcomes to target also help explain the slow pace of biodiversity conservation and
restoration. The main goal of PES schemes, for example, including REDD+, is to secure ecosystem services, such as the provision of biodiversity habitat, carbon sequestration, and hydrological services. Yet most PES programs and studies evaluating them focus on avoiding forest loss—a proxy for progress that can be deceptive (Börner et al. 2020; Alpízar et al. 2020). Interventions that prevent only a relatively small area of forest loss could successfully secure ecosystem services if implemented in exactly the right forest (Börner et al. 2020). The use of more exacting outcome measures, then, such as biodiversity indices can enhance both program efficacy and cost effectiveness (Alpízar et al. 2020).

Another complication is that biodiversity conservation and restoration policies do not exist in isolation. Rather, they interact with other, preexisting policies. Sometimes the policies will complement each other; for example, the decentralization of administrative authority for rural areas from the national to the local level will, in some cases, improve conservation outcomes (Samii et al. 2014). Unfortunately, preexisting policies instead often dampen the effectiveness of conservation policies. Perhaps most important, agricultural subsidies, which are common in the region, can encourage ecosystem loss and degradation. In an analysis of data from eight countries, Bulte et al. (2007) find that agricultural subsidies have led both to the expansion of agricultural areas and (surprisingly) lower productivity, because farmers have incentive to adopt inefficient modes of production that entail using more and more land to garner the subsidies. Similarly, Moffette and Alix-Garcia (2024) find that livestock subsidies in Mexico have increased deforestation by 7 percent. And Peres and Schneider (2012) argue that subsidized farmer relocation programs have caused significant deforestation in Brazil.

An additional impediment to the efficacy of conservation policies has to do with their funding, which is often inadequate. Moreover, many conservation interventions are funded by national budgets or international cooperation, which makes them vulnerable to changing political and macroeconomic conditions (Alpízar et al. 2020). Protected areas, for example, are sometimes downgraded (that is, made less strict), downsized (reduced in size), or degazetted (effectively eliminated) as financial and political factors change (Mascia et al. 2014).

Finally, active monitoring of all the instruments described here requires greater collaboration among policymakers, researchers, funders, and implementing agencies (Arkema et al. 2013; Snilsveit et al. 2019; Baylis et al. 2016), which should take place early on in design phases (Börner et al. 2020). Until that happens, conservation programs will remain high-risk strategies whose effectiveness is unclear (Alpízar et al. 2020; Snilsveit et al. 2019).
## Biodiversity and the Financial Sector

In this section we briefly explore the relationship of the financial sector with the coupled challenges of biodiversity loss and climate change. The goal is to illustrate the importance of understanding, measuring, and addressing the threats from these challenges throughout the whole economic system (WEF 2020).

On the positive side, the financial sector increasingly is committing to stemming biodiversity loss through pledges, alliances, and disclosure commitments involving large financial operators (NGFS 2022; UNEP Finance Initiative, n.d.).<fnref n="1" /> This is very much needed. As discussed in Chapter 6, the financial sector plays a crucial role in the economies of Latin American and Caribbean countries, allocating resources to the sectors and places where they are needed most and aggregating and distributing risks. Its ability to do so is closely linked not only to macroeconomic conditions but to well-functioning natural ecosystems, which, as discussed above, provide vital services that underpin a variety of economic sectors (Van Toor et al. 2020).

The financial sector is, by design, highly responsive to risks and uncertainty, and it both is exposed and aims to mitigate risks associated with climate change. These include physical risks, which have to do with damage to assets from, for example, storms and floods, as well as transition and liability risks that stem from regulatory limitations imposed by national or supranational authorities. Some climate risks—for instance, risks from extreme hydrometeorological events in small island states—are idiosyncratic. Others are systematic (everybody is affected at once) or systemic (the risk spreads through the system), as is the case with increasing temperatures affecting the tourist industry everywhere in the world.

The loss of biodiversity carries very similar implications in terms of risk for the financial sector (Kurth et al. 2021), and, in fact, the same terminology is becoming the standard for describing it (Kedward, Ryan-Collins, and Chenet 2020; Kedward and Ryan-Collins 2022). The collapse of pollinators worldwide, for example, and increases in pests and diseases in key crops pose physical risks to the financial sector, with potentially systemic consequences. The new Deforestation Regulation<fnref n="2" /> approved by the European Union in June 2023 is a good example of transition and liability risk. It gives exporters eighteen months to prove that commodities (cattle, cocoa, coffee, oil palm,

<footnote n="1">¹ See also the website for Finance for Biodiversity, at https://www.financeforbiodiversity.org/.</footnote>

<footnote n="2">² https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32023R1115.</footnote>
<figure><img src="image_33.png" /><figcaption>**Figure 4.4.** Interaction of Biodiversity and Climate Change Challenges with the Financial Sector</figcaption></figure>

Source: Based on Jada, Alpizar, and Sen (2023).

Note: The arrows show the potential impacts.

soya, and wood) destined for the European market are 100 percent clean with respect to deforestation.

The link between the financial sector and nature-related risks is two-way: financial entities are not only exposed to nature-related risks; they affect them. By providing capital to nature-damaging activities or encouraging consumption and production decisions that are incompatible with sustainable practices, the financial sector can increase exposure to transition risks and even physical risks (see Figure 4.4).

Looking ahead, the financial sector needs to go beyond traditional risk management approaches and implement a forward-looking, scenario-based assessment of risk. Traditional stress testing, for example, has
increasingly drawn attention and been the subject of research because of both its current importance and its well-recognized limitations. The sector also needs to go beyond overly simplistic, single-threat approaches to stress testing and start looking at system-level threats (ECB 2020; Svartzman et al. 2021; Calice, Diaz Kalan, and Miguel 2021).

Ultimately, the goal is to transform the financial sector to be capable of integrating nature risk considerations into its decisions, while actively contributing to a societal transition toward nature-positive activities (OECD 2019b; World Bank 2021).

## A Symbiotic Approach to the Twin Challenges

This chapter has described the interlinked challenges of climate change and biodiversity loss and made the case that they must be addressed in tandem because they are mutually reinforcing, share common drivers, can be addressed by many of the same policies and programs, and have similar impacts on people and their livelihoods. It has also made a case that Latin America and the Caribbean is extremely rich in biodiversity and in the accumulated experience of local and Indigenous communities living in harmony with nature. All of these attributes should help the region advance toward a climate-neutral and nature-positive future.

The region's policy responses to the twin challenges of climate change and biodiversity loss thus far have demonstrated creativity and a willingness to implement change. Going forward, the region will need to improve the efficacy, efficiency, scope, and scale of these responses. The design of policies to mitigate and adapt to climate change while promoting biodiversity conservation and restoration should be coherent with the existing policy architecture and finetuned to the diversity of the ecosystems and peoples of Latin America and the Caribbean. These responses should not be perceived as part of some “green agenda.” Rather, they should be regarded as central to the creation of wealth and social well-being in the region.
<figure><img src="image_34.png" /></figure>

# Feeding a Warmer World

Food security in Latin America and the Caribbean faces growing challenges as climate change weakens food availability, access, use, and stability. In turn, food insecurity contributes to climate vulnerability. Breaking this feedback loop requires a multifaceted policy response, including promoting productivity-enhancing and climate-smart technologies, strengthening sectoral governance, expanding and targeting cash transfers, diversifying crops and livelihood strategies, fortifying food safety and pest management, and expanding agricultural insurance.

While climate change is a global challenge, it disproportionately affects regions with heightened vulnerabilities. In Latin America and the Caribbean, food insecurity stands out as one such vulnerability that climate change significantly worsens through multiple pathways. For instance, it reduces food availability by lowering agricultural productivity, restricts access to food by exacerbating poverty, degrades the nutritional quality of food through the spread of agricultural pests and diseases, and undermines food stability by increasing crop losses due to natural disasters. This chapter delves into the complex connections among climate vulnerability, disasters, and food security in Latin America and the Caribbean, offering policy recommendations to strengthen the resilience of the region's agrifood systems.

Throughout the chapter, data from the United Nations Food and Agriculture Organization, drawn from the organization's FAOSTAT database,<fnref n="1" /> are used to characterize food security; data from the Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index<fnref n="2" /> are used to characterize climate vulnerability; and data from the Emergency Events Database (EM-DAT)<fnref n="3" /> are used to count catastrophic events for which governments

<footnote n="1">¹ https://www.fao.org/faostat.</footnote>

<footnote n="2">² https://gain.nd.edu/our-work/country-index.</footnote>

<footnote n="3">³ https://www.emdat.be.</footnote>
have issued formal national emergency declarations, based on their severity. Graphical and correlational statistical analyses using these data provide a heuristic framework for organizing and motivating the discussion. The chapter begins with aggregate measures of food security, climate vulnerability, and disasters and then turns to four more specific dimensions of food security: availability, access, use, and stability.

## Food Insecurity

The 1996 World Food Summit defined food security as physical and economic access to sufficient, safe, and nutritious food that meets people's dietary needs and preferences and enables them to live active, healthy lives. Climate change is amplifying the already considerable challenge of achieving food security (Mbow et al. 2019). Food insecurity refers to the lack of food security. FAO's Food Insecurity Experience Scale (FIES) measures the prevalence of severe or moderate food insecurity. Severe food insecurity is defined as having experienced hunger for one or more days as a result of a shortage of food. Moderate food insecurity is defined as having faced uncertainty about the ability to obtain food, resulting from lack of resources, along with occasional reductions in food quality and/or quantity (FAO et al. 2023). In Latin America and the Caribbean, the incidence of these two types of food insecurity has exceeded the world average every year since 2014. In 2022, 38 percent of the population of the region faced moderate or severe

<figure><img src="image_35.png" /><figcaption>**Figure 5.1.** Food Insecurity in the World and Latin America and the Caribbean</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT's Food Insecurity Experience Scale.
food insecurity, as compared to 30 percent in the rest of the world. Moreover, with the exception of 2017–18, the incidence of moderate and severe food insecurity has been growing every year since 2014 (Figure 5.1). The COVID-19 pandemic and other recent international crises have helped drive this trend by exacerbating poverty, inequality, and inflation (FAO et al. 2023).

Food insecurity, particularly chronic food insecurity, is also measured in terms of undernourishment, which is defined as the proportion of the population that lacks caloric intake sufficient to maintain a healthy life (FAO et al. 2023). Although Latin America and the Caribbean achieved the Millennium Development Goal of cutting the proportion of undernourished people by half between 1990 and 2015 (UN 2015), undernourishment has since increased. Its prevalence rose even before the pandemic, from 5.3 percent in 2014 to 6 percent in 2018 (Figure 5.2, Panel A). Very recently, in 2022, undernourishment declined. This recent improvement was mainly driven by Andean countries, however; in the Caribbean and the Southern Cone, undernourishment continued to grow (Figure 5.2, Panel B).<fnref n="4" />

<caption>**Figure 5.2.** Undernourishment in the World and Latin America and the Caribbean</caption>

**A. World and Latin America and the Caribbean**

<figure><img src="image_36.png" /></figure>

(continued on next page)

<footnote n="4">⁴ Subregions in Latin America and the Caribbean are defined as follows: Central America includes Belize, Costa Rica, the Dominican Republic, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Panama; the Andean countries are Bolivia, Colombia, Ecuador, and Peru; the Caribbean comprises the Bahamas, Barbados, Guyana, Haiti, Jamaica, Suriname, and Trinidad and Tobago; and the Southern Cone consists of Argentina, Brazil, Chile, Paraguay, and Uruguay.</footnote>
<caption>**Figure 5.2.** Undernourishment in the World and Latin America and the Caribbean (continued)</caption>

B. Subregions of Latin America and the Caribbean

<figure><img src="image_37.png" /></figure>

Source: IDB staff calculations, based on data from FAOSTAT.

Note: Values for Central America and the Caribbean are reported by FAOSTAT. For the Andean countries and the Southern Cone, aggregates are reported based on country values using population-weighted averages.

Climate Vulnerability

As noted above, the ND-GAIN Country Index is used to measure climate vulnerability (Chen et al. 2015); Box 5.1 describes the index and its application in brief. The ND-GAIN Country Index has been widely used to explore the links among climate vulnerability and various economic, social, and

**Box 5.1.** The Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index

As discussed by Chen et al. (2015) and Chen, Noble, et al. (2023), the ND-GAIN Country Index is composed of two key dimensions: vulnerability and readiness. Vulnerability measures a country's propensity to be negatively affected by climate change, while readiness measures its ability to make effective use of investments for adaptation actions based on its having a safe and efficient business environment.

Here, the focus is on the vulnerability dimension, which has three components: exposure, sensitivity, and adaptive capacity. Exposure considers the degree to which a system will be subjected to climate hazards from a bio-physical perspective over the coming decades, given predictions about the

(continued on next page)
**Box 5.1. The Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index (continued)**

effects of climate change. Sensitivity measures both dependence on sectors adversely affected by climate hazards and the proportion of the population exposed to climate hazards as a result of such factors as topography or demography. Finally, adaptive capacity reflects the ability to reduce the potential for damage and to respond to the negative consequences of climate events. Each component includes key indicators for six “life-supporting” sectors: food, water, health, ecosystem services, human habitat, and infrastructure.

In total, the vulnerability part of the ND-GAIN Country Index comprises 36 indicators, which are used to construct a univariate index ranging from 0 to 1 (or 0 to 100), with a higher score representing greater vulnerability (that is, a higher score is less desirable).

institutional outcomes; see, for example, Halkos et al. (2020); Cevik and Jalles (2020a); Grecequet et al. (2017); Werrell, Femia, and Sternberg (2015); and Namdar, Karami, and Keshavarz (2021).

As shown in Figure 5.3, Panel A, the climate vulnerability of Latin America and the Caribbean in 2019 as measured by the ND-GAIN Country

<figure><img src="image_38.png" /><figcaption>**Figure 5.3. Climate Vulnerability in the World and Latin America and the Caribbean**</figcaption></figure>

(continued on next page)
<caption>**Figure 5.3.** Climate Vulnerability in the World and Latin America and the Caribbean

B. ND-GAIN vulnerability in subregions of Latin America and the Caribbean</caption>

<figure><img src="image_39.png" /><figcaption></figcaption></figure>

Source: IDB staff calculations, based on data from the ND-GAIN Country Index.

Index was, at 42.4, higher than for Europe and Central Asia (25.3) but lower than for South Asia (52.5) and Sub-Saharan Africa (53.7). It has been falling, albeit slowly, since 1995. This improvement has been mainly driven by the region's adaptative capacity, which implies that it is better prepared to deal with the negative impacts of climate-related events. Within the region, as Figure 5.3, Panel B, indicates, climate vulnerability was highest for Central America (44.1) and lowest for the Southern Cone (38.7). The lower levels of climate vulnerability observed in the Southern Cone relative to the other subregions are explained by considerable gains in adaptive capacity according to the ND-GAIN. This suggests that Southern Cone countries are better equipped to reduce potential damage from climate events. Vulnerability also varies significantly across countries. It has, for example, declined in Bolivia, Chile, Ecuador, and Peru but increased in Colombia and Venezuela, while remaining relatively stable in Caribbean countries.

## Disasters

In Latin America and the Caribbean, as in other parts of the world, climate change has increased the frequency and the severity of disasters, and it will continue to do so (IPCC 2023a; Magrin et al. 2014). Droughts, floods,
<figure><img src="image_40.png" /><figcaption>**Figure 5.4.** Disaster Events in Latin America and the Caribbean, 2000–2019</figcaption></figure>

Source: IDB staff calculations, based on data from EM-DAT.

and storms are the most prevalent climate-related disasters in the region. According to EM-DAT, the region as a whole registered 69 droughts between 2000 and 2019, as shown in Figure 5.4, Panel A. Central America had a total of 28, the most of any subregion. By contrast, the Southern Cone and Andean countries averaged only 1 or 2 droughts per year, while the Caribbean experienced them in only 5 of the 20 years included in the sample.
During the same period, Latin America and the Caribbean experienced 759 storms or floods, averaging a total of 40 per year (Figure 5.4, Panel B). As with droughts, Central America was the subregion most affected, registering 325 (an average of 16 events per year), nearly twice as many as in the other subregions. The second most affected subregion was the Southern Cone, with a total of 193, averaging 10 per year.

Droughts, storms, and floods wreak havoc on the agricultural sector. Globally, losses incurred by the sector between 2008 and 2018 in lower-to lower-middle-income countries accounted for just over one-quarter of the overall losses from medium- to large-scale disasters. According to FAO, Latin America and the Caribbean lost about US$29 billion during the same period as a result of reductions in crop and livestock production following disasters (FAO 2021).

The impact of disasters on food security depends on the intensity, duration, and type of disaster, as well as the nutritional condition of the affected population (PAHO and WHO 2019). Hurricanes, floods, and tsunamis adversely affect food availability by destroying crops and livestock, but, as discussed below, they can also affect food access, use, and stability by damaging infrastructure and supply chains, disrupting communications, and interrupting employment (PAHO and WHO 2019).

## The Whole Picture

The background provided above has shown that, for Latin America and the Caribbean as a whole, food security has worsened in recent decades, while climate vulnerability has fallen slightly, and the incidence of disasters caused by weather-related events, particularly droughts, has risen. It has also shown that these trends have differed across subregions. Graphical and correlational statistical analysis sheds light on the links among these trends. As has been noted, the aim is more to provide a heuristic framework for discussing these links than to identify causal effects.

## Graphical Analysis

Graphical analysis suggests that food insecurity, as measured by both the FIES and the prevalence of undernourishment, is positively correlated with climate vulnerability in Latin America and the Caribbean.<fnref n="5" /> Figure 5.5

<footnote n="5">⁵ While the correlation between the ND-GAIN Country Index vulnerability score and the FIES seems stronger (0.88) than the correlation between the ND-GAIN Country Index vulnerability score and undernourishment (0.69), the difference is at least</footnote>
<figure><img src="image_41.png" /><figcaption>**Figure 5.5.** Correlation between Food Insecurity and Climate Vulnerability</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.
Note: The dotted lines represent region averages. Correlation coefficient is 0.88 for Panel A and 0.69 for Panel B.

partly explained by the sample of countries included in each analysis. FIES is available for 15 countries while data on undernourishment are available for 25 countries. When both correlations are calculated using the first subsample of 15 countries, they are similar (0.88 vs 0.82).
identifies countries with relatively high levels of both climate vulnerability and food insecurity that may be at high risk of an acute food crisis following a climate-related disaster. These are the countries in the northeast quadrant of the two panels of the figure: Belize, Bolivia, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Nicaragua, and Peru. To reduce the risk of an acute food crisis, it is crucial for these countries to strengthen the resilience of their agrifood systems to climate change.

The correlation between food insecurity, as measured by the prevalence of undernourishment, on the one hand, and the incidence of climate-related disasters (droughts, floods, and storms) on the other, is not as strong as the correlation between food insecurity and climate vulnerability (Figure 5.6).

## Econometric Analysis

Graphical analysis suggests positive correlations between food insecurity on the one hand and climate vulnerability and, to a lesser extent, the incidence of climate-related disasters on the other. These correlations are further explored with two-way fixed effects panel data models that control for unobserved time-invariant confounding factors and observed time-varying ones (Box 5.2).

Regression results indicate that, even when controlling for both observed time-varying confounding factors and unobserved time-invariant ones, climate

<caption>**Figure 5.6.** Correlation between Food Insecurity and Disaster Events, 2000–2019</caption>

### A. Undernourishment and drought events

<figure><img src="image_42.png" /></figure>

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<caption>**Figure 5.6.** Correlation between Food Insecurity and Disaster Events, 2000–2019 (continued)</caption>

B. Undernourishment and flood and storm events

<figure><img src="image_43.png" /></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dotted lines represent region averages. Correlation coefficient is 0.88 for Panel A and 0.69 for Panel B.

vulnerability remains positively correlated with the prevalence of undernourishment (Table 5.1). Specifically, for the region as a whole, an increase of 1 point in the ND-GAIN Country Index vulnerability score is associated with a 1.15 percentage point increase in the prevalence of undernourishment. According to

Box 5.2. Regression Models

The sample for the econometric analysis presented here includes twenty-five Latin American and Caribbean countries: Argentina, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad and Tobago, Uruguay, and Venezuela. For each, longitudinal data span the twenty years from 2000 to 2019. The more recent years, which reflect the idiosyncratic effects of the COVID-19 pandemic, are excluded.

The following two-way fixed effects equations are estimated

$$Y_{i,t} = \alpha + \rho V_{i,t} + \beta_1 GDP_{i,t} + \beta_2 AGGDP_{i,t} + \gamma_1 + \theta_1 + \varepsilon_{i,t} \quad (1)$$

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Box 5.2. Regression Models (continued)

where *i* indexes countries and *y* indexes years; *Y* is the percentage of the population that is undernourished; *V* is the independent variable of interest, either the ND-GAIN Country Index climate vulnerability score or a count of disasters (floods, storms, and droughts); *GDP* is gross domestic product per capita; *AGGDP* is agriculture as a percentage of total GDP; *γ* are country fixed effects that control for time-invariant confounding factors (such as geophysical features that affect climate risk exposure); *θ* are year fixed effects that control for time-varying factors (like global commodity prices that affect all countries the same way); and *ε* is an error term. In addition, in some specifications, interaction terms are included to explore subregion effects, with the Southern Cone region designated as the base category.

<table><caption>**Table 5.1.** Regression Results: Effect of Climate Vulnerability on Undernourishment</caption><thead><tr><td>Covariate</td><td>Coefficient</td></tr></thead><tbody><tr><td>ND-GAIN Country Index climate vulnerability score</td><td>1.151***</td></tr><tr><td>Country Fixed Effects</td><td>Yes</td></tr><tr><td>Year Fixed Effects (2001–2019)</td><td>Yes</td></tr><tr><td>No. Countries</td><td>24</td></tr><tr><td>No. Observations</td><td>456</td></tr></tbody></table>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dependent variable is undernourishment, measured using FAO's Food Insecurity Experience Scale. The independent variable of interest is climate vulnerability, measured using the ND-GAIN Country Index climate vulnerability score. The regression includes time-varying gross domestic product and agricultural gross domestic product.

***p-value < 0.01.

data from 2019, this would imply that an additional half million people in Latin America and the Caribbean will experience undernourishment.

A similar econometric model demonstrates climate-related disasters (floods, storms, and droughts) are not significantly correlated with undernourishment by country. There may be two reasons. First, disasters may have geographically limited effects on undernourishment that cannot be discerned at the national level. In addition, disasters may affect acute food insecurity more than the chronic food insecurity that undernourishment measures.

## Food in Four Dimensions

Although the empirical strategy and results presented in this subsection are not strong enough to establish causality, they do indicate that climate
vulnerability is associated with food insecurity. To explore this relationship further, the rest of this chapter discusses links between climate vulnerability and the four dimensions of food security—availability, access, use, and stability. Also included are boxes listing specific policies to address challenges in these areas, as well as examples of programs that have been implemented in Latin America and the Caribbean to carry them out.

## Food Availability

Food availability refers to the supply of food that can be obtained both domestically and via imports. As a region, Latin American and the Caribbean produces food with sufficient calories to feed its population. It is also a net food exporter, producing 12 percent of the world's food (FAOSTAT).

Nevertheless, adequate food availability is a problem in the region for at least three reasons. First, food production is not evenly distributed across the region's countries. In 2021, South America produced 83 percent of the region's food, Central America 15 percent, and the Caribbean 2 percent (Figure 5.7). Furthermore, just three countries—Brazil, Argentina, and Mexico—produced 70 percent of the region's food.

<figure><img src="image_44.png" /><figcaption>**Figure 5.7.** Value of Food Production in Latin America and the Caribbean, 2000–2020</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT.

Note: Constant US$ 2014–16.
Second, recent estimates suggest that the Latin American and Caribbean population will continue to grow until 2058 (UN 2019). To feed its inhabitants, the region will need to boost food availability by increasing food production without increasing greenhouse gas emissions, clearing forestland, or otherwise degrading the environment and natural resources. The key to achieving this ambition will be boosting agricultural productivity, particularly for nutritious food crops.

And third—and most important for the discussion in this chapter—mounting global evidence shows that climate change tends to reduce agricultural productivity, especially in warmer regions (Ortiz-Bobea et al. 2021; Descheemaeker et al. 2018; Hristov et al. 2018; Myers et al. 2017; CEPAL 2013, 2014; Fernandes et al. 2012). The main causes are changes in temperature and precipitation, shorter production cycles, and diminished availability of water (Fernandes et al. 2012). Glacier setbacks as a result of climate change also adversely affect agricultural production (Handmer et al. 2012).

In Latin America and the Caribbean, agricultural productivity growth has been decreasing since 2011, at least partly because of climate change.<fnref n="6" /> One recent study finds that, since 1961, fully one-quarter of growth of agricultural total factor productivity (a measure of how much output can be produced with a given amount of inputs) has been lost to the cumulative impact of anthropogenic climate change (Ortiz-Bobea et al. 2021). According to another study, increases in temperature, precipitation variability, and hot dry periods are expected to lead to declining yields of staple crops and the degradation of land suitable for them in El Salvador and Honduras (Bouroncle et al. 2015; USAID 2017).

Aside from its adverse impacts on crop productivity, climate change is expected to reduce both livestock productivity and fishery productivity. With respect to the former, it will reduce the quantity and quality of forage (in terms of protein content and digestibility) and cattle fertility, while at the same time increasing animals' energy requirements (Hristov et al. 2018). Heat stress has been shown to have a negative effect on milk and meat production (Lemes et al. 2021).

As for aquatic resources, fisheries are threatened by ocean acidification and rising sea temperatures and levels (Pörtner et al. 2022; Ding et al. 2017; Allison et al. 2009). Even though the availability of fishery resources is projected to increase by more than 30 percent in high-latitude areas, it is expected to fall by up to 40 percent in the tropics (Cheung et al. 2010). The populations of developing countries that rely on fishery resources for protein intake—particularly in coastal areas—are highly vulnerable to food insecurity resulting from these impacts of climate change (Ding et al. 2017).

<footnote n="6">⁶ Agricultural production increased during this period, but mostly as a result of the expansion of the agricultural frontier, particularly in the Southern Cone.</footnote>
In addition to having direct effects on agricultural, livestock, and fishing productivity, climate change can adversely affect these activities by reducing labor productivity, particularly in countries where these activities are labor intensive (Myers et al. 2017; Dunne, Stouffer, and John 2013). In recent decades, increases in relative humidity have reduced work capacity by 10 percent during the warmer months and are expected to spur a 20 percent reduction by 2050 (Dunne, Stouffer, and John 2013).

Finally, climate change can increase the prevalence of pests and plagues that destroy agricultural products. It can have this effect by modifying the incidence and geographical distribution of pests and diseases in plants and animals (Mbow et al. 2019; Adedayo, Fasona, and Kuti 2014); favoring the proliferation of certain pests (Huot et al. 2017; Evans et al. 2007); and contributing to the emergence or reemergence of infectious diseases (Van den Bossche and Coetzer 2008).

Given the evidence summarized above, measures of food production and climate vulnerability are expected to be negatively correlated—a hypothesis that is confirmed by data for Latin America and the Caribbean. Food availability as measured by FAO's Gross Production Index for food items is negatively correlated with climate vulnerability as measured by the ND-GAIN Country Index (Figure 5.8).<fnref n="7" /> Countries particularly at risk because of high levels of climate vulnerability and low levels of food availability are the Bahamas, Belize, Ecuador, El Salvador, Guatemala, and Haiti.

What can Latin American and Caribbean countries do to mitigate the adverse effects of climate change on food availability? In general, the answer entails developing and disseminating new productivity-enhancing, climate-smart, and post-harvest technologies and practices. Table 5.2 lists policies that could be adopted to address specific impacts, and Box 5.3 describes PAGRICC, a program designed by the Inter-American Development Bank (IDB) and the government of Nicaragua to advance the use of climate-smart technologies to enhance food security.

## Food Access

Food access refers to the availability of financial and physical household resources needed to acquire adequate food. Climate change and disasters can reduce food access by reducing rural household income from

<footnote n="7">⁷ The Gross Production Index for food items is an annual index, calculated at the country level by FAO, to measure production of edible food crops that contain nutrients. The index shows the level of food production for each year relative to the base period of 2014–16. An index above 100 implies higher food production compared to the baseline, while an index below 100 indicates lower food production.</footnote>
agricultural activities and by increasing food prices (Fanzo et al. 2017; WFP and Met Office Hadley Center 2012). The first effect stems from the adverse impacts of climate change on agricultural productivity and the second from reduced food availability, both already discussed above.

Simulation analyses generally find that climate change will raise food prices and exacerbate poverty and food insecurity. At the global level,

<figure><img src="image_45.png" /><figcaption>**Figure 5.8.** Correlation between Food Production and Climate Vulnerability</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dotted lines represent region averages. Correlation coefficient is -0.1.

<table><caption>**Table 5.2.** Policies to Mitigate Effects of Climate Change on Food Availability</caption><thead><tr><th>Adverse impact</th><th>Policy</th></tr></thead><tbody><tr><td>Reduced productivity of crops and livestock</td><td><ul><li>Adopt technologies and practices that increase the resilience of agricultural systems, improve production efficiency, and have environmental and mitigation co-benefits. Examples include agroforestry and silvopastoral systems; improved, diverse, and flood- or drought-resistant crop varieties; improved forage; and water-efficient irrigation. See, for example, Mbow et al. (2019); Miralles-Wilhelm (2021); Sonneveld et al. (2018); and González-Flores and Le Pommellec (2019).</li></ul></td></tr><tr><td>Reduced productivity of fisheries</td><td><ul><li>Strengthen research on the governance, management, and monitoring of fishery resources and the diversification of livelihood strategies (Sparre and Venema 1998; Gaudin and De Young 2007; Mora et al. 2009; Matić-Skoko et al. 2011; FAO 2022).</li></ul></td></tr></tbody></table>

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<table><caption>**Table 5.2.** Policies to Mitigate Effects of Climate Change on Food Availability (continued)</caption><thead><tr><th>Adverse impact</th><th>Policy</th></tr></thead><tbody><tr><td>Increased frequency and intensity of droughts and floods</td><td><ul><li>Promote water conservation via more efficient irrigation systems, water harvests, and aquifer protection (Salazar et al. 2016; Salazar and Lopez 2017).</li><li>Promote research on and adoption of drought-, flood-, and heat-resistant crop varieties (Atlin, Cairns, and Das 2017; Thomas 2015).</li><li>Provide more accurate and frequent climate information (Lambert et al. 2019; Bouroncle et al. 2019).</li></ul></td></tr><tr><td>Increased incidence, persistence, and intensity of pests and plagues</td><td><ul><li>Promote adoption of post-harvest technologies and practices that prevent the spread of diseases and pest infestations, including the use of silos, storage units, and cold chain equipment (Tefera, Mugo, and Likhayo 2011; Stathers et al. 2020; Odjo et al. 2022; Olorunfemi and Kayode 2021; Acedo 2019).</li><li>Promote the adoption of integrated pest management (IPM) and good agricultural practices (GAPs) to reduce toxic inputs in crops through improved knowledge of food handling and biological control. See, for example, Waddington et al. (2014).</li></ul></td></tr></tbody></table>

Source: IDB staff, adapted from Mbow et al. (2019).

**Box 5.3. Climate-Smart Technologies for Food Security in Nicaragua**

In 2010, IDB approved the Environmental Program for Disaster Risk and Climate Change Management (PAGRICC, for its Spanish acronym) to reduce the vulnerability of rural communities to climate change through natural resource management. The program was implemented in hydrological basins prioritized according to their vulnerability and had three complementary components: one geared to helping small farmers adopt environmental restorative systems (SRAs, for the Spanish acronym) at the farm level; a second to supporting investments in public infrastructure in critical areas to reduce losses from disasters; and a third to strengthening municipal governance related to disaster risk management and the implementation of mechanisms for maintaining environmental services in prioritized subbasins, among others.

Component 1 was closely linked to adaptation to and mitigation of climate change impacts on food security. It had three key elements. First, participants received technical assistance to develop a farm plan geared to implementing one of seven possible SRAs. These involved the adoption of nature-based solutions (technologies or packages of technologies) to improve soil quality, reduce erosion, and increase water harvesting, infiltration, and retention, while at the same time improving yields, diversifying the production portfolio of participants, and improving resilience to climate change. Second, participants received smart subsidies to partially finance the implementation of their SRAs. Finally, tailor-made technical assistance, including monthly visits

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Box 5.3. Climate-Smart Technologies for Food Security in Nicaragua (cont.)

and meetings with farmers, was provided to participants for several years to support the successful implementation of their SRAs.

After the program concluded in 2016, an impact evaluation of PAGRICC was conducted in 2019 with a sample of 1,033 households (519 beneficiaries and 514 controls), using quasi-experimental methods with panel data. The program was found to have increased agricultural productivity, measured as the value of production per hectare in annual crops (US$195/ha); the area under tree cover (3 hectares); the volume of water collected (204 m³); and the number of nature-based technologies adopted. Regarding food security, PAGRICC improved protein consumption, measured as own livestock consumed, and income from milk sales during the dry season.

Additional environmental co-benefits are expected in terms of diet diversification, as well as carbon capture as a result of the substantial increase in tree cover. The success of the program exemplifies how nature-based solutions can have many co-benefits related to improving the natural resource base, productivity, and food security, as well as mitigation co-benefits.

Source: IDB staff, based on González-Flores and Le Pommellec (2019).

Nelson et al. (2010) find that climate change will significantly raise the real international prices of maize (by 87 to 106 percent), rice (31 to 78 percent), and wheat (44 to 59 percent) by 2050.

Increases in food prices can, in turn, lead to more poverty and food insecurity in developing countries (Myers et al. 2017; Anderson, Ivanic, and Martin 2014; Ivanic and Martin 2014). Simulations at the global level, for example, indicate that a 10 percent rise in food prices increases poverty by 0.8 percentage points (Hallegatte et al. 2016; Ivanic and Martin 2014).

Along the same lines, simulations for Latin America and the Caribbean suggest that a 20 percent rise in food prices would increase poverty in the region by an average of 2.6 percentage points, causing 63.5 million people to become poor (Figure 5.9). An increase of this magnitude would raise the percentages of people in poverty and extreme poverty for all countries in the region except Barbados (Figure 5.9). Those most affected would be Guatemala, Nicaragua, and Peru, where the increase would be more than 4 percentage points. In addition, simulations show urban areas are more affected by food price increases, facing a 3.9 percentage point poverty increase versus 2.3 percentage points in rural areas.

The hypothesis that food access is correlated with climate vulnerability is explored using the same graphical approach as in previous sections
(Figure 5.10). Food access is measured using an inverse proxy: the percentage of the population living in poverty, defined as living on less than US$6.85 per day. A positive correlation is expected between this measure and climate vulnerability, and, in fact, a strong positive correlation is found. Countries that are particularly at risk because of high levels of climate vulnerability and

<caption>**Figure 5.9.** Simulated Effect of a 20 Percent Increase in Food Prices on Poverty</caption>

A. Effect on poverty

<figure><img src="image_46.png" /></figure>

(continued on next page)
<figure><img src="image_47.png" /><figcaption>**Figure 5.9.** Simulated Effect of a 20 Percent Increase in Food Prices on Poverty (continued)</figcaption></figure>

Source: IDB staff calculations, based on the IDB's SCL Policy Simulator.

Note: Poverty rates reported are based on national poverty definitions.

low levels of food access are Colombia, the Dominican Republic, Ecuador, El Salvador, Honduras, and Peru. Note that Caribbean countries are excluded from the analysis because the requisite data are not available.

What can Latin American and Caribbean countries do to mitigate the adverse effects of climate change on food access? In general, the answer
<figure><img src="image_48.png" /><figcaption>**Figure 5.10.** Correlation between Population Living in Poverty and Climate Vulnerability</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dotted lines represent region averages. Correlation coefficient is 0.66.

entails cash transfers, cash for work programs, interventions to boost and diversify rural incomes, and promoting the adoption of climate-smart agricultural technologies and agricultural insurance. Table 5.3 lists specific policies, and Box 5.4 presents a program in Bolivia that successfully applied some of them.

## Food Use

Food use refers to the nutritional quality of food. It is a function of food safety, feeding practices, food preparation, diet diversity, and food distribution within households (EC-FAO 2008).

Climate change affects food use via at least three pathways. First, the nutritional quality of diets tends to decline in response to climate shocks (Hallegatte et al. 2017; Baez, de la Fuente, and Santos 2010). Empirical evidence shows that children born in areas affected by disasters are more likely to suffer from stunting (WFP and Met Office Handley Center 2012). Children are classified as stunted if their height for their age is below average, based on an internationally agreed-on measure from the World Health Organization (Bukari et al. 2022). Stunting is a common measure of food use because the main (though not only) cause is chronic malnutrition during
<table><caption>**Table 5.3.** Policies to Mitigate Effects of Climate Change on Food Access</caption><thead><tr><td>Adverse impact</td><td>Policy</td></tr></thead><tbody><tr><td>Reduced productivity of crops and livestock<br>Increased food prices and poverty</td><td><ul><li>See Table 5.2, particularly in the context of smallholder farmers who depend on their food production for their own consumption.</li><li>Expand cash transfers to vulnerable populations, particularly women, children, and Indigenous groups, who cannot afford to buy food. Registries of social protection programs can be used to target households most vulnerable to the economic effects of extreme climate events. See, for example, Barca and O'Brien (2017); Stampini et al. (2021); and Bagolle, Costella and Goyeneche (2023).</li><li>Implement interventions that improve rural income through financial and technical support. See, for example, Barham and Chitemi (2009); Markelova et al. (2009); Markelova and Mwangi (2010); Fischer and Qaim (2012); Fischer and Qaim (2014); Chagwiza, Muradian, and Ruben (2016); and Orsi et al. (2017).</li></ul></td></tr><tr><td>Increased vulnerability of agricultural activities</td><td><ul><li>Promote climate-smart agricultural technologies (for instance, modern irrigation systems). See, for example, Salazar et al. (2016); Aramburu et al. (2019); Salazar and Lopez (2017); and Salazar et al. (2021).</li><li>Promote income diversification through agricultural and nonagricultural strategies to increase climate resilience. See, for example, Salazar, Fahsbender, and Kim (2018); Akrasi, Eddico, and Adarkwah (2020); and Adem et al. (2018).</li><li>Expand access to agricultural insurance and other risk reduction strategies (Nnadi et al. 2013; Marza, Angelescu, and Tindeche 2015).</li><li>Implement cash-for-work programs related to resilient infrastructure, such as projects to carry out community road maintenance, watershed reforestation, cleaning of irrigation canals and removal of debris, and mangrove reforestation, among others. See, for example, Doocy et al. (2006); Tilman et al. (2017); and Loewe and Zintl (2021).</li></ul></td></tr></tbody></table>

Source: IDB staff, adapted from Mbow et al. (2019).

infancy, when nutritious foods are crucial for healthy long-term development (Caulfield et al. 2006; WHO 2015b).

Second, climate change affects food safety by increasing the incidence, persistence, and intensity of foodborne and waterborne diseases (Fanzo et al. 2017; Schmidhuber and Tubiello 2007; Duchenne-Moutien and Neetoo 2021; Tirado et al. 2010). Affected populations may not be able to assimilate nutrients and, therefore, have poorer nutritional outcomes.

Third, elevated carbon dioxide levels reduce the levels of protein, iron, and zinc in various crops, thereby increasing the number of people at risk of macro- and micronutrient deficiency (Myers et al. 2017; Medek, Schwartz, and Myers 2017; Smith, Golden, and Myers 2017; Myers et al. 2015). For example, Medek, Schwartz, and Myers (2017) report that climate change may
Box 5.4. Agricultural Technologies for Food Security in Bolivia

In 2011, the IDB approved the Direct Support Program for the Creation of Rural Agrifood Initiatives (CRIAR, for its Spanish acronym) to improve rural food security in Bolivia by increasing both income and productivity. The program sought to increase the climate resilience of small landholder farmers through technology adoption and technical assistance. Implemented in five departments of Bolivia, it targeted 33 municipalities and 1,355 communities deemed particularly vulnerable to food insecurity. CRIAR provided non-reimbursable vouchers that financed 90 percent of the total cost of an agricultural technology selected by the producer, along with personalized technical assistance. Among the technologies were post-harvest storage, modern irrigation systems, fruit dehydrators, mills, pulp machines, electric fences, greenhouses, and livestock innovations.

In 2016, an impact evaluation of CRIAR was conducted with a sample of 1,287 households (817 beneficiaries and 470 controls), using an instrumental variable approach. The results indicated that participation in the program improved household income (agricultural and nonagricultural) by 36 percent, agricultural income by 19 percent, and productivity by 92 percent (measured as the annual value of production per hectare). This implies that household incomes became more diversified. Furthermore, the project reduced the probability of experiencing food insecurity by 20 to 32 percent. Specifically, 22 percent of beneficiary households reported being less concerned about lacking food at home, 14 percent were more likely to be eating nutritious food, and 17 percent were less likely to have diets lacking in variety. Participation reduced the probability of adults' missing a meal by 14 percent and not eating during the whole day by 10 percent. The project concluded in 2016, and its favorable outcomes resulted in the approval and implementation of a second phase (CRIAR II), implemented from 2016 to 2022.

The success of CRIAR shows that interventions that target smallholders, promote the adoption of key technologies, and provide tailor-made technical assistance can have positive impacts on food availability, access, and use.

Source: IDB staff, based on Salazar et al. (2016).

contribute to an increase of 148 million people with protein intake deficiency by 2050 because of lower protein content in rice (reduced by 7.6 percent), wheat (7.8 percent), barley (14.1 percent), and potatoes (6.4 percent).

The graphical analysis conducted for food use measures this dimension of food insecurity with an inverse proxy: the prevalence of children under five years of age who are stunted (Figure 5.11), with a positive correlation expected between climate vulnerability and stunting. This relationship is confirmed. Countries that are particularly at risk because of high levels of climate vulnerability and of stunting are Belize, Bolivia, Ecuador, Guatemala, Haiti, Honduras, and Nicaragua.
<figure><img src="image_49.png" /><figcaption>**Figure 5.11.** Correlation between Stunting and Climate Vulnerability</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dotted lines represent region averages. Correlation coefficient is 0.56.

What can Latin American and Caribbean countries do to mitigate the adverse effects of climate change on food use? In general, the answer entails promoting nutritionally sensitive agricultural and food safety interventions, agricultural diversification, post-harvest technologies and practices, integrated pest management (IPM) and good agricultural practices (GAP) programs, and water, sanitation, and hygiene (WASH) interventions. Table 5.4 lists specific policies, and Box 5.5 presents a program in Nicaragua that successfully applied some of them.

## Food Stability

Food stability refers to uninterrupted access to nutritious food over time (FAO 2008). The graphical analysis of this food insecurity dimension uses the volatility in the FAO Food Price Index (FPI) to proxy for food stability. Following Tothova (2011), volatility in a given year is measured as the coefficient of variation of monthly prices (the ratio of the annualized standard deviation over the mean). Higher values imply higher price volatility. A positive correlation is found between climate vulnerability and the FPI volatility. Countries particularly at risk because of high levels of climate vulnerability and high FPI volatility are Bolivia, the Dominican Republic, Guatemala, Haiti, and Jamaica (Figure 5.12).
<table><caption>**Table 5.4.** Policies to Mitigate Effects of Climate Change on Food Use</caption><thead><tr><td>Adverse impact</td><td>Policy</td></tr></thead><tbody><tr><td>Reduced nutritional quality of food</td><td><ul><li>Promote nutritionally sensitive agriculture and food safety interventions, including homestead food production systems, home gardens, biofortified crops, traceability systems, good agricultural and manufacturing practices, and agricultural diversification. See, for example, Ruel et al. (2017); Ruel (2019); Ruel, Quisumbing, and Balagamwala (2018); and Sharma et al. (2021).</li><li>Promote agricultural diversification to achieve more resilient production systems and improve dietary diversity. See, for example, Dillon, McGee, and Oseni (2015) and Jones, Shrinivas, and Bezner-Kerr (2014).</li></ul></td></tr><tr><td>Increased incidence, persistence, and intensity of foodborne and waterborne diseases</td><td><ul><li>Promote the adoption of post-harvest technologies and practices, including the use of silos, storage units, and cold chain equipment (Tefera, Mugo, and Likhayo 2011; Stathers et al. 2020; Odjo et al. 2022; Olorunfemi and Kayode 2021; Acedo, 2019).</li><li>Promote the adoption of IPM and GAPs to reduce toxic inputs in agricultural crops through improved knowledge of food handling and biological control. See, for example, Waddington et al. (2014).</li><li>Promote WASH interventions as part of an emergency response within 12 months of a flood event (Yates et al. 2021). Expand access to potable water systems and sanitation (Kiendrebeogo 2012; Cassivi et al. 2019).</li></ul></td></tr></tbody></table>

Source: IDB staff, adapted from Mbow et al. (2019).

**Box 5.5. Women's Income Diversification and Empowerment for Food Use in Nicaragua**

With the support of the IDB, the Productive Support for Agrifood Program (APAGRO, for its Spanish acronym) was implemented in Nicaragua from 2010 to 2014. Targeting small landholder female producers, APAGRO's goal was to enhance the food security and nutrition of rural households by diversifying income to improve climate resilience. The program offered transfers of different small livestock assets, combined with technical assistance that included training in financial management, good animal practices to reduce sanitary problems, and other topics. More than 10,000 female farmers benefited from this intervention.

An impact evaluation of APAGRO was conducted in 2018, with a sample of 1,081 producers (570 beneficiaries and 511 controls), using a difference in difference methodology. The findings suggested that participation increased agricultural production (by 60 percent), home consumption (18 percent), income from livestock sales (over 200 percent), food consumption (11 percent), and protein intake (12 percent). Furthermore, the analysis confirmed that the program reduced women's disempowerment (measured using the Women's Empowerment Index in Agriculture, or WEAI) and gender imbalance within households.

Source: IDB staff, based on Salazar, Fahsbender, and Kim (2018).
<figure><img src="image_50.png" /><figcaption>**Figure 5.12.** Correlation between FPI Volatility and Climate Vulnerability</figcaption></figure>

Source: IDB staff calculations, based on data from FAOSTAT and the ND-GAIN Country Index.

Note: The dotted lines represent region averages. Correlation coefficient is 0.2.

Latin American and Caribbean countries can mitigate the adverse effects of climate change on food stability by strengthening mechanisms that enable them to respond to episodes of instability. Cash transfer and insurance programs are particularly useful (Table 5.5).

<table><caption>**Table 5.5.** Policies to Mitigate Effects of Climate Change on Food Stability</caption><thead><tr><td>Adverse impact</td><td>Policy</td></tr></thead><tbody><tr><td>Increased volatility of income and food prices</td><td><ul><li>Promote cash transfer programs. These are useful tools to plan and target for emergency preparedness. Cash transfers can be expanded to maintain food access and reduce negative impacts on children's nutritional outcomes before and during emergencies. Registries of social protection programs can be used to target households most vulnerable to the effects of extreme climate events on food stability. This often requires expanding coverage. See, for example, Pople, Dercon, and Brunckhorst (2023) and Barca and O'Brien (2017).</li></ul></td></tr><tr><td>Increased crop and livestock losses</td><td><ul><li>Provide financial products and services, such as weather index-based insurance products for agriculture (Pishbahar et al. 2019; Chen, Lu, et al. 2023). See, for example, Di Falco et al. (2014); Carter et al. (2014); and Moore et al. (2019).</li></ul></td></tr></tbody></table>

Source: IDB staff, adapted from Mbow et al. (2019).
## A Menu of Options to Ensure Food Security

The literature reviewed in this chapter confirms that climate vulnerabil-
ity has a substantial, multifaceted adverse effect on food security in Latin
America and the Caribbean, including on all four of its dimensions: availabil-
ity, access, use, and stability. The data and analysis presented suggest that
higher levels of climate vulnerability, as measured by the ND-GAIN Country
Index vulnerability score, are associated with higher levels of undernourish-
ment (food insecurity), lower scores on the Gross Production Index for food
items (food availability), higher rates of poverty (food access), higher prevalence of stunting for children under five years of age (food use), and greater
variability in food prices (food stability).

The literature also suggests that an array of policies can mitigate
these effects. These include promoting the development and adoption of
productivity-enhancing, climate-smart, and post-harvest agricultural tech-
nologies and practices; strengthening sectoral governance; expanding and
better targeting cash transfer programs; diversifying crops and livelihood
strategies; strengthening food safety and pest management; and provid-
ing agricultural insurance. Furthermore, evaluations of IDB-supported
initiatives demonstrate that, if carefully designed and implemented, such
interventions can have their intended impacts.

While this review of policies is certainly not exhaustive, it clearly demon-
strates that policy responses are needed at different levels and in different
sectors. That finding, in turn, argues for an overarching approach that con-
siders the entire agrifood system, including its intersections with social
protection, agriculture, health, energy, water and sanitation, and disaster
risk management. Since conditions and capacities differ across and within
countries, further analysis is needed to identify the most appropriate inter-
ventions at the country level.
<figure><img src="image_51.png" /></figure>
# Forecast for Financial Sector Resilience

Banking system stress tests in 17 countries in Latin America and the Caribbean suggest that, while capital losses may occur under different climate scenarios, they are expected to be manageable, posing a limited threat to the overall solvency of banks in the region. Deficiencies in terms of data and technology, however, pose substantial barriers to effective risk assessment and management. Addressing these shortfalls requires enhanced data collection and information processing, and more dynamic approaches are needed from supervisory and regulatory bodies to enable financial system regulation to keep pace with the evolving nature of climate risks.

Banks play a vital role in Latin America and the Caribbean. They support people and firms by providing financing for mitigation and adaptation measures, as well as through risk transfer mechanisms and instruments to increase resilience. While elsewhere in the world banks are relatively less important than capital markets, in the region they hold most financial savings and serve as the backbone of financial intermediation, channeling funds from savers to borrowers. Any significant impact on the stability of the banking system can have far-reaching consequences for economic growth, investment, and savings.

This chapter explores the impacts of climate change on banking systems in Latin America and the Caribbean with a focus on assessing the risks that climate change brings to banking system stability. Historically, financial stability has been lacking in the region. Yet it is critical to the proper functioning of the financial system. Without stability, banks cannot perform the financing and risk transfer functions necessary to help countries cope with climate change. The comprehensive risk assessment presented in this chapter, for most countries in Latin America and the Caribbean, is the first one conducted to date.
Climate change holds at least two kinds of risk for financial stability: physical risk and transition risk (Grippa, Schmittmann, and Suntheim 2019; Rudebusch 2021; Fabris 2020; ECB 2020). Physical risk encompasses damage to property, infrastructure, and land from extreme weather events, such as floods and drought, and the ensuing drops in productivity.<fnref n="1" /> Transition risk refers to the revaluation of carbon-intensive assets (for example, oil refineries) and the various effects of adjustment to a lower-carbon economy, including changes in the labor market and comparative advantages. Transition costs can be triggered by the sudden adoption of more stringent climate policies and associated regulation and taxes, by technological changes that reduce the costs of renewable energy, and by changes in market sentiment and preferences that induce shifts in the supply of and demand for more sustainable goods and services.

Stress tests are a key tool for evaluating the resilience of banking systems to climate change–induced financial risks, encompassing a wide range of climate scenarios and their financial implications. Although widely used in other regions, such testing is novel for Latin America and the Caribbean.

Stress tests provide invaluable insights by forecasting how banks can withstand adverse climate-related scenarios. They help identify vulnerabilities within banks' portfolios, particularly in the face of physical and transition risks, offering a preemptive look at potential systemic shocks. This foresight allows for the development of more resilient financial strategies and regulatory policies, ensuring that banks and the broader financial system are better prepared for the multifaceted impacts of climate change.

But results from climate risk stress tests must be taken with a grain of salt (Reinders, Schoenmaker, and Van Dijk 2023). Although they provide useful information, they have significant shortcomings that may result in underestimating the possibility of systemwide financial losses. Besides physical and transition risks, climate change exacerbates liability risks, which arise when parties who have suffered loss or damage from its impacts seek compensation from those they hold responsible. On a sufficiently large scale, such claims could lead to significant legal and financial implications for businesses and governments. Systemic risks also are associated with the interconnectedness of financial institutions and markets, where climate change impacts could trigger a chain reaction affecting the capital market and, therefore, the broader financial system. As stress tests tend to rely on macroeconomic models with insufficient sectoral and spatial details, they often neglect these and various other types of climate shocks.

<footnote n="1">¹ Physical risk also includes slow-onset events, such as environmental degradation and sea-level rise.</footnote>
In view of these limitations, the chapter also explores other impacts of climate change on the financial sector, including changes in asset values and credit risk and the chances of systemic financial disruptions, given the exposure of banks to sovereign credit risk. It highlights the complex transmission channels by which risks associated with climate change can affect financial stability, sheds light on the vulnerabilities of the region's banking systems to these risks, and identifies the unknowns and uncertainties that require monitoring. In doing so, the chapter traces a path forward for policymakers, regulators, and banks to guide policy action. Through careful consideration of the risks, stakeholders can take informed actions to safeguard financial stability in an increasingly uncertain climate future.

## From Climate Risks to Financial Risks: Banking Stress Tests

Climate change exposes banks to physical and transition risks both directly and indirectly. Direct risks are posed by the increasing costs of energy, the costs of compliance with climate-friendly building and regulatory standards, and the vulnerability of data centers to flooding or cyclones. Indirect risks result from the disruptive effect of adverse macroeconomic conditions and transition paths on the creditworthiness of the banks' clients and the value of the assets the banks hold.

One avenue for assessing whether banks can withstand the effects of climate change is stress testing. Stress tests are simulation exercises conducted by individual and central banks to assess the resilience of a bank or group of banks under alternative severe but plausible scenarios (Baudino and Svoronos 2021). The forward-looking nature of the methodology makes it well-suited to produce, under a few plausible scenarios, forecasts of exposures and losses that cannot be extrapolated from historical data, such as data related to ongoing unprecedented climate risk. As climate hazards may manifest asymmetrically across sectors and geographical locations, the effects will also likely differ across banks, depending on their specific balance sheet structures in general and their loan portfolios in particular.<fnref n="2" />

Translating climate change impacts into financial impacts on bank exposures requires four main analytical steps. The first is to use climate scenarios produced by the scientific community to project future temperatures.

<footnote n="2">² Here the central bank also has a role in determining the parameters of the scenarios against which banks will test themselves. Otherwise, the results from each bank's stress test will not be comparable to the others. Relatedly, by leading the process, central banks play the key role of raising awareness among banks about the need to take preventive measures against the adverse impacts of climate change on bank solvency, profitability, and liquidity.</footnote>
The second involves mapping these climate metrics to traditional macro-economic indicators, such as gross domestic product (GDP).<fnref n="3" /> Third, a more granular analysis should be carried out, as climate hazards manifest asymmetrically across sectors and geographical locations. Finally, all the previous elements must be combined to compute the impact on banks.<fnref n="4" />

Running a stress test entails a great deal of uncertainty, stemming from the difficulty of inferring potential future effects from historical data, the non-negligible likelihood of *tail events*—that is, events, such as extreme weather events, that are highly unlikely but can have significant, often severe, consequences if they do happen—and the emergence of second-round effects and feedback among climate change variables, macroeconomic variables, and financial outcomes (Battiston et al. 2019; Monasterolo 2020).<fnref n="5" /> The interaction between climate change on the one hand and the economy as a whole and the banking system on the other is highly dependent on the climate policies chosen over time at the national and international levels. The lack of certainty about the pathway of future emissions, their impacts on climate and economic activity, and the pace of technological progress—plus the policies to be put forward in coming years and decades—adds complexity to the task by forcing the analyst to ponder alternative outcomes under various scenarios.

## International Evidence: Stressed but Stable

With climate change becoming increasingly topical on the global policy agenda, central banks and multilateral institutions have been leading a new wave of stress tests on its long-term impacts. The Network of Central Banks and Supervisors for Greening the Financial System (in NGFS 2021) and the

<footnote n="3">³ This can be done using either a top-down or a bottom-up approach. In the former, risk is first dimensioned based on a rather consolidated unit of analysis (for example, sectors or the economy as a whole) and then is attributed to the component parts of that unit (for example, firms operating in each sector). In contrast, the bottom-up approach starts by characterizing the risk more granularly and then moves up to produce an aggregated measure.</footnote>

<footnote n="4">⁴ This implies substantial modeling complexity and uncertainty, as it requires combining various models not initially designed to operate together. More detailed descriptions and comparisons of the different modeling approaches are provided by Brunetti et al. (2022), the Basel Committee on Banking Supervision (BCBS 2021), and the Network of Central Banks and Supervisors for Greening the Financial System (NGFS 2019). It must also be noted that banks and bank supervisors usually do not run all four steps themselves. Still, they borrow most inputs from other sources (such as the NGFS scenarios, discussed later) and then focus on the bank-specific impact.</footnote>

<footnote n="5">⁵ Tail events are referred to as “*black swans*”—that is, they are extremely rare and unpredictable and have a significant impact. The term “*green swans*” is often used to describe similar events that are specifically related to environmental issues.</footnote>
Financial Stability Board (in FSB 2022) survey the core features of the stress tests, both planned and finalized, in 36 jurisdictions globally. They argue that the following is true of most of the exercises:

* They adopt a 30-year timespan, as a compromise between long-term view and credibility, and three Shared Socioeconomic Pathway (SSP) scenarios: orderly transition, disorderly transition, and hot-house world.

* They more frequently cover transition risk than physical risk (as the former seems more pressing, while the latter is expected to manifest more intensely in the second half of the century) and credit risk more often than other risks (which is justified by the overwhelming weight of loans in banks' portfolios).

* They focus on the banking system (although several extend their scope to include insurance companies and pension funds).

* They favor a static balance sheet and focus on first-order effects (as opposed to a dynamic approach with second-round, or feedback, mechanisms, which would bring about more realism but at the cost of additional modeling complexity).

* They are split evenly between top-down and bottom-up approaches, with widely varied data granularity.

Although strict comparison of numerical results across studies is not possible because of significant differences in the types of risks assessed and in outcome metrics, assumptions, and methodologies, a common thread is that widespread, climate-driven banking instability seems unlikely, except for financial institutions with loan portfolios excessively concentrated in some geographical regions and sectors. The studies reveal, for instance, that an average of 15 percent of the banking system's credit book has been issued to transition-sensitive sectors—that is, high emitters of greenhouse gases—which does not stand out as a severe threat to solvency, even if a loan portfolio rebalancing is ruled out. Still, exposures are not uniform across banks. In Colombia, for example, risky loan exposure ranges from 1 to 26 percent (FSB 2022).

While the focal point of the above applications is bank credit risk, a recent body of research has started digging into the relationship among sovereign debt, climate change, and financial stability. As banks and other intermediaries invest part of their resources in government securities, the impacts of climate change on public sector finances can spill over into the health of the financial system. One line of research, for example, concludes that climate change can affect financial stability through the sovereign debt nexus.
In particular, the studies collectively illustrate the multifaceted impacts of climate change on the financial sector, showing how government debt affects banking sector health in emerging markets, how climate vulnerability influences sovereign bond spreads, and how climate risks could lead to credit rating downgrades (Deghi et al. 2022; Cevik and Jalles 2020b; Klusak et al. 2021). Still, detailed case studies for Colombia and the United Kingdom suggest that, while significant financial losses may occur under certain climate scenarios, the stability of banking capital and the sustainability of public debt are not necessarily at risk.<fnref n="6" />

Box 6.1 discusses the impact of climate change on the insurance industry. This link is particularly relevant due to the feedback loop between financial stability and that sector. A case study of Puerto Rican banks in the aftermath of Hurricane Maria in 2017 (Anagnostakos et al. 2023) concludes that banks were surprisingly resilient due to the stabilizing effects of property insurance, federal mortgage insurance, disaster assistance, and, to a lesser extent, their own underwriting. These attenuating factors may be threatened by climate change if insurers withdraw from disaster zones or increase coverage costs prohibitively.

**Box 6.1. Climate Change Impact on the Insurance Industry**

As the Latin American and Caribbean region has a bank-centered financial system, this chapter does not deal with the effects of climate change on the insurance industry. But that sector is also at the mercy of climate risks (see, for instance, IAIS 2018). Physical risks may increase the coincidence of previously uncorrelated weather events, thus raising the volume of claims by affecting many policyholders at the same time. Insurers may retain overly risky clients and/or dismiss proper policy repricing if they do not factor in these latent hazards. And, on top of this, sluggish economic growth may be deleterious to the demand for insurance. In parallel, transition factors, by disrupting conventional sources of energy, business models, and the demand for and supply of different goods and services, may open up new business opportunities. Also, with extreme weather events becoming more frequent and severe, insurance premiums may increase and availability may decrease.

To date, the bulk of the analysis on the long-term stability of this market has been based primarily on qualitative, self-reported surveys among industry insiders. Insurance supervisors and companies increasingly are adopting

(continued on next page)

<footnote n="6">⁶ See, for example, Reinders et al. (2021) for a case study of Colombia and OBR (2021) for the United Kingdom.</footnote>
Box 6.1. Climate Change Impact on the Insurance Industry (continued)

more rigorous approaches, like the banking system stress test methodologies. Along these lines, IAIS (2022) concludes that the overall amount of losses by 2100 relating to the equity, corporate, and sovereign bonds, loans, and real estate held by the insurance sector across the world will range from a lower bound of 0.5 percent of the total assets in an orderly transition up to 3 percent in a “too little, too late” scenario. In sum, much like the findings for the banking sector, the results point to a manageable outcome. Battiston et al. (2019) reach a similar conclusion in their investigation of the risks posed by sovereign debt holdings in the insurance companies’ portfolios.

Nevertheless, these studies do not provide a comprehensive analysis of the potential losses for the insurance sector from climate change, as they fail to incorporate the risks associated with the potential materialization of the risks insured by these companies, which would hit the liabilities side, not the assets side, of their balance sheets.

Climate Change and Banking System Stress

In the case of Latin America and the Caribbean, the international evidence from the NGSF and FSB is limited to Brazil, Chile, Colombia, and Mexico. For this reason, this analysis is expanded to include 17 countries in the region: Argentina, the Bahamas, Barbados, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Panama, and Peru. In terms of the data required, the simulations combine banking and macroeconomic information to envisage the effects of climate change on the health of the banking system as of the year 2050.

The physical and transition risks of climate change can affect financial intermediaries through various channels. Given these risks, the primary goal of climate stress tests is to quantify how much capital banks will lose between 2022 and 2050 under different scenarios.

The simulation relies on a sequential analysis, linking climate change risk to sectoral and aggregate GDP growth and bank asset quality, spanning private sector loans and government debt (Figure 6.1); the methodological details are explained in Box 6.2. These stress tests must, however, be interpreted with caution, as uncertainty persists about the long-term effects of climate change on economic activity and the resulting bank solvency. Since the situation is unprecedented, the available economic models can produce only conjectures about the evolution of future GDP levels and growth. A particularly crucial limitation of these models is that they fail to incorporate
<figure><img src="image_52.png" /><figcaption>Figure 6.1. Impact Chain of Climate Change Risks</figcaption></figure>

Box 6.2. Potential Losses for Banks: Methodological Approach

In this analysis of the possible implications of climate change for financial stability in Latin America and the Caribbean, a simple simulation approach is used to estimate the potential losses of banking systems associated with their loan and government debt portfolios.ª Regarding private sector loans, the change in the value of total assets for the banking system is simulated in two stages. In the first, the ratio of nonperforming loans (NPLs) to total private sector loans (henceforth, NPL/L) in 2050 is projected as

$$ \left(\frac{NPL}{L}\right) = \sum_s \alpha^s \times \varepsilon_{NPL,g} \times g_s $$

where $\alpha^s$ is the share of each sector S (including both productive activities and the household sector) in total loans. In turn, $\varepsilon_{NPL,g}$ is the elasticity of total NPL/L to GDP growth, for which, for comparison purposes, two alternative values are taken. The first, -0.96, is the average of the empirical elasticity, as unveiled by 146 studies (Macháček, Melecký, and Šulganová 2018); this value is assumed to be the same for all sectors.ᵇ The second value ranges from -1.1 to -1.5 and represents the authors' estimate, based on Colombian data (see Box 6.3), for each broad division (primary, manufacturing, services, and household sectors). Finally, $g_s$ is the projected GDP growth for the sector, taken from the dynamic neoclassical general equilibrium model estimated by Koch and Leimbach (2023) under the five Shared Socioeconomic Pathway (SSP) scenarios in 194 countries.ᶜ An advantage of this model is its provision of a broad representation of Latin American and Caribbean countries and a three-sector GDP breakdown (primary, manufacturing, and services).

The equation says the projected overall NPL/L ratio is a weighted average of the sectoral NPL/L ratios. This figure is then compared to a recent NPL/L ratio (December 2022) to compute the expected change in this ratio, $\Delta(NPL/L)$.

In the second stage, the additional loan losses are computed in terms of the initial level of capital (K):

$$ \left(\frac{\Delta LL}{K}\right) = \left(\frac{\Delta NPL}{L}\right) \times LGD \times \left(\frac{L}{A}\right) = \left(\frac{A}{K}\right), $$

(continued on next page)
Box 6.2. Potential Losses for Banks: Methodological Approach (continued)

where LL, A, K, and LGD stand for loan losses, total assets, total capital (or net worth), and loss-given default, respectively. For practical purposes, LGD takes a value of 0.5, as suggested by Dagher et al. (2020).

The equation seeks to answer the following question: What proportion of their initial capital (as of late 2022) would banks lose as a result of additional loan defaults in a disorderly transition and a hothouse scenario vis-à-vis an orderly scenario? The equation says the losses will be larger the larger the elasticity of NPL/L to GDP growth, the loss given default, the share of loans to assets (that is, the overall exposure to credit risk), and the inverse of the capitalization ratio (that is, higher capital banks are better prepared to absorb a given loan loss).

The potential effect on the valuation of public debt holdings is first estimated by exploiting the following finance equation:

$$ \begin{aligned} \Delta DP &= \Delta SP \times D \\ &= -(\delta_{sp,g} \times g + \delta_{sp,y} \times y) \times D, \end{aligned} $$

where ΔDP is the percentage change in the value of public debt, ΔSP is the change in the sovereign spread under the two adverse climate scenarios (relative to the most benign one), and D is the average duration of public debt. D is assumed to equal 8, in line with the average public debt maturity in the region, as reported by Prats and Pereira (2022). δ<sub>sp,g</sub> (-0.13) and δ<sub>sp,y</sub> (-1.2) are the elasticities of the sovereign spread with respect to GDP growth and level (both measured relative to the benchmark), respectively, with the empirical elasticities borrowed from Presbitero et al. (2015).<sup>d</sup> As before, projected country g and data come from Koch and Leimbach (2023). The erosion of capital triggered by the potential public debt downward repricing is estimated as follows:

$$ \left(\frac{\Delta PD}{K}\right) = \Delta PD \times \left(\frac{PD}{A}\right) = \left(\frac{A}{K}\right). $$

The equation says that these market losses, in terms of capital, will increase with the elasticities of sovereign spreads to GDP growth and level, the duration of the debt, the share of government securities to assets, and, as before, the inverse of the capitalization ratio.

(continued on next page)

<footnote><sup>a</sup> Key assumptions in the methodology are (i) only the banking system is under scrutiny, in light of its overwhelming dominance in the region over other financial intermediaries; (ii) although climate risks might have ramifications for the market, liquidity, and operational risks, the exercise here focuses on credit risk, not only because it is more clearly defined and measurable but, mainly, because it appears prima facie as the most significant source of vulnerability in the region; and (iii) the asset structure of the banking system does not change throughout</footnote>
Box 6.2. Potential Losses for Banks: Methodological Approach (continued)

the period of analysis. This assumption of a fixed balance sheet, whereby asset composition remains unchanged, is not entirely realistic, as banks may gradually reduce their exposure to climate risks. A dynamic balance sheet approach, however, would require equally strong assumptions, as these portfolio changes are extremely hard to foresee. Also assumed is (iv) all sovereign debt holdings are valued at market prices, and regular loans to the public sector are not included; and (v) all bank assets are in local currency so exchange rate considerations can be ruled out. These simplifying assumptions are crucial to broadening the coverage of Latin American and Caribbean countries as much as possible, so as to provide a first regional assessment of the quantifiable threat posed by climate risks to financial stability.

b For other estimates of this empirical elasticity, see Beck, Jakubik, and Pilouí (2013) and Jakubik and Kadioglu (2022).

c The model considers energy and some natural resources (oil and gas) as productive inputs, which affect the GDP trajectory through energy prices and the cost-minimizing choice of energy sources by producers (see Château, Dellink, and Lanzi 2014). Depending on the relative price of fossil and renewable energies, each sector—which has its own energy and other input intensity—decides whether to invest in sources alternative to conventional fuel. The sectoral breakdown enables the identification of sectors particularly affected by climate policies, such as emission-intensive industries. It is worth noting that the various stakeholders in the area have widely adopted the SSPs. They are, for instance, the basis for the scenarios developed by the Network of Central Banks and Supervisors for Greening the Financial System (see NGFS 2020).

d Presbitero et al. (2015) estimate a multivariate panel regression on a sample of 49 developing and emerging countries. Maurini (2011) reports similar results for a broad sample of emerging countries.

extreme (that is, low probability but high impact) events and feedback mechanisms that would magnify the aggregate impact of sectoral shocks. Because of this, critics claim such projections boast overly optimistic scenarios (Buhaug and Vestby 2019; Pindyck 2013, 2022).

For this analysis, projected GDP growth up to 2050 is borrowed from Koch and Leimbach (2023), who run growth trajectories under five Shared Socioeconomic Pathways (SSPs) in 194 countries.<fnref n="7" /> The SSPs are scenarios developed by the climate change research community to promote integrated and consistent economic research in this area. This analysis focuses on three of these—SSP1, SSP2, and SSP3—to cover a broad spectrum: SSP1 is an orderly transition scenario with limited physical and transition risk; SSP2 is a disorderly transition scenario where correction policies are delayed, resulting in high transition costs and medium physical costs; and SSP3 paints a “hothouse” world in which no additional advances are achieved on the mitigation and adaptation fronts, giving rise to low transition costs but extremely high physical risk.<fnref n="8" /> The other information needed is a set of banking system

<footnote n="7">⁷ We thank Professor Marian Leimbach for generously sharing their dataset.</footnote>

<footnote n="8">⁸ In terms of the original SSP narrative, SSP1 can be considered the gold standard, featuring sustainable technologies driving economic growth and no major national or international conflicts that could hamper mitigation and adaptation policies; SSP2 is a “business as usual” scenario preserving historical socioeconomic patterns; and, finally,</footnote>
<table><caption>**Table 6.1.** Banking Indicators in 17 Latin American and Caribbean Countries, as of End-2022</caption><thead><tr><th>Variable</th><th>Mean</th><th>Median</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Private credit to assets</td><td>52.2</td><td>50.3</td><td>28</td><td>79.4</td></tr><tr><td>Private credit composition</td><td></td><td></td><td></td><td></td></tr><tr><td>Household</td><td>45.3</td><td>47.5</td><td>15.1</td><td>83</td></tr><tr><td>Enterprise</td><td>54.7</td><td>52.5</td><td>17</td><td>84.9</td></tr><tr><td>Enterprise credit composition</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>8.5</td><td>5.7</td><td>0.7</td><td>24.4</td></tr><tr><td>Manufacturing</td><td>23.5</td><td>18.3</td><td>4</td><td>64.2</td></tr><tr><td>Services</td><td>68</td><td>74.1</td><td>29.9</td><td>95.3</td></tr><tr><td>Public debt to assets</td><td>15.8</td><td>15.3</td><td>1.1</td><td>40.2</td></tr><tr><td>Net worth to assets</td><td>10.7</td><td>10.5</td><td>7.1</td><td>18.3</td></tr><tr><td>NPL ratio</td><td>3.7</td><td>2.8</td><td>1.6</td><td>9.6</td></tr></tbody></table>

indicators, which come from the websites of the central banks or the bank supervisory agencies.⁹ The outcome metric is the change in bank capitalization prompted by the sluggish GDP growth in the SSP2 and SSP3 scenarios relative to the benchmark, the SSP1 scenario. Specifically, the calculations require data on the initial conditions (in this case, at the end of 2022) regarding balance sheet structure (private credit, public debt securities, and the ratio of net worth to assets), as well as private credit allocation by sector (enterprise credit partitioned into primary, manufacturing, and services—matching the available GDP disaggregation in the projections up to 2050—plus household credit). These indicators are reported in Table 6.1.

SSP3 is the least desirable case, in which nationalism, widening inequality within countries, and little care for climate-driven damage all give rise to disappointing economic growth. A comparison of median annual GDP growth across different sectors from 2022 to 2050 under SSP2 and SSP3 scenarios relative to SSP1 indicates that GDP growth lags under SSP2 and SSP3 by 1 and 2.3 percentage points, respectively. It is important to note these figures are, again, relative to the SSP1 benchmark scenario and not absolute growth rates. A drop of two percentage points in capital to assets under SSP2, for instance, means the indicator is two points lower than it would be under SSP1.⁹ Throughout the analysis, we assume a fixed balance sheet, meaning the asset structure of the banking system, in terms of asset class and sector allocation, remains unchanged throughout the period. This may not be completely realistic, as banks may gradually reduce their exposure to climate risks, and, also, borrowers may seek to reduce their carbon footprints. A more dynamic balance sheet approach, however, would need data inputs that are extremely hard to foresee.
<figure><img src="image_53.png" /><figcaption>**Figure 6.2.** Loan Losses under SSP2 and SSP3 Relative to SSP1, as of 2050</figcaption></figure>

Source: Authors' analysis.

Note: The boxplot summarizes the results of the analysis, with the box representing the interquartile range (IQR, or the 50 percent of data points lying above the first 25 percent and below the 75 percent). The line within the box is the median. The extreme values of the line attached to the box are the “maximum” (75 percent plus 1.5 IQR) and the “minimum” (the first 25 percent minus 1.5 IQR), and the dots indicate outliers, which in this case correspond to Ecuador and Haiti, with the latter being the largest. “Literature elasticity” and “elasticity estimates” refer to the elasticity of the NPL to GDP growth, for which the authors alternatively use the average empirical values from a large number of studies (“literature”) and their own sectoral estimates, based on Colombian data and reported in Box 6.3 (“estimates”).

The simulation results under different assumptions on the elasticity of total NPL/L to GDP growth show that median loan losses amount to 5.4 percent of initial capital (see Figure 6.2) in the most pessimistic scenario. Overall losses—the sum of both loan and public debt losses (which amount to 6.4 percent in the worst scenario)—are reported in Figure 6.3.<fnref n="10" /> These losses appear manageable and unlikely to threaten financial stability. Dagher et al. (2016), for example, examine the deterioration of NPL ratios around all banking crises since 1970 and conclude, in their baseline calculation, that a ratio of capital to risk-weighted assets of 14.8 percent would have been enough to deal with those episodes. The median value of this indicator in the region is 16.2 percent, and it would drop to about 14.7 percent under the worst climate change scenario evaluated in the simulations (see

<footnote n="10">¹⁰ The main results appear mostly unaltered by whether an NPL elasticity is borrowed from the literature or the authors' own elasticities from Colombia data are used; see Boxes 6.2 and 6.3 for further details of these elasticities.</footnote>
<figure><img src="image_54.png" /><figcaption>**Figure 6.3.** Loan and Public Debt Losses under SSP2 and SSP3 Relative to SSP1, as of 2050</figcaption></figure>

Source: Authors' analysis.

Note: The boxplot summarizes the results of the analysis, with the box representing the interquartile range (IQR, or the 50 percent of data points lying above the first 25 percent and below the 75 percent). The line within the box is the median. The extreme values of the line attached to the box are the “maximum” (75 percent plus 1.5 IQR) and the “minimum” (the first 25 percent minus 1.5 IQR), and the dots indicate outliers, which in this case correspond to Brazil and Guatemala in the SSP2 scenario and Brazil, Haiti, and Guatemala in the SSP3 scenario, with Guatemala being the largest. “Literature elasticity” and “elasticity estimates” refer to the elasticity of the NPL to GDP growth, for which the authors alternatively use the average empirical values from a large number of studies (“literature”) and their own sectoral estimates, based on Colombian data and reported in Box 6.3 (“estimates”).

Figure 6.5). A more detailed analysis using more granular data for Colombia is reported in Box 6.3.

Capital losses depend on how much loan quality deteriorates as a result of climate change and how much of the initial capital the overall loan portfolio represents. Two country cases illustrate why this happens. In the first, Argentina, loan losses under the SSP2 scenario stand at only 0.5 percent of capital. Why? Because the initial private credit exposure is just 28 percent of assets, and banks are highly capitalized, with a capital to assets ratio of 18.3 percent. On the other hand, the same losses climb to 6.7 percent in Haiti, where private credit and capital represent 61.7 percent and just 7.7 percent of assets, respectively.

The results suggest that climate change is not likely to cause widespread banking instability, except among financial institutions whose loan portfolios are highly concentrated in specific geographical areas and sectors. These results require some qualifications, however.
<figure><img src="image_55.png" /><figcaption>**Figure 6.4.** Regulatory Capital to Risk-Weighted Assets, as of Late 2022</figcaption></figure>

Source: Authors' elaboration, based on data extracted from the websites of central banks and other bank supervisors.

First, as highlighted earlier, GDP growth rates may fall below those used in the above stress tests under extreme climate conditions, resulting in additional losses. Tail risks—that is, the materialization of extreme weather events that are expected to get more frequent and severe with climate change (see Chapter 2)—can give rise to deeper and wider adverse climate events not captured by the typical climate research framework. Their materialization would substantially threaten financial stability, as they can lead to abrupt and profound economic and financial disruptions that are difficult to predict and manage.

Second, at present, banks in the region hold ample capital, so they are well prepared to absorb the losses in the negative scenarios. Specifically, the median actual capital ratio in the sample is 54.7 percent above the standard Basel III requirement of 10.5 percent of risk-weighted assets (see Figure 6.4).<fnref n="11" /> Even in the most adverse simulated scenario, the excess capital would drop to 32 percent, and no country would fall below or even get close to the minimum requirement (see Figure 6.5).<fnref n="12" /> These ample levels

<footnote n="11">¹¹ In the same spirit, Bebczuk, Fernandez Diez, and Tamola (2021) show that for an average of 15 countries in the region, as of mid-2020, the actual capital was 61 percent higher than the minimum requirement.</footnote>

<footnote n="12">¹² This assumes that risk-weighted and book capital (the measures used in the simulations above) are equal or perfectly correlated. Also, the market risk attached to</footnote>
<figure><img src="image_56.png" /><figcaption>**Figure 6.5.** Basel Capital Ratios under SSP2 and SSP3 Relative to SSP1, as of 2050</figcaption></figure>

Note: The boxplot summarizes the results of the analysis, with the box representing the interquartile range (IQR, or the 50 percent of data points lying above the first 25 percent and below the 75 percent). The line within the box is the median. The extreme values of the line attached to the box are the “maximum” (75 percent plus 1.5 IQR) and the “minimum” (the first 25 percent minus 1.5 IQR). The baseline comes from the distribution of Basel capital ratios data at the country level (without employing simulation). “Literature elasticity” and “elasticity estimates” refer to the elasticity of the NPL to GDP growth, for which the authors alternatively use the average empirical values from a large number of studies (“literature”) and their own sectoral estimates, based on Colombian data and reported in Box 6.3 (“estimates”).

of capitalization are, however, intended to shield banks from risks that are not necessarily related to climate change and/or may change given climate change. To date, neither the regulators nor the financial institutions themselves have factored climate risks into their ongoing capitalization decisions. So, the question arises as to whether additional capital will be needed in the future to account fully for risks that have not yet been fully quantified or are unknown, especially if the other, non-climate-related, risks are realized, wipping out the current excess capital.

All in all, the simulations from banking stress tests in the region should not be interpreted as proof that there are no banking-related risks to worry about. Actual outcomes, with low but positive probability, may

government debt holdings may or may not be realized. The simulation assumes that all this debt is valued at market prices, which is not always the case; even if market priced, the regulator may allow these assets to be recorded at nominal or near-nominal value. What ultimately matters, however, is the actual, underlying value, regardless of whether the regulator allows the recorded value to deviate from the economic one.
Box 6.3. Robustness Checks with Bank-Level Data for Colombia

Because of data limitations, the simulations presented so far in this chapter have been performed (a) at the banking system level; (b) with a low level of sectoral disaggregation (agriculture, manufacturing, services, and households); and (c) using the elasticity of the ratio of nonperforming loans to total private sector loans (NPL/L) and GDP growth from Macháček, Melecký, and Šulganová (2018), rather than estimating it. Moreover, this elasticity is assumed to be the same for all sectors, and (d) growth forecasts are applied that do not consider extreme sectoral GDP growth scenarios. Given these constraints, a country-specific exercise is conducted on Colombia, the only country in the region with the public data required to relax them.

The dataset used contains the commercial and household loan portfolio of every bank operating in Colombia between the first quarter of 2012 and the first quarter of 2020.ª The data span the performing and nonperforming loans (NPLs) to a maximum of 81 productive subsectors, plus the household sector, and include the quarterly real GDP of each of the subsectors. The elasticity of the NPL/L to GDP for agricultural, manufacturing, and service activities and the household loan portfolio are estimated based on this information (see Bebczuk, Celis, and Galindo, forthcoming, for details).ᵇ

Afterward, following the same methodology as the previous exercises, several additional stress tests are run, with some sectors identified that are especially vulnerable to climate change. The industries likely to be severely affected by physical risks include crop and animal production, forestry and logging, fishing, and the manufacturing of food and beverage products (see Chapter 5). Transition risks can be anticipated to take a toll on crude petroleum and natural gas; mining; the manufacture of coke and other refined petroleum products, paper and paper products, and rubber and plastic products; and electricity and gas.

The first stress scenario (scenario 1) involves varying NPL/L ratios according to the estimated sectoral GDP elasticities and the same sectoral GDP forecasts, as opposed to relying on the elasticity borrowed from the literature and assuming it to be the same across sectors. However, to produce more extreme scenarios that may capture local disasters, which may be the biggest risk for sectoral or geographically concentrated banks, two additional stress scenarios are posed:c

• Scenario 2: NPL/L ratios double with respect to the observed levels as of December 2022 in the climate-vulnerable sectors.

• Scenario 3: NPL/L ratios go up to 25 percent in the climate-vulnerable sectors, no matter the initial value.

Figures 6.3.1 (using GDP growth forecast under SSP2) and 6.3.2 (using GDP growth forecast under SSP3) summarize the Basel capital ratio for each bank predicted to result under each respective scenario.

(continued on next page)
Box 6.3. Robustness Checks with Bank-Level Data for Colombia (continued)

<figure><img src="image_57.png" /><figcaption>**Figure B6.3.1.** Basel Capital Ratios by Bank under Alternative Stress Scenarios under SSP2 Relative to SSP1, as of 2050</figcaption></figure>

Note: The boxplot summarizes the results of the analysis under the macroeconomic SSP2 scenario, with the box representing the interquartile range (IQR, or the 50 percent of data points lying above the first 25 percent and below the 75 percent). The line within the box is the median. The extreme values of the line attached to the box are the “maximum” (75 percent plus 1.5 IQR) and the “minimum” (the first 25 percent minus 1.5 IQR). The dotted line represents the regulatory capital ratio threshold of 10.5 percent. The baseline comes from the distribution of Basel capital ratios data at the level of Colombian banks (without employing simulation). In scenario 1 (SC1), the NPL ratio varies according to the estimated elasticities only. Scenario 2 (SC2) doubles the NPL ratio in the sensitive sectors. In scenario 3 (SC3), the NPL ratio in every sensitive sector is increased to 25 percent.

Results show that regulatory capital ratios remain largely unaffected even in the extreme scenarios. Still, a few banks would see a material capital contraction that would make them fall below the 10.5 percent required ratio. This, in itself, could be problematic, if the failure of a few banks—even small ones—can translate to a systemic run against the financial system.

What explains this low expected impact? Mainly, the fact that banks are already only marginally exposed to these risky sectors. Food manufacturing (8.8 percent of the banking system) and electricity and gas (10.3 percent) are the only sensitive sectors displaying a somewhat significant share of the overall commercial loan portfolio. Although there is some dispersion across banks, neither of these two activities has a participation surpassing 20 percent.

Last but not least, as discussed earlier in this chapter, the effect will be lower, the lower the overall exposure to credit risk. In the Colombian case, at the system level, total commercial loans represent 2.2 percent of total capital. For a ratio of capital to risk-weighted assets of 16.2 percent, this means that in the most extreme case considered, the capital ratio would drop slightly to 14 percent, which suggests systemic financial stability would not be seriously jeopardized. Some banks would, however, drop below the minimum capital

(continued on next page)
Box 6.3. Robustness Checks with Bank-Level Data for Colombia (continued)

<figure><img src="image_58.png" /><figcaption>**Figure B6.3.2.** Basel Capital Ratios by Bank under Alternative Stress Scenarios under SSP3 Relative to SSP1, as of 2050</figcaption></figure>

Note: The boxplot summarizes the results of the analysis under the macroeconomic SSP3 scenario, with the box representing the interquartile range (IQR, or the 50 percent of data points lying above the first 25 percent and below the 75 percent). The line within the box is the median. The extreme values of the line attached to the box are the “maximum” (75 percent plus 1.5 IQR) and the “minimum” (the first 25 percent minus 1.5 IQR). The dotted line represents the regulatory capital ratio threshold of 10.5 percent. The baseline comes from the distribution of Basel capital ratios data at the level of Colombian banks (without employing simulation). In scenario 1 (SCI), the NPL ratio varies according to the estimated elasticities only. Scenario 2 (SC2) doubles the NPL ratio in the sensitive sectors. In scenario 3 (SC3), the NPL ratio in every sensitive sector is increased to 25 percent.

requirements. This does not necessarily mean banks would fail, since this is a
hypothetical exercise that assumes their asset structure remains fixed despite
rising risks. Failures may be avoided by reshaping the structure of balance
sheets in response to higher climate risks. Nonetheless, it is worth noting that,
in case some banks fail as a result of climate risks, the way in which this is man-
aged may avert a systemic impact.

<footnote>a The analysis does not include information after early 2020 because of regulatory forbearance during the COVID-19 crisis that permitted banks to postpone the downgrading of loans incurring in arrears.</footnote>

<footnote>b The empirical elasticities range between -1.1 and -1.5—that is, slightly higher but otherwise on the same order of magnitude as the average -0.96 produced by the related literature and used in the simulations presented elsewhere in this chapter. For the interested reader, the estimation relies on a model of local projections with bank-level fixed effects. More details, and the econometric output, are available upon request.</footnote>

<footnote>c The exercise assumes that the NPL/L ratios in the less sensitive sectors (those not listed in the previous paragraph) evolve according to the estimated GDP elasticities and the available projected GDP growth rates.</footnote>

<footnote>d It must be noted that the banks under threat are initially less capitalized than the average.</footnote>

<footnote>e Also important to emphasize is the assumption that the banks will maintain the same exposure to the sensitive sectors throughout the next few decades. It should be expected, however, that, in the face of potential future losses, those banks would sooner or later reduce such exposure, attenuating the results even further.</footnote>
be notably worse than those that have been incorporated into the analysis to date.<fnref n="13" />

## Climate-Related Risks in the Financial Sector

The findings from stress tests underscore the need for robust policy frameworks to reduce the impending risks. Policy recommendations include enhanced data collection to detect the risks, the development of forward-looking risk assessment methodologies to anticipate them, and the strengthening of regulatory and supervisory frameworks to prepare the financial system for climate-related challenges.

Financial regulation and supervision are crucial in addressing proactively the repercussions of climate-related physical and transition risks on financial stability. Despite a flourishing policy and academic literature, only educated conjectures can be made about what the distant future holds in terms of the economic and financial impacts of these unprecedented events. Detailed data are scarce, and the shortage may bias the current assessments of financial impacts. As the Colombian exercise shows, banks display very different balance sheet structures. They should not, therefore, be expected to be hit uniformly, and a vigilant eye must be kept on some individual intermediaries with higher exposure to sensitive sectors. Central banks, in general, and regulators, in particular, should lead the design and implementation of stress tests—which provide the critical input for monitoring, identifying, and mitigating otherwise unanticipated risks—because they have unique access to the granular bank data and highly skilled technical staff needed to perform the tests.

To assess the preparedness of financial regulators and supervisors in Latin America and the Caribbean, the Inter-American Development Bank (IDB), with the support of the Association of Supervisors of Banks of the Americas (ASBA), surveyed central banks and bank supervisors to identify the disclosure practices of financial institutions with regard to climate-related risks and to take stock of climate change-related financial regulation and environmental, social, and corporate governance (ESG)

<footnote n="13">¹³ Although these and similar projections are widely used in policy and academic applications, the positive projected GDP growth rates, even in the most adverse scenario, may be controversial. Specifically, recall that the exercise indicates how much worse things could get compared to an orderly transition scenario. The projected average annual GDP per capita growth rate in the period 2022–50 stands at 3.5 percent, 2.5 percent, and 1.2 percent for SSP1, SSP2, and SSP3, respectively, and the analysis does not work with these projections directly but, rather, relative to SSP1, the most benign scenario.</footnote>
practices. Financial regulators and supervisors in 21 countries responded to the survey.<fnref n="14" />

The results, highlighted in Table 6.2 and described in detail by the two organizations (in IDB and ASBA, forthcoming), paint a nuanced picture. Broadly speaking, there is a discernible interest among countries in addressing the looming challenges climate change presents to their financial systems. Fifteen have some institutional strategy to include climate-related and other environmental and social risks in their regulatory and supervisory frameworks. This pursuit reflects a growing awareness of the intertwined relationship of environmental shifts and economic well-being. Only four countries, however, have regulatory policies that explicitly include measuring climate-related risks vis-á-vis credit or other financial risks.

Beyond the lack of a robust regulatory framework leading to the incorporation of climate-related risks in regulation and supervision, many countries have obstacles they need to overcome, including a lack of institutional capacity for financial supervision and the limited availability of data and technology for assessing climate-related risks. These limitations can be addressed if explicit regulation is put into place. Among other requirements, risks must be quantified by geographical and sectoral exposure, stress tests focused on these dimensions must be conducted, and provisioning and capital requirements specific to climate-related risks must be designed (Herrera et al. 2023).

Supervising the multifaceted risks induced by climate change also requires a sizeable and well-trained workforce capable of acknowledging and assessing the complexities of climate finance and environmental risk management. Concerning the capacities of supervisory agencies, one-third of the countries that responded to the survey said they have a specific department or dependency dedicated to climate-related risks. Of these, three have more than three persons working full-time on those tasks. Four appear to be training their staff on these issues. Only three countries have developed technological frameworks to supervise climate-related risks. In sum, the survey has identified a need to invest in increasing the number and upgrading the skills of financial sector staff to develop a group of qualified professionals specialized in climate-related financial risks.

The availability of accurate, comprehensive, timely data is a foundation for any robust supervisory strategy. Among the primary challenges

<footnote n="14">¹⁴ The countries responding to the survey were Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, and Uruguay.</footnote>
<table><caption>**Table 6.2. Highlights of the Survey on Preparedness for Climate Change Impacts on Financial Stability**</caption><thead><tr><th>Survey category</th><th>Percentage of countries that answered the survey</th></tr></thead><tbody><tr><td><strong>Regulatory Framework</strong></td><td></td></tr><tr><td>Countries with an institutional strategy to include climate-related and other environmental and social risks in the supervisory and regulatory framework</td><td>71%</td></tr><tr><td>Countries with a regulatory act that includes the measurement of climate risks related to credit or other financial risks</td><td>19%</td></tr><tr><td><strong>Capacity of Supervisory Agency to Monitor Climate-Related Risks</strong></td><td></td></tr><tr><td>Countries where the supervisory agency has a specific area/ dependency devoted to climate risks</td><td>33%</td></tr><tr><td>Countries with more than three people working on climate issues most of the time</td><td>14%</td></tr><tr><td>Countries training the regulatory or supervisory personnel on climate-related and other ESG risks</td><td>71%</td></tr><tr><td>Countries training more than 20% of staff</td><td>19%</td></tr><tr><td>Countries that have developed or implemented third-party technology products to supervise climate-related and ESG risks</td><td>14%</td></tr><tr><td><strong>Data Capacity of Countries to Identify and Monitor Climate-Related Risks</strong></td><td></td></tr><tr><td>Countries that have granular data on asset locations to assess physical risks</td><td>33%</td></tr><tr><td>Countries with a mapping of climate-vulnerable assets/regions and carbon-intensive industries</td><td>10%</td></tr><tr><td>Countries looking at adding climate tags to the credit bureau to identify defaulted loans related to climate impacts</td><td>19%</td></tr><tr><td>Countries with data on how financial institutions' significant exposures to climate risks are mitigated by insurance or other types of collaterals</td><td>10%</td></tr><tr><td>Countries that identify and monitor physical risk exposures in financial institutions' banking and trading books</td><td>14%</td></tr><tr><td>Countries identifying economic sectors vulnerable to transition risks, including liability risk</td><td>10%</td></tr><tr><td><strong>Climate-Related Stress Tests</strong></td><td></td></tr><tr><td>Countries using or evaluating the use of climate-related issues in financial system stress tests</td><td>24%</td></tr><tr><td>Countries considering or applying forward-looking climate scenarios for stress test purposes</td><td>14%</td></tr></tbody></table>

Source: IDB and ASBA, forthcoming.
highlighted by the survey are the data constraints. Many countries in the region find themselves hamstrung by a lack of key data, which makes it difficult to gauge the full spectrum of climate-related risks, let alone formulate targeted strategies to reduce them. Only seven of the countries have granular data on asset allocation with which to assess the physical risks associated with the loans provided by financial institutions. Very few countries comprehensively map climate-vulnerable assets and regions and carbon-intensive industries. Such types of data are crucial for assessing climate-related risks of loans that encompass several risky dimensions. If these data were available and tagged to loans in credit bureaus, assessing the future risks of loans properly would be feasible. Only four countries in the region have plans for that, however.

Supervisors in very few countries have access to information on the existence of instruments to mitigate climate-related risks. Only three countries can identify and monitor physical risk exposures in financial institutions' lending and trading books, and only two can identify and monitor loans extended to sectors that face climate transition risks. Without reliable data in these and other dimensions, formulating strategies or making informed decisions to combat the financial implications of climate change becomes an uphill battle.

All of the above explains in part the difficulties of conducting climate stress tests in the financial systems of the countries in Latin America and the Caribbean. As discussed previously, only five of the countries do this, and only three have forward-looking scenarios.

In Latin America and the Caribbean, financial authorities are willing to monitor the impact of climate-related risks on financial stability. Before they can, however, the practical challenges need to be solved. For that, a legally defined regulatory framework that explicitly addresses the need to incorporate climate-related risks in financial regulation and supervision would be useful. Under the umbrella of strong legal frameworks, countries could invest in data infrastructure and capacity building, particularly in human resources. This could ensure the region is able to quantify the impending risks and is equipped to handle them if they materialize.

## Mixed Messages

In the realm of banking systems within Latin America and the Caribbean, stress test exercises evaluating the impact of risks related to climate change lend themselves to mixed policy conclusions. On the one hand, at face value, they suggest that while there may be capital losses under different climate scenarios, these losses are expected to be manageable, posing a
limited threat to the overall solvency of banks. On the other hand, however, this reassurance is tempered by the recognition that our understanding of how climate change intersects with economic and financial developments is still nascent. The use of historical evidence to project future outcomes remains limited. Current stress tests cover a limited number of risks and scenarios and dismiss the economic outcomes under extreme climate events. In this light, they must be interpreted cautiously, as being able to provide useful evidence to policymakers but far from offering bulletproof guidance on the adequate levels of capital needed to confront adverse scenarios with a low but positive probability of occurrence.

The inherent uncertainty in the economic and climatic projections underscores the vital role of financial regulatory authorities. They are called upon to take the lead in gathering detailed data, specific to each bank and borrower, about exposures to emerging risks. The authorities are also tasked with raising awareness among the financial intermediaries they oversee. This proactive approach is vital in navigating the complexities of climate-related financial risks.

While the impending uncertainties do not leave room for complacency, the empirical results of stress tests done for the first time for a wide sample of countries in Latin America and the Caribbean suggest that regulatory overreach—the imposition of excessive or overly burdensome rules and regulations—can be counterproductive. Banking systems in the region are strong and well-capitalized, making them capable of absorbing losses under the scenarios that were used in the stress tests. Regulatory overreach can lead to unintended negative consequences for financial systems, such as stifling innovation, creating unnecessary administrative costs, or hindering financial depth. Instead, a more measured approach that keeps the regulatory stance open to future adjustments would be consistent with the available evidence and the existing uncertainties. Adaptability to incoming information is key, allowing for responses to evolve as new empirical results emerge and unanticipated events unfold.

In the interim, the focus should be on enhancing data collection and investing in the technical training of staff. These efforts are essential for conducting forward-looking analyses that are inherently complex. By building this capacity, financial authorities can better equip themselves to navigate the uncertain waters of climate change impacts on the financial sector, ensuring stability and resilience in the face of these emerging challenges.
<figure><img src="image_59.png" /></figure>
# Delivering Resilient Infrastructure Services

<figure><img src="image_60.png" /></figure>

Climate change increasingly disrupts essential water, energy, transportation, and telecommunication services, with poor infrastructure quality and inadequate maintenance exacerbating these vulnerabilities. Building resilience calls for upgrading existing systems, enhancing materials and design, improving maintenance practices, and integrating nature-based solutions into climate change adaptation and mitigation. Among the strategies needed are diversification, decentralization, the addition of redundancy, and the enhancement of information systems. Strengthening institutions and planning for future uncertainties are essential to ensure service continuity in the face of escalating climate impacts.

Societies have built their infrastructure within ecosystems that existed before them, and it has always depended on nature's delicate equation. This balance is being altered by slow changes, such as sea-level rise and shifting precipitation patterns, and by extreme events, like droughts, heavy rains, landslides, and hurricanes, that are increasing in frequency and intensity as a result of climate change. Given the poor quality of infrastructure in the region and the inadequate quality of services that already hinders productivity and well-being, these disruptions are particularly concerning. As infrastructure systems were not originally designed to cope with climate change, the risks of service interruptions and the cascading effects on the economy and society become even more pronounced.

Extreme weather events disrupt essential infrastructure services on which societies depend, including water, sanitation, electricity, telecommunications, and transportation. In 2023, for instance, Montevideo, Uruguay, experienced its worst drought in 74 years. By early July, freshwater reserves were at 2 per-cent of their capacity, forcing authorities to use higher-salinity water from the ocean to maintain supply (Díaz Campanella 2023). Citizens reported dissatisfaction and damage to their appliances. Authorities warned of adverse effects on pregnant women and those with hypertension and provided financial
support to purchase bottled water. In the same year, Hurricane Otis went from a simple tropical storm to a maximum category hurricane in less than 12 hours, causing extensive destruction and an estimated US$16 billion in economic losses in Acapulco, Mexico. A week after the disaster, water, electricity, and internet services were still intermittent (Suárez 2023).

The destruction left in the wake of disasters does not represent their only cost; to it should be added the cost of loss of service. When transportation, telecommunications, energy, or water and sanitation services are disrupted, losses in society begin to cascade. Households cannot store or cook food safely, businesses cannot deliver their products to their customers, health service providers struggle to care for their patients, and authorities have difficulty communicating and coordinating search and rescue efforts.

Another challenge is the uncertainty climate change brings. The same atmospheric dynamics that make it impossible to predict for sure the intensity of a hurricane like Otis only 12 hours in advance make it impossible to predict precisely the precipitation patterns, temperatures, and frequency and severity of extreme events that will prevail in a few decades. This makes planning and designing infrastructure challenging. But, as this chapter will show, there are ways of achieving resilient services.

## Double Trouble: Disruption in Both Supply and Demand

A key step to ensuring a climate-resilient supply of infrastructure services is to understand how climate changes modify consumer needs and the capacity of utilities to provide services.

<figure><img src="image_61.png" /><figcaption>**Figure 7.1.** Infrastructure Damage by Sector and Type of Event in Latin America and the Caribbean</figcaption></figure>

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<figure><img src="image_62.png" /><figcaption>**Figure 7.1.** Infrastructure Damage by Sector and Type of Event in Latin America and the Caribbean (continued)</figcaption></figure>

Source: Authors' elaboration, based on data from DesInventar (1970–2013).
A look at past disasters shows that different types of infrastructure have different susceptibilities. For the past fifty years, authorities in Latin America and the Caribbean have reported when extreme weather events and landslides have damaged the infrastructure used to supply water, energy, transportation, and telecommunications services (Figure 7.1). Floods are the problem most frequently encountered by utilities in all sectors; they represent 33 percent of the damaging events affecting the energy sector and up to 44 percent in transportation. But the second most frequent problem is different for each sector. In the water sector, it is rainfall, as excess water overwhelms sanitation and distribution infrastructure. In energy and telecommunications, it is storms; they affect powerlines and antennas. And, in transportation, the second most important threat is landslides, which can destroy bridges and block roads.

Of course, different parts of the region are affected differently, as well. While Figure 7.1 provides averages across Latin America and the Caribbean, a focus on the Caribbean would reveal a higher impact of storms across sectors, and a focus on Andean countries would highlight landslides. Moreover, the figure only shows how frequently different events affect infrastructure, without indicating the magnitude of the damage they cause. Some events, like hurricanes, are relatively infrequent but can be highly destructive.

## Water and Sanitation

Looking ahead, climate change will affect each type of infrastructure and each country differently.

In the water sector, for instance, higher temperatures translate into greater demand, especially when incentive for efficient consumption is lacking because of inadequate pricing or an absence of metering. Freshwater demand will rise simultaneously for agriculture, electricity generation, mining, and other water-intensive industries (Caretta et al. 2022), exacerbating competition across different uses. This will be particularly challenging in countries where people already live in areas with medium-high to extreme water stress, such as Chile (82 percent live in such areas), Mexico (79 percent), and Venezuela (54 percent; Libra et al. 2022). In addition, higher demand for water raises the volume entering sewerage, which is already burdened by rapid urbanization and population growth. When their capacity is exceeded, pipes rupture and overflow, and flooding occurs.

At the same time, climate change disrupts water supply and affects sanitation infrastructure (Talbot-Wright, Pérez-Urdiales, and Vogt-Schilb 2023). Glaciers act as natural reservoirs, but they are retreating as a result of rising temperatures. This, in turn, reduces water availability and disrupts seasonal
flow patterns, affecting supply during dry periods. In Bolivia, Colombia, Ecuador, Peru, and Venezuela, the reduction in area of Andean glaciers during the second half of the 20th century ranged from 20 to 50 percent (Magrin 2015). Moreover, runoff from melting glaciers can trigger landslides that damage or destroy water intake and wastewater treatment infrastructure and overwhelm water treatment infrastructure by making the water too muddy. Changes in rainfall patterns similarly disrupt seasonal flow patterns. In an Inter-American Development Bank (IDB) survey of 61 water utilities, 77 percent claimed to have been affected by reduced river flows, and 70 percent reported increased sediment load (Solis and Serebrisky 2023; see Table 7.1).

The sediment load in rivers is also augmented by soil erosion, triggered by heavy rains and wildfires created by heatwaves, while higher water temperatures are making harmful algal blooms more frequent, severe, and widely distributed, affecting the taste and odor of the water (Caretta et al. 2022). Furthermore, more rainfall and sea-level rise can collapse stormwater systems and cause sewage overflows (Hughes et al. 2021). These events pose risks to human health, increase pressure on groundwater sources, and raise costs for water utilities, encompassing both investment and operational expenditures. Of the water operators responding to the IDB survey, 64 percent reported increases in expenditure on chemicals for water treatment as a result of climate change (Solis and Serebrisky 2023).

## Energy

Climate change also threatens the provision of energy (Talbot-Wright, Hallack, and Vogt-Schilb 2023). In Latin America and the Caribbean, where hydropower

<table><caption>**Table 7.1.** Main Impacts of Climate Change Reported by Latin American and Caribbean Water Utilities</caption><thead><tr><td>Impacts</td><td>Percentage of utilities</td></tr></thead><tbody><tr><td>Reduction in river flow</td><td>77%</td></tr><tr><td>Increase in the sediment load of surface sources</td><td>70%</td></tr><tr><td>Collapse of sewage or stormwater drainage networks due to heavy rains</td><td>66%</td></tr><tr><td>Unexpected changes in water demand due to changes in temperature</td><td>52%</td></tr><tr><td>Reduction or depletion of the groundwater table in aquifers</td><td>41%</td></tr><tr><td>Power outages as a result of disasters</td><td>39%</td></tr><tr><td>Landslides causing damage to infrastructure</td><td>33%</td></tr><tr><td>Increased presence of algae in water sources</td><td>31%</td></tr></tbody></table>

Source: Authors' elaboration, based on Solis and Serebrisky (2023).
provided 53 percent of the electricity generated between 2000 and 2021, changes in precipitation will affect streams and, ultimately, hydropower production. In Suriname, water inflow to the Afobaka hydropower plant could be reduced by around 5 to 9 percent by midcentury and by 10 to 14 percent by 2100 (San Salvador Del Valle et al. 2022). Electricity production from wind farms and solar panels will be altered by changes in wind patterns, air density, and cloudiness, while extreme heat will reduce the yield of solar panels and thermal power plants and the capacity of the grid to carry electricity (IEA 2022a). During droughts and heatwaves, sparks in transmission lines could lead to wildfires, which in turn will threaten the transmission lines (IEA 2021a).

When extreme weather events damage power plants or transmission lines, operators often struggle to reestablish service. After Hurricane

**Box 7.1. How Climate Change Disrupts Electricity Services: The Case of Hurricane Odile**

In September 2014, Hurricane Odile struck Baja California Sur, Mexico. Strong winds destroyed power lines and compromised the electricity services of large urban centers, affecting 50 to 100 percent of users, depending on the municipality. An ongoing IDB research project seeks to estimate the effect of Odile on electricity service and economic activity by using satellite data on nighttime light intensity, an indicator recognized as a good proxy for the evolution of economic activity (Henderson, Storeygard, and Weil 2012; Addison and Stewart 2015; Pérez-Sindín, Chen, and Prishchepov 2021).

The recovery of electricity services is estimated by comparing the nighttime light to pre-hurricane values, capturing the severity of the hurricane's impact and how long different areas take to recover. Recovery ratios are calculated as the percentage of nighttime light in a specific area during a specific week following the hurricane compared to the average nighttime light of the same area in the corresponding month of 2012.

Preliminary descriptive results show that, the first week after the hurricane, nighttime light in urban areas of Baja California Sur was 75 percent of that before it. Not all urban areas were affected the same way, however. The recovery ratio of Cabo San Lucas and San José del Cabo, for instance, reached only 33 percent the week after the hurricane, while La Paz (a larger city with a smaller share of compromised electricity users) had a recovery ratio of 87 percent (Figure B7.1.1).

Although electricity was restored two weeks after the hurricane, the effects of service interruptions on households, businesses, and industries continued to affect electricity usage and economic activity for much longer. Five weeks after the hurricane, the recovery ratios of Cabo San Lucas and San José del

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**Box 7.1. How Climate Change Disrupts Electricity Services: The Case of Hurricane Odile (continued)**

Cabo were at 67 percent and 58 percent, respectively, and they were still at only 83 percent and 74 percent ten weeks after the hurricane. La Paz was 92 percent recovered five weeks after the hurricane and had completely recovered after two months. In some areas, the return of nighttime light to pre-hurricane levels may take over a year (Figure B7.1.2).

<figure><img src="image_63.png" /><figcaption>**Figure B7.1.1. Map of Recovery Ratios in Baja California Sur after Hurricane Odile**</figcaption></figure>

Source: IDB staff elaboration, based on NASA Black Marble Data.

Note: The line represents the hurricane's path. Week 1, week 5, and week 10 correspond to the number of weeks after the hurricane made landfall.

<figure><img src="image_64.png" /><figcaption>**Figure B7.1.2. Evolution of Recovery Ratios for Selected Urban Centers**

A. All urban centers</figcaption></figure>

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**Box 7.1. How Climate Change Disrupts Electricity Services: The Case of Hurricane Odile (continued)**

<figure><img src="image_65.png" /><figcaption>**Figure B7.1.2. Evolution of Recovery Ratios for Selected Urban Centers (continued)**</figcaption></figure>

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**Box 7.1. How Climate Change Disrupts Electricity Services: The Case of Hurricane Odile** (continued)

<caption>**Figure B7.1.2. Evolution of Recovery Ratios for Selected Urban Centers**
*(continued)*</caption>

D. San José del Cabo

<figure><img src="image_66.png" /></figure>

Source: IDB staff elaboration, based on NASA Black Marble Data.

Note: Hurricane Odile made landfall on September 15, 2014.

Odile, some neighborhoods in Baja California Sur, Mexico, took up to a year to return to their pre-hurricane levels of electricity consumption (Box 7.1).

Even as climate change threatens the supply of electricity, it raises the demand for it. Temperature anomalies increase the need for cooling and heating. In Colombia, consumption rises with higher temperatures, and the increase is greater for higher-income households that can afford air conditioning and fans (McRae 2023). For each degree Celsius rise in temperature, wealthier households consume 6 percent more electricity, compared to the 2 percent increase observed for low-income households (Figure 7.2). The case of Colombia also suggests that, as lower-income households through the region ascend to the middle class, their use of air conditioning and fans will go up as well, further driving up consumption.

Policy in other sectors will also cause the demand for electricity to increase. Dealing with water scarcity, for example, will entail a stronger reliance on energy-intensive technologies, such as groundwater extraction and desalinization plants (IEA 2021a). Finally, policies to reduce greenhouse gas emissions will have an effect; it is estimated that if 10 percent of vehicles in
<figure><img src="image_67.png" /><figcaption>**Figure 7.2.** Effect of Temperature on Household Electricity Consumption by Income, Colombia</figcaption></figure>

Source: IDB staff elaboration, based on McRae (2023).

Note: The analysis only considers hot regions in Colombia.

Latin America are electric by 2030, power demand will increase 1.2 percent (Lopez et al. 2022).

## Transportation and Telecommunications

Transportation is a cornerstone of the economy; most goods and services require it to some extent. But climate change can significantly affect roads, railroads, seaports, inland waterways, and public transportation systems (Talbot-Wright, Calatayud, et al. 2023). Strong winds, for instance, can bring about temporary closures of ports and airports, while heavy precipitation can contribute to weather-related crashes and the flooding of transportation facilities. In Colombia, projections until 2100 indicate increased rainfall will cause transportation productivity losses of between 1.8 percent and 3.1 percent as a result of temporary road closures caused by landslides (Gordillo et al. 2015).

Droughts affect world trade by reducing the capacity of canals, such as the Panama Canal (Box 7.2), while increases in temperatures cause road deterioration and asphalt rutting, as well as the loss of inland navigation capacity as a result of low water levels (ITF 2016). Sea-level rise can lead to the erosion of coastal roads and floods in airports, ports, and highways. In Latin America and the Caribbean, 19 regional ports, which together handled 12.8 million tonnes of shipping in 2019, could be subject to flooding by 2100 because of sea-level rise (Calatayud et al. 2023).
Box 7.2. Climate Change Disruptions to Maritime Transportation and World Trade: The Panama Canal

The Panama Canal, one of the world's four major maritime passageways, handles about 3 percent of global maritime trade, connecting 1,700 ports worldwide. In 2022, 14,000 vessels crossed it, generating US$2.5 billion in revenues. In contrast to the Suez Canal, which carries seawater, the Panama Canal relies on freshwater, whose availability is affected by climate change. This has required authorities to contend with water shortages during droughts.

In 2019, a rainfall deficit of 20 percent relative to the 70-year average led to a 16 percent reduction in daily crossings. In 2020, to provide incentive to shippers to optimize their transit schedules, the Canal Authority introduced a surcharge for vessels longer than 38.1 meters and wider than 27.7 meters. This also generated revenues for investments in making the canal's operations more reliable. In 2022, droughts led to a reduction in draft depth from 50 to 47.5 feet, while as of May 30, 2023, the depth was down to 44 feet. Especially affected by the shallower water are the largest container ships, which have been forced to transport less merchandise to reduce weight.

Based on historical data and climate change impact scenarios over the next 30 years, the IDB estimates that draft reductions will mainly affect international trade from the East Coast of the United States and the Gulf of Mexico, which account for 72 percent of canal cargo (IDB 2022b). During dry seasons, draft depth could drop to 42 feet, barring the canal to the three out of four ships using it that require a draft of 44 feet to cross safely. This could lead to notable price increases for containerized cargo, grains, chemicals, and fuels. It would also mean a decrease in revenues for the Panamanian government of about 0.5 percent of gross domestic product, based on 2022 figures.

Source: Calatayud et al. 2023.

When extreme weather events damage transportation infrastructure, the lost value from service interruptions adds to that from damage to assets. In the Dominican Republic, hurricanes, river surges, earthquakes, and tsunamis generate US$1 million in damage to the transportation network annually. On top of that, the losses incurred by users in terms of time lost in transport are nearly three times larger (Olaya González et al. 2022). Similarly, Hurricane Dorian (2010), Tropical Storm Eta (2020), and Hurricane Iota (2020) together caused US$51 million in damage to transportation infrastructure in the Bahamas and US$82 million in Honduras. Losses associated with disruptions to transportation services were estimated at US$37 million in the Bahamas and US$59 million in Honduras (Deopersad et al. 2020; IDB 2021a). Businesses are particularly affected by disruptions to transportation, as it is
a fundamental enabler of supply chains. In particular, 56 percent of losses suffered by businesses in Latin America and the Caribbean after a disaster can be attributed to transportation disruptions (Rozenberg et al. 2019).

Telecommunications are also affected by climate change. Extreme weather events and landslides caused by heavy rainfall can damage physical telecommunications assets, including internet servers, electronic components, and telephone poles. In the Bahamas, Hurricane Matthew in 2020 caused US$9.9 million in damage to utility pole infrastructure, wires, fiber optic backhaul systems, and antennas. Restoration of communication lines was then hampered by the need to wait for repairs to power utility poles, while the progress of those repairs was, in turn, hindered by debris blocking access roads to cell tower sites (Bello et al. 2020).

Table 7.2. summarizes the impacts of climate change on infrastructure services, and Table 7.3 lists global infrastructure assets and the risks to which they are subject as a result of exposure to climate hazards by 2030.

## On the Supply Side: Making Services Resilient

Utilities have to reduce the impacts of climate change on citizens and industries. Infrastructure must be designed to enable the continuous provision of services, even during emergencies.

<table><caption>**Table 7.2.** Impacts of Climate Change and Disasters on Infrastructure Services</caption><thead><tr><th rowspan="2">Infrastructure Service</th><th colspan="3">Impacts of climate change</th></tr><tr><th>Higher temperatures and droughts</th><th>Extreme precipitation, extreme winds, cyclones, and floods</th><th>Changes in sea level</th></tr></thead><tbody><tr><td>Water and sanitation</td><td><ul><li>Increase in water and sanitation demand*</li><li>Water storage evaporation</li><li>Reduction of groundwater levels</li><li>Increase in pumping costs</li><li>Cracks and ruptures in distribution networks from land subsidence</li></ul></td><td><ul><li>Damage to physical infrastructure from debris and inundation</li><li>Overflows in sewage and stormwater systems</li><li>Riverbank erosion</li><li>Stormwater systems overflows</li><li>Increase in sediment load of surface water sources because of erosion</li></ul></td><td><ul><li>Aquifer salinization from sea-level intrusion</li><li>Flooding of desalinization plants</li><li>Flooding of coastal wastewater treatment plants</li></ul></td></tr></tbody></table>

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<table><caption>**Table 7.2.** Impacts of Climate Change and Disasters on Infrastructure Services (continued)</caption><thead><tr><th rowspan="2">Infrastructure Service</th><th colspan="3">Impacts of climate change</th></tr><tr><th>Higher temperatures and droughts</th><th>Extreme precipitation, extreme winds, cyclones, and floods</th><th>Changes in sea level</th></tr></thead><tbody><tr><td></td><td><ul><li>Increase in sediment load in rivers and lakes because of wildfires</li><li>Increase in the frequency, severity, and geographical distribution of harmful algal bloom in surface water sources</li><li>Increased odors in wastewater treatment facilities</li></ul></td><td></td><td></td></tr><tr><td>Transportation</td><td><ul><li>Melting road surfaces and railway lines</li><li>Road damage from ground defrosting</li><li>Loss of navigation capacity from low water levels</li><li>Road deterioration requiring more maintenance</li><li>Land subsidence from aquifer overexploitation in drought</li></ul></td><td><ul><li>Aviation grounding</li><li>Roads and railways covered by water, mud, and debris</li><li>Road and railway losses from soil erosion</li><li>Bridge collapse from soil erosion and debris</li><li>Physical destruction of seaports</li><li>Temporary closures of ports and airports caused by extreme winds</li><li>Flooding and damage to subway stations, bus stops, rolling stock, buses, etc.</li></ul></td><td><ul><li>Flooding of coastal roads</li><li>Flooding of seaport infrastructure (sea routes closing)</li><li>Flooding of coastal airports</li><li>Bridge collapses from rising water levels</li></ul></td></tr><tr><td>Energy</td><td><ul><li>Increase in electricity demand, led by more use of air conditioning*</li><li>Increase in electricity demand led by groundwater pumping and desalinization plants*</li></ul></td><td><ul><li>Infrastructure flooding</li><li>Damage to power lines and hydro generation infrastructure by landslides</li></ul></td><td><ul><li>Flooding of coastal power plants</li></ul></td></tr></tbody></table>

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<table><caption>**Table 7.2.** Impacts of Climate Change and Disasters on Infrastructure Services (continued)</caption><thead><tr><th rowspan="2">Infrastructure Service</th><th colspan="3">Impacts of climate change</th></tr><tr><th>Higher temperatures and droughts</th><th>Extreme precipitation, extreme winds, cyclones, and floods</th><th>Changes in sea level</th></tr></thead><tbody><tr><td></td><td><ul><li>Reduced efficiency of solar panels</li><li>Reduction in hydro storage and electricity generation</li><li>Reduced power output from thermoelectric plants as a result of warmer water</li><li>Power outages from unmanageable peak loads</li><li>Transmission line inefficiencies</li><li>Increased risk of wildfires from high temperatures and expanded transmission lines touching vegetation</li></ul></td><td><ul><li>Power line collapses</li><li>Energy supply disruption</li><li>Damage to wind turbines and solar panels due to extreme wind speed</li><li>Damage to physical hydropower assets</li></ul></td><td></td></tr><tr><td>Telecommunications</td><td><ul><li>Need for early warning systems and rescue and reconstruction coordination*</li><li>Damage to physical assets (internet servers, electronic components)</li></ul></td><td><ul><li>Radio transmission disruption due to heavy rain and thunderstorms</li><li>Network lines and radio masts collapse</li><li>Underground infrastructure inundation</li></ul></td><td></td></tr></tbody></table>

Source: IDB staff elaboration, based on Woetzel et al. (2020) and OECD (2018).

Note: Asterisks (*) denote impacts on demand.

At the national level, countries have the dual challenge of addressing the current inequities in access to infrastructure services and ensuring the quality and continuity of those services in the future. Where fiscal resources are limited, this can only be achieved if countries prioritize high-quality projects that incorporate climate sustainability (Delgado et al. 2021). In 2022, for example, with support from the IDB, Peru published its National
<table><caption>**Table 7.3. Climate-Fueled Risk to Infrastructure Assets by 2030**</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td rowspan="12">Energy</td><td rowspan="2">Water</td><td>Wastewater treatment systems</td><td>Sea-level rise and tidal flooding</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Water treatment systems</td><td>Riverine and pluvial flooding</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">T&D</td><td>Freshwater infrastructure</td><td>Hurricanes, storms, and typhoons</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Substations</td><td>Tornadoes and other wind</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Generation</td><td>Lines</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hydroelectric plants</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Telecom</td><td>Solar power plants</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wind power plants</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Transportation</td><td>Thermo power plants</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Data centers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Generation</td><td>Fixed infrastructure</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wireless infrastructure</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">Energy</td><td rowspan="5">Water</td><td>Seaports</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rivers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Roads</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rail</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Airports</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody><tfoot><tr><td colspan="12">Little to no risk</td><td>Increased risk</td></tr></tfoot></table>

Source: Woetzel et al. 2020.

Note: T&D = transmission and distribution.
Sustainable Infrastructure Plan for Competitiveness 2022–2025, address-
ing, for the first time, sustainable infrastructure development to ensure the
environmental sustainability (including climate resilience) of its investment
projects.

At the project level, countries can develop resilience to climate change and disasters by incorporating risk assessment into their national investment system standards for project preparation, design, and implementation. Climate change risks can often be easily mitigated if the choice of project location is informed by a risk assessment. Mitigation can be as simple as siting new roads farther inland, away from the coast. When location is constrained, building protection is an option. By applying the IDB's Climate Change and Disaster Risk Assessment method, for example, Colombian authorities have been able to analyze the causes of landslides and propose engineering (in the form of retaining walls) and nature-based solutions (the use of vegetation) to reduce their occurrence (IDB 2015).

Building stronger structures is also key. Design tweaks to consider include making foundations deeper, elevating infrastructure, using more resistant materials, and introducing innovative technology. Solar panels can, for example, be made to rotate to prevent their destruction by strong winds (IAEA 2019). Roads, bridges, and airport runways can be made to withstand extreme heat through the choice of asphalt and the design of joints. With correct information on the location and intensity of hazards, the cost of making new infrastructure stronger in low- and middle-income countries equates to only 3 to 6 percent of capital costs in the power sector, 1 to 2 percent in the water sector, and around 5 percent in transportation (Hallegatte, Rentschler, and Rozenberg 2019). While efforts to incorporate resilience and disaster risk prevention might be seen (incorrectly) as a source of delay and extra costs in project preparation, evidence shows the benefits are about four to seven times higher than the costs (Barandiarán et al. 2019).

Tools to Plan for an Uncertain Climate

Project planners need to consider that climate change alters the frequency,
intensity, and duration of hazards. Traditional risk assessment relies on his-
torical records to identify hazards and their likelihood, but, with climate
change, the historical record is no longer a sound basis for assessing risk
(Barandiarán et al. 2019). Hurricanes of the magnitude of Dorian, for exam-
ple, which affected 70,000 people and caused US$5 billion in damage in the
Bahamas in 2019, used to happen every 50 to 100 years but may now occur
as often as once every 25 years (IDB 2020a).
Complicating matters, climate change itself is *deeply uncertain*. “Deep uncertainty” means that, because of limited data and imperfect models, experts cannot agree on the relative likelihood of different events (Marchau et al. 2019). While it is known, for instance, that the sea level will certainly rise, no one can say by exactly how much or how fast. The ways in which global climate change will affect local weather also cannot be predicted. In the U.S. state of Maryland, a review of model results found that, by 2045, temperatures could increase by less than 1°C or more than 2°C, while precipitation could decline by more than 8 percent or increase by more than 10 percent (Fischbach et al. 2015).

To select and design infrastructure projects in the face of these unknowns, utilities can make use of the “decision making under deep uncertainty” (DMDU) toolset (Marchau et al. 2019). This approach generates many different scenarios, identifies investments that pay off under a large variety of them (that is, “no-regrets” options), emphasizes reversible strategies, and encourages the making of contingent plans that can be adjusted over time as new information becomes available (Hallegatte 2009).

In Monterrey, Mexico, for example, a DMDU study found that by 2026, water planners should prioritize relatively inexpensive investments in conservation practices, a new reservoir, and groundwater wells (Molina-Pérez et al. 2019). The decision to build a more expensive desalination plant can be delayed until 2039 and may become necessary only if water consumption exceeds what groundwater can supply. Dams and aqueducts, which are even more costly, only make sense in the longer term if demand increases significantly. Similarly, in Lima, Peru, a DMDU study enabled the water utility to identify investments that would allow it to meet at least 90 percent of demand 90 percent of the time, while saving US$600 million, or 25 percent of the investment budget, compared to its previous plan (Kalra et al. 2015).

Combining the use of DMDU tools with stakeholder consultation helps build consensus among local stakeholders. Molina-Pérez et al. (2019) find that locals in Monterrey initially had divergent views on whether to prioritize conservation measures or large-scale infrastructure projects. The solution was first to establish common goals among them (the goals being reliability and cost) and then show the value of a portfolio approach staged over time. The study highlights that while conservation efforts are universally beneficial and should be encouraged, they are insufficient alone to ensure water security. Conversely, while major infrastructure investments are very effective, their high costs and the adequacy of smaller projects in most scenarios mean they can be postponed until further evidence confirms their necessity.
Another tool that can support planning in the face of an uncertain cli-
mate is the so-called *blue spot analysis* method. It allows policymakers to
analyze the risks imposed by climate change on transportation systems and
prioritize adaptation actions (Lempert et al. 2021; Koks et al. 2023). A blue spot
analysis helps officials accomplish the following (Talbot-Wright, Calatayud,
et al. 2023):

* Pinpoint transportation assets, such as sections of road networks or bridges, that are exposed to extreme weather events under different climate change scenarios

* Estimate the degree of vulnerability of these assets—for instance, whether they would be destroyed by a flood of a given severity

* Estimate the criticality of each asset, based on the economic consequences of its being disrupted—for instance, a bridge that is the sole means of access to a hospital or port

* Assess different interventions to reinforce the network

The Dominican Republic recently developed a blue spot analysis to pri-
oritize investments and improve the resilience of its road transportation
network in the face of hurricanes, river surges, earthquakes, and tsunamis
(Olaya et al. 2022). The government conducted research to guide its main-
tenance schedule and determine the roads and bridges to be prioritized in
reconstruction efforts following a hurricane. It then presented these invest-
ment projects to international development banks interested in financing
adaptation (Olaya et al. 2022; IDB 2022b).

The Power of Prevention with Maintenance and Retrofitting

One way to ease the stress exerted by climate change on existing resources
is for utilities to increase their efficiency. Many cities in Latin America and
the Caribbean, for example, must contend with water scarcity but, paradox-
ically, experience significant losses from their water distribution systems.
A critical indicator of these losses is nonrevenue water—a measure of how
much water is lost in the distribution system as a result of leaks, lack of
metering, or illegal connections. This indicator shows losses of 40 percent in
26 of the region's countries (Carrera et al. 2018), reaching 75 percent for the
system operator in Jamaica (Janson, Burkhard, and Jones 2021).

Providing funding for operation and maintenance is essential to increas-
ing efficiency and preventing asset degradation. Throughout an asset's
lifespan, capital costs represent just 15 to 30 percent of total expenses, with the
remaining portion going to operations and maintenance (CDRI 2023). In Latin
America and the Caribbean, maintenance and asset replacement account for 40 percent of the investments needed to provide basic infrastructure services to all by 2030 (Brichetti et al. 2021). According to Aquarating, however—an IDB system that evaluates the performance of water and sanitation utilities based on indicators and management practices—51 percent of companies lack a maintenance and asset replacement plan grounded in risk analysis and cost considerations (Pastor 2019). Rozenberg and Fay (2019) find that for low- and middle-income countries, the lack of proper routine maintenance increases capital costs for replacement by at least 50 percent in the transportation sector and 60 percent in the water sector for the period 2015–30.

Existing infrastructure must also be adapted or retrofitted to increase durability and minimize exposure to hazards. The risk of wildfires can be reduced, for instance, by coordinating vegetation management surrounding transmission lines and burying transmission and distribution lines (IEA 2021a), which will protect them from other climate-related events, such as cyclones or landslides, as well.

## Having a Plan B: The Value of Diversification, Decentralization, and Redundancy

The provision of infrastructure services relies on interconnected networks. Increasing diversification, decentralization, and redundancy is a key strategy to ensure the continuous provision of services through a working network, even when an event affects one of its components.

Cities can diversify water sources to increase their ability to produce drinking water even when some sources are affected by extreme weather events; an example of this approach is provided by Santiago de Chile, the capital city of Chile, as presented in Box 7.3. Aquifers are strategic natural assets that allow cities to ensure water provision when drought or excessive sediment load causes surface waters to become unavailable (Solís 2023). Since excessive reliance on this source can lead to overexploitation, some countries have also begun to invest in seawater desalinization plants. In Chile, desalinization has long been used to supply freshwater to mining projects, and, since December 2021, it has also provided tap water to 200,000 people in the Atacama region.

A key diversification strategy is reusing water. Treated wastewater has many applications, including agricultural irrigation and industrial processes, as exemplified by San Luis Potosí, Mexico's, Tenorio plant, which reuses up to 45 percent of the city's wastewater (World Bank 2018). This practice is limited in Latin America and the Caribbean, however, where only 60 percent of the population has access to sewage systems and a mere 30 to 40 percent
Box 7.3. How Diversification Helps Santiago de Chile Keep Tap Water Flowing

Santiago de Chile provides a clear example of how the effects of climate change are becoming more frequent and intense and having greater impacts on the provision of infrastructure services. When heavy rainfall occurs, it displaces materials that end up in the Maipo and Mapocho rivers. These rivers are the main sources for drinking water for the city, and the presence of displaced materials forces the utility to shut down water treatment facilities temporarily. The number of extreme turbidity events—when the water is too muddy or carries too much sediment for the existing infrastructure to make it safe to drink—increased from four per decade in 1992–2001 to fifty-one in 2012–21.

Aguas Andinas, the local water utility, has designed a resilience plan to keep tap water flowing for more than 6 million citizens even when the Mapocho and Maipo are too muddy to use. After investing around US$190 million, Aguas Andinas can keep the water on for up to 37 hours during extreme turbidity events, up from only 4 hours in 2011 (Solís 2023). The investments included the construction of wells and storage ponds, including the Pirque Mega Ponds in 2020 and the Cerro Negro and Lo Mena wells, 300 meters deep, which started operations in 2022. Looking ahead, Aguas Andinas plans to invest in additional projects and reach 48 hours of continuous water provision during turbidity events.

of wastewater is treated (Rodríguez et al. 2019; World Bank 2019a). Expanding water reuse opportunities means building more infrastructure and addressing pollution through strategies like reducing agrochemical use and enhancing water treatment methods.

Rainwater harvesting can also contribute to diversification on a small scale. While not a perfect substitute for piped water, rainwater can meet demand for certain uses and help reduce pressure on water sources. An IDB cost-benefit analysis shows that rainwater harvesting systems in El Salvador can be a clean and cost-effective solution for off-grid users and those who buy water because of poor service (Rovira, Sánchez, and Rovira 2020). In Mexico City, overexploitation of groundwater prompted the launch of a rainwater harvesting program in low-income neighborhoods in 2019. It has already benefited over 50,000 households in areas marked by water scarcity, poverty, and urban marginalization.

In transportation, telecommunications, and electricity distribution, redundancy is key. When cities invest in mass transit along with pedestrian-friendly sidewalks and cycling routes, for example, they enhance people's ability to cope with events that affect their movements. Diverse options
enhance network connectivity, ensuring the availability of a functional path between origin and destination even if some segments are disrupted (Hallegatte, Rentschler, and Rozenberg 2019).

Finally, decentralization also contributes to reliable services. Excessive geographical concentration of infrastructure makes the systems more vulnerable to extreme events. Wind and solar plants scattered through the country are less likely to be affected all at the same time. In Peru, although electricity generation capacity is highly concentrated in the central region, some decentralization is being gradually achieved. The generation of solar energy in the south and wind energy in the north is increasing, representing 7 percent of total generation (Schmerler et al. 2019). The benefits of geographical diversification can be reinforced by promoting distributed generation at the residential and industrial levels.

## Green Solutions for Grey Infrastructure

Service provision does not rely exclusively on structures made from steel and concrete. Often forgotten is the importance of ecosystems and how they can substitute for or complement conventional, or so-called “grey,” infrastructure (Table 7.4). Nature-based solutions are interventions motivated and supported by nature that provide services and increase resilience. These actions protect, sustainably manage, and restore natural or modified ecosystems and provide a variety of valuable benefits to society, contributing to water security and the reduction of flood and landslide risks (IPCC 2022; Ozment et al. 2021). They include investments in both “natural” and “green” infrastructure. The former involves projects that preserve or restore existing natural landscapes to benefit from ecosystem services. The latter commonly refers to engineered interventions in urban areas that interact with grey infrastructure, providing hybrid solutions like permeable pavement and green roofs (Nature Conservancy 2019).

Nature-based solutions increasingly are recognized for their many benefits in providing infrastructure services as effectively as—and sometimes more cost effectively than—grey infrastructure. Coral reefs, mangroves, forests, constructed wetlands, green roofs, green spaces, and other such solutions offer a wide range of ecosystem services, including flood control, water purification, and stormwater runoff regulation (Cavallo, Powell, and Serebrisky 2020). Coral reefs, for instance, can attenuate the energy of ocean waves by an average of 97 percent, significantly reducing risks of coastal flooding (Ferrario et al. 2014), while constructed wetlands provide a viable, low-cost alternative to wastewater treatment, taking advantage of natural processes to remove pollutants. In New York City, maintaining afforestation
<table><caption>**Table 7.4.** Examples of Grey Infrastructure and Nature-Based Solutions in Water and Sanitation Services</caption><thead><tr><th rowspan="2">Process</th><th rowspan="2">Grey Infrastructure</th><th colspan="2">Nature-based Solutions</th></tr><tr><th>Natural / green Infrastructure</th><th>Ecosystem Service (Benefits)</th></tr></thead><tbody><tr><td>Water treatment</td><td>Water treatment plant</td><td>Forestland, riparian areas surrounding water sources, mangroves</td><td>Water quality regulation: They naturally filter biological and chemical impurities, trap sediment and reduce erosion.</td></tr><tr><td>Increase in water availability</td><td>Water transfers</td><td>Forests, wetlands, floodplains</td><td>Water availability: During dry periods, they increase storage capacity, improve base flows, and enhance water quality.</td></tr><tr><td>Storage</td><td>Reservoirs, dams</td><td>Aquifers</td><td>Water storage: Aquifers turn unstable phenomena such as rainfall into a stable water source.</td></tr><tr><td>Stormwater management</td><td>Stormwater drainage system</td><td>Green roofs, porous pavements, wetlands, bioswales, rain gardens</td><td>Aquifer recharge and urban flood management: Water infiltration in the ground, overflowing and flooding prevention</td></tr><tr><td>Wastewater treatment</td><td>Wastewater treatment plant</td><td>(Constructed) wetlands</td><td>Treatment: Wastewater natural treatment reducing treatment needs</td></tr></tbody></table>

Source: Authors' elaboration, based on Browder et al. (2019) and Cavallo, Powell, and Serebrisky (2020).

in the contributing watershed (comprising approximately 75 percent of the land) will allow the city to avoid building a treatment plant that would cost between US$8 billion and US$10 billion (Abell et al. 2017).

Moreover, these green solutions deliver co-benefits that enhance bio-diversity, support fisheries, promote tourism, and facilitate nutrient cycling, contributing to overall ecological and social well-being (Cavallo Powell, and Serebrisky 2020). Whether their benefits and co-benefits outweigh the costs of these solutions, however, depends on the local context. By definition, the effectiveness of nature-based solutions depends on the local ecosystems and geography. The efficacy of a coral reef in stemming storm surges, for example, depends on its position relative to the coastline, its size, its submergence, and its structural characteristics. More generally, the use of nature-based solutions requires careful consideration of local conditions, specialized knowledge, and, often, innovative financing strategies to realize
their full potential. Nonetheless, as the evidence for their benefits and cost effectiveness continues to grow, it becomes increasingly clear that leveraging the region's natural capital through targeted, well-planned investments can play a crucial role in climate change adaptation.

São Paulo, Brazil, offers a prime example. The city has suffered severe droughts in recent years, and the set of watersheds that provides half of the water consumed in the metropolitan area has lost 70 percent of its forests (Abell et al. 2017). Reforestation could significantly reduce sediment and nutrient loading in water sources (Ozment et al. 2018). A recent study estimates the economic losses of the 2014–15 drought at US$316 million. If nature-based solutions had been implemented, this cost would have been reduced by 28 percent (Stein Ciasca et al. 2023). In Bogotá, Colombia, a natural infrastructure portfolio of US$5.3 million could generate cost avoidance totaling US$44.6 million over 30 years (Izquierdo-Tort et al. 2023).

Regarding implementation, a survey conducted by the IDB with 61 water operators in 11 countries in the region finds that 36 percent have not yet adopted any nature-based solutions. Forty-three percent, however, are already investing in reforesting the upper parts of the watershed and 30 percent in the protection or restoration of ecosystems, such as lakes or wetlands (Solis and Serebrisky 2023). This substantial level of investment may be explained by the gains these solutions have made in public policy and their promotion by authorities. In Peru, the Water Regulation Authority has approved the allocation of a percentage of water tariffs toward the financing of projects to restore or conserve water sources. Water operators across the country collect a total of around US$6.9 million annually for these initiatives (Benites Elorreaga and Gammie 2021).

## Dealing with the Demand Side

While conventional approaches to relieving climate-related stress on infrastructure assets mainly involve enhancing, expanding, or strengthening the supply side, increasing demand efficiency can help, as well. In the case of water, for example, growing scarcity calls for greater efficiency in meeting all types of water demand: agricultural, industrial, and human consumption. Undoubtedly, countries should make substantial efforts in agriculture, which, in Latin America and the Caribbean, accounts for 76 percent of water extractions and operates with an efficiency level for irrigation water use that is not quite half of the global average (CAF 2022). To reduce excessive water withdrawals, farmers can use such irrigation technologies as drip and sprinkler systems, make use of terracing and soil restoration techniques, switch to saline water when feasible, or grow foods that consume less water per
unit of calorie or protein produced (Talbot-Wright, Pérez-Urdiales, and Vogt-Schilb 2023).

Households also have a role to play. Introducing incentives for consumers to be more efficient is always beneficial. These interventions are cost effective regardless of the climate scenarios, and, in the face of uncertain climate conditions, they increase resource availability, helping utilities meet increasing demand. If water demand grows at a moderate or lower rate in Monterrey, Mexico, for example, implementing a new tariff schedule could promote more efficient water consumption and lead to a 3 percent reduction in demand, resulting in turn in a less expensive portfolio of projects to ensure water reliability (Molina-Pérez et al. 2019).

Asking households to be more efficient can be a delicate matter in Latin America and the Caribbean, where many suffer from patchy, low-quality services. Many countries are unable to guarantee a continuous water supply, with 20 percent of households receiving water four days a week or less (Gómez-Vidal, Machado, and Datshkovsky 2021). Nonetheless, evidence indicates disparities in water consumption in the region, which means there is room to reduce wasteful consumption. While the World Health Organization recommends a daily per capita water consumption of 100 liters, for instance, the average consumption in some neighborhoods of Lima is as high as 280 liters (Gómez-Lobo et al. 2023) and in Mendoza, Argentina, as high as 460 liters (Toribio 2023).

With regard to both water and energy, metering is crucial for demand-side policies, allowing authorities to manage consumption through both pricing and other strategies. An IDB study in Quito, Ecuador, for example, shows that metering can reduce water consumption by 8 percent (Carrillo, Contreras, and Scartascini 2021). Barriers to its implementation, however, can include initial consumer resistance and a lack of quality in infrastructure services—such as an unreliable or intermittent water supply—that can impede accurate metering. But incentives to reduce consumption can only be introduced if people know how much they consume and, thus, if their bill depends on it. This is not a trivial issue, since the prevalence of water metering is low in some cities of the region (Solís and Serebrisky 2023). A further step is smart metering, which can empower and engage consumers and provide consumption information in real time. When used in tandem with dynamic tariff design, smart metering can allow the modification of prices to balance water consumption based on availability (Rougé et al. 2018). In the case of efficient energy consumption, it can shift electricity demand to times with plentiful generation or when aggregate demand is lower.

Cities can also consider restrictions on specific water uses, the promotion of water- or energy-saving devices, mandating the disclosure of
water and energy consumption on appliance labels, and the provision of
consumption information to consumers on utility bills (Ong, Tortajada, and
Arora 2023). Examples include schedule restrictions on landscape watering
in the U.S. state of Texas; rebates and replacement programs for residen-
tial users and businesses, such as were implemented during the so-called
millennium drought in Australia (Low et al. 2015); and refrigerator replace-
ment, which resulted in an 8 percent reduction in electricity consumption
in Mexico (Davis, Fuchs, and Gertler 2014). When using communication on
bills as a means of increasing efficiency, simply stating consumers' absolute
consumption levels is often insufficient, while social comparison is usually
more effective (Bergquist and Nilsson 2018; Schultz et al. 2015). An interven-
tion in Belen, Costa Rica, that involved neighborhood comparisons led to
a reduction in water consumption by users ranging from 3.7 to 5.6 percent
(Datta et al. 2015). Consumption patterns can also be shaped by educational
campaigns and by recent experiences of water scarcity. In an IDB study con-
ducted in five megacities across Latin America, 68 percent of respondents
reported conserving water during daily hygiene activities. This percent-
age was higher in cities that had endured prolonged droughts, such as São
Paulo and Mexico City (Balza et al. 2023). Policies using price increases, on
the other hand, while cost effective and easier to enforce than other solu-
tions, can lead to distributional issues and political resistance.

In sum, there is no universal solution for decreasing water and energy
consumption, and demand management policies should align with the
target group's characteristics (Pérez-Urdiales, García-Valiñas, and Martínez-
Espiñeira 2016; Wichman, Taylor, and Von Haefen 2016). With reference to
Lima's water tariff reform, Gómez-Lobo et al. (2023) stress the importance
of using household socioeconomic data to target tariff increases and tak-
ing a gradual approach to allow for adjustments in consumption over time.

Information and Institutions: Building Blocks of Resilience

Resilience is also about the intangible. Information, for instance, allows
countries to concentrate their investment efforts, ensuring they get the
best value for money, which is particularly important in reduced fiscal
spaces. Having information on natural hazards and the exposure of assets
allows for investments that specifically improve and protect those assets.
In low- and middle-income countries, investments can cost ten times more
without information on which localities are exposed to hazards, which
results in resilience investment's being spread (more expensively) on the
system as a whole rather than focused on the specific water, sanitation,
energy, and transportation assets most in need of strengthening (Halegatte,