## 1armea2022003

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### Mission and participants
- IMF Fiscal Affairs Department in-person mission to Yerevan from July 14 to 25 to quantify long term fiscal risks from climate change.
- Mission leadership and team:
  - Led by Mr. Jason Harris (FAD).
  - Team members: Mr. Bryn Battersby (FAD), Mr. Mehdi Raissi (IMF Resident Representative), Mr. John Zohrab (FAD regional advisor), Mr. Jyoti Rahman (FAD short-term expert).
- Government counterparts met include Minister of Finance Mr. Tigran Khachatryan and multiple deputy ministers and department directors across MoF, MoE, MEnv, CBA, and other agencies.
- Development partners met: World Bank, UNDP, Asia Development Bank, EU Delegation to Armenia.
- Technical contributors and presenters: Mr Richard Hughes (UK OBR), Pierre-Alain Bruschez (Swiss Federal Finance Administration), Shota Gunia (Georgian Ministry of Finance).

### Executive summary — key climate and socioeconomic findings
- Historical and projected temperature changes:
  - Average temperature in Armenia has already increased by 1.2 degrees Celsius since the 1990s.
  - RCP8.5 (unmitigated): projected rise around 5.6 degrees above the 1990s average by the 2090−2100 decade.
  - RCP6.0: projected rise around 3.5 degrees by 2090−2100.
  - Under the unmitigated scenario: number of summer days projected to increase and number of frost and ice days expected to fall substantially by 2100.
- Variability and extremes:
  - Armenia’s temperature variance is already high—only three countries have more variable average annual temperatures in the referenced dataset.
  - Historical seasonal change (1966–2016): average summer temperature rose by around 1.3 degrees; winter temperatures rose by 0.4 degrees.
  - Under the unmitigated scenario: average summer temperature projected to be 7 degrees higher in 2090−2100 than the 1990s; average winter temperature projected to be around 5 degrees higher.
- Water, hydrology and precipitation:
  - Most rivers do not have constant flow; around 55 percent of total annual river flow formed by snowmelt and spring precipitation.
  - Under the unmitigated scenario: projected fall in precipitation could lead to up to 39 percent reduction in water flow by 2100.
  - Water flows into Lake Sevan projected to fall by a third; flow into some reservoirs could fall by more than half.
- Mitigation and emissions:
  - Armenia pledged to reduce emissions to 40 percent below 1990 levels by 2030.
  - 1990 total GHG emissions: 25.9 million GHG CO2 equivalent tons.
  - 2017 total GHG emissions: 10.6 million GHG CO2 equivalent tons (around 60 percent below 1990 level).
  - GHG emissions growing by an annual average rate of 3.2 percent per year since 2000.
  - 2018 primary energy supplies: 65 percent natural gas, 10 percent oil products.
  - Energy sector produced over 95 percent of all CO2 emissions in 2017 (or 66 percent of all GHG emissions).
  - Planned mitigation includes new nuclear power plant and smaller hydroelectric power plants; without these Armenia likely to exceed its Paris Agreement target.
- Adaptation readiness and international indices:
  - INFORM Risk Index: Armenia ranked as a high-risk country.
  - ND-GAIN rank: 52nd least vulnerable out of 181 countries, with above-average readiness.
- National Adaptation Plan:
  - First National Adaptation Plan produced in 2021 listing 26 measures.
  - Assessed cost AMD560m (0.01 percent of GDP).
  - Plan focuses on establishing technical capacity rather than specific resilience actions.
  - Action 2.1 and Action 2.4 (guideline integration and mapping/database) delayed from early 2022 to 2023.

### Macroeconomic and fiscal scenario outcomes (comparative results and parameters)
- Scenarios modelled: Paris scenario, unmitigated scenario (RCP8.5), volatile scenario (higher temperatures plus increased volatility).
- Scenario outcomes by 2072 (relative to baseline, absent fiscal response unless noted):
  - Volatile scenario: climate change could reduce GDP per capita by 18 percent relative to baseline by 2072; public debt could increase to 140 percent of GDP by 2072.
  - Accounting for temperature alone (not volatility): GDP would be 3 percent smaller and public debt 62 percent of GDP by 2072.
  - Paris scenario: GDP unchanged from baseline and public debt 46 percent of GDP by 2072.
- Adaptation effects:
  - Reducing adaptation time from 30 to 20 years in the unmitigated scenario could reduce GDP impact by 1.7 percentage points and lower public debt from 62 to 54 percent of GDP by 2072.
- Fiscal adjustment trade-offs:
  - Trimming expenditure to match slower growth could avoid debt buildup but implies a 14 percent reduction in real primary spending per capita relative to baseline in 2072.

### Fiscal policy implications and numeric consolidation requirements
- With inflation assumed unchanged, decline in labor productivity reduces nominal GDP; natural disasters could cause inflation spikes.
- Revenue assumptions: revenue declines in line with nominal GDP so revenue-to-GDP ratios held constant.
- Primary expenditure assumption: rigid in dram terms and held unchanged from baseline (initial assumption).
- Consolidation requirements to stabilize debt-to-GDP in 2072:
  - Baseline scenario: 0.5 percent of GDP required.
  - Unmitigated scenario: 1.3 percent of GDP required.
  - Volatile scenario: 5.3 percent of GDP required.
- Example of expenditure flexibility:
  - If primary expenditure reduced in line with revenue in the volatile scenario, debt-to-GDP ratio rises by only 8 percentage points relative to baseline in 2072; achieving this requires real primary expenditure per capita to be 14 percent lower relative to baseline in 2072.
- Adaptation investment context:
  - Global public adaptation needs in 2030 are estimated at around ¼ percent of world GDP per year.

### Disaster module (Natural Disaster Shock Module) — mechanics and availability
- Purpose: assess impact of different scales and magnitudes of natural disasters on macroeconomic and fiscal outcomes.
- Simulated shocks: pandemic, flood, drought, earthquake, severe storms.
- Features:
  - Records discretionary fiscal policy or materialization of contingent liabilities.
  - Underpinned by a simple production function; partial equilibrium with external and monetary sectors exogenous.
  - Generates scenarios from two variables: severity of disaster and preparedness of the country.
  - Adaptable to country circumstances; requires national macroeconomic and fiscal data with global impacts as starting point.
- Availability: Module piloted and will be available on the IMF’s Fiscal Risk Portal in 2023.

### Discrete fiscal risks, SOEs, PPPs, guarantees and sector exposures
- Two general types of discrete climate risk:
  - Direct physical risks to assets (higher temperatures, reduced precipitation/water flow, increased natural disasters).
  - Transition risks (policy, technology, international commitments affecting asset and contract viability).
- Channels of fiscal exposure: PPPs, SOEs, government guarantees, government projects.
- Concentration of SOE assets and contingent liabilities:
  - More than 60 percent of SOE assets (equivalent to around AMD473bn or 7.0 percent of GDP) concentrated in energy sector.
  - Another 6 percent of SOE assets in water sector, and 4.5 percent in transportation sector.
  - Liabilities of energy sector SOEs: around AMD400bn (5.7 percent of GDP).
  - Government contingent liabilities:
    - Around AMD256bn (3.6 percent of GDP) from power-purchase agreements in the energy sector.
    - AMD87bn (1.2 percent of GDP) from a PPP in the water sector.
- Total SOE and contingent exposure aggregates:
  - Total assets of SOEs: AMD702bn (10.0 percent of GDP).
  - Total contingent liabilities from PPP & PPA: AMD482bn (6.9 percent of GDP).
- Sectors with concentrated climate risk: energy, water, transport — risks include reduced hydroelectric generation, infrastructure damage, force majeure on PPPs, compromised thermal plant efficiency and distribution networks.
- Ongoing disclosures: Ministry of Finance has begun initial disclosures in FRSs and intends to expand with ADB support.

### Hydroelectric power projections and losses (selected figures)
- Aggregate and cascade projections under the unmitigated scenario (Projected Change in River Flow in 2100 and Projected 2100 Power Generation):
  - Vorotan HPPs Cascade:
    - Power generated in 2007: 1,030 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): 45%
    - Projected 2100 Power Generation: 1,030 (million kWh)
    - Decrease from Current Generation: 0 (million kWh)
  - Sevan-Hrazdan HPPs Cascade:
    - Power generated in 2007: 521 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -36%
    - Projected 2100 Power Generation: 334 (million kWh)
    - Decrease from Current Generation: 188 (million kWh)
  - Dzora HPP:
    - Power generated in 2007: 86 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -25%
    - Projected 2100 Power Generation: 64 (million kWh)
    - Decrease from Current Generation: 21 (million kWh)
  - Small Scale HPPs:
    - Power generated in 2007: 216 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -24%
    - Projected 2100 Power Generation: 164 (million kWh)
    - Decrease from Current Generation: 52 (million kWh)
  - Total:
    - Power generated in 2007: 1,853 (million kWh)
    - Projected 2100 Power Generation: 1,592 (million kWh)
    - Total Decrease from Current Generation: 261 (million kWh)

### Project-level example: Akhouryan Irrigation Project (climate considerations)
- Design forecasts to 2100 included increased temperatures (4-6 degrees), reduced precipitation (22 percent), and river flow reduction (37 percent).
- Project dual role: climate adaptation (irrigation, gravity supply) and mitigation (reduce emissions by switching from pumped water to gravity flow enabling small hydropower).
- Major risks: farmers’ capacity to adapt and invest; regulatory and policy environment.
- Study did not analyze different climate and macroeconomic scenarios.

### Climate Public Investment Management (PIM) assessment and insurance-related fiscal risks
- Recommendation: undertake a Climate Public Investment Management Assessment to evaluate integration of climate risk in PIM and identify priorities.
- Suggested requirement: environmental and social impact studies at feasibility and appraisal stages to:
  - include analysis of the risk of climate change on the project, and
  - include analysis of the risk the project presents to meeting Armenia’s mitigation targets.
- Example: Environmental and Social Impact Study for Yerevan 2 CC Power Plant ArmPower CJSC includes detailed CO2 emissions assessment and consistency with NDC commitments.
- Agricultural insurance subsidy figures:
  - AMD256m in 2020 (around 50 percent of insured premia).
  - Doubled to AMD500m in 2021.
- Recommendation: Ministry of Finance should track liabilities under agricultural insurance and project future subsidy needs under different climate scenarios.

### Recommendations and immediate analytical/institutional priorities
- Long-term fiscal projections and analytical program:
  - 1. Adopt and refine long-term fiscal projections using Armenian medium term fiscal framework (MTFF) and Baseline macro assumptions (MoF, 2022-23).
    - First stage: refine projections using MTFF and agree assumptions within MoF, MoE and CBA (remainder of 2022).
    - Subsequently: analyze climate change effects on capital stock, labor supply, and monetary and external sectors (2023−24).
  - 2. Quantify effects of ageing and climate change on revenues and expenditures under different demographic and emissions scenarios; aim for publication in late 2023 (MoF, 2023).
  - 3. Analyze and publish effects of earthquakes and other non-climate but macro-fiscal risks (MoF, 2023-24).
  - 4. Initiate cross-government work program on fiscal implications of climate change adaptation and mitigation (MoF, CBA, MoE, MEnv, 2022−24).
- Discrete fiscal risk work:
  - 5. Examine discrete climate change-related fiscal risks, including budget vulnerabilities from PPAs, other major long-term contracts, and contingent liabilities (FRAD, 2022).
  - 6. Participate in National Adaptation Plan Action 2.4 (mapping and database on climate-change related risks) and incorporate into MoF’s fiscal risk database (MoF and Ministry of Environment, 2022).
  - 7. Undertake a Climate Public Investment Management Assessment (C-PIMA) and continue reforms recommended by the 2018 PIMA (MoF and MoE, 2023).
- Broader fiscal risk communication and coverage:
  - 8. Provide clearer FRS summaries of risks, magnitudes and likelihoods, and link key risks to policy measures (FRAD, Dec 2022; June 2023).
  - 9. Increase FRS coverage to include missing areas (e.g., natural resources and demographics) and summaries of risks covered elsewhere (FRAD, June 2023).
  - 10. Publish special one-off FRS chapters on particular concerns, starting with climate change in June 2023 and demographics in June 2024.
- Immediate institutional priorities:
  - Build Ministry of Finance capacity to assess long-term fiscal risks from climate change.
  - Coordinate across MoF, MoE, CBA, MEnv, and other agencies to integrate climate risk data (e.g., National Adaptation Plan mapping) into fiscal risk management.
  - Advance scenario-based fiscal projections incorporating productivity, interest rate, demographic and climate-driven macroeconomic effects.
  - Conduct sectoral and project-level assessments for water-dependent infrastructure and major energy projects vulnerable to reduced water flows and increased temperature volatility.

*Source: 1armea2022003 - PREFACE and selected sections (IMF staff mission report and Executive Summary extracted from the referenced PDF).*

### PREFACE ___________________________________________________________________________________________________ 6

### PREFACE

### Mission and Participants
- At the request of the Deputy Minister of Finance of Armenia, a team from the IMF’s Fiscal Affairs Department (FAD) undertook an in-person mission from July 14 to 25 in Yerevan to quantify long term fiscal risks from climate change.
- Mission leadership and team:
  - Led by Mr. Jason Harris (FAD).
  - Team members: Mr. Bryn Battersby (FAD), Mr. Mehdi Raissi (IMF Resident Representative), Mr. John Zohrab (FAD regional advisor), Mr. Jyoti Rahman (FAD short-term expert).
- Government counterparts met:
  - Minister of Finance, Mr. Tigran Khachatryan.
  - Deputy Minister Avag Avanesyan.
  - Mr. Ara Avetisyan (Head of Fiscal Risk Management Department), Mr. Narek Karapetyan (Macro Policy Department), Hrayr Yesayan (Budget Department), Samvel Khanvelyan (Public Debt department).
  - Deputy Minister Gayane Gabrielyan (Ministry of Environment), Deputy Minister Arman Khojoyan (Ministry of Economy), Acting Director Levon Azizyan (Bureau of Meteorology), Deputy President Davit Grigoryan (Urban Development Committee), Deputy Ministers Vache Terteryan and Kristine Ghalechyan (Ministry of Territorial Administration and Infrastructure), Board Member Hasmik Ghahramanyan (Central Bank of Armenia) and Director Armen Nurbekyan (Dilijan Training and Research Center).
- Development and international partners met:
  - Armineh Manookian Salmasi and Irina Ghaplanyan (World Bank), Natia Natsvlishvili, Diana Harutunyan and Hovhannes Ghazaryan (UNDP), Joao Pedro Farinha and Gregorio Belaunde (Asia Development Bank), Frank Hess and Andrea Baggioli (EU Delegation to Armenia).
- Technical contributors and presenters:
  - Mr Richard Hughes (Chair, United Kingdom Office of Budget Responsibility), Pierre-Alain Bruschez (Swiss Federal Finance Administration), Shota Gunia (Georgian Ministry of Finance).
- Acknowledgements: Assistance from Messrs. Ara Avetisyan and Hayk Ohanyan, Mr. Mehdi Raissi and team, and interpretation support from Ms. Lilit Simonyan and Ms. Marietta Sahakyan.

### Activities Undertaken
- Introduced approaches to fiscal and climate change modelling in working sessions with Ministry of Finance, Ministry of Economy and Central Bank staff.
- Discussed country experiences in assessing and reporting fiscal risks from climate change with international experts.
- Identified areas for further analytical work and cross-government collaboration on fiscal implications of climate change.

### Executive Summary — Key Findings (extracted from Preface and Executive Summary)
- Historical and projected temperature changes:
  - Average temperature in Armenia has already increased by 1.2 degrees Celsius since the 1990s.
  - Under the Representative Concentration Pathway (RCP) 8.5 or ‘unmitigated’ emissions scenario, Armenia’s average annual temperature is projected to rise by around 5.6 degrees above the 1990s average by the 2090−2100 decade.
  - Under RCP 6.0, the average annual temperature is projected to rise by around 3.5 degrees.
  - Under the unmitigated scenario, the number of summer days is projected to increase and the number of frost and ice days are expected to fall substantially by 2100.
- Variability and extremes:
  - Armenia’s temperature variance is already high—only three countries have more variable average annual temperatures—and is projected to increase further under unmitigated scenarios.
  - Historical seasonal change (1966–2016): average summer temperature rose by around 1.3 degrees; winter temperatures rose by 0.4 degrees.
  - Under the unmitigated scenario, average summer temperature is projected to be 7 degrees higher in 2090−2100 than the 1990s; average winter temperature projected to be around 5 degrees higher.
- Macroeconomic and fiscal impacts (scenario comparisons):
  - Three scenarios explored: Paris scenario, unmitigated scenario, volatile scenario (higher temperatures plus increased volatility).
  - Under the volatile scenario: climate change could reduce GDP per capita by 18 percent relative to baseline by 2072, and absent fiscal policy response, public debt could increase to 140 percent of GDP by 2072.
  - Accounting for temperature alone (not volatility): GDP would be 3 percent smaller and public debt 62 percent of GDP by 2072.
  - Paris scenario: GDP unchanged from baseline and public debt 46 percent of GDP by 2072.
  - Adaptation effect: reducing adaptation time from 30 to 20 years in the unmitigated scenario could reduce GDP impact by 1.7 percentage points and lower public debt from 62 to 54 percent of GDP by 2072.
  - Fiscal adjustment implication: trimming expenditure to match slower growth could avoid debt buildup but results in a 14 percent reduction in real primary spending per capita relative to baseline in 2072.
- Discrete fiscal risks and sector exposures:
  - Key discrete risks concentrated in energy, water, and transport sectors; large hydroelectric and water transmission projects vulnerable to a projected 40 percent decline in water flows.
  - Climate-related natural disasters could damage infrastructure, generate force majeure events for PPPs, and compromise thermal power plant efficiency and the transmission framework.
  - State exposures include guarantees, on-lent loan portfolio, SOEs and PPPs.
- Fiscal transparency and risk reporting:
  - Since the 2018 Fiscal Transparency Evaluation, coverage expanded to include risks from the environment, litigation, and financial sector, and more comprehensive PPP assessments.
  - Recommendations to increase impact and tractability of the Fiscal Risk Statement (FRS): include clearer summaries of key risks, broaden coverage to summarize risk assessments published elsewhere, standardize assessments, link identified risks to concrete policy measures, and publish one-off chapters on major issues (e.g., climate change, demographics).

### Recommendations (summary of Preface/Executive Summary recommendations)
- Long-term fiscal projections and analytical program:
  - 1. Adopt and refine long-term fiscal projections using the Armenian medium term fiscal framework (MTFF) and Baseline scenario macroeconomic assumptions (MoF, 2022-23).
    - First stage: refine projections using the MTFF and agree assumptions within MoF, MoE and CBA (remainder of 2022).
    - Subsequently: analyze climate change effects on capital stock, labor supply, and monetary and external sectors (2023−24).
  - 2. Quantify effects of ageing and climate change on revenues and expenditures under different demographic and emissions scenarios, aiming for publication in late 2023 (MoF, 2023).
  - 3. Analyze and publish effects of earthquakes and other non-climate but macro-fiscal risks (MoF, 2023-24).
  - 4. Initiate a cross-government work program on fiscal implications of climate change adaptation and mitigation (MoF, CBA, MoE, MEnv, 2022−24).
- Discrete fiscal risk work:
  - 5. Examine discrete climate change-related fiscal risks, including budget vulnerabilities related to power purchase agreements, other major long-term contracts, and contingent liabilities (FRAD, 2022).
  - 6. Participate in National Adaptation Plan Action 2.4 (mapping and database on climate-change related risks) and incorporate into MoF’s fiscal risk database (MoF and Ministry of Environment, 2022).
  - 7. Undertake a Climate Public Investment Management Assessment (C-PIMA) and continue reforms recommended by the 2018 PIMA (MoF and MoE, 2023).
- Broader fiscal risk communication and coverage:
  - 8. Provide clearer FRS summaries of risks, magnitudes and likelihoods, and link key risks to policy measures (FRAD, Dec 2022; June 2023).
  - 9. Increase FRS coverage to include missing areas (e.g., natural resources and demographics) and summaries of risks covered elsewhere (FRAD, June 2023).
  - 10. Publish special one-off FRS chapters on particular concerns, starting with climate change in June 2023 and demographics in June 2024.

### Immediate Analytical and Institutional Priorities Noted
- Build Ministry of Finance capacity to assess long-term fiscal risks from climate change.
- Coordinate across MoF, MoE, CBA, MEnv, and other agencies to integrate climate risk data (e.g., National Adaptation Plan mapping) into fiscal risk management.
- Advance scenario-based fiscal projections that incorporate productivity, interest rate, demographic and climate-change-driven macroeconomic effects.
- Conduct sectoral and project-level assessments for water-dependent infrastructure and major energy projects vulnerable to reduced water flows and increased temperature volatility.

*Source: 1armea2022003 - PREFACE (IMF staff mission report and Executive Summary extracted from the referenced PDF).*

### 3.      While the rise in temperature is projected to be fairly consistent across the country,

### 3.      While the rise in temperature is projected to be fairly consistent across the country,

### Observed and projected temperature and precipitation changes
- Average annual temperature in the key agricultural marzes of Ararat, Tavush and Syunik valleys is projected to rise from 10−14 degrees to 16−18 degrees, with greater increases in summer.
- Temperature increase under the unmitigated scenario is projected to be slightly higher in Ararat, Vayots Dzor, and Syunik than in other marzes.
- Precipitation is projected to fall in all marzes under the unmitigated scenario; the largest projected falls are in Ararat, Kotayk, and Gegharkunik (the immediate catchment area for Lake Sevan).
- Increased temperatures and reduced precipitation in agricultural valleys would exacerbate aridity and threaten the viability of the sector.

### Water resources and hydrology
- Most rivers in Armenia do not have a constant flow and dry out in the summer, with around 55 percent of the total annual river flow formed by snowmelt and precipitation in spring.
- Under the unmitigated scenario, the projected fall in precipitation would lead to a higher reduction in water flow of as much as 39 percent by 2100.
- Water flows into Lake Sevan would fall by a third.
- Flow into some reservoirs would fall by more than half.
- Some river basins (Vorotan and Voghji) may see more stability or even an increase in water flow where increased precipitation possibly outweighs faster rates of evaporation.

### Temperature variability, extreme events, and drought trends
- Between 1971−2000 and 1991−2020, both the mean and standard deviation of average annual temperatures have risen.
- Only three countries have a reported higher variance of temperatures than Armenia in the referenced dataset.
- The length of droughts each year has increased by 33 days over the period between 2000 and 2017, with the boundary of the drought zone expanding to now also include mountainous areas (UNFCC).
- From 1935 to 2016, average annual precipitation decreased by 9 percent.
- Under the unmitigated scenario, Armenia is projected to become substantially drier, with the SPEI index reaching -2 (or severe drought conditions) by the end of the century.
- Higher year-to-year variation is likely to lead to an increase in extreme weather events at the right tail of the distribution, increased health problems, lower productivity, drought-related water and food shortages, damage to infrastructure, and disruption in supply chains.

### Climate-related natural disaster frequency
- The most observed hazardous phenomena in Armenia during 1975−2016 were frost, hailstorms, strong winds and extremely heavy rainfalls.
- The frequency of climate-related natural disasters has been rising in Armenia.

### International exposure and adaptation readiness
- The European Commission’s INFORM Risk Index ranks Armenia as a high-risk country.
- The Notre-Dame Global Adaptation Initiative Index (ND-GAIN) ranks Armenia 52nd least vulnerable out of 181 countries, with above-average readiness.
- ND-GAIN notes mounting challenges in managing impacts on water resources, infrastructure, and climate-sensitive industries like tourism, but observes recent improvements in regulatory quality, rule of law, control of corruption, and business environment that improve adaptation capacity.

### Mitigation commitments, emissions and energy profile
- Armenia has pledged to reduce emissions to 40 percent below the emission levels in 1990 by 2030.
- Total greenhouse gas (GHG) emissions in 1990 were 25.9 million GHG CO2 equivalent tons.
- In 2017, total GHG emissions were 10.6 million GHG CO2 equivalent tons, or around 60 percent below the emissions level of 1990, and well below the target for 2030.
- GHG emissions have been growing by an annual average rate of 3.2 percent per year since 2000.
- In 2018, 65 percent of primary energy supplies came from natural gas, and another 10 percent came from oil products.
- The energy sector produced over 95 percent of all CO2 emissions in 2017 (or 66 percent of all GHG emissions).
- Energy emissions are expected to have risen in 2020 with the installation of a large new thermal power plant, and are projected to rise in the coming years with increasing demand.
- Planned mitigation includes construction of a new nuclear power plant and smaller hydroelectric power plants; without these measures Armenia is likely to exceed its GHG target under the Paris Agreement.

### National Adaptation Plan and institutional actions
- Armenia produced its first National Adaptation Plan in 2021, which lists 26 measures focused on (a) introducing and enhancing the National Adaptation Plan process in Armenia and (b) enhancing institutional and technical capacity for the National Adaptation Plan process.
- The National Adaptation Plan has an assessed cost of AMD560m (0.01 percent of gross domestic product (GDP)).
- The plan does not contain specific actions to build resilience and adapt to climate change; it focuses on establishing technical capacity to assess risks and needs.
- Actions of direct relevance for fiscal risk management include:
  - Action 2.1: The development of the guideline on integration of climate-related risk management considerations into sectoral and regional development strategies.
  - Action 2.4: The mapping and development of a database on climate-change related risks.
- Both Action 2.1 and Action 2.4 were originally due to be completed in early 2022 but have been delayed and are not expected to be completed until 2023.

### Fiscal risks from climate change: transmission channels and impacts
- Climate change can cause supply- and demand-side vulnerabilities affecting labor productivity, capital accumulation, and human health.
- Supply-side transmission channels (examples):
  - Land: land degradation with reduction in agricultural potential; scarce land resources in some regions.
  - Capital: faster depreciation of machinery and equipment; reallocation of resources from productive capital to adaptation investment.
  - Productivity: infrastructure degradation; deteriorations in population health.
  - Labor: reduced human performance due to higher temperature; loss of hours worked due to extreme temperatures.
- Direct public-finance impacts (examples):
  - Public spending to replace damaged infrastructure or buildings.
  - Social transfers to households affected by natural disasters.
  - Materialization of explicit contingent liability, e.g., insurance schemes backed by state guarantees.
  - Public investments and subsidies to mitigate and adapt to climate change.
  - Natural disaster emergency spending, compensation for financial losses, and repairing and rebuilding assets.
- Indirect public-finance impacts (examples):
  - Reduction of tax revenue due to a reduction in economic activity.
  - Increase of health care spending due to more diseases.
  - Materialization of implicit contingent liabilities, e.g., support to financial institutions in distress.
  - Impact on sovereign capacity to pay debt obligations over the medium-term due to budgetary funds reallocation towards recovery and reconstruction.

### Approaches to quantifying climate change fiscal risks
- Analyzing the long-run effect of climate change on the economy and consequences for the fiscal position: designs simplified long-run frameworks, models scenarios of climate change impacts on economy and fiscal position, and refines as new research becomes available.
- Analyzing the potential fiscal impact of climate-change related natural disasters: quantifies impacts by analyzing historical and projected vulnerabilities to natural disasters and affiliated economic and budgetary costs.
- Analyzing other discrete fiscal risks related to climate change: e.g., budgetary implications of reduced power-generating capacity of hydropower installations because of drought or reduced river flows.
- Quantitative analysis of climate change impacts on government balance sheets: natural disasters and gradual environmental changes could damage or destroy public assets, requiring increased expenditure on maintenance and increased depreciation rates for public assets because of shorter life cycles.

*Source: 1armea2022003*

### Box 2. Climate Change Fiscal Risk Analysis in the United Kingdom and Georgia

### Box 2. Climate Change Fiscal Risk Analysis in the United Kingdom and Georgia

### United Kingdom: OBR framework and illustrative scenario
- Framework steps:
  - Created a simple long-term fiscal baseline for the budget deficit called the “stable deficit baseline.”
  - Layered the additional impact of periodic fiscal risks on top of that baseline to create the “historical shocks baseline.”
  - Added an “unmitigated global warming scenario” building on the RCP8.5-scenario.
- Key assumptions of the unmitigated global warming scenario:
  - Cost of adaptation: 0.3 percent of GDP a year.
  - Cost of natural disasters: twice as large (relative to baseline assumptions).
  - Frequency of natural disasters: occur twice as frequently (relative to baseline assumptions).
- Purpose and scope:
  - The simple framework provides illustrative scenarios to show the potential fiscal scale of climate change risks in the United Kingdom.

### Georgia: IMF-assisted fiscal risk assessment
- Analytical perspectives used by Georgia’s Ministry of Finance with IMF technical assistance:
  - Macroeconomic channel: examined the growing impact of higher temperatures on the macroeconomy and consequences for public finances.
  - Natural disasters channel: modelled the fiscal cost of more frequent and severe natural disasters, particularly floods, landslides, and droughts.
  - Discrete fiscal risks: qualitatively reviewed climate change-related discrete fiscal risks such as long-run power contracts, guarantees, and on-lent loans to state‑owned enterprises (SOEs) that may be affected by changing weather patterns.
- Key quantitative findings:
  - Climate change could reduce GDP per capita by 13 percent by the end of the century, relative to the baseline.
  - Climate change could increase public debt levels by 18 percent of GDP, relative to the baseline.
- Source references cited:
  - OBR (2021), Fiscal Risk Report July 2021.
  - Harris, J., et. al, (2022), “Georgia: Updating the Balance Sheet and Quantifying Fiscal Risks from Climate Change”, IMF Technical Assistance Report.

*Source: OBR (2021), Fiscal Risk Report July 2021, and Harris, J., et. al, (2022), IMF Technical Assistance Report.*

### 32.      With inflation assumed to remain unchanged, the decline in labor productivity

### 1armea2022003 - 32.      With inflation assumed to remain unchanged, the decline in labor productivity

### Impact on GDP, inflation, and external balances
- With inflation assumed to remain unchanged, the decline in labor productivity directly reduces nominal GDP.
- Climate change could cause spikes in inflation through natural disasters and other supply chain disruptions.
- Climate change may affect Armenia’s external balances, presenting another potential inflation risk.
- Historical single-event reference: a drought in 2000 likely caused around ½ percent of GDP damages with fiscal effect of around ¼ percent of GDP.

### Fiscal implications across climate scenarios
- Revenues:
  - Under each climate change scenario, revenue is assumed to decline in line with nominal GDP, so revenue-to-GDP ratios are held constant.
- Primary expenditure:
  - Primary expenditure is assumed to be rigid, and held unchanged in Armenian dram terms from the baseline (initial projection assumption).
- Outcomes:
  - Slower economic growth and worsening primary net borrowing result in gradual but significant increases in debt-to-GDP ratio over time.
  - Volatile scenario: increasing primary net borrowing requirements lead to a sharp ramp up in debt-to-GDP ratio as well as interest expenditures by the 2040s; public finances would be on an unsustainable trajectory without a fiscal adjustment.
  - Unmitigated scenario: primary net borrowing requirements are 0.8 of a percentage point larger relative to the baseline in 2072, leading to a 15 percentage points higher debt-to-GDP ratio.

### Disaster Module (Box 3) — purpose and mechanics
- Purpose:
  - The Natural Disaster Shock Module for the Fiscal Stress Test allows authorities to assess the impact of different scales and magnitudes of natural disasters on macroeconomic and fiscal outcomes.
- Shocks simulated:
  - Supply side disaster shocks—pandemic, as well as flood, drought, earthquake, and severe storms.
- Uses:
  - At onset of a disaster or for medium to long term scenario analysis to analyze:
    - the impacts of a disaster on individual sectors of the economy;
    - how these impacts feed into tax, expenditure, and deficit/financing/debt changes; and
    - implications for the macroeconomy and public finances overall.
- Features and assumptions:
  - Option to record discretionary fiscal policy or materialization of contingency liabilities.
  - Underpinned by a simple production function; analysis is partial equilibrium with external and monetary sectors assumed exogenous.
  - Generates macroeconomic and fiscal scenarios from two variables: severity of the disaster and preparedness of the country.
  - Adaptable to country circumstances; requires national macroeconomic and fiscal data with global impacts as a starting point.
- Availability note:
  - The Module is currently being piloted and will be available on the IMF’s Fiscal Risk Portal in 2023.

### Expenditure rigidity, adaptation, and policy implications
- Expenditure rigidity assumption:
  - Baseline projections assume spending under climate scenarios remains at the same levels as the baseline path, implying policymakers do not adjust fiscal settings in response to climate-induced slowdown (likely overstates fiscal impact).
- Flexibility parameter:
  - A parameter allows relaxation of expenditure rigidity; varies between 0 (fully flexible) and 1 (completely rigid).
  - As the parameter approaches 0, real primary expenditure per capita reduces so that the expenditure-to-GDP ratio is held constant; primary expenditure becomes significantly lower than the baseline.
- Quantitative example (volatile scenario):
  - If primary expenditure is reduced in line with revenue, debt-to-GDP ratio rises by only 8 percentage points relative to the baseline in 2072.
  - To achieve this outcome, real primary expenditure per capita needs to be 14 percent lower relative to the baseline in 2072.
  - Even with that reduction, this translates into a 2.5 percent a year increase in real primary expenditure per capita over the five decades to 2072.
- Adaptation parameter (m) and faster adaptation:
  - Kahn et al (2021) framework: countries adapt to higher temperatures over 30 years (central assumption).
  - Lowering the adaptation parameter to 20 (adapt in 20 years rather than 30) in the unmitigated scenario:
    - Reduces net primary borrowing requirements by 0.4 of a percentage point in 2072 compared with the central adaptation scenario.
    - Reduces debt by 8 percent of GDP compared with the central adaptation scenario.
- Adaptation investment:
  - Global public adaptation needs in 2030 are estimated at around ¼ percent of world GDP per year (Aligishiev and others).
  - No econometric analysis yet links adaptation investment with macroeconomic and fiscal outcomes for Armenia; IMF analysis suggests adaptation investment may be more costly than traditional public investment but could reduce fiscal impacts of natural disasters by increasing resilience.
  - Not all adaptation measures require public expenditure (examples: changing work hours by regulation; shifting agricultural output).
  - There is currently no detailed quantitative analysis of climate adaptation investment in Armenia.

### Implications for fiscal policy settings
- Consolidation requirements to stabilize debt-to-GDP in 2072:
  - Baseline scenario: fiscal consolidation of 0.5 percent of GDP required.
  - Unmitigated scenario: required consolidation is 1.3 percent of GDP.
  - Volatile scenario: required consolidation is 5.3 percent of GDP.
- Policy trade-offs:
  - A fiscal policy setting that is sustainable under the baseline may cease to be so with climate change.
  - Investing in climate adaptation and recalibrating fiscal policies as climate change unfolds can improve long-term fiscal aggregates.
  - Example outcomes:
    - In the unmitigated scenario, faster adaptation may raise debt in 2072 by 7 percent of GDP relative to the baseline instead of a 15 percent increase under central adaptation.
    - If primary expenditures decline in line with revenues in the unmitigated scenario, debt-to-GDP ratio would be less than one percentage point higher compared to the baseline.
    - In the volatile scenario, if primary expenditure evolves in line with revenue, debt would be around 8 percentage points higher in 2072 relative to the baseline.

### Discrete fiscal risks to government assets and contracts (SOEs, PPPs, guarantees)
- Two general types of discrete fiscal risk related to climate change:
  - Direct physical risks to assets (increasing temperature, reduced precipitation and water flow, increased natural disasters).
  - Transition risks (changing policy, technology and international commitments affecting asset and contract viability).
- Channels: PPPs, SOEs, government guarantees, government projects.
- Concentration of SOE assets and contingent liabilities:
  - More than 60 percent of SOE assets (equivalent to around AMD473bn or 7.0 percent of GDP) are concentrated in the energy sector.
  - Another 6 percent of SOE assets are in the water sector, and 4.5 percent in the transportation sector.
  - Liabilities of energy sector SOEs: around AMD400bn (5.7 percent of GDP).
  - Government exposure to contingent liabilities:
    - Around AMD256bn (3.6 percent of GDP) from power-purchase agreements in the energy sector.
    - AMD87bn (1.2 percent of GDP) in contingent liabilities from a PPP in the water sector.
- Sectors with concentrated climate risk:
  - Energy, water, and transport face significant risks from increasing temperatures, extreme temperature events, reduced water flows, increased wildfires, and landslides.
  - Impacts include reduced hydroelectric power generation, infrastructure damage, force majeure events for PPP contracts, and compromised efficiency of thermal power plants and distribution networks.
- Ongoing risk identification:
  - Ministry of Finance has begun initial disclosures of these risks in the FRSs and intends to expand disclosures with support from the Asian Development Bank.

### Examples of SOE and PPP climate exposures (selected figures)
- Total assets of SOEs: AMD702bn (10.0 percent of GDP).
- Total contingent liabilities (CLs) from PPP & PPA: AMD482bn (6.9 percent of GDP).
- Selected sector-level exposures (from Table 4):
  - Energy sector SOE assets (examples): AMD327bn (4.6% of GDP) tied to Hydro power exposure; PPA CLs AMD102.1bn (1.5% of GDP).
  - Energy sector SOE assets (other exposures): AMD129bn (1.8% of GDP); PPA CLs AMD231bn (3.3% of GDP).
  - Transportation SOE assets: AMD15bn (0.2% of GDP); PPP CLs AMD139bn (2.0% of GDP).
  - Aggregate examples (multiple entries): SOE assets AMD280bn (4.0% of GPD) with PPA CLs AMD129bn (1.8% of GDP).
- Source of data for exposures: Republic of Armenia, (2022), 2023-2025 Medium Term Expenditure Framework.

### Project-level example: Akhouryan Irrigation Project (Box 4)
- Project context:
  - Kaps dam on the Akhouryan River near Gyumri was started but not completed; incomplete dam is a hazard due to potential extreme floods affecting downstream areas including Gyumri.
  - A 2014 feasibility study envisaged reconstruction of the dam and gravity supply of irrigation; at the time, assessment of climate change impacts was relatively new.
- Climate factors identified by the study:
  - Around 80 percent of land plots in Armenia characterized by desertification and various land degradation levels.
  - More than half of cultivated lands were irrigated and their share in crops production was 70 percent.
  - Temperature rise and decreased precipitation will expand areas needing irrigation; increased evaporation could result in secondary salinization.
  - Heavy rainfall and floods will intensify water erosion; droughts and southern winds will cause further wind erosion.
  - Irrigation was the largest user of water in the Akhouryan basin with low use efficiency and high losses; reducing irrigation water demand via increased system efficiency would help combat runoff effects from climate change.
  - Project is a climate change adaptation project and also a mitigation project by reducing emissions from switching from energy-intensive pumping to gravity flow, and enabling renewable electricity by small hydropower plants.
  - Climate change will reduce water quality in the basin; combined with the project, the basin ecology would need further assessment.
- Specific design and forecasts included:
  - Study incorporated forecasts by 2100 of increased temperatures (4-6 degrees), reduced precipitation (22 percent), and river flow (37 percent).
  - Designs included facilities to cope with increasing hydro-meteorological hazardous events, including floods and mudslides.
- Major project risks noted:
  - Capacity of farmers to adapt practices and invest in on-farm facilities.
  - Regulatory and policy environment.
  - The study did not include analyses of possible impacts of different climate and macroeconomic scenarios.

### Hydroelectric power projections and losses (Table 5)
- Projected hydropower changes under the unmitigated scenario (Projected Change in River Flow in 2100 and Projected 2100 Power Generation):
  - Vorotan HPPs Cascade:
    - Power generated in 2007: 1,030 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): 45%
    - Projected 2100 Power Generation: 1,030 (million kWh)
    - Decrease from Current Generation: 0 (million kWh)
  - Sevan-Hrazdan HPPs Cascade:
    - Power generated in 2007: 521 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -36%
    - Projected 2100 Power Generation: 334 (million kWh)
    - Decrease from Current Generation: 188 (million kWh)
  - Dzora HPP:
    - Power generated in 2007: 86 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -25%
    - Projected 2100 Power Generation: 64 (million kWh)
    - Decrease from Current Generation: 21 (million kWh)
  - Small Scale HPPs:
    - Power generated in 2007: 216 (million kWh)
    - Projected Change in River Flow in 2100 (from baseline): -24%
    - Projected 2100 Power Generation: 164 (million kWh)
    - Decrease from Current Generation: 52 (million kWh)
  - Total:
    - Power generated in 2007: 1,853 (million kWh)
    - Projected 2100 Power Generation: 1,592 (million kWh)
    - Total Decrease from Current Generation: 261 (million kWh)
- Note: HPP is Hydroelectric Power Plant.
- Source for table: Stanton, et. al, (2008), “The Socio-Economic Impact of Climate Change in Armenia”, Stockholm Environment Institute.

*Source: Excerpt from IMF staff report content provided in the supplied PDF chapter/section.*

### 47.      It would be useful to undertake a Climate Public Investment Management

### 1armea2022003 - 47.      It would be useful to undertake a Climate Public Investment Management

### Climate Public Investment Management (PIM) assessment
- Recommend undertaking a Climate Public Investment Management Assessment to evaluate how climate change risk management is incorporated in Armenia’s PIM system and to identify priorities for improvement.
- Specific suggestion: require environmental and social impact studies in the feasibility and appraisal stage of projects to:
  - include analysis of the risk of climate change on the project, and
  - include analysis of the risk the project presents to meeting Armenia’s mitigation targets.
- Example provided: The Environmental and Social Impact Study for the Yerevan 2 CC Power Plant ArmPower CJSC includes a detailed assessment of expected CO2 emissions from operation of the plant and the consistency of those projections with Armenia’s NDC commitments.

### Climate-related fiscal risks and insurance exposure
- Climate-related fiscal risks from multiple sources should be regularly and carefully evaluated.
- Example risk: scheme for compensating landowners during droughts should be assessed given increasing severity and regularity of droughts in Armenia.
- Financial-sector linked climate risks should be considered; central banks are increasingly examining climate-related risk management.
- In Armenia, the Ministry of Finance should continue to track liabilities under the agricultural insurance system and project future subsidy requirements under different climate scenarios.
- Exact subsidy figures for the agricultural insurance scheme:
  - AMD256m in 2020, or around 50 percent of the insured premia,
  - doubled to AMD500m in 2021.
- Note: Insurance programs can distribute risk effectively, but government exposure to these programs should be carefully assessed given increasing climate change-related events.

*Italicized footnotes and references in the source omitted from this summary.*

### Future work program — overall objective
- The analysis presented provides a preliminary, broad-brushed analysis of macro-fiscal risks from climate change that should be refined.
- The mission provided the analytical framework and simulations to the Ministry of Finance, which should continue to improve the analysis over the remainder of 2022 before presenting results in the next FRS.

### A. Refinements and extensions to macro-fiscal analysis
- Update input data:
  - Replace WEO data used in the analysis with Armenia’s medium term fiscal framework. This task can be performed in the next few months.
- Reassess productivity convergence assumptions:
  - Baseline scenario currently assumes long-run convergence to the OECD historical average.
  - Suggest using countries that successfully transitioned from centrally planned to market economy as a more appropriate convergence benchmark.
  - Authorities could perform a productivity analysis for Armenia in the next few months to set the productivity convergence assumption.
- Reassess interest rate assumptions:
  - Current analysis assumes an interest rate of 4.6 percent.
  - Long-run interest rates may be affected by possible loss of concessional loans, risk premia, and slowing productivity growth.
  - Authorities could perform a detailed analysis of long-term interest rates in the coming months.
- Assess Armenia’s climate adaptation needs and quantify fiscal effects:
  - Work across relevant government parts to analyze adaptation policies beginning in the next few months.
  - Initial stages: qualitative assessment, followed by quantification of costs and benefits, and ultimately financing options and strategies.
- Incorporate the effect of ageing on expenditure:
  - Baseline assumptions abstract away from ageing effects on the expenditure path.
  - Analytical framework can be amended to incorporate ageing effects, particularly healthcare and pensions.
  - If the Baseline is recalibrated with Armenia’s medium term fiscal framework and productivity adjustments in the next six months, this task can be performed in the first half of 2023.
- Assess sustainability of the revenue base in various demographic and climate scenarios:
  - Current analysis assumes revenue-to-GDP ratio remains constant in the long term.
  - Both climate change and ageing could affect the revenue base.
  - Authorities could explore long-run trends in Armenia’s revenue base in late 2023, assuming prior tasks are completed.
- Include external and monetary shocks:
  - Explore effects of external and monetary shocks from climate change, ageing, natural disasters, or geopolitical events on long-term fiscal projections.
  - Examples: climate change raising Armenia’s risk premia; supply chain disruptions causing inflation and exchange rate depreciation.
  - Given majority of Armenia’s public debt is denominated in foreign currency, these situations would present significant fiscal risk.
  - Include qualitative discussion in next FRS, followed by quantification approaches in 2023, possibly using the MoF’s macroeconomic model.
- Analyze the effect of climate change on the capital stock:
  - Disaggregate labor productivity projections into capital-labor ratio and total factor productivity using historical national accounts data and economic growth literature parameters.
  - Begin in 2023, starting in a stylized way and moving toward more robust analysis.

### Discrete climate risk analysis and Fiscal Risk Unit actions
- The FRS should provide additional analysis of discrete risks associated with climate change.
- The Fiscal Risk Unit should carefully examine discrete climate change-related fiscal risks, including vulnerabilities of the government budget to:
  - power purchase agreements (PPAs),
  - other major long-term contracts,
  - contingent liabilities.
- Include analysis of transition risks related to possible policy changes to mitigate and adapt to climate change.
- Ministry of Finance should participate in National Adaptation Plan Action 2.4 (mapping and development of a database on climate-change related risks) and map these risks into the Ministry of Finance’s database of fiscal risks.

### B. Broader fiscal risk work program and improvements to FRSs
- Recent institutional context:
  - Ministry of Finance’s Fiscal Risk Management Department has been reinvigorated and is now focused on preparing analysis and advice on policy measures to mitigate risks in addition to preparing annual FRSs.
- 2018 Fiscal Transparency Evaluation (FTE) assessment:
  - Fiscal risk reporting and management assessed as reasonably good but with clear areas for improvement.
  - Stronger areas: reporting and analysis of risks from macroeconomic, guarantees, financial sector, sub-nationals and public corporations.
  - Areas for improvement: standardizing and collecting various strands of risk analysis into a single document; development of long-term demographic related risk analysis and environmental risks.
- Improvements since 2018 in FRSs:
  - Expanded reporting and assessment of fiscal risks to include natural disasters, including climate change and government risk mitigation and adaptation policies, other environmental risks, litigation risks, financial sector risks and biological risks.
  - Expanded discussion around government financial assets, particularly the on-lending portfolio.
  - More detailed and comprehensive assessments of PPP fiscal risks.
- High-level improvements to incorporate in future FRSs (including ADB roadmap and FTE guidance):
  - Provide a clear upfront summary and guidance on where major fiscal risks lie, their size and probability.
  - Broaden fiscal risk statement to include key summaries of risk assessments published elsewhere (e.g., macroeconomic risk assessment in the medium-term expenditure framework, assessments of debt fiscal risks).
  - Incorporate missing areas per FTE categorization, including demographic and natural resources fiscal risks, and use of key risk mitigation measures such as reserves.
  - Further expand standardization and clearer descriptions of risks; ensure discussions form coherent analysis rather than collections of facts and observations.
  - Link individual risks to specific management and policy measures to reduce or react to risks when they crystallize.
  - Publish special chapters providing deep one-off analysis on particular areas of concern, examples include:
    - climate change fiscal risks from a macro-fiscal perspective (as exemplified in this report and the UK’s Office of Budget Responsibility’s July 2021 report),
    - demographic fiscal risks, as exemplified in long-term fiscal sustainability statements published in several countries, including Kazakhstan starting in 2022 and Georgia planned to start in 2023.

*Source: 1armea2022003 - 47.      It would be useful to undertake a Climate Public Investment Management (excerpt).*

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_Source: https://www.imf.org/-/media/files/publications/cr/2022/english/1armea2022003.pdf_