## clnea2022001

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### Box 1 — An Introduction to Climate Science: predictability, trends, extremes, and uncertainty
- Climate versus weather; predictability
  - The climate system is chaotic and hard to predict in the short term but can be predictable in the long term.
  - Chaos limits accurate weather prediction beyond a few days.
  - Boundary conditions (geography, global heat balance, greenhouse gases) constrain weather; stable weather patterns emerge over time if boundary conditions are not affected.
  - Climate = long-term distribution of weather; scientists use a 20- to 30-year time window to define a region’s climate.
- Observed trends
  - Temperature is increasing virtually everywhere.
  - Very high temperatures (based on historical climate) are becoming more frequent; very cold days are becoming less frequent.
  - Warming is faster at high latitudes and on land.
  - Strong confidence exists in detecting these trends and attributing them to increasing greenhouse gas concentrations.
- Extreme events, detection, and attribution challenges
  - Natural variability, especially for rainfall and extremes, can be mistaken for climate change; strong variability makes detection and projection hard in many areas.
  - To identify robust trends in climate extremes, many years of data are necessary; for extreme heat, extreme precipitation, and droughts the IPCC uses 50 years of data (Arias and others 2021).
  - Past observational biases (for example, missed hurricanes pre-satellite era) can distort trend estimates.
  - Attribution science compares simulated climates with and without growing greenhouse gas emissions to estimate changes in event probabilities; a significant change in probability supports attribution to climate change for increased likelihood, not for that specific event.
- Sources of uncertainty in climate scenarios
  - Internal variability: dominates the first 20 years of each scenario; quantifiable probabilistically with enough data.
  - Model uncertainty: unknown exact functioning of climate models; main source of uncertainty 20 to 50 years into the future.
  - Scenario uncertainty: depends on future technologies, socioeconomic developments, and policy; becomes more important 50 years into the future.
- Evidence synthesis and guidance for policymakers
  - Climatologists combine observations, models, and theory to detect trends, attribute causes, and project futures.
  - Economic policymakers should rely on scientific assessments (for example, IPCC reports and the interactive IPCC Atlas).

### Section 2 — Adaptative Capacity Indices: methodology, findings, and policy implications
- Frontier analysis and data sources
  - Staff calculations based on 2015–18 data from the European Commission, United Nations University Institute for Environment and Human Security, University of Notre Dame, and April 2020 World Economic Outlook.
  - AE = advanced economies; AFR = sub-Saharan Africa; APD = Asia and Pacific; EME = emerging markets; EUR = Europe; LIC = low-income countries; MCD = Middle East and North Africa; WHD = Western Hemisphere.
  - Method: stochastic production model of log adaptive capacity with single input = log GDP per capita (US$); disturbances = mixture of half-normal (inefficiency) and normal (measurement error).
  - Estimated linear coefficient = 1.7 with p-value < 0.001. Null of no technical inefficiency rejected at the 0.001 level.
- Composite indices construction
  - Built country-by-country adaptive capacity and exposure indices using:
    - European Commission’s Index for Risk Management,
    - World Risk Index (United Nations University Institute for Environment and Human Security),
    - University of Notre Dame’s Global Adaptation Index (disaggregated data obtained).
  - Adaptive capacity index captures socioeconomic, infrastructure, and institutional characteristics for climate risk management.
  - Exposure index measures exogenous physical disaster risks.
  - Composite indices standardized before averaging across the three sources.
- Key empirical findings: trends in disaster impacts and losses
  - Mortality trends
    - Deaths from natural disasters have steadily declined over the last hundred years despite population growth.
    - The deadliest natural disasters—drought, extreme temperature, floods, and storms—caused 25 deaths per 100,000 people in the 1920s compared with 0.33 deaths per 100,000 people in the 2010s, a 98 percent reduction.
    - Natural disasters accounted for 0.20 percent of all deaths in the 2010s (Global Burden of Disease 2018).
    - Cardiovascular diseases accounted for 31 percent of all deaths (for comparison).
    - Among climate disasters, deaths due to extreme temperature (both cold and hot) were the leading cause: 0.16 per 100,000 people.
    - Data sources: EM-DAT (CRED/UCLouvain) and global population data from the US Census; compiled by Our World in Data.
  - Economic losses
    - When accounting for rising wealth and population growth, economic losses due to climate disasters show modest or no upward trend globally.
    - Nominal economic losses increased over time, largely because populations are larger and wealthier.
    - Losses as a share of GDP sometimes show no trend or a more modest positive trend compared with nominal damages (literature: Bouwer 2011; Weinkle and others 2018; Pielke 2021; Barthel and Neumayer 2012; Estrada, Botzen, and Tol 2015).
  - Drivers of contained increase in losses
    - Development co-benefits and deliberate investment in risk reduction.
    - Technological and economic progress enable investments in protection; richer societies invest more in safety standards that reduce climate risks.
- Inequality, poverty traps, and distributional vulnerabilities
  - Adaptation is harder for the poor; rich households have greater adaptive capacity across all income levels.
  - Farmers in isolated rural areas are less able to adapt than urban dwellers.
  - Poor households suffer more from the same shocks because they live in more vulnerable locations and lack recovery resources (savings, finance, networks).
  - Climate change expected to be worse for the poor due to higher exposure and lower adaptive capacity.
  - Regional risks: sub-Saharan Africa, the Caribbean, and some countries in MCD and APD face particular risk with limited adaptation capacity.
  - Some countries may enter a vicious cycle of low growth and increasing climate vulnerability resembling a poverty trap; avoiding this may require capacity development, large investments, or external help.
- Integrating adaptation into the development agenda: principles and tools
  - Adaptation should be integral to development planning (Paris Agreement Article 7: “the global goal of enhancing adaptive capacity, strengthening resilience and reducing vulnerability to climate change, with a view to contributing to sustainable development”).
  - Resource allocation and trade-offs
    - Governments must allocate limited fiscal resources across competing needs, including adaptation, considering distributional effects.
    - Requires information on effectiveness and distributional impacts across programs and generations.
  - Cost-Benefit Analysis (CBA)
    - CBA assesses net contribution to society’s welfare, measures market and non-market impacts (environment, mortality, morbidity), monetizes via methods including Value of Statistical Life.
    - Standard procedure: estimate standing, monetize impacts, discount and sum net benefits to compute NPV; finance programs with NPV > 0.
    - Financing costs and macro-fiscal stability impacts should be included; positive NPV projects are not necessarily self-funding.
  - Constraints and prioritization
    - Fiscal space and capacity limits in some countries may prevent financing all viable projects.
    - Joint consideration of adaptation and other development priorities; rank projects using benefit-to-cost ratios if financing fungible.
  - Challenges in applying CBA to adaptation
    - Lack of data, uncertainty in estimating avoided costs, limited capacity.
    - Estimating benefits is especially challenging for systemic changes and tipping points.
    - Capacity building in public financial management and project implementation is important.
  - Long-lived projects and discounting
    - Long-lived adaptation projects have large upfront costs and slowly accruing benefits.
    - For programs > 50 years, a declining discount rate could be used.
    - Political difficulty: reluctance to invest beyond electoral cycles; CBA helps acknowledge full-lifetime benefits.
  - Distribution and equity within CBA
    - NPV measures aggregate net benefit; distributional impacts are critical and governments should estimate them.
    - Kaldor-Hicks principle: standard CBA treats everyone equally; governments can redistribute via compensation or fiscal policy.
    - When NPV < 0, standard recommendation is not to invest; governments may compensate those negatively affected instead.
    - Governments can use adaptation policies for redistribution within and between countries, balancing against other redistribution instruments.

### Box 2 — Adaptation Strategies for Countries Vulnerable to Droughts: impacts, policy areas, and decision frameworks
- Recent impacts and vulnerability
  - Recent droughts in Central Asia, Africa, and South America threatened millions and have long-lasting economic effects (food insecurity, migration, conflict).
  - As climate change intensifies, drought intensity and frequency will increase.
  - In sub-Saharan Africa, each drought reduces medium-term annual growth by 1 percentage point — about eight times that in the rest of the world’s emerging market and developing economies.
  - Low-income households are most severely affected; striking channels include increased school dropouts and deterioration of infant health.
- Key adaptation policy areas (IMF research findings)
  - Raise agricultural productivity and reduce sensitivity to inclement weather
    - Multipronged actions: improved mobile networks, irrigation, erosion protection, seed quality.
    - Ghana example: improved seed varieties, shade trees, improved irrigation bolstered cocoa drought resistance.
    - Ethiopia example: some farmers’ yields rose by up to 40 percent with wheat varieties resistant to rust.
    - Better mobile networks improve access to early warnings and weather information.
  - Invest in diversified energy sources
    - Electricity powers irrigation and deep tube-well pumps.
    - One-fifth of sub-Saharan Africa’s electricity is from hydropower and is susceptible to droughts.
    - Diversifying green electricity increases electrification, reduces emissions, and creates jobs.
    - Kenya example: access to electricity rose from 40 to 70 percent of the population largely through small, off-grid, solar-powered plants.
    - Pay-as-you-go mobile money models may improve accessibility and scalability.
  - Strengthen coping mechanisms
    - Higher incomes from diversification, access to finance, and social assistance buffer post-drought impacts.
    - Social protection programs can be adapted to cover drought and climate shocks.
- Equity considerations: compensation versus protective infrastructure
  - Compensation may be more efficient than investment when protective infrastructure has negative NPV even after non-market impacts.
  - Authorities should consider whether negative NPV costs align with society’s willingness to pay to address other inequities.
  - Alternatives: relocation subsidies or supports with less stringent conditionality if negative NPV beyond willingness to pay.
  - Consider moral hazard where post-disaster relief expectations reduce ex ante adaptation.
- Decision frameworks and analytical approaches
  - Multigoal analysis: incorporate goals beyond economic efficiency (poverty reduction, cultural heritage); monetize impacts where possible and assess trade-offs transparently.
  - Distributionally weighted CBA: calculate net benefits for groups and multiply by weights reflecting relative importance.
  - Social welfare function: combine goals and inequality aversion to maximize welfare subject to budget constraint (difficult in practice).
  - Cost-effectiveness analysis: when goals are specified, find least-expensive solution to attain them; beware of setting goals without cost–benefit comparison.
- When government intervention is warranted
  - Market failures justify intervention: positive externalities, public goods, coordination needs for networks, credit constraints, moral hazard.
  - Regulations (zoning, building codes, mandatory insurance) can mitigate moral hazard.
  - Removing policy-induced distortions (input subsidies, subsidized water) should be considered with distributional implications.
- Role of subsidies and government compensation
  - Subsidies needed for adaptations with positive externalities (R&D, protection of key assets).
  - In absence of externalities, subsidies can be inefficient; unconditional income transfers may be preferred for poverty alleviation.
  - Pricing risks correctly improves efficiency; government compensation can create excessive risk taking.
- International aid considerations
  - Growing demand for increased aid to low-income vulnerable countries based on equity arguments.
  - Conditionality of aid is double-edged: can mobilize resources but may produce investments that contribute less to society’s goals than alternative uses.
- CBA amid risks and deep uncertainty
  - CBA can weight NPVs across known-probability scenarios; expected NPV equals WTP if preferences are risk-neutral.
  - Idiosyncratic, poolable risks covered by actuarially fair insurance reduce uncertainty—expected NPV then approximates WTP.
  - Systematic, non-poolable risks require other valuation approaches (for example, option price measuring WTP before outcomes are known).
  - Deep uncertainty calls for complementing CBA with robust decision-making: choose solutions delivering acceptable outcomes across scenarios or optimized for worst-case outcomes.
  - Governments benefit from establishing transparent rules consistently applied across programs.
- Timing and sequencing guidance
  - Adaptation is dynamic and should be integrated into development planning now.
  - Timing depends on investment characteristics, local vulnerabilities, present climate, future expectations, risk tolerance, and finance access.
  - Waiting strategies: delay replacement of soon-to-be-obsolete capital; retrofit long-lived capital when appropriate.
  - Distinguish adaptations by capital depreciation relative to climate change rate.
  - Practical starting points: address present climate adaptation gaps, emergency preparedness, and retrofit wholly inadequate infrastructure.
- Guidance on climate projections by investment horizon
  - Up to 20 years
    - Natural variability dominates locally except for temperature and sea-level rise.
    - Model uncertainty dominated by natural variability; scenario uncertainty limited until midcentury.
    - Best estimates and very likely ranges for temperature and sea-level rise across emission scenarios are very similar until 2040.
  - 20 to 30 years
    - Model uncertainty becomes most important; use full set of climate model scenarios.
    - Distributions are not objective probabilities; averages are “best estimates”; 90 percent ranges reflect broad consensus.
  - Longer than 30 years
    - Scenario uncertainty becomes dominant; analysts should consider scenario runs separately and apply robust decision-making.
    - Match emission scenarios with appropriate socioeconomic scenarios.
  - Known robust outcomes: temperature increase, sea-level rise, glacier melt, and increased frequency/intensity of extremes in at least some regions.
- Reference lifespans for infrastructure (guidance)
  - Thermal power plants: 20 to 60 (years)
  - Buildings: 30 to 150 (years)
  - Transport: 30 to 200 (years)
  - Water: 30 to 200 (years)
  - Land-use plans: > 100 (years)

### Annex 1 — Climate Change Scenarios: SSPs, temperature, sea level, and socioeconomic matching
- Emission scenarios and SSPs
  - IPCC uses Shared Socio-Economic Pathways (SSPs) matched with radiative forcing (RF) in 2100 (W/m2). Example: SSP1–1.9 means SSP1 with RF = 1.9 W/m2 in 2100.
  - Effects of emissions on temperature become relevant around midcentury; sea-level rise effects become relevant after midcentury.
  - Sea-level rise has strong inertia; scenarios lead to similar best estimates at least up to 2050.
  - Annex excludes very-fast sea-level rise scenarios; a low-likelihood marine ice sheet instability scenario with extreme RCP8.5 could add an additional meter of sea-level rise by 2100 but is deeply uncertain.
- Temperature and sea-level projections (IPCC best estimates and ranges)
  - Best estimate of mean temperature change in 2046 to 2065 versus 1850–1900 ranges between 1.6°C and 2.4°C.
  - Best estimate of mean temperature change in 2081 to 2100 ranges between 1.4°C and 4.4°C.
  - Mean sea-level rise above 1995–2014 level:
    - 2050: 0.18 m to 0.23 m (range across scenarios).
    - 2100: 0.38 m to 0.77 m (range across scenarios).
  - Historical notes:
    - Global mean surface temperature increased by 1.1°C between 1850–1900 and 2010–2019.
    - Global mean sea level increased by 20 cm between 1901 and 2018.
- Annex Table 1.1 — Global Mean Surface Temperature Change (°C) by scenario and period (selected entries)
  - 2021–2046
    - SSP1–1.9: Best Estimate 1.5; Very Likely range 1.2 to 1.7
    - SSP5–8.5: Best Estimate 1.6; Very Likely range 1.3 to 1.9
  - 2046–2065
    - SSP1–1.9: Best Estimate 1.6; Very Likely range 1.2 to 2.0
    - SSP5–8.5: Best Estimate 2.4; Very Likely range 1.9 to 3.0
  - 2081–2100
    - SSP1–1.9: Best Estimate 1.4; Very Likely range 1.0 to 1.8
    - SSP5–8.5: Best Estimate 4.4; Very Likely range 3.3 to 5.7
- Annex Table 1.1 — Global Mean Sea-Level Rise (m) by scenario and year (selected entries)
  - 2030
    - SSP1–1.9: Best Estimate 0.09; Likely Range 0.08 to 0.12
    - SSP5–8.5: Best Estimate 0.10; Likely Range 0.09 to 0.12
  - 2050
    - SSP1–1.9: Best Estimate 0.18; Likely Range 0.15 to 0.23
    - SSP5–8.5: Best Estimate 0.23; Likely Range 0.20 to 0.30
  - 2100
    - SSP1–1.9: Best Estimate 0.38; Likely Range 0.28 to 0.55
    - SSP5–8.5: Best Estimate 0.77; Likely Range 0.63 to 1.02
- Socioeconomic scenarios (matching emissions and development pathways)
  - Analysts must match emission scenarios with socioeconomic scenarios because vulnerability and adaptation costs depend on both climate and development.
  - IPCC matched RCPs and SSPs: highest emission scenario now labeled SSP5–8.5; lowest emission scenarios matched with SSP1 (SSP1–1.9 and SSP1–2.6).
  - Some RCP–SSP combinations are incompatible; GHG emission scenarios and socioeconomic scenarios cannot be chosen independently.
  - Most models indicate that with no mitigation, RF would be between 6.0 and 7.0 w/m2 in 2100.
  - With national climate pledges and mitigation measures, global temperature is expected to rise by 2.7°C by the end of the century, in line with the SSP2–4.5 scenario (UN Environment Programme 2021).
- Annex Table 2.1 — Global Economic and Population Scenarios (selected years)
  - Table entries present GDP (T PPP), Population (M), GDP/Capita (‘000 PPP).
  - 2020
    - SSP1: GDP 102; Population 7,576; GDP/Capita 13,439
    - SSP2: GDP 101; Population 7,611; GDP/Capita 13,302
    - SSP3: GDP 97; Population 7,698; GDP/Capita 12,659
    - SSP5: GDP 102; Population 7,552; GDP/Capita 13,493
  - 2030
    - SSP1: GDP 291; Population 8,062; GDP/Capita 36,133
    - SSP2: GDP 231; Population 8,262; GDP/Capita 27,996
    - SSP3: GDP 174; Population 8,514; GDP/Capita 20,396
    - SSP5: GDP 365; Population 8,054; GDP/Capita 45,282
  - 2100
    - SSP1: GDP 565; Population 6,958; GDP/Capita 81,258
    - SSP2: GDP 539; Population 9,032; GDP/Capita 59,711
    - SSP3: GDP 270; Population 12,620; GDP/Capita 21,415
    - SSP5: GDP 1,031; Population 7,375; GDP/Capita 139,797

*Source: clnea2022001*

### Box 1. An Introduction to Climate Science

### Box 1. An Introduction to Climate Science

### Climate versus weather; predictability
- The climate system is chaotic and hard to predict in the short term but can be predictable in the long term.
- Chaos limits accurate weather prediction beyond a few days.
- Weather is constrained by stable exogenous forces called boundary conditions including geography and the global heat balance regulated by, among others, greenhouse gases.
- Stable weather patterns emerge over time assuming boundary conditions are not affected (for example, by increased anthropogenic greenhouse gas emissions).
- The long-term distribution of weather is called “climate.”
- Scientists use a 20- to 30-year time window to define the climate of a region; deviations from the norm must be persistent to imply that climate has changed.

### Observed trends
- It is certain that temperature is increasing virtually everywhere.
- Very high temperatures, defined based on the historical climate, are becoming more frequent.
- Very cold days are becoming less frequent.
- Warming is faster at high latitudes and on land.
- There is strong confidence in detecting these trends and in attributing them to increasing greenhouse gas concentrations.

### Extreme events, detection, and attribution challenges
- For rainfall and extreme events, natural variability of weather can be mistaken for climate change.
- Strong natural variability in rainfall patterns makes it hard to detect trends and to make projections in many areas of the world.
- Unusually long meteorological droughts, strong tropical cyclones, and extreme rainfall are rare but part of the historic climate of many regions.
- To identify robust trends in climate extremes, it is necessary to use many years of data.
  - For extreme heat, extreme precipitation, and droughts the Intergovernmental Panel on Climate Change (IPCC) uses 50 years of data (Arias and others 2021).
- Past observations were not as good as today; for example, bias in counting strong hurricanes before the satellite era (1970s) may reveal a secular positive trend that disappears after accounting for missed storms (Vecchi and others 2021).
- Attributing any extreme event to climate change is difficult.
  - Attribution science simulates climate with and without growing greenhouse gas emissions to estimate changes in the probability of observing a certain event.
  - If there is a significant difference in probabilities, it is reasonable to suspect that climate change is responsible for the increase in probability of observing a certain event, but not that specific event.

### Sources of uncertainty in climate scenarios
- Three sources of uncertainty:
  - Internal variability: due to the chaotic dynamics of climate; dominates the first 20 years of each scenario and can be quantified probabilistically if enough data is available.
  - Model uncertainty: emerges because the exact functioning of the climate system is unknown; it is impossible to know with what probability each model is the correct representation of the true climate system. This is a cause of deep uncertainty and is usually the main source of uncertainty 20 to 50 years into the future.
  - Scenario uncertainty: emerges because future concentrations of greenhouse gases depend on future technologies, socioeconomic developments, and policy. As these factors cannot be predicted in probabilistic terms, this is a second cause of deep uncertainty and becomes more important 50 years into the future.

### Evidence synthesis and guidance for policymakers
- Climatologists use multiple lines of evidence—observations, models, theory—to detect trends, attribute them to climate change, and make projections.
- Economic policymakers should rely on assessments by climate scientists.
- Useful references include IPCC reports and the interactive IPCC Atlas (Gutiérrez and others 2021).

*Source: clnea2022001 - Box 1. An Introduction to Climate Science*

### 2. Adaptative Capacity Indices

### 2. Adaptative Capacity Indices

### Frontier analysis of adaptive capacity
- Data and sources:
  - Staff calculations based on 2015–18 data from the European Commission, the United Nations University Institute for Environment and Human Security, the University of Notre Dame, and the April 2020 World Economic Outlook.
  - Note: AE = advanced economies; AFR = sub-Saharan Africa; APD = Asia and Pacific; EME = emerging markets; EUR = Europe; LIC = low-income countries; MCD = Middle East and North Africa; WHD = Western Hemisphere.
- Methodology:
  - Fitted a stochastic production model of log adaptive capacity with a single input: the logarithm of log GDP per capita in US dollars.
  - Disturbances assumed to be a mixture of two components:
    - A strictly nonnegative half-normal distribution capturing inefficiency.
    - A symmetric normal distribution capturing measurement errors and other shocks unrelated to adaptation (allowing a country to lie above the frontier).
  - The estimated linear coefficient is 1.7 with a p-value < 0.001.
  - The null hypothesis of no technical inefficiency is rejected at the 0.001 level.
- Composite indices construction:
  - Country-by-country composite indices for adaptive capacity and exposure to climate change risks built using:
    - European Commission’s Index for Risk Management,
    - World Risk Index by the United Nations University Institute for Environment and Human Security,
    - University of Notre Dame’s Global Adaptation Index (disaggregated information obtained).
  - The composite adaptive capacity index captures socioeconomic, infrastructure, and institution characteristics that contribute to climate risk management capacity.
  - The exposure index measures exogenous risks based on physical characteristics of natural disasters.
  - Composite indices built for every source using the aggregation rules inherent to each source.
  - Composite indices standardized before averaging across the three sources.

### Key empirical findings: trends in disaster impacts and losses
- Mortality trends:
  - Deaths from natural disasters have steadily declined over the last hundred years, despite fast global population growth.
  - The deadliest natural disasters—drought, extreme temperature, floods, and storms—caused 25 deaths per 100,000 people in the 1920s compared with 0.33 deaths per 100,000 people in the 2010s, a 98 percent reduction.
  - Natural disasters accounted for 0.20 percent of all deaths in the 2010s (Global Burden of Disease 2018).
  - Cardiovascular diseases accounted for 31 percent of all deaths (for comparison).
  - Among climate disasters, deaths due to extreme temperature (both cold and hot) were the leading cause: 0.16 per 100,000 people.
  - Data on victims of natural catastrophes are from EM-DAT, CRED/UCLouvain, Brussels, Belgium – www.emdat.be. Global population data from the US Census; compiled by Our World in Data.
- Economic losses:
  - When accounting for rising wealth and population growth, economic losses due to climate disasters exhibit a modest or no upward trend at the global level.
  - Nominal economic losses have increased over time, but a large fraction is due to a growing and wealthier population.
  - Losses expressed as a share of GDP sometimes show no trend or a more modest positive trend compared with nominal damages (cited literature: Bouwer 2011; Weinkle and others 2018; Pielke 2021; Barthel and Neumayer 2012; Estrada, Botzen, and Tol 2015).
- Drivers of contained increase in losses:
  - Development cobenefits and deliberate investment in risk reduction.
  - Technological and economic progress enable individual and collective investment in protection; richer societies invest more in safety standards that reduce climate risks.

### Inequality, poverty traps, and distributional vulnerabilities
- Differential adaptive capacity:
  - Adaptation is particularly challenging for the poor: rich households have greater ability to adapt than poor households in countries of all income levels.
  - Farmers in isolated rural areas have more limited adaptation capacity than urban dwellers.
  - Poor households suffer more from the same climate shocks because they tend to:
    - Live in more vulnerable dwellings and areas (e.g., steep terrain, low-lying flood-prone areas).
    - Lack access to resources that help recovery (savings, access to finance, support networks).
- Potential for worsened inequality:
  - Climate change is expected to generally be worse for the poor due to both higher exposure to climate hazards and lower adaptive capacity.
- Regional risks and the vicious cycle:
  - Sub-Saharan Africa, the Caribbean, and some countries in the Middle East and Asia and the Pacific are at particular risk from extreme weather with limited adaptation capacity.
  - Some countries may risk entering a vicious cycle of low economic growth and increasing climate vulnerability resembling a poverty trap.
  - Avoiding such a vicious cycle may require capacity development, large investments, or both, and external help may be necessary.
  - Many adaptation efforts overlap with other development needs (for example, enhancing agricultural technologies, improving communications, widening access to electric power, and strengthening coping mechanisms).

### Integrating adaptation into the development agenda: principles and tools
- Overall framing:
  - Adaptation should be an integral part of development planning in all countries; the Paris Agreement (Article 7) establishes adaptation as “the global goal of enhancing adaptive capacity, strengthening resilience and reducing vulnerability to climate change, with a view to contributing to sustainable development.”
  - Investments in climate change adaptation are similar to other development investments: objective is to maximize future welfare given available resources.
- Resource allocation and trade-offs:
  - Governments must allocate limited fiscal resources across competing needs, including adaptation, while considering distributional effects.
  - Requires information on effectiveness of spending across alternative programs and distributional impacts to prioritize interventions across sectors, income groups, and generations for a just and efficient sustainable development trajectory.
- Cost-Benefit Analysis (CBA):
  - CBA is a tool to assess net contribution of a program to society’s welfare based on society’s preferences.
  - CBA aims to measure all impacts, including externalities and non-market impacts (environment, mortality, morbidity), and monetize them using different methods.
  - CBA uses the Value of Statistical Life to value avoided deaths and captures morbidity impacts via direct health service costs and disutility of illness.
  - Standard procedure: estimate standing, monetize market and non-market impacts, discount and sum net benefits to compute net present value (NPV). Standard prescription: only programs with NPV > 0 should be financed.
  - Even projects with positive NPV are not necessarily self-funding; financing costs (welfare cost of raising taxes or impact of additional borrowing on costs and macro-fiscal stability) should be included in CBA.
- Constraints and prioritization:
  - Lack of fiscal space and capacity may prevent some countries (especially lower-income) from financing all viable projects; constraints also limit investment in education, health, and other development goals.
  - Adaptation and other development priorities should be jointly considered, including synergies and trade-offs.
  - If financing is fully fungible, competing programs should be ranked using CBA and programs with highest ranking financed; if not fully fungible, same logic and methods apply within earmarked financing projects.
  - Ranking projects using benefit-to-cost ratios helps select the most efficient and affordable set of projects.
- Challenges in applying CBA to adaptation:
  - Lack of data, uncertainty in estimating benefits (avoided costs), and limited capacity may limit practical implementation.
  - Estimating benefits is especially challenging for systemic changes and poorly understood tipping points; standard valuation methods may be inaccurate.
  - Large adaptation projects (for example, protecting large portions of coastline from sea-level rise) pose high cost and complexity in management.
  - Capacity building to improve public financial management (PFM) and project implementation is important.
- Long-lived projects and discounting:
  - Adaptation projects with very long lifetimes (coastal protection, climate-resilient infrastructure, water distribution systems) have large upfront costs and benefits that accrue slowly; predicting and monetizing long-term benefits is difficult and uncertain.
  - These challenges are shared with other long-lived investments (early age education, railroads, hydroelectric plants); CBA guidance from other sectors is applicable.
  - Choice of discount rate is very important and subject to uncertainty.
  - For programs stretching more than 50 years, a declining discount rate could be used.
  - A major political difficulty is reluctance to invest in projects with time horizons longer than usual electoral cycles; CBA helps acknowledge benefits over the entire lifetime.
- Distribution and equity within CBA framework:
  - NPV measures aggregate net benefit but distributional impacts are critical and should be estimated and assessed by governments.
  - Standard CBA sums gains and losses across society and generations, treating everyone equally (Kaldor-Hicks compensation principle).
  - Governments can redistribute aggregate gains via compensation or fiscal policy; distributional choices are normative and for governments to decide.
  - When NPV < 0 (cost of protection larger than its benefit), standard recommendation is not to invest; governments could compensate those negatively affected instead of financing protection.
  - Governments may use adaptation policies for redistribution within and between countries, potentially scaling adaptation beyond what CBA recommends for vulnerable groups or scaling back when net benefits accrue mostly to the wealthy.
  - Governments should weigh costs and benefits of redistributive adaptation programs against other redistribution instruments; a combination of efficient adaptation policies with dedicated redistributive programs could yield larger aggregate net benefits.

*Source: IMF Staff Climate Notes (section "2. Adaptative Capacity Indices")*

### Box 2. Adaptation Strategies for Countries Vulnerable to Droughts

### Box 2. Adaptation Strategies for Countries Vulnerable to Droughts

### Recent impacts of droughts and vulnerability
- Recent droughts in Central Asia, Africa, and South America still threatened the lives of millions.
- Droughts have long-lasting adverse economic effects and contribute to food insecurity, mass migration, conflict and fragility, and other socioeconomic concerns.
- As climate change intensifies, so will the intensity and frequency of droughts.
- In sub-Saharan Africa, each drought reduces medium-term annual growth by 1 percentage point.
- This is about eight times that in the rest of the world’s emerging market and developing economies.
- Low-income households are most severely affected because their coping mechanisms are limited and they often depend on rain-fed agriculture.
- A striking channel of impact is a marked increase in school dropouts and deterioration of infant health.

### Key adaptation policy areas (IMF research findings)
- When integrated into near- and medium-term budgets, the following policy areas can reduce vulnerability to droughts and support sustainable and inclusive growth:
  - Raise agricultural productivity and reduce sensitivity to inclement weather:
    - Multipronged actions: improvements in mobile networks, irrigation, erosion protection, and seed quality.
    - Ghana example: cocoa’s drought resistance was bolstered with improved seed varieties, planting non-coca trees for shade, and improving irrigation systems.
    - Ethiopia example: some farmers’ yields rose by up to 40 percent with the development of wheat varieties resistant to rust.
    - Better mobile phone networks improve access to early warning systems and weather information, including simple voice messages, enabling more productive and climate smart agriculture.
    - Raising farmers’ awareness accelerates implementation of measures.
  - Invest in diversified energy sources:
    - Electricity powers irrigation systems and deep tube-well pumps.
    - One-fifth of sub-Saharan Africa’s electricity is generated from hydropower, which is susceptible to droughts.
    - Diversifying green electricity sources increases electrification, reduces carbon emissions, and creates jobs.
    - Kenya example: access to electricity rose from 40 to 70 percent of the population in large part through small, off-grid, solar-powered energy plants.
    - The pay-as-you-go mobile money model may have advantages in accessibility and scalability.
  - Strengthen coping mechanisms:
    - Higher incomes from diversified sources, access to finance, and social assistance act as buffers post-drought.
    - Policies can support diversifying income sources and improving access to finance.
    - Social protection programs, such as health and unemployment insurance, can be adapted to cover impacts from droughts and other climate shocks.

### Equity considerations: compensation versus protective infrastructure
- Compensation might be more efficient than investments to achieve society’s equity preferences when protective infrastructure would have a negative NPV even after factoring non-market impacts.
- Authorities should consider whether the cost implied by a negative NPV is consistent with society’s willingness to pay to address other inequitable situations (for example, spending to reduce health risks).
- If the negative NPV is beyond what society would be willing to pay, alternative supports include relocation subsidies or other supports with less stringent conditionality.
- Moral hazard issues should be considered where post-disaster relief might still be expected in addition to ex ante compensation for those who chose to stay in risky areas.
- Governments can standardize how they assess trade-offs across different groups and goals to ensure consistency across programs and development investments.

### Decision frameworks and analytical approaches
- Multigoal analysis:
  - Governments establish goals beyond economic efficiency (for example, reducing poverty, preserving cultural heritage).
  - If impacts can be monetized (for example, via willingness to pay), they should be added to NPV calculations; if not, trade-offs should be assessed transparently and consistently.
  - Compare alternative development programs to select the most efficient combination (for example, means-tested cash transfers vs adaptation programs to reduce poverty).
- Distributionally weighted CBA:
  - Calculate net benefits for different groups and multiply by weights reflecting relative importance (examples cited: Adler 2016; Fleurbaey and Abi-Rafeh 2016; Robinson, Hammitt, and Zeckhauser 2016).
  - Using the same set of weights for all government programs helps deal with equity issues consistently.
- Social welfare function:
  - Combines alternative goals and inequality aversion; choose the combination of programs that maximizes social welfare given a budget constraint.
  - Hard to implement in practice because estimating a social welfare function is difficult.
- Cost-effectiveness analysis as alternative:
  - If there is a strong preference for specific results, cost-effectiveness analysis finds the least expensive solution to attain desired goals without estimating benefits.
  - Governments should be careful setting goals without comparing costs and benefits, because even the least-cost alternative may lead to a net loss for society if benefits are low.

### When government intervention is warranted: market failures and public goods
- In perfectly competitive markets, autonomous adaptation by firms and individuals would be efficient; this is a benchmark.
- In imperfect markets, adaptation can be inefficient for several reasons:
  - Positive externalities and public goods (information about climate change, emergency preparedness plans, seawalls, basic research) are underprovided by markets.
  - Networks (dikes, water, transportation) require coordination because adaptation in one component affects the rest; private adaptation tends to be underprovided.
  - The scope of cooperation needed depends on the externality’s extent (city, national, international).
  - Broader market imperfections (poor business environment, inefficient credit markets) hamper investment in climate-appropriate capital.
  - Moral hazard can cause insufficient adaptation investment if expectations of central government relief exist; regulations (zoning, building codes, mandatory insurance) can mitigate moral hazard.
  - Policy-induced distortions (for example, input subsidies, subsidized water use) can worsen scarcity and misallocate resources; removing distortions should be considered alongside distributional implications.
- Market failures, especially credit constraints for low-income consumers and small firms, can be severe; targeted market reforms enhance development and adaptation.

### Role of subsidies and government compensation
- Subsidies are necessary to provide socially optimal amounts for adaptations with positive externalities (for example, R&D in drought-resistant seeds, protection of key assets).
- In absence of externalities, subsidies can be inefficient and distort choices away from optimal consumption and investment.
- Increased taxation to finance subsidies may cause additional deadweight losses; unconditional income transfers may be preferred if the primary goal is alleviating poverty.
- If governments are expected to compensate agents for climate-event losses, subsidies to private adaptation may lower future government liabilities and reduce moral hazard; these effects show up as changes in government surplus in CBA.
- Overall efficiency improves if risks are fully priced; government compensation can lead to inefficient (excessive) risk taking at the social level.

### International aid considerations
- There is growing demand to increase international aid toward low-income vulnerable countries with large adaptation needs based on equity arguments.
- Conditionality of international aid on adaptation investments is double-edged:
  - Conditioning may mobilize resources toward vulnerable countries.
  - Conditioning may lead to investments that contribute less to society’s goals than alternative development projects.
  - Donors should weigh costs and benefits of adaptation investments against alternative uses of aid if adaptation finance is not additional to existing development assistance or lacks other strong development benefits.

### Cost-Benefit Analysis (CBA) amid risks and deep uncertainty
- CBA can account for risks with known probabilities by weighting NPV across scenarios to calculate expected NPV; aggregation may lose welfare effects of risk attitudes.
  - If preferences are risk-neutral, probability-weighted NPV equals society’s WTP.
  - With risk-averse preferences, risk matters beyond expected NPV.
- Idiosyncratic risks that can be pooled and covered by actuarially fair insurance reduce uncertainty to certain outcomes; expected NPV is then correct for WTP.
  - Example: pooling hail damage across many farmers approximates expected losses for society.
- Systematic risks (catastrophic, non-poolable) require different valuation approaches:
  - Option price: estimate society’s WTP for the program before knowing actual future outcomes; accounts for large welfare cost from rare catastrophic outcomes but is difficult to estimate.
- Deep uncertainty affecting long-term adaptation benefits and costs means CBA should be complemented by alternative decision methods:
  - Develop measures of social net benefits under alternative scenarios to inform choices reflecting societal attitudes toward known and unknown probabilities.
  - Policymakers may prefer solutions that deliver acceptable outcomes across most scenarios rather than higher expected benefits with high probability of large negative outcomes, or choose programs optimized for worst-case outcomes.
  - No single optimal method exists; various robust decision-making approaches have been proposed.
- Governments benefit from establishing transparent rules consistently applied across programs for adaptation and other development goals.

### Practical guidance on timing and sequencing of adaptation
- Adaptation is a dynamic, ongoing process that should be integrated into development planning; any new investment should reflect future climate effects over the investment’s lifetime.
- Adaptation should start now because climate has changed and will continue to change.
- Timing of investments depends on:
  - Characteristics of the investment, local vulnerabilities, present climate, expectations about future climate, risk tolerance, and impediments such as access to finance.
- Waiting strategies:
  - If capital will become obsolete soon, waiting to replace it with better-adapted capital may contain adaptation costs and result in tolerable damage.
  - If capital has a long remaining lifetime, retrofitting can be viable though more costly.
- Distinguish adaptations by capital depreciation rates:
  - Capital that depreciates faster than climate change: adaptation likely occurs as gradual adjustments over time; long-term planning may be less necessary.
  - Capital that depreciates more slowly than climate change: discrete investment choices can lead to suboptimal outcomes; real options and advanced planning can save costs (for example, building larger-than-needed foundations for seawalls).
  - Allowing faster depreciation of non-resilient capital can free resources for resilient capital.
- Practical starting points:
  - Address adaptation gaps to the present climate, especially when gaps are large and involve short-lived capital.
  - Emergency preparedness plans have large present and future benefits without infrastructure investment.
  - Rebuild or retrofit infrastructure wholly inadequate for present climate risks to accumulate knowledge and experience before forecasting long-term scenarios.

### Guidance on using climate projections by investment horizon
- For local-level climate projections, consult climatologists to adapt available knowledge to local conditions. Recurring features by investment horizon:
  - Up to 20 years:
    - Natural variability of climate likely stronger at local level than underlying climatic trends, except for temperature and sea-level rise.
    - Model uncertainty is dominated by natural variability; scenario uncertainty is limited until approximately midcentury.
    - Best estimates and very likely ranges of temperature and sea-level rise across emissions scenarios are very similar until 2040.
    - Uncertainty can be quantified probabilistically using observed climate distributions corrected by robust trends.
    - Deep uncertainty cannot rule out abrupt departures, especially of extremes, but scenarios typically display strong inertia in this time frame.
  - 20 to 30 years:
    - Model uncertainty is the most important source of uncertainty.
    - Use the full set of climate model scenarios to study distributions of future temperature and other variables, including extremes.
    - The distribution is not an objective probability distribution; the average is a “best estimate,” not an expected value.
    - The 90 percent range represents a broad area of consensus among climatologists, not a 90 percent confidence interval.
    - Scenarios can inform subjective probabilities for CBA or underpin robust decision-making methods under uncertainty.
  - Longer than 30 years:
    - Scenario uncertainty becomes dominant.
    - Analysts should consider different scenario model runs separately and apply robust decision-making methods under uncertainty.
    - Match emission scenarios with appropriate socioeconomic scenarios.
- Uncertainty declines from local to regional to global scales.
- Known robust outcomes: temperature will increase, sea level will rise, glaciers will melt, and extreme events will become more frequent and intense in at least some regions.
- Develop solutions now even if exact timing and locations are uncertain; flexible adaptation capital (for example, upgradable seawalls) is particularly valuable.

### Reference lifespans for infrastructure (as a guide)
- Thermal power plants: 20 to 60 (years)
- Buildings: 30 to 150 (years)
- Transport: 30 to 200 (years)
- Water: 30 to 200 (years)
- Land-use plans: > 100 (years)

*Prepared by Seung Mo Choi (African Department).*

### Annex 1. Climate Change Scenarios

### Annex 1. Climate Change Scenarios

### Emission scenarios and SSPs
- Climate scientists develop scenarios of future warming using representative emission scenarios from integrated assessment models.
- In the most recent Intergovernmental Panel on Climate Change assessment report (IPCC 2021b), each emission scenario is based on a storyline chosen among five standardized future socioeconomic, technological, and policy developments, called Shared Socio-Economic Pathway (SSP).
- The emission scenario is indicated with its level of radiative forcing (RF) in 2100. RF is the excess energy trapped by Greenhouse Gases (GHGs) in the atmosphere above the pre-industrial level and is measured in watts per square meter (W/m2).
- Example: the lowest warming scenario is SSP1–1.9, which indicates that SSP1 was used and the level of RF in 2100 is equal to 1.9 W/m2.
- The effects of alternative emissions trajectories on temperature become relevant around midcentury and on sea-level rise after midcentury.
- Sea-level rise has strong inertia, so all scenarios lead to similar best estimates at least up to 2050.
- Annex Table 1.1 does not include scenarios of very fast sea-level rise that are very uncertain. A low-likelihood marine ice sheet instability scenario, with the extreme Representative Concentration Pathway (RCP) 8.5 emission scenario, that could add an additional meter of sea level rise by 2100 cannot be ruled out due to deep uncertainties (Arias and others 2022, Box TS.4).

### Temperature and sea-level projections (IPCC best estimates and ranges)
- Best estimate of mean temperature change in 2046 to 2065 with respect to the 1850- to 1900-level ranges between 1.6°C and 2.4°C, and between 1.4°C and 4.4°C in 2081 to 2100.
- Mean sea-level rise above the 1995 to 2014 level:
  - Ranges from 0.18 m to 0.23 m in 2050.
  - Ranges from 0.38 m to 0.77 m in 2100.
- Note: Global mean surface temperature has increased by 1.1°C between 1850–1900 and 2010–2019.
- Note: Global mean sea level has increased by 20 cm between 1901 and 2018.
- The scenarios project sea-level rise in meters relative to a baseline of 1995–2014 sea level.

### Annex Table 1.1 — Global Mean Surface Temperature Change (°C) by scenario and period
- 2021–2046: Best Estimate and Very Likely range
  - SSP1–1.9: Best Estimate 1.5; Very Likely range 1.2 to 1.7
  - SSP1–2.6: Best Estimate 1.5; Very Likely range 1.2 to 1.8
  - SSP2–4.5: Best Estimate 1.5; Very Likely range 1.2 to 1.8
  - SSP3–7.0: Best Estimate 1.5; Very Likely range 1.2 to 1.8
  - SSP5–8.5: Best Estimate 1.6; Very Likely range 1.3 to 1.9
- 2046–2065: Best Estimate and Very Likely range
  - SSP1–1.9: Best Estimate 1.6; Very Likely range 1.2 to 2.0
  - SSP1–2.6: Best Estimate 1.7; Very Likely range 1.3 to 2.2
  - SSP2–4.5: Best Estimate 2.0; Very Likely range 1.6 to 2.5
  - SSP3–7.0: Best Estimate 2.1; Very Likely range 1.7 to 2.6
  - SSP5–8.5: Best Estimate 2.4; Very Likely range 1.9 to 3.0
- 2081–2100: Best Estimate and Very Likely range
  - SSP1–1.9: Best Estimate 1.4; Very Likely range 1.0 to 1.8
  - SSP1–2.6: Best Estimate 1.8; Very Likely range 1.3 to 2.4
  - SSP2–4.5: Best Estimate 2.7; Very Likely range 2.1 to 3.5
  - SSP3–7.0: Best Estimate 3.6; Very Likely range 2.8 to 4.6
  - SSP5–8.5: Best Estimate 4.4; Very Likely range 3.3 to 5.7

### Annex Table 1.1 — Global Mean Sea-Level Rise (m) by scenario and year
- 2030: Best Estimate and Likely Range
  - SSP1–1.9: Best Estimate 0.09; Likely Range 0.08 to 0.12
  - SSP1–2.6: Best Estimate 0.09; Likely Range 0.08 to 0.12
  - SSP2–4.5: Best Estimate 0.09; Likely Range 0.08 to 0.12
  - SSP3–7.0: Best Estimate 0.10; Likely Range 0.08 to 0.12
  - SSP5–8.5: Best Estimate 0.10; Likely Range 0.09 to 0.12
- 2050: Best Estimate and Likely Range
  - SSP1–1.9: Best Estimate 0.18; Likely Range 0.15 to 0.23
  - SSP1–2.6: Best Estimate 0.19; Likely Range 0.16 to 0.25
  - SSP2–4.5: Best Estimate 0.21; Likely Range 0.18 to 0.26
  - SSP3–7.0: Best Estimate 0.22; Likely Range 0.19 to 0.28
  - SSP5–8.5: Best Estimate 0.23; Likely Range 0.20 to 0.30
- 2100: Best Estimate and Likely Range
  - SSP1–1.9: Best Estimate 0.38; Likely Range 0.28 to 0.55
  - SSP1–2.6: Best Estimate 0.44; Likely Range 0.33 to 0.61
  - SSP2–4.5: Best Estimate 0.56; Likely Range 0.44 to 0.76
  - SSP3–7.0: Best Estimate 0.68; Likely Range 0.55 to 0.90
  - SSP5–8.5: Best Estimate 0.77; Likely Range 0.63 to 1.02

### Socioeconomic scenarios (Annex 2) — matching emissions and development pathways
- Economists and analysts must match emission scenarios and socioeconomic scenarios because vulnerability and adaptation costs depend on both future climate and future levels of development.
- Each socioeconomic scenario (SSP) provides an internally consistent set of assumptions on economic development, society, and technology.
- With the last assessment report, the IPCC has matched RCPs and SSPs to ensure consistency:
  - The highest emission scenario was previously labeled as RCP8.5 and now as SSP5–8.5.
  - The lowest emission scenarios, RCP2.6 and RCP1.9, are matched with SSP1 (SSP1–1.9 and SSP1–2.6).
- Some combinations of RCPs and SSPs are incompatible. The socioeconomic scenarios do not include climate change feedbacks on the economy nor the impact of economic growth on emissions.
- Hence, GHG emission scenarios and socioeconomic scenarios cannot be chosen independently: low (high) development scenarios are inconsistent with high (low) emission scenarios. The highest emission scenario, RCP8.5, is compatible only with SSP5.
- The highest emission scenario is often referred to as a “no policy” scenario but can be supported only by extreme use of coal and very fast economic growth.
- Most models indicate that with no mitigation, RF would be between 6.0 and 7.0 w/m2 in 2100.
- With national climate pledges and mitigation measures, global temperature is expected to rise by 2.7°C by the end of the century, in line with the SSP2–4.5 scenario (UN Environment Programme 2021).

### Annex Table 2.1 — Global Economic and Population Scenarios (selected years and indicators)
- Note: Table entries present GDP (T PPP), Population (M), GDP/Capita (‘000 PPP).
- 2020
  - SSP1: GDP 102; Population 7,576; GDP/Capita 13,439
  - SSP2: GDP 101; Population 7,611; GDP/Capita 13,302
  - SSP3: GDP 97; Population 7,698; GDP/Capita 12,659
  - SSP5: GDP 102; Population 7,552; GDP/Capita 13,493
- 2030
  - SSP1: GDP 291; Population 8,062; GDP/Capita 36,133
  - SSP2: GDP 231; Population 8,262; GDP/Capita 27,996
  - SSP3: GDP 174; Population 8,514; GDP/Capita 20,396
  - SSP5: GDP 365; Population 8,054; GDP/Capita 45,282
- 2050
  - SSP1: GDP 291; Population 8,062; GDP/Capita 36,133
  - SSP2: GDP 231; Population 8,262; GDP/Capita 27,996
  - SSP3: GDP 174; Population 8,514; GDP/Capita 20,396
  - SSP5: GDP 365; Population 8,054; GDP/Capita 45,282
- 2100
  - SSP1: GDP 565; Population 6,958; GDP/Capita 81,258
  - SSP2: GDP 539; Population 9,032; GDP/Capita 59,711
  - SSP3: GDP 270; Population 12,620; GDP/Capita 21,415
  - SSP5: GDP 1,031; Population 7,375; GDP/Capita 139,797
- Source note: This table is based on data from the Shared Socio-Economic Pathways (SSP) database hosted by the IIASA Energy Program. The underlying scientific data was published in Riahi and others (2017).

*Source: Annexes 1 and 2, clnea2022001 — Annex 1. Climate Change Scenarios*

### 2020. Invention and Global Diffusion of Technologies for Climate Change Adaptation: A Patent

### clnea2022001 - 2020. Invention and Global Diffusion of Technologies for Climate Change Adaptation: A Patent

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### Document context and placement
- NOTESNOTES
- Economic Principles for Integrating Adaptation to Climate Change into Fiscal Policy
- IMF STAFF CLIMATE NOTE 2022/001

*IMF | Staff Climate Notes*

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_Source: https://www.imf.org/-/media/files/publications/staff-climate-notes/2022/english/clnea2022001.pdf_