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Unprivileged groups are less served by green cooling services in major European urban areas

Nature Cities volume 1, pages 424–435 (2024)Cite this article

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Abstract

Heat stress is the leading climate-related cause of premature deaths in Europe. Major heatwaves have struck Europe recently and are expected to increase in magnitude and length. Large cities are particularly threatened due to the urban morphology and imperviousness. Green spaces mitigate heat, providing cooling services through shade provision and evapotranspiration. However, the distribution of green cooling and the population most affected are often unknown. Here we reveal environmental injustice regarding green cooling in 14 major European urban areas. Vulnerable residents in Europe are not concentrated in the suburbs but in run-down central areas that coincide with low-cooling regions. In all studied areas, lower-income residents, tenants, immigrants and unemployed citizens receive below-average green cooling, while upper-income residents, nationals and homeowners experience above-average cooling provision. The fatality risk during extreme heatwaves may increase as vulnerable residents are unable to afford passive or active cooling mitigation.

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Fig. 1: Green cooling and lower-income spatial distribution.
Fig. 2: Green cooling distribution.
Fig. 3: Population exposed to cooling.
Fig. 4: GCoS correlation with socioeconomic indicators.
Fig. 5: Socioeconomic indicators heatmap.
Fig. 6: Enviromental injustices.

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Data availability

The model inputs used in this study and the resultant maps are publicly available on the original open access source (Extended Data Table 1). The EC measurements used to validate the model should be requested for each location separately. The social indicators are available at the ArcGIS Living Atlas of the World under the Esri agreement or at the national census bureau of each country. Esri provides the standardization and redistribution of the indicators in granulated points from the sources: Michael Bauer Research GmbH (EU), Nexiga (DE), AIS and Instituto Nacional de Estadistica (Spain) and 4orange (the Netherlands). The green cooling simulations and the 10-m-resolution GCoS maps for all functional urban areas are available via Zenodo at https://doi.org/10.5281/zenodo.10708300 (ref. 69) Functional urban areas from Global Human Settlement Layer (GHSL-FUA)68 used to define the urban areas is available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_STAT_UCDB2015MT_GLOBE_R2019A/V1-2/ (last accessed 4 April 2023).

Code availability

The SCOPE model (2.0) code for MATLAB (R2018b or higher) is available at ref. 70. The R package to download, preprocess the input data and run the SCOPE model is available by rSCOPE (2.0)71 at https://doi.org/10.5281/zenodo.6204580. The code to calculate and map the GCoS is available at https://github.com/AlbyDR/GCoS (https://doi.org/10.5281/zenodo.10708300)69.

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Acknowledgements

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Research Training Group ‘Urban Water Interfaces’ (GRK 2032-2) to A.D.R. Einstein Research Unit ‘Climate and Water under Change’ (CliWaC) from the Einstein Foundation Berlin and Berlin University Alliance (ERU-2020-609) to S.V. The EC data from Berlin provided by the Urban Climate Observatory (UCO) were supported by Urban Climate Under Change [UC]², funded by the German Ministry of Research and Education (FKZ 01LP1602A) to F.M. The EC observations in Amsterdam have been supported by the Institute for Advanced Metropolitan Solutions (AMS Institute, project VIR16002) and the Netherlands Organisation for Scientific Research (NWO) (project 864.14.007). We acknowledge support from the 4TU-program HERITAGE (HEat Robustness In relation To AGEing cities), funded by the High Tech for a Sustainable Future (HTSF) program of 4TU, the federation of the four technical universities in the Netherlands, to G.-J.S. EC observations of Helsinki were provided by the Research Council of Finland (project numbers 321527, 37549 and 335201) to L.J. We thank C. Feigenwinter for the data from Basel (CH) used in this activity CH-BaK (2019–20) and CH-BaA (2019–20). The kind permission of British Telecom (BT) is acknowledged for the London measurements. Equipment was part-funded by the Engineering and Physical Sciences Research Council (EP/G029938/1) to J.F.B and C.H. C.H. also acknowledges funding from the UK Natural Environment Research Council (NE/H003169/1, NE/K002279/1 and NE/T001798/2). For the Vienna EC measurements, we acknowledge funding from the Vienna Science and Technology Fund (10.47379/ESR20030) and the City of Vienna (MA7-596744/17), as well as the support of A1 Telekom AG, to B.M. G.N. thanks the support of the ICOS Pilot Application in Urban Landscapes (PAUL) Horizon2020 Project (GA101037319).

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Authors and Affiliations

  1. Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Berlin, Germany

    Alby Duarte Rocha, Stenka Vulova, Michael Förster & Birgit Kleinschmit

  2. Department of Environmental Meteorology, University of Kassel, Kassel, Germany

    Stenka Vulova

  3. Institute of BioEconomy, National Research Council (IBE-CNR), Florence, Italy

    Beniamino Gioli

  4. Department of Forest- and Soil Sciences, Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Austria

    Bradley Matthews

  5. Environment Agency Austria, Vienna, Austria

    Bradley Matthews

  6. UK Center for Ecology and Hydrology, Penicuik, UK

    Carole Helfter

  7. Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Berlin, Germany

    Fred Meier

  8. Meteorology and Air Quality Section, Wageningen University, Wageningen, the Netherlands

    Gert-Jan Steeneveld

  9. Department of Meteorology, University of Reading, Reading, UK

    Janet F. Barlow

  10. Institute for Atmospheric and Earth System Research/Physics, Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, Finland

    Leena Järvi

  11. Remote Sensing Lab, Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, Heraklion, Greece

    Nektarios Chrysoulakis

  12. CMCC Foundation, Euro-Mediterranean Center on Climate Change, Viterbo, Italy

    Giacomo Nicolini

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Contributions

A.D.R., M.F., S.V. and B.K. designed the overall research goals and aims. B.M., C.H., F.M., G.-J.S., N.C., G.N., B.G., L.J., A.D.R. and M.F. were responsible for data selection and preprocessing (EC data, forcing data and socioeconomic indicators, respectively). A.D.R. conducted the data simulations, analysis and paper draft preparation. The paper received inputs about the urban areas and their climatological characteristics from B.G., B.K., B.M., C.H., F.M., G.-J.S., G.N., J.F.B., L.J., N.C. and S.V. All authors discussed the results and contributed to reviewing and editing the final version of the paper.

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Correspondence to Alby Duarte Rocha.

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Nature Cities thanks Theodore Endreny, Robert McDonald and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 ET model validation per EC towers location.

Model validation of ET per month and hourly average for each urban area with EC towers measurements – 2019/2021. Note: The values up to six hours after a rain event were excluded.

Extended Data Fig. 2 Spatial distribution of GCoS at different scales in Berlin.

GCoS distribution at a neighbourhood scale in Berlin (a), and at a 10 m resolution in a central area (b and c). Background image source (b and c): Leaflet | Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community.

Extended Data Table 1 Model inputs and data sources used to simulate ET and soil temperature from SCOPE
Full size table
Extended Data Table 2 Model accuracy based on hourly ET for the cities with EC tower measurements – 2019-2021
Full size table

Supplementary information

Supplementary Information

Supplementary maps, Figs. 1–14 and Table 1.

Reporting Summary

Source data

Source Data Figs. 4–6

Statistical source data (Pearson correlation for all indicators and urban areas used in the paper).

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Rocha, A.D., Vulova, S., Förster, M. et al. Unprivileged groups are less served by green cooling services in major European urban areas. Nat Cities 1, 424–435 (2024). https://doi.org/10.1038/s44284-024-00077-x

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