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Biodiversity-rich recreational areas near cities as a nature-based mental health solution

Nature Cities volume 2, pages 532–542 (2025)Cite this article

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Abstract

Nature experiences in biodiversity-rich recreational areas (recreation-permitted protected areas, Key Biodiversity Areas and intact forest landscapes) can provide an extra benefit to mental health compared to managed urban green spaces. However, to what extent urban residents have access to these experiences globally and their cost-effectiveness as mental health treatments remain unclear. Assessing 9,034 cities globally, we find that over 96% of cities have biodiversity-rich recreational areas within two hours, with the affordability of experiences and visiting rates being highest in Europe, Oceania and North America. The extra benefits of experiences in biodiversity-rich recreational areas near cities reduces 137,299 (90% uncertainty range: 10,368–598,509) disability-adjusted life years of depression and anxiety and appears as a cost-effective public health intervention in developed settings in Europe, North America and South America. Lowering traveling costs by establishing new highly biodiverse recreation-permitted protected areas near cities would be a nature-based mental health intervention with potential to benefit biodiversity and urban residents’ health.

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Fig. 1: Accessibility in terms of traveling time to biodiversity-rich recreational areas, PAs, KBAs and IFLs in cities of the world.
Fig. 2: Travel costs and affordability for urban residents to have experiences in biodiversity-rich recreational areas.
Fig. 3: Distribution of mental health benefits.
Fig. 4: Distribution of cost-effectiveness in cities of the world.

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Data availability

The data used in this study were collected from the following sources: global urban boundaries were from the GHS Urban Centre Database of European Commission (https://doi.org/10.2905/4606D58A-DC08-463C-86A9-D49EF461C47F); protected areas datasets were from the World Database on Protected Areas (WDPA) (https://www.protectedplanet.net/en), Resource and Environment Science Data Center (https://www.resdc.cn/data.aspx?DATAID=272) and OpenStreetMap (Geofabrik Download Server); Key Biodiversity Areas boundaries dataset was from The World Database of Key Biodiversity Areas (https://www.keybiodiversityareas.org/); Intact Forest Landscapes dataset was from Intact Forest Landscapes website (Intact Forest Landscapes); the population dataset was from WorldPop (https://doi.org/10.5258/SOTON/WP00647); the global friction surface was from work published by Weiss et al.39 (https://doi.org/10.1038/nature25181); the national minimum monthly wage data were from the statutory nominal gross monthly minimum wage statistics in the ILOSTAT database (https://rshiny.ilo.org/dataexplorer75/?lang=en); the GNI per capita, GDP per capita, exchange rates and historical inflation rates were from the World Bank (World Development Indicators|Data Catalog); the purchase prices of cars were from Numbeo (https://www.numbeo.com/cost-of-living); the gasoline prices were from globalPetrolPrices.com (https://www.globalpetrolprices.com/gasoline_prices/); the train travel prices were from FleetLogging (https://fleetlogging.com/transport-price-index/); and the data of global burden of mental disease for 2019 were from the Institute for Health Metrics and Evaluation (Global Burden of Disease Study 2019 (GBD 2019) Data Resources|GHDx). Country-level administrative boundaries were from geoBoundaries (geoBoundaries). All data are available for direct download or upon request from the data owner. Supplementary Table 1, containing maximum, minimum and mean travel time from all pixel locations within selected major cities to the nearest PAs, KBAs and IFLs, is publicly available via figshare at https://doi.org/10.6084/m9.figshare.28875569 (ref. 62).

Code availability

Spatial data processing and analysis were conducted using ArcGIS Pro 3.1.2. The Monte Carlo simulations on the extra benefits of high biodiversity on anxiety and depression were conducted using R 4.3.3., and the code is available via figshare at https://doi.org/10.6084/m9.figshare.28875596 (ref. 63).

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Acknowledgements

We thank P. Ran for his suggestions on the visualization of the results, which is crucial to improve the readability. Funding: S.H. was supported by the Key Project from the National Social Science Foundation of China (grant number 2021LJ10192) and the National Natural Science Foundation of China (grant number 42171272). C.X. was supported by the scholarship under the China Scholarships Council program (number 202106410053).

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Authors and Affiliations

  1. School of Public Administration, China University of Geosciences (Wuhan), Wuhan, People’s Republic of China

    Cong Xia, Shougeng Hu & Zongnan Hu

  2. Department of Biological Sciences, National University of Singapore, Singapore, Republic of Singapore

    Cong Xia, Yanyun Yan & L. Roman Carrasco

  3. Key Laboratory for Rule of Law Research, Ministry of Natural Resources, Wuhan, People’s Republic of China

    Shougeng Hu

  4. Department of Social Sciences, Wageningen University and Research, Wageningen, the Netherlands

    Zongnan Hu

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  1. Cong Xia
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Contributions

Conceptualization: L.R.C. and S.H. Methodology: L.R.C. and C.X. Investigation: C.X. and Z.H. Visualization: C.X. Supervision: L.R.C. and S.H. Writing—original draft: C.X. Writing—review and editing: L.R.C., S.H. and Y.Y.

Corresponding authors

Correspondence to Shougeng Hu or L. Roman Carrasco.

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Nature Cities thanks Penny Cook, Fu Li 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 Estimated visiting rates to biodiversity-rich recreational areas.

(a) The annual visiting rate per capita to biodiversity-rich recreational areas in the world’s cities. (b) Total number of visits to biodiversity-rich recreational areas at the national level.

Extended Data Fig. 2 Distribution of cost-effectiveness in cities of the world under the 5th and 95th percentile estimates.

(a) Individual cost-effectiveness of biodiversity-rich recreational areas experiences in the prevention of depression and anxiety under the 5th percentile estimates. (b) The spatial distribution of different cost-effectiveness levels under the 5th percentile estimates. (c) Individual cost-effectiveness of biodiversity-rich recreational areas experiences in the prevention of depression and anxiety under the 95th percentile estimates. (d) The spatial distribution of different cost-effectiveness levels under the 95th percentile estimates.

Supplementary information

Supplementary Information

Supplementary Methods, Results 1 and 2, Figs. 1–3, Tables 1–4 and References.

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Xia, C., Hu, S., Hu, Z. et al. Biodiversity-rich recreational areas near cities as a nature-based mental health solution. Nat Cities 2, 532–542 (2025). https://doi.org/10.1038/s44284-025-00251-9

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