CoChat
Sign Up
Cited by 13Urban Heat Island MitigationClimate Change and Health ImpactsUrban Green Space and Health

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Billions of people exposed to increasing heat but decreasing greenness from 2000 to 2022

Open access
Ye, Tingting, Xu, Rongbin, Huang, Wenzhong, Yang, Zhengyu, Yu, Pei, Yu, Wenhua, Liu, Yanming, Wu, Yao, Wen, Bo, Zhang, Yiwen, Hart, Jaime E., Nieuwenhuijsen, Mark, Abramson, Michael J., Guo, Yuming, Li, Shanshan·2025·The Innovation
DOI 10.1016/j.xinn.2025.100870PMC PMC12105507
Open this paper in CoChat — free account

Abstract

Rising heat stress due to climate warming poses a significant threat to human health, and greenness offers a nature-based solution to mitigate heat-related health impacts and enhance resilience. Although global greenness has increased, it remains unclear whether these trends align with the population's heat mitigation needs. In this study, we integrated spatially resolved demographic data with satellite-derived greenness metric and reanalysis-based heat stress data to construct a global profile of joint exposure at 1 × 1 km resolution from 2000 to 2022. We found that 69.3% of global populated areas and 41.3% of the global population (∼2.9 billion people) were exposed to increasing heat stress but decreasing greenness (IHDG), representing the most concerning situation for heat mitigation. Urban populations were disproportionately affected, with 50.8% exposed compared to 27.1% in rural areas. Low- and middle-income countries exhibited more pronounced trends of increasing heat stress and bore the greatest burden from IHDG, accounting for 85% of total exposed populations. Moreover, there was a notable demographic shift in IHDG-exposed populations toward older groups, exacerbating the heat mitigation crisis. This study advances the understanding of the joint dynamics of heat stress and greenness and provides a profile of population exposure at a fine grid level. By highlighting the scale of IHDG conditions, our findings emphasize the urgent need to address this environmental challenge and a significant opportunity for improving greenness to mitigate increasing heat globally. The spatially detailed assessment maps offer essential data for informed decision-making.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.xinn.2025.100870

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Satellite products provide spatially detailed land surface temperature information, while gridded population datasets can be used to estimate population exposure.

    “Satellite products provide spatially detailed land surface temperature information, while gridded population datasets can be used to estimate population exposure [7, 8].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [7]

    The Materials and methods sections on Population data and Mapping population exposure profiles describe using 1 × 1 km WorldPop population data to estimate exposure. The Heat stress data acquisition and processing section instead uses reanalysis-based UTCI, not satellite-derived land surface temperature; the satellite product measures greenness. The land-surface-temperature part is therefore unsupported by this paper. (LLM, conf=0.89)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 34

  1. Perkins-Kirkpatrick, 2020, Increasing trends in regional heatwaves, Nat. Commun., 11, 3357, 10.1038/s41467-020-16970-7 · DOI
  2. Romanello, 2023, The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms, Lancet, 402, 2346, 10.1016/S0140-6736(23)01859-7 · DOI · Paper page
  3. Zhao, 2021, Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study, Lancet Planet. Health, 5, e415, 10.1016/S2542-5196(21)00081-4 · DOI
  4. Hansen, 2011, Older persons and heat-susceptibility: the role of health promotion in a changing climate, Health Promot. J. Aust., 22, 17, 10.1071/HE11417 · DOI
  5. Li, 2013, Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts, J. Appl. Meteorol. Climatol., 52, 2051, 10.1175/JAMC-D-13-02.1 · DOI
  6. Romanello, 2022, The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels, Lancet, 400, 1619, 10.1016/S0140-6736(22)01540-9 · DOI · Paper page
  7. Zhang, 2021, Effect of heatwaves and greenness on mortality among Chinese older adults, Environ. Pollut., 290, 10.1016/j.envpol.2021.118009 · DOI
  8. Ye, 2024, Heat Exposure, Preterm Birth, and the Role of Greenness in Australia, JAMA Pediatr., 178, 376, 10.1001/jamapediatrics.2024.0001 · DOI
  9. Ding, 2020, Nearly Half of Global Vegetated Area Experienced Inconsistent Vegetation Growth in Terms of Greenness, Cover, and Productivity, Earths Future, 8, 10.1029/2020EF001618 · DOI
  10. Pan, 2018, Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends, Rem. Sens. Environ., 214, 59, 10.1016/j.rse.2018.05.018 · DOI
  11. Matthew McConnachie, 2010, Public green space inequality in small towns in South Africa, Habitat Int., 34, 244, 10.1016/j.habitatint.2009.09.009 · DOI
  12. Mitchell, 2008, Effect of exposure to natural environment on health inequalities: an observational population study, Lancet, 372, 1655, 10.1016/S0140-6736(08)61689-X · DOI · Paper page
  13. Kovats, 2008, Heat stress and public health: a critical review, Annu. Rev. Publ. Health, 29, 41, 10.1146/annurev.publhealth.29.020907.090843 · DOI
  14. Sadeh, 2021, Remote sensing metrics to assess exposure to residential greenness in epidemiological studies: A population case study from the Eastern Mediterranean, Environ. Int., 146, 10.1016/j.envint.2020.106270 · DOI
  15. Jendritzky, 2012, UTCI—Why another thermal index?, Int. J. Biometeorol., 56, 421, 10.1007/s00484-011-0513-7 · DOI · Paper page
  16. Blazejczyk, 2012, Comparison of UTCI to selected thermal indices, Int. J. Biometeorol., 56, 515, 10.1007/s00484-011-0453-2 · DOI · Paper page
  17. Krüger, 2021, Literature Review on UTCI Applications, 23
  18. Di Napoli, 2020, ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis, Geoscience Data Journal, 8, 2, 10.1002/gdj3.102 · DOI
  19. Lloyd, 2019, Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets, Big Earth Data, 3, 108, 10.1080/20964471.2019.1625151 · DOI
  20. Sen, 1968, Robustness of Some Nonparametric Procedures in Linear Models, Ann. Math. Stat., 39, 1913, 10.1214/aoms/1177698021 · DOI
  21. Fernandes, 2005, Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors, Rem. Sens. Environ., 95, 303, 10.1016/j.rse.2005.01.005 · DOI
  22. Ebi, 2021, Hot weather and heat extremes: health risks, Lancet, 398, 698, 10.1016/S0140-6736(21)01208-3 · DOI · Paper page
  23. Kerr, 2007, Global Warming Is Changing the World, Science, 316, 188, 10.1126/science.316.5822.188 · DOI
  24. Marengo, 2009, Future change of temperature and precipitation extremes in South America as derived from the PRECIS regional climate modeling system, Int. J. Climatol., 29, 2241, 10.1002/joc.1863 · DOI
  25. Leal Filho, 2018, Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context, J. Clean. Prod., 171, 1140, 10.1016/j.jclepro.2017.10.086 · DOI
  26. Li, 2020, On the influence of density and morphology on the Urban Heat Island intensity, Nat. Commun., 11, 2647, 10.1038/s41467-020-16461-9 · DOI
  27. Sy, 2015, Land use patterns and related carbon losses following deforestation in South America, Environ. Res. Lett., 10, 10.1088/1748-9326/10/12/124004 · DOI · Paper page
  28. Mirzabaev, 2022, Economic efficiency and targeting of the African Great Green Wall, Nat. Sustain., 5, 17, 10.1038/s41893-021-00801-8 · DOI
  29. Zhai, 2023, Assessing the effects of China's Three-North Shelter Forest Program over 40 years, Sci. Total Environ., 857, 10.1016/j.scitotenv.2022.159354 · DOI
  30. Meade, 2020, Physiological factors characterizing heat-vulnerable older adults: A narrative review, Environ. Int., 144, 10.1016/j.envint.2020.105909 · DOI
  31. Wolf, 2010, Social capital, individual responses to heat waves and climate change adaptation: An empirical study of two UK cities, Glob. Environ. Change, 20, 44, 10.1016/j.gloenvcha.2009.09.004 · DOI
  32. Didan, 2015, 35, 2
  33. Iungman, 2023, Cooling cities through urban green infrastructure: a health impact assessment of European cities, Lancet, 401, 577, 10.1016/S0140-6736(22)02585-5 · DOI · Paper page
  34. Van Ryswyk, 2019, Does urban vegetation reduce temperature and air pollution concentrations? Findings from an environmental monitoring study of the Central Experimental Farm in Ottawa, Canada, Atmos. Environ., 218, 10.1016/j.atmosenv.2019.116886 · DOI

Cited by

Sorted by citations

Metadata

Authors
Ye, T., Xu, R., Huang, W., Yang, Z., Yu, P., Yu, W., Liu, Y., Wu, Y., Wen, B., Zhang, Y., Hart, J. E., Nieuwenhuijsen, M., Abramson, M. J., Guo, Y., & Li, S.
Affiliations
  • Chongqing University (CN)
  • Monash University (AU)
  • Brigham and Women's Hospital (US)
  • Universitat Pompeu Fabra (ES)
Corresponding author
  • Guo, Yuming
  • Li, Shanshan
ORCID
Venue
The Innovation
Publisher
Elsevier BV
Published
2025-05
Volume
6
Issue
5
Pages
100870
ISSN
2666-6758 (ISSN-L)
DOI
10.1016/j.xinn.2025.100870
PMC
PMC12105507
OpenAlex
W4408240603
Cited by
13
References
4
Citations by year
2026: 9 · 2025: 4
FWCI
3.60
Type
journal article
Language
en
Open access
Licence
Locations
  • The Innovation (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Australian Research Council (DP210102076)
  • National Health and Medical Research Council (GNT2009866, GNT2000581, GNT2008813)
  • Faculty of Medicine, Nursing and Health Sciences, Monash University
  • China Scholarship Council (202006010044, 202006380055, 202006010043, 201906320051)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Social Sciences › Urban Studies › Urban Green Space and Health
Keywords
heat stress, vegetation greenness, joint exposure, global warming, heat mitigation, nature-based solutions, urban-rural disparities, low- and middle-income countries, population exposure, demographic change

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review