## wpiea2025144

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### Literature overview — context, methodology, and key long-term projections
- Major context and motivation:
  - Between 2003 and 2019, Peru experienced over 61,000 emergencies linked to natural hazards (World Bank, 2022).
  - Acute physical risk profile dominated by floods, landslides, droughts, and storms.
  - Economic losses and damages from disasters in 1982-83, 1997-98, and 2017 amounted to 11.6, 6.2, and 1.6 percent of GDP, respectively (World Bank, 2016).
  - Approximately 40 percent of total damages in 2017 were inflicted on the road network.
  - The IMF-adapted ND-GAIN index ranks Peru as the most vulnerable country in Latin America 5 (LA5) to chronic physical risks.
  - Paper argument: quantifying long-term output losses from physical climate risks and evaluating returns on containment policies is essential for informed fiscal allocation (IMF, 2020; Bellon and Massetti, 2022a).

- Short-run empirical findings summarized (Local Projection method, Jordà, 2005; sample 1980–2023):
  - Fish production falls by 70 percent in the year following a strong El Ni ño episode.
  - Agricultural output falls by 11 percent in the year following a strong El Ni ño episode.
  - Recovery to pre-shock trends typically takes over a year.
  - Disaster-related expenditures increase while tax revenues fall, reducing the primary balance by 2 percentage points of GDP.
  - Evidence of inflation pass-through from non-core to core prices that takes approximately one calendar year to materialize.
  - These short-term estimates feed into long-term projections distinguishing chronic and acute risks.

- Methodological and literature positioning:
  - Global-level El Ni ño literature (e.g., Cashin et al., 2017); developing-country findings (Smith and Ubilava, 2017).
  - Peru-specific projections (e.g., Chirinos, 2021) and regional climate modeling (SENAMHI, 2009).
  - Methodological advances referenced: 3SLS, DSGE calibration, Markov-switching dynamic (FGG) model; this paper adapts the FGG model to more frequent, less severe events (El Ni ño).

- Peru’s vulnerability, exposure, and adaptive capacity:
  - El Ni ño Costero: recurring warming of sea surface temperature along Peru’s coast (Ni ño 1+2 region) occurring every four to five years.
  - Sectoral dependence and exposure:
    - Agriculture and fish production jointly constitute 7.6 percent of GDP and employ 28 percent of the workforce (OECD, 2023).
    - These sectors account for over 18 percent of total exports.
  - Adaptive capacity constraints include insufficient water management capacity, relatively low quality of infrastructure, weak public investment management, poor coordination across government levels, capacity constraints within the civil service, and chronic under-execution of capital budgets.

- Long-term projections and resilience assessment (framework and headline outcomes):
  - Framework distinguishes acute and chronic physical risks; acute risks modelled with extended regime-switching DSGE (FGG) including non-destructive repeated disaster shocks; chronic risks quantified using Chirinos (2021) output elasticities with gradual natural adaptation.
  - Projected cumulative income losses:
    - Between 13.9 and 18.6 percent of GDP by 2050.
    - Between 22.0 and 50.6 percent of GDP by 2100.
  - Potential output gains from strengthening structural resilience and closing implementation gaps:
    - Potential output projected to increase by 9.3 to 12.3 percent by 2050 (relative to a baseline with extreme weather events and persistent temperature changes).
    - Potential output projected to increase by 12.4 to 31 percent by 2100 (relative to the same baseline).
    - Gains are gradual and backloaded and do not fully offset expected chronic and acute losses.
  - Net fiscal savings from resilience investments:
    - Estimated to range from 1.2 to 1.6 percent of GDP per annum by 2050.
    - Estimated to range from 2.3 to 4.6 percent of GDP per annum by 2100.
  - Conclusion: structural resilience investments can generate meaningful long-term output and fiscal dividends and can be fiscally self-sustaining over the long term by reducing reconstruction and relief needs and expanding the tax base via stronger growth.

- Empirical model and data specifics (Local Projection IRFs; El Ni ño shock identification):
  - SST anomaly data for Ni ño 1+2 region from NOAA; 3-month rolling average variant of the Oceanic Ni ño Index (ONI).
  - An El Ni ño Costero episode defined as beginning when this index exceeds +0.5°C.
  - Analysis focuses on strong and very strong events: episodes in which the index exceeds +1.5°C at any point during an identified event.
  - This yields five shocks in the quarterly sample and four shocks in the monthly sample.
  - Data frequency and samples:
    - Monthly sample: January 1992 to September 2023.
    - Quarterly sample: first quarter of 1990 to the third quarter of 2023.
  - Variables: monthly IRFs for core and headline inflation; quarterly IRFs for sectoral output and primary fiscal balance; controls include oil price indices, a global fertilizer index, and a local production index (monthly only).

### Short-term macro-fiscal implications of El Ni ño shocks — empirical short-run impacts
- Inflation dynamics:
  - Strong El Ni ño Costero episodes increase both headline and core inflation.
  - Headline inflation: immediate impact, peaking at a 4.4 percentage point year-over-year increase by the end of the first year, then gradually declining and turning negative as prices revert toward pre-El Ni ño levels.
  - Core inflation: more gradual effect, with a noticeable increase emerging about 12 months after the initial shock.
  - Food-price channel:
    - Non-core food inflation (perishables) peaks at an 8.1 percentage point increase by the end of the first year.
    - Core food prices begin to rise in the second year, reaching a 2.3 percentage point increase about 20 months after onset.
  - 2023 episode specific spikes:
    - Fish and Seafood category CPI inflation rose by 11 percentage points between February and June 2023.
    - Fruits CPI inflation rose by 22.6 percentage points between February and September 2023.

- Sectoral output impacts and aggregate GDP:
  - Typical duration: strong El Ni ño events typically last slightly longer than one year.
  - Fisheries and agriculture:
    - Fish production: drops by 70 percent within the first year on average, followed by rapid recovery as SST normalize and fish stocks replenish.
    - Agricultural output: declines by about 11 percent over the same period but recovers more gradually; output remains 3.4 percent below pre-El Ni ño levels 18 months after the shock.
  - Aggregate effect:
    - Accounting for spillovers, El Ni ño Costero events typically reduce real GDP by approximately 5 percent within the first year.
    - Increased public spending on reconstruction and support aims to contain the overall decline.

- Fiscal impacts:
  - Central government primary balance: generally deteriorates by about 2 p.p. of GDP within a year of the shock.
  - 2023 episode fiscal outcome: between March and November 2023, the central government’s annual primary balance to GDP ratio declined by approximately 1.2 p.p.

- 2023 El Ni ño Costero episode observed deviations (March–November 2023, relative to trend):
  - Fishing output: 27.3 percent below trend.
  - Agricultural output: 4.9 percent below trend.
  - Construction output: declined by 9.2 percent.
  - Manufacturing output: declined by 6.6 percent.
  - Central government primary balance: annual primary balance to GDP ratio declined by approximately 1.2 p.p. over the same period.

### Potential growth dividends — long-term effectiveness of resilience and adaptation
- Role of investments:
  - Adaptation and structural resilience plus increased public investment efficiency deliver sizable long-term output gains by reducing disaster impacts on natural capital and productive government infrastructure and partially offsetting temperature-driven declines in long-run growth.
  - Structural resilience: climate-proofing public infrastructure (roads, bridges, schools).
  - Adaptation investments: knowledge systems, irrigation and water management, crop and livestock diversification, etc.

- National Adaptation Plan (NAP) scope and effectiveness:
  - Full implementation of measures in the NAP could mitigate up to one-third of the short-term impact of El Ni ño Costero on the country’s natural capital.
  - Under SSP2-4.5, NAP measures expected to reduce by approximately one-third the adverse effects on productivity in the agriculture and energy sectors, as well as on labor productivity.

- Quantitative projections:
  - Combined with resilient government infrastructure, total potential output gains can be as high as 9.3-12.3 percent by 2050 and 12.4-31 percent by 2100.
  - Policy package does not fully offset projected decline in potential output due to climate impacts but is cost-effective under all three global warming scenarios.
  - Adaptation measures meet the positive net present value (NPV) condition for society in the measures analyzed; they raise private sector surplus and strengthen government fiscal position through net savings.
  - Key risk: low quality of public investment and constraints related to absorptive capacity.

### Fiscal savings — costs, savings, and discounted returns
- Fiscal cost estimates (2024–2030 and ongoing):
  - Additional 0.4 percent of GDP in public investment between 2024 and 2030 for climate-proofing current investment pipeline, retrofitting public assets, and coastal protection.
  - 0.2 percent of GDP in additional annual expenditure for disaster risk management (early warning systems, emergency response equipment, other DRMS components).
  - Full implementation of costed NAP measures: 0.8 percent of GDP per year over the same period.
  - Ongoing fiscal spending assumption: equal to 6 percent of the acquired public capital stock annually to cover depreciation and preserve the asset base.

- Estimated fiscal savings and drivers:
  - Between 2024 and 2050: average annual fiscal savings ranging from 1.2 to 1.6 percent of GDP.
  - From 2024 to 2100: average annual savings ranging from 2.3 to 4.6 percent of GDP.
  - Savings primarily driven by growth-induced revenue gains; cumulative tax revenue increases by the end of the century.
  - Investments remain cost-effective across all three global warming scenarios after discounting.

- Discounted fiscal savings (Present value at a 6 percent annual discount rate, in percent of 2023 GDP) — summary of Table 2:
  - SSP1-2.6 / 2050 / 2100:
    - Total Return (a): 120.25 / 262.36
    - Stock saving: 4.53 / 6.84
    - Flow saving: 16.28 / 31.00
    - Potential growth: 99.43 / 224.52
    - Total Cost (b): 10.53 / 11.65
    - Adaptation: 4.84 / 4.92
    - Resilience: 5.69 / 6.73
    - Net saving (a) - (b): 109.72 / 250.71
  - SSP2-4.5 / 2050 / 2100:
    - Total Return (a): 122.34 / 286.80
    - Stock saving: 4.71 / 6.89
    - Flow saving: 16.58 / 33.92
    - Potential growth: 101.04 / 245.99
    - Total Cost (b): 10.53 / 11.65
    - Adaptation: 4.84 / 4.92
    - Resilience: 5.69 / 6.73
    - Net saving (a) - (b): 111.81 / 275.15
  - SSP3-7.0 / 2050 / 2100:
    - Total Return (a): 143.05 / 336.35
    - Stock saving: 4.78 / 6.96
    - Flow saving: 19.52 / 39.97
    - Potential growth: 118.75 / 289.42
    - Total Cost (b): 10.53 / 11.65
    - Adaptation: 4.84 / 4.92
    - Resilience: 5.69 / 6.73
    - Net saving (a) - (b): 132.52 / 324.70

- Overall fiscal conclusion:
  - Even after discounting, investments in adaptation and structural resilience yield positive net savings and discounted returns across scenarios and time horizons, driven by stock and flow savings and potential-growth-induced revenue gains.

*Source: IMF staff paper (sections 1.1, 3.3, 5.1, 5.2).*

### 1.1    Literature overview .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  

### 1.1    Literature overview

### Major context and motivation
- Between 2003 and 2019, Peru experienced over 61,000 emergencies linked to natural hazards (World Bank, 2022).
- The acute physical risk profile is dominated by floods, landslides, droughts, and storms.
- Economic losses and damages from disasters in 1982-83, 1997-98, and 2017 amounted to 11.6, 6.2, and 1.6 percent of GDP, respectively (World Bank, 2016).
- Approximately 40 percent of total damages in 2017 were inflicted on the road network.
- The IMF-adapted ND-GAIN index ranks Peru as the most vulnerable country in Latin America 5 (LA5) to chronic physical risks.
- The paper argues that quantifying long-term output losses from physical climate risks and evaluating returns on containment policies is essential for informed fiscal allocation (IMF, 2020; Bellon and Massetti, 2022a).

### Short-run empirical findings summarized
- Method: Local Projection method (Jordà, 2005) used to estimate impact of El Ni ño Costero events on Peru’s economy between 1980 and 2023.
- Key short-term empirical results:
  - Fish production falls by 70 percent in the year following a strong El Ni ño episode.
  - Agricultural output falls by 11 percent in the year following a strong El Ni ño episode.
  - Recovery to pre-shock trends typically takes over a year.
  - Disaster-related expenditures increase while tax revenues fall, reducing the primary balance by 2 percentage points of GDP.
  - Evidence of inflation pass-through from non-core to core prices that takes approximately one calendar year to materialize.
- These estimates are used as inputs for long-term projections distinguishing chronic and acute risks.

### Literature and methodological positioning
- Global-level El Ni ño literature: Cashin et al. (2017) using a GVAR model show heterogeneous impacts on growth, inflation, and commodity prices across countries.
- Developing-country focus: Smith and Ubilava (2017) find El Ni ño events reduce GDP growth by 1-2 percent annually, with stronger effects in tropical regions.
- Peru-specific and regional studies:
  - Chirinos (2021) projects a 9 percent reduction in per capita income by 2050 from longer-term climate anomalies, with agriculture and fisheries most at risk.
  - SENAMHI (2009) predicts changes in temperature and precipitation patterns by 2030 using global and regional models.
- Methodological advances:
  - Choi and Fisher (2003) used a 3SLS regression framework for expected losses from increased natural disasters.
  - Auffhammer (2018) highlights challenges in quantifying climate damages and heterogeneity across regions and time.
  - Gallic and Vermandel (2020) develop a DSGE model for weather shocks to agricultural productivity, calibrated with SVAR estimates.
  - Fernandez-Corugedo et al. (2023) introduce a Markov-switching dynamic (FGG) model to study long-run macro returns from adaptation investment; this paper adapts the FGG model to more frequent, less severe events (El Ni ño).

### Peru’s vulnerability and exposure (key statistics and channels)
- El Ni ño Costero: recurring warming of sea surface temperature along Peru’s coast (Ni ño 1+2 region) occurring every four to five years.
- Climatic impacts vary geographically:
  - Northern coast: heavy rainfall → infrastructure damages from floods, lower agricultural yields, slowdown in construction.
  - Southern regions: reduced precipitation → lower rain-fed agriculture output.
  - Coastal waters: higher sea surface temperature → reduced fish production (e.g., lower anchovy catches) and disruption to fishmeal/fish oil processing.
  - Higher air temperatures → disrupted flowering and pollination (e.g., blueberries, avocados, mangoes, olives).
- Sector dependence and exposure:
  - Agriculture and fish production jointly constitute 7.6 percent of GDP and employ 28 percent of the workforce (OECD, 2023).
  - These sectors account for over 18 percent of total exports.
- Medium-term climate variability: Cai et al. (2021) suggest El Ni ño-related precipitation patterns will become more intense and shift eastward.
- Adaptive capacity constraints:
  - Peru exhibits one of the lowest adaptive capacities in the region, driven by insufficient water management capacity, relatively low quality of infrastructure, weak public investment management, poor coordination across government levels, capacity constraints within the civil service, and chronic under-execution of capital budgets.

### Long-term projections and resilience assessment (summary results)
- Framework: Distinguish between acute and chronic physical risks; acute risks modelled with an extended regime-switching DSGE (FGG) framework including non-destructive repeated disaster shocks; chronic risks quantified assuming gradual natural adaptation using output elasticity estimates from Chirinos (2021).
- Projected cumulative income losses:
  - Between 13.9 and 18.6 percent of GDP by 2050.
  - Between 22.0 and 50.6 percent of GDP by 2100.
- Potential output gains from strengthening structural resilience and closing implementation gaps:
  - Potential output projected to increase by 9.3 to 12.3 percent by 2050 (relative to a baseline with extreme weather events and persistent temperature changes).
  - Potential output projected to increase by 12.4 to 31 percent by 2100 (relative to the same baseline).
  - Gains are gradual and backloaded, and do not fully offset expected chronic and acute losses.
- Fiscal savings from resilience investments (net fiscal savings = fiscal gains minus costs of measures):
  - Estimated to range from 1.2 to 1.6 percent of GDP per annum by 2050.
  - Estimated to range from 2.3 to 4.6 percent of GDP per annum by 2100.
- Conclusion: Investments in structural resilience can generate meaningful long-term output and fiscal dividends and can be fiscally self-sustaining over the long term by reducing reconstruction and relief needs and expanding the tax base via stronger growth.

### Empirical model and data specifics
- Local Projection regression (Jordà, 2005) used to estimate impulse response functions (IRFs). Equation (3.1) specifies cumulative percent change in the dependent variable from t−1 to t+h as a function of the shock, lags of the dependent variable, and control variables.
- El Ni ño shock identification:
  - Use sea surface temperature (SST) anomaly data for the Ni ño 1+2 region from NOAA.
  - Compute a 3-month rolling average of the SST anomaly to yield a variant of the Oceanic Ni ño Index (ONI).
  - An El Ni ño Costero episode is defined as beginning when this index exceeds +0.5°C.
  - Shocks are binary variables equal to 1 in the first month or quarter of each identified event and 0 otherwise.
  - Analysis focuses on strong and very strong events: sample restricted to episodes in which the index exceeds +1.5°C at any point during an identified event.
  - This yields five shocks in the quarterly sample and four shocks in the monthly sample.
- Data frequency and samples:
  - Monthly sample: January 1992 to September 2023.
  - Quarterly sample: first quarter of 1990 to the third quarter of 2023.
- Variables and controls:
  - Monthly IRFs estimated for core and headline inflation.
  - Quarterly IRFs estimated for sectoral output and the primary fiscal balance.
  - Variables seasonally adjusted by Haver or E-views X-13.
  - Inflation constructed from CPI data from Banco Central de Reserva del Perú (BCRP) as year-over-year growth rate.
  - Sectoral output and central government primary balance from Haver Analytics and BCRP.
  - Set of controls includes (a) oil price indices for Peru’s main oil import partners, (b) a global fertilizer index, and (c) a local production index for monthly frequency estimates. Quarterly controls exclude the local production index.

*Source: IMF staff paper (section 1.1, Literature overview).*

### 3.3    Short-term macro-fiscal implications of El Ni ̃no shocks

### 3.3    Short-term macro-fiscal implications of El Ni ̃no shocks

### Inflation dynamics
- Strong El Ni ̃no Costero episodes increase both headline and core inflation.
- Headline inflation:
  - Immediate impact, peaking at a 4.4 percentage point year-over-year increase by the end of the first year, then gradually declining and turning negative as prices revert toward pre-El Ni ̃no levels.
- Core inflation:
  - More gradual effect, with a noticeable increase emerging about 12 months after the initial shock—suggesting a potential pass-through from headline to core inflation.
- Food-price channel:
  - Non-core food inflation (perishables such as fresh fruits and vegetables) closely follows the trajectory of headline inflation but with greater magnitude, peaking at an 8.1 percentage point increase by the end of the first year.
  - Core food prices (less sensitive to weather shocks) begin to rise in the second year, reaching a 2.3 percentage point increase in the inflation rate about 20 months after the onset of an El Ni ̃no Costero event.
- 2023 episode specific spikes:
  - Fish and Seafood category CPI inflation rose by 11 percentage points between February and June 2023.
  - Fruits CPI inflation rose by 22.6 percentage points between February and September 2023.

### Sectoral output impacts and aggregate GDP
- Typical duration:
  - Strong El Ni ̃no events typically last slightly longer than one year.
- Fisheries and agriculture:
  - Within four quarters a strong El Ni ̃no Costero shock sharply reduces fishery and agricultural output.
  - Fish production: drops by 70 percent within the first year on average, followed by a rapid recovery as sea surface temperatures normalize and fish stocks are replenished.
  - Agricultural output: declines by about 11 percent over the same period but recovers more gradually; output remains 3.4 percent below pre-El Ni ̃no levels 18 months after the shock.
- Manufacturing, construction, mining and aggregate effect:
  - Accounting for spillovers, El Ni ̃no Costero events typically reduce real GDP by approximately 5 percent within the first year.
  - Increased public spending on infrastructure reconstruction and support for affected populations aims to contain the overall decline in output.

### Fiscal impacts
- Central government primary balance:
  - Generally deteriorates by about 2 p.p. of GDP within a year of the shock, reflecting both higher expenditures and reduced revenue collection.
- 2023 episode fiscal outcome:
  - Between March and November 2023, the central government’s annual primary balance to GDP ratio declined by approximately 1.2 p.p.

### 2023 El Ni ̃no Costero episode (Peru)
- Classification and timing:
  - The 2023 El Ni ̃no Costero reached the “strong” classification in April 2023.
- Observed sectoral deviations between March and November 2023 (relative to trend):
  - Fishing output: 27.3 percent below trend.
  - Agricultural output: 4.9 percent below trend.
  - Construction output: declined by 9.2 percent.
  - Manufacturing output: declined by 6.6 percent.
- Central government primary balance:
  - Annual primary balance to GDP ratio declined by approximately 1.2 p.p. over the same period.

### Impulse-response and modeling notes (short-term focus)
- Impulse-response estimates (Figure 5) show sharp short-term reductions in fishery and agricultural output within four quarters of a strong El Ni ̃no Costero shock.
- The paper’s empirical results highlight the immediate inflationary pressure via food prices, followed by gradual pass-through to core inflation, and substantial short-run sectoral and fiscal impacts that amplify aggregate GDP and budgetary effects.

*Source: IMF staff calculations as presented in 3.3 Short-term macro-fiscal implications of El Ni ̃no shocks.*

### 5.1    Potential growth dividends

### 5.1    Potential growth dividends

### Long-term output gains from resilience and adaptation
- Investments in adaptation and structural resilience combined with increased public investment efficiency deliver sizable output gains in the long-term.
- Investing in resilience and adaptation offsets the impact of natural hazards and climate change on productive factors and growth by reducing the impact of natural disasters on natural capital and productive government infrastructure and partially offsetting the reduction in the long run growth rate due to positive temperature anomalies.
- Structural resilience: making public infrastructure, like roads, bridges, and schools, climate-proof—a shift from standard to resilient capital.
- Adaptation investments: expenditures on knowledge systems, irrigation and water management, and diversification of crops and livestock, among other items.

### Scope and effectiveness of National Adaptation Plan measures
- Based on discussions with the authorities, full implementation of measures in the National Adaptation Plan could mitigate up to one-third of the short-term impact of El Niño Costero on the country’s natural capital.
- Under the intermediate emissions scenario (SSP2-4.5), these measures are expected to reduce by approximately one-third the adverse effects on productivity in the agriculture and energy sectors, as well as on labor productivity.

### Quantitative projections of potential output gains
- Combined with benefits from more resilient government infrastructure, total potential output gains can be as high as 9.3-12.3 percent by 2050 and 12.4-31 percent by 2100.
- While the proposed policy package does not fully offset the projected decline in potential output due to climate impacts, it remains cost-effective under all three global warming scenarios.
- Adaptation measures meet the positive net present value (NPV) condition for society in the measures analyzed, as they simultaneously enhance private sector surplus—by raising the income trajectory—and strengthen the government’s fiscal position through net savings.
- A key risk to this assessment: low quality of public investment and constraints related to absorptive capacity (not explored in depth in this study).

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### 5.2    Fiscal savings

### Fiscal cost estimates (2024–2030 and ongoing)
- Climate-proofing infrastructure in the current investment pipeline, retrofitting existing public assets, and implementing coastal protection against sea level rise: additional 0.4 percent of GDP in public investment between 2024 and 2030.
- Further spending needs related to disaster risk management (early warning systems, emergency response equipment, other national disaster risk management strategy components): 0.2 percent of GDP in additional annual expenditure.
- Full implementation of the costed measures outlined in the National Adaptation Plan: 0.8 percent of GDP per year over the same period.
- Once these investments are made, ongoing fiscal spending assumed equal to 6 percent of the acquired public capital stock annually to cover depreciation and preserve the asset base.

### Estimated fiscal savings and drivers
- Between 2024 and 2050, these measures are estimated to generate average annual fiscal savings ranging from 1.2 to 1.6 percent of GDP.
- From 2024 to 2100, average annual savings increase to between 2.3 and 4.6 percent of GDP.
- These savings are primarily driven by growth-induced revenue gains: a higher GDP trajectory translates into a higher path for tax receipts.
- Although annual growth dividends are relatively modest and do not fully offset initial output losses, their cumulative effect leads to substantial increases in tax revenue levels by the end of the century.
- Even after accounting for the time value of money and a range of discount rates, investments in adaptation and structural resilience remain cost-effective across all three global warming scenarios.

### Discounted fiscal savings (Present value at a 6 percent annual discount rate, in percent of 2023 GDP) — Table 2 summary
- SSP1-2.6 / 2050 / 2100:
  - Total Return (a): 120.25 / 262.36
  - Stock saving: 4.53 / 6.84
  - Flow saving: 16.28 / 31.00
  - Potential growth: 99.43 / 224.52
  - Total Cost (b): 10.53 / 11.65
  - Adaptation: 4.84 / 4.92
  - Resilience: 5.69 / 6.73
  - Net saving (a) - (b): 109.72 / 250.71
- SSP2-4.5 / 2050 / 2100:
  - Total Return (a): 122.34 / 286.80
  - Stock saving: 4.71 / 6.89
  - Flow saving: 16.58 / 33.92
  - Potential growth: 101.04 / 245.99
  - Total Cost (b): 10.53 / 11.65
  - Adaptation: 4.84 / 4.92
  - Resilience: 5.69 / 6.73
  - Net saving (a) - (b): 111.81 / 275.15
- SSP3-7.0 / 2050 / 2100:
  - Total Return (a): 143.05 / 336.35
  - Stock saving: 4.78 / 6.96
  - Flow saving: 19.52 / 39.97
  - Potential growth: 118.75 / 289.42
  - Total Cost (b): 10.53 / 11.65
  - Adaptation: 4.84 / 4.92
  - Resilience: 5.69 / 6.73
  - Net saving (a) - (b): 132.52 / 324.70

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*Source: IMF staff calculations using the FGG model (Fernandez-Corugedo et al., 2023), Massetti and Tagklis (2023), Harris et al. (2020), and Chirinos (2021).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025144.pdf_