Temperature
Days
Mortality costs as share of GDP
Energy expenditures as share of GDP
Climate Projections
Damage Projections
Mortality costs
The methodology for estimating the mortality costs of future climate change is described in full in Carleton et al. (2022). This study uses comprehensive historical mortality records to quantify how death rates across the globe have been affected by observed climate changes.
Carleton et al. (2020) compile the largest sub-national vital statistics database in the world, detailing 399 million deaths across 41 countries accounting for 55 percent of the global population. By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on death rates. These relationships are modified by the climate and income levels of the affected population. Carleton et al. (2020) use these results to model how adaptation affects the sensitivity of a population to extreme temperatures.
Estimates of the mortality-temperature relationship are used to generate projections of the future impacts of climate change on mortality rates for areas across the globe, dividing the world into 24,378 distinct regions (each containing roughly 300,000 people, about the size of a U.S. county). Using a revealed preference technique to measure the total cost of adaptive behaviors and technologies, these projections capture the full mortality risk of climate change, accounting for both adaptation benefits and costs, in addition to direct mortality impacts.
These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying economic and health data.
Projected impacts of climate change on mortality rates are then monetized and used to determine the costs of excess mortality risk in a given year. The full mortality risk of climate change mapped here includes the monetized value of both changes in mortality rates and changes in expenditures on adaptation. Damages are valued at an income-scaled value of statistical life (VSL) relying the U.S. EPA’s VSL estimate of $10.95 million (2019 USD). Damages aggregated at a higher geographical level than impact region are totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database).
Energy costs
The methodology for estimating the energy use costs of future climate change is described in full in Rode et al. (2021). This study uses comprehensive historical energy consumption data derived from International Energy Agency data files to quantify how a population’s use of electricity and other fuels (for example, natural gas, oil and coal) energy consumption responds to climate changes. The authors utilize the World Energy Balances dataset of the International Energy Agency, which describes electricity and direct fuel consumption across residential, commercial, industrial, and agricultural end-uses in 146 countries during 1971-2010.
By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on energy consumption. These relationships differ by energy type (electricity, other fuels) and are modified by the income levels and climate of the affected population. The study uses these results to model how income growth and adaptation affect the sensitivity of energy consumption to extreme temperatures.
The authors then use these estimates of the energy-temperature relationship to generate projections of the future impacts of climate change on electricity and direct fuel consumption for areas across the globe, dividing the world into 24,378 distinct regions. Each region contains roughly 300,000 people—about the size of a U.S. county. The projected impacts capture the effects of adaptive behaviors that populations undertake as they become richer and exposed to warmer climates.
These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying data.
Projected changes in energy expenditures (both electricity and other fuels) are then monetized and used to determine the costs in a given year. Quantiles are calculated using the delta method along with Newton’s method. Expenditure changes and GDP used to construct percentages are aggregated to higher geographical levels as totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Future prices are assumed to grow at 1.4% per year. Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3.
Temperature
Days
Mortality costs as share of GDP
Energy expenditures as share of GDP
Climate Projections
1.
Damage Projections
Mortality costs
The methodology for estimating the mortality costs of future climate change is described in full in Carleton et al. (2022). This study uses comprehensive historical mortality records to quantify how death rates across the globe have been affected by observed climate changes.
Carleton et al. (2020) compile the largest sub-national vital statistics database in the world, detailing 399 million deaths across 41 countries accounting for 55 percent of the global population. By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on death rates. These relationships are modified by the climate and income levels of the affected population. Carleton et al. (2020) use these results to model how adaptation affects the sensitivity of a population to extreme temperatures.
Estimates of the mortality-temperature relationship are used to generate projections of the future impacts of climate change on mortality rates for areas across the globe, dividing the world into 24,378 distinct regions (each containing roughly 300,000 people, about the size of a U.S. county). Using a revealed preference technique to measure the total cost of adaptive behaviors and technologies, these projections capture the full mortality risk of climate change, accounting for both adaptation benefits and costs, in addition to direct mortality impacts.
These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying economic and health data.
Projected impacts of climate change on mortality rates are then monetized and used to determine the costs of excess mortality risk in a given year. The full mortality risk of climate change mapped here includes the monetized value of both changes in mortality rates and changes in expenditures on adaptation. Damages are valued at an income-scaled value of statistical life (VSL) relying the U.S. EPA’s VSL estimate of $10.95 million (2019 USD). Damages aggregated at a higher geographical level than impact region are totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database).
Energy costs
The methodology for estimating the energy use costs of future climate change is described in full in Rode et al. (2021). This study uses comprehensive historical energy consumption data derived from International Energy Agency data files to quantify how a population’s use of electricity and other fuels (for example, natural gas, oil and coal) energy consumption responds to climate changes. The authors utilize the World Energy Balances dataset of the International Energy Agency, which describes electricity and direct fuel consumption across residential, commercial, industrial, and agricultural end-uses in 146 countries during 1971-2010.
By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on energy consumption. These relationships differ by energy type (electricity, other fuels) and are modified by the income levels and climate of the affected population. The study uses these results to model how income growth and adaptation affect the sensitivity of energy consumption to extreme temperatures.
The authors then use these estimates of the energy-temperature relationship to generate projections of the future impacts of climate change on electricity and direct fuel consumption for areas across the globe, dividing the world into 24,378 distinct regions. Each region contains roughly 300,000 people—about the size of a U.S. county. The projected impacts capture the effects of adaptive behaviors that populations undertake as they become richer and exposed to warmer climates.
These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying data.
Projected changes in energy expenditures (both electricity and other fuels) are then monetized and used to determine the costs in a given year. Quantiles are calculated using the delta method along with Newton’s method. Expenditure changes and GDP used to construct percentages are aggregated to higher geographical levels as totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Future prices are assumed to grow at 1.4% per year. Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3.