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Written by Kathryn Foster, Cornell University
Projections and forecasts are widely used within the demographic and climate communities (Balk et al., 2022). Projections are an especially useful tool in climate-epidemiology research, to estimate the future impacts of climate change on public health. A recent study, however, strongly cautions against projections that attribute future health risks to climate change without considering underlying demographic changes that may increase vulnerable populations’ susceptibility to climate hazards, such as global aging and population growth (Kephart and Bilal, 2026). The article delineates three drivers of future health risk that are often conflated in climate-health projections. First, there may be changes in the frequency or severity of climate hazards (e.g., longer heat waves, more intense hurricanes). Second, population growth (or decline) may contribute to the changes in the total number of people exposed. Third, demographic shifts in the population, such as increases in life expectancy and greater shares of older adults, may change the vulnerabilities of people exposed. Kephart and Bilal argue that if studies group together distinct drivers of future health risks, results will indicate only if and to what extent health risk may arise. But they will not clearly indicate why health risks arise, and for whom they may disproportionately impact. Amid global aging and increasing environmental hazards, differentiating between these drivers allows researchers to accurately attribute risks to either climate or demographic changes.
Kephart and Bilal (2026) provide four ways to incorporate population dynamics into climate-health projections to improve both transparency in research and attribution of factors that can serve as a basis for public health and climate adaptation (see Table 1): (1) Report climate and demographic counterfactuals on the same spatial or temporal scale; (2) Include normalized measures of health burdens alongside absolute measures in research; (3) Include age-standardized rates if available; (4) Avoid misattributions within titles, abstracts, or other products available to the public.
The three examples below demonstrate innovations in current research that disentangle population aging, climate hazards, and health risks in projection modeling. In accordance with Kehart and Bilal’s recommendations, the following studies show how sociodemographic dynamics like aging, migration, and inequality interact with climate hazards to shape future climate-health risks. These examples each use a different spatial scale and extents from continental to cities to sub-city, respectively.
Example 1: Population aging and heat exposure in the 21st century: which US regions are at greatest risk and why?
This study by Carr et al. (2024) shows how population aging and rising temperatures may create regions in the U.S. where these two trends collide, increasing population exposure to heat. Drawing from two main data sources, county-level projections and gridded daily temperature projections, this research identifies changes from 2020 to 2100 in person-degree days, which is the cumulative population-level chronic heat exposure multiplied by the population over 69 years of age in the county at the given time period. The authors simulate future demographic dynamics under 5 different climate scenarios using a shared socioeconomic pathways (SSP) approach.
Why use the SSPs to understand population futures?
The SSP approach uses expert opinion about future changes in demographic rates (fertility, life expectancy, migration between countries) and socioeconomic characteristics (education, urbanization, economic growth, and technological development) consistent with different scenarios of future carbon emissions (e.g., sprawling or concentrated spatial patterns of growth, economic growth consistent with fossil-fuel usage or sustainability futures). Using SSPs to guide narratives about the future, researchers and practitioners can quantify aspects of socioeconomic and environmental conditions and the pathways that lead to those outcomes (Climate Central (2021).
SSPs—and the use of scenarios—have a long history. Researchers and other users of scenarios around the world periodically reflect upon them. A Scenario Evolution Process is currently underway, and users (or potential users) are invited to participate in the scientific discourse.
In addition to SSPs, this study is consistent with Kephart and Bilal’s advice to avoid conflating demographic change with increased climate exposure. They use decomposition to calculate the proportion of the change in older adults’ chronic heat exposure attributable to climate change, population size, and population aging.
The decomposition findings show that across the U.S., all three drivers contribute relatively equally to projected changes in older adults’ chronic heat exposure, with the contribution of climate change being slightly smaller than population size and aging. However, when examining the findings by U.S. Census Divisions, regional differences emerge. Climate change will account for a larger share of population heat exposure in historically colder regions, such as the Midwest and the northeastern regions of New England. However, population growth and aging are larger drivers of changes in older adults’ chronic heat exposure in the Southern and Mountain regions. These findings highlight regions in the U.S. that could be “hotspots” for both population aging and increased heat exposure.
(Interested readers can find the same methodology applied to global regions in Falchetta et al. 2024.)
Example 2: Temperature-related mortality burden and projected change in 1368 European regions: a modelling study
This study by García-León et al. (2024) disentangles and projects the impacts of an aging population on temperature-related mortality risks by 2100 in 1368 regions in Europe. The authors use a three-stage modeling framework (found here) to estimate temperature-related mortality risks across age groups. The risks were then used to create temperature thresholds of mortality percentiles for both raw and age-standardized mortality rates for heat and cold. Similar to Kephart and Bilal’s suggestion, the authors isolated the effect of demographic change on future temperature mortality by calculating the difference in mortality between cases identifying mortality with (1) the present climate combined with the present population and (2) the present climate combined with future population exposure projections.
While Europe varies by region, climate change is expected to widen disparities in mortality, more so than population aging, particularly in southern Europe, where heat-related deaths are projected to increase. The authors find that while the mean heat in Northern Europe is expected to increase, it will not cause additional deaths. Rather, the projected aging of the population will make the region more susceptible to extreme heat but will not cause additional deaths. Furthermore, they find that the effects of aging are much more pronounced for cold-related mortality in eastern regions of Europe, specifically for elderly populations, the authors do not pin-point mechanisms that may mediate this relationship such as whether they expect cold-weather mortality to increase due to increased exposure to cold and winter storms (e.g., falling on ice) or due to increased respiratory illnesses that are seasonally dominant and disproportionately affect older adults (e.g., influenza and pneumonia) For both heat- and cold-related mortality risks, the authors note they vary by region (and regional economic characteristics) and under projected demographic changes.
Example 3: Equity dimensions of future vulnerabilities to climate hazards: Long-term spatial population projections for New York City consistent with Shared Socioeconomic Pathways (updated study is forthcoming, early results prepared for New York City’s Mayor’s Office were reported in here).
This study by Balk et al. (forthcoming 2026) adopts a multidimensional, multiregional demographic projection method to forecast long-term county-level population in order to understand the future health-related costs of morbidity and mortality associated with climate change in New York City (NYC) (McPherson et al. 2024). Most SSP-based projections are applied to large regional or national-level areas, whereas this study uses a fine-scale geography (counties) for population projections and downscales these projections to sub-city, fine-scale grid cells. The authors develop a novel set of local-level SSPs by notably including both migration within national borders and demographic rates for population subgroups that represent the diversity of a city (not previously done in SSPs). By applying these SSPs, they created multiple possible population scenarios for NYC, avoiding misattribution in population projections and providing a new pathway for local climate-health projections. The research team rescaled county projections to smaller units using historical parcel and Census block group data, allowing them to better assess local population change and future climate impacts with a detailed spatial scale.
The research finds that determinants of population growth change under different scenarios and across geographical locations. Results suggest that across most SSPs, population growth will decrease across NYC within the coming decades due to decreasing migration and fertility rates and increasing mortality rates and population aging. Through creating multiple population scenarios and spatial downscaling, the authors demonstrate a method for more accurate population-health projections and other impacts of future climate impacts on populations at risk at smaller geographical scales. Going beyond the recommendations of Kephart and Bilal, this work suggests a need to consider uncertainties in future demographic trends that may alter population size and age structure in a local area.
Future Research and Policy: In climate-health projections, it is important to disaggregate the drivers of future health risks. Determining what process is impacting future health risks, whether it is climate or demographic change, will inform subsequent adaptation and policy intervention.
Carr et al. (2024) and García-León et al. (2024) highlight regions where population aging may pose a greater health risk or where climate change may be the greater contributor to future risk. In ‘hotspot’ areas, or areas where aging populations and rising temperatures equally contribute to future health risks, they suggest that public initiatives should focus on educating older adults about heat risks by sending heat alerts, informing them about and offering transportation to cooling centers, and offering home-based cooling solutions (Carr et al., 2024). Based on the varying size and age structure in the future NYC example (Balk et al., 2026), policies in cities, which may be particularly vulnerable to heat due to the urban heat island effect and could have more (or less) diverse populations, may also need to embrace increased uncertainties in future population.
Accurately attributing demographic change and climate change in future health risk and exposure research yields projections with more usable information, subsequently, better evidence for policy and planning. These practices from researchers will support effective action in protecting aging populations and mitigating the hazards associated with future temperature extremes.
References
- Balk, D., Tipaldo, J., Leiwen, J., & Zoraghein, H. (2026). Equity of future vulnerabilities to climate hazards: Long-term spatial population projections for New York City consistent with Shared Socioeconomic Pathways [Forthcoming]. Proceedings of the National Academy of Sciences of the United States of America.
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Carr, D., Falchetta, G., & Sue Wing, I. (2024). Population aging and heat exposure in the 21st century: which US regions are at greatest risk and why?. The Gerontologist, 64(3), gnad050.
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Climate Matters. 2021. “IPCC 6th Assessment Report – the Physical Science Basis | Climate Central.” Climate Central. Retrieved July 9, 2026 (https://www.climatecentral.org/climate-matters/ipcc-6th-assessment-report-the-physical-science-basis).
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Falchetta, G., De Cian, E., Sue Wing, I., & Carr, D. (2024). Global projections of heat exposure of older adults. Nature Communications, 15(1), 3678.
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García-León, D., Masselot, P., Mistry, M. N., Gasparrini, A., Motta, C., Feyen, L., & Ciscar, J. C. (2024). Temperature-related mortality burden and projected change in 1368 European regions: a modelling study. The Lancet Public Health, 9(9), e644-e653.
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Kephart, J. L., & Bilal, U. (2026). Disentangling climate hazards from demographic change in climate–health projections. Nature medicine, 1-2.
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McPhearson, T., Towers, J., Balk, D., Horton, R., Madajewicz, M., Montalto, F., Neidell, M., Orton, P., Rosenzweig, B., Bader, D., Chen, Z., DeGaetano, A., Evans, C., Golkhandan, M. R., Gurian, P., Kaatz, J., Herreros-Cantis, P., Lo, R., Ortiz, L., Braneon, C., Branco, B., Campbell, L., Dubay, F., Graziano, K., Jiang, L., Johnson, M., Kennedy, C., Kioumourtzoglou, M.-A., Kleyman, J., Kleyman, J., Knowlton, K., Limaye, V., Matte, T., Munoz Perez, S., Reed, D., Shakya, M., Svendsen, E., Tipaldo, J., Zoraghein, H. (2024). New York City Town+Gown Climate Vulnerability, Impact, and Adaptation Analysis Final Report. New York City Mayor’s Office of Climate and Environmental Justice.