## clnea2022002

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**Canonical URL:** [clnea2022002](https://www.imf.org/-/media/files/publications/staff-climate-notes/2022/english/clnea2022002.pdf)

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---

### Differences and limitations of adaptation estimates
- Definitions and assumptions drive wide variation in estimates:
  - Inclusion/exclusion of broad development investments that would be needed even without climate change.
  - Different baseline development assumptions change baseline climate vulnerability and adaptation needs (example: public health progress assumptions affect adaptation estimates).
  - Assumptions about adaptation goals (economic efficiency versus preserving current risk levels or reducing climate damages to zero), returns to adaptation, and extent of future warming.
- Additional limitations:
  - Many studies focus on one sector or one source of climate risk; comprehensive national adaptation needs assessments and cross-country comparisons are scarce.
  - Aggregation can mask large negative effects concentrated in vulnerable countries and omit low-probability, high-impact events and nonmarket impacts.

### Need for granular, country- and sector-level analyses
- Climate and economic activity vary at national and local levels; key vulnerabilities arise where climate risks intersect with densely populated areas or focal infrastructure.
- Country documents and assessments (National Adaptation Plans; Disaster Resilience Strategies; IMF-World Bank Climate Change Policy Assessments) are useful; forthcoming World Bank Country Climate and Development Reports and IMF Climate Macroeconomic Assessment Program Reports expected to be relevant.

### Focus of the Note: two adaptation investment types
- The Note illustrates magnitude and heterogeneity of adaptation needs by focusing on:
  - Strengthening physical assets (infrastructure resilience).
  - Investing in coastal protection.
- Rationale: strengthening infrastructure resilience is estimated to be the costliest among adaptation policies and essential to safeguarding inclusive growth.

### Estimates of strengthening public infrastructure resilience
- Approach:
  - Cross maps of natural hazards and infrastructure location; assume different initial resilience by country income group.
  - Estimate share of assets needing strengthening and apply strengthening unit costs.
- Estimated costs (annual, 2021 to 2025):
  - Strengthening exposed existing public assets: 0.2 percent of GDP.
  - Strengthening exposed projected public assets: 0.4 percent of GDP.
  - Estimates correspond to strengthening with benefit-to-cost ratios above one in a very large range of scenarios.
- Distribution:
  - Emerging markets face the largest costs, followed by low-income countries.
  - Private-sector asset resilience costs could be twice as high and more evenly distributed across income groups.
- Complementary regional estimates:
  - Sub-Saharan Africa adaptation needs over the next decade: US$30–50 billion (2–3 percent of regional GDP).
  - Some Middle East and Central Asia countries: between 0.1 percent and 3.3 percent of GDP.
  - Small islands exposed to tropical cyclones and sea-level rise can face very large adaptation costs; some small island developing states face double digit costs.

### Estimates of coastal protection needs
- Aggregate estimate:
  - 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).
  - Simple average of coastal protection needs across all countries is estimated at 1 percent of annual GDP.
- Methodology:
  - Based on Nicholls and others (2019): define protection level that minimizes sum of construction costs, maintenance costs, and residual flood damage to assets to 2100.
  - Report average construction and maintenance costs in the first 15 years where most new dikes would be built.
- Distribution and outcomes:
  - Costs vary significantly; tend to be larger for low-income and small developing states; coastal protection estimated to help contain residual risks to low levels (at or below 0.2 percent of GDP).
- Note on aggregation:
  - Coastal protection cost estimates were done independently from strengthening costs; there could be some overlap.

### Selection, execution, and maintenance to keep adaptation affordable
- To keep adaptation affordable:
  - Monitor asset conditions.
  - Ensure efficient selection, execution, and maintenance of investment projects.
- Strategic options:
  - Do not strengthen assets facing small climate risks or that are less essential.
  - Relocate assets in areas with overwhelming climate risks when cost effective.
  - Strengthen governance to avoid substitution with cheaper design, lower-quality materials, and skimping on maintenance.

### Financing constraints and distributional considerations
- Challenges for lower-income countries:
  - Limited fiscal space, large competing development needs, and the largest damages despite limited contributions to global warming.
- Financing options:
  - Domestic revenue mobilization.
  - Reprioritization of investment plans or other spending.
  - Support from donor community.
  - Combinations of the above.
- Table highlights (pre–COVID-19 fiscal space and adaptation costs):
  - Median total strengthening costs are estimated at 0.2 percent of GDP.
  - Fiscal space and debt distress assessments cited are pre–COVID-19 and do not reflect developments since the pandemic.

### International adaptation finance: gaps and challenges
- Tracking and scale:
  - Adaptation finance embedded in many projects complicates tracking; private sector and within-country public sector investment data often missing.
- Quantified gaps and flows:
  - Developed countries committed to mobilize $100 billion per year by 2020 for climate finance for developing countries.
  - Adaptation needs are estimated to be around $0.25 trillion per year by the midcentury.
  - Identified adaptation financing flows: $22 billion in 2015 to 2016 and $30 billion in 2017 to 2018 on average.
  - Annual aid for adaptation from official creditors to low-income developing countries was $10 billion in 2018.

### Barriers to effective aid disbursement and rationale for additional support
- Barriers:
  - Donor conditionalities often require public financial management (PFM) standards before disbursing support; target countries needing support most tend to have PFM standards below donor requirements.
  - Unachievable requirements can hold up progress if donor financing is necessary.
- Equity and needs-based rationale:
  - Adaptation needs tend to be higher in countries with less fiscal space, lower institutional capacity, and larger shares of the global poor.
  - Strong case for additional international support that does not crowd out existing aid; international organizations can support capacity development and catalyze financial assistance.

### Adaptation investment as cost-effective alternative to disaster relief
- Potential efficiency gains:
  - International support for adaptation investment can reduce total donor spending by lowering future disaster relief needs.
- Case studies:
  - Maldives: cumulated discounted fiscal savings in post-disaster relief from an infrastructure resilience program are more than double the extra spending to finance it.
  - Samoa: CMAP pilot estimated savings of a similar magnitude; savings increase with expected intensity of natural disasters.

---

### Box 2 — Pacific island countries: urgency, access challenges, and recommendations
- Urgency and financing needs:
  - PICs are among the most exposed to climate hazards.
  - IMF (2021c) estimates 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.
- Access challenges and GCF evidence:
  - PICs face limited fiscal space, low administrative capacity, and underdeveloped private sectors; rely on bilateral partners or grant-based instruments from multilaterals and major climate funds (such as the Green Climate Fund [GCF]).
  - Direct access accreditation difficulties due to low PFM capacity; indirect access via international accredited entities has been more successful with faster approvals and larger projects.
  - Total amount approved by the GCF for all PIC projects since 2015 has been about half of the estimated annual adaptation needs for the region; about half of approved amounts have been disbursed.
  - Tuvalu is an outlier: approved climate adaptation funding as percent of GDP at over 200 percent.
- Policy recommendations:
  - For PICs: strategically allocate financing channels; establish dedicated climate units within ministries of finance where resources allow; continue to build PFM capacity focused on audit, control frameworks, and public investment practices.
  - For climate funds: consider further streamlining accreditation and approval requirements for small and fragile countries; consider increasing reliance on ex post monitoring.
  - For the IMF: further integrate climate analysis into macroeconomic surveillance and provide capacity development to build stronger PFM and public investment management practices.

---

### Box 3 — Malawi: mainstreaming climate change into macroeconomic projections
- Context:
  - Malawi is highly vulnerable to climate change; recent severe droughts, floods, and insect infestations prompted adjustments to baseline macroeconomic projections.
  - Since 2019, macro-fiscal implications of climate change have been integrated into IMF analysis for Malawi.
- Three-step approach:
  1. Incorporate climate shocks into baseline macroeconomic projections using event studies.
  2. Include amplified tail-risk scenarios in the debt sustainability analysis.
  3. Discuss improvements in statistical data to enable more sophisticated modeling.
- Event-study approach details:
  - Near-term real GDP growth: expected value combines growth absent shocks (example: 5 percent) and growth if a climate shock hits (example: 3 percent = 5 percent – 2 percent), where the shock impact is based on historical drops from event studies (example: 2 percent).
  - Medium-term growth: factors in positive impact from resilience-building policies net of projected disaster impacts, derived from historical data.
  - Inflation: projected with adjustments for monetary policy and increased medium-term resilience of agricultural production.
  - Public investment and social protection: assumed to increase in line with latest PDNA; PDNA after 2019 floods estimated climate-resilient rebuilding would cost 4.5 percent of GDP—spread over five years.
  - Financing assumptions for domestically financed portion included revenue mobilization (including consideration of a carbon tax), reducing nonpriority spending, and larger fiscal deficits.
- Debt sustainability and tail risks:
  - DSA included amplified tail risk scenarios to assess fiscal space and adequacy of buffers; highlighted potential for large additional balance of payments and fiscal financing needs.
- Data and modeling needs:
  - Improved statistical and climate data would enable dynamic general equilibrium models, vector autoregression models, and simulations of future climate shocks.
- Illustrative simulation detail:
  - PDNA-based investment need: 4.5 percent of GDP in climate-resilient rebuilding following 2019 severe flooding, spread over five years.

---

### Annex 1 — Methods to estimate climate change costs and adaptation benefits
- Two broad method groups:
  - Simulation models: parameterized models (sectoral or economy-wide) that can simulate climates not yet experienced and switch adaptation options on/off.
  - Econometric models: reduced-form estimations using cross-sectional or panel data; project adaptation from observed cross-climate behavior.
- Integrated Assessment Models (IAMs):
  - Integrate energy, economy, climate, and sometimes land; examples include DICE and RICE, FUND, PAGE.
  - Typical limitations: few sectors modeled in detail; countries aggregated; adaptation sometimes modeled without private adaptation costs or technological progress.
- CGE models:
  - Include many sectors and regions; rely on exogenous climate shocks; DSGE models not yet applied widely for global climate cost estimates in reviewed literature.
- Econometric approaches:
  - Cross-sectional econometrics: rely on observed behavior across climates but face confounding variable challenges; generally do not provide direct estimates of economic benefit of adaptation.
  - Panel econometrics: use weather shocks and short-term elasticities; may not capture slow stock-based adaptation; some studies attempt to detect adaptation over time or across climates.
- Representative global aggregate estimates (2100 relative to reference without climate change):
  - For +1.5°C to +2.5°C (approximately SSP1–2.6): Median loss: 1.5 percent [–13.0, +0.1] of annual global GDP.
  - For +2.9°C to +4.3°C (approximately SSP3–7.0): Median loss: 3.3 percent [–23, –0.8] of annual global GDP.
- Selected numerical findings from surveyed literature (examples):
  - Tol (2013): 1.0°C → –1.4 percent global GDP loss.
  - Nordhaus (2017): 1.0°C → –0.2 percent global GDP loss.
  - Burke, Hsiang, and Miguel (2015): 4.3°C → –23.0 percent global GDP loss.
  - Kahn and others (2021): 1.6°C → –0.6 percent (Fast adaptation), 1.6°C → –1.6 percent (Medium adaptation), 1.6°C → –1.1 percent (Slow adaptation).
- Analytical and policy implications:
  - Choice of method matters; simulation models can quantify adaptation investment needs and residual damages, while econometric models leverage observed adaptation but may miss slow-moving adjustments.
  - IAMs may both over- and under-estimate adaptation benefits; net bias is uncertain.
  - Policy priorities: account for distributional impacts, recognize limitations of global-average GDP losses, and invest in adaptation where models indicate measurable benefits while acknowledging uncertainties and missing tail risks.

---

### Annex 2 — Costs of making infrastructure more resilient
- Focus and current risks:
  - Vulnerabilities to floods and storms are estimated to be the costliest source of climate risks now and into the future.
  - Annex focuses on cost of strengthening existing exposed assets and investment projects to improve resilience to floods and storms.
- Methodology overview:
  - Bottom-up cross-country estimation using maps of hazards and infrastructure (roads and railways as proxy).
  - A kilometer of road/railway assessed as exposed if its construction standards lead it to be damaged at least once every hundred years.
  - Construction standards differ by income group and increase with income.
- Unit cost assumptions and data sources:
  - Incremental costs use average values from Miyamoto International 2019; unit cost bases from engineers and experts assessments.
  - Public and private investment projections from the World Economic Outlook (IMF 2020f).
  - Public capital stock and depreciation rates from IMF Investment and Capital Stock Dataset 2019; 2017 levels of public capital stock used when relevant.
- Estimation for investment projects (2021–2025):
  - Strengthening costs computed using average investment projections multiplied by share of exposed assets and unit cost = 15 percent.
  - Average exposure of future projects assumed equal to exposure of existing assets.
  - When projections unavailable, future investment-to-GDP ratios assumed constant at last observed level.
  - Costs expressed in annual values.
- Estimation for existing assets:
  - Strengthening costs computed as capital stock that won’t be depreciated by 2030 (in 10 years) multiplied by share of exposed assets and unit cost = 50 percent.
  - Fraction of capital stock that won’t be depreciated within 10 years = (1 – δ)^10, where δ denotes depreciation rate.
  - Total costs annualized by assuming constant investment in percent of GDP over next 10 years.
- Key numeric assumptions:
  - Unit cost for projects = 15 percent.
  - Unit cost for existing stock = 50 percent.
  - Time horizon for non-depreciated stock = 10 years.
  - Fraction non-depreciated = (1 – δ)^10, where δ is the depreciation rate.
- Cross-country averages (simple average):
  - Strengthening investment projects: 0.1 percent of GDP.
  - Strengthening existing assets: 0.3 percent of GDP.
  - Interpretation: only 10 percent of assets are estimated to be exposed to floods and storm on average.
- Distributional findings:
  - Advanced economies: lower costs due to higher construction standards and lower public investment as share of GDP.
  - Low-income countries and small developing states: assets and projects tend to be most exposed, leading to high-cost estimates.
  - Emerging economies: typically have the highest costs because they combine larger exposure with large stocks of existing assets and projected investment.
- Private-sector costs (annual, 2021–25):
  - Strengthening exposed future private assets: 0.4 percent of GDP annually.
  - Strengthening exposed existing private assets: 0.6 percent of GDP annually.
  - Observations: private-sector costs almost twice public-sector estimates and more evenly distributed across income groups.
- Caveats and practical considerations:
  - Technical solutions used do not guarantee complete protection and do not include all possible options.
  - In some cases, abandoning or tearing down and rebuilding exposed assets may be more cost-effective than strengthening.
  - Calculations assume average exposure of future projects equals exposure of existing assets.
  - Achieving the Sustainable Goals would require additional investments that would need to be made more resilient and would add to costs.

*Source: IMF Staff Climate Notes — clnea2022002.*

### Box 1. Differences and Limitations of Estimates of Global Adaptation Needs

### Box 1. Differences and Limitations of Estimates of Global Adaptation Needs

### Sources of variation in adaptation estimates
- Definitions and assumptions drive wide variation in estimates of adaptation investment needs:
  - Inclusion or exclusion of broad development investments that would be needed even without climate change (Hallegatte and others 2018).
  - Different assumptions about baseline development imply different baseline climate vulnerability and therefore different adaptation needs.
  - Example: assumptions about progress in public health directly impact adaptation estimates because public health capacity can reduce infectious diseases faster than climate change increases them; studies include a lower or greater share of future public health investment in adaptation needs (Tol, Ebi, and Yohe 2007).
- Additional differences arise from assumptions about:
  - Adaptation goals (economic efficiency versus preserving current risk levels or reducing climate damages to zero).
  - Returns to adaptation policies.
  - The extent of future warming and climate change impacts.

### Need for granular, country- and sector-level analyses
- Climate and economic activity vary tremendously at national and local levels; key vulnerabilities emerge where climate risks intersect with densely populated areas or focal infrastructure (Hallegatte and others 2019).
- Comprehensive national adaptation needs assessments and cross-country comparisons are scarce; most studies focus on one sector or one source of climate risk.
- Country documents and assessments (National Adaptation Plans; Disaster Resilience Strategies; IMF-World Bank Climate Change Policy Assessments) provide useful references; forthcoming World Bank Country Climate and Development Reports and IMF Climate Macroeconomic Assessment Program Reports expected to be relevant.

### Focus in this Note: two adaptation investment types
- The Note illustrates magnitude and heterogeneity of adaptation needs by focusing on:
  - Strengthening physical assets (infrastructure resilience).
  - Investing in coastal protection.
- Rationale: Strengthening infrastructure resilience is estimated to be the costliest among adaptation policies 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.

### Estimates of strengthening public infrastructure resilience
- Approach:
  - Identify exposed infrastructure by crossing maps of natural hazards and infrastructure location.
  - Assume different levels of initial resilience by country income group (Annex 2).
  - Estimate share of assets needing strengthening and apply strengthening unit costs.
- Estimated costs:
  - Strengthening exposed existing public assets could cost 0.2 percent of GDP annually for 2021 to 2025.
  - Strengthening exposed projected public assets could cost 0.4 percent of GDP annually for 2021 to 2025.
  - Estimates correspond to strengthening with benefit-to-cost ratios above one in a very large range of scenarios.
- Distribution:
  - Emerging markets face the largest costs, followed by low-income countries, due to a base effect (more assets and projects) and greater exposure.
  - Costs of improving asset resilience in the private sector could be twice as high, but more evenly distributed across income groups (Annex 2).
- Complementary IMF and regional estimates:
  - Sub-Saharan Africa adaptation needs over the next decade: US$30–50 billion (2–3 percent of regional GDP) (IMF 2020a).
  - Some Middle East and Central Asia countries: between 0.1 percent and 3.3 percent of GDP (IMF 2020c; Duenwald and others, forthcoming).
  - Small islands exposed to tropical cyclones and sea-level rise can face very large adaptation costs (Grenada 2021; IMF 2019a, 2021a, 2021b, 2021f).

### Estimates of coastal protection needs
- Aggregate estimate:
  - 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).
- Methodology:
  - Based on Nicholls and others (2019): define protection level that minimizes sum of construction costs, maintenance costs, and residual flood damage to assets to 2100.
  - Report average construction and maintenance costs in the first 15 years where most new dikes would be built (Annex 2).
- Distribution and outcomes:
  - Costs vary significantly and tend to be larger for low-income and small developing states; 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).
- Note on aggregation:
  - The estimation of coastal protection costs was done independently from the strengthening costs study; there could be some overlap.
  - The simple average of coastal protection needs across all countries is estimated at 1 percent of annual GDP.

### Addressing challenging adaptation needs: selection, execution, and maintenance
- To keep adaptation affordable:
  - Monitor asset conditions.
  - Ensure efficient selection, execution, and maintenance of investment projects.
- Strategic options:
  - Assets facing small climate risks or that are less essential may not need strengthening.
  - Assets in areas with overwhelming climate risks can be relocated and possibly rebuilt when cost effective.
  - Strengthen governance to avoid substitution with cheaper design or lower-quality materials and to avoid skimping on maintenance.

### Financing constraints and distributional considerations
- Financing large adaptation costs will challenge many countries’ fiscal space, especially lower-income countries which:
  - Typically have limited fiscal space.
  - Have large and competing development needs.
  - Suffer from the largest damages despite limited contributions to global warming (IMF 2018, 2020a, 2020b, 2021a, 2021d, 2021f).
- Financing options:
  - Domestic revenue mobilization.
  - Reprioritization of investment plans or other spending.
  - Support from donor community.
  - Combinations of the above (Cevik and Nanda 2020; IMF 2013; Cohen and Jalles 2020).
- Table highlights (pre–COVID-19 fiscal space and adaptation costs):
  - Median total strengthening costs are estimated at 0.2 percent of GDP.
  - Countries listed across categories of fiscal space and above/below-median strengthening costs (sources: Hallegatte and others (2019); Hallegatte, Rentschler, and Rozenberg (2019); IMF staff reports; staff calculations).
  - Note: Fiscal space and debt distress assessments are pre–COVID-19 and do not reflect developments since the pandemic.

### International adaptation finance: gaps and challenges
- Tracking and scale:
  - Adaptation finance is embedded in many projects not purely related to adaptation, complicating tracking.
  - Data on private sector and within-country public sector investment is often missing (Richmond and others 2020).
- Quantified gaps:
  - Developed countries committed to mobilize $100 billion per year by 2020 for climate finance for developing countries (UNFCCC 2009b).
  - Adaptation needs are estimated to be around $0.25 trillion per year by the midcentury (Figure 2).
  - Identified adaptation financing flows: $22 billion in 2015 to 2016 and $30 billion in 2017 to 2018 on average (Buchner and others 2019).
  - Annual aid for adaptation from official creditors to low-income developing countries was $10 billion in 2018 (IMF 2020c).

### Barriers to effective aid disbursement and the case for additional support
- Barriers:
  - Donor conditionalities often require public financial management (PFM) standards before disbursing support.
  - Target countries that need support most tend to have PFM standards below donor requirements and limited scope to improve them quickly.
  - Unachievable requirements can hold up progress if donor financing is necessary.
- Equity and needs-based rationale:
  - Adaptation needs tend to be higher in countries with less fiscal space, lower institutional capacity, and larger shares of the global poor.
  - There is a strong case for additional international support that does not crowd out existing aid, given these countries’ limited capacity and historical contribution to global warming.
  - International organizations, including the IMF, can support capacity development and catalyze financial assistance.

### Adaptation investment as cost-effective alternative to disaster relief
- Potential efficiency gains:
  - International support for adaptation investment can reduce total donor spending by lowering future disaster relief needs.
  - Case studies:
    - Maldives: cumulated discounted fiscal savings in post-disaster relief from an infrastructure resilience program are more than double the extra spending to finance it (Melina and Santoro 2021).
    - Samoa: Climate Macroeconomic Assessment Program pilot estimated savings of a similar magnitude; savings increase with expected intensity of natural disasters (IMF, forthcoming).

*Source: Box 1. Differences and Limitations of Estimates of Global Adaptation Needs (clnea2022002).*

### Box 2. Unlocking Access to Climate Finance for Pacific Island Countries

### Box 2. Unlocking Access to Climate Finance for Pacific Island Countries

### Urgency and financing needs
- Pacific island countries (PICs) are globally among the most exposed to climate hazards and 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.

### Access challenges and empirical evidence on GCF financing
- PICs generally have limited fiscal space, low administrative capacity, and underdeveloped private sectors, constraining them to rely on bilateral partners or grant-based instruments from multilaterals and major climate funds (such as the Green Climate Fund [GCF]).
- Direct access accreditation difficulties:
  - 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).
  - 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 performance:
  - 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.
- Financing shortfall and disbursement:
  - 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.
  - Out of this amount, only about half has actually been disbursed.
- Distributional observation:
  - Countries more exposed to climate change have, on average, had more funds approved for climate adaptation.
  - Note: Tuvalu is an outlier in terms of amount of approved climate adaptation funding as percent of GDP, at over 200 percent.

### Policy recommendations and institutional responses
- For Pacific island countries (PICs):
  - Strategically decide which 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, establish dedicated climate units, preferably within ministries of finance, to coordinate the financing of priority climate projects.
  - Continue to build PFM capacity, with a focus on strong audit, robust control frameworks, and strengthened public investment practices, which are at the center of GCF accreditation requirements.
- For climate funds:
  - Consider further streamlining accreditation and approval requirements for small and fragile countries.
  - Consider increasing reliance on ex post monitoring.
- For the IMF:
  - Further integrate analysis of climate issues into macroeconomic surveillance.
  - Continue to provide capacity development support to countries to build stronger PFM and public investment management practices.

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

### Box 3. Malawi: Mainstreaming Climate Change into Macroeconomic Projections

### Box 3. Malawi: Mainstreaming Climate Change into Macroeconomic Projections

### Context and motivation
- Malawi is one of the most vulnerable countries to climate change in sub-Saharan Africa and has experienced severe droughts, floods, and insect infestations over the past decade.
- Recent trends in the frequency and intensity of natural disasters warranted adjustments to baseline macroeconomic projections.
- Since 2019, the macro-fiscal implications of climate change have been integrated into the IMF’s analysis for Malawi.

### Approach overview
- The analysis described is one of several approaches applied to mainstream climate change into macroeconomic projections for Malawi.
- The approach involved three main steps: (1) incorporating climate shocks into baseline macroeconomic projections using event studies, (2) including amplified tail-risk scenarios in the debt sustainability analysis, and (3) discussing improvements in statistical data with authorities to enable more sophisticated modeling.

### Incorporating climate shocks into baseline projections (event-study approach)
- Real GDP growth — near term:
  - 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 combined: (1) growth absent any climate shocks (for example, 5 percent) and (2) growth if a climate shock hits (for example, 3 percent = 5 percent – 2 percent)—the latter 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).
- Real GDP growth — medium term:
  - Medium-term real GDP growth projections factored in the positive impact from resilience-building policies (for example, infrastructure) net of projected disaster impacts; these effects were derived from historical data.
- Inflation:
  - 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 and social protection:
  - Public investment and social protection targeting resilience were assumed to increase in line with the latest Post-Disaster Needs Assessments (PDNA).
  - 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.
  - Assumptions were made, based on discussions with development partners, on what development partners would finance and whether grants or loans would be applied.
  - Domestically financed portion relied on: (1) revenue mobilization, including consideration of a carbon tax; (2) reducing nonpriority spending; and (3) larger fiscal deficits—all of which had implications for debt projections.
- Balance of payments:
  - Balance of payments effects were a function of assumptions on development partner financing and net exports.
  - Export and import projections applied 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:
  - 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.

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

### Data and modeling needs
- Statistical and climate data considered key to enhancing the analysis were discussed with the authorities.
- Improved data would enable more sophisticated analysis, including applying dynamic general equilibrium models, vector autoregression models, and model simulations of future climate shocks.

### Illustrative simulation details (as presented)
- Simulation basis: IMF staff calculations based on a simulation exercise for Malawi, as of March 2020.
- PDNA-based investment need: 4.5 percent of GDP in climate-resilient rebuilding following 2019 severe flooding, spread over five years.
- Domestic policy options for financing the domestically financed portion included: revenue mobilization (including consideration of a carbon tax), reducing nonpriority spending, and larger fiscal deficits—each with implications for the primary balance and debt projections.

*Source: IMF staff calculations based on a simulation exercise for Malawi, as of March 2020. This box was prepared by Pritha Mitra, Jung Eun Yoon, and Mai Farid.*

### Annex 1. Estimating Climate Change Costs and

### Annex 1. Estimating Climate Change Costs and Adaptation Benefits

### Methods Overview
- Two broad groups of methods to estimate climate change impacts and adaptation:
  - Simulation models: parameterized models that simulate economy or sectoral outcomes as functions of exogenous variables and model parameters. Can simulate climates not yet experienced and switch adaptation options on and off.
  - Econometric models: reduced-form estimations using cross-sectional or panel data to estimate how climate affects economic or sectoral variables; rely on observed behavior and often project adaptation from current cross-climate differences.

### Simulation Models
- General characteristics:
  - Can be sectoral (engineering, forestry, agriculture) or economy-wide (partial equilibrium, CGE, IAM).
  - Advantages: can measure effects of different adaptation solutions by turning them on/off; estimate adaptation investment needs and residual damage.
  - Disadvantages: rely on many assumptions on behavior, technology, and costs; sensitive to parameterizations.

- Integrated Assessment Models (IAMs):
  - Integrate the energy system, the economy, the climate system, and sometimes land.
  - Examples used in surveyed studies: DICE and RICE, FUND, PAGE; other IAMs for mitigation include MERGE and WITCH.
  - Typical features and limitations:
    - Focus on long-term dynamics (capital accumulation, energy-sector investment, climate).
    - Few sectors modeled in detail; countries aggregated in macro-regions or a single global region.
    - Calibrate a macroeconomic damage function relating global GDP and temperature from sectoral/regional studies and econometrics.
    - Adaptation: can be modeled as efficient or inefficient; cost of private adaptations is not always included (which can overestimate adaptation benefits).
    - Technological progress in adaptation is not always included (which can underestimate adaptation benefits). Net bias is uncertain.
    - Include temperature change, sea-level rise, tropical cyclones, catastrophic events; sometimes monetize nonmarket losses (ecosystems, amenity values).
    - Criticisms: limited ability to model catastrophic outcomes and dynamics leading to societal collapse.

- Computable General Equilibrium Models (CGEs):
  - Include many sectors and regions with trade; greater sectoral/geographic detail typically for shorter horizons than IAMs.
  - Rely on exogenous climate shocks and parameterization using econometric evidence.
  - Can estimate channel contributions (for example, international trade) to reduce/amplify climate impacts.
  - DSGE models belong to this class but have not yet been applied to estimate global climate change costs in the reviewed literature.

### Econometric Methods
- Cross-sectional econometrics:
  - Estimate reduced-form functions where economic/sectoral outcomes (GDP per capita, agricultural rents, crop yields, water use) are functions of climate and controls.
  - Use observed present-day adaptation across climates to project future adaptation.
  - Advantage: rely on observed behavior and project adaptations from current cross-climate responses.
  - Main challenge: controlling for confounding variables correlated with both climate and adaptation (geographic, socioeconomic characteristics).
  - Generally do not provide direct estimates of the economic benefit of adaptation.

- Panel (time-series) econometrics:
  - Identify effects of weather shocks on changes in GDP per capita using fixed effects or first differences.
  - Use short-term elasticities; typically do not capture adaptation that relies on stocks or slow adjustments.
  - Limitations:
    - Short-term weather shock responses may not reflect long-term adaptation (example: adoption of air conditioning as heat extremes become more frequent).
    - Inferring long-run climate impacts from short-term shocks may imply permanent short-term imperfections and understate long-term adaptation.
  - Some studies attempt to detect adaptation by comparing weather-shock impacts across climates or over time; examples include Schlenker and Roberts (2009), Deschênes and Greenstone (2011), Burke and Emerick (2016), and Kahn and others (2021).
    - Kahn and others (2021) use weather shocks relative to a trailing moving average and estimate that adaptation has the potential to halve the long-term cost of warming.

### Estimates of Climate Change Damages (Summary of Review)
- Reported central estimates and ranges from the surveyed literature (global aggregates, 2100 relative to reference without climate change):
  - For global warming in the range of +1.5°C to +2.5°C (approximately SSP1–2.6):
    - Median loss: 1.5 percent [–13.0, +0.1] of annual global GDP in 2100.
  - For global warming in the range of +2.9°C to +4.3°C (approximately SSP3–7.0):
    - Median loss: 3.3 percent [–23, –0.8] of annual global GDP in 2100.
  - These ranges reflect literature status but do not span all possible outcomes due to study limitations.

- Reasons global-average estimates can understate concerns:
  - Global averages mask large negative effects in developing countries that are already hot and in small vulnerable nations; very large loss-to-GDP ratios in small countries contribute little to global aggregates.
  - Some studies omit worst-case scenarios or tipping-point dynamics; low-probability, high-impact events may be missing or only partially represented.
  - Nonmarket impacts (biodiversity loss, amenity loss) are imperfectly included and may be underestimated.
  - Studies do not consider crossing societal tipping points (social conflict, wars, disruptive migration) due to lack of empirical quantification.
  - GDP measures output and is a partial measure of welfare; it does not capture welfare losses from shifting resources to reconstruction or distributional impacts that concentrate losses among poorer groups.

### Representative Numerical Findings from Annex Table 1.1 (selected entries)
- Integrated Assessment Models (examples)
  - Tol (2013): 1.0°C → –1.4 percent global GDP loss
  - Nordhaus (2017): 1.0°C → –0.2 percent global GDP loss
  - Hope (2006): 2.5°C → –0.9 percent global GDP loss
  - Nordhaus (2014): 3.0°C → –10.6 percent global GDP loss
  - Nordhaus (2017): 4.3°C → –4.4 percent global GDP loss

- Cross-Section Econometrics (examples)
  - Horowitz (2009): 1.0°C → –3.8 percent global GDP loss
  - Choinière and Horowitz (2000): 1.1°C → –7.4 percent global GDP loss
  - Mendelsohn and others (2000): 2.5°C → +0.1 percent global GDP change

- Panel Econometrics (examples)
  - Pretis and others (2018): 1.5°C → –8.0 percent global GDP loss
  - Kahn and others (2021): 1.6°C → –0.6 percent (Fast adaptation), 1.6°C → –1.6 percent (Medium adaptation), 1.6°C → –1.1 percent (Slow adaptation)
  - Burke, Hsiang, and Miguel (2015): 4.3°C → –23.0 percent global GDP loss

- Computable General Equilibrium Models (examples)
  - Bosello and others (2012): 1.9°C → –0.5 percent global GDP loss
  - Dellink and others (2014): 2.5°C → –1.1 percent global GDP loss
  - Roson and Van der Mensbrugghe (2012): 2.9°C → –1.8 percent global GDP loss

- Other methods and expert elicitation (examples)
  - Tol (2002): 1.0°C → –2.7 percent (aggregation from sectoral studies)
  - Howard and Sylvan (2015): 3.0°C → –5.0 percent (expert elicitation; median response of economists), 3.0°C → –7.1 percent (expert elicitation; mean response)

### Key Analytical and Policy Implications (from the annex discussion)
- Choice of method matters for estimated impacts and adaptation benefits:
  - Simulation models can quantify adaptation investment needs and residual damages by switching adaptation options on/off.
  - Econometric models leverage observed adaptation across climates but may not capture slow-moving, stock-based adaptation.
- IAMs and many studies may both over- and under-estimate adaptation benefits due to omission of private adaptation costs and technological progress in adaptation; net bias is uncertain.
- Policy-relevant priorities include:
  - Accounting for distributional impacts (large negative effects concentrated in vulnerable countries and groups).
  - Recognizing limitations of global-average GDP losses and factoring in catastrophic and nonmarket risks.
  - Investing in adaptation where models indicate measurable benefits, while acknowledging model uncertainties and missing tail risks.

*Source: Annex 1. Estimating Climate Change Costs and Adaptation Benefits — IMF Staff Climate Notes (clnea2022002).*

### Annex 2. The Costs of Making Infrastructure More

### Annex 2. The Costs of Making Infrastructure More Resilient

### Current climate risks and focus
- The current climate is already a source of risks for physical infrastructure; vulnerabilities to floods and storms are estimated to be the costliest source of climate risks in the present and into the future.
- The annex focuses on the cost of strengthening existing exposed economic assets and investment projects to improve their resilience to floods and storms, as investing in infrastructure resilience is among the costliest adaptation policies.

### Methodology for estimating upgrading costs
- Approach: systematic cross-country, bottom-up estimation based on country exposure to natural hazards and additional costs to make exposed assets more resilient.
- Proxy for infrastructure location: location of roads and railways.
- Exposure identification:
  - Two global maps are used: one for natural hazards and one for roads and railways (Koks and others 2019).
  - A kilometer of road or railway is assessed to be exposed if its construction standards are such that it gets damaged at least once every hundred years.
  - Construction standards differ by income group (high-income, upper-middle-income, and all others) and increase with income following Rozenberg and Fay 2019.
- Incremental cost assumptions:
  - Incremental costs use average values corresponding to the set of technical options from Miyamoto International 2019.
  - Technical solutions are economically sensible but do not guarantee zero damage nor encompass all possible risk-reduction options.
- Unit cost bases and data sources:
  - Unit cost estimates based on engineers and experts’ assessments (Hallegatte, Rentschler, and Rozenberg 2019; Miyamoto International 2019).
  - Public and private investment projections from the World Economic Outlook (IMF 2020f).
  - Public capital stock and depreciation rates from the IMF Investment and Capital Stock Dataset 2019.
  - 2017 levels of public capital stock used in calculations when relevant.

### Estimation of strengthening costs for investment projects
- Procedure:
  - Strengthening costs for investment projects are computed using average investment projections over 2021 to 2025.
  - Investment projections are multiplied by the estimated share of exposed assets and by a unit cost of 15 percent.
  - The average exposure of future projects is assumed to be the same as the exposure of existing assets.
  - When projections are unavailable, future investment-to-GDP ratios are assumed to remain constant at the last observed level in the IMF Investment and Capital Stock Dataset 2019.
  - Costs are expressed in annual values.
- Key numeric assumptions:
  - Unit cost = 15 percent.

### Estimation of strengthening costs for existing assets
- Procedure:
  - Strengthening costs of existing assets are computed as the capital stock that won’t be depreciated by 2030 (in 10 years), multiplied by the estimated share of exposed assets and by a unit cost of 50 percent.
  - The fraction of the capital stock that won’t be depreciated within 10 years is equal to (1 – δ)^10, where δ denotes the depreciation rate.
  - Total costs are annualized by assuming constant investment in percent of GDP over the next 10 years.
- Key numeric assumptions:
  - Time horizon for non-depreciated stock = 10 years.
  - Unit cost = 50 percent.
  - Fraction non-depreciated = (1 – δ)^10, where δ is the depreciation rate.

### Cross-country averages and distributional findings
- Simple cross-country average adaptation costs:
  - Strengthening investment projects: 0.1 percent of GDP.
  - Strengthening existing assets: 0.3 percent of GDP.
- Interpretation:
  - These averages are similar to weighted averages in the main text; small countries with very high costs are averaged with many countries with very small cost estimates.
  - Estimates reflect that only 10 percent of assets are estimated to be exposed to floods and storm on average.
- Variation across country groups:
  - Advanced economies: lower costs due to higher construction standards (smaller shares of exposed assets) and lower public investment as a share of GDP; however, larger stock of existing assets makes strengthening costs relatively closer to other countries.
  - Low-income countries and small developing states: assets and investment projects tend to be most exposed, leading to high-cost estimates.
  - Emerging economies: typically have the highest costs because they combine larger exposure (as in low-income countries and small developing states) with large stocks of existing assets and investment projects.

### Private sector cost estimates
- Annual costs of strengthening private assets resilience to current storms and flood risks (2021–25):
  - Strengthening exposed future private assets: 0.4 percent of GDP annually between 2021 and 2025.
  - Strengthening exposed existing private assets: 0.6 percent of GDP annually between 2021 and 2025.
- Observations:
  - Private-sector costs are estimated to be almost twice as large as in the public sector.
  - Private sector costs are more evenly distributed across income groups and across new and existing infrastructure, reflecting higher private investment and larger shares of private-owned infrastructure in more advanced countries, which compensates for lower exposure.
  - These cost estimates represent strengthening costs (not optimal investment) and are expected to be lower than avoided damages in a large range of scenarios.

### Caveats and practical considerations
- Technical solutions used in cost estimates do not guarantee complete protection and do not include all possible options, including potentially more cost-effective or more expensive alternatives.
- In some cases, abandoning or tearing down and rebuilding exposed assets may be more cost-effective than strengthening.
- The calculations assume the average exposure of future projects equals exposure of existing assets.
- Achieving the Sustainable Goals would require additional investments; those investments would need to be made more resilient and would add to the costs.

*Sources: Hallegatte, Rentschler, and Rozenberg (2019); Hallegatte and others (2019); IMF, Capital Stock 2019 Dataset; IMF, World Economic Outlook database; and staff calculations.*

### References

### References

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### Integrated Assessment Models, Climate-Economy Modeling, and Theory
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### Empirical Studies on Temperature, Weather, and Economic Impacts
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### Sea-Level Rise, Coastal Flooding, and Infrastructure
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### Adaptation, Resilience, and Disaster Risk Management
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- Miyamoto International. 2019. Overview of Engineering Options for Increasing Infrastructure Resilience. Washington, DC: World Bank.
- Marto, Ricardo, Chris Papageorgiou, and Vladimir Klyuev. 2018. “Building Resilience to Natural Disasters: An Application to Small Developing States.” Journal of Development Economics 135: 574–86.

### Data Sources, Indexes, and Technical Reports
- Centre for Research on the Epidemiology of Disasters. n.d. “EM-DAT: The CRED/OFDA International Disaster Database.” www.emdat.be
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### Health, Water, and Sectoral Impact Studies
- Bosello, Francesco, Roberto Roson, and Richard S. J. Tol. 2006. “Economy-Wide Estimates of the Implications of Climate Change: Human Health.” Ecological Economics 58 (3): 579–91.
- Hurd, Brian, Mac Callaway, Joel Smith, and Paul Kirshen. 2004. “Climatic Change and US Water Resources: From Modeled Watershed Impacts to National Estimates.” Journal of the American Water Resources Association 129–48.
- Blanc, Elodie, and John Reilly. 2017. “Approaches to Assessing Climate Change Impacts on Agriculture: An Overview of the Debate.” Review of Environmental Economics and Policy 11 (2): 247–57.
- Blanc, Elodie, and Wolfram Schlenker. 2017. “The Use of Panel Models in Assessments of Climate Impacts on Agriculture.” Review of Environmental Economics and Policy 11 (2): 258–79.

### Methods, Solution Techniques, and Meta-Analyses
- Fernández-Villaverde, Jesús, and Oren Levintal. 2018. “Solution Methods for Models with Rare Disasters.” Quantitative Economics 9 (2): 903–944.
- Levintal, Oren. 2018. “Taylor Projection: A New Solution Method for Dynamic General Equilibrium Models.” International Economic Review 59 (3): 1345–1373.
- Howard, Peter H., and Thomas Sterner. 2017. “Few and Not so Far between: A Meta-Analysis of Climate Damage Estimates.” Environmental and Resource Economics 68 (1): 197–225.
- Howard, Peter H., and Derek Sylvan. 2015. “The Economic Climate: Establishing Expert Consensus on the Economics of Climate Change.” Institute for Policy Integrity 438–41.
- Fisher-Vanden, Karen, David Popp, and Ian Sue Wing. 2014. "Introduction to the Special Issue on Climate Adaptation: Improving the Connection between Empirical Research and Integrated Assessment Models." Energy Economics 46: 495–99.

*References as listed in the source document.*

### 31. Cambridge, MA: MIT Press.

### 31. Cambridge, MA: MIT Press.

### Works by William Nordhaus and related modeling of climate economics
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- Nordhaus, William. 2008. A Question of Balance. New York: Yale University Press.
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- Nordhaus, William. 2017. “Revisiting the Social Cost of Carbon.” Proceedings of the National Academy of Sciences 114 (7): 1518–23.
- Nordhaus, William. 2019. “Economics of the Disintegration of the Greenland Ice Sheet.” Proceedings of the National Academy of Sciences 116 (25): 12261–69.
- Nordhaus, William D., and Joseph Boyer. 2000. Warming the World: Economic Models of Global Warming. Cambridge, MA: MIT Press.
- Nordhaus, William D., and Zili Yang. 1996. “A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies.” The American Economic Review: 741–65.

### Integrated assessment models, damage estimation, and uncertainty
- Plambeck, Erica L., and Chris Hope. 1996. “PAGE95: An Updated Valuation of the Impacts of Global Warming.” Energy Policy 24 (9): 783–93.
- Pindyck, Robert S. 2013. "Climate Change Policy: What Do the Models Tell Us?" Journal of Economic Literature 51 (3): 860–72.
- Weitzman, Martin L. 2011. "Fat-tailed Uncertainty in the Economics of Catastrophic Climate Change." Review of Environmental Economics and Policy 5 (2): 275–92.
- Weyant, John. 2017. “Some Contributions of Integrated Assessment Models of Global Climate Change.” Review of Environmental Economics and Policy 11 (1): 115–37.
- Schauer, Michael J. 1995. “Estimation of the Greenhouse Gas Externality with Uncertainty.” Environmental and Resource Economics 5 (1): 71–82.
- Sterner, Thomas, and U. Martin Persson. 2020. “An Even Sterner Review: Introducing Relative Prices into the Discounting Debate.” Review of Environmental Economics and Policy 2 (1).

### Damage functions, sectoral impacts, and economic growth interactions
- Tol, Richard S. J. 1995. “The Damage Costs of Climate Change toward More Comprehensive Calculations.” Environmental and Resource Economics 5 (4): 353–74.
- Tol, Richard S. J. 1997. “On the Optimal Control of Carbon Dioxide Emissions: An Application of FUND.” Environmental Modeling & Assessment 2 (3): 151–63.
- Tol, Richard S. J. 2002. “Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates.” Environmental and Resource Economics 21 (1): 47–73.
- Tol, Richard S.J. 2009. “The Economic Effects of Climate Change.” The Journal of Economic Perspectives 23 (2): 29–51.
- Tol, Richard S. J. 2013. “The Economic Impact of Climate Change in the 20th and 21st Centuries.” Climatic Change 117 (4): 795–808.
- Tol, Richard S.J. 2014. “Correction and Update: The Economic Effects of Climate Change.” Journal of Economic Perspectives 28 (2): 221–26.
- Tol, Richard S. J. 2018. “The Economic Impacts of Climate Change.” Review of Environmental Economics and Policy 12 (1): 4–25.
- Roson, Roberto, and Dominique Van der Mensbrugghe. 2012. “Climate Change and Economic Growth: Impacts and Interactions.” International Journal of Sustainable Economy 4 (3): 270–85.
- Pretis, Felix, Moritz Schwarz, Kevin Tang, Karsten Haustein, and Myles R Allen. 2018. “Uncertain Impacts on Economic Growth When Stabilizing Global Temperatures at 1.5 C or 2 C Warming.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376 (2119): 20160460.

### Agriculture, timber, and sector-specific studies
- Schlenker, Wolfram, and Michael J. Roberts. 2009. “Nonlinear Temperature Effects Indicate Severe Damages to US Crop Yields under Climate Change.” Proceedings of the National Academy of Sciences 106 (37): 15594–98.
- Schlenker, Wolfram, W. Michael Hanemann, and Anthony C. Fisher. 2005. “Will US Agriculture Really Benefit from Global Warming? Accounting for Irrigation in the Hedonic Approach.” The American Economic Review 95 (1): 395–406.
- Reilly, John, Francesco Tubiello, Bruce McCarl, D. Abler, R. Darwin, K. Fuglie, S. Hollinger, C. Izaurralde, S. Jagtap, and J. Jones. 2003. “US Agriculture and Climate Change: New Results.” Climatic Change 57 (1–2): 43–67.
- Tubiello, Francesco N, and Günther Fischer. 2007. “Reducing Climate Change Impacts on Agriculture: Global and Regional Effects of Mitigation, 2000–2080.” Technological Forecasting and Social Change 74 (7): 1030–56.
- Sohngen, Brent, and Robert Mendelsohn. 1998. “Valuing the Impact of Large-Scale Ecological Change in a Market: The Effect of Climate Change on US Timber.” American Economic Review: 686–710.
- Sohngen, Brent, Robert Mendelsohn, and Roger Sedjo. 2001. “A Global Model of Climate Change Impacts on Timber Markets.” Journal of Agricultural and Resource Economics: 326–43.

### Sea level rise, adaptation, and losses & damages
- Yohe, Gary, James Neumann, Patrick Marshall, and Holly Ameden. 1996. “The Economic Cost of Greenhouse-Induced Sea-Level Rise for Developed Property in the United States.” Climatic Change 32 (4): 387–410.
- Simpson, Murray C., and M. Harrison. 2010. Quantification and Magnitudeof Losses and Damages Resulting from the Impacts of Climate Change: Modelling the Transformational Impacts and Costs of Sea Level Rise in the Caribbean. Geneva: United Nations Office for Disaster Risk Reduction.
- Nordhaus, William. 2019. “Economics of the Disintegration of the Greenland Ice Sheet.” Proceedings of the National Academy of Sciences 116 (25): 12261–69.

### Adaptation finance, infrastructure, and policy assessments
- United Nations Framework Convention on Climate Change (UNFCCC). 2009a. “Investment and Financial Flows to Address Climate Change - An Update.” Bonn, Germany.
- United Nations Framework Convention on Climate Change (UNFCCC). 2009b. “Copenhagen Accord.” Bonn, Germany.
- Organisation for Economic Co-operation and Development (OECD). 2021. “Statement from OECD Secretary-General Mathias Cormann on Climate Finance in 2019.” https://www.oecd.org/environment/statement-from-oecd-secretary-general-mathias-cormann-on-climate-finance-in-2019.htm
- Richmond, Morgan, Chavi Meattle, Valerio Micale, Padraig Oliver, and Rajashree Padmanabhi. 2020. “A Snapshot of Global Adaptation Investment and Tracking Methods.” Climate Policy Initiative, London.
- World Bank. 2010a. “Economics of Adaptation to Climate Change - Synthesis Report.” Washington, DC.
- World Bank. 2010b. “The Cost to Developing Countries of Adapting to Climate Change - New Methods and Estimates.” Washington, DC.
- World Bank. 2010c. “World Development Report 2010: Development and Climate Change.” Washington DC.
- World Bank. 2017. "Climate Change and Disaster Management." World Bank Group, Washington, DC.
- Rozenberg, Julie, and Marianne Fay. 2019. Beyond the Gap: How Countries Can Afford the Infrastructure They Need While Protecting the Planet. Washington, DC: World Bank Publications.

### Health, infectious disease, and regional studies
- Tol, Richard S.J., Kristie L. Ebi, and Gary W. Yohe. 2007. “Infectious Disease, Development, and Climate Change: A Scenario Analysis.” Environment and Development Economics 12 (5): 687–706.
- Westphal, Michael I., Gordon A. Hughes, and Jörn Brömmelhörster. 2015. "Economics of Climate Change in East Asia." Asian Development Bank, Mandaluyong City, Philippines.

### Reviews, assessments, and broader syntheses
- Stern, Nicholas. 2007. The Economics of Climate Change: The Stern Review. Cambridge, MA: Cambridge University Press.
- World Bank. 2010a. “Economics of Adaptation to Climate Change - Synthesis Report.” Washington, DC.
- United Nations Environment Programme (UNEP). 2014. “Adaptation Gap Report 2014.” UN Environment Programme, Nairobi, Kenya.

*IMF STAFF CLIMATE NOTE 2022/002*

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_Source: https://www.imf.org/-/media/files/publications/staff-climate-notes/2022/english/clnea2022002.pdf_