## 1. Climate Vulnerabilities and Policy and Institutional Readiness

## Source details

**Canonical URL:** [1. Climate Vulnerabilities and Policy and Institutional Readiness](https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021062-print-pdf.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/wp/2021/english/wpiea2021062-print-pdf.pdf.md)
- [Structured JSON version](/-/media/files/publications/wp/2021/english/wpiea2021062-print-pdf.pdf.json)

---

### Introduction and purpose
- Climate-related hazards and subsequent disasters affect lives, livelihoods, socio-economic systems, and infrastructure, resulting in high costs for economic growth and development.
- Purpose:
  - Analyze two aspects for Small Developing States (SDS) exposed to climate-related hazards: (i) additional spending needs to achieve selected SDGs and (ii) options to finance these needs.
  - Extend the IMF SDG Costing methodology (Gaspar and others 2019) for five SDGs and focus on general trends across income groups, regions, and sectors using publicly available data.

### Scope, definitions, and data vintage
- SDS definition: developing countries that are Fund members with a population below 1.5 million as of 2011 and income per capita below International Development Assistance-related level (IMF 2018).
- Five SDGs analyzed: health (SDG 3), education (SDG 4), WASH (SDG 6.1 and 6.2), energy (SDG 7.1.1), and roads (SDG 9.1.1).
- Country sample: 25 SDS distributed across lower middle-, upper middle-, and high-income groups in the Caribbean, Pacific, Sub-Saharan Africa (SSA), and the Middle East and North Africa (MENA).
- Climate vulnerability selection: SDS with substantial (i.e., above global average) climate vulnerabilities based mainly on the Notre-Dame Global Adaptation Initiative Index (ND-GAIN).
- Estimates based on information and data available at the end-2019.
- Data assembly: multidimensional database for 25 SDS from text mining and analysis of about 800 reports, projects, and development plans; validation with sectoral experts and development partners.

### Methodological contributions
- Country-specific costing accounting for climate exposure and vulnerabilities, including:
  - Spending to increase climate resilience of existing and new infrastructure in energy, roads, and WASH.
  - Maintenance, rehabilitation and upgrading, and faster capital depreciation of new infrastructure.
  - Use of country-specific unit costs from sectoral projects and estimates where possible.
- Creation of a multidimensional database for 25 SDS using extensive document mining and expert validation.
- Discussion of financing options: estimation of domestic revenue potential (including carbon taxation), examination of access to external financing for climate-related disasters, and consideration of debt vulnerabilities.

### Key quantitative findings (aggregate)
- Median additional annual spending needs for the median SDS to meet selected targets under the five SDGs between 2019 and 2030:
  - about 6.7 percent of 2030 GDP in 2030.
  - Physical infrastructure would need an additional 3.7 percent of 2030 GDP per year to reach SDG targets in 2030.
  - Health and education spending would need to increase by 3 percentage points of GDP until reaching 8 percent of GDP in 2030.
- Heterogeneity across income groups and regions:
  - Lower middle-income SDS: 8.6 percent of 2030 GDP (largest additional annual spending needs).
  - High-income SDS: 6 percent of 2030 GDP (lowest additional annual spending needs).
  - By region: Caribbean 7.7 percent of 2030 GDP; Pacific 6.5 percent of 2030 GDP; SSA and MENA 6.2 percent of 2030 GDP.
- Drivers of cost variation:
  - Country-specific unit costs, SDG performance and targets, rehabilitation, upgrades, maintenance, and climate resilience requirements.
  - Meeting SDGs often requires improving spending efficiency and reallocation, particularly in SDS with already significant health and education spending.

### Spending composition and sectoral results
- Median additional annual spending needed to meet the five SDS development goals: around 6.7 percent of 2030 GDP in 2030 (median).
- Physical infrastructure (energy, roads, WASH) median additional costs: 55 percent of total additional costs.
  - Equivalent: additional 3.7 percent of 2030 GDP every year until 2030 for physical infrastructure.
- Health and education:
  - SDS would need to increase spending by 3 percentage points of GDP to reach about 8 percent of GDP in 2030 (from about 5 percent now).
- By income group and sector:
  - Lower middle-income SDS: additional physical infrastructure spending about 60 percent of total additional cost; health requires a 2 percentage points of GDP increase by 2030 (from median current ~5 percent of GDP).
  - Upper middle-income SDS: health accounts for ~20 percent of total additional cost; health requires ~1.5 percentage points of GDP by 2030 (from ~5 percent now); education accounts for ~30 percent of total additional costs.
  - High-income SDS: health requires ~1 percentage point increase (compared to ~5.8 percent now); education increases are largest among income groups due to current lower level (~3 percent of GDP).
- By region and sector:
  - Additional costs largest in Caribbean SDS, then Pacific, Sub-Saharan Africa, and SSA+MENA.
  - Pacific SDS—largest additional costs in energy and roads.
  - SSA and MENA SDS—lowest additional spending needs for energy and roads but largest for WASH and health.
  - Education additional needs largest in Caribbean SDS.

### Cost of climate-resilient infrastructure
- Road infrastructure: climate-resilience cost could explain between 2-40 percent of the total cost to build new roads in the SDS sample.
- Rehabilitation, upgrading and maintaining existing road infrastructure accounts for about 36 percent of the total additional costs in the sample.
- WASH: capital maintenance estimated around 30 percent of initial capital; resilience improvement costs can vary between 5 to more than 100 percent of total capital cost depending on sensitivity to extreme events.
- Energy: uses country-specific estimates for new renewable generation, improved transmission efficiency, and maintenance.

### Spending reallocation and efficiency gains
- Median additional increase in spending to reach higher health outcomes varies between 1-2 percentage points of GDP by 2030, noting SDS median health expenditure already exceeds 5 percent of GDP per year.
- Median changes by income group (health):
  - Additional health spending (ppt of GDP): Lower Middle-Income 2.0; Upper Middle-Income 1.5; High-Income 1.0
  - Number of medical staff per 100 patients: 2.2; 1.2; 0.4
  - Other current and capital in total spending: -0.7; -17.3; -21.4
  - Wages to GDP per capita: -4.4; 0.7; 2.4
- Education median changes by income group:
  - Additional education spending (ppt of GDP): Lower Middle-Income 1.3; Upper Middle-Income 2.2; High-Income 2.6
  - Students to teacher ratio: 0.3; -1.6; 1.5
  - Enrolment rate in pre-primary to tertiary education: 22; 17; 22
  - Other current and capital spending in total spending: -6.9; -0.6; 7.0
  - Wages to GDP per capita: -1.3; 0.1; 0.2
- Reallocation and efficiency gains, together with improved public financial management and infrastructure governance, can provide some room to finance additional spending.

### Example applications and sectoral exposure
- Climate risks (8.5 scenario): by 2100 impacts of coastal flooding will affect anywhere between 5 to 80 percent of the population (country dependent).
  - Infrastructure and essential services likely impacted, particularly in coastal areas.
  - Travel and tourism can make up anywhere from 10 to 56 percent of a country’s GDP among the SDS.
- Energy example (SDS ‘A’, 2030):
  - Target: 100 percent renewable energy to all by 2030.
  - Baseline (2019): 100 percent population access to electricity; 67 percent RE; 33 percent fossil (diesel).
  - Consumption per capita: 2019: 662 KWh per capita; 2030: 1,755 KWh per capita.
  - Weighted unit cost: 6,100 US$/kW.
  - Years to complete: 11.
  - Average annual cost of required investment: 0.23 US$ billion.
  - Cost of depreciation at completion: 0.07 US$ billion.
  - % of 2030 GDP: 2.06%.
  - Assumption: immediate scale-up of spending maintained until 2030.
- Health example (SDS ‘B’):
  - Doctors per 1,000 population: 0.2 → 1.1.
  - Other medical personnel per 1,000 population: 1.0 → 3.8.
  - Doctor wages (ratio to GDP per capita): 13.9 → 8.0.
  - Other current and capital spending (% total spending): 70.2 → 63.8.
  - Results:
    - Health spending (percent of GDP): 3.3 → 6.6.
    - Per capita spending (USD 2019): 97.4 → 316.7.
    - Health outcomes (adjusted health and access index): 0.2 → 0.45.
  - Implication: increase overall health spending by about 3.3 percentage points of GDP between 2019 and 2030, plus targeted staffing and capital spending increases to peer medians; assumes wage bill declines toward peer ratios by 2030 with efficiency gains comparable to peers.

### Financing options, potential, and constraints
- Domestic revenue mobilization:
  - Estimated potential revenue gains: about 1 to 4 percentage points of GDP.
  - Increasing domestic revenue requires country-specific tax policy and administration efforts and continuous capacity development; gains will take time due to limited capacity, large informal sectors, and narrow tax bases in SDS.
  - Domestic revenue mobilization alone is insufficient to meet development needs.
- Energy and carbon taxation:
  - Introducing a carbon tax on fuel at $35 per ton could help generate additional revenues of between 0.4 - 1 percent of GDP in 2025 in modeled SDS.
  - Removing state utility subsidies for diesel costs and electricity tariff subsidies could reduce expenditure by about 1 percent of GDP in some SDS.
  - Domestic revenue gains from fuel taxation will erode over time with the switch toward renewable energy.
- External financing and climate funds:
  - MDBs in 2019 committed a total of US$61 billion in climate finance in all economies where they operate, US$41 billion of which was for low- and middle-income countries. Roughly $US38.5 million was from MDBs own accounts for low- and middle-income economies.
  - Climate funds, multilateral, bilateral, regional funds, regional insurance mechanisms, contingent financing arrangements, and private sector instruments exist but are often hard for SDS to access due to administrative requirements, capacity constraints, data availability, and high transaction costs.
- Barriers to accessing climate finance:
  - Complex application and accreditation procedures, heavy demands on project preparation and co-financing, limited digital infrastructure, lack of robust data, limited management capacity, weak alignment of public financial management with SDG/climate goals, and small domestic market scale.
- Fiscal space, debt sensitivity, and buffers:
  - SDS general government debt averaged around 58 percent of GDP at end-2019.
  - Debt by income group: 36 percent of GDP for lower middle-income SDS; 61 and 66 percent of GDP for high- and upper middle-income countries (respectively).
  - Following the pandemic, SDS debt ratios could increase up to 24 percentage points of GDP in two years.
  - Frequent shocks require maintaining fiscal and reserve buffers and limit borrowing space; quasi-continuous post-disaster reconstruction strains financing availability for other development goals.

### Tax frontier estimation (Annex 5) — key points
- Annex 5 approach accounts for size of tourism and agriculture sectors, government wage bills, geographic dispersion, and extent of remittances.
- Estimated potential to increase domestic revenue: about 1 to 4 percentage point of GDP.
- Tax gaps by income group:
  - High-income SDS: average gap between potential and actual tax revenue is about 4 percentage points of GDP.
  - Upper middle-income and lower middle-income SDS: gap about 1-1.8 percentage points of GDP.
- Domestic revenue gains are relatively small and backloaded compared to substantial development spending requirements.

### Key quantitative needs and conclusions
- Annual spending needs to reach SDG targets in health, education and physical infrastructure are around 6.7 percent of 2030 GDP in 2030, with significant variation across countries and sectors.
- Physical infrastructure accounts for 55 percent of total additional costs; an additional 3.7 percent of 2030 GDP per year is needed for physical infrastructure.
- Domestic revenue mobilization alone is insufficient and takes time; climate taxation has a signaling role to mobilize resources and access external financing.
- High debt sensitivity to frequent exogenous shocks quickly limits SDS borrowing space.
- Non-financial challenges include small economy size, limited implementation and administrative capacity, and data availability shortfalls.
- COVID-19 has exacerbated challenges and limited financing options; international technical and financial support is crucial to avoid development setbacks and to build forward better.

### Caveats, uncertainty, and next steps
- Estimates rely on aggregate information from existing sectoral projects and studies and are subject to significant uncertainty (e.g., impact of ongoing climate change).
- Assessment needs updating by country economists with authorities and sectoral experts to account for country specificity and the impact of the COVID-19 crisis on SDG performance, targets, and available financing—particularly in tourism-dependent SDS.
- Further refinement would improve reflection of risks given data availability constraints.

*Source: wpiea2021062-print-pdf - 1. Climate Vulnerabilities and Policy and Institutional Readiness — estimates and analysis based on data available at end-2019.*

### 1. Climate Vulnerabilities and Policy and Institutional Readiness _________________________________ 13

### 1. Climate Vulnerabilities and Policy and Institutional Readiness _________________________________ 13

### Introduction and purpose
- Climate-related hazards and subsequent disasters affect lives, livelihoods, socio-economic systems, and infrastructure, resulting in high costs for economic growth and development.
- Climate-related disasters and exposure to natural hazards can keep or move people back into poverty (Hallegatte and others 2016, 2017), creating a strong nexus between increasing climate resilience and achieving sustainable development.
- Purpose of the paper:
  - Analyze two aspects for Small Developing States (SDS) exposed to climate-related hazards: (i) additional spending needs to achieve selected SDGs and (ii) options to finance these needs.
  - Focus on general trends across income groups, regions, and sectors using publicly available data and information, extending the IMF SDG Costing methodology (Gaspar and others 2019) for five SDGs.

### Scope, definitions, and data vintage
- SDS definition used: developing countries that are Fund members with a population below 1.5 million as of 2011 and income per capita below International Development Assistance-related level as identified in the IMF Guidance Note (IMF 2018).
- Five SDGs analyzed: health (SDG 3), education (SDG 4), WASH (SDG 6.1 and 6.2), energy (SDG 7.1.1), and roads (SDG 9.1.1).
- Country sample: 25 SDS (Annex 1), distributed across lower middle-, upper middle-, and high-income country groups in the Caribbean, Pacific, Sub-Saharan Africa (SSA), and the Middle East and North Africa (MENA).
- Climate vulnerability selection: SDS with substantial (i.e., above global average) climate vulnerabilities based mainly on the Notre-Dame Global Adaptation Initiative Index (ND-GAIN).
- Estimates based on information and data available at the end-2019.
- Data assembly: constructed a multidimensional database for SDS largely excluded from prior work by gathering country and sectoral data through text mining and analysis of about 800 reports, projects, and development plans; validation with sectoral experts and development partners.

### Methodological contributions
- Three main contributions:
  - Country-specific costing that accounts for climate exposure and vulnerabilities. Cost estimates for physical infrastructure include spending to increase climate resilience of existing and new infrastructure in energy, roads, and WASH, and account for maintenance, rehabilitation and upgrading, and faster capital depreciation of new infrastructure. To the extent possible, country-specific unit costs derived from sectoral projects and estimates are used.
  - Creation of a multidimensional database for 25 SDS using extensive document mining and expert validation.
  - Discussion of financing options: estimation of domestic revenue potential (including carbon taxation), examination of access to external financing for climate-related disasters, and discussion of considerations (e.g., debt vulnerabilities) for development financing space in SDS.

### Key quantitative findings
- Median additional annual spending needs for the median SDS to meet selected targets under the five SDGs between 2019 and 2030:
  - about 6.7 percent of 2030 GDP in 2030.
  - Spending for physical infrastructure would need to be scaled up immediately by an additional 3.7 percent of 2030 GDP per year to reach the SDG targets in 2030.
  - Health and education spending would need to increase by 3 percentage points of GDP until reaching 8 percent of GDP in 2030.
- Heterogeneity across income groups and regions:
  - Additional annual spending needs are the largest in lower middle-income SDS: 8.6 percent of 2030 GDP.
  - Additional annual spending needs are the lowest for high-income SDS: 6 percent of 2030 GDP.
  - By region: Caribbean 7.7 percent of 2030 GDP; Pacific 6.5 percent of 2030 GDP; SSA and MENA 6.2 percent of 2030 GDP.
- Drivers of cost variation:
  - Country-specific unit costs, SDG performance and targets, rehabilitation, upgrades, maintenance of existing infrastructure, and climate resilience requirements.
  - Meeting SDGs often requires improving spending efficiency and reallocation, particularly in SDS with already significant health and education spending.

### Financing options, potential, and constraints
- Domestic revenue mobilization:
  - Potential revenue gains are relatively small—estimated between 1 and 4 percentage points of GDP.
  - Increasing domestic revenue requires consistent country-specific tax policy and administration efforts coupled with continuous capacity development; revenue gains will take time to materialize given SDS’ limited capacity, large informal sectors, and narrow economic and tax bases.
  - Domestic revenue mobilization efforts alone are insufficient to meet development needs.
- External financing and climate funds:
  - Untapped external financing options for climate change adaptation, mitigation, disaster preparedness and response exist, but SDS face difficulties accessing them due to administrative requirements, prerequisite arrangements, limited resources and capacity (including in public financial management), data availability, and high transaction costs.
- Fiscal space and shocks:
  - Frequent shocks (natural, climate-related, economic, and health disasters) limit the scope to finance development needs because SDS must maintain adequate buffers.
  - Debt is highly sensitive and increases rapidly following disasters, limiting borrowing space.
- Implication: external technical and financial support from the international community (e.g., in line with UNFCCC Paris Climate Commitments) is critical to ensure progress and avoid development setbacks following shocks.

### Caveats, uncertainty, and next steps
- Estimates rely on aggregate information from existing sectoral projects and studies to estimate the cost of climate-resilient infrastructure and are subject to significant uncertainty (e.g., impact of ongoing climate change).
- The assessment needs to be updated by country economists in collaboration with authorities and sectoral experts to account for country specificity and the impact of the COVID-19 crisis on SDG performance and targets, and available financing—particularly in tourism-dependent SDS.
- The paper’s estimates are subject to data availability constraints and would benefit from further refinement to better reflect risks.

*Source: wpiea2021062-print-pdf - 1. Climate Vulnerabilities and Policy and Institutional Readiness — estimates and analysis based on data available at end-2019.*

### Box 1: COVID-19 Crisis and Progress toward Sustainable Development in SDS

### Box 1: COVID-19 Crisis and Progress toward Sustainable Development in SDS

### Change in initial conditions
- COVID-19 impact on SDGs depends on (i) where each country was in terms of meeting its SDG targets at the pandemic’s outbreak (so-called ‘initial conditions’) and (ii) how the shock has affected SDG performance until now (‘change in initial conditions’).
- Initial economic conditions in terms of size of fiscal buffers and financing space are important determinants of shock absorption.
- Countries with better-prepared health and WASH sectors and/or that managed to timely and effectively channel resources toward these sectors saw fewer changes in SDG performance (IMF 2020 a b).
- Reallocation of efforts (including spending, particularly in countries with lower fiscal buffers) to fight the pandemic could have setbacks for other health or WASH outcomes, especially in countries with weaker preparedness and high vulnerabilities to epidemics, vector- and water-borne diseases, non-communicable diseases, nutrition and immunological challenges.
- The COVID-19 outbreak and containment measures have resulted in school closures and interrupted many ongoing investment projects, affecting education performance and causing capital losses.
- Capital losses are larger for SDS with high climate vulnerabilities and lower infrastructure resilience, resulting in a failure to provide regular and emergency infrastructure maintenance.
- Under the UNFCCC, advanced economies have committed to mobilizing US$100 billion per year by 2020 to help developing countries tackle and adapt to climate change; only a small part of this financing has been mobilized so far.

### Channels (how the shock transmits to SDG progress)
- Availability of financing is the key factor (‘channel’) for maintaining progress toward sustainable development and reaching 2030 targets for many countries, including SDS (Section V).
- The pandemic already affected SDS domestic revenue (e.g., from tourism) and constrains their capacity to mobilize domestic revenue and access external funds.
- Financing constraints will result in lowering and reallocating development spending in the next decade.
- Political economy will largely influence the type and speed of measures, including spending reallocation; socio-political priorities may shift given lack of available financing, inputs and mobility restrictions.
- Financing constraints, coupled with higher input costs, capacity constraints and socio-political pressures, pose serious threats to climate resilience in infrastructure and social sectors.

### Outcomes (sectoral effects and possible distributional patterns)
- Outcomes vary across SDGs depending on (a change in) initial conditions, financing and structural constraints, and the efficiency of implemented policy measures.
- There may be sectoral winners if health and well-being become top short-, medium- or long-term priorities, or if SDS concentrate efforts to adapt faster to on-line learning and work (Georgieva 2020).
- There may be sectoral losers if input cost and availability (through supply chain distribution) force SDS to postpone plans to increase climate resilience and move toward safely managed WASH and clean, affordable energy.
- Overall, SDS could fail in building back better if post-pandemic recovery plans do not result in increased preparedness and resilience of disaster risk and emergency systems, sectors, and infrastructure.
- The threat to sustainable development progress is larger if all countries fail to align policies that hasten economic recovery with those that lead to positive environmental outcomes; continued reliance on fossil fuels for energy generation, transport and production would compound future challenges.

### Conceptual considerations and implications for SDS
- The paper uses a simple conceptual framework linking (Change in) Initial Conditions → Channels (smaller fiscal space, political economy spending reallocation, input cost/availability) → Outcomes (Unchanged SDG Goals, Delays in Unchanged SDG Goals, Lower SDG Targets).
- Maintaining progress toward sustainable development and reaching 2030 targets requires more substantial reform and spending efforts than before the pandemic.
- COVID-19 can compound future climate challenges if mitigation efforts are not pursued as part of recovery plans.

### Data, scope and key numeric and classification points cited in the box
- Most data and information obtained for this study are from 2019, or the latest official country documents, communications or available sectoral projects.
- The authors focus on SDSs (IMF 2018) that have at least average exposure to past and future climate change risks and natural hazards; the country sample comprises 25 SDS:
  - Antigua and Barbuda, the Bahamas, Belize, Bhutan, Cabo Verde, Comoros, Djibouti, Dominica, Fiji, Federated States of Micronesia (FSM), Grenada, Guyana, Kiribati, Maldives, Mauritius, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Seychelles, Solomon Islands, Timor-Leste, Tuvalu and Vanuatu.
- In adapting infrastructure costing, the assessment increases depreciation to 6 percent, compared to 5 percent used in Gaspar and others (2019), to account for higher depreciation in climate-vulnerable SDS.
- In terms of road coverage, each country aims to reach at least 75 percent in the Rural Access Index (RAI) as in Gaspar and others (2019).
- IMF Guidance Note (IMF 2018) classifies SDS income groups as:
  - high-income SDS: GDP per capita above US$12476,
  - upper middle-income: GDP per capita between US$4036 and US$12475,
  - lower middle-income SDS: GDP per capita between US$1026 US$4035,
  - low-income SDS: GDP per capita below US$1025. (The sample does not include low-income SDS.)
- Over 110 countries have pledged to carbon neutralize by 2050.

*Source: Box 1: COVID-19 Crisis and Progress toward Sustainable Development in SDS, wpiea2021062-print-pdf*

### 8.5 scenario, by 2100 it is expected that impacts of coastal flooding will affect anywhere between 5 to 80

### wpiea2021062-print-pdf - 8.5 scenario, by 2100 it is expected that impacts of coastal flooding will affect anywhere between 5 to 80 percent of the population (country dependent)

### Climate risks and sectoral exposure
- Under an 8.5 scenario, by 2100 impacts of coastal flooding will affect anywhere between 5 to 80 percent of the population (country dependent).
- Infrastructure and essential services are likely to be impacted, particularly in coastal areas (Climate Central 2019).
- Travel and tourism sectors are likely to be affected by coastal flooding and coastal change; the sector can make up anywhere from 10 to 56 percent of a country’s GDP among the SDS (World Travel & Tourism Council 2019).

### Example application: Energy / Electricity (SDS ‘A’, 2030)
- SDS ‘A’ target: provide 100 percent renewable energy (RE) to all by 2030 using solar, wind and geothermal.
- Baseline (2019): 100 percent population access to electricity; 67 percent RE (mainly solar); 33 percent fossil (diesel).
- Consumption per capita:
  - 2019: 662 KWh per capita
  - 2030: 1,755 KWh per capita
- Weighted unit cost (generation, transmission, maintenance, distribution): 6,100 US$/kW
- Years to complete: 11
- Average annual cost of required investment: 0.23 US$ billion
- Cost of depreciation at completion: 0.07 US$ billion
- % of 2030 GDP: 2.06%
- Assumption: country A will immediately scale up spending and maintain this level until 2030.

### Other physical infrastructure: roads and WASH
- Road costing approach: additional kilometers estimated to ensure road access for all (proxied by RAI at least 75 percent), accounting for population and economic growth, rehabilitation, upgrade, maintenance, and a weighted unit cost by road type.
- WASH costing: calibrated using Hutton and Varughese (2016), and climate-event adjustments from Miyamoto (2019 a b) and Hallegatte and others (2019 a b); targeted population calibrated using population growth and internal migration projections until 2030.

### Social development sectors: health and education (methodology)
- Approach: estimate change in spending needed to improve health and education outcomes relative to peers with similar income and climate vulnerability but better sectoral outcomes, also using ND-GAIN Index for climate preparedness.
- Peer sample size per SDS: between 7 and 16 countries.
- Change in spending between 2019 and 2030 is a function:
  ∆ Spending_i,2030-2019 = f(Inputs_i^r, Gap2030, Costs_i^r, X_i,e,2030)
  - Inputs (I): numbers of teachers, doctors, other medical staff; current and capital spending on health and education vs. median peer.
  - Unit costs (C): average teacher and medical staff wages vs. median peer.
  - Other factors (X): population growth/structure, enrolment rates, economic growth.

### Example application: Health sector (SDS ‘B’)
- Main factors (2019 → 2030 targets / peers):
  - Doctors per 1,000 population: 0.2 → 1.1
  - Other medical personnel per 1,000 population: 1.0 → 3.8
  - Doctor wages (ratio to GDP per capita): 13.9 → 8.0
  - Other current and capital spending (% total spending): 70.2 → 63.8
- Results:
  - Health spending (percent of GDP): 3.3 → 6.6
  - Per capita spending (USD 2019): 97.4 → 316.7
  - Health outcomes (adjusted health and access index): 0.2 → 0.45
- Implications:
  - Country B would need to increase overall health spending by about 3.3 percentage points of GDP between now and 2030.
  - Additional spending to increase number of doctors and medical staff and other current and capital spending toward peer medians.
  - Assumes medical wage bill would progressively decrease to peer levels by 2030 while retaining qualified professionals and assumes efficiency gains comparable to peers.

### Aggregate results: cost of meeting development and climate goals (2030)
- Median additional annual spending needed to meet the five SDS development goals: around 6.7 percent of 2030 GDP in 2030 (median).
- Physical infrastructure (energy, roads, WASH) median additional costs: 55 percent of total additional costs.
  - Equivalent: SDS would need to spend 3.7 percent of 2030 GDP more every year until 2030 to reach SDG targets in physical infrastructure.
- Health and education:
  - SDS would need to increase spending by 3 percentage points of GDP to reach about 8 percent of GDP in 2030 (from about 5 percent now).
- By income group:
  - Lower middle-income SDS: additional physical infrastructure spending accounts for about 60 percent of total additional cost.
  - Lower middle-income: additional spending on health is significant — a 2 percentage points of GDP increase by 2030 compared to current median spending of about 5 percent of GDP.
  - Upper middle-income SDS: additional health spending accounts for about 20 percent of the total; health requires ~1.5 percentage points of GDP by 2030 (from ~5 percent currently). Education accounts for ~30 percent of total additional costs.
  - High-income SDS: health requires increasing current spending by about 1 percentage point (compared to about 5.8 percent of GDP now); education increases are largest among income groups due to current lower level (3 percent of GDP).
- By regions:
  - Additional costs largest in: Caribbean SDS, then Pacific, Sub-Saharan Africa, and SSA+MENA.
  - By sector: Pacific SDS — largest additional costs in energy and roads. Sub-Saharan Africa and MENA SDS — lowest additional spending needs for energy and roads but largest for WASH and health. Education additional needs largest in Caribbean SDS.

### Spending composition and drivers
- Drivers of additional spending:
  - Level of development and SDG performance.
  - Country-specific development targets (e.g., renewable energy goals).
  - Variation in unit costs of construction and service provision across regions.
  - Vulnerabilities to climate change and disasters.
  - Structural factors: geographic location and dispersion, imports, labor market, institutional preparedness, data availability.
- Interaction effects: weak governance, lack of maintenance, and limited data can increase unit costs and widen gaps.

### Cost of climate-resilient infrastructure
- For road infrastructure, climate-resilience cost could explain between 2-40 percent of the total cost to build new roads in the SDS sample.
- Rehabilitation, upgrading and maintaining existing road infrastructure accounts for about 36 percent of the total additional costs in the sample.
- For WASH, capital maintenance estimated around 30 percent of initial capital; resilience improvement costs can vary between 5 to more than 100 percent of total capital cost depending on sensitivity to extreme events.
- Energy infrastructure costing uses country-specific estimates for new renewable generation, improved transmission efficiency, and maintenance.

### Spending reallocation and efficiency gains
- Median additional increase in spending to reach higher health outcomes varies between 1-2 percentage points of GDP by 2030, noting SDS median health expenditure already exceeds 5 percent of GDP per year.
- Table: SDS median changes by income group (health)
  - Additional health spending (ppt of GDP): Lower Middle-Income 2.0; Upper Middle-Income 1.5; High-Income 1.0
  - Number of medical staff per 100 patients: 2.2; 1.2; 0.4
  - Other current and capital in total spending: -0.7; -17.3; -21.4
  - Wages to GDP per capita: -4.4; 0.7; 2.4
- Education median changes by income group:
  - Additional education spending (ppt of GDP): Lower Middle-Income 1.3; Upper Middle-Income 2.2; High-Income 2.6
  - Students to teacher ratio: 0.3; -1.6; 1.5
  - Enrolment rate in pre-primary to tertiary education: 22; 17; 22
  - Other current and capital spending in total spending: -6.9; -0.6; 7.0
  - Wages to GDP per capita: -1.3; 0.1; 0.2
- Reallocation and efficiency gains can provide some room to finance additional spending; improved public financial management and infrastructure governance are important.

### Financing for climate and development
- SDS tax revenue context:
  - Current tax ratio for lower middle-income SDS: about 17 percent of GDP, which is 2 percentage points of GDP below comparable peers.
  - Tax levels for upper middle- and high-income countries: broadly similar at around 19.5 percent of GDP and in line with peers’ median.
- Tax potential gaps:
  - High-income SDS: average gap between potential and actual tax revenue is about 4 percentage points of GDP.
  - Upper middle-income and lower middle-income SDS: gap is about 1-1.8 percentage points of GDP, implying need for structural and fiscal measures.
- Policy instruments mentioned: energy and fuel taxation reforms (e.g., excise taxation, a carbon tax, removing distortions) suggested as measures with far-reaching effects.

*Source: Authors’ estimates, IMF working paper content provided.*

### Annex 5 discusses the approach applied to estimate the tax frontier for SDS. These estimates account for, for

### wpiea2021062-print-pdf - Annex 5 discusses the approach applied to estimate the tax frontier for SDS. These estimates account for, for instance, the size of tourism and agriculture sectors, government wage bills, geographic dispersion of the country and the extent of remittances.

### Tax frontier estimation and general revenue findings
- Annex 5 applies an approach to estimate the tax frontier for SDS, accounting for factors such as the size of tourism and agriculture sectors, government wage bills, geographic dispersion, and the extent of remittances.
- Estimated potential to increase domestic revenue: about 1 to 4 percentage point of GDP.
- Domestic revenue gains from fuel taxation will erode over time, for example due to the switch towards renewable energy.
- These domestic revenue gains are relatively small and backloaded compared to substantial development spending requirements.

### Energy taxation: scope, calibration, and projected revenue
- Many SDS still rely on imported petroleum products, particularly diesel, for their energy needs.
- Policy options include adjusting indirect taxation (value-added tax and excise), import duties, and introducing carbon taxes on fuel products to reflect environmental and health externalities (e.g., GHG emissions and local air pollution).
- Electricity pricing should reflect supply costs that vary by type of electricity generation and depend on generation efficiency.
- Moving toward renewable energy, such as solar, can reduce both GHG emissions and generation costs and provide scope to remove electricity subsidies without large tariff increases.
- Various estimates show that introducing a carbon tax on fuel at $35 per ton (e.g., through increasing existing excise taxes) could help generate additional revenues of between 0.4 - 1 percent of GDP in 2025.
- In some SDS, removing the subsidy provided to state utilities for diesel costs and electricity tariff subsidies would reduce expenditure by about 1 percent of GDP.
- These additional gains fall short of fully financing development spending needs but serve an important signaling role that SDS are contributing to mitigation and mobilizing resources for resilience.

### Modeling and evidence base for carbon taxation estimates
- Results are based mainly on a mitigation spreadsheet model developed by Parry and others (2016 2017) and applied to Belize, St. Lucia, Grenada, Maldives, Micronesia, Seychelles and Tonga (Bonato and others 2018, Cheasty and others 2017, 2018, Daniel and others 2021, Davies and others 2019 a and b, Authors’ estimates).
- The sectoral model projects energy use as a function of factors such as exogenous technological change, global oil prices and domestic carbon taxation. The model calibration is country specific.
- For many SDS, incorporating the externalities from GHG emissions into existing fuel excises would have a similar effect as a carbon tax, since the bulk of their GHG emissions are from the combustion of fuels.

### Access to climate financing: instruments, scale, and challenges
- Multilateral Development Banks (MDBs) offer grants, contingent grants and concessional loans; together, the MDBs in 2019 committed a total of US$61 billion in climate finance in all economies where they operate, US$41 billion of which was for low- and middle-income countries. A significant portion of MDB climate finance includes trust-funded operations that are channeled through the MDBs. Nonetheless, roughly $US38.5 million was from MDBs own accounts for low- and middle-income economies.
- Climate Funds or Climate-Related Financial Intermediary Funds offer grants, contingent grants, concessional loans, equity, guarantees and results-based financing; these funds can mobilize additional private sector funding but the climate fund architecture is complex with many eligibility and processing requirements.
- Regional, country group or sector-specific funds provide targeted support (examples cited include Energy and Environment Partnership Africa and the Asia Pacific Climate Finance Fund).
- Bilateral funds (e.g., Global Climate Change Alliance (GCCA+), Nordic Development Fund) support mainstreaming, resilience, and sector-based adaptation and mitigation strategies.
- Regional insurance mechanisms (e.g., CCRIF, PCRIC) enable multi-country risk pooling and parametric policies that provide short-term liquidity when triggered; initiatives like PCRAFI provide disaster risk modeling and technical assistance.
- Contingent financing arrangements include World Bank CERC, CAT-DDO; IMF Rapid Credit Facility and Rapid Financing Instrument; ADB Policy-Based Contingent Financing Instrument; IMF Catastrophe Containment and Relief Trust for debt relief grants.
- Private sector leverage: governments seek private equity, debt, grants, guarantees, and institutional investments (pension funds, insurance companies). National Development Banks can play a role in supporting the low-carbon transition. IFC estimates the NDCs of 21 emerging market economies will represent US$ 23 trillion by 2030 in investment opportunities.
- Opportunities exist to mobilize national-level instruments such as green credits or loans for mitigation and the low-carbon transition.

### Barriers to accessing climate finance for SDS
- Common challenges include:
  - Climate funds can be complex with heavy demands on project preparation, co-financing, and prerequisite arrangements beyond the capacity of many small states.
  - Limited digital infrastructure and lack of access to robust data hinder proposal submission, monitoring and reporting.
  - Accreditation processes to become an intermediary are complex and rigorous.
  - Limited capacity affects management of climate-related projects, including infrastructure, social sectors and early warning systems.
  - Weak alignment of public financial management processes with SDG and climate resilience goals is a common institutional constraint.
  - Many fund processes reduce predictability and sustainability through lengthy, capacity-intensive accreditation and approval procedures that can take months or years without guaranteed results.
  - Limitations to accessing private financing stem from small domestic markets, inadequate policy environment and signaling, lack of information/data, and lack of necessary scale given geographic isolation, high cost and high risks.

### Fiscal space, debt sensitivity, and buffers
- SDS general government debt averaged around 58 percent of GDP at end-2019.
- Debt levels varied: 36 percent of GDP for lower middle-income SDS, 61 and 66 percent of GDP for high- and upper middle-income countries.
- Following the pandemic, SDS debt ratios could increase up to 24 percentage points of GDP in two years (Figure 8 based on the October 2020 and October 2019 editions of the World Economic Outlook).
- Frequent shocks necessitate maintaining liquid fiscal and reserve buffers, for instance in the form of emergency funds, and careful management of fiscal risks.
- Quasi-continuous post-disaster reconstruction and emergency repairs of climate-vulnerable infrastructure strain financing availability for other development goals.
- Limited fiscal space requires careful calibration of fund allocation across sectors, including cost-benefit analysis that considers second-round effects of additional SDG spending on economic growth and climate resilience.

### Key quantitative needs and conclusions
- Annual spending needs to reach SDG targets in health, education and physical infrastructure are around 6.7 percent of 2030 GDP in 2030, with significant variation across countries and sectors (e.g., largest additional cost estimated for lower middle-income SDS).
- Domestic revenue mobilization alone is insufficient to finance development needs and takes time; climate taxation plays an important signaling role to access external financing.
- High debt sensitivity to frequent exogenous shocks quickly limits SDS borrowing space and forces difficult choices across and within sectors.
- Non-financial challenges include small economy size, limited implementation and administrative capacity (e.g., public financial management, financing instrument analysis and use), and data availability shortfalls.
- The COVID-19 crisis has exacerbated challenges and limited financing options, with possible severe and long-lasting effects on SDS economies and populations.
- Advancing international cooperation through further technical and financial support is crucial; the post-pandemic recovery presents an opportunity to build forward better and improve SDS climate resilience.

*Source: wpiea2021062-print-pdf - Annex 5 discusses the approach applied to estimate the tax frontier for SDS. These estimates account for, for instance, the size of tourism and agriculture sectors, government wage bills, geographic dispersion of the country and the extent of remittances.*

### Annex 1. Country Sample

### Annex 1. Country Sample

### Sources and classification
- Sources: WEO (2019)
- Note: IMF Guidance Note (IMF 2018) classifies SDS in four income groups:
  - (i) high-income SDS, as countries with GDP per capita above US$12476,
  - (ii) upper middle-income, as countries with GDP per capita between US$4036 and US$12475;
  - (iii)  lower middle-income SDS as countries with GDP per capita between US$1026 US$4035; and
  - (iv) low-income SDS with GDP per capita below US$1025.
- Our sample does not include low-income SDS.

### Country sample (Income Group | Region | Island/Coastal/Landlocked as listed)
- Antigua & Barbuda — High-income WHDY
- Bahamas — High-income WHDY
- Belize — Upper middle-incomeWHDY
- Bhutan — Lower middle-incomeAPDY
- Cabo Verde — Lower middle-incomeAFRY
- Comoros — Lower middle-incomeAFRY
- Djibouti — Lower middle-incomeMENAY
- Dominica — Upper middle-incomeWHDY
- Fiji — Upper middle-incomeAPDY
- Grenada — Upper middle-incomeWHDY
- Guyana — Upper middle-incomeWHDY
- Kiribati — Lower middle-incomeAPDY
- Maldives — High-income APDY
- Mauritius — Upper middle-incomeAFRY
- Micronesia — Lower middle-incomeAPDY
- Saint Kitts and Nevis — High-income WHDY
- Saint Lucia — Upper middle-incomeWHDY
- Saint Vincent and the Grenadines — Upper middle-incomeWHDY
- Samoa — Upper middle-incomeAPDY
- Sao Tome and Principe — Lower middle-incomeAFRY
- Seychelles — High-income AFRY
- Solomon Islands — Lower middle-incomeAPDY
- Timor-Leste — Lower middle-incomeAPDY
- Tuvalu — Lower middle-incomeAPDY
- Vanuatu — Lower middle-incomeAPDY

### Aggregate figure reported in the sample
- Total SDS252131

*Source: WEO (2019).*

### REFERENCES

### REFERENCES

### Multilateral and development bank reports
- African Development Bank, Asian Development Bank, Asian Infrastructure Investment Bank, the European Bank for Reconstruction and Development, the European Investment Bank, the Inter-American Development Bank Group, the Islamic Development Bank, and the World Bank Group, 2020, Joint Report on Multilateral Development Banks’ Climate Finance, August 2020
- Climate Investment Funds, 2019, Preparing Outer Island Sustainable Electricity Development Project
- Green Climate Fund https://www.greenclimate.fund/about
- OECD and World Bank, 2016, “Climate and Disaster Resilience Financing in Small Island Developing States,” joint report by OECD, SISRI and the Climate Change Group of the World Bank, Washington, DC.
- OECD, 2018, “Climate-resilient Infrastructure. Policy Perspective,” OECD Environmental Policy Paper. 14, Paris, France.
- OECD, 2020, “Climate Finance Provided and Mobilized by Developed Countries in 2013-2018,” OECD, Paris, France.
- World Bank, 2016, “World Bank Group Engagement in Small States the Cases of the OECS, Pacific Island Countries, Cabo Verde, Djibouti, Mauritius, and the Seychelles — Clustered Country Program Evaluation,” Washington, DC.
- World Bank, 2020, “Understanding Poverty – Topics – Water – Overview,” World Bank Group Water Global Practice, Washington, DC. https://www.worldbank.org/en/topic/water/overview#2 (accessed 15 October 2020).
- World Travel & Tourism Council, 2019, Travel & Tourism Economic Impact Reports. https://wttc.org/Research/Economic-Impact.

### IMF policy papers, working papers, and staff notes
- Allen, R., M. El Rayess, L. Doherty, and P. Goel, 2020, “Review of the Public Financial Management Reform Strategy for Pacific Island Countries, 2010-2020,” IMF Working paper 20/183, International Monetary Fund, Washington, DC.
- Daniel, J., A. Banerji, A. Blackman, S. Ester, T. Moeaki, R. Neves, N. Palu, V. Piatkov, D. Prihardini, C. Sandoz, and A. Zdzienicka, 2020, “Climate Change Policy Assessment for Tonga,” April 2020, International Monetary Fund, Washington, DC.
- Daniel, J., L. Doherty, G. Preston, T. Schneider (2021) “Barriers Preventing Pacific Island Countries Access to Climate Financing,” IMF Working Paper forthcoming, International Monetary Fund, Washington, DC.
- Fenochietto, R. and C. Pessino. 2013, “Understanding Countries’ Tax Effort,” IMF Working Paper 13/244, International Monetary Fund, Washington, DC.
- Gaspar, V., D. Amaglobeli, M. Garcia-Escribano, D. Prady, and M. Soto, 2019, “Fiscal Policy and Development: Human, Social, and Physical Investment for the SDGs,” Staff Discussion Note 19/03, International Monetary Fund, Washington, DC.
- Gupta, S.; D. Coady; M. Fouad; R. Hughes; M. Garcia-Escribano; T. Curristine; C. Abdallah; K. Dybczak; Y. Endegnanew; M. Francese; T. Hansen; L.F. Jirasavetakul; M. Nozaki; B. Shang; M. Simmonds; and M. Soto, 2016, “Managing Government Compensation and Employment - Institutions, Policies, and Reform Challenges,” Policy Papers 47 (2016), International Monetary Fund, Washington, DC.
- Mitchell, A., M. Wickham, and M. Torres, 2020, “Strengthening Public Investment Management in the Eastern Caribbean Currency Union: Getting more bang for the dollar!,” IMF Working Paper, 20/177, International Monetary Fund, Washington, DC.
- Nishizawa, H., S. Roger, and H. Zang. 2019, “Fiscal Buffers for Natural Disasters in Pacific Island Countries,” IMF Working Paper 19/152, International Monetary Fund, Washington, DC.
- International Monetary Fund, 2016, “Small States’ Resilience to Natural Disasters and Climate Change—Role of the IMF,” IMF Policy Paper, Washington, DC.
- International Monetary Fund, 2018, “2017 Staff Guidance Note on the Fund's Engagement with Small Developing States,” Washington, DC.
- International Monetary Fund, 2019, “Building Resilience in Developing Countries Vulnerable to Large Natural Disasters,” IMF Policy Paper 19/020, Washington, DC.
- International Monetary Fund, 2019, 2020 (a), World Economic Outlook, “The Great Lockdown,” Washington, DC. April 2020.
- International Monetary Fund, 2020 (b), World Economic Outlook, “A Long and Difficult Ascent,” Washington, DC. October 2020.
- Georgieva, K., 2020, “Beyond the Crisis,” Finance & Development, June 2020, vol. 57/2.

### Country climate policy assessments and case studies (IMF country reports and related)
- Bonato, L., A. Cheasty, M. Pigato, K. Antoine, A. De Kleine Feige, A. Guerson, S. Lakhtakia, I. Parry, G. Salinas, and D. Stephan, 2018, St. Lucia. “Climate Change Policy Assessment,” IMF Country Report 18/181, International Monetary Fund, Washington, DC.
- Cheasty, A., B. Garnaud, T. Konuki, I. Parry, and W. Samuel, 2017, “Seychelles. Climate Change Policy Assessment,” IMF Country Report 17/162, International Monetary Fund, Washington, DC.
- Cheasty, A., D. Leigh, I. Parry, D. Vasilyev, M. Boyer, R. Gunasekera, and R. Alfaro-Pelico, 2018. Belize. “Climate Change Policy Assessment,” IMF Country Report 18/329, International Monetary Fund, Washington, DC.
- Davies, M., B. Lissovolik, I. Parry, A. Guerson, G. Huang, T. Komatsuzaki, W. Mitchell and M. Boyer, 2019 (a), “Grenada. Climate Change Policy Assessment,” IMF Country Report 19/193, International Monetary Fund, Washington, DC.
- Davies, M., M. Nozaki, R. Singh, K. Abdelkader, A. Le, D. Prihardini, G. Huang, and S. Esler, 2019 (b), “Federated States of Micronesia. Climate Change Policy Assessment,” IMF Country Report 19/292, International Monetary Fund, Washington, DC.
- Daniel, J., A. Banerji, A. Blackman, S. Ester, T. Moeaki, R. Neves, N. Palu, V. Piatkov, D. Prihardini, C. Sandoz, and A. Zdzienicka, 2020, “Climate Change Policy Assessment for Tonga,” April 2020, International Monetary Fund, Washington, DC.

### Methodology and empirical studies on tax effort, stochastic frontiers, and efficiency
- Aigner, D., C.A.K. Lovell and P. Schmidt, 1977, “Formulation and estimation of stochastic frontier production function models”, Journal of Econometrics 6: 21-37.
- Bahl, R.W., 1971, “A regression approach to tax effort and tax ratio analysis,” Staff Papers, 18(3), pp.570-612.
- Battese, G.E. and T.J. Coelli. 1995, “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data,” Empirical Economics 20: 325-332.
- Greene, W.H., 2005. “Fixed and Random Effects in Stochastic Frontier Models,” Journal of Productivity Analysis, 23: 7–32. https://centerforpolicyimpact.org/wp-content/uploads/sites/18/2019/04/CPIGH-Report_Tax-report_Enhancing-Domestic-Revenues__April-2019_FINAL.pdf
- Cyan, M., J. Martinez-Vazquez and V. Vulovic. 2013, “Measuring tax effort: Does the estimation approach matter and should effort be linked to expenditure goals?,” International Center for Public Policy Working Paper 13-08, Atlanta, GA.
- Caldeira, E., A. Compaoré ,A. Adessé Dama, M. Mansour,G. Rota-Graziosi, 2020, “Tax effort in Sub-Saharan African countries: evidence from a new dataset,” Working Papers hal-02543162, HAL.
- Fenochietto, R. and C. Pessino. 2013, “Understanding Countries’ Tax Effort,” IMF Working Paper 13/244, International Monetary Fund, Washington, DC.
- Glenday, G., I. Bharali, and Z. Wang. 2019, “A Cross-Country Comparative Study of Tax Capacity, Effort and Gaps.” 2019, 171.
- Langford, B. and T. Ohlenburg. 2016, “Tax revenue potential and effort – an empirical investigation,” S-43202-UGA-1. International Growth Centre, London, UK.

### Infrastructure, resilience, water, sanitation, and climate adaptation
- Hallegatte, S., M. Bangalore, L. Bonzanigo, M. Fay, T. Kane, U. Narloch, J. Rozenberg, D. Treguer, A. Vogt-Schilb, 2016, “Shock Waves: Managing the Impacts of Climate Change on Poverty,” Climate Change and Development Series, World Bank, Washington, DC.
- Hallegatte, S., A. Vogt-Schilb, M. Bangalore, and J. Rosenberg, 2017, “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters,” Climate Change and Development Series, World Bank, Washington, DC.
- Hallegatte, S., J. Rentschler, and J. Rosenberg, 2019 (a), “Lifelines. The Resilient Infrastructure Opportunity,” World Bank, Washington, DC.
- Hallegatte, S., J. Rentschler, J. Rosenberg, C. Nicolas, C, Fox, 2019 (b), “Strengthening New Infrastructure Assets. A Cost-Benefit Analysis,” Policy Research Working Paper 8896, World Bank, Washington, DC.
- Rozenberg, J. and M. Fay, 2019, “Beyond the Gap: How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure,” World Bank, Washington, DC.
- Miyamoto International, 2019 a, “Overview of Engineering Options for Increasing Infrastructure Resilience,” Background Paper for Lifeline Report, World Bank, Washington, DC.
- Miyamoto International , 2019 b, “Increasing Infrastructure Resilience -- Technical Annex,” Technical annex to background paper above, World Bank, Washington, DC.
- Burdescu, R, C. van den Berg, N. Janson, and O. Alvarado, 2020, “A Benchmark for the Performance of State-Owned Water Utilities in the Caribbean,” World Bank, Washington, DC.
- Hutton, G. and M. Varughese, 2016, “The Cost of Meeting the 2030 Sustainable Development Goal Targets on Drinking Water, Sanitation, and Hygiene,” World Bank, Washington, DC.
- UNICEF and WHO, 2019, “Progress on household drinking water, sanitation and hygiene 2000-2017. Special focus on inequalities,” New York, NY.
- Climate Central, 2019, Source Data for Coastal DEM, “Flooded Future: Global vulnerability to sea level rise worse than previously understood.” https://www.climatecentral.org/news/report-flooded-future-global-vulnerability-to-sea-level-rise-worse-than-previously-understood.
- IPCC, 2019, “Special Report on the Ocean and the Cryosphere in a Changing Climate,” https://www.ipcc.ch/srocc/

### Climate finance, national development banks, and access to funds
- Griffith-Jones, S; Attridge, S; and Gouett, M., 2020, “Securing Climate Finance Through National Development Banks,” ODI Report January 2020
- Tanner, T., Bisht, H., Quevedo, A., Malik, M., Nadiruzzaman, M., Biswas, S., 2019, “Enabling Access to the Green Climate Fund: Sharing Country Lessons From South Asia,” Action On Climate Today Learning Paper, Oxford Policy Management and ODI, UK.
- Daniel, J., L. Doherty, G. Preston, T. Schneider (2021) “Barriers Preventing Pacific Island Countries Access to Climate Financing,” IMF Working Paper forthcoming, International Monetary Fund, Washington, DC.
- Commonwealth Parliamentary Association, 2019, “Climate Change and Small States Parliamentarian’s Toolkit: A guide for effective climate action,” CPA Small Branches Network, London, UK.

*Source: wpiea2021062-print-pdf - REFERENCES*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021062-print-pdf.pdf_