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### Executive summary — key findings
- Economic losses from climate-related extremes in Europe reached around half a trillion euros over the past four decades.
- About 70,000 deaths were attributed to abnormal temperature (extreme heatwaves) in Europe in 2022.
- Temperatures in Europe have increased at more than twice the global average—by about 0.5 degrees Celsius on average per decade.
- Europe’s overall vulnerability to climate risks is lower than other regions’, but notable disparities exist:
  - Central and Eastern Europe (CESEE) countries are comparatively more vulnerable and experience greater human and economic costs from climate disasters.
  - CESEE’s higher vulnerability is driven by weaker adaptation capacity, higher economic dependence on climate-sensitive sectors (such as agriculture), and lower investment in climate-resilient infrastructure.
- Climate analogue mapping is used to:
  - Project the future climate for each European country using an ensemble of climate models.
  - Identify countries whose present climate best approximates a given country’s projected future climate.
  - Provide tangible information on climatic characteristics countries should anticipate and to calibrate macro analyses of climate-related shocks.
- Investments in adaptation infrastructure can:
  - Significantly reduce output losses from natural disasters.
  - Mitigate medium-term economic scarring.
  - Support sustainable long-term growth and help reduce inequality.
- Growth benefits from infrastructure investments are amplified when complemented with reforms to improve public investment efficiency (PIE).
- Due to limited domestic financial resources, external support—ideally in the form of grants or concessional loans—plus efforts on domestic revenue mobilization and expenditure rationalization, will be critical to meet many adaptation needs in CESEEs without endangering debt sustainability.
- Improving PIE (strengthening governance and quality of institutions) can further boost real GDP growth by leveraging new private investment opportunities.

### Introduction — context and motivation
- Europe has faced substantial climate impacts across populations and economies:
  - Economic losses from climate-related extremes in Europe reached around half a trillion euros over the past four decades.
  - About 70,000 deaths were attributed to abnormal temperature (extreme heatwaves) in Europe in 2022.
  - Temperatures in Europe have increased at more than twice the global average—by about 0.5 degrees Celsius on average per decade.
- Documented climate changes: increased frequency and intensity of floods, storms, heatwaves, and unprecedented cold winters.
- Sectoral impacts:
  - Agriculture: shifting growing seasons and unpredictable weather → diminished crop yields and risks to food security.
  - Coastal regions: heightened risks from rising sea levels and altered precipitation patterns affecting infrastructure and livelihoods.
- Even under more optimistic GHG emission scenarios, ongoing changes in global climate conditions will continue to pose substantial risks, necessitating immediate adaptation actions.
- The paper proposes climate analogues to:
  - Calibrate macro analyses of climate-related shocks using the IMF's DIGNAD (Debt-Investment-Growth and Natural Disaster) Model.
  - Simulate the role of investments in adaptive infrastructure and their sustainable financing strategies.

### Vulnerability to climate change and readiness for adaptation actions
- Aggregate regional patterns:
  - Europe’s vulnerability to climate risks is below the global average.
  - Advanced economies (AEs) in Europe have the lowest aggregate vulnerability compared to other regions.
  - CESEE countries are more vulnerable to natural disasters than AEs.
- Drivers of higher vulnerability in CESEE:
  - Exposure to climate risks is similar for AEs and CESEE (largely reflecting geographical location).
  - CESEE faces large adaptive capacity gaps and weaker readiness for effective implementation of adaptation actions.
- Data sources and indicators:
  - Vulnerability and readiness assessments are based on ND-GAIN data.

### ND-GAIN (Box 1) — structure and empirical findings
- ND-GAIN structure:
  - Two aggregate indices: vulnerability index and readiness index.
  - Vulnerability sub-indices: Exposure, Sensitivity, and Adaptative capacity.
  - Readiness index dimensions: Economic, Governance, and Social.
- Vulnerability components definitions:
  - Exposure: extent to which society and sectors are stressed by future changing climate conditions; based on mid-term projections (2040–2069).
  - Sensitivity: degree to which people and dependent sectors are affected by climate perturbations.
  - Adaptative capacity: society’s ability to adjust, reduce potential damage, and respond to negative consequences.
- Empirical findings (CESEE vs AEs):
  - Cumulative cost of climate-related disasters (in percent of 2019 GDP): the cumulative cost is about 1 percentage point higher in CESEE compared to AEs.
  - Countries with largest cumulative costs include:
    - Moldova (close to 12 percent)
    - Bosnia and Herzegovina (about 7 percent)
    - North Macedonia (about 5.5 percent)
  - Some AEs with large cumulative damages: Spain, Portugal, and Greece (about 5 percent of 2019 GDP cumulatively).
  - Human and social impact (average affected population over past 30 years): top 5 affected are from CESEE, including:
    - Moldova (average 3 percent of the population affected)
    - North Macedonia (2 percent)
    - Bosnia and Herzegovina (1.5 percent)
  - ND-GAIN projections:
    - CESEE exposure projected to remain broadly similar to AEs.
    - CESEE faces more prominent risks for agriculture (projected change of cereal yields) and flood hazards.
    - AEs likely to be more exposed to sea level rise and change in annual runoff.
  - Sensitivity patterns:
    - CESEE: larger rural share, higher dependence on agriculture, weaker infrastructure quality → relatively higher sensitivity.
    - AEs: concentration in smaller urban areas → sensitivity to extreme temperature and urban exposure.
  - Adaptative capacity and readiness:
    - CESEE: lower agriculture adaptation capacity, weaker transport and trade-related infrastructure quality, weaker health sector preparedness, weaker disaster preparedness strategies.
    - Readiness: CESEE exhibits governance challenges and lower social readiness (innovation, education, ICT, social inequality); less-favorable business environment for private climate-resilient investment.

### Climate analogues mapping — method and uses
- Methodology:
  - Use an ensemble of approximately thirty climate models of temperature and precipitation (CMIP6 via World Bank Climate Knowledge Portal) under different GHG emission concentration paths.
  - Baseline scenario: “rocky road” (SSP3–7.0) with end-of-21st century radiative forcing level of 7 watts per square meter and a very likely range of increase in average global surface temperature of 2.8 to 4.6 degrees Celsius.
  - Additional scenarios: SSP2–4.5 and SSP5–8.5.
  - Model ensemble median used to project climate variables for each country.
- Identification method (summary):
  - Ω set includes seasonal temperature and seasonal precipitation, #Ω=8.
  - Procedure:
    1. Project each climate variable through 2100 under assumed GHG pathway.
    2. Quantify dissimilarities between early and late 21st century climates using standardized Euclidean distance (SED) across Ω, with means for 2002–2021 and 2080–2099 and standard deviation from interannual variability for 2002–2021.
    3. Assemble matrix D of SED values; identify analogue as candidate with minimum SED.
  - Candidate set sizes:
    - m=52 when limiting candidate analogues to Europe.
    - m=243 when expanding to every state in the world.
- Uses of climate analogues:
  - Provide relatable, tangible information about climatic characteristics countries should anticipate.
  - Calibrate impact of natural disasters in macro models (DIGNAD).
  - Inform extent and nature of adaptation required and illuminate uncertainty under different emission scenarios.
- Note: Annex II presents a “global search” variant where analogues may be located anywhere globally; that mapping excludes seasonal variations due to limited data availability.

### Calibrating impacts, investments, and financing — DIGNAD model overview
- Model type and purpose:
  - DIGNAD: dynamic general equilibrium model describing a small open economy; quantifies impact of climate disasters and policy scenarios for adaptation infrastructure investment.
- Three interdependent blocks:
  - Private demand block: two household types (savers with financial instruments; liquidity-constrained without); households earn labor income, receive remittances and transfers, consume domestic and imported goods.
  - Private supply block: representative firms produce tradable and non-tradable goods with Cobb-Douglas production; firm output increases with TFP, which depends on public infrastructure stock (standard and climate-resilient).
  - Policy block: financing options for government investment include consumption taxes, labor taxes, net transfers, issuance of domestic and/or external commercial debt, external concessional debt, or receiving grants. Two policy perspectives: strict fiscal rule (taxes adjust automatically) and no automatic tax adjustment (debt financing allowed).
- Disaster channels affecting GDP:
  - Destruction of public infrastructure stock → fiscal rebuilding costs.
  - Destruction of private capital → private reconstruction with adjustment costs.
  - Reduction in TFP (exogenous, recovers at assumed pace).
  - Increased government borrowing costs via risk premium; reduced efficiency of government infrastructure investment after disasters.
- Climate-resilient infrastructure features:
  - Split between standard and climate-resilient public infrastructure.
  - Climate-resilient infrastructure reduces output cost of natural disasters, has higher upfront cost, lower depreciation rate, and higher rate of return.

### Case study: Moldova — context and projected impacts
- Context and challenges:
  - Moldova’s exposure to adverse climate events is broadly similar to the rest of Europe, but adaptation capacity is significantly weaker even when controlling for income.
  - Contributing factors: limited investments in climate-resilient infrastructure, weaknesses in climate risk and disaster management, and weaknesses in PFM frameworks.
  - Moldova is identified as the country with the largest economic costs due to climate disasters.
- Projected climate shifts under the “rocky road” GHG scenario:
  - Annual temperature likely to increase by about 3.5 degree Celsius on average (from an average increase of 1.5 degree Celsius in the past 50 years).
  - Annual precipitation shows a sharp increase compared to the historical declining trend; seasonal precipitations expected to be more volatile.
  - Moldova’s analogue under this scenario: Spain.
- Changes in frequency of adverse events (next three decades vs historical):
  - Flood events: could be five times more frequent.
  - Storms: could occur thirteen times more often.
  - Droughts: frequency expected to more than double.
  - Episodes of extreme temperatures: frequency expected to triple.
  - New risk: wildfires could occur despite not being recorded in recent past.
- Economic impact estimates:
  - Under Moldova’s current weaker adaptation capacity, future climate disasters would have an economic impact amounting to about 10 percent GDP on average per occurrence.
  - Historical impact (past disasters): about 6 percent of GDP per occurrence.
  - Note: the 10 percent estimate is described as conservative and based on higher frequency of shocks (excluding new ones) and assuming similar costs as past events.

### Baseline calibration and simulation design for Moldova — key parameters
- Simulation horizon and timing:
  - Simulations cover a 20-year horizon.
  - Government increases investment in infrastructure (standard or climate-resilient) during the first 5 years.
  - A natural disaster hits in Year 6; reconstruction starts immediately and lasts 5 years.
  - Assumed no impact on risk premium on government commercial external debt.
- Calibration parameters (as presented):
  - Public infrastructure investment to GDP ratio: 4.0%
  - Public adaptation infrastructure investment to GDP ratio: 0.0%
  - Consumption tax rate (VAT): 20.0%
  - Labor income tax rate: 12.0%
  - Public domestic debt to GDP ratio: 9.6%
  - Public concessional debt to GDP ratio: 26.0%
  - Public external commercial debt to GDP ratio: 0.0%
  - Private external debt to GDP ratio: 50.6%
  - Real interest rate on public domestic debt: 4.0%
  - Real interest rate on public external commercial debt: 6.0%
  - Grants to GDP ratio: 0.5%
  - Natural resources revenues to GDP ratio: 0.0%
  - Remittances to GDP ratio: 14.1%
  - Imports to GDP ratio: 58.9%
  - Trend per capita growth rate in absence of natural disasters: 6.0%
  - Value added in NT-sector: 60.0%
  - Efficiency of public infrastructure investment: 65.0%
  - Ability of adaptation capital to withstand natural disaster: 30.0
  - Cost ratio adaptation vs standard investment: 25.0%
  - Initial return standard on infrastructure investment: 25.0%
  - Initial return on adaptation infrastructure investment: 35.0%
  - Depreciation rate of public capital (standard infrastructure): 7.5%
  - Depreciation rate of public capital (adaptation): 3.0%
  - Division of fiscal adjustment parameter - Transfers: 20.0%
  - Division of fiscal adjustment parameter - Consumption tax: 40.0%
  - Division of fiscal adjustment parameter - Labor income tax: 40.0%
  - Public debt adj. between commercial external and domestic: 50.0%

### Investment needs for Moldova (Box 4) — estimates and approaches
- Two estimation approaches:
  - Sectoral approach (World Bank, 2016): estimated total adaptation cost of about 2 percent of GDP per year over the next 10–15 years.
  - Frontier analysis approach (adaptive capacity frontier using full sample of European countries): estimated investment need of about 2.5 percent of GDP per year in adaptation over the next 20 years to close adaptation gaps.
- Methodological note:
  - Adaptive capacity frontier estimated by fitting a production function with the logarithm of per capita GDP in USD as input using 2020 data from the WEO and ND-GAIN.
- Simulation assumption:
  - Use the lower bound estimate of 2 percent of GDP annually for additional public investment in simulations.

### Standard vs. Climate-Resilient investment scenarios (Moldova)
- Scenario definitions:
  - Unchanged policies scenario: no additional public infrastructure investment beyond baseline.
  - Standard investment scenario: additional 2 percent of GDP for public standard infrastructure investment annually.
  - Adaptation investment scenario: additional 2 percent of GDP annually directed entirely towards climate-resilient infrastructure.
- Simulation results (pre-disaster, shock, post-disaster):
  - Pre-disaster:
    - New infrastructure boosts GDP growth by about 1 ppt above the baseline during the investment phase.
    - Private investment and consumption decline due to tax increases and cuts in public transfers used to finance additional public investment.
  - Shock:
    - Under unchanged policy or standard investment scenarios, the climate disaster shock causes GDP to contract by about 10 percent.
    - Under adaptation investment scenario, GDP contraction is about 4 precent, suggesting resilient infrastructure could absorb more than half of the disaster impact on economic activity.
    - Public debt:
      - Under unchanged policy or standard investment scenarios, public debt increases from 35 percent to about 39.5 percent of GDP.
      - Under adaptation investment scenario, public debt increases to about 37 percent of GDP.
    - Given the stricter fiscal rule in these simulations, the debt-to-GDP increase is exclusively driven by the denominator effect (change in GDP).
  - Post-disaster:
    - Unchanged and standard investment scenarios: medium-term scarring significant, with GDP growth about 4 to 5 p pts below the steady state 5 years after the disaster; in the longer term, GDP growth stands at about 2 ppts below the steady state more than a decade after the shock.
    - Long-term debt-to-GDP ratio slightly above steady state by about 1.5 ppts under unchanged/standard scenarios.
    - Adaptation investment scenario: economic activity recovers faster:
      - GDP returns closer to the steady state level within a bit more than a decade after the disaster.
      - Debt-to-GDP ratio converges back to about 35 percent by the end of the simulation horizon.

### Alternative financing options (Box 4 continued) — setup and outcomes
- Additional financing assumptions:
  - Additional 0.5 percent of GDP in grant financing is available annually (bringing baseline grant financing to 1 percent of GDP).
  - Increased access to concessional loans by 1 percent of GDP annually.
  - Remaining financing gap of about 0.5 percent of GDP annually to fully finance climate-resilient infrastructure.
- Options to close financing gap:
  - (1) increase public commercial debt, or
  - (2) mobilize additional tax revenues and/or generate savings from current transfers.
- Public Investment Efficiency (PIE) scenario:
  - Considered increase in PIE by 15 ppts to 80 percent, similar to top performers among emerging countries.
- Financing Option 1: Increased public debt financing:
  - Scenario 1 (commercial debt only): public debt would peak at about 54 percent of GDP (from about 35.5 percent) and remain broadly at that level 10 years after the shock.
    - New adaptation investment boosts growth by about 1 ppt above the pre-disaster baseline, limits economic impact by more than half, and reduces medium-term scarring.
  - Scenario 2 (additional grants and concessional borrowing available): growth impact larger by about 0.2 ppt by end of investment cycle and post-shock; debt-to-GDP ratio reaches a maximum of 49 percent, before declining to 47 percent by end of forecast horizon.
  - Role of PIE: improving PIE supports growth impact; with strengthened PIE, growth stands about 0.3 ppt higher by end of investment phase; post-shock recovery is faster.
- Financing Option 2: Tax revenue mobilization and expenditure savings:
  - Growth benefits and resilience comparable to Financing Option 1 when grants and concessional debt are present.
  - Growth outcomes larger when additional grants/concessional debt are available than when adaptation investment is fully financed through taxes and expenditure rationalization.
  - Tax increases depress private investment and consumption, weakening growth impact of public infrastructure when financed by taxes.
  - Improving PIE yields positive impacts similar to debt-financing scenarios.
  - Debt-to-GDP ratio peaks at about 41 percent following the shock and declines gradually to 38 percent by end of forecast horizon; preserves public debt sustainability while providing similar growth and resilience impact.

### Donor trade-offs and net savings
- Donor considerations:
  - Given Moldova’s limited financial resources, constrained access to commercial domestic and external debt, and limited fiscal space, donor support ex ante (before disasters) is particularly efficient.
- Donors’ net savings calculation:
  - If donors provide financial assistance for all reconstruction efforts post-disaster, the net present value of future reconstruction costs compared to ex-ante adaptation investment shows:
    - Donors’ savings amount to about 26 percent of total ex post reconstruction costs if they support adaptation investments ex ante (Average Impact).
    - Donors’ savings under larger future disaster impacts:
      - Average Impact + 30%: 30.2 (percent of reconstruction cost)
      - Average Impact +50%: 32.1 (percent of reconstruction cost)
      - Average Impact +100%: 35.2 (percent of reconstruction cost)

### Key conclusions and policy recommendations
- Main conclusions:
  - Adaptation infrastructure from public investments can significantly reduce output losses from natural disasters and mitigate medium-term economic scarring.
  - Such investments support sustainable long-term growth and can reduce inequality and support Sustainable Development Goals.
  - Increasing PIE (strengthening governance and institutions) further boosts GDP growth by leveraging new investment opportunities.
- Challenges:
  - Limited financial resources could delay adaptation investments and endanger public debt sustainability or weaken growth potential if donors’ support is absent.
  - External support is critical to help vulnerable countries close adaptation gaps; donors’ ex-ante support yields significant net savings relative to ex-post reconstruction costs.
  - Emerging European countries are relatively less prepared for implementation of adaptation actions due to weaker governance quality and gaps in innovation technologies.
- Policy recommendations:
  - Prioritize country-specific resilience strategies informed by climate analogues to bridge adaptation gaps.
  - Scale up adaptation infrastructure investments to reduce output losses and medium-term scarring.
  - Improve public investment efficiency through public financial management and public investment management reforms.
  - Mobilize external finance—preferably grants or concessional loans—coupled with domestic revenue mobilization and expenditure rationalization to finance adaptation without jeopardizing debt sustainability.
  - Maintain or bolster high education investment and outcomes to support innovation and climate-resilient strategies.
  - Stimulate a favorable business environment to crowd in private investments for climate actions.

*Source: EXECUTIVE SUMMARY, wpiea2024109-print-pdf*

### EXECUTIVE SUMMARY ___________________________________________________________________________ 6

### EXECUTIVE SUMMARY

### Executive summary — key findings
- Economic losses from climate-related extremes in Europe reached around half a trillion euros over the past four decades.
- About 70,000 deaths were attributed to abnormal temperature (extreme heatwaves) in Europe in 2022.
- Temperatures in Europe have increased at more than twice the global average—by about 0.5 degrees Celsius on average per decade.
- Europe’s overall vulnerability to climate risks is lower than other regions’, but notable disparities exist:
  - Central and Eastern Europe (CESEE) countries are comparatively more vulnerable and experience greater human and economic costs from climate disasters.
  - This higher vulnerability in CESEE is driven by weaker adaptation capacity, higher economic dependence on climate-sensitive sectors (such as agriculture), and lower investment in climate-resilient infrastructure.
- Climate analogue mapping is used to:
  - Project the future climate for each European country using an ensemble of climate models.
  - Identify countries whose present climate best approximates a given country’s projected future climate.
  - Provide tangible information on climatic characteristics countries should anticipate and to calibrate macro analyses of climate-related shocks.
- Investments in adaptation infrastructure can:
  - Significantly reduce output losses from natural disasters.
  - Mitigate medium-term economic scarring.
  - Support sustainable long-term growth and help reduce inequality.
- Growth benefits from infrastructure investments are amplified when complemented with reforms to improve public investment efficiency (PIE).
- Due to limited domestic financial resources, external support—ideally in the form of grants or concessional loans—plus efforts on domestic revenue mobilization and expenditure rationalization, will be critical to meet many adaptation needs in CESEEs without endangering debt sustainability.
- Improving PIE (strengthening governance and quality of institutions) can further boost real GDP growth by leveraging new private investment opportunities.

### Introduction — context and motivation
- Europe has faced substantial climate impacts across populations and economies:
  - Economic losses from climate-related extremes in Europe reached around half a trillion euros over the past four decades.
  - About 70,000 deaths were attributed to abnormal temperature (extreme heatwaves) in Europe in 2022.
  - Temperatures in Europe have increased at more than twice the global average—by about 0.5 degrees Celsius on average per decade.
- Climate changes documented include increased frequency and intensity of floods, storms, heatwaves, and unprecedented cold winters.
- Sectoral impacts noted:
  - Agriculture faces shifting growing seasons and unpredictable weather, leading to diminished crop yields and risks to food security.
  - Coastal regions face heightened risks from rising sea levels and altered precipitation patterns affecting infrastructure and livelihoods.
- Even under more optimistic GHG emission scenarios, ongoing changes in global climate conditions will continue to pose substantial risks, necessitating immediate adaptation actions.
- The paper proposes climate analogues to:
  - Calibrate macro analyses of climate-related shocks using the IMF's DIGNAD (Debt-Investment-Growth and Natural Disaster) Model.
  - Simulate the role of investments in adaptive infrastructure and their sustainable financing strategies.

### Vulnerability to climate change and readiness for adaptation actions
- Aggregate regional patterns:
  - Europe’s vulnerability to climate risks is below the global average.
  - Advanced economies (AEs) in Europe have the lowest aggregate vulnerability compared to other regions.
  - CESEE countries are more vulnerable to natural disasters than AEs.
- Drivers of higher vulnerability in CESEE:
  - Exposure to climate risks is similar for AEs and CESEE (largely reflecting geographical location).
  - CESEE faces large adaptive capacity gaps and weaker readiness for effective implementation of adaptation actions.
- Data sources and indicators:
  - Vulnerability and readiness assessments are based on ND-GAIN data (see Box 1).

### Climate analogues mapping — method and uses
- Methodology:
  - Use an ensemble of climate models to project future climates for each European country.
  - Identify present-day countries/regions whose climates best approximate projected future climates (climate analogues).
- Uses of climate analogues:
  - Provide relatable, tangible information about climatic characteristics countries should anticipate.
  - Calibrate the impact of natural disasters in macro models (DIGNAD).
  - Inform the extent and nature of adaptation required and illuminate uncertainty under different emission scenarios.

### Calibrating impacts, investments, and financing — overview of approach
- Modeling framework:
  - The IMF's DIGNAD Model is used to simulate macroeconomic impacts of natural disasters and the role of adaptation investments (see Box 3).
- Calibration:
  - Climate analogue information calibrates country-level parameters used in macro analyses of climate-related shocks.
- Investment scenarios considered:
  - Standard investments vs. climate-resilient investments.
  - Alternative financing options including debt financing and fiscal financing are analyzed.
- Key results (summary):
  - Adaptation infrastructure investments can substantially reduce output losses from natural disasters and medium-term scarring.
  - Investments are more effective when paired with improvements in public investment efficiency (PIE).
  - External support in the form of grants or concessional loans and domestic revenue mobilization are important to avoid endangering debt sustainability.

### Case study highlights (Moldova)
- The paper includes a case study that:
  - Calibrates the macro impact of future natural disasters based on climate analogues.
  - Presents baseline calibration of model parameters for Moldova.
  - Compares outcomes across Standard vs. Climate-Resilient investment strategies.
  - Assesses alternative financing options and trade-offs for donors.
- The case study demonstrates how tailored adaptation investments and financing strategies can materially alter macroeconomic trajectories and resilience outcomes.

### Policy implications and recommendations
- Prioritize country-specific resilience strategies informed by climate analogues to bridge adaptation gaps.
- Scale up adaptation infrastructure investments to reduce output losses and medium-term scarring.
- Improve public investment efficiency (PIE) through governance and institutional quality reforms to amplify growth returns from adaptation spending.
- Mobilize external finance—preferably grants or concessional loans—coupled with domestic revenue mobilization and expenditure rationalization to finance adaptation without jeopardizing debt sustainability.
- Consider donor trade-offs and design financing instruments that minimize debt risks while maximizing adaptation impact.

### Conclusion
- Climate change poses growing risks to Europe despite lower aggregate vulnerability relative to other regions; significant intra-regional disparities warrant targeted adaptation.
- Climate analogues offer a practical tool to inform calibration of macro models and design of adaptation strategies.
- Investment in adaptation infrastructure, supported by improvements in PIE and prudent financing (external concessional support and domestic fiscal measures), can reduce damages, support long-term growth, and lower inequality.

*Source: EXECUTIVE SUMMARY, wpiea2024109-print-pdf*

### Box 1. Notre Dame Global Adaptation Index (ND-GAIN)

### Box 1. Notre Dame Global Adaptation Index (ND-GAIN)

### Overview of ND-GAIN
- ND-GAIN is composed of two aggregate indices:
  - vulnerability index: measures the propensity or predisposition of human societies to be negatively impacted by climate hazards.
  - readiness index: captures preparedness to make effective use of investments for adaptation actions thanks to a safe and efficient business environment.
- The vulnerability index is broken down into 3 sub-indices: Exposure, Sensitivity, and Adaptative capacity.
- Each of the three sub-indices are constructed based on data covering six sectors: health, food, ecosystem, habitat, water, and infrastructure.
- The readiness index encompasses three dimensions: Economic, Governance, and Social.

### Vulnerability components (definitions and scope)
- Exposure:
  - Defined as the extent to which human society and its supporting sectors are stressed by future changing climate conditions.
  - Captures physical factors external to the system that contribute to vulnerability.
  - Based on mid-term projections (2040–2069) of a set of indicators covering the six sectors described above.
- Sensitivity:
  - The degree to which people and the sectors they depend upon are affected by climate related perturbations.
  - Factors include dependency on climate-sensitive sectors and proportion of populations sensitive to climate hazard due to topography and demography.
- Adaptative capacity:
  - The ability of society and its supporting sectors to adjust to reduce potential damage and respond to negative consequences of climate events.
  - Indicators seek to capture tools (e.g., disaster preparedness strategies) readily deployable to deal with sector-specific climate change impacts.

### Readiness index (dimensions)
- Economic: Capacity of the economy to attract adaptation investment.
- Governance: Capacity to promote and maintain a sound governance/institutional framework, contributing to attract external financing and support deployment of adaptation actions and adaptation-related policies.
- Social: Social characteristics (wealth, education, access to technology) that support resilience to extreme climate events and foster implementation of adaptation strategies, including innovation capacity.

### Exposure and sensitivity — empirical findings for Europe (CESEE vs AEs)
- Cumulative cost of climate-related disasters (in percent of 2019 GDP):
  - The cumulative cost is about 1 percentage point higher in the CESEE compared to AEs.
  - Countries with the largest cumulative costs include:
    - Moldova (close to 12 percent)
    - Bosnia and Herzegovina (about 7 percent)
    - North Macedonia (about 5.5 percent)
  - Some AEs with large cumulative damages: Spain, Portugal, and Greece (about 5 percent of 2019 GDP cumulatively).
- Human and social impact (average affected population over past 30 years):
  - Top 5 countries with most affected population are from CESEE, including:
    - Moldova (average 3 percent of the population affected)
    - North Macedonia (2 percent)
    - Bosnia and Herzegovina (1.5 percent)
- ND-GAIN projections:
  - CESEE countries’ exposure to climate change projected to remain broadly similar to AEs.
  - CESEE faces more prominent risks for agriculture (projected change of cereal yields) and flood hazards.
  - AEs likely to be more exposed to impacts of sea level rise and change in annual runoff.
- Sensitivity patterns:
  - Sensitivity is broadly similar in CESEE and AEs, with differing sources:
    - CESEE: larger share of rural population, higher dependence on agricultural activities (including subsistence farming), and weaker infrastructure quality → relatively higher sensitivity to climate changes and disasters.
    - AEs: significant concentration of population in smaller urban areas → more sensitivity to adverse climate conditions (e.g., extreme temperature).

### Adaptative capacity and readiness — empirical findings for Europe
- Adaptation challenges magnify impacts in CESEE compared to AEs:
  - Agriculture adaptation capacity in CESEE (captured by irrigation capacity and availability of fertilizers and automotive infrastructure) is below AEs average.
  - Quality of infrastructure, including transport and trade-related infrastructure, is weaker in CESEE on average.
  - Health sector preparedness is weaker in CESEE compared to AE peers (crucial for mitigating human and social costs).
  - Disaster preparedness strategies are comparatively weaker in CESEE.
- Readiness for effective implementation of adaptation actions:
  - CESEE exhibits governance challenges (limited institutional capacity and lack of coordination), with some governance indicators (political stability, control of corruption, respect of the rule of law, regulatory quality) well below AE.
  - Social readiness in CESEE lags AEs (innovation, education, ICT infrastructure, social inequality).
  - A less-favorable business environment in CESEE may curtail private sector involvement in climate-resilient infrastructure and limit catalyzation of private adaptation investment.

### Climate analogues mapping — purpose and interpretation
- Climate analogue mapping matches a country’s projected future climate to the current climate of another location to inform adaptation choices (behavior, technologies, policies).
- Analogues are identified to minimize seasonal differences in temperature and precipitation between one country’s future climate and another’s present climate.
- The approach gives concrete, relatable sense of adaptation required and indicates general shifts (in Europe, a general shift to the south reflecting projected temperature changes).

### Scenarios, modeling, and baseline assumptions
- Projections use an ensemble of approximately thirty climate models of temperature and precipitation under different GHG emission concentration paths; projection data from CMIP6 (Coupled Model Inter-comparison Projects) accessed through the World Bank Group’s Climate Knowledge Portal.
- Baseline scenario used in this paper: “rocky road” (SSP3–7.0), characterized by:
  - end-of-21st century radiative forcing level of 7 watts per square meter.
  - a very likely range of increase in average global surface temperature of 2.8 to 4.6 degrees Celsius.
- Additional scenarios presented:
  - SSP2–4.5 (lower emissions / more mitigation than baseline).
  - SSP5–8.5 (higher emissions / less mitigation than baseline).
- The model ensemble median is used to project climate variables for each country; using an ensemble minimizes model bias.
- The IPCC AR6 uses five illustrative scenarios based on SSPs and RCPs; SSP narratives summarized in text:
  - SSP1: Sustainability - Taking the Green Road (Low challenges to mitigation and adaptation)
  - SSP2: Middle of the Road (Medium challenges to mitigation and adaptation)
  - SSP3: A Rocky Road - Regional Rivalry (High challenges to mitigation and adaptation)
  - SSP5: Fossil fueled Development (High challenges to mitigation, low challenges to adaptation)
- Note on comparability of analogues in this paper: focus on candidate analogues from Europe (m=52 when limiting to Europe) to keep differences in level of economic development relatively contained; expanding to every state in the world gives m=243.

### Analytical method for identification of climate analogues (summary of Box 2)
- Variables and seasons:
  - Ω is the set of climate variables: seasonal temperature and seasonal precipitation, so #Ω=8 (average temperature in each of four seasons, and average precipitation in each of four seasons).
- Procedure (three steps):
  1. For each target country j, project each climate variable through 2100 under an assumed GHG emission concentration pathway.
  2. For each target country j, quantify dissimilarities between early and late 21st century climates by calculating the standardized Euclidean distance (SED) across Ω, using 2002–2021 and 2080–2099 means for climate variables and the standard deviation of interannual variability for 2002–2021.
  3. For a given scenario, assemble the matrix D of SED values where each column corresponds to a target country j and each row to a candidate analogue i; identify the analogue as the candidate with minimum SED (minimum value in each column of D).
- Candidate set sizes:
  - m=52 when limiting candidate analogues to Europe.
  - m=243 when expanding candidate analogues to every state in the world.
- Data used:
  - Historical annual average temperature and precipitation for every country in the world.
  - Historical seasonal average temperature and seasonal precipitation for every country in Europe.
  - Projected values of same climate variables for every country in Europe, into 2100.
- Methodological note:
  - Procedure differs from comparing indices of averages; comparing averages could erroneously pick cases with vastly different climatic patterns but similar averages.

*Source: wpiea2024109-print-pdf — Box 1. Notre Dame Global Adaptation Index (ND-GAIN).*

### Annex II shows the results based on a “global search”, whereby the analogue for a country in Europe can be located in an

### wpiea2024109-print-pdf - Annex II shows the results based on a “global search”, whereby the analogue for a country in Europe can be located in any part of the globe. In this case however, the mapping excludes seasonal variations in temperature and precipitations due to limited data availability.

### Case study: Closing the Adaptation Gaps and Building Resilience — Moldova
- Context and challenges
  - Moldova’s exposure to adverse climate events is broadly similar to the rest of Europe, but adaptation capacity is significantly weaker even when controlling for income.
  - Contributing factors: limited investments in climate-resilient infrastructure, weaknesses in climate risk and disaster management, and weaknesses in PFM frameworks.
  - Moldova is identified as the country with the largest economic costs due to climate disasters (see Annex III note in source).
- Projected climate shifts under the “rocky road” GHG scenario
  - Annual temperature likely to increase by about 3.5 degree Celsius on average (from an average increase of 1.5 degree Celsius in the past 50 years).
  - Annual precipitation shows a sharp increase compared to the historical declining trend; seasonal precipitations expected to be more volatile.
  - Moldova’s analogue under this scenario: Spain.
- Changes in frequency of adverse events (next three decades vs historical)
  - Flood events: could be five times more frequent.
  - Storms: could occur thirteen times more often.
  - Droughts: frequency expected to more than double.
  - Episodes of extreme temperatures: frequency expected to triple.
  - New risk: wildfires could occur despite not being recorded in recent past.
- Economic impact estimates
  - Under Moldova’s current weaker adaptation capacity, future climate disasters would have an economic impact amounting to about 10 percent GDP on average per occurrence.
  - Historical impact (past disasters): about 6 percent of GDP per occurrence.
  - Note: the 10 percent estimate is described as conservative and based on higher frequency of shocks (excluding new ones) and assuming similar costs as past events.

### The DIGNAD model: framework to evaluate macroeconomic impact of climate-resilient infrastructure
- Model type and purpose
  - Dynamic general equilibrium model describing a small open economy; quantifies impact of climate disasters and policy scenarios for adaptation infrastructure investment.
- Three interdependent blocks
  - Private demand block: household consumption and saving decisions with two household types (savers with access to financial instruments; liquidity-constrained without). Households earn labor income, receive remittances and transfers, and consume domestic and imported goods.
  - Private supply block: firm decisions on labor and capital demand; tradable and non-tradable goods produced by representative firms following a Cobb-Douglas production function; firm output increases with TFP, which depends on the stock of public infrastructure (climate-resilient and standard).
  - Policy block: financing options for government investment in standard or climate-resilient infrastructure; fiscal instruments include consumption taxes, labor taxes, net transfers; government can issue domestic and/or external commercial debt, external concessional debt, or receive grants. Two policy perspectives modeled: strict fiscal rule (taxes adjust automatically) and no automatic tax adjustment (debt financing allowed).
- Channels through which natural disasters affect GDP
  - Destroying stock of public infrastructure (rebuilding by government generates fiscal costs).
  - Destroying stock of private capital (rebuilt by private investment subject to adjustment costs).
  - Reducing TFP (exogenous, recovers at assumed pace).
  - Additional mechanisms: increased government borrowing costs via risk premium; reduced efficiency of government infrastructure investment after disasters.
- Features of climate-resilient infrastructure in the model
  - Total public infrastructure split into standard and climate-resilient.
  - Climate-resilient infrastructure reduces output cost of natural disasters but has higher upfront cost.
  - Climate-resilient infrastructure assumed to have lower depreciation rate and higher rate of return.

### Baseline calibration and simulation design for Moldova (modeling assumptions)
- Simulation horizon and timing of events
  - Simulations cover a 20-year horizon.
  - Government increases investment in infrastructure (standard or climate-resilient) during the first 5 years.
  - A natural disaster hits in Year 6; reconstruction starts immediately and lasts 5 years.
  - Assumed no impact on risk premium on government commercial external debt (Moldova has not incurred new commercial debt on international markets in at least the past 5 years).
- Calibration parameters (Table 2: Moldova: Main Parameters Calibration)
  - Public infrastructure investment to GDP ratio: 4.0%
  - Public adaptation infrastructure investment to GDP ratio: 0.0%
  - Consumption tax rate (VAT): 20.0%
  - Labor income tax rate: 12.0%
  - Public domestic debt to GDP ratio: 9.6%
  - Public concessional debt to GDP ratio: 26.0%
  - Public external commercial debt to GDP ratio: 0.0%
  - Private external debt to GDP ratio: 50.6%
  - Real interest rate on public domestic debt: 4.0%
  - Real interest rate on public external commercial debt: 6.0%
  - Grants to GDP ratio: 0.5%
  - Natural resources revenues to GDP ratio: 0.0%
  - Remittances to GDP ratio: 14.1%
  - Imports to GDP ratio: 58.9%
  - Trend per capita growth rate in absence of natural disasters: 6.0%
  - Value added in NT-sector: 60.0%
  - Efficiency of public infrastructure investment: 65.0%
  - Ability of adaptation capital to withstand natural disaster: 30.0
  - Cost ratio adaptation vs standard investment: 25.0%
  - Initial return standard on infrastructure investment: 25.0%
  - Initial return on adaptation infrastructure investment: 35.0%
  - Depreciation rate of public capital (standard infrastructure): 7.5%
  - Depreciation rate of public capital (adaptation): 3.0%
  - Division of fiscal adjustment parameter - Transfers: 20.0%
  - Division of fiscal adjustment parameter - Consumption tax: 40.0%
  - Division of fiscal adjustment parameter - Labor income tax: 40.0%
  - Public debt adj. between commercial external and domestic: 50.0%
- Additional calibrated observations from analogues and historical series
  - Seasonal volatility of precipitation and temperature measured as standard deviations over 1950-2021 (figures shown in source).
  - Frequency of adverse climate events (Number of disasters, 1950-2021) compared between Moldova and its analogue (figures shown in source).

### Investment needs: quantifying adaptation costs for Moldova (Box 4)
- Two estimation approaches and results
  - Sectoral approach (World Bank, 2016 analysis of Moldova’s climate adaptation investment planning): estimated total adaptation cost of about 2 percent of GDP per year over the next 10–15 years.
  - Frontier analysis approach (adaptive capacity frontier using full sample of European countries): estimated investment need of about 2.5 percent of GDP per year in adaptation over the next 20 years to close adaptation gaps.
- Methodological note
  - The adaptive capacity frontier is estimated to fit a production function with a single input, the logarithm of per capita GDP in USD. Sources and calculations based on 2020 data from the WEO and University of Notre Dame’s Global Adaption Index (as described in the source).

_International Monetary Fund — IMF Working Papers: Weathering Tomorrow: Climate Analogues and Adaptation Gaps in Europe (content unit provided)_

### Box 4: Quantifying Adaptation Investment Needs for Moldova (concluded)

### Box 4: Quantifying Adaptation Investment Needs for Moldova (concluded)

### Methodology and assumptions
- Moldova’s 2020 Nationally Determined Contribution estimates adaptation investment needs to be 2.5 percent of GDP per year over the next 10–15 years. The simulations use the lower bound of this estimates range (2 percent of GDP).
- Peer benchmarking: identify a country with similar income per capita but closer to the adaptative capacity frontier (example: UKR), then simulate a public capital investment profile for Moldova over the next 20 years to reach that peer level, adjusting for differences in public investment management.
- Data limitations: due to lack of historical data on climate adaptation investment, project total public capital investment and assume additional capital expenditure (on top of the baseline) is aimed at strengthening adaptation to climate risks.
- Fiscal constraints:
  - Government faces a tight budgetary constraint and fiscal policy is guided by a strict fiscal rule that does not allow debt financing.
  - No new external grants or concessional loans are available beyond baseline assumptions in the first set of simulations.
  - Any new spending is financed through tax increases or savings from reduced public transfers.

### Standard vs. Climate-Resilient investment scenarios
- Three scenarios:
  - Unchanged policies scenario: key macro-variables under the baseline are kept unchanged; no additional public infrastructure investment beyond baseline.
  - Standard investment scenario: assumes an additional 2 percent of GDP for public standard infrastructure investment annually.
  - Adaption investment scenario: the additional 2 percent of GDP (annually) public investment is entirely directed towards climate-resilient infrastructure to strengthen Moldova’s adaptation capacity (Box 3).

### Simulation results (Figure 7) — Pre-disaster, Shock, Post-disaster
- Pre-disaster:
  - New infrastructure contributes to boost GDP growth by about 1 ppt above the baseline during the investment phase.
  - Private investment and consumption decline due to the tax increase and cut in public transfers used to finance additional public investment.
- Shock:
  - Under unchanged policy or standard investment scenarios, the climate disaster shock causes GDP to contract by about 10 percent.
  - Under the pre-disaster accumulation of adaptation investment scenario, GDP contraction is about 4 precent, suggesting resilient infrastructure could absorb more than half of the disaster impact on economic activity.
  - Public debt:
    - Under unchanged policy or standard investment scenarios, public debt increases from 35 percent to about 39.5 percent of GDP.
    - Under the adaptation investment scenario, public debt increases to about 37 percent of GDP.
  - Given the stricter fiscal rule in these simulations, the debt-to-GDP increase is exclusively driven by the denominator effect (change in GDP).
- Post-disaster:
  - Unchanged and standard investment scenarios: medium-term scarring is significant, with GDP growth remaining about 4 to 5 p pts below the steady state 5 years after the disaster; in the longer term, GDP growth stands at about 2 ppts below the steady state more than a decade after the shock.
  - Long-term debt-to-GDP ratio is slightly above the steady state by about 1.5 ppts under unchanged/standard scenarios.
  - Adaptation investment scenario: thanks to more resilient infrastructure (and milder destruction of capital stock), economic activity recovers faster:
    - GDP returns closer to the steady state level within a bit more than a decade after the disaster.
    - Debt-to-GDP ratio converges back to about 35 percent by the end of the simulation horizon.

### Alternative financing options — setup and parameters (Table 3)
- Additional financing assumptions:
  - Additional 0.5 percent of GDP in grant financing is available annually (bringing baseline grant financing to 1 percent of GDP).
  - Increased access to concessional loans by 1 percent of GDP annually.
  - Remaining financing gap of about 0.5 percent of GDP annually to fully finance climate-resilient infrastructure.
- Options to close financing gap:
  - (1) increase public commercial debt, or
  - (2) mobilize additional tax revenues and/or generate savings from current transfers.
- Public Investment Efficiency (PIE):
  - Consider scenario where PIE increases by 15 ppts to 80 percent, similar to top performers among emerging countries.
- Table 3 parameter summary (as presented):
  - Debt financing column and Tax and exp. rationalization column with Scenarios 1–3 showing combinations of Grant: + 0.5 ppt; Concessional loans: + 1 ppt; Commercial debt: ~; tax and exp. rationalization: ~; PIE: + 15 ppts (in scenarios where applied).

### Financing Option 1: Increased public debt financing to close the gap
- Scenario comparisons:
  - Scenario 1: government finances adaptation infrastructure exclusively by increasing commercial debt (no extra grants/concessional borrowing).
    - Public debt would peak at about 54 percent of GDP (from about 35.5 percent) and remain broadly at that level 10 years after the shock.
    - New investment in adaptation infrastructure boosts growth by about 1 ppt above the pre-disaster baseline, helps limit economic impact of climate disaster by more than half, and reduces medium-term scarring.
  - Scenario 2: additional grants and concessional borrowing available.
    - Under this scenario the growth impact of adaptation investment is larger by about 0.2 ppt by the end of the investment cycle and over the entire post-shock period.
    - Debt-to-GDP ratio reaches a maximum of 49 percent, before declining to 47 percent by end of the forecast horizon.
- Role of PIE:
  - Improving PIE further supports the impact of adaptation investment on growth regardless of financing modality.
  - With strengthened PIE, growth stands at about 0.3 ppt higher by the end of the investment phase.
  - Post-shock, the economy recovers faster, taking growth back to the steady state level by end of the forecast horizon.

### Financing Option 2: Mobilizing tax revenue and expenditure savings to close the gap
- Macroeconomic impacts:
  - Growth benefits of adaptation infrastructure before and after the shock, and resilience to climate disaster, are of comparable magnitude to Financing Option 1.
  - Growth outcomes are larger in the scenario with additional grants and concessional debt financing compared to a scenario where adaptation investment is fully financed through taxes and expenditure rationalization.
  - Tax increases (on income and consumption) depress private investment and consumption, weakening the growth impact of public infrastructure investment when financing is tax/expenditure-based.
  - Improving PIE yields positive impacts similar to the debt-financing scenarios.
- Debt sustainability implications:
  - Debt-to-GDP ratio peaks at about 41 percent following the shock and declines gradually to 38 percent by end of the forecast horizon.
  - This financing option preserves public debt sustainability while providing similar growth and climate-resilience impact.

### Trade-offs and considerations for donors; donors’ net savings
- Closing adaptation gaps involves trade-offs between bolstering climate-resilient infrastructure, supporting economic activity, and maintaining debt sustainability.
- Given Moldova’s limited financial resources, constrained access to commercial domestic and external debt, and limited fiscal space, donor support ex ante (before disasters) is particularly efficient.
- Donors’ net savings calculation:
  - Analysis assumes donors provide financial assistance for all reconstruction efforts following a disaster; compute net present value of future reconstruction costs and compare to cost of ex-ante investment in climate-resilient infrastructure.
  - Donors’ net savings are large:
    - Donors’ savings would amount to about 26 percent of total ex post reconstruction costs if they were to support adaptation investments ex ante (Average Impact).
    - Donors’ savings under larger future disaster impacts:
      - Average Impact + 30%: 30.2 (percent of reconstruction cost)
      - Average Impact +50%: 32.1 (percent of reconstruction cost)
      - Average Impact +100%: 35.2 (percent of reconstruction cost)

### Key conclusions and policy implications
- Adaptation infrastructure from public investments can significantly reduce output losses from natural disasters and mitigate medium-term economic scarring.
- Such investments support sustainable long-term growth and can reduce inequality and support Sustainable Development Goals.
- Increasing PIE (strengthening governance and institutions) further boosts GDP growth by leveraging new investment opportunities.
- Challenges:
  - Limited financial resources could delay adaptation investments and endanger public debt sustainability or weaken growth potential if donors’ support is absent.
  - External support is critical to help vulnerable countries close adaptation gaps; donors’ ex-ante support yields significant net savings relative to ex-post reconstruction costs.
  - Emerging European countries are relatively less prepared for implementation of adaptation actions due to weaker governance quality and gaps in innovation technologies.
- Policy recommendations:
  - Provide external (donor) financial assistance ex ante to support resilient investments.
  - Strengthen public investment efficiency through public financial management and public investment management reforms.
  - Maintain or bolster high education investment and outcomes to support innovation and climate-resilient strategies.
  - Stimulate a favorable business environment to crowd in private investments for climate actions.

*Source: IMF Working Paper — Box 4: Quantifying Adaptation Investment Needs for Moldova (concluded)*

### Annex II. Climate Analogues with Global Search

### Annex II. Climate Analogues with Global Search

### Maps and scope
- Sources: World Bank Climate Change Knowledge Portal, IMF, and IMF staff calculations.
- Maps show CESEE (top-left panel) and non-CESEE AE (top-right panel) countries at location of their analogues—i.e., countries with present climates close to the former countries’ future climate.
- Additional panels label climate analogues for CESEE (bottom-left panel) and AE (bottom-right panel).

### Key labeling and data panels (as presented)
- Title context: IMF WORKING PAPERS Weathering Tomorrow: Climate Analogues and Adaptation Gaps in Europe.
- Panels include country-group comparisons: Advanced Europe, CESEE, MDA.
- Visual scales shown for many indicators run from 0.0 to 1.0 for normalized projections, sensitivity, adaptive capacity, and readiness metrics.

### Projected-change and exposure indicators (scale 0.0–1.0)
- Projected change of cereal yields
- Projected population change
- Projected change of annual runoff
- Projected change of annual groundwater recharge
- Projected change of deaths from climate change induced diseases
- Projected change of length of transmission season of vector-borne diseases
- Projected change of biome distribution
- Projected change of marine biodiversity
- Projected change of warm period
- Projected change of flood hazard
- Projected change of hydropower generation capacity
- Projection of Sea Level Rise impacts
- Exposure categories: Food, Infrastructure, Habitat, Ecosystem, Health, Water

### Sensitivity indicators (scale 0.0–1.0)
- Food import dependency
- Rural Population
- Fresh water withdrawal rate
- Water dependency ratio
- Slum population
- Dependency on external resource for health services
- Dependency on natural capital
- Ecological footprint
- Urban concentration
- Age dependency ratio
- Dependency on imported energy
- Population living under 5m above sea level
- Sensitivity categories: Infrastructure, Habitat, Ecosystem, Health, Water, Food

### Adaptive capacity indicators (scale 0.0–1.0)
- Agriculture capacity (Fertilizer, Irrigation, Pesticide, Tractor...)
- Child malnutrition
- Access to reliable drinking water
- Dam capacity
- Medical staffs (physicians, nurses and midwives)
- Access to improved sanitation facilities
- Protected biomes
- Engagement in International environmental...
- Quality of trade and transport-related infrastructure
- Paved roads
- Electricity access
- Disaster preparedness
- Adaptive Capacity categories: Infrastructure, Habitat, Ecosystem, Health, Water, Food

### Readiness indicators (scale 0.0–1.0)
- Doing business
- Political stability and non-violence
- Control of corruption
- Rule of law
- Regulatory quality
- Social inequality
- ICT infrastructure
- Education
- Innovation
- Readiness categories: Social, Governance, Economic

### Moldova: costs of past natural disasters and vulnerability/readiness panels
- Sources: University of Notre Dame’s Global Adaption Index, EM-DAT, and IMF staff calculations.
- Visual axis labels and scales included:
  - Normalized indicator scales run 0.0, 0.2, 0.4, 0.6, 0.8, 1.0.
  - Population Affected by Natural Disasters: axis ticks 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5; labeled "Average number of total affected people (thousand)" and "Average affected people in % of total population".
  - Population Affected chart country ordering (as shown): ESP, RUS, FRA, UKR, MDA, TUR, ISR, BIH CZE, MKD, ALB, LTU, GBR DEU, ROU, HUN, POL, NLD, BLR, SRB PRT, ITA, BEL, GRC BGR, SVN, AUT, SVK, HRV SWE, MNE, NOR, IRL, CHE LUX, CYP, FIN, LVA, EST, DNK, ISL.
- Cumulative Cost of Natural Disasters (1990–2019) axis labeled "percent of 2019 GDP" with ticks 0, 2, 4, 6, 8, 10, 12. Country ordering (as shown): MDA, BIH, MKD, PRT SRB, ESP, GRC, ITA, CZE, ROU, DEU UKR, FRA, BGR, DNK, HUN, POL, AUT GBR, HRV, CHE, SVN NLD, LVA, LUX, SVK, BEL, SWE, RUS, LTU, ISL, ISR, TUR, EST, BLR ALB, NOR, IRL, CYP, FIN, MNE.
- CESEE Average and AE Average markers are presented on the cumulative-cost panel.

### References (selected, as shown in the annex)
- Aligishiev, Z., Ruane, C. and Sultanov, A. 2023. “User Manual for the DIGNAD Toolkit.” IMF Technical Notes and Manuals 2023/03, International Monetary Fund, Washington, DC.
- Ballester, J., van Daalen, K.R., Chen, Z.Y., et al. 2024. “The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study.” The Lancet Regional Health–Europe 36 (January). doi: 10.1016/j.lanepe.2023.100779.
- Bellon, M. and Massetti, E. 2022. “Economic Principles for Integrating Climate Change Adaptation into Fiscal Policy.” IMF Staff Climate Note 2022/001, International Monetary Fund, Washington, DC.
- Chen, C., I. Noble, J. Hellman, J. Coffee, M. Murillo, and N. Chawla. 2023. “University of Notre Dame global adaptation initiative-country index technical report.”
- World Bank Group. 2021. “User Manual Climate Change Knowledge Protal (CCKP).” World Bank Group Manuals 2021/09, World Bank Group, Washington, DC.

*Annex II. Climate Analogues with Global Search — from Working Paper No. WP/2024/109, Weathering Tomorrow: Climate Analogues and Adaptation Gaps in Europe*

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_Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024109-print-pdf.pdf_