4 Discussion i THE COST OF INACTION Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries CLIMATE INVESTMENT FUNDS © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@ worldbank.org. Attribution Please cite the work as follows: World Bank. 2024. The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries. Washington, DC: World Bank. Translations If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by the World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations If you create an adaptation of this work, please add the following disclaimer along with the attribution: This is an adaptation of an original work by the World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by the World Bank. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; e-mail: pubrights@worldbank.org. Contents iii Foreword vi Acknowledgments vii Abbreviations viii Units of Measurement viii Summary ix 1. Introduction 1 2. Methods 4 2.1 | Selection of Countries 5 2.2 | Selection of Climate Scenarios 5 2.3 | Modeling and Analytical Approach 8 2.4 | Data 13 3. Impacts of Climate Change on Health 14 and the Economic Cost of Inaction 3.1 | Impacts of Climate Change on Health 15 3.2 | Economic Cost of Inaction 16 4. Discussion 19 4.1 | Summary of Key Findings 20 4.2 | Comparison of Findings with Key Literature 21 4.3 | Study Caveats 23 4.4 | Policy Implications and Future Directions 25 Annexes 26 iv Annex 1 | Countries Included in the Analysis 26 Annex 2 | SSPs and RCPs 29 Annex 3 | Estimating the Impacts of Climate Change on Morbidity 33 and Mortality Annex 4 | Modeling Fatality Rates 38 Annex 5 | Estimating YLL from Climate Change 42 Annex 6 | Estimating the Economic Cost of Climate-Related 45 Health Impacts Annex 7 | Cumulative Estimates of the Impacts of Climate Change 65 on Morbidity and Mortality in the Short Term and Long Term Annex 8 | Economic Cost of the Health Impacts of Climate Change 67 in the Short Term and Long Term References 68 v FIGURES 1 SSPs and RCPs 7 2 Distribution of the Economic Cost of the Health Impacts of Climate Change 18 Across Regions: 2026-2050 A.1 Population Growth under SSPs 29 A.2 Economic Growth under SSPs 29 TABLES 1 Shared Socioeconomic Pathways 6 2 Comparative Analysis of Approaches: WHO (2014) and this Report 8 3 Cumulative Number of Cases Attributable to Climate Change: 2026-2050 15 4 Cumulative Number of Deaths Attributable to Climate Change: 2026-2050 16 5 Cumulative Economic Cost of the Health Impacts of Climate Change: 2026-2050 17 A.1 LMICs Included in the Analysis 26 A.2 Total Projected Regional Population 30 A.3 Total Projected Regional GDP (trillion USD) 30 A.4 Modeling Malaria’s Fatality Rate 40 A.5 Estimating Life Expectancies 42 A.6 Age Distribution of Deaths from Malaria in Nigeria in 2020 43 A.7 Age Distribution of Deaths from Malaria in Cameroon in 2020 44 A.8 Estimated Country-Specific Values for VSL (USD) – SSP3 46 A.9 Estimated Country-Specific Values for VSL (USD) – SSP2 48 A.10 Estimated Country-Specific Values for COI Dengue (USD) – SSP3 51 A.11 Estimated Country-Specific Values for COI Dengue (USD) – SSP2 53 A.12 Estimated Country-Specific Values for COI Malaria (USD) – SSP3 55 A.13 Estimated Country-Specific Values for COI Malaria (USD) – SSP2 57 A.14 Estimated Country-Specific Values for COI Diarrhea (USD) – SSP3 59 A.15 Estimated Country-Specific Values for COI Diarrhea (USD) – SSP2 61 A.16 Cumulative Number of Cases Attributable to Climate Change: 2026-2030 65 A.17 Cumulative Number of Deaths Attributable to Climate Change: 2026-2030 65 A.18 Cumulative Number of Cases Attributable to Climate Change: 2026-2100 66 A.19 Cumulative Number of Deaths Attributable to Climate Change: 2026-2100 66 A.20 Economic Cost of the Health Impacts of Climate Change: 2026-2030 67 A.21 Economic Cost of the Health Impacts of Climate Change: 2026-2100 67 Foreword vi Climate change has profound and extensive adverse impacts on health, and these are expected to intensify in the coming decades. As casualties and fatalities increase, the climate-related health crisis risks overwhelming health care facilities and systems, particularly in low- and middle-income countries. As a result, the health impacts of a warming planet could push millions of people into extreme poverty. A vital first step toward addressing the health emergency arising from climate change involves identifying the multiple health impacts and estimating the cost of inaction. Building on an earlier effort by the World Health Organization, this World Bank report quantifies the impacts and costs of projected climate change on health in low- and middle-income countries. The study reveals that climate change-related health impacts will be severe, even in the short term, and certain regions like Sub-Saharan Africa and South Asia will bear a disproportionate share of the global burden. The cost of inaction is expected to be far higher than projected in the report, which did not cover all of the health risks linked to climate change. These projections should galvanize decision-makers and spur urgent, transformative action. Countries must adopt bold measures to limit the impacts of climate change and significantly boost the resilience of their health care sys- tems. This cannot be about addressing the impact on specific diseases alone. Instead, we must focus on strengthe- ning health systems so they can adapt and mitigate the broader impacts of climate change on health conditions. The World Bank aims to reach 1.5 billion people with quality health services by 2030. This goal will not be reached, without expanding our investments in climate and health to help countries build high-quality, climate-resilient, and low-carbon health systems. These are not just words. In fact, last year climate investments in health projects amounted to a third of the Bank’s total financing for health. Further we are focusing on assessing country-specific climate-health vulnerabilities to inform the design of tailo- red solutions to guide our investments to build resilient, low-carbon health systems; as well as deepening part- nerships at the global, regional, and country levels to support these efforts. Looking ahead, the World Bank is developing a full range of financing instruments for both adaptation and mitigation activities, which will enable us to increase our support to help low- and middle-income countries tackle climate-health challenges. Climate change is a global crisis – we must join forces now to address its direct and indirect impacts on health and limit the high human and economic costs. This is a wake-up call for all of us to act decisively and urgently to safeguard our future. Juan Pablo Uribe Global Director, Health, Nutrition and Population The World Bank Acknowledgments THE COST OF INACTION vii  f Climate Change on Health in Low- and Middle-Income Countries Quantifying the Impact o This report was produced by the Climate and Health Program in the Health, Nutrition, and Population Global Practice of the World Bank. The report is authored by Lorie Rufo (Senior Climate Change Specialist, World Bank); Benoit Laplante (Economics Consultant, World Bank); Steven Ceglia Smith (Data Analyst Consultant, World Bank); and David Alexander Clary (Economics Consultant, World Bank). The report received overall guidance from Tamer Samah Rabie (Global Program Lead, Climate and Health, World Bank). The report greatly benefited from the valuable contributions of the following peer reviewers: Patrick Hoang- Vu Eozenou (Senior Economist, World Bank); Jed Friedman, (Lead Economist, World Bank); Xian Fu Lu (Climate Change Consultant, Climate Investment Funds); and Urvashi Narain (Program Leader, World Bank). The authors would also like to thank the following external peer reviewers of the report for their insightful com- ments: Enis Baris (Director, Global Health Policy, Institute for Health Metrics and Evaluation); Diarmid Campbell- Lendrum (Head, Climate Change and Health Unit, World Health Organization); Sari Kovats (Associate Professor, London School of Hygiene and Tropical Medicine); Christopher Murray (Founding Director, Institute for Health Metrics and Evaluation); Frank Pega (Technical Officer, Environment, Climate Change and Health, World Health Organization); and Alexander Preker (Adjunct Associate Professor, Columbia University). The authors sincerely appreciate the valuable contributions provided by Alethea Cook (Health Consultant, World Bank); Stephen Dorey (Senior Health Specialist, World Bank); Toni Joe Lebbos (Economist, World Bank); Yuna Nakayasu (Health Consultant, World Bank); Wameq Azfar Raza (Senior Health Specialist, World Bank); Jyotirmoy Saha (Health Consultant, World Bank); Zara Shubber (Senior Health Specialist, World Bank); and Penny Williams (Manager, People Vice-Presidency, World Bank). Sincere thanks are also extended to the World Bank Climate Change Knowledge Portal team who provided gui- dance on the appropriate use of the most recent climate data available on the portal. Further thanks to Kah Ying Choo who diligently performed the copyediting of the report. The authors would like to express their gratitude to Juan Pablo Uribe (Global Director, Health, Nutrition, and Population, World Bank) and Monique Vledder (Practice Manager, Health, Nutrition, and Population, World Bank) for their unwavering support. Finally, the authors are thankful to the Climate Investment Funds for its financial support. viii ABBREVIATIONS UNITS OF MEASUREMENT AR6 Sixth Assessment Report (of the IPCC) °C Degree Celsius CCKP Climate Change Knowledge Portal Gt/yr Gigatons Per Year CMIP6 Sixth Phase of the Coupled Model W/m2 Watt Per Square Meter Intercomparison Project CO2 Carbon Dioxide COI Cost of Illness COP Conference of the Parties (to the UNFCCC) COP26 26th Conference of the Parties EAP East Asia and Pacific ECA Europe and Central Asia GDP Gross Domestic Product IAM Integrated Assessment Model ILO International Labour Organization IPCC Intergovernmental Panel on Climate Change LAC Latin America and the Caribbean LMICs Low- and Middle-Income Countries MENA Middle East and North Africa OECD Organisation for Economic Co-operation and Development RCP Representative Concentration Pathway SA South Asia SIDS Small Island Developing States SRES Special Report on Emissions Scenarios SSA Sub-Saharan Africa SSP Shared Socioeconomic Pathway UN United Nations UNEP United Nations Environment Programme UNFCCC United Nations Framework Convention on Climate Change US United States USD United States Dollar VSL Value of Statistical Life WEF World Economic Forum WHO World Health Organization YLL Years of Life Lost Summary 4. Discussion ix Climate change is impacting human health in myriad ways, including by increasing the frequency of extreme weather events, the emergence and spread of infectious diseases, and disruptions to food systems. The impacts of climate change on health—already profound—are only expected to worsen over time. Not only will the number of diseases and deaths from climate-sensitive health risks increase, but so too will the geographical range of these diseases. Low- and middle-income countries (LMICs) are expected to face a disproportionate burden of these impacts due to their higher levels of poverty and income inequality, and weak healthcare systems. With growing recog- nition that the climate crisis is a health crisis, the international community has expressed urgent calls for action on climate and health. The response of the health community and researchers has been largely focused on studying the link between climate change and health. A limited number of studies have established empirical links between climate conditions and the variability in the number of different diseases in specific national (or subnational) contexts, particularly focusing on vector-borne and waterborne disea- ses. In addition, few studies have aimed to assess the economic cost associated with the health impacts of projected climate change. This report aims to address the existing knowledge gap and provide a deeper understanding of the interconnection between climate and health, in terms of the risks to human health and the economic burden of these risks. Specifically, it provides a quantitative assessment of the potential impacts of climate change based on the number of cases and the number of deaths resulting from selected vector- and water-borne diseases, stunting, and extreme heat. An assessment of the economic cost of climate change on health (in terms of both morbidity and mortality) is also provided. The analysis covers 69 low-income and middle-income countries with national populations excee- ding 10 million people in the base year 2020. These 69 countries comprise 96 percent of the total population of all LMICs. Estimates of the impacts of climate change on health are provided for different time periods in- cluding 2026-2030, 2026-2050, and 2026-2100 in the context of two socioeconomic development scenarios featured in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC)—namely, SSP3-7.0 and SSP2-4.5 (referred to hereafter as SSP3 and SSP2). These two scenarios represent middle-of-the-road development paths between the worst-case (RCP8.5) and the increasingly unlikely best-case scenarios (RCP2.6 and RCP1.9). SSP3 represents a cha- llenging pathway, assuming high population growth, limited economic development, and reduced x investments in health and education, whereas SSP2 presents moderate challenges, characterized by steady population and economic growth. The main findings of this report are as follows: 1. The impacts of climate change on health are significant and call for immediate ac- tion at the global and country levels. Between 2026 and 2050, climate change is projec- ted to cause between 4.1 billion (SSP2) and 5.2 billion (SSP3) cases across LMICs. The impact of climate change on mortality will be equally stark. By 2050, the number of deaths caused by climate change could reach between 14.5 million (SSP2) and 15.6 million (SSP3). Immediate, decisive action is needed to avert these devastating impacts on health across LMICs. 2. Scaling climate-health action is needed to avert trillions of dollars in economic costs arising from these selected health impacts of climate change in the coming deca- des. By 2050, the economic cost of the health impacts of climate change is projected to reach between USD 8.6-15.4 trillion under SSP3. These costs translate to between 0.7 percent and 1.2 percent of the gross domestic product (GDP) of LMICs. These estimates are higher under SSP2, reaching between USD 11.0-20.8 trillion translating to 0.7 percent and 1.3 percent of GDP in LMICs. 3. Sub-Saharan Africa (SSA) and South Asia (SA) will bear the brunt of the health im- pacts of climate change. These impacts will be particularly severe in SSA, which is projected to experience approximately 71 percent of all cases and nearly one-half of all deaths caused by climate change between 2026 and 2050 under both socioeconomic development scena- rios. SA is projected to experience approximately 18 percent of all cases and one-quarter of all deaths under both scenarios. In these two regions combined, the number of deaths caused by climate change is projected to reach between 10.5 million (SSP2) and 11.7 million (SSP3) by 2050. The economic cost will be significantly higher for SSA than for any other re- gion—by 2050, this cost will amount to between 2.7 percent and 3.6 percent of the region’s GDP under both scenarios. 4. The health impacts of climate change presented in this report are significant but li- kely to be just the tip of the iceberg. The analysis presented in this report includes the po- tential impacts of climate change on a limited number of health risks. The potential impacts of climate change on other health risks, such as non-communicable diseases and mental health, are not included in this report. Moreover, the analysis of these health risks has not conside- red the change in their geographical trajectory because factors, such as migration and water stress, can only be captured through a dynamic model. As a result, the results presented here xi should be understood as a notable underestimate of the scale of the real impacts of climate change on health in LMICs. The findings of this report confirm the AR6’s projection of a significant increase in cases and deaths due to climate change and the uneven geographical distribution of this future burden, with SSA and, to a lesser extent, SA bearing the brunt of the projected increase. Furthermore, by estimating the associated economic costs of these health impacts, this report argues for intensifying and accelerating efforts to reduce greenhouse gas emissions as well as for LMICs to prioritize invest- ments in health systems. These investments are needed to build resilient and sustainable health systems that can weather the adverse impacts of climate change on health. It must be clearly sta- ted that this report does not advocate for a vertical approach to the health risks included in the analysis. A health systems approach is needed to tackle the projected impacts of climate change on health effectively and efficiently. 1. Introduction THE COST OF INACTION 1  f Climate Change on Health in Low- and Middle-Income Countries Quantifying the Impact o 1. Introduction 1. Introduction 2 Climate change has been impacting human health at an accelerated pace over the past decade. This includes increases in heat-related illnesses, waterborne and vector-borne diseases (including outbreaks), and malnutrition from reduced crop productivity, among numerous others. These effects are expected to worsen over time, with changes not only in the number of diseases and deaths from climate-sensitive health risks but also in their geographical range (George et al. 2024). In addition to impacting health outcomes, climate change is projected to adversely impact health systems. As a result of poverty, income inequality, and weak healthcare systems, low- and midd- le-income countries (LMICs) are expected to face disproportionate increases in morbidity, as well as increasing losses and damages to health facilities. Urgent calls for action have been expressed by the global community (Fielding 2023; Intergovernmental Panel on Climate Change [IPCC] 2023; Romanello et al. 2023; United Nations Environment Programme [UNEP] 2023). Over 200 health journals have recently called on the United Nations (UN), political leaders, and health professionals to treat the ongoing climate and nature crises as one global health emergency (Abbasi et al. 2023). Despite the scale of this crisis, evidence quantifying the impact of climate change on health re- mains limited. The underlying physiological factors linking climate change and the incidence of vector-borne and waterborne diseases have been discussed by numerous experts (George et al. 2024; Semenza et al. 2023; Thomson et al. 2022; Wong 2023). However, the extent of the risk of climate change on health remains poorly quantified (Mora et al. 2022). Only a limited number of papers have aimed to transform this knowledge into quantified assessments of the potential impacts of climate change on specific health risks.1 Given the scarcity of public resources, future policy responses on their allocations require going beyond understanding the underlying nature of climate change and health links to quantify the extent of the linkages in terms of future inci- dences, mortality, and economic costs (Ebi 2022, 2024). This lack of comprehensive quantification may partially explain why health-specific climate actions represent only 6 percent of total adapta- tion funding (World Health Organization [WHO] 2023). It has been estimated that LMICs require at least USD11 billion in funding per year this decade to adapt to climate and health impacts and increase the resilience of their health systems (UNEP 2023). To address the evidence gap, this report provides estimates of the economic cost of inaction on selected health risks linked to climate change. It provides a quantitative assessment of the 1  A limited number of studies have established and estimated empirical links between climate conditions, variability, and the incidence of diseases in specific national (or subnational) contexts. Examples include Brazil (Barcellos et al. 2014); China (Xiang et al. 2018; Zheng et al. 2017); Colombia (Quinterro-Herrera et al. 2015); Iran (Salahi-Moghaddam et al. 2017); Philippines (Su 2008); Sierra Leone (George et al. 2023); Singapore (Struchiner et al. 2015); Tanzania (Kulkarni et al. 2016); Uganda (Boyce et al. 2016); Vietnam (Xuan et al. 2014); and Zambia (Bennett et al. 2016). 1. Introduction 3 potential impacts of climate change on (1) the number of cases and deaths resulting from selected health risks: extreme heat, waterborne diseases (diarrhea), stunting, and vector-borne diseases (dengue and malaria); (2) the number of years of life lost (YLL) from deaths arising from these health risks attributable to climate change; and (3) the economic cost of the incremental number of cases and deaths attributable to climate change. These impacts are estimated for 69 LMICs whose population exceeds 10 million people in the base year 2020. Estimates of the impacts are generated for the short term (2026–2030), medium term (2026–2050), and long term (2026–2100) based on two (of the five) climate scenarios forming the basis of the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) — SSP2 and SSP3. This report builds on previous work and leverages new analysis to further deepen the unders- tanding of the health impacts of climate change. It expands on an assessment conducted by the World Health Organization (WHO 2014) on the impacts of climate change on morbidity and mor- tality resulting from dengue, diarrhea, extreme heat, malaria, and stunting for the years 2030 and 2050.2 This report adopts the same methodological approach as a starting point but extends the analysis in numerous directions as explained in Section 2 below. 2  WHO (2014) also included an analysis of the impacts of climate change on deaths resulting from floods. Floods are not included in the current analysis: the nature of the modeling required differs significantly from the modeling of the impacts of climate change on diseases. THE COST OF INACTION  f Climate Change on Health in Low- and Middle-Income Countries Quantifying the Impact o 2. Methods 2. Methods 5 2.1 Selection of Countries The analysis presented in this report focuses on the LMICs of the six regions based on the World Bank Group’s classification.3 A total of 69 countries with a national population exceeding 10 million in the baseline year 2020 are included. The list of countries included in the analysis is provided in Annex 1. These 69 countries represent 96.2 percent of the total population of all LMICs.4 When grouped by region, East Asia and Pacific (EAP) constitute 32.5 percent of this total population of LMICs, followed by South Asia (SA — 29.6 percent) and Sub-Saharan Africa (SSA — 17.0 percent). This analysis is conducted at the country (national) level. As such, it does not capture subnational variability pertaining to climate projections, socioeconomic characteristics, and levels of health risks. The consequences of this constraint of the analysis are discussed further below. 2.2 Selection of Climate Scenarios Climate data of relevance were obtained from the latest projections made available by the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). These projections also formed the foundation of IPCC’s AR6. Furthermore, this analysis also relied on IPCC’s shared socioeconomic pathways (SSPs) in combi- nation with representative concentration pathways (RCPs) to illustrate potential climate futures. As such and for ease of use in this report, we refer to each specific combination of SSPs and RCPs as climate scenarios. For its preparation of the AR6, IPCC developed five SSPs — extending from SSP1 to SSP5. They represent socioeconomic narratives for possible 21st-century global developments in the absence of new climate policies. Each SSP depicts a different development storyline in terms of assumed population projections, economic growth, technological breakthroughs, and land use, among numerous other variables (Table 1). 3  The six regions are East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), South Asia (SA), and Sub-Saharan Africa (SSA). 4  The total population of all LMICs in 2020 reached 6.6 billion. 2. Methods 6 Table 1. Shared Socioeconomic Pathways Pathway Description Development Storyline SSP1 Sustainability Population decline and significant economic growth with increased commitment to sustainable development SSP2 Middle of the Moderate population and economic growth with slow progress toward the road Sustainable Development Goals Regional rivalry High population growth and limited economic growth with limited investments in SSP3 human development SSP4 Inequality Moderate population growth and limited economic growth with unequal investments in human capital SSP5 Fossil-fuel Population decline and significant economic growth with rapid technological development progress, strong investments in human development, and exploitation of fossil fuel resources. Projections of carbon dioxide (CO2) are associated with each development pathway. For example, based on the development assumptions for SSP5, CO2 emissions are expected to increase rapidly from the current 40 gigatons per year (Gt/yr) to approximately 130 Gt/yr by 2100. On the other hand, under both SSP1 and SSP2, emissions are projected to decline (starting almost immediately under SSP1 and around the mid-century under SSP2), reaching approximately 10 Gt/yr under SSP2 and negative values under SSP1. Under SSP3, emissions will double approximately in value from the existing level by 2100. These emissions pathways are mapped onto RCPs, whose projected value of radiative forcing (me- asured in watts per square meter [W/m2]) corresponds directly with CO2 emissions levels. Higher levels of emissions lead to greater atmospheric concentration levels of greenhouse gases that, in turn, lead to higher levels of radiative forcing. The levels have a range of 1.9 to 8.5 W/m2. The RCPs are numbered accordingly from RCP1.9 to RCP8.5 to reflect the projected value of radiative forcing. Although five scenarios — SSP5-8.5, SSP3-7.0, SSP2-4.5, SSP1-2.6, and SSP1-1.9 — are explored in great detail in IPCC’s AR6 (Figure 1), this assessment has selected SSP3–7.0 and SSP2-4.5 (hen- ceforth, SSP3 and SSP2, for short) for analysis. These two scenarios represent middle-of-the-road development pathways between RCP8.5 (deemed to be a worst-case scenario) and the increasin- gly unlikely best-case scenarios (RCP2.6 and RCP1.9). The selected scenarios were used to project changes in socioeconomic conditions and climate conditions in each of the 69 countries included in the assessment. Additional details concerning SSPs and RCPs are provided in Annex 2. 2. Methods 7 Figure 1 SSPs and RCPs Source: Meinshausen et al. (2020). The assumptions about socioeconomic development contribute significantly to the estimates ge- nerated with and without climate change in order to clearly isolate the impact of climate change. Concomitantly, the selection of scenarios also has a substantial influence on the estimated health effects of climate change. The choice of different scenarios also partially accounts for the differences between the results presented in this analysis and previous modeling efforts. For example, Carleton et al. (2022) used climate projections from the combination of RCP4.5 and 8.5 with SSP2, SSP3, and SSP4, while WEF (2024) used the sole combination of SSP2-6.0 for its study. WHO (2014) — to which this analysis is most closely related — employed a single emissions scena- rio, known at the time as SRES A1b, from the Special Report on Emissions Scenarios (SRES). The A1 family of scenarios assumes a future world of very rapid economic growth (all other things being equal, rapid economic growth results in a smaller number of cases of health risks), a global popu- lation that increases until the mid-century and declines thereafter, and perhaps, more importantly, 2. Methods 8 the rapid introduction of new and more efficient technologies. The subset, A1b, further assu- mes the balanced use of all fossil-intensive and non-fossil energy sources (IPCC 2000). At the time of the WHO assessment, only three general circulation models were used to provide climate projections.5 As indicated in WHO (2014), “[n]ew post-SRES emissions scenarios (Representative Concentration Pathways) were developed for the IPCC fifth assessment report, but scenario data for these were not available at the time (…)” (p. 99). While WHO (2014) did use the best available information at the time, the A1b SRES scenario, in retrospect, may be overly optimistic, with pre- CMIP6 climate projections currently outdated. 2.3 Modeling and Analytical Approach As mentioned previously, this assessment closely follows and builds on the methodology and mo- deling developed by WHO (2014) to estimate climate-related impacts on health. Key similarities and differences between the methodological approach described below and that of WHO (2014) are summarized in Table 2. Table 2. Comparative Analysis of Approaches: WHO (2014) and This Report Parameters WHO (2014) This Report Health risks Dengue, diarrhea, extreme heat, malaria, Dengue, diarrhea, extreme heat, malaria, and stunting stunting, and coastal flooding Climate SRES A1b (from the Special Report on SSP3-RCP7.0 and SSP2-RCP4.5 (from IPCC’s AR6 of scenarios Emissions Scenarios published in 2000) 2021) Baseline year 2000 2020 Time horizon 2030, 2050 2030, 2050, 2100 Countries All countries of the WHO regions 69 LMICs with populations greater than 10 million in all six regions of the World Bank Malaria Statistical modeling: Strictly monotonic Statistical modeling: Inverted U-shape as a function of increase with temperature temperature Malaria fatality No modeling of malaria fatality rate Malaria fatality modeled as a function of GDP per rate capita and female education Dengue Spline function: Monotonic increase with Spline function: Inverted U-shape as a function of temperature-precipitation temperature-precipitation Stunting Stepwise approach based on establishing 2030 and 2050 using the WHO estimates; 2100 using estimates a correlation between climate change and income elasticity due to lack of data undernutrition 5  These models were BCM (from the Bjerknes Centre for Climate Research, University of Bergen, Norway), EGMAM (from the Freie Universitaet Berlin, Institute for Meteorology, Berlin, Germany), and CM4v1 (from the Institut Pierre Simon Laplace, Paris, France). 2. Methods 9 Parameters WHO (2014) This Report Extreme heat Statistical modeling Same as WHO Diarrhea Statistical modeling Same as WHO Economic No Yes assessment Estimating the Impacts of Climate Change on Morbidity and Mortality Modeling The analysis presented in this report utilized individual models to estimate the impacts of climate change on morbidity and mortality for each health risk. Except for stunting, statistical models were used to estimate the future number of cases for each health risk in each country and the year of interest. For stunting, the assessment used regional stunting data for cases and deaths due to climate change from WHO (2014) to derive country-level data. The individual statistical models used for dengue, malaria, diarrhea, and extreme heat, as well as the model used to derive data for stunting, are described in detail in Annex 3. Calculating the Number of Cases and Deaths Except for stunting, estimates of the future likelihood (probability) of diseases were obtained using socioeconomic and climate projections from the datasets described later in this section. The es- timated future likelihood levels were then applied to the national country populations projected under the relevant SSP scenario to estimate morbidity levels in each country in the years 2030, 2050, and 2100. For dengue and malaria, fatality rates (number of deaths per case of each disease) were calcu- lated for the base year 2020. In the first version of this analysis, it was assumed that the fatality rates of dengue and malaria would remain constant over 2020–2100, in line with the WHO (2014) approach. In this updated analysis, a model was developed to estimate malaria fatality rates for 2030, 2050, and 2100 based on GDP per capita; a proxy for socioeconomic development that is assumed to be negatively correlated with fatality rates; and female education, which has been shown to be negatively associated with the likelihood of mortality from extreme weather events 2. Methods 10 (Blankespoor et al. 2010). Although a similar model was tested for dengue, the results were incon- clusive;6 thus, a constant fatality rate was assumed for 2020, 2050, and 2100. Details are presen- ted in Annex 4. For stunting, the analysis used estimates from WHO (2014) for 2030 and 2050 and followed the WHO (2014) approach to derive estimates for the year 2100. The estimates provide a measure of the health impacts attributable to climate change. It is im- portant to note that the modeling assumes no adaptation and does not include any assumptions about specific breakthroughs in vaccines, medicine, or increased coverage in protection measu- res. These were assumed to remain at the same level as the baseline year 2020. Estimating the Number of Years of Life Lost from Climate Change To estimate the number of years of life lost (YLL), the analysis first derived estimates of life expec- tancy for each of the 69 countries in the baseline year 2020 and then for 2030, 2050, and 2100. The life expectancy for any given country for 2020 was obtained from the World Bank’s World Development Indicators database. To estimate the life expectancies for future years, the analysis relied on published literature from Kc and Lutz (2017). Kc and Lutz (2017) outlined assumptions concerning the demographic and human capital compo- nents (specifically fertility, mortality, migration, and education) corresponding to each SSP to pro- ject national populations over time. In the case of mortality, SSP2 was characterized as a “medium” mortality scenario: life expectancy was assumed to increase by two years per decade. SSP3 was considered a “high” mortality scenario: life expectancy was assumed to increase by one year per decade. These assumptions for mortality were used to estimate life expectancies for all countries for the years 2030, 2050, and 2100. Further assumptions were made for the different health risks. As mortality typically affects young children for stunting and diarrhea, the analysis assumed that the number of YLL is equal to the estimated life expectancy of every death associated with stunting and diarrhea for the purpose of simplification. Concerning extreme heat, the analysis adopted methods developed by WHO (2014): it assumed that the number of deaths between the expected life expectancy in the year of interest and 65 years would be uniformly distributed. For malaria and dengue, the analysis first 6  Coefficients were not statistically significant and the R2 coefficients were very low in all specifications. The further testing of various specifications, for example, by including the three levels of female education and testing non-linear and logistic regressions, also led to inconclusive results. A study by Ali (2024) showed a significant and positive correlation between mortality rates in the most recent dengue outbreak in Bangladesh with population density and air quality index. This finding could account for the lack of relationships among dengue, GDP per capita, and female education. 2. Methods 11 derived the distribution of mortality across the age groups for each country, followed by estimates of the number of deaths by age group to derive the number of YLL associated with these health risks. Annex 5 provides a detailed description of the methods and calculations. Estimating the Economic Cost of the Health Impacts of Climate Change The analysis applied two approaches to estimate the economic cost associated with the health impacts of climate change: the first is based on the value of a statistical life (VSL), while the second is based on YLL. Both approaches include the cost of illness (COI). It is readily understood that the approach based on YLL will yield a lower estimate of the economic cost of climate change than the approach based on VSL, as the VSL does not account for the fact that certain diseases impact different cohorts of individuals (for example, the statistical modeling of extreme heat assumes an impact only on those above 65 years old). Approach Based on VSL The first approach to estimate the economic cost of mortality was the VSL. VSL serves as a measu- re of a population’s willingness to pay for risk reduction and the marginal cost of enhancing safety. The use of VSL is a common approach in cost-benefit analyses to measure the economic benefit individuals receive from enhancements to their health and safety, and to assess the economic cost of premature deaths. Banzhaf (2022) provides a thorough review of the concept and of its use. This approach estimated the national VSL with a benefit-transfer approach, using an estimated VSL of USD6 million in the United States as a starting point. To estimate country-specific VSL, the VSL in the base year 2020 was adjusted across each of the 69 countries to account for differences in GDP per capita across the countries in 2020, assuming an income elasticity of one. Once a VSL was estimated for 2020 for any given country, the VSL for 2030, 2050, and 2100 was then calcu- lated by multiplying the 2020 estimated VSL with the ratio of GDP per capita in 2030, 2050, and 2100 to GDP per capita in 2020. Annex 6 outlines the approach in detail, provides the equations used, and presents the estimated values of VSL for all 69 countries and both SSPs for 2020 (ba- seline), 2030, 2050, and 2100. Approach Based on YLL A noted drawback of the VSL approach is that it does not explicitly account for the fact that various diseases impact individuals in different age groups. For example, mortality from diarrhea is known to be more significant for young children and infants, while heat-related mortality impacts mostly older individuals. The use of YLL allows for such control. As a result, the economic cost of mortality 2. Methods 12 is lower using YLL than VSL. This approach measured the economic cost of climate-associated health impacts solely on mortality by multiplying the estimated YLL in any given country in 2030, 2050, and 2100 by the average annual VSL in the same country for the same years. Annex 6 pro- vides further details and the equation used. Estimating COI The value of COI is specific to diseases, country, and time. Numerous studies provide COI estima- tes for malaria (mostly in the SSA countries) and for dengue (mostly in the countries of Asia and Latin America). In the case of malaria, excellent reviews of studies are provided in Devine et al. (2019) and Andrade et al. (2022). Specific estimates of COI for malaria are available for Burkina Faso (Duval et al. 2022), Gambia (Duval et al. 2022), Ghana (Dalaba et al. 2018), India (Singh et al. 2019), Kenya (Ayieko et al. 2009; Chuma et al. 2010), Malawi (Hennessee et al. 2017), Mozambique (Alonso et al. 2019), Myanmar (Cho and Gatton 2004), Nigeria (Ezenduka et al. 2017; Onwujekwe et al. 2013; Salawu et al. 2016), and Sri Lanka (Attayanake et al. 2000), among others. For this analysis, we used the afo- rementioned estimates when country-specific COIs were available. However, as all these studies were conducted prior to 2020—the base year used in this analysis, the COI values provided in the various papers were adjusted for 2020 using the national consumer price index. For both stunting and extreme heat, the estimated economic costs include only the economic cost of mortality, not the economic cost of morbidity, as estimates of COI for stunting and extreme heat were not available. Information from the literature to derive COI for developing countries on stun- ting and extreme heat was very limited. For the purpose of estimating the morbidity cost arising from diarrhea, it was determined that only 0.5 percent of all cases of diarrhea require treatment (Lamberti et al. 2012). When original COI estimates are not available for any given country, national COI values were esti- mated by using the benefit-transfer approach, which was adjusted for differences in the GDP per capita (similar to the approach for estimating the country-specific VSL). The COI values of 2020 for any given country were estimated for the years 2050 and 2100. The COI estimates are included in both the VSL and YLL approaches to estimating the cumulative economic cost. The cumulative economic cost of cases and deaths was estimated for 2030, 2050, and 2100 in relation to its value as a percentage of GDP. Estimated economic costs are presented in dollars for the year of interest in real terms, not in present-value terms. 2. Methods 13 Estimated values of COI for all 69 countries and both SSPs for 2020 (baseline), 2030, 2050 and 2100 are presented in Annex 6 for dengue, malaria, and diarrhea. 2.4 Data Socioeconomic, climate, and health data were used to assess the impacts of climate change on the number of cases and deaths related to the health risks included in this analysis. Socioeconomic data for the year 2020 (demographic and economic) were obtained from the World Development Indicators. Socioeconomic data for the years 2030, 2050, and 2100 for both SSP sce- narios were obtained from the SSP website.7 Mortality rates for SSP2 and SSP3 were derived from the World Population Prospects 2022 web page of the UN Department of Economic and Social Affairs Population Division.8 Climate data (namely precipitation levels and temperatures) were obtained through the World Bank’s (2021) Climate Change Knowledge Portal (CCKP).9 For each climate variable, under each time period and each development scenario, projections were made available from the multi-mo- del ensemble developed under CMIP6, resulting in a probability distribution of projections. CCKP makes available values for the 10th percentile, median, and 90th percentile of the probability dis- tribution. This study used the national median value. Finally, health data on the global burden of disease were obtained from the Institute for Health Metrics and Evaluation’s (2024) Global Health Data Exchange website for the years 2019 and 2020.10 On some occasions, the year 2019 was selected as the baseline due to insufficient data as a consequence of the COVID-19 pandemic during the year 2020. The cyclical nature of some risk factors (for example, dengue) demands that a 10-year average be used to estimate values for the baseline year 2020, instead of a single year.11 7 IIASA (International Institute for Applied Systems Analysis), 2018, SSP Database (Shared Socioeconomic Pathways) — Version 2.0, December 2018, https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10. 8 Department of Economic and Social Affairs Population Division, 2024, Mortality, https://population.un.org/wpp/Download/ Standard/Mortality/. 9 The World Bank Group, 2021, Climate Change Knowledge Portal, https://climateknowledgeportal.worldbank.org. 10 IHME (Institute for Health Metrics and Evaluation), 2024, Global Health Data Exchange, https://ghdx.healthdata.org/. 11 For example, Bangladesh experienced a high peak in the number of cases of dengue in 2019 (Ali 2024). THE COST OF INACTION 14  f Climate Change on Health in Low- and Middle-Income Countries Quantifying the Impact o 3. Impacts of Climate Change on Health and the Economic Cost of Inaction 3. Impacts of Climate Change on Health and the Economic 15 15 Cost of Inaction 3.1 Impacts of Climate Change on Health This section presents the cumulative estimates of the impacts of climate change on morbidity and mortality for the selected health risks analyzed for the medium term (2026–2050). Supplementary estimates for the short term (2026–2030) and long term (2026–2100) are available in Annex 7. Without decisive action, climate change will have a devastating impact on human heal- th across LMICs, with SSA and SA bearing the brunt of these impacts. By 2050, climate change is projected to cause between 4.1 billion (SSP2) and 5.2 billion (SSP3) cases across LMICs (Table 3). SSA and SA will experience a disproportionate burden of these impacts. Under both scenarios, SSA will experience the majority (approximately 71 percent) of all cases, while SA will experience approximately 18 percent of all cases. The impact of climate change on mortality is equally stark (Table 4). By 2050, the number of deaths caused by climate change could reach be- tween 14.5 million (SSP2) and 15.6 million (SSP3). The se impacts will be the greatest in SSA and, to a lesser extent, SA and EAP. SSA is projected to experience between 6.8 million (SSP2) and 7.8 million deaths (SSP3), accounting for roughly one-half of all deaths under both scenarios. SA will experience roughly one-quarter of all deaths under both scenarios, while EAP will experience be- tween 21 percent (SSP3) and 24 percent (SSP2) of all deaths. Table 3. Cumulative Number of Cases Attributable to Climate Change: 2026–2050 SSP3 SSP2 (million) (million) EAP 240.6 186.1 ECA 60.0 29.6 LAC 151.1 95.1 MENA 147.0 72.3 SA 944.4 733.8 SSA 3,640.1 2,991.2 Total 5,183.1 4,108.1 3. Impacts of Climate Change on Health and the Economic 16 16 Cost of Inaction Table 4. Cumulative Number of Deaths Attributable to Climate Change: 2026–2050 SSP3 SSP2 (thousands) (thousands) EAP 3,234.7 3,413.3 ECA 149.2 136.2 LAC 271.2 273.5 MENA 226.2 159.7 SA 3,982.9 3,677.0 SSA 7,753.8 6,813.3 Total 15,618.0 14,473.0 3.2 Economic Cost of Inaction The projected impacts of climate change on health shown above will impose significant costs on LMICs. As discussed earlier, this assessment applied two approaches to estimate the economic costs arising from selected climate-related health risks. The first is the VSL approach, which asses- ses the economic cost of mortality. The second is the YLL approach, which is based on monetizing the number of YLL by multiplying the estimated number of YYL in any given country by the average VSL in the same country. Both approaches include the estimated COI.12 The application of these two approaches provides a range of estimated economic costs. This section presents estimates of the economic costs arising from the impacts of climate change on health for the medium term (2026–2050). Supplementary estimates for the short term (2026–2030) and long term (2026–2100) are available in Annex 8. By 2050, the economic cost of the health impacts of climate change may well reach and surpass USD20.8 trillion across LMICs, with SSA and SA suffering significantly higher costs as a share of GDP. Under SSP3, the economic cost of health impacts attributable to clima- te change is estimated to reach between USD8.6 trillion (YLL approach) and USD15.4 trillion (VSL approach) by 2050 (Table 5). These costs amount to approximately 0.7 percent and 1.2 percent of the GDP of LMICs under the YLL and VSL approaches, respectively. The estimates are moderately 12 Two types of results may be expected when these approaches are applied. First, all other things being equal. Second, all other things being equal, the economic cost of climate change will be higher in upper-middle-income countries relative to lower- middle-income countries and higher in lower-middle-income countries relative to low-income countries. This is simply because both VSL and COI are higher in upper-middle-income countries than in the countries under the other two income levels in this analysis. 3. Impacts of Climate Change on Health and the Economic 17 17 Cost of Inaction higher under SSP2, reaching between USD11.0 trillion (YLL approach) and USD20.8 trillion (VSL approach) by 2050, equivalent to 0.7 percent and 1.3 percent of the GDP in LMICs, respectively.13 A larger share of this economic cost will arise in EAP and, to a lesser extent, in SA and SSA (Figure 2). The higher VSL and COI in EAP reflect the higher cost in this region, despite the absolute number of cases and deaths from climate change being higher in SSA. Nonetheless, SSA will bear a significantly heavier economic burden than any other region, amoun- ting to between 2.7 percent (YLL approach) and 3.6 percent (VSL approach) of the regional GDP un- der both scenarios. The economic cost for SA — amounting to between 1.2 percent (YLL approach) and 2.6 percent (VSL approach) of regional GDP under both SSP2 and SSP3 — will also be significant. Table 5. Cumulative Economic Cost of the Health Impacts of Climate Change: 2026–2050 SSP3 SSP2 YLL VSL YLL VSL Cost Share of Cost Share of Cost Share of Cost Share of (Billions GDP (%) (Billions GDP (%) (Billions GDP (%) (Billions GDP (%) USD) USD) USD) USD) EAP 3,338.0 0.46 6,036.4 0.83 4,540.1 0.51 8,389.6 0.94 ECA 101.9 0.10 491.8 0.47 70.8 0.06 530.2 0.43 LAC 285.3 0.19 588.3 0.40 338.3 0.20 763.3 0.45 MENA 98.7 0.18 246.8 0.44 65.8 0.11 233.2 0.38 SA 2,278.8 1.27 4,601.7 2.56 2,749.8 1.19 6,355.9 2.76 SSA 2,546.7 2.73 3,398.4 3.64 3,192.6 2.61 4,482.8 3.66 Total 8,649.5 0.66 15,363.4 1.17 10,957.4 0.68 20,755.1 1.30 13 Estimated costs are higher under SSP2 despite the higher number of cases and deaths under SSP3 than under SSP2. This is due to the assumption of significantly higher GDP under SSP2, which leads to higher VSL and COI figures under SSP2. 3. Impacts of Climate Change on Health and the Economic 18 18 Cost of Inaction Figure 2. Distribution of the Economic Cost of the Health Impacts of Climate Change Across Regions: 2026–2050 SSP3 (VSL Approach) SSP2 (VSL Approach) 22% 22% 40% 39% 30% 31% 3% 2% 4% 2% 1%4% SSP3 (YLL Approach) SSP2 (YLL Approach) 30% 29% 39% 41% 26% 25% 1% 3% 1% 1% 3% 1% SSA EAP ECA LAC MENA SA THE COST OF INACTION  f Climate Change on Health in Low- and Middle-Income Countries Quantifying the Impact o 4. Discussion 4. Discussion 20 Using the latest available climate data (CMIP6) and socioeconomic data as modeled under SSP3 and SSP2, this report provides updated estimates of the impacts of climate change on the number of deaths and cases linked to selected health risks. It also presents estimates of the economic costs associated with these health impacts, illustrating the critical costs of inaction on climate change in LMICs. It is important to note that these estimates should not be interpreted as “predictions” or “best guesses” of what a future without climate action may look like. The estimates are conditional to the socioeconomic projections of specific SSPs, which are plausible (and not necessarily equally likely) storylines of global socioeconomic and technological development trajectories. 4.1 Summary of Key Findings The main findings are as follows: Without bold and decisive action, climate change will have a dire impact on health in LMICs. By 2050, climate change will cause between 4.1 billion (SSP2) and 5.2 billion (SSP3) cases and between 14.5 million (SSP2) and 15.6 million (SSP3) deaths across LMICs. SSA and SA will bear the brunt of these impacts, experiencing nearly one-half and one-quarter of all deaths caused by climate change, respectively, under both climate scenarios. The health impacts of climate change presented in this report, though significant, are likely to be only the tip of the iceberg. This is because the assessment includes the potential impacts of climate change on only five selected health risks; other health risks, such as non-com- municable diseases and mental health, are not included in this study. In addition, this assessment covers only a subset (69) of all developing countries. The analysis also does not consider the chan- ge in the geographical trajectory of these health risks because factors, such as migration and wa- ter stress, can only be captured through a dynamic model. Consequently, the results presented here should be understood as a notable underestimate of the scale of the real impacts of climate change on health. In the absence of strong mitigation and adaptation measures, the economic cost arising from the health impacts of climate change in the LMICs could reach and likely surpass USD21 trillion by 2050. This report finds that the economic cost is projected to reach between USD8.6 trillion (YLL approach) and USD15.4 trillion (VSL approach) under SSP3. The estimated eco- nomic cost is higher under SSP2, reaching between USD10.9 trillion (YLL approach) and USD20.8 trillion (VSL approach). These figures would amount to approximately 0.7 percent and 1.3 percent of the GDP of the LMICs, respectively. 4. Discussion 21 The findings of this report confirm the IPCC’s AR6 reporting of a projected significant increase in disease burden and associated mortality due to climate change. This assess- ment also verifies the uneven geographical distribution of this future burden, with SSA and, to a lesser extent, SA bearing the brunt of the projected increases in illnesses and deaths. SSA will face a significantly higher cost than other regions, amounting to between 2.7 percent (YLL approach) and 3.6 percent (VSL approach) of regional GDP by 2050, regardless of the socioeconomic deve- lopment scenarios. These results provide considerable empirical support for the urgent call for action to tackle the health impacts of climate change, particularly in these two regions. 4.2 Comparison of Findings with Key Literature The findings presented in this report are assessed in comparison to other estimates of the heal- th impacts of climate change, particularly the projections from the 2014 WHO analysis as well as other recent global studies utilizing alternative modeling approaches. WHO (2014) — whose methodology serves as a basis for the current analysis — estimated that climate change would take the lives of an additional 250,000 per year due to infectious diseases, undernutrition, diarrhea, heat stress, and flooding over 2030–2050. However, we find this earlier result to be a significant underestimate of the potential impacts of climate change on mortality over this period. The estimates from this assessment suggest approximately 0.8 million deaths per year on average over the same period. Among the numerous factors contributing to this diffe- rence (as summarized in Table 2), a key factor is that WHO (2014) used datasets available in 2010 and 2011 for its projections of socioeconomic variables (population and GDP),14 while the present study drew on the more recent SSP framework for this purpose. Other models utilize different approaches to estimate the climate-driven health impacts. Integrated assessment models (IAMs) used to estimate the social cost of carbon15 include modeling approa- ches to project future climate-driven health impacts. However, these approaches have been criti- cized for relying on outdated data and providing human mortality impacts that “do not reflect the latest scientific understanding” (Bressler et al., 2021). Based on a comparison of climate-driven mortality estimates from vector-borne diseases and diarrhea derived from WHO (2014) and those from one such IAM (the Climate Framework for Uncertainty, Negotiation and Decision – FUND) for 14  Population projections used the medium variant of the UN 2010 population projections (UN 2011). Projections of economic growth used 2010 and 2011 datasets made available by the World Bank, the International Monetary Fund, and the Organisation for Economic Co-operation and Development (OECD). 15  The social cost of carbon quantifies the net cost of emitting one additional metric ton of carbon-dioxide-equivalent at a certain point in time. 4. Discussion 22 2030 and 2050, Bressler et al. (2021) showed that FUND’s estimates for vector-borne diseases were significantly lower in 2030 (more than 10-fold lower) and even more so by 2050 (more than 30-fold lower) than reported in WHO (2014). For diarrheal disease, FUND’s estimates were similar to WHO’s in 2030 but were half as large in 2050. Bressler et al. (2021) conclude that this disparity is likely due to larger income effects assumed in FUND, though lower sensitivity to temperature changes could not be ruled out. The results presented in the current assessment suggest that IAMs are significantly underestimating the climate-driven health impacts. In recognizing the limitations of IAM models to estimate climate-driven mortality impacts, Bressler et al. (2021) produced country-level mortality damage functions for all countries globally by ex- trapolating mortality projections from temperature-related mortality (heat and cold) from a large epidemiological study.16 The mortality estimates derived from this study for 2050 due to the direct impacts of cold and heat were nine times larger than WHO’s estimates for deaths due to vec- tor-borne diseases and diarrhea, further underscoring the point that the estimates provided by this study are likely only the tip of the iceberg given the focus on only selected climate and health impact pathways. The World Economic Forum recently presented estimates of the impacts of “climate events” (floods, droughts, heat waves, tropical storms, wildfires, and sea-level rises) on morbidity and mor- tality (WEF 2024). These impacts of climate events were assessed under SSP2-6.0 scenario. The findings of the analysis showed that by 2050, climate change is likely to cause an additional 14.5 million deaths (WEF 2024). Given the nature of the “climate events” included in the WEF (2024) analysis, results cannot be immediately compared to those obtained in this analysis. However, barring significant overlaps, it may be more appropriate to think of climate events resulting in 14.5 million deaths in addition to climate change resulting in an additional 15.6 million deaths from the selected health risks included in this analysis. WEF (2024) further reports that climate change is likely to cause an additional $12.5 trillion in eco- nomic losses. This economic impact is measured as the sum of the productivity loss caused by the increase in detrimental health outcomes, and the cost of treatment – a measure generally close to the COI approach. It is of interest to note that VSL is not used as a measure of mortality cost in the study. This alone would tend to provide lower estimates of the economic cost of the impacts of climate change on health. 16  Gasparrini et al. 2017. 4. Discussion 23 Carleton et al. (2022) estimated the impact and economic cost of projected rising temperature on mortality under various combinations of SSPs and RCPs. The study used VSL to convert projected changes in mortality rates into dollars. This VSL was then transformed into a value per life-year lost to allow computing the total value of expected life-years lost due to climate change, accounting for different mortality-temperature relationships across age groups. The VSL is allowed to vary with in- come, using an income elasticity of one to adjust the U.S. estimates of the VSL to different income levels across the world and over time. The economic valuation approach used in in Carleton et al. (2022) and in this report are similar. Also, while limited to the sole impact of temperature rise on mortality, Carleton et al. (2022) estimates that the mean global increase in the mortality risk due to climate change could amount to 3.2 percent of global gross domestic product (GDP) in 2100. The Lancet Commission on Investing in Health recently recognized that climate change will concei- vably have large consequences for human mortality – albeit of an uncertain magnitude, especially in the long run (Jamison et al. 2024). The results presented in this report support this claim and calls for urgent action to address the climate-health crisis. 4.3 Study Caveats There are several caveats to this assessment that are important to highlight. First, this assessment modeled the relationship between temperature and precipitation and mor- bidity and mortality for only selected health risks. There are many additional causal pathways be- tween climate change and health, both direct and indirect, that are not considered by this analysis. These include the health impacts arising from extreme weather events, other climate-sensitive diseases, and climate-induced migration and conflict, among others. Second, the analysis does not explicitly account for the characteristics of different geographies that may impact infectious disease transmission (such as population density and altitude). Nor does it consider the projected change in the geographical trajectory of the modeled health risks as a result of either (or both) changes in climate conditions or the migration of populations, including urbanization, as a response to climate change. Third, the modeling used to estimate morbidity and mortality from infectious diseases is also sta- tic in nature. As such, it does not account for the dynamic effects between the vector and human 4. Discussion 24 populations or the seasonal/year-to-year variability in transmission. While such modeling is cha- llenging to do at a global scale, capturing such dynamics could be done at the national or subna- tional levels, with dynamic modeling that explicitly captures changes in vector and other pathogen populations and other dynamics.17 Fourth, the health impacts of climate change were derived from the use of estimated statistical models that are based on exposure-response functions (that is, by associating temperature and precipitation with health outcomes). These functions are primarily based on current and historical data associations between climate change and health impacts. Therefore, they do not account for potential future individual (including physiological) and systemic adaptations to climate change or specific national and subnational climate and socioeconomic conditions. While the assessment infers future societal adaptative capacity using plausible SSP scenarios by accounting for socioe- conomic factors such as demographic change, economic growth, human development, and tech- nological progress, there remain significant uncertainties concerning what a future without climate action would look like. Fifth, the analysis utilizes modeled data for the baseline morbidity and mortality estimates, which inherently include uncertainties. Furthermore, there is a lack of data from the literature to allow us to derive COI estimates for stunting and extreme heat. Therefore, the estimated economic costs derived from this study are likely to be an underestimate even beyond the likely underestimates of the burden of disease. Lastly, the methodology based on which this analysis is conducted relies on VSL estimates extra- polated from the United States, adjusted for GDP per capita, due to a lack of data from LMICs. While common in the literature, a noted limitation is that this approach does not account for the potential impacts of variables other than differences in GDP per capita in the measurement of country-specific estimates of VSL Furthermore, VSL is often critiqued for its reliance on assump- tions that may not fully reflect individual preferences or behaviors. Despite these challenges, VSL remains a practical tool for policymakers, offering a way to quantify trade-offs between mortality risk reductions and regulatory costs, though it requires careful consideration of ethical and con- text-specific factors to enhance its applicability and acceptance. Recognizing these limitations, this study also employed the use of the YLL methodology in its analysis to provide a range of economic impacts. 17  For example, Servadio et al. (2018) found that warming at lower temperatures in South and Southeast Asia may increase vector and pathogen proliferation while warming at higher temperatures may decrease vector-borne disease outbreaks. Such effects cannot be captured when modeling is taking place at national levels. 4. Discussion 25 4.4 Policy Implications and Future Directions Despite the above limitations, this report provides significant findings that underscore the need for urgent action to address the climate-health crisis. It has demonstrated the urgency of the is- sue by showing that a substantial number of people will suffer and die due to climate-sensitive diseases in the near future. Urgent and scaled-up action is needed to transition to low-carbon care pathways and strengthen the capacity of health systems and communities to prevent, detect, and respond to climate-related health threats. Such action will require significant financing and investment in health systems. However, the cost of inaction is far higher. Therefore, it is crucial that policymakers and development partners — both in LMICs and globally — work to increase health financing to address the climate and health crisis. A key step toward this objective is ensuring that health adaptation is prioritized in national adaptation agendas. It must be clearly stated that this report does not advocate for a vertical disease approach to the selected health risks included in the analysis. Instead, it calls for a health systems approach to effectively and efficiently tackle the projected impacts of climate change on health. As this report is limited to identifying and assessing the impacts and economic costs of climate change on health, it does not, as such, provide an assessment of the costs and benefits of alter- native actions aimed at mitigating these impacts. An immediate next step should thus be aimed at providing guidance on how the climate resilience of the health sector can be enabled. Annexes 26 Annex 1. Countries Included in the Analysis Table A.1 LMICs Included in the Analysis Regiona Countries Population (in 2020)b Income classificationc East Asia and China 1,411,100,000 UM Pacific (EAP) Indonesia 271,857,970 LM Philippines 112,190,977 LM 8 countries Viet Nam 96,648,685 LM Thailand 71,475,664 UM Myanmar 53,423,198 LM Malaysia 33,199,993 UM Cambodia 16,396,860 LM Total — EAP 2,066,293,347 Europe and Central Russian Federation 144,073,139 UM Asia (ECA) Turkey 83,384,680 UM Ukraine 44,132,049 LM 7 countries Uzbekistan 34,232,050 LM Romania 19,265,250 UM Kazakhstan 18,755,666 UM Azerbaijan 10,093,121 UM Total — ECA 353,935,955 27 Regiona Countries Population (in 2020)b Income classificationc Latin America and Brazil 213,196,304 UM Caribbean (LAC) Mexico 125,998,302 UM Colombia 50,930,662 UM Argentina 45,376,763 UM 12 countries Peru 33,304,756 UM Venezuela 28,490,453 UM Ecuador 17,588,595 UM Guatemala 16,858,333 UM Bolivia 11,936,162 LM Haiti 11,306,801 LM Dominican Republic 10,999,664 UM Honduras 10,121,763 LM Total — LAC 576,108,558 Middle East and Egypt 107,465,134 LM North Africa Iran 87,290,193 LM (MENA) Algeria 43,451,666 LM Iraq 42,556,984 UM 9 countries Morocco 36,688,772 LM Yemen 32,284,046 L Syria 20,772,595 L Tunisia 12,161,723 LM Jordan 10,928,721 UM Total — MENA 393,599,834 South Asia (SA) India 1,396,387,127 LM Pakistan 227,196,741 LM Bangladesh 167,420,951 LM 6 countries Nepal 29,348,627 LM Afghanistan 38,972,230 L Sri Lanka 21,919,000 LM Total — SA 1,881,244,676 28 Regiona Countries Population (in 2020)b Income classificationc Sub-Saharan Africa Nigeria 208,327,405 LM (SSA) Ethiopia 117,190,911 L Congo 92,853,164 L 27 countries Tanzania 61,704,518 LM South Africa 58,801,927 UM Kenya 51,985,780 LM Sudan 44,440,486 L Uganda 44,404,611 L Angola 33,428,486 LM Ghana 32,180,401 LM Mozambique 31,178,239 L Madagascar 28,225,177 L Cote d’Ivoire 26,811,790 LM Cameroon 26,491,087 LM Niger 24,333,639 L Burkina Faso 21,522,626 L Mali 21,224,040 L Malawi 19,377,061 L Zambia 18,927,715 LM Chad 16,644,701 L Somalia 16,537,016 L Senegal 16,436,120 LM Zimbabwe 15,669,666 LM Guinea 13,205,153 L Rwanda 13,146,362 L Benin 12,643,123 LM Burundi 12,220,227 L Total — SSA 1,079,911,431 69 countries Total all regions 6,351,093,801 18 countries Total L countries 608,532,284 31 countries Total LM countries 3,296,182,540 20 countries Total HM countries 2,446,378,977 a Regional grouping of countries according to the World Bank. b Source: World Bank Development Indicators. c Income classification according to the World Bank: L = Low-income countries; LM = Lower-middle-income countries; UM = Upper-middle-income countries. 29 Annex 2. SSPs and RCPs The five core SSPs developed by the IPCC represent socioeconomic narratives for possible 21st-century global developments in the absence of new climate policies. Each SSP depicts a di- fferent development storyline—in terms of assumed population projections, economic growth, technological breakthroughs, and land use among numerous other variables. For example, SSP3 assumes high population growth (Figure A.1) and limited economic growth (Figure A.2). From the point of view of both mitigation and adaptation, SSP3 represents a challen- ging development scenario (a large population with relatively low income on average). Investments in education and health are low, with countries finding it difficult to sustain living standards and provide access to safe water, improved sanitation, and healthcare for marginalized populations. SSP2, occasionally referred to as the “middle-of-the-road” scenario (Riahi et al. 2017), is characteri- zed by moderate population and economic growth. Progress toward achieving sustainable develo- pment goals—including improved living conditions and access to education, safe water, and health care—is slow under SSP2. SSP1 represents a sustainable development path, whereby populations decline rapidly and economies grow significantly over 2050-2100. Population decline, combined with relatively high income per capita, would facilitate the mitigation of greenhouse gases and adaptation. Figure A.1 Population Growth under SSPs Figure A.2 Economic Growth under SSPs Source: Kc and Lutz (2017) Source: Dellink et al. (2017) 30 The projected total regional populations under both SSP2 and SSP3 are reported in Table A.2. Observe that population estimates are significantly higher under the SSP3 pathway. Also note that while the population of SA is approximately twice the population of SSA in the baseline year 2020, the populations of these two regions are almost identical by 2100. Table A.2 Total Projected Regional Population 2020 2030 2050 2100 Regions SSP3  EAP 2,066,293,347 2,107,858,206 2,099,259,189 1,910,123,444 ECA 354,686,703 356,292,237 373,265,466 436,905,985 LAC 576,108,558 652,030,921 766,679,648 975,957,701 MENA 393,599,834 441,079,207 567,696,140 847,697,265 SA 1,881,244,676 2,175,760,563 2,718,675,735 3,731,808,658 SSA 1,079,911,431 1,319,304,138 1,967,870,576 3,450,632,176 Regions SSP2 EAP 2,066,293,347 2,165,263,367 1,987,146,447 1,385,022,963 ECA 354,686,703 359,816,074 363,879,772 313,251,233 LAC 576,108,558 627,901,155 667,314,762 606,546,778 MENA 393,599,834 415,875,700 489,855,010 520,993,748 SA 1,881,244,676 2,065,822,726 2,371,640,671 2,284,337,526 SSA 1,079,911,431 1,244,738,245 1,686,342,599 2,296,139,696 The projected total regional GDPs under both socioeconomic development pathways are repor- ted in Table A.3. Note that the regional GDPs are significantly higher under SSP2 than under SSP3 for all regions. Table A.3 Total Projected Regional GDP (Trillion USD) 2020 2030 2050 2100 Regions SSP3 EAP 17.397 26.201 33.846 39.113 ECA 2.879 3.768 4.917 7.761 LAC 3.861 5.162 7.169 12.851 MENA 1.185 1.716 3.035 7.417 31 2020 2030 2050 2100 Regions SSP3 SA 3.483 5.617 9.579 21.344 SSA 1.596 2.527 5.486 26.497 Regions SSP2 EAP 17.397 29.948 44.989 64.840 ECA 2.879 4.087 6.128 10.822 LAC 3.861 5.412 8.741 21.047 MENA 1.185 1.736 3.550 11.259 SA 3.483 6.046 13.847 47.055 SSA 1.596 2.721 8.016 65.292 Projections of carbon dioxide (CO2) are associated with each development pathway. For example, based on the development assumptions for SSP5, CO2 emissions are expected to increase rapidly from the existing 40 gigatons per year (Gt/yr) to reach approximately 130 Gt/yr by 2100. On the other hand, under both SSP1 and SSP2, emissions are projected to decline (starting almost im- mediately under SSP1 and around the mid-century under SSP2), reaching approximately 10 Gt/yr under SSP2 and negative values under SSP1. Under SSP3, emissions will double approximately in value from the existing level by 2100. These pathways are mapped onto RCPs, whose projected value of radiative forcing (measured in watts per square meter [W/m2]) corresponds directly with CO2 emissions levels. Higher levels of emissions lead to greater levels of atmospheric concentration of greenhouse gases which in turn leads to higher levels of radiative forcing. The levels have a range of 1.9 to 8.5 W/m2. The RCPs are numbered accordingly from RCP1.9 to RCP8.5 to reflect the projected value of radiative forcing. At first glance, it may seem as though there are a large number of possible combinations of SSPs and RCPs, leaving analysts with the freedom to combine any SSP with any RCP for modeling pur- poses. However, the following two issues should be noted: First, as indicated earlier, any given SSP assumes a specific development pattern that, in turn, leads to a specific path of emissions, radiative forcing, and global warming. Simultaneously, while different SSPs can result in a different range of radiative forcing, some SSPs exclude the realiza- tion of some degrees of radiative forcing. As such, SSP1-7.0, SSP1-8.5, SSP3-1.9, SSP3-2.6, and SSP5-1.9 cannot exist, just as SSP5 is the only development narrative found to be consistent with the radiative forcing level of 8.5 W/m2 (Kriegler et al. 2017). Hence, the selection of climate 32 projections (arising from the different degrees of radiative forcing) must be consistent with the selection of a socioeconomic development scenario that generates the quantity of CO2 underl- ying the selected RCP. Second and just as importantly, differences in the model outputs between RCPs with higher ra- diative forcing (for example, 8.5 or 7.0), when compared to model outputs arising from RCPs with lower radiative forcing (for example, 2.6 or 1.9), should not be interpreted as the benefits of green- house gas mitigation policies. As noted in Pielke (2021), although such comparisons are common in the literature, this is invalid because socioeconomic, technological, and biophysical assumptions differ considerably across RCPs (since RCPs emerge from SSPs). In AR6, IPCC emphasizes five scenarios in its climate experiments: SSP5-8.5, SSP3-7.0, SSP2-4.5, SSP1-2.6, and SSP1-1.9. Two of those scenarios—SSP3 and SSP2—were retained for this assess- ment of the health impacts of climate change. These two scenarios represent middle-of-the-road development pathways between the worst-case (RCP8.5) and the increasingly unlikely best-case scenarios (RCP2.6 and RCP1.9). 33 Annex 3. Estimating the Impacts of Climate Change on Morbidity and Mortality The individual models used to estimate the future number of cases for each health risk are pre- sented below. In the case of dengue, malaria, diarrhea, and extreme heat, the values for the clima- te and socioeconomic exogenous variables in the statistical models projected to prevail in 2030, 2050, and 2100 in each of the SSPs, which are specific to each of the 69 countries for climate and socioeconomic conditions, are provided. Dengue The statistical model used a splined interaction among the annual mean temperature and the annual mean precipitation and a standalone variable of GDP per capita: (1) where18 Malaria WHO (2014) used a statistical model in which the likelihood (probability) of malaria is a monotoni- cally increasing function of both temperature and precipitation:19 18 The spline function is used to draw a risk function curve: it allows for a flexible relationship and interaction between climate factors (temperature and precipitation) and the probability of dengue transmission areas. 19  In the context of specific national or subnational modeling efforts, statistical modeling can capture behavioral variables, such as the extent of the use of bed nets. Beloconi et al. (2023) provided an example of such modeling with data extracted from Siaya County, which is located on the shores of Lake Victoria, Kenya. 34 (2) where Logit(Malaria): Probability of malaria; Tmin: Mean temperature of the coldest month; and PRmax: Mean precipitation of the wettest month. Previous malaria models have considered 30°C to be the maximum temperature suitable for ma- laria transmission (Mordecai 2013). However, recent research suggests that an ideal range of ma- laria transmission occurs between 25°C and 27°C. It also notes that malaria transmission drops off sharply at temperatures below 16°C and above 34°C, with rates of malaria transmission de- creasing symmetrically outside the ideal range (Suh 2024; Yamba 2023). This is in line with the methodology of other studies that indicate an increase in temperatures, eventually resulting in a decrease in malaria transmission (Murdock 2016). In light of these findings, the model presented in Equation (2) was modified to increase monoto- nically (at the rate at temperatures from 16°C to 25°C, plateau between 25°C and 27°C, and then decrease monotonically (at the same rate ) at temperatures beyond 27°C. The likelihood (probabi- lity) of malaria was assumed to be nil for any temperature values lower than 16°C or above 34°C. In mathematical terms, the statistical model used in this paper is as follows: (3) where Tideal_lower: Lower bound of ideal temperature range for malaria transmission; and Tideal_upper: Upper bound of ideal temperature range for malaria transmission. 35 The above functional relationship suggests that some areas of the globe currently experiencing malaria may see a reduction in the number of cases of malaria as temperature becomes less than ideal for the carrying vector. It should also be noted that the malaria burden is sensitive to factors beyond simply temperature and precipitation, including land disturbance and baseline water, sanitation, and hygiene infras- tructure. Variations in GDP per capita may serve as a proxy for some of these other variables. Further modeling efforts may aim to explicitly capture the impacts of these other variables. Diarrhea The statistical model is as follows: (4) where nc,t Number of diarrhea deaths attributable to climate change among children aged under 15 years in country c in year t; Nc,t Number of diarrhea deaths in children aged under 15 years in a future without climate change in country c in year t; ∆Tc,t Temperature anomaly in country c in year t; and β = log(1+α) Mid-estimate of the log-linear increase in diarrheal deaths per degree of temperature increase, with α being the linear increase in diarrheal deaths per degree of temperature increase Extreme heat Based on Honda et al. (2014), the temperature-mortality function is assumed to be V-shaped, and the temperature value at which mortality is the lowest is defined as the optimum temperature. For temperatures above the optimum temperature, the mortality difference was defined as the heat-related mortality: (5) 36 where: Dav Daily average number of deaths in people aged 65 years and over; and RRt Ratio of mortality at temperature index t, compared to mortality at the optimum temperature (84th percentile). The above approach focuses on older ages (65 and above). It may be noted that heat shocks can also affect people of younger ages. The existing focus on the population of 65 years and over thus leads to underestimating the impacts of extreme heat. On the other hand, it may also be noted that in some countries (or perhaps more precisely—in some areas of some countries), increases in temperature may lead to a reduction in mortality associated with exposure to extreme cold. When this effect is not captured, the impact of rising temperatures will be overestimated. Stunting This study used the regional stunting data for cases and deaths due to climate change from the WHO (2014) study to derive country-level data. The WHO (2014) study established the links be- tween climate change and undernutrition. The model developed by WHO comprises the following steps:20 Step 1: Estimate future post-global food trade national calorie availability for the years selected for analysis, with and without climate change. Step 2: Estimate the proportion of the population considered to be undernourished. Step 3: Estimate regional-level child stunting using an undernutrition model that accounts for pro- jections of GDP per capita. Step 4: Estimate mortality attributable to child stunting. The results of the analysis by WHO (2014) were reported as the percentage of children aged under five, who were moderately or severely stunted, in 2030 and 2050, as well as the number of deaths of children due to stunting—with and without climate change. 20  Note that WHO (2014) provides estimates for the years 2030 and 2050. This report extends the analysis to the year 2100. 37 These estimates were used in the current assessment, with estimates of mortality for the year 2100 calculated in the following way (for each of the 69 countries): Step 1: Estimate the percentage change (decrease) in the proportion of children aged under five who are stunted, based on the percentage increase in the GDP per capita between the years 2030 and 2050 (akin to a measure of income elasticity). Step 2: Use the estimates from Step 1 to estimate the proportion of stunted children in 2100 ba- sed on the projected percentage increase in the GDP per capita between 2050 and 2100. Given the different projections of the GDP per capita in SSP2 and SSP3, estimates for the year 2100 vary across SSPs. 38 Annex 4. Modeling Fatality Rates Malaria and Dengue For both dengue and malaria, the fatality rates (the number of deaths per case of each disease) were calculated for the base year 2020. In the first version of this analysis, it was assumed that the fatality rates for dengue and malaria remained constant over 2020-2100. This approach was also employed by WHO (2014) for the selected period of analysis up to 2050. However, if the pro- bability of malaria and dengue were negatively correlated with GDP per capita as indicated in the statistical functions presented above (Equations [1] and [2]), it can be assumed that fatality rates may also be negatively correlated with socioeconomic development for which GDP per capita ser- ves as a proxy. Previous work has also indicated that the likelihood of mortality from extreme events—be it cli- matic or geological in nature—is negatively associated with female education. Blankespoor et al. (2010) have shown that in any given population, the more educated females are, the lower the probability of death. These impacts were used in assessing the economic cost of adaptation in the influential World Bank (2010) report, Economics of Adaptation to Climate Change: Synthesis Report, in which investing in female education was highlighted as a means for adapting to the pro- jected impacts of climate change. It was thus hypothesized in the current analysis that female education could have the same impact on malaria and dengue fatality rates. The general model being tested is the following: (5) FRjit= αi+β1 GDPit+β2 FEMit+β3 GDPit* FEMit+ ε where FRjit is the fatality rate for disease j in country i at time t, with j standing for malaria or dengue; GDPit is GDP per capita in country i at time t; and FEMit is female education in country i at time t. 39 Annual GDP per capita figures over the 2000-2022 period were obtained for all countries from the World Bank database. Figures for female education were obtained from UN Statistics. For the purpose of testing, female education was defined in various ways: • Proportion of female population with primary education (Fempri); • Proportion of female education with lower secondary education (Femsec); and • Proportion of female education with upper secondary education (Femup) Malaria Given the overwhelming concentration of global cases of malaria in SSA (accounting for 95 percent of all cases of malaria), the above model was tested using the 27 Sub-Saharan African countries in- cluded in our analysis; all 27 countries had positive fatality rates for malaria in the base year 2020. It was tested by using fatality rates found in WHO (2023), with a balanced panel dataset construc- ted covering the 2000-2022 period. Various specifications were tested, including a non-linear specification for GDP per capita and lo- gistic regressions. Estimated coefficients were statistically significant only for the linear model that offered the best explanatory power. Results for the specifications of various variables of interest are presented in Table A.4. Three models were generated. Note that GDP per capita is strongly significant in all model specifi- cations: as GDP per capita increases, malaria fatality rates fall. Taken individually, Fempri, Femsec, and Femup had a similar effect on fatality rates. However, Femup (Model 3) was noted to offer a much better fit than the other two models. Therefore, the estimated coefficients from Model 3 were used to estimate fatality rates in 2030, 2050, and 2100, based on the values of the GDP per capita and female education as projected in SSP2 and SSP3 for the same years. 40 Table A.4 Modeling Malaria’s Fatality Rate Dependent variable: Malaria’s fatality rate Independent variable Model (1) Model (2) Model (3) GDP -0.134** -0.093*** -0.084*** (0.054) (0.028) (0.011) Fempri -3.874 *** (0.969) GDP * Fempri 0.002*** (0.001) Femsec -5.053*** (1.756) GDP * Femsec 0.002*** (0.0003) Femup -14.499*** (1.925) GDP * Femup 0.004*** (0.0003) Observations 360 271 195 R 2 0.073 0.104 0.452 Adjusted R2 -0.005 0.004 0.359 F statistics 8.703 *** 9.400 *** 45.626*** (df = 3; 331) (df = 3; 243) (df = 3; 166) Note: *p < 0.1; **p < 0.05; ***p < 0.01 Dengue For dengue, the model was tested by using 55 countries (out of 69) with non-zero fatality rates in the base year 2020.21 However, as dengue fatality rates are available only in the 2009 to 2019 period, a balanced panel dataset was constructed over that period. 21  Fourteen countries had a zero fatality rate from dengue in 2020. 41 Similar specifications were used to model dengue’s fatality rate as for malaria’s fatality rate. However, the overall results were not conclusive: many of the coefficients obtained were not sta- tistically significant and the R2 coefficients were very low in all specifications. The further testing of various specifications, for example, by including the three levels of female education and testing non-linear and logistic regressions, also led to inconclusive results. A study by Ali (2024) showed a significant and positive correlation between mortality rates in the most recent dengue outbreak in Bangladesh with population density and air quality index. This finding could account for the lack of relationships among dengue, GDP per capita, and female education. Hence, a constant fatality rate (equal to the 2020 level) was assumed for dengue. 42 Annex 5. Estimating YLL from Climate Change Life Expectancy In order to estimate the number of years of life lost (YLL), estimates of life expectancy are needed for each of the 69 countries in the baseline year 2020, and then in the years 2030, 2050, and 2100. We used LEXPECi,t to represent the life expectancy of country, i, in year, t. The life expectancy of any given country, i, among the 69 countries included in the analysis for the baseline year 2020 (noted as LEXPECi, 2020) can be found in the World Bank Indicators database. The life expectancies of the other years, that is, LEXPECi,2030, LEXPECi,2050, and LEXPECi,2100, have to be estimated. Unfortunately, although the SSP database covers numerous socioeconomic variables, it does not include the values of life expectancy. In order to obtain values of life expectancy, the analysis relied on Kc and Lutz (2017). The authors provided details on the assumptions concerning the demographic and human capital compo- nents of the SSPs (fertility, mortality, migration, and education). Concerning the mortality compo- nent, SSP2 was characterized as “medium” mortality while SSP3 was characterized as “high” morta- lity. Kc and Lutz (2017) asserted that a “medium” mortality assumption implies that life expectancy increases by two years per decade while a “high” mortality assumption implies an increase in life expectancy of one year per decade. We retained these assumptions in projecting the life expec- tancies for the years 2030, 2050, and 2100. Furthermore, we assumed that these increases would apply equally to all countries included in the analysis. The calculation of life expectancies is shown in Table A.5. Table A.5 Estimating Life Expectancies SSP2 LEXPECi,2020 + 2 LEXPECi,2020 + 6 LEXPECi,2020 + 16 SSP3 LEXPECi,2020 + 1 LEXPECi,2020 + 3 LEXPECi,2020 + 8 43 Calculating Years of Life Lost Mortality from stunting and diarrhea typically affects young children. For simplification purposes, it is thus assumed that the number of YYL is equal to the estimated life expectancy for every death associated with stunting and diarrhea. Regarding extreme heat, it is noted that the statistical modeling used in WHO (2014) applies only to individuals above 65 years old. For the purpose of estimating the number of YYL resulting from extreme heat death, it is assumed that the number of deaths is uniformly distributed between LEXPECi,t and 65. Hence, the number of YYL for that age group is [(LEXPECi,t – 65) / 2], provided that LEXPECi,t is larger than 65. In circumstances where LEXPECi,t is less than 65, it is assumed that the number of YYL is 0. In order to estimate the number of YYL associated with both malaria and dengue deaths, the distribution of mortality across age groups is required. The mortality numbers from malaria and dengue across the age groups is available for each country included in the analysis with a positive number of deaths for the baseline year 2020.22 For the purpose of illustration, the age distribution of deaths from malaria in Nigeria and Cameroon in the year 2020 is shown in Tables A.6 and A.7, respectively. Similar tables were constructed for each country with malaria and dengue deaths in the baseline year 2020. Table A.6 Age Distribution of Deaths from Malaria in Nigeria in 2020 Age Group Number of Deaths % Distribution of Deaths Less than 5 years old 129,822 55.59 5-14 years old 13,756 5.89 15-49 years old 40,657 17.41 50-69 years old 33,521 14.35 70+ years old 15,794 6.76 Total 233,551 100 Source: https://ourworldindata.org. 22  For malaria, the information is available at: https://ourworldindata.org/grapher/malaria-deaths-by-age. For dengue, the information is available in World Bank Indicators. 44 Table A.7 Age Distribution of Deaths from Malaria in Cameroon in 2020 Age Group Number of Deaths % Distribution of Deaths Less than 5 years old 15,213 48.06 5-14 years old 1,394 4.40 15-49 years old 6,858 21.67 50-69 years old 5,571 17.60 70+ years old 2,619 8.27 Total 31,654 100 Source: https://ourworldindata.org. We estimate the number of deaths by age group. Within each age group, it is assumed that the number of deaths is uniformly distributed within the age group. As a result, the number of YLL in country, i, in year, t (with t = 2030, 2050, 2100): For the age group less than 5 years old: LEXPECi,t – 2.523 For the age group 5-14 years old: LEXPECi,t – 9.5 For the age group 15-49 years old: LEXPECi,t – 32 For the age group 50-69 years old: LEXPECi,t – 59.5 For the age group 70+ years old: LEXPECi,t – 70 For the purpose of estimating the number of YLL, it is finally assumed that the percentage distri- bution of deaths across the age groups in the years 2030, 2050, and 2100 is similar as observed in the year 2020. 23  “2.5”, “9.5”, “32”, “59.5”, and “70” are the mid-point values of the respective age groups. 45 Annex 6. Estimating the Economic Cost of Climate-Related Health Impacts Value of Statistical Life The national VSL were estimated with a benefit-transfer approach, using an estimated VSL of USD6 million in the United States (US) in 2020 as a starting point.24 A country-specific VSL was then estimated in the base year 2020 for each of the 69 countries, using the ratio of GDP per capita in the country of interest to GDP per capita (in PPP terms) in the US (Equation [6]). This approach assumes that VSL has an income elasticity of 1.25 This approach allows the VSL in 2020 for each country to be estimated. (6) where: VSL2020,i is the estimated VSL in 2020 in the country, i, GDP2020,i is the GDP per capita in 2020 in the country, i, and GDP2020,US is GDP per capita in 2020 in the US. Once a VSL in 2020 was estimated for any given country, the VSL in 2030, 2050, and 2100 for that country were calculated by multiplying the 2020 estimated VSL with the ratio of GDP per capita in 2030, 2050, and then 2100 to GDP per capita in 2020 (Equation [7]—which similarly assumes a unitary income elasticity): (7) 24 A VSL of approximately USD6 million was estimated in OECD countries in the mid-2000s. More recently, Viscusi (2020) used a VSL of USD11 million. A similar value was recently used by Carleton et al. (2022). This analysis uses the lower value of USD6 million as a baseline value for VSL, based on the preference of underestimating the economic cost associated with mortality. 25 Viscusi (2020) adopted an income elasticity figure of 1.0 to estimate the global economic cost of the COVID-19 pandemic. The use of the income elasticity of 1 is consistent with the meta-regression analyses of the revealed preference labor market data in Viscusi and Masterman (2017a, 2017b). Similarly, a meta-regression analysis of the stated preference data in Masterman and Viscusi (2018) estimated that the overall income elasticity of the VSL ranged from 0.94 to 1.05 across countries. An income elasticity of 1 across time and geographical regions was also used by Carleton et al. (2022). 46 where: VSLt,i is VSL in country, i, at time, t, with t = 2030, 2050, or 2100 and GDPt,i is GDP per capita in country, i, at time, t, with t = 2030, 2050, or 2100. In Equation (7), GDPt,i not only varies across time and country but also the SSPs, with SSP2 showing a higher GDP per capita than SSP3 for all the countries and years. The estimated values of VSL for all 69 countries and both SSPs for 2020 (baseline), 2030, 2050, and 2100 are presented below in Table A.8 and Table A.9 for SSP3 and SSP2 respectively. Table A.8 Estimated Country-Specific Values for VSL (USD) – SSP3 Country 2020 2030 2050 2100 Afghanistan 194,240 238,049 380,247 1,171,832 Algeria 1,228,880 1,522,371 1,562,756 2,804,399 Angola 884,574 858,679 656,431 1,442,703 Argentina 2,603,498 3,139,257 3,663,703 4,878,483 Azerbaijan 1,371,923 1,550,281 1,474,724 2,375,253 Bangladesh 333,877 489,091 733,680 1,613,220 Benin 217,964 271,570 408,496 1,167,993 Bolivia 798,317 1,040,672 1,473,159 3,224,734 Brazil 1,747,278 2,063,543 2,246,594 2,324,466 Burkina Faso 210,100 273,368 402,433 1,075,220 Burundi 95,844 135,893 244,634 889,794 Cambodia 456,812 654,763 990,005 2,100,646 Cameroon 363,480 491,863 779,353 1,960,239 Chad 196,752 246,646 412,664 1,222,609 China 1,928,327 2,925,640 3,867,701 4,734,957 Colombia 1,517,280 1,754,464 2,117,938 3,520,566 Congo, DR 67,491 123,168 268,148 1,051,658 Cote d’Ivoire 336,113 571,609 1,052,209 2,441,970 Dominican Republic 1,517,096 1,803,721 2,171,160 3,510,295 47 Country 2020 2030 2050 2100 Ecuador 1,205,379 1,420,718 1,776,433 3,378,722 Egypt 982,983 1,352,955 1,984,679 3,871,077 Ethiopia 189,611 253,319 400,815 1,209,471 Ghana 340,965 466,727 663,098 1,659,815 Guatemala 624,829 737,587 1,004,761 2,134,802 Guinea 272,764 465,920 820,239 1,967,130 Haiti 199,908 296,222 537,901 1,696,886 Honduras 556,063 690,168 991,728 2,213,202 India 654,287 925,698 1,260,823 1,862,056 Indonesia 874,605 1,311,913 1,942,977 2,985,028 Iran 1,554,603 1,911,783 2,653,906 3,799,189 Iraq 823,619 1,079,443 1,401,315 1,648,835 Jordan 845,529 1,174,546 1,900,289 4,380,105 Kazakhstan 2,373,738 3,583,606 4,627,499 4,524,302 Kenya 262,244 338,246 500,750 1,479,346 Madagascar 127,157 171,042 306,575 1,149,514 Malawi 136,981 184,020 296,660 1,150,393 Malaysia 2,376,407 2,952,202 3,868,254 6,315,435 Mali 143,004 189,208 305,523 873,635 Mexico 2,045,542 2,397,662 2,841,860 3,823,935 Morocco 845,476 1,189,128 1,819,029 3,186,494 Mozambique 181,733 256,900 403,603 1,153,203 Myanmar 321,712 440,088 541,623 671,604 Nepal 180,852 231,379 338,401 931,095 Niger 115,163 139,705 201,276 719,240 Nigeria 416,268 554,709 788,966 2,203,405 Pakistan 379,577 469,368 666,715 1,476,281 Peru 1,794,937 2,374,122 3,116,373 5,055,980 Philippines 628,446 773,713 1,052,499 2,449,547 Romania 1,887,783 2,414,692 2,955,523 4,495,724 Russian Federation 2,703,222 3,592,915 4,621,660 6,133,900 48 Country 2020 2030 2050 2100 Rwanda 208,574 286,385 425,224 1,133,511 Senegal 286,362 360,055 507,812 1,263,590 Somalia 5,050 6,976 12,112 58,913 South Africa 1,696,305 2,154,195 2,748,392 4,068,813 Sri Lanka 1,050,723 1,500,735 2,010,613 2,936,687 Sudan 338,440 434,982 627,354 1,487,141 Syrian Arab Republic 878,340 1,208,245 1,856,760 3,599,869 Tanzania 238,072 332,854 538,993 1,480,536 Thailand 1,493,017 2,056,981 2,907,128 4,847,994 Tunisia 1,536,704 2,234,471 3,499,106 5,564,913 Turkey 2,277,972 2,706,597 3,207,772 4,130,196 Uganda 208,522 279,257 440,834 1,399,146 Ukraine 1,196,370 1,641,949 2,351,631 4,615,154 Uzbekistan 634,705 928,517 1,415,610 3,081,060 Venezuela 1,744,845 2,107,415 3,370,897 5,342,364 Vietnam 637,914 932,552 1,466,767 3,014,518 Yemen, Republic of 291,307 369,645 562,888 1,354,987 Zambia 270,963 373,205 604,011 1,832,436 Zimbabwe 72,646 101,280 235,534 1,380,054 Table A.9 Estimated Country-Specific Values for VSL (USD) – SSP2 Country 2020 2030 2050 2100 Afghanistan 194,240 259,249 550,050 4,059,516 Algeria 1,228,880 1,611,346 2,213,968 6,782,743 Angola 884,574 901,829 971,368 5,048,862 Argentina 2,603,498 3,414,099 5,090,673 10,663,927 Azerbaijan 1,371,923 1,561,647 1,858,423 5,401,424 Bangladesh 333,877 545,008 1,178,305 5,477,045 Benin 217,964 306,355 683,729 4,933,748 Bolivia 798,317 1,189,515 2,421,618 9,576,759 Brazil 1,747,278 2,228,730 3,183,610 7,197,721 49 Country 2020 2030 2050 2100 Burkina Faso 210,100 319,327 752,902 5,240,042 Burundi 95,844 158,835 470,932 4,341,914 Cambodia 456,812 732,934 1,524,311 6,828,685 Cameroon 363,480 557,704 1,262,241 6,498,942 Chad 196,752 277,456 689,694 5,301,730 China 1,928,327 3,198,784 5,306,407 10,144,168 Colombia 1,517,280 1,903,498 3,051,011 8,570,414 Congo, DR 67,491 142,995 494,281 4,377,246 Cote d’Ivoire 336,113 691,256 1,984,771 9,336,611 Dominican Republic 1,517,096 1,982,931 3,094,945 7,651,320 Ecuador 1,205,379 1,574,554 2,666,848 8,585,770 Egypt 982,983 1,494,762 2,947,555 8,845,187 Ethiopia 189,611 292,422 741,611 5,577,314 Ghana 340,965 535,189 1,174,970 6,043,698 Guatemala 624,829 876,482 1,709,332 7,363,580 Guinea 272,764 622,820 1,837,279 9,823,379 Haiti 199,908 340,493 953,902 6,427,046 Honduras 556,063 779,346 1,587,580 7,460,221 India 654,287 1,050,264 2,088,816 6,726,670 Indonesia 874,605 1,443,754 2,897,550 8,662,902 Iran 1,554,603 2,015,755 3,163,407 7,763,142 Iraq 823,619 1,058,961 1,461,527 4,496,679 Jordan 845,529 1,288,382 2,676,684 8,800,514 Kazakhstan 2,373,738 3,624,235 4,896,518 7,510,826 Kenya 262,244 390,798 859,037 4,905,011 Madagascar 127,157 194,162 515,075 4,220,352 Malawi 136,981 207,438 507,899 4,582,694 Malaysia 2,376,407 3,196,581 5,116,961 11,713,070 Mali 143,004 215,626 542,548 4,176,500 Mexico 2,045,542 2,566,018 3,905,805 10,319,880 Morocco 845,476 1,362,010 2,931,536 9,891,526 50 Country 2020 2030 2050 2100 Mozambique 181,733 295,542 733,635 6,088,111 Myanmar 321,712 483,299 823,310 2,535,833 Nepal 180,852 271,766 623,102 4,120,834 Niger 115,163 162,160 377,006 4,022,578 Nigeria 416,268 620,659 1,309,779 7,298,900 Pakistan 379,577 531,053 1,114,511 5,372,717 Peru 1,794,937 2,601,899 4,386,154 11,115,107 Philippines 628,446 859,110 1,590,852 6,117,223 Romania 1,887,783 2,593,981 3,968,903 9,414,624 Russian Federation 2,703,222 3,754,874 5,440,287 9,991,107 Rwanda 208,574 333,241 762,405 4,506,278 Senegal 286,362 423,391 955,571 5,964,809 Somalia 5,050 10,035 34,956 539,225 South Africa 1,696,305 2,299,296 3,621,892 8,345,570 Sri Lanka 1,050,723 1,654,488 3,066,034 8,935,757 Sudan 338,440 494,529 1,043,700 5,774,801 Syrian Arab Republic 878,340 1,345,067 2,706,542 9,211,826 Tanzania 238,072 385,080 902,993 5,202,281 Thailand 1,493,017 2,277,028 4,272,421 11,635,272 Tunisia 1,536,704 2,387,862 4,422,568 9,735,056 Turkey 2,277,972 2,961,581 4,449,355 10,030,464 Uganda 208,522 326,293 782,997 5,623,225 Ukraine 1,196,370 1,788,906 3,227,552 8,810,788 Uzbekistan 634,705 1,037,523 2,066,913 5,982,778 Venezuela 1,744,845 2,167,103 3,282,841 8,318,452 Vietnam 637,914 1,015,399 2,019,008 6,800,314 Yemen, Republic of 291,307 413,989 863,535 3,344,140 Zambia 270,963 429,801 1,032,153 6,400,551 Zimbabwe 72,646 118,484 414,901 4,349,512 51 Cost of Illness Table A.10 Estimated Country-Specific Values for COI Dengue t Country 2020 2030 2050 2100 Afghanistan 120 148 236 726 Algeria 870 1,077 1,106 1,985 Angola 626 608 465 1,021 Argentina 1,069 1,288 1,504 2,002 Azerbaijan 971 1,097 1,044 1,681 Bangladesh 207 303 455 1,000 Benin 154 192 289 827 Bolivia 328 427 605 1,324 Brazil 691 816 888 919 Burkina Faso 149 193 285 761 Burundi 68 96 173 630 Cambodia 612 877 1,327 2,815 Cameroon 257 348 552 1,387 Chad 139 175 292 865 China 2,107 3,197 4,226 5,173 Colombia 974 1,126 1,360 2,260 Congo, DR 48 87 190 744 Cote d’Ivoire 238 405 745 1,728 Dominican Republic 623 740 891 1,441 Ecuador 495 583 729 1,387 Egypt 696 957 1,404 2,739 Ethiopia 134 179 284 856 Ghana 241 330 469 1,175 Guatemala 307 362 494 1,049 Guinea 193 330 580 1,392 Haiti 82 122 221 696 Honduras 228 283 407 908 India 246 348 475 701 Indonesia 867 1,300 1,926 2,959 Iran 1,100 1,353 1,878 2,689 52 Country 2020 2030 2050 2100 Iraq 583 764 992 1,167 Jordan 598 831 1,345 3,100 Kazakhstan 1,680 2,536 3,275 3,202 Kenya 186 239 354 1,047 Madagascar 90 121 217 813 Malawi 97 130 210 814 Malaysia 2,838 3,526 4,620 7,543 Mali 101 134 216 618 Mexico 840 984 1,166 1,569 Morocco 598 842 1,287 2,255 Mozambique 129 182 286 816 Myanmar 358 489 602 747 Nepal 112 143 210 577 Niger 82 99 142 509 Nigeria 295 393 558 1,559 Pakistan 235 291 413 915 Peru 737 974 1,279 2,075 Philippines 639 787 1,070 2,491 Romania 1,336 1,709 2,092 3,181 Russian Federation 1,913 2,543 3,271 4,341 Rwanda 148 203 301 802 Senegal 203 255 359 894 Somalia 4 5 9 42 South Africa 1,200 1,524 1,945 2,879 Sri Lanka 651 930 1,246 1,821 Sudan 240 308 444 1,052 Syrian Arab Republic 622 855 1,314 2,547 Tanzania 168 236 381 1,048 Thailand 1,858 2,559 3,617 6,032 Tunisia 1,087 1,581 2,476 3,938 Turkey 1,612 1,915 2,270 2,923 Uganda 148 198 312 990 Ukraine 847 1,162 1,664 3,266 Uzbekistan 449 657 1,002 2,180 Venezuela 482 582 931 1,476 53 Country 2020 2030 2050 2100 Vietnam 459 671 1,055 2,168 Yemen, Republic of 206 262 398 959 Zambia 192 264 427 1,297 Zimbabwe 51 72 167 977 Table A.11 Estimated Country-Specific Values for COI Dengue (USD) – SSP2 Country 2020 2030 2050 2100 Afghanistan 120 160 340 2,508 Algeria 870 1,135 1,560 4,780 Angola 626 627 675 3,510 Argentina 1,069 1,393 2,078 4,352 Azerbaijan 971 1,105 1,314 3,820 Bangladesh 207 336 725 3,372 Benin 154 214 478 3,449 Bolivia 328 483 984 3,893 Brazil 691 877 1,252 2,832 Burkina Faso 149 223 526 3,657 Burundi 68 111 330 3,045 Cambodia 612 974 2,025 9,071 Cameroon 257 391 885 4,559 Chad 139 195 483 3,717 China 2,107 3,469 5,754 11,000 Colombia 974 1,217 1,950 5,479 Congo, DR 48 100 346 3,061 Cote d’Ivoire 238 481 1,381 6,497 Dominican Republic 623 808 1,261 3,118 Ecuador 495 641 1,086 3,498 Egypt 696 1,050 2,071 6,214 Ethiopia 134 205 520 3,912 Ghana 241 375 824 4,238 Guatemala 307 419 817 3,519 Guinea 193 421 1,241 6,638 Haiti 82 139 388 2,615 54 Country 2020 2030 2050 2100 Honduras 228 317 645 3,033 India 246 392 779 2,508 Indonesia 867 1,422 2,854 8,532 Iran 1,100 1,413 2,217 5,441 Iraq 583 744 1,028 3,161 Jordan 598 905 1,880 6,182 Kazakhstan 1,680 2,556 3,453 5,296 Kenya 186 273 600 3,427 Madagascar 90 136 362 2,963 Malawi 97 146 357 3,224 Malaysia 2,838 3,793 6,072 13,898 Mali 101 151 381 2,930 Mexico 840 1,049 1,596 4,217 Morocco 598 950 2,045 6,900 Mozambique 129 207 514 4,268 Myanmar 358 534 910 2,802 Nepal 112 166 379 2,509 Niger 82 113 264 2,815 Nigeria 295 436 920 5,124 Pakistan 235 326 685 3,302 Peru 737 1,060 1,787 4,530 Philippines 639 867 1,606 6,175 Romania 1,336 1,826 2,793 6,626 Russian Federation 1,913 2,645 3,832 7,037 Rwanda 148 234 534 3,158 Senegal 203 296 668 4,169 Somalia 4 6 22 339 South Africa 1,204 1,627 2,563 5,906 Sri Lanka 651 1,018 1,887 5,500 Sudan 240 346 730 4,037 Syrian Arab Republic 622 939 1,889 6,430 Tanzania 168 269 632 3,641 Thailand 1,858 2,809 5,271 14,354 Tunisia 1,087 1,679 3,111 6,847 Turkey 1,612 2,080 3,126 7,046 55 Country 2020 2030 2050 2100 Uganda 148 228 546 3,921 Ukraine 847 1,255 2,265 6,183 Uzbekistan 449 726 1,446 4,186 Venezuela 482 596 902 2,287 Vietnam 459 725 1,441 4,853 Yemen, Republic of 206 290 604 2,339 Zambia 192 300 720 4,467 Zimbabwe 51 83 290 3,038 Table A.12 Estimated Country-Specific Values for COI Malaria (USD) – SSP3 Country 2020 2030 2050 2100 South Africa 1,204 1,627 2,563 5,906 Afghanistan 20 24 38 118 Algeria 114 142 145 261 Angola 124 121 92 203 Argentina 98 118 138 184 Azerbaijan 128 144 137 221 Bangladesh 34 49 74 163 Benin 31 38 57 164 Bolivia 30 39 56 122 Brazil 66 78 85 88 Burkina Faso 16 21 30 81 Burundi 13 19 34 125 Cambodia 42 61 92 195 Cameroon 57 78 123 310 Chad 28 35 58 172 China 179 272 360 440 Colombia 26 30 36 60 Congo, DR 9 17 38 148 Cote d’Ivoire 47 80 148 343 Dominican Republic 57 68 82 132 Ecuador 45 54 67 127 Egypt 91 126 185 360 Ethiopia 27 36 56 170 56 Country 2020 2030 2050 2100 Ghana 23 32 46 114 Guatemala 24 28 38 81 Guinea 38 65 115 276 Haiti 8 11 20 64 Honduras 21 26 37 84 India 77 109 148 218 Indonesia 81 122 181 278 Iran 145 178 247 353 Iraq 77 100 130 153 Jordan 79 109 177 407 Kazakhstan 221 333 430 421 Kenya 37 47 70 208 Madagascar 26 35 63 236 Malawi 19 26 42 162 Malaysia 221 274 360 587 Mali 20 27 43 123 Mexico 77 90 107 144 Morocco 79 111 169 296 Mozambique 46 65 102 291 Myanmar 30 41 50 62 Nepal 18 23 34 94 Niger 16 20 28 101 Nigeria 34 46 65 181 Pakistan 32 40 56 124 Peru 105 138 182 295 Philippines 58 72 98 228 Romania 176 225 275 418 Russian Federation 251 334 430 570 Rwanda 29 40 60 159 Senegal 40 51 71 177 Somalia 1 1 2 8 South Africa 232 295 376 557 Sri Lanka 106 151 203 296 Sudan 48 61 88 209 Syrian Arab Republic 82 112 173 335 57 Country 2020 2030 2050 2100 Tanzania 33 47 76 208 Thailand 139 191 270 451 Tunisia 143 208 325 517 Turkey 212 252 298 384 Uganda 29 39 62 196 Ukraine 111 153 219 429 Uzbekistan 59 86 132 286 Venezuela 66 80 127 202 Vietnam 59 87 136 280 Yemen, Republic of 27 34 52 126 Zambia 38 52 85 257 Zimbabwe 10 14 33 194 Table A.13 Estimated Country-Specific Values for COI Malaria (USD) – SSP2 Country 2020 2030 2050 2100 Afghanistan 20 26 55 408 Algeria 114 149 205 628 Angola 124 124 134 697 Argentina 98 128 191 400 Azerbaijan 128 145 173 502 Bangladesh 34 55 118 548 Benin 31 42 95 684 Bolivia 30 44 90 358 Brazil 66 84 119 270 Burkina Faso 16 24 56 389 Burundi 13 22 66 604 Cambodia 42 68 140 629 Cameroon 57 87 198 1,018 Chad 28 39 96 737 China 179 295 490 936 Colombia 26 32 52 146 Congo, DR 9 20 69 608 Cote d’Ivoire 47 95 274 1,289 Dominican Republic 57 74 116 287 58 Country 2020 2030 2050 2100 Ecuador 45 59 100 321 Egypt 91 138 272 816 Ethiopia 27 41 103 776 Ghana 23 36 80 412 Guatemala 24 32 63 270 Guinea 38 84 246 1,317 Haiti 8 13 36 240 Honduras 21 29 59 279 India 77 122 243 782 Indonesia 81 133 268 800 Iran 145 186 291 715 Iraq 77 98 135 415 Jordan 79 119 247 812 Kazakhstan 221 336 454 696 Kenya 37 54 119 680 Madagascar 26 40 105 861 Malawi 19 29 71 640 Malaysia 221 295 473 1,082 Mali 20 30 76 581 Mexico 77 96 147 388 Morocco 79 125 269 907 Mozambique 46 74 184 1,523 Myanmar 30 45 76 234 Nepal 18 27 62 408 Niger 16 23 52 558 Nigeria 34 51 107 594 Pakistan 32 44 93 449 Peru 105 151 254 644 Philippines 58 79 147 565 Romania 176 240 367 871 Russian Federation 251 347 503 925 Rwanda 29 46 106 627 Senegal 40 59 133 827 Somalia 1 1 4 67 South Africa 233 296 377 559 59 Country 2020 2030 2050 2100 Sri Lanka 106 166 307 894 Sudan 48 69 145 801 Syrian Arab Republic 82 123 248 845 Tanzania 33 53 125 722 Thailand 139 210 394 1,073 Tunisia 143 221 409 900 Turkey 212 273 411 926 Uganda 29 45 108 778 Ukraine 111 165 298 812 Uzbekistan 59 95 190 550 Venezuela 66 81 123 312 Vietnam 59 94 186 628 Yemen, Republic of 27 38 79 307 Zambia 38 60 143 886 Zimbabwe 10 16 58 603 Table A.14 Estimated Country-Specific Values for COI Diarrhea (USD) – SSP3 Country 2020 2030 2050 2100 Afghanistan 72 88 141 435 Algeria 438 542 556 999 Angola 208 202 154 339 Argentina 1,457 1,757 2,050 2,730 Azerbaijan 603 681 648 1,044 Bangladesh 61 89 134 294 Benin 63 79 119 339 Bolivia 288 376 532 1,164 Brazil 923 1,090 1,186 1,227 Burkina Faso 58 75 111 296 Burundi 38 54 98 356 Cambodia 344 494 746 1,584 Cameroon 101 136 216 543 Chad 76 96 160 474 China 455 690 912 1,116 60 Country 2020 2030 2050 2100 Colombia 969 1,120 1,352 2,248 Congo, DR 33 60 132 516 Cote d’Ivoire 123 209 385 893 Dominican Republic 553 657 791 1,280 Ecuador 706 832 1,040 1,978 Egypt 178 245 360 702 Ethiopia 97 129 204 617 Ghana 263 360 511 1,279 Guatemala 445 525 715 1,519 Guinea 46 79 139 334 Haiti 99 147 267 841 Honduras 334 415 596 1,331 India 107 151 206 304 Indonesia 460 690 1,022 1,569 Iran 772 949 1,318 1,886 Iraq 345 452 587 691 Jordan 287 399 645 1,486 Kazakhstan 450 679 877 858 Kenya 181 234 346 1,023 Madagascar 42 57 102 384 Malawi 70 94 152 589 Malaysia 1,512 1,878 2,461 4,018 Mali 97 128 207 592 Mexico 972 1,140 1,351 1,817 Morocco 505 711 1,087 1,905 Mozambique 67 94 148 424 Myanmar 322 441 543 673 Nepal 65 83 122 336 Niger 40 49 71 253 Nigeria 174 232 330 923 Pakistan 74 92 131 290 Peru 510 675 886 1,438 Philippines 664 818 1,112 2,588 Romania 351 449 550 836 Russian Federation 411 546 703 933 61 Country 2020 2030 2050 2100 Rwanda 187 257 382 1,018 Senegal 101 126 178 444 Somalia 71 98 171 829 South Africa 851 1,081 1,379 2,042 Sri Lanka 422 603 807 1,179 Sudan 261 336 485 1,149 Syrian Arab Republic 73 100 154 298 Tanzania 64 89 144 395 Thailand 728 1,003 1,417 2,364 Tunisia 446 648 1,015 1,615 Turkey 480 570 676 870 Uganda 99 133 209 665 Ukraine 101 139 199 390 Uzbekistan 142 208 316 689 Venezuela 796 962 1,539 2,438 Vietnam 386 564 888 1,824 Yemen, Republic of 168 213 324 779 Zambia 129 178 288 874 Zimbabwe 78 108 252 1,475 Table A.15 Estimated Country-Specific Values for COI Diarrhea (USD) – SSP2 Country 2020 2030 2050 2100 Afghanistan 72 96 203 1,501 Algeria 438 571 785 2,405 Angola 208 208 224 1,165 Argentina 1,457 1,900 2,833 5,934 Azerbaijan 603 686 816 2,372 Bangladesh 61 98 213 990 Benin 63 88 196 1,416 Bolivia 288 425 866 3,425 Brazil 923 1,171 1,672 3,781 Burkina Faso 58 87 204 1,423 Burundi 38 63 187 1,722 62 Country 2020 2030 2050 2100 Cambodia 344 548 1,139 5,103 Cameroon 101 153 347 1,786 Chad 76 107 265 2,038 China 455 748 1,242 2,374 Colombia 969 1,210 1,940 5,449 Congo, DR 33 69 240 2,122 Cote d’Ivoire 123 249 714 3,358 Dominican Republic 553 718 1,120 2,769 Ecuador 706 915 1,549 4,988 Egypt 178 269 531 1,593 Ethiopia 97 148 375 2,820 Ghana 263 409 897 4,614 Guatemala 445 607 1,183 5,096 Guinea 46 101 298 1,591 Haiti 99 167 469 3,157 Honduras 334 464 946 4,443 India 107 170 337 1,087 Indonesia 460 754 1,514 4,526 Iran 772 991 1,556 3,817 Iraq 345 441 609 1,872 Jordan 287 434 902 2,965 Kazakhstan 450 685 925 1,419 Kenya 181 267 587 3,351 Madagascar 42 64 171 1,398 Malawi 70 106 259 2,333 Malaysia 1,512 2,020 3,234 7,403 Mali 97 145 364 2,804 Mexico 972 1,214 1,848 4,884 Morocco 505 803 1,728 5,829 Mozambique 67 108 267 2,218 Myanmar 322 481 820 2,526 Nepal 65 96 221 1,459 Niger 40 56 131 1,397 Nigeria 174 258 544 3,032 Pakistan 74 103 217 1,045 Peru 510 735 1,238 3,138 63 Country 2020 2030 2050 2100 Philippines 664 901 1,669 6,417 Romania 351 480 734 1,742 Russian Federation 411 568 823 1,512 Rwanda 187 296 678 4,009 Senegal 101 147 332 2,070 Somalia 71 125 436 6,725 South Africa 854 1,154 1,818 4,188 Sri Lanka 422 660 1,222 3,562 Sudan 261 377 797 4,407 Syrian Arab Republic 73 110 221 752 Tanzania 64 102 238 1,373 Thailand 728 1,101 2,065 5,624 Tunisia 446 689 1,275 2,808 Turkey 480 619 930 2,098 Uganda 99 153 367 2,632 Ukraine 101 150 271 739 Uzbekistan 142 229 457 1,322 Venezuela 796 984 1,491 3,778 Vietnam 386 610 1,213 4,084 Yemen, Republic of 168 235 491 1,901 Zambia 129 202 486 3,011 Zimbabwe 78 125 438 4,587 It is important to note that the estimates of the economic cost are limited to the application of the estimated COI to the number of cases of morbidity. The true economic cost of morbidity is, in all likelihood, larger than the one used here. For example, it is well documented that both ex- treme heat and stunting are accompanied by important productivity losses (Galasso et al. [2018]; Heltberg [2008]; International Labour Organization [ILO] 2019). However, productivity losses (with the exception of days of illness) are typically not included in the estimates of COI. The cumulative economic cost of cases and deaths is estimated for 2026-2030, 2031-2050, and 2051-2100 in relation to its value as a percentage of GDP. Results are presented for the 69 coun- tries as a whole and each of the six regions (following the World Bank’s regional groupings). 64 Years of life lost This approach measures the economic cost of climate change solely on mortality by multiplying the estimated YLL in any given country in each of the years—2030, 2050, and 2100—by the avera- ge annual VSL in the same country for the same years. The average VSL in any country i at time t, noted as AVSLi,t, is simply estimated as (8) AVSLi,t = VSLi,t / LEXPECi,t, with t = 2030, 2050, 2100. Note that AVSLi,t is specific to SSP2 and SSP3, as both VSLi,t and LEXPECi,t vary across SSPs. 65 Annex 7. Cumulative Estimates of the Impacts of Climate Change on Morbidity and Mortality in the Short Term and Long Term Short Term (2026-2030) Table A.16 Cumulative Number of Cases Attributable to Climate Change: 2026-2030 SSP3 SSP2 Number of cases Share of Total Number of cases Share of Total (Millions) (%) (Millions) (%) EAP 30.1 5.82 26.2 4.96 ECA 8.2 1.58 5.5 1.05 LAC 19.3 3.72 14.5 2.74 MENA 18.9 3.66 12.2 2.31 SA 99.1 19.14 95.2 18.01 SSA 342.4 66.09 374.9 70.93 Total 518.0 528.5 Table A.17 Cumulative Number of Deaths Attributable to Climate Change: 2026-2030 SSP3 SSP2 Number of deaths Share of Total Number of deaths Share of Total (Thousands) (%) (Thousands) (%) EAP 289.9 16.72 384.6 20.62 ECA 16.4 0.95 16.0 0.86 LAC 27.2 1.57 31.6 1.70 MENA 23.9 1.38 21.8 1.17 SA 388.5 22.41 413.7 22.18 SSA 988.0 56.98 997.6 53.48 Total 1,734.0 1,865.2 66 Long Term (2026-2100) Table A.18 Cumulative Number of Cases Attributable to Climate Change: 2026-2100 SSP3 SSP2 Number of cases Share of Total Number of cases Share of Total (Millions) (%) (Millions) (%) EAP 1,266.0 3.21 694.8 4.38 ECA 232.7 0.59 67.4 0.42 LAC 725.9 1.84 313.8 1.98 MENA 804.9 2.04 206.9 1.30 SA 9,647.4 24.48 3,343.9 21.08 SSA 26,727.2 67.83 11,238.2 70.84 Total 39,404.0 15,865.0 Table A.19 Cumulative Number of Deaths Attributable to Climate Change: 2026-2100 SSP3 SSP2 Number of deaths Share of Total Number of deaths Share of Total (Thousands) (%) (Thousands) (%) EAP 24,009.0 18.30 16,600.4 25.98 ECA 988.8 0.75 699.8 1.10 LAC 2,225.1 1.70 1,431.9 2.24 MENA 2,674.3 2.04 938.3 1.47 SA 47,852.5 36.47 20,928.1 32.75 SSA 53,465.8 40.75 23,295.3 36.46 Total 131,215.3 63,893.9 67 Annex 8. Economic Cost of the Health Impacts of Climate Change in the Short Term and Long Term Short Term (2026-2030) Table A.20 Economic Cost of the Health Impacts of Climate Change: 2026-2030 SSP3 SSP2 YLL VSL YLL VSL Cost Share of Cost Share of Cost Share of Cost Share of GDP (Billions GDP (%) (Billions GDP (%) (Billions GDP (%) (Billions (%) USD) USD) USD) USD) EAP 231.0 0.19 411.9 0.34 339.1 0.25 603.7 0.44 ECA 13.9 0.08 46.1 0.26 11.4 0.06 47.8 0.25 LAC 27.5 0.11 51.4 0.21 31.2 0.12 64.4 0.25 MENA 11.6 0.14 20.5 0.25 9.5 0.12 19.5 0.24 SA 192.3 0.74 360.5 1.39 209.9 0.76 437.8 1.58 SSA 267.5 2.28 355.2 3.03 298.4 2.39 408.3 3.27 Total 743.8 0.35 1,245.7 0.59 899.5 0.39 1,581.5 0.69 Long Term (2026-2100) Table A.21 Economic Cost of the Health Impacts of Climate Change: 2026-2100 SSP3 SSP2 YLL VSL YLL VSL Cost Share of Cost Share of Cost Share of Cost Share of (Billions GDP (%) (Billions GDP (%) (Billions GDP (%) (Billions GDP (%) USD) USD) USD) USD) EAP 39,181.3 1.53 64,593.3 2.53 54,066.1 1.48 92,952.0 2.55 ECA 659.4 0.16 4,215.8 0.99 476.0 0.09 4,783.9 0.87 LAC 3,182.6 0.49 6,714.0 1.03 3,624.7 0.39 8,746.4 0.95 MENA 1,612.6 0.50 5,062.9 1.58 719.8 0.17 4,404.3 1.01 SA 43,465.6 4.53 85,040.9 8.87 42,414.9 2.40 100,371.7 5.67 SSA 35,401.8 3.92 46,980.7 5.20 41,278.4 2.08 60,334.4 3.04 Total 123,503.3 2.13 212,607.6 3.66 142,579.9 1.53 271,592.6 2.92 References 68 Abbasi, Kamran, Parveen Ali, Virginia Barbour, Thomas Benfield, Kirsten Bibbins-Domingo, Stephen Hancocks, Richard Horton, et al. 2023. “Time to Treat the Climate and Nature Crisis as One Indivisible Global Health Emergency: Joint Action Is Essential for Planetary and Human Health.” The British Medical Journal 383: 2355. doi: 10.1136/bmj.p2355. 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