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Acute mental health benefits of urban nature

Nature Cities volume 2, pages 720–731 (2025)Cite this article

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Abstract

Mental disorders are more prevalent in cities, yet the global impact of urban nature on mental health remains insufficiently understood. Here we address this gap by systematically reviewing 449 peer-reviewed studies and conducting a meta-analysis of 78 field-based experiments to quantify the effects of various urban nature types on 12 mental health outcomes. Our meta-analysis demonstrates that exposure to urban nature provides substantial benefits for a broad spectrum of mental health outcomes. Green spaces such as urban forests and parks emerged as key elements in mitigating negative moods, such as depression and anxiety, and enhancing overall mental well-being. In particular, the benefits of nature exposure are most pronounced among young adults, although consistent positive effects are evident across all age groups. These findings highlight the importance of safeguarding and expanding access to urban nature as a key strategy for enhancing public health and well-being in cities worldwide.

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Fig. 1: Geographic distribution of studies and the extracted mental health outcomes related to nature exposure.
Fig. 2: Pooled effect sizes (Hedges’ g) for the impact of nature exposure on mental health outcomes.
Fig. 3: Subgroup analysis of pooled effect sizes for mental health outcomes by nature type.
Fig. 4: Subgroup analysis of pooled effect sizes for mental health outcomes by age group.
Fig. 5: Literature screening process.

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Data availability

All the data used in this analysis are extracted from previously published studies. The full list of included papers, along with the processed data used for analysis and figure generation, is available via GitHub at https://github.com/Yingjie4Science/nature-health-ma. The global basemap vector data used in this study are freely available from Natural Earth (https://www.naturalearthdata.com/). Urban density data were primarily derived from the Global Human Settlement Layer produced by the Joint Research Centre of the European Commission (https://human-settlement.emergency.copernicus.eu/ghs_ucdb_2024.php).

Code availability

All code used for the analysis and figure generation is available via GitHub at https://github.com/Yingjie4Science/nature-health-ma.

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Acknowledgements

We acknowledge funding support from the Stanford Woods Institute’s Realizing Environmental Innovation Program, Cyrus Tang Foundation, Marcus and Marianne Wallenberg Foundation, Heinz Foundations, Winslow Foundation, Enlight Foundation, and individual contributors J. Miller and K. Hsiao. We also thank E. Brieant, Z. Galli, R. M. Bertrand and C. Purisima for their assistance with data extraction from the selected literature.

Author information

Authors and Affiliations

  1. Natural Capital Project, Woods Institute for the Environment, Doerr School of Sustainability, Stanford University, Stanford, CA, USA

    Yingjie Li, Lisa Mandle, Anders Rydström, Roy P. Remme, Tong Wu, Yougeng Lu, Gretchen C. Daily & Anne D. Guerry

  2. Department of Biology, Stanford University, Stanford, CA, USA

    Yingjie Li, Anders Rydström, Yougeng Lu & Gretchen C. Daily

  3. Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

    Yuanyuan Mao & Roy P. Remme

  4. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA

    Xin Lan

  5. Environmental Science and Policy Program, Michigan State University, East Lansing, MI, USA

    Xin Lan

  6. State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, China

    Chao Song

  7. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Kari C. Nadeau

  8. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

    Andreas Meyer-Lindenberg

  9. Center for Conservation Biology, Stanford University, Stanford, CA, USA

    Gretchen C. Daily

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Contributions

Y. Li, A.D.G. and L.M. conceptualized the research idea. Y. Li, Y.M., A.D.G. and L.M. designed the study. Y. Li, Y.M., A.R. and X.L. contributed to data collection and extraction. Y. Li, Y.M., A.R., C.S. and A.M.-L. contributed to the methodology. Y. Li performed the formal analysis, data visualization and drafted the original manuscript. A.D.G., L.M. and G.C.D. supervised the project. A.D.G., L.M., T.W. and G.C.D. secured the funding. All authors contributed to the interpretation of the results and provided critical feedback on the manuscript.

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Correspondence to Yingjie Li.

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The authors declare no competing interests.

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Nature Cities thanks John Ji, Fu Li and David Rojas-Rueda for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Top 20 mental health (MH) indicators (a) and measurement tools (b) reported in the literature.

Full names of each tool: Profile of Mood States (POMS), Positive and Negative Affect Schedule (PANAS), Restoration Outcome Scale (ROS), General Health Questionnaire (GHQ-12), Perceived Stress Scale (PSS), Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), World Health Organisation 5 Wellbeing Index (WHO-5), State-Trait Anxiety Inventory (STAI), Depression, Anxiety and Stress Scale - 21 Items (DASS-21), The Short-Form 36 or 12 Health Survey (SF-36, or SF-12), Perceived Restorativeness Scale (PRS), Subjective Vitality Scales (SVS), Patient Health Questionnaire (PHQ), Mental Health Inventory - 5 Items (MHI-5), Kessler Psychological Distress Scale (K10), Strengths and Difficulties Questionnaire (SDQ), Rosenberg Self-Esteem Scale (RSE), Satisfaction with Life Scale (SWLS), and Center of Epidemiologic Studies Depression Scale, 10-item version (CES-D-10). For the meta-analysis, we selected the top seven measurement tools (POMS, PANAS, ROS, PSS, STAI, DASS-21, and SVS) used in experimental studies. We did not include the Perceived Restorativeness Scale (PRS) because more than half of the studies used it as a confounding variable, instead of an outcome.

Extended Data Fig. 2 Boxplots of mean effect sizes by urban nature type.

The horizontal line inside each box represents the median, and the boxes span the interquartile range (IQR, 25th to 75th percentile). Whiskers extend to 1.5× IQR, and individual points beyond this range are considered outliers. Overlaid jittered dots represent individual data points, slightly displaced horizontally to improve visibility and illustrate data distribution within each category. The Kruskal-Wallis test was used to detect if there is a significant difference among groups. Post-hoc Dunn’s test with Bonferroni correction was used for pairwise comparisons. P-value annotations (*, **, ***) indicate significant differences (p < 0.05, p < 0.01, p < 0.001) between groups, with only statistically significant comparisons displayed. The sample size (n) of each subgroup corresponds to those reported in Fig. 3, with the underlying data available in the Source Data.

Source Data

Extended Data Fig. 3 Boxplots of mean effect sizes by age group.

Statistical elements and methods are as described in Extended Data Fig. 2. Sample sizes for each subgroup correspond to those in Fig. 4. The underlying data are provided in the Source Data.

Source Data

Extended Data Fig. 4 Subgroup analysis of pooled effect sizes for mental health outcomes by duration of nature exposure.

TMD, Total Mood Disturbance.

Source Data

Extended Data Fig. 5 Boxplots of mean effect sizes by duration of nature exposure.

These plots suggest potential dose–response relationships between time spent in nature and various mental health outcomes. Red diamonds and lines indicate group means and trends across exposure durations. Other statistical elements and methods are as described in Extended Data Fig. 2. Sample sizes for each subgroup correspond to those in Extended Data Fig. 4. The underlying data are provided in the Source Data.

Source Data

Extended Data Fig. 6 Subgroup analysis of pooled effect sizes for mental health outcomes by nature exposure type.

All statistical elements in the forest plots—including error bars, significance levels, sample sizes (n), and heterogeneity statistics (I²)—are as described in Fig. 3.

Source Data

Extended Data Fig. 7 Boxplots of mean effect sizes by nature exposure type.

Statistical elements and methods are as described in Extended Data Fig. 2. Sample sizes for each subgroup correspond to those in Extended Data Fig. 6. The underlying data are provided in the Source Data.

Source Data

Extended Data Fig. 8 Subgroup analysis of pooled effect sizes for mental health outcomes by gender representation.

All statistical elements in the forest plots—including error bars, significance levels, sample sizes (n), and heterogeneity statistics (I²)—are as described in Fig. 3. We categorized studies based on the proportion of female and male participants to account for potential gender differences, as most experimental studies included mixed-gender samples with limited effect size data available for male-only or female-only groups.

Source Data

Extended Data Fig. 9 Boxplots of mean effect sizes by gender representation.

Statistical elements and methods are as described in Extended Data Fig. 2. Sample sizes for each subgroup correspond to those in Extended Data Fig. 8. The underlying data are provided in the Source Data.

Source Data

Extended Data Fig. 10 Subgroup analysis of pooled effect sizes for mental health outcomes by region.

All statistical elements in the forest plots—including error bars, significance levels, sample sizes (n), and heterogeneity statistics (I²)—are as described in Fig. 3.

Source Data

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–7, Tables 1–7, Methods, Results and Discussion.

Reporting Summary (download PDF )

Source data

Source Data (download XLSX )

Statistical source data for Figs. 2–4 and Extended Data Figs. 2–10.

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Li, Y., Mao, Y., Mandle, L. et al. Acute mental health benefits of urban nature. Nat Cities 2, 720–731 (2025). https://doi.org/10.1038/s44284-025-00286-y

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