Macro-Fiscal Implications of Adaptation to Climate Change

Adaptation to climate change is an integral part of sustainable development and a necessity for advanced and developing economies alike. How can adaptation be planned for and mainstreamed into fiscal policy? Setting up inclusive coordination mechanisms and strengthening legal foundations to incorporate climate change can be a prerequisite. This Note identifies four building blocks: 1. Taking stock of present and future climate risks, identifying knowledge and capacity gaps, and establishing guidance for next steps. 2. Developing adaptation solutions. This block can be guided by extending the IMF three-pillar disaster resilience strategy to address changes in both extreme and average weather and would cover the prevention of risks, the alleviation of residual risks, and macro-fiscal resilience. 3. Mainstreaming these solutions into government operations. This requires strengthening public financial management institutions by factoring climate risks and adaptation plans into budgets and macro-frameworks, and in the management of public investment, assets and liabilities. 4. Providing for transparent evaluations to inform future plans. This involves continually monitoring progress and regularly updating adaptation plans.

Abstract

Adaptation to climate change is an integral part of sustainable development and a necessity for advanced and developing economies alike. How can adaptation be planned for and mainstreamed into fiscal policy? Setting up inclusive coordination mechanisms and strengthening legal foundations to incorporate climate change can be a prerequisite. This Note identifies four building blocks: 1. Taking stock of present and future climate risks, identifying knowledge and capacity gaps, and establishing guidance for next steps. 2. Developing adaptation solutions. This block can be guided by extending the IMF three-pillar disaster resilience strategy to address changes in both extreme and average weather and would cover the prevention of risks, the alleviation of residual risks, and macro-fiscal resilience. 3. Mainstreaming these solutions into government operations. This requires strengthening public financial management institutions by factoring climate risks and adaptation plans into budgets and macro-frameworks, and in the management of public investment, assets and liabilities. 4. Providing for transparent evaluations to inform future plans. This involves continually monitoring progress and regularly updating adaptation plans.

Summary

Summary

Adaptation reduces climate change damages but is costly and cannot eliminate all risks. Governments have to decide on an acceptable balance of residual risks and to determine adaptation investment needs by weighing costs, benefits, and distributional effects. A literature review suggests that well-designed and well-implemented adaptation can have large returns. Global public adaptation needs in 2030 are estimated in the literature at around ¼ percent of world GDP per year, but with very large disparities across countries and high uncertainty. Our analysis points to annual adaptation costs exceeding 1 percent of GDP for some developing countries, and above 10 percent of GDP for some island states. Many of these countries—despite typically not having contributed to global warming—face high adaptation needs while being challenged by limited fiscal space, limited capacity, or both, calling for additional support from the international community. To help guide national fiscal policies, countries could integrate climate risks and the cost of adaptation into their macro-fiscal frameworks. Shock scenarios are useful to reflect short-term impacts of climate disasters, while the long-term analysis of risks and uncertainties surrounding climate change requires scenarios that cover impacts from changes in both average and extreme events, as well as adaptation policies.

This Staff Climate Note is part of a series of three Notes (IMF Staff Climate Note 2022/001, 2022/002, and 2022/003) that discuss fiscal policies for climate change adaptation. The first Note (Bellon and Massetti 2022a, henceforth Note 1) examines the economic principles that can guide the integration of climate change adaptation into fiscal policy. It argues that climate change adaptation should be part of a holistic, sustainable, and equitable development strategy. To maximize the impact of scarce resources, governments need to prioritize among all development programs, including but not limited to adaptation. To this end, they can use cost-benefit analysis while ensuring that the decision-making process reflects society’s preferences about equity and uncertainty. This second Note discusses the macro-fiscal implications of climate change adaptation. A third Note (Bellon and Massetti 2022b, henceforth Note 3) considers how to translate adaptation principles and estimates of climate impacts into effective policies. It argues that adaptation solutions can be guided by an extension of the IMF’s (2019b) three-pillar disaster resilience strategy to address changes in both extreme and average weather, for all countries. It suggests that public financial management (PFM) institutions can support an efficient implementation of adaptation solutions by factoring climate risks and adaptation plans into budgets and macro-frameworks, and in the management of public investment, assets, and liabilities.

Adaptation Benefits, Costs, and Needs

Climate change adaptation is the process needed to minimize losses (and maximize benefits) from climate change. Adaptation is needed to address risks from changes both in average conditions and in frequency and intensity of extreme weather, for example by improving dryland agriculture, using water resources better, managing sea-level rise, and making infrastructure more resilient. As climate is changing and will continue to change even with intensive mitigation efforts, adaptation to climate change is a necessity for advanced and developing economies alike (Note 1).

Despite all the potential benefits, adaptation cannot replace mitigation (Intergovernmental Panel on Climate Change [IPCC] 2022). Adaptation to climate change aims at controlling local climate damages, whereas climate change mitigation aims at controlling global climate change. Mitigation is a global coordination problem because it will fail if too many or large enough countries opt out of global efforts. For the most part, however, adaptation can succeed (or fail) at the local level independently from adaptation efforts in the rest of the world. For example, protection from sea-level rise in one island country does not depend on adaptation in other countries. Adaptation and mitigation are both needed to help reduce damages from climate change. Without strong mitigation, it is not possible to stabilize global temperature, and adaptation would become either impossible or too expensive.

Governments would benefit from starting to evaluate the macro-fiscal implications of adaptation to climate change. Adaptation can help to substantially reduce climate change costs. In the long term, well-managed adaptation frees resources and increases fiscal space if the reference scenario assumes unmitigated climate change impacts. But adaptation competes for budget resources with other public programs in the short term and may seem to reduce fiscal space if the reference scenario does not include climate change damages.

The full cost of climate change is the sum of the cost of adaptation and the cost of residual risks. Climate risks that are either impossible or too expensive to eliminate will put additional strains on the economy and public finances. Governments can start planning for climate change by estimating and incorporating projected climate damages as well as adaptation benefits and costs in their macro-fiscal frameworks. While all countries would benefit from this exercise, it will be most important for vulnerable developing economies, and especially in small developing island states threatened by both climate disasters and sea-level rise.

Assessing macro-fiscal impacts of adaptation to climate change requires estimating climate change impacts and the effectiveness and cost of adaptation. This information is needed for governments to decide on an acceptable level of residual climate change risks and to determine adaptation investment needs. These decisions involve weighing facts and preferences, including society’s specific ethical norms and risk aversion. While economic principles cannot be the sole criteria in this process, they can play an important role. In particular, cost-benefit analysis (CBA), complemented with an assessment of distributional impacts, can help decision makers maximize overall social welfare by avoiding wasting scarce resources (Note 1).

Adaptation Benefits: The Avoided Cost of Climate Change

The benefit of adaptation can be estimated by comparing the cost of climate change with and without adaptation. This principle applies at both the project (for example, building a bridge with or without higher resilience standards) and macroeconomic levels. However, determining benefits can be challenging because of difficulties in estimating alternative projections with climate change impacts where the only difference is how agents adapt (see Massetti and Mendelsohn 2018 for a literature review of methods). Using different combinations of climate change and socioeconomic projections allows for the estimation of adaptation benefits under a range of possible conditions.

Estimating adaptation benefits is difficult and characterized by large uncertainties, but there is growing consensus that benefits are large. Sectoral studies report climate adaptation returns (benefit-cost ratios) as high as 100 to 900 percent (Hallegatte, Rentschler, and Rozenberg 2019; Global Center on Adaptation [GCA] 2018). For example, long-term savings from investment in resilience and coping mechanisms can reach 300 percent for droughts and 1,200 percent for storms in sub-Saharan Africa (IMF 2020a). Large benefits of investing in adaptation are also found in other regions (Asian Development Bank 2013; Ahmed and Suphachalasai 2014; Westphal, Hughes, and Brbmmelhbrster2015; Agarwal and others 2021; IMF 2021a; Duenwald and others, forthcoming). These extremely high returns can be surprising. They cover programs such as disaster preparedness and public health measures with low costs and high benefits. They can also be found when adaptation is about marginal shifts in production, such as when farmers switch crops (Massetti and Mendelsohn 2018), or when improvements in building design helps prevent the collapse of large infrastructure.

As total investment in adaptation increases, adaptation costs are expected to increase faster than benefits, eventually leading to smaller net benefits. It is useful to distinguish between marginal and average returns to adaptation. As for many other investments, there is evidence that investment in adaptation may have declining marginal returns. The literature uses macroeconomic simulations over the entire century to estimate global average returns for the “optimal” level of adaptation. These models assume diminishing returns of adaptation investment, from high initial levels down to zero for the last investment projects at the optimum. Results point to high average returns, from 80 to 100 percent (De Bruin, Dellink, and Tol 2009; Bosello, Carraro, and De Cian 2010; Agrawala and others 2011). This is higher than typical average returns of investing in standard infrastructure in countries with large investment gaps, which is estimated to be between 15 and 20 percent (see Berg and others 2012 for a literature review).

As direct evidence on the benefits of adaptation is limited, useful insights can be inferred from estimates of climate change costs that use different assumptions on adaptation. Figure 1 provides a review of climate costs estimated under various methods. Linear trend lines highlight average costs respectively from integrated assessment models (IAMs) and panel econometric studies at different levels of global warming. IAMs produce consistently lower economic costs at each level of warming because they include a substantial amount of adaptation, whereas panel econometric studies include limited or no adaptation at all. As a result, costs of climate change are roughly twice as large with panel econometric models than with IAMs. As costs estimated with IAMs include a larger range of hazards than panel econometric studies, the comparison may underestimate the benefit of adaptation (see Annex 1 for a review of methods and limitations, and Annex Table 1.1 for a list of studies).

Figure 1.
Figure 1.

Climate Change Costs and Estimation Methods

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Staff elaboration compiled from surveys in Tol (2009, 2014), Kahn and others (2021), and Howard and Sterner (2017).Note: Global mean temperature change with respect to 1850–1900 and percentage global loss of GDP with respect to a scenario with no climate change in the year in which the temperature level is reached. Different colors denote different methods. The trend lines are a linear fit of damages estimated with integrated assessment models and panel econometric studies. Estimates of damages for warming greater than 5°C are not included because impacts are highly speculative at those levels of warming and unlikely to be observed in this century. Studies that provide estimates of damages only for some regions and that use the social cost of carbon as a metric of climate change costs are not included because they are not easily comparable. See Annex 1 for details and a list of studies included in the figure.

While Figure 1 is based on a comprehensive literature review of global climate change cost estimates, all studies share limitations that likely underestimate total climate change costs. None of the studies considers the cost from crossing climate tipping points (for example, quick melting of the Greenland Ice Sheet) or from societal collapse (for example, mass migration and war), and coverage of nonmarket impacts is limited (for example, biodiversity loss). It is unclear how effective adaptation can be to limit the cost of these large impacts. Furthermore, these estimates are global costs expressed as a percentage of global GDP. Costs in developing countries and in small vulnerable economies are much larger as a percentage of their national GDP. See Annex 1 for a discussion of the literature.

Reaping the full benefits of public adaptation projects will take time and capacity building. Both micro- and macro-level estimates of adaptation benefits tend to underestimate the cost of adaptation because they assume optimal design and execution. Large investments may face obstacles because benefits accrue in the long term while costs are upfront (Guo and Quayyum 2020). Large adaptation returns are estimated on specific projects (for example, early warning systems, incremental building upgrades), can take years to materialize (when disaster losses are avoided), and assume perfect implementation. In practice, the execution of sizable public investment in adaptation will at least face the same challenges as other public investments with typically large efficiency losses (IMF 2015, 2020b), and with possibly larger losses, because authorities are building capacity in this new area.

Defining Adaptation Investment Needs

Investment needs in climate change adaptation can be defined as the difference between optimal investment levels with and without climate change (strict additionality definition). Quantification of adaptation needs requires defining optimality and developing economic models that can derive optimal investment levels under scenarios with and without climate change (see Annexes 1 and 2 in Note 1 for a synthesis and discussion of climate and socioeconomic scenarios used in the literature).

This definition can be applied to identify planned or executed climate change adaptation spending. For specific projects in practice, a simplified application is to count the projects that are only implemented because of climate change and, for those who would be implemented anyway, to count the additional cost incurred to adapt them to climate change impacts. This definition can be relevant to track climate change adaptation spending in national budgets and in international aid.

The adoption of strict additionality to define climate change adaptation needs excludes the cost of closing the gap to achieve an optimal level of adaptation to current climate conditions. This distinction can have important implications in countries that are far from optimally adapted to the current climate. For these countries, investment needs to make their economy more resilient and better adapted to the current climate can be substantial relative to (or even exceed) the additional cost of strengthening adaptation investments to address the additional effects from climate change. For example, some flood-prone areas could benefit from investing in protective infrastructure under current climate conditions, and optimal adaption to climate change may imply additional costs for stronger infrastructure. The additional costs implied by climate change can be small compared to the cost of the project designed for current conditions.

A broader definition of adaptation needs that includes adaptation to the current climate should still exclude investment needs that are primarily driven by other development needs. Adaptation costs can be hard to separate from other development needs when development and adaptation are self-reinforcing. For example, education has sometimes been cited as an adaptation cost because climate change makes education investment even more needed, especially for the most vulnerable. A more educated population can learn faster about new technologies and can transition more easily to climate-resilient practices and sectors. However, climate change will likely have negligible effects, if any, on education goals. Therefore, the bulk of education needs should not generally be counted as adaptation needs. In specific cases, a fraction of the spending that is primarily directed to other goals can be counted toward adaptation needs, but only when specific costs can be related to climate impacts. This would be the case when investing in education infrastructure incurs additional costs because buildings need to be made more resilient to new climate extremes. For example, cyclones can inflict heavy damages to schools and prevent their use for a long time (IMF 2020e). If tropical cyclones intensify in Tonga due to climate change, for example, the increase in the cost of protection is attributable to climate change and satisfies the requisite of additionality.

Alternative definitions that would classify investments that are primarily driven by development needs as adaptation needs would lead to very large and potentially misleading estimates of adaptation gaps. Such definitions may lead countries and international donors to double count some development needs.

Estimates of Adaptation Needs

Global public adaptation needs are estimated at around ¼ percent of world GDP per year on average but may not be representative of the challenge faced by many countries. Estimates in the literature indicate that global investment needs in climate change adaptation for developing countries range from insignificant amounts to $300 billion annually in 2030 and between $50 billion and $500 billion annually in 2050. As a share of average annual global GDP, investment needs in 2030 range from almost nothing to about 0.3 percent of projected 2030 global GDP per year (Figure 2). In 2050, investment needs increase in all studies, even after accounting for global economic growth. These estimates are highly uncertain because they rely on just a few studies and they are not recent. The wide ranges can be explained by differences in the definition of needs and in assumptions about future development and climate change (Box 1).

Figure 2.
Figure 2.

Estimates of Global Investment Needs in Adaptation

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Staff meta-analysis based on the studies cited below..Note: In 2014, the United Nations Environment Programme (UNEP 2014) started an “Adaptation Gap Report” series in which they publish annually a review of the literature that estimates investment needs in adaptation. The literature reviewed has not changed over the years and consists of three studies: United Nations Framework Convention on Climate Change (UNFCCC; 2009a), World Bank (WB; 2010a, 2010b, 2010c), and a study led by the Organisation for Economic Co-operation and Development in 2009–2011 (Agrawala and others 2011). Figure 2 reports the UNEP review and estimates in the studies reviewed by the UNEP separately.

Countries’ bottom-up self-assessment of their needs tends to be larger, even as high as 100 or 250 times higher than global averages for some lower-income vulnerable countries. The 46 countries that included adaptation cost estimates in their Nationally Determined Contribution estimated a total, collective cost for these measures of $783 billion by 2030 (or about 1.5 percent of their GDP annually from 2015 on average) (Buchner and others 2019).

Small island countries face among the greatest challenges. Climate change effects are projected to entail very large costs for all small island countries, and sea-level rise threatens the very existence of some small low-lying islands (World Bank 2017). For example, estimates of cumulative resilience needs for Dominica, Fiji, Grenada, and Tonga range from 100 to 500 percent of GDP, although some of these needs might be defined very broadly (Grenada 2021; IMF 2020e, 2021b; Government of the Republic of Fiji 2017).

Differences and Limitations of Estimates of Global Adaptation Needs

Estimates of adaptation investment needs can vary widely because of different definitions of adaptation needs and different assumptions about future development levels. Some differences can be attributed to the inclusion or exclusion of broad development investments that would be needed even without climate change (Hallegatte and others 2018). Some studies assume more baseline development, implying lower baseline climate vulnerability and therefore lower adaptation needs, while others do the opposite and obtain larger needs. For example, studies might differ in how much progress in public health is assumed, with a direct impact on adaptation estimates. Public health capacity is beneficial to address climate change as it can reduce infectious diseases faster than climate change makes them worse. However, public health would be expected to improve as economies grow, even without climate change, and different studies include a lower or greater share of future public health investment in adaptation needs (Tol, Ebi, and Yohe 2007).

To a lesser extent, differences in estimates of adaptation needs also stem from different assumptions about adaptation goals, climate change, and adaptation technology. Some studies assume economic efficiency to define adaptation goals. Other studies aim at achieving other goals, such as preserving the current level of risk or reducing climate damages to zero. Studies can also make different assumptions about the returns to adaption policies and about the extent of future warming and climate change impacts.

Beyond estimates of global adaptation needs, granular analyses of climate vulnerabilities at the national and local level are also essential. Climate can vary tremendously at the national and local level. In addition, economic activity tends to be very unevenly distributed over space and can rely on well-functioning critical infrastructure (for example, the main road connecting an otherwise isolated production center). As a result, key vulnerabilities emerge where climate risks intersect with densely populated areas or focal infrastructure (Hallegatte and others 2019).

The multifaceted impacts of climate change are challenging and explain the scarcity of comprehensive assessments of national adaptation needs and cross-country comparisons. While studies that focus on one sector or one source of climate risk abound, rigorous independent studies that consider adaptation gaps across sectors and risks at the national or regional level are less frequent, even in advanced economies (the review in Organisation for Economic Co-operation and Development 2021 points to some exceptions). Country documents such as National Adaptation Plans (Note 3), Disaster Resilience Strategies (for example, for Dominica and Grenada; IMF 2021b; Grenada 2021), or the joint IMF-World Bank Climate Change Policy Assessments also provide useful references (for example, IMF 2020e), as should the forthcoming World Bank Country Climate and Development Reports and IMF Climate Macroeconomic Assessment Program Reports.

In this Note, we illustrate the magnitude of adaptation needs and cross-country heterogeneity by focusing on two types of adaptation investment: strengthening physical assets and investing in coastal protection. Knowing the size of adaptation investment needs is often critical for the policymakers in charge of designing affordable adaptation strategy.

Strengthening climate resilience of physical assets is one of the essential aspects of addressing adaptation gaps. Among various adaptation policies, investing in infrastructure resilience is estimated to be the costliest and essential to safeguarding inclusive growth (GCA 2018; Hallegatte and others 2019). Other costly adaptation policies include better water management systems, dryland agriculture, and disaster relief.

Starting by adapting infrastructure to existing climate risks can be a practical way forward. Even the costs of adapting to the present climate can be challenging to estimate because they require knowledge about the current resilience of infrastructure, the optimal level of resilience, and the costs of upgrading infrastructure. With these challenges in mind, we provide a systematic analysis of the magnitude of cross-country adaptation needs by focusing on existing gaps and on specific climate risks— floods and storms—which are estimated to be the major sources of infrastructure disruption (Lange and others 2020). Our estimation approach relies on the identification of exposed infrastructure by crossing two detailed global maps, on the location of natural hazards and on the location of infrastructure, by assuming different levels of initial resilience for different country income groups (Annex 2). We estimate the cost of strengthening public infrastructure, both existing assets and infrastructure projects, to improve their resilience to floods and storms. We do so by estimating the share of assets that needs strengthening and by applying strengthening unit costs.

Strengthening exposed existing and projected public assets could cost, respectively, 0.2 and 0.4 percent of GDP annually for 2021 to 2025, with large disparities across countries (Annex 2). The estimates correspond to strengthening that was estimated to have benefit-to-cost ratios above one in a very large range of scenarios (estimates of the optimal level of strengthening would be much harder to derive at the global level). Figure 3, panel 1, shows that emerging markets face the largest costs, followed by low-income countries. Large costs result from the combination of a base effect (emerging markets have more assets and more investment projects than low-income countries) and greater exposure (infrastructure in emerging markets and low-income countries are more likely to be exposed). A similar exercise shows that the costs of improving asset resilience in the private sector could be twice as high, but somewhat more evenly distributed across income groups (Annex 2).

Figure 3.
Figure 3.

Public Sector Adaptation Costs to Selected Current and Future Climate Risks

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Hallegatte and others (2019); Hallegatte, Rentschler, and Rozenberg (2019); Rozenberg and Fay (2019); Nicholls and others (2019); IMF, Capital Stock 2019 Dataset; IMF, World Economic Outlook database; and staff calculations.Notes: AE = advanced economies; EME = emerging market economies; LIC = low-income countries; SDS = small developing states.

Other recent IMF research estimates costs of similar magnitude. Studies find that over the next decade adaptation needs in sub-Saharan Africa range between US$30–50 billion (2–3 percent of regional GDP) (IMF 2020a) and in some Middle East and Central Asia countries between 0.1 percent and 3.3 percent of GDP (IMF 2020c; Duenwald and others, forthcoming). In small islands exposed to both tropical cyclones and sea-level rise, adaptation costs can be very large (Grenada 2021; IMF 2019a, 2021a, 2021b, 2021f).

Where future risks are better identified, like those stemming from sea-level rise, countries can and should develop adaptation plans now. When possible, a gradual approach to land planning that bans new construction close to exposed coasts can incentivize the relocation of assets at low cost. In some, but not all, places, it can be efficient to build infrastructure for coastal protection. In some places, restoring or planting mangrove forests can be an efficient nature-based protection with environmental cobenefits (GCA 2018). Coastal protection costs have been extensively studied and countries can start deciding now where and when to build protective infrastructure and how to finance such construction.

Coastal protection needs are estimated at 0.1 percent of GDP annually for 15 years, with much larger costs for small developing states in the Pacific and the Caribbean.1 Figure 3, panel 2, provides a second illustration of cross-country adaptation needs focusing on the cost of coastal protection. It reports estimates based on Nicholls and others (2019). Protection needs are defined as the cost of reaching a protection level that minimizes the sum of construction costs, maintenance costs, and residual flood damage to assets to 2100 (Annex 2). Figure 3, panel 2, reports the average construction and maintenance costs in the first 15 years where most of the new dikes would be expected to be built. Costs vary significantly and tend to be larger for low-income and small developing states. Even among small developing states, there are large disparities as some small island developing states face double digit costs. Coastal protection is estimated to help contain (and sometimes lower) residual risks to low levels (at or below 0.2 percent of GDP).

Addressing Challenging Adaptation Needs

To keep adaptation investment affordable, it is crucial to monitor asset conditions and ensure efficient selection, execution, and maintenance of investment projects. For example, physical assets that face small climate risks or that are less essential to the rest of the economy may not need strengthening. Assets in areas that face overwhelming climate risks can be relocated, possibly rebuilt, when cost effective. Strengthening governance to avoid substitution with cheaper design or lower-quality material at the construction stage and to avoid skimping on maintenance is essential to save on future repair or replacement costs.

Financing large adaptation costs will challenge many countries’ fiscal space. This is especially the case for lower-income countries, which have typically not contributed much to global warming yet suffer from the largest damages, are burdened with large and competing development needs, and typically have limited fiscal space (IMF 2018, 2020a, 2020b, 2021a, 2021d, 2021f). Financing adaptation costs will require domestic revenue mobilization, a reprioritization of investment plans or other spending, the support of the donor community, or a combination of all these sources (Cevikand Nanda 2020; IMF 2013; Cohen and Jalles 2020). Table 1 lists countries with high adaptation costs that may face financing challenges.

Table 1.

Adaptation Costs and Pre-COVID-19 Fiscal Space

article image
Sources: Hallegatte and others (2019); Hallegatte, Rentschler, and Rozenberg (2019); IMF staff reports; and staff calculations.Note: Fiscal space assessments are estimated for advanced and emerging market economies and are based on the debt sustainability assessment from the last published IMF Article IV report; risks of debt distress are estimated for low-income countries and are taken from the last published debt sustainability assessment. These assessments were done pre-COVID-19 and do not reflect developments since the outset of the pandemic. Median total strengthening costs are estimated at 0.2 percent of GDP. Missing countries are absent because of the lack of data.

International financing flows toward adaptation are hard to track and fall short of commitments and needs. The financing of adaptation-related activities is often embedded in projects that are not purely related to adaptation, which makes adaptation finance challenging to track. Furthermore, data on private sector and within-country public sector investment is often missing (Richmond and others 2020). In 2009, developed countries committed to jointly mobilize $100 billion per year by 2020 in climate finance for action in developing countries, including but not only for adaptation (UN Framework Convention on Climate Change 2009b). Adaptation needs are estimated to be around $0.25 trillion per year by the midcentury (Figure 2). Yet studies identify only $22 billion in adaptation financing flows in 2015 to 2016 and $30 billion in 2017 to 2018 on average (Buchner and others 2019). Further, annual aid for adaptation from official creditors to low-income developing countries was just $10 billion in 2018 (IMF 2020c).

The disbursement of international aid for adaptation is hampered by demanding conditionalities and low capacity in targeted countries (Box 2). Donors often require that some PFM standards are in place before disbursing financial support. However, the countries that need support and that would benefit the most from it also tend to have PFM standards below requirements and little scope to improve them on a timely basis. While strengthening PFM is essential to ensure effective progress on adaptation (Note 3), unachievable requirements might hold up all progress if donor financing is necessary.

There is a strong case for additional international support toward vulnerable countries based on needs and equity considerations. The cost analysis makes clear that adaptation needs tend to be higher in countries with less fiscal space, lower institutional capacity, and a larger share of the global poor. For many of these countries, securing multilateral and bilateral aid is essential as their needs exceed revenue mobilization potential and Sustainable Development Goals require greater, not less, spending in other areas. It will also be important to improve their project design and implementation capacity. Also, given that these countries did not cause global warming, there seems to be a strong case for additional (that is, that does not crowd out existing) support from the donor community (Box 2). International organizations such as the IMF could play a useful role in supporting these countries via capacity development and catalyzing financial assistance.

International support for climate adaptation investment can be a cost-effective alternative to disaster relief for international donors. Increased spending in adaptation investment can lead to a net reduction of total spending for donors that already provide support for disaster alleviation by reducing climate change risks before they materialize. The scope for savings should be assessed on a case-by-case basis and not taken for granted, but there can be scope for large efficiency gains in protection. These gains are particularly pronounced in the case of small island states. Melina and Santoro (2021) show that the cumulated discounted fiscal savings in post-disaster relief from an investment program to increase resilience of public infrastructure in the Maldives are more than double the extra spending to finance it. The recent Climate Macroeconomic Assessment Program pilot estimated savings of a similar magnitude in the case of Samoa, highlighting that savings are increasing in the expected intensity of natural disasters (IMF, forthcoming).

Unlocking Access to Climate Finance for Pacific Island Countries1

Pacific island countries (PICs), which are globally among the most exposed to climate hazards, have an urgent need to invest in climate adaptation. IMF (2021c) estimates that PICs need almost $1 billion every year over the next 10 years, or about 6½ to 9 percent of GDP on average, to upgrade and retrofit public sector infrastructure and coastal protection systems. However, access to climate finance has been disappointing so far, and PICs face many challenges (Fouad and others 2021).

These critical investment needs notwithstanding, PICs generally have limited fiscal space, low administrative capacity, and underdeveloped private sectors. As such, they are typically constrained to rely on bilateral partners, or grant-based instruments from multilaterals and major climate funds (such as the Green Climate Fund [GCF]). Thus far, PICs have faced significant challenges in getting accredited for direct access from facilities such as the GCF due to low capacity in public financial management (PFM). Unfortunately, the speed at which sufficient PFM capacity for direct access could be developed in PICs is inconsistent with the speed at which these countries need to adapt to climate change.

Indirect access to GCF through international accredited entities (such as the World Bank, Asian Development Bank, or the UN Development Programme) has been more successful for PICs, with faster approvals and larger projects. However, even with attempts to streamline requirements, the total amount approved by the GCF for all PIC projects since 2015 has only been about half of the estimated annual adaptation needs for the region. And out of this amount, only about half has actually been disbursed (Figure 2.1). The good news is that the countries more exposed to climate change have, on average, had more funds approved for climate adaptation (Figure 2.2) (Chen and others 2015; Organisation for Economic Co-operation and Development 2021). Nevertheless, faster approvals and disbursements are urgently needed for all PICs.

Figure 2.1.
Figure 2.1.

Green Climate Fund Financing versus Estimated Annual Adaptation Needs for Pacific Island Funding

(Millions of US dollars)

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Green Climate Fund (GCF); and IMF staff calculations.
Figure 2.2.
Figure 2.2.

Exposure to Climate Change and Approved Climate Adaptation Funding

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Staff elaboration based on Chen and others (2015) and Organisation for Economic Co-operation and Development (2021).Note: Tuvalu is not shown on chart, as it is an outlier in terms of amount of approved climate adaptation funding as percent of GDP, at over 200 percent.

To meet the challenge of addressing climate adaptation needs, PICs, climate funds, and the IMF could work better together. PICs could strategically decide which of their priority projects are best financed through bilateral channels, which are better suited for climate funds, and obtained through international or direct access. Where resource constraints allow, PICs could establish dedicated climate units, preferably within ministries of finance, to coordinate the financing of priority climate projects. PICs should continue to build PFM capacity, with a focus on strong audit, robust control frameworks, and strengthened public investment practices, that are at the center of GCF accreditation requirements. Climate funds could consider further streamlining accreditation and approval requirements for small and fragile countries and consider increasing reliance on ex post monitoring. The IMF could further integrate analysis of climate issues into macroeconomic surveillance and continue to provide capacity development support to countries to build stronger PFM and public investment management practices.

1 This box was prepared by Natalija Novta (Asia and the Pacific Department) and Gemma Preston (Fiscal Affairs Department).

Integrating Adaptation into Macro-Fiscal Frameworks

Macro-Framework Implications of Climate Risks and Adaptation Policies

Macro-fiscal policies should be supported by frameworks that reflect climate risks and envisaged adaptation policies. IMF guidance for both low-income and market-access countries recommends analyzing the impact of climate when assessing fiscal sustainability (IMF 2016, 2018, 2021e). For vulnerable countries, the success of macro-fiscal policies can be compromised by present and future climate risks. Additional specific measures may be needed to cope with those risks (for example, the accumulation of fiscal buffers).

It is useful to study separately the macro-fiscal implications of a weather-disaster shock and of cumulative climate change effects (Figure 4 and Box 3). The short-term macro forecast baseline may not include the impact of weather disasters as these risks could instead be covered by alternative shock scenario analyses. Temporary-shock scenarios developed using short-to medium-term projections (3 to 10 years) help assess structural and financial protection strategies and prepare for post-disaster needs. At longer time horizons, macro-framework projections could be updated to reflect the cumulative effects of climate risks, covering both slow-moving gradual changes in average climate conditions and cumulative changes in extreme weather. This is a complex exercise that requires capacity building and a continuous dialogue between policymakers, economists, and scientists. It would be equally recommended to assess the cumulative impact of these changes on key macro-fiscal variables (such as potential growth, spending, and debt). Scenarios can also be useful to reflect risks about cumulative effects.

Figure 4.
Figure 4.

Illustrative Growth Projections/Scenarios with Disaster Impacts

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Sources: IMF 2016; and staff modifications.

The implications of weather disasters can be macro-critical and are best examined with short- to medium-term shock scenarios (IMF 2016). Relevant scenarios would examine the economy’s response to a temporary weather disaster of relevant scale (either based on the average size over 10 to 20 years or based on tail events with recurring frequencies of 50 or 100 years). These scenarios would determine the adequate size of buffers and risk-reduction strategies as these cannot be derived from historical averages that dilute severe disaster impacts. Scenarios would help to assess medium-term fiscal and debt sustainability and supplement the analysis of risks to growth potential.

  • Natural disasters reduce GDP through multiple channels. By destroying assets and harming human lives, disasters cut production inputs (Acevedo 2016; Acevedo and others 2018). Additionally, disasters have adverse effects on productivity. Strong complementarities between assets imply that the destruction of some infrastructure can lower the productivity of other assets. For example, the destruction of power infrastructure will make it difficult to operate plants, damages to transportation networks prevent labor and intermediary inputs getting to where they are needed, and hampered communication infrastructure aggravates the misallocation of inputs. Decreasing returns to scale also imply congestion effects (when surviving infrastructure is overwhelmed) that reduce productivity further.

  • Humanitarian relief and reconstruction to address disrupted productivity have large multipliers. In addition to moral reasons, humanitarian relief is essential to avoid permanent adverse effects on labor. Fast reconstruction is justified by high returns exceeding predisaster levels: restored assets have positive externalities as they allow other assets to return to normal operations and help to ease congestion effects.

  • Natural disasters tend to worsen the fiscal balance ex post in several ways (IMF 2019b). The drop in GDP reduces government revenues while grants from the international community may provide some relief. Lifeline support to affected populations (provision of public services, social assistance, in-kind transfers) and public and private sector reconstruction support (grants to other government units, transfers) lead to increases in current expenditure. The reconstruction of damaged public assets raises capital expenditure, whereas disruption may slow the execution of other public investment projects. This creates “explicit” liabilities (impact on government-owned assets) and “implicit” liabilities (expected government support even in the absence of formal contract). Below the line, financing needs are likely to trigger additional borrowing. The destruction of assets and borrowing damage the government balance sheet. Assessing disaster risks to the baseline should inform the adequate size of fiscal buffers and spending on risk-reduction infrastructure. Addressing the consequences of large climate shocks requires ex ante policy adjustments. For example, ex post financial and liquidity constraints could be addressed ex ante with additional fiscal buffers (self-insurance) or by contracting contingent financing instruments (see also Note 3; IMF 2021a). Examining liabilities, both explicit and implicit, can inform financing plans and also prepare for post-disaster response. In some cases, additional investment in risk-reduction infrastructure to limit ex post liabilities is also desirable, both for countries and international donors (IMF 2019a).

For most countries until 2030, weather disasters are not expected to be significantly different from those already experienced (Note 1), but a careful re-evaluation of existing risks can be beneficial. If the effect of present climate risks is already fully accounted for in growth projections, it is unlikely that a reduction in GDP growth baseline is needed. However, in some cases, a re-evaluation of recent local data on the materialization of climate risks and on socioeconomic changes (for example, change in land use with implication for climate impacts and new developments in areas more at risk) may predict greater impact from disasters than previously envisaged in the baseline. Such cases would warrant lowering GDP potential for some years. Alternatively, the updated risk assessment and new opportunities could lead a country to invest in reducing these climate risks and protect, or possibly improve, future growth potential (Box 3).

At longer horizons, climate change is projected to create new challenges, some of which will not be caused by weather disasters. Permanently higher average temperatures and different rainfall patterns will lead to slow-moving but pervasive changes in productivity (for example, new tourism and agricultural conditions). Sea-level rise will transform vast coastal areas, creating challenges for coastal cities and infrastructure, and threatening the existence of small low-lying islands. Previously unexperienced droughts and floods can put hydropower plants under stress, while power demand may simultaneously soar due to increased use of air conditioning. All significant climate change effects, including those that won’t take the form of disaster episodes, should be reflected in macro-frameworks.

Climate change could create large risks that would affect long-term debt sustainability. In long-term scenarios that assume little or no climate change mitigation, the literature predicts that vulnerable countries will have significantly lower GDP per capita levels by 2050 relative to a baseline without climate change (Mendelsohn, Dinar, and Williams 2006; Cevik and Nanda 2020; Cevik and Jalles 2020, 2021; IMF 2021b, 2021e; Grenada 2021) within the time horizon considered in debt sustainability assessments. Country or regional studies can be leveraged to identify areas of vulnerabilities (sometimes with impact estimates) and can be supplemented by studies that analyze specific risks in depth (for example, for temperature increase, see Kahn and others 2021; for sea-level rise, see Desmet and others 2018; and for the Caribbean and Pacific Islands, see Simpson and Harrison 2010 and Asian Development Bank 2013).

Long-term sustainability would be best assessed with long-term scenarios accounting for the cumulative effects of climate change and underpinned by coherent adaptation policy narratives. Macroeconomists can use sensitivity analysis and explore the implications of alternative macro-fiscal and climate projections in stress scenarios using relevant studies from the literature. It would be useful for these scenarios to explicitly account for adaptation policies. This can be illustrated by the case of sea-level rise where damages can be estimated to be large, but adaptation such as relocation or coastal protection investment can reduce damages effectively. For example, without coastal protection against sea-level rise, Suriname is estimated to incur annual losses gradually increasing to around 1% percent of GDP by 2080 (Simpson and Harrison 2010). Data from a follow-up study suggest that investing in coastal protection of 2 percent of GDP for 10 years would contain these losses to no more than 0.1 percent until 2100. Coastal protection is not the only adaptation strategy. Desmet and others (2018) show that relocation, in the form of migration and the rise of new productive clusters away from exposed coasts, is an effective adaptation strategy and can reduce GDP losses by a factor of 10. For Suriname, their model predicts annual costs below 0.3 percent of GDP and migration outflows that would reduce population by 2.4 percent by 2200. Taken together, these studies highlight the important role of adaptation policies in long-term scenarios, the need to explain adaptation assumptions in projections, and suggest that a well-planned gradual approach starting now can be cost effective.

Climate change and adaptation policies will impact inequality. Within countries, climate effects tend to have a disproportionate effect on the poor (Hallegatte and others 2016a). There is also some evidence that adaptation investment can reduce inequality (as done with Debt, Investment, Growth, and Natural Disaster [DIGNAD] in IMF 2020a) but more work is needed to better understand the distributional impact of adaptation.

General Equilibrium Models of Adaptation to Analyze Growth-Debt Trade-Offs

General equilibrium (GE) models are useful to determine whether development plans, including adaptation, and financing strategies are coherent. Investments in adaptation deliver benefits, but they can be costly. In particular, costly investment in resilience puts strain on public budgets but shores up growth when natural disasters occur. Therefore, general equilibrium models can be useful to assess whether specific adaptation strategies are sustainable under a plausible calibration of key economic relationships and climate shock realizations.

GE models can also be used to compare the costs and benefits of reform packages. Reforms aimed at protecting the economy from climate risks can be combined and supported by supplementary measures. The success of protection strategies can hinge on these supplementary reforms whose effects are best illustrated with GE model simulations, like those generated by the IMF DIGNAD model (Annex 1). For example, improvements in public finance management can be instrumental for the development and implementation of adaptation investment plans to deliver on expected returns. Lowering borrowing costs by mobilizing concessional financing from development partners or domestic revenues can help to safeguard debt sustainability despite costly adaptation measures. Alternatively, simulations can be used to determine the magnitude of the reforms needed to ensure adaptation plans are sustainable.

The implications of climate uncertainty for physical protection and insurance policy options need to be analyzed by running large numbers of shock scenarios. This approach can be implemented by repeating simulations of deterministic models based on different random climate shocks and by stochastic models. Such an approach also encompasses dynamic stochastic general equilibrium (DSGE) models, like the IMF’s Flexible System of Global Models (see also IMF 2019b; Cantelmo and others 2019; Fernandez-Corugedo, Gonzalez, and Guerson, forthcoming) that can explicitly incorporate the distributions of climate shocks. This approach improves on dynamic general equilibrium models that are limited to the study of a few specific disaster shock scenarios.

DSGE models can be used to analyze the long-term implications of climate shocks and shifts in their distribution on investment. Recent developments in structural modeling (see Levintal 2018; Fernández-Villaverde and Levintal 2018) provide a way to evaluate how the distribution of climate shocks affects macroeconomic outcomes. For example, Cantelmo, Melina, and Papageorgiou (2019) assume that agents’ expectations about weather-related disaster shocks affect the stochastic steady state of their model and find that the higher expected frequency of natural disasters hinders private investment spending even in the absence of an actual disaster realization, as the return on investment is expected to be lower, and thereby contribute to aggravate the effect of climate change on GDP.

DSGE models are useful to assess welfare trade-offs of climate change policies (Box 4). Although climate change is associated with substantial welfare losses (Donadelli and others 2017), policies designed to counter its effects do not necessarily lead to higher welfare outcomes. Financing adaptation investment through taxes, for instance, also includes some adverse effects. In this case, adaptation investment improves social welfare by cushioning the economy from the impact of climate change but also produces a welfare loss as higher taxes tend to decrease private consumption and incentives to work. Considering such trade-offs is imperative to the formulation of cost-effective adaptation policies, and this can be facilitated by nonlinear DSGE models. The micro-foundations on which these models are built allow economists to disentangle the channels through which climate change and climate change policies affect labor market outcomes and households’ consumption choices and can help with the design of supplementary policy to alleviate negative effects.

DSGE models can also quantify the effects of stochastic shocks and the expected cost-benefits of adaptation policies that reduce risks, including insurance policy options (IMF 2016). For example, DSGE models can be used to calculate the probability that post-disaster financing needs would exceed a particular level in a given year (or group of years) under different insurance options (standard commercial insurance contracts, self-insurance by accumulating savings, state-contingent debt instruments such as catastrophe bonds). Stochastic DSGE models have been coupled with deterministic models to study near term dynamics to study, for example, impacts on income distribution (for example, Guerson, Guo, and Mendes-Tavares 2022).

The wealth of information provided by DSGE models comes at a cost. In addition to their stochastic capabilities, DSGE models can typically feature many sectors, including detailed energy sectors, and can be tailored to study distribution issues across heterogeneous agents. However, using, developing, or calibrating these models to new settings requires capacity and time.

DSGE results can also be very sensitive to model calibration and, as for other models, to the assumed probability distribution of climate variables, which is uncertain and changing. As discussed in Note 1, analysts can use observed climate risks for scenarios with a time horizon of less than 20 years, possibly updated with observed robust climate trends. For scenarios with a time horizon of between 20 and 30 years, analysts can use all the climate scenarios available in the literature to define probability distributions of extreme weather and other climate outcomes while keeping in mind that these are not objective risks (Note 1). For simulations at very long horizons (more than 30 years into the future), analysts could rely on different combinations of greenhouse gas emission scenarios and socioeconomic scenarios (Note 1).

Malawi: Mainstreaming Climate Change into Macroeconomic Projections1

Malawi is one of the most vulnerable countries to climate change in sub-Saharan Africa. Over the past decade, it has experienced severe droughts, floods, and insect infestations. The recent trends in the frequency and intensity of these natural disasters warranted some adjustments to the baseline macroeconomic projections. Post-disaster studies also found that ramping up resilience to climate shocks could contribute to a virtuous cycle of self-reinforcing adaptation and development. Consequently, since 2019, the macro-fiscal implications of climate change have been integrated into the IMF’s analysis for Malawi. The following describes one of several approaches which have been applied.

First, baseline macroeconomic projections incorporated climate shocks, applying event studies:

  • Near-term real GDP growth was calculated based on the expected value of growth adjusted for offsetting factors such as the fiscal multiplier effect from public investment and social protection spending. The expected value of growth was based on growth (1) absent any climate shocks (for example, 5 percent) and (2) if a climate shock hits (for example, 3 percent = 5 percent – 2 percent)—calculated as growth absent a shock less the historical drop in growth when a climate shock hits based on event studies (for example, 2 percent).

  • Medium-term real GDP growth projections factored in the positive impact from resilience-building policies (for example, infrastructure) net of projected disaster impacts—in this case, these were derived from historical data.

  • Inflation was projected following the same logic as for real GDP growth with adjustments for anticipated monetary policy adjustments and, over the medium term, increased resilience of agricultural production to climate shocks.

  • Public investment social protection targeting resilience were assumed to increase in line with the latest Post-Disaster Needs Assessments (PDNA). Figure 3.1 provides an example, where, following severe flooding in 2019, the PDNA estimated climate-resilient rebuilding (mainly infrastructure) would cost 4.5 percent of GDP—spread out over five years (Figure 3.1, panel 1). Based on discussions with development partners, assumptions were also made on what they would finance and whether grants or loans would be applied (Figure 3.1, panel 2). Keeping in mind limited fiscal space, the domestically financed portion relied on (1) revenue mobilization, including consideration of a carbon tax; (2) reducing nonpriority spending; and (3) larger fiscal deficits (Figure 3.1, panels 3 and 4)—all of which had implications for debt projections.

  • Balance of payments effects were a function of assumptions on development partner financing and net exports. Export and import projections applied a similar logic to those for real GDP and inflation, except import projections also relied on the marginal propensity to import related to public investment.

  • Financial sector impact was assumed to be limited due to the low levels of access to finance in Malawi. Large firms who would borrow from the banking system tend to have already invested in their own resilient infrastructure.

Figure 3.1.
Figure 3.1.

Malawi: Investment Scenario with Fiscal Impacts

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: IMF staff calculations based on a simulation exercise for Malawi, as of March 2020.Note: Current primary balance is defined as the sum of planned climate-dedicated taxation, crowding out of investment, and current spending on climate subventions less domestically financed development spending.

Second, Malawi’s debt sustainability analysis included alternative tail risks scenarios, where the magnitude of the climate shocks assumed in the baseline were amplified. This helped to assess fiscal space and the adequacy of existing and planned buffers. Moreover, it highlighted the potential for large additional balance of payments and fiscal financing needs.

Third, statistical data that would be key to enhancing the analysis was discussed with the authorities. These include macroeconomic and climate data that would enable more sophisticated analysis, including applying dynamic general equilibrium models, vector autoregression models, and model simulations of future climate shocks.

1 This box was prepared by Pritha Mitra, Jung Eun Yoon, and Mai Farid.

Social Welfare in Disaster-Prone Countries

Natural disasters take a heavy toll on social welfare in disaster-prone countries. Disaster-prone countries, which are often low-income developing states, are a subset of all climate-vulnerable countries with a substantially higher probability of experiencing weather-related natural disasters in any given year. Being small in surface, population, or the size of their economy, such countries experience much larger capital damages and economic losses as a fraction of GDP in the aftermath of natural disasters (Centre for Research on the Epidemiology of Disasters n.d.). On average, disaster-prone countries have lower levels of income, private expenditure, and social welfare. Cantelmo, Melina, and Papageorgiou (2019) find that more frequent and powerful weather disasters in these countries cause an average welfare loss equivalent to a permanent reduction in consumption of 1.6 percent.

In the absence of adaptation, climate change will exacerbate the divergence in welfare and income between disaster-prone and non-disaster-prone countries. Climate change has the potential to make weather-related natural disasters more frequent and more powerful, putting greater pressure on the social welfare and growth prospects of disaster-prone countries (Intergovernmental Panel on Climate Change 2021, 2022; Gutiérrez and others 2021; Note 1). Macroeconomic models that can simulate uncertainty about weather and economic growth can be used to estimate the effects of changes in the probability distribution of natural disasters, their assumed intensity, or both. For example, Cantelmo, Melina, and Papageorgiou (2019) use a nonlinear dynamic stochastic general equilibrium model with a stochastic steady state to estimate the welfare and macroeconomic effects of an increase in the frequency of natural disasters by 35 percent and an increase of damages per disaster by 82 percent. Under this scenario, they find that the welfare losses from natural disasters increase by a factor of seven.

Adaptation investment can reduce welfare losses from more frequent and more severe natural disasters, but programs paid for by domestic resources imply a welfare trade-off. Ex ante adaptation can cushion the economy from the impact of natural disasters (Marto, Papageorgiou, and Klyuev 2018; Acevedo and others 2018; Cantelmo, Melina, and Papageorgiou 2019) and, therefore, increase social welfare, but financing adaptation via taxes can also reduce welfare. The net effect depends on a multitude of factors, including the return on adaptation capital and the efficiency of public finance management. As shown in Figure 4.1, for the disaster-prone countries studied Cantelmo, Melina, and Papageorgiou (2019), an increase in adaptation investment has positive effects up to a point (35 percent share of resilient capital in the total capital stock) where the marginal utility of an additional unit of resilient capital (benefits) becomes equal to the marginal disutility implied by the need to finance it (costs). The marginal disutility of investing beyond this point is larger than the marginal utility. Additional investment beyond this point implies net costs, and they would not be warranted based on pure cost-benefit considerations (see Note 1 for a discussion of cost-benefit analysis applied to climate change adaptation). Similar trade-offs should be expected with other sources of financing that do not expand the resource envelope of the economy.

Figure 4.1.
Figure 4.1.

Welfare Gain from Investing in Resilient Capital in Disaster-Prone Countries

Citation: Staff Climate Notes 2022, 002; 10.5089/9798400201608.066.A001

Source: Cantelmo, Melina, and Papageorgiou (2019)

International donors can relax domestic trade-offs by expanding the resource envelope of the economy, but cost-effectiveness considerations remain important. Higher grant disbursements improve social welfare, and a sufficiently strong contribution from international donors can, in principle, close the welfare gap. Cantelmo, Melina, and Papageorgiou (2019) illustrate this point by showing that financing the entire extra cost of accumulating 35 percent share of resilient capital (the optimal level in Figure 4.1) with grants reduces the welfare gap between the average disaster-prone and non-disaster-prone countries by half.

Donors would maximize the welfare impact of their contribution and recipient countries the impact of scarce international aid by financing the most cost-effective programs and by strengthening recipient countries’ capacity. Ex post relief accelerates the recovery, preserves debt sustainability, reduces the need to mobilize resources from the population, and contributes to a less volatile consumption path. However, the same improvement in social welfare can be achieved through financing ex ante resilience with, often, only a fraction of the funding. International aid is limited, and governments would maximize its impact by channeling it toward projects with the highest benefit-cost ratio (Note 1). The development community would also benefit from improving vulnerable countries’ capacity to access climate funds and to implement adaptation programs (Box 2), including by providing more technical assistance.

Macro-Fiscal Implications of Adaptation to Climate Change
Author: Mr. Zamid Aligishiev, Emanuele Massetti, and Mr. Matthieu Bellon
  • View in gallery

    Climate Change Costs and Estimation Methods

  • View in gallery

    Estimates of Global Investment Needs in Adaptation

  • View in gallery

    Public Sector Adaptation Costs to Selected Current and Future Climate Risks

  • View in gallery

    Green Climate Fund Financing versus Estimated Annual Adaptation Needs for Pacific Island Funding

    (Millions of US dollars)

  • View in gallery

    Exposure to Climate Change and Approved Climate Adaptation Funding

  • View in gallery

    Illustrative Growth Projections/Scenarios with Disaster Impacts

  • View in gallery

    Malawi: Investment Scenario with Fiscal Impacts

  • View in gallery

    Welfare Gain from Investing in Resilient Capital in Disaster-Prone Countries