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A generalized relationship between dose of greenness and mental health response

An Author Correction to this article was published on 22 August 2025

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Abstract

Exposure to green spaces is a boon to urbanites. Over the last four decades, an increasing number of researchers have shown interest in exploring the relationship between the dose of greenness and mental health response. Early studies suggested a linear dose–response relationship, making it challenging to identify the most beneficial doses of greenness. However, findings from a rapidly growing body of recent research indicate the possible existence of a generalized curvilinear pattern. Despite this, these studies have used varying measures and contexts, resulting in inconclusive evidence. Without fully understanding the nature of the relationship, we do not know how to allocate green landscape resources to maximize mental health benefits. This study aimed to identify a generalized pattern to describe the dose–response relationship between urban greenness and mental health. Through a meta-analysis of all relevant studies, we found sufficient samples to generalize the dose–response curve for greenness intensity. Our analysis revealed that a quadratic pattern best fits most of the published greenness curves, and we identified the highly beneficial and best doses of eye-level greenness and top-down greenness. This study identifies and rationalizes a generalized quadratic pattern describing the dose of greenness–mental health response curves, addressing a critical knowledge gap across multiple fields. In practice, a moderate ‘dose’ of urban greenness exposure provides the most salubrious supply of mental health benefits.

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Fig. 1: Four circumstantial doses of green landscapes–mental health curve theories.
Fig. 2: Meta-analysis of curve refitting between dose of greenness and mental health performance.
Fig. 3
Fig. 4

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Data availability

All datasets supporting the findings of this study are publicly accessible via GitHub at https://github.com/jli1102/Dose.git. The repository comprises five structured databases: (1) Data 1_Eligibility_784 studies; (2) Data 2_Inclusion_133 studies; (3) Data 3_Extraction_69 curves; (4) Data 4_Curve refitting results; (5) Data 5_Risk of Assessment results. Database 1 includes the full list of 784 eligible studies with key information after screening. The ‘source’ variable indicated the search iteration, with six iterations conducted: sources 0 and 1 searched Web of Science and Scopus from 1985 to 2021; sources 2 and 3 searched the Web of Science and Scopus from 2021 to 2022; sources 4 and 5 included additional studies from citations or alternative sources; source 6 searched the Web of Science and Scopus from 2022 to 2025 and PubMed from 1985 to 2025. Database 2 contains the full list of 133 studies for inclusion with key information, namely, study ID, title, first author, study year, continent, relationship (causal–correlational), mental health type, mental health description, dose type, dose measured range, optimal dose, satisfactory dose and checked (Y/N). Database 3 is the full list of 69 curves with extracted key information for the final curve refitting. The information included curve ID, citation, title, published year, continent, study design type, relationship, DV, DV description, IV presentation methods, dose audit methods, dose description, dose range, optimal dose, satisfactory dose and quality check results. Dataset 4 includes the standardized curve data points and curve-fitting results across four worksheets: eye-level dose curve data points and curve-refitting results; quality-verified eye-level curve-fitting results after risk-of-bias assessment; top-down dose curve data points and curve-refitting results; and quality-verified top-down curve-fitting results after risk-of-bias assessment. Database 5 contains results for the risk-of-bias assessment and quality check. The files have three worksheets: evaluation criteria; quality check results with total points; percent possible points, final quality results and summary chart.

Code availability

The code supporting the findings of this study, especially the R code in the file titled ‘curve fitting code’, is available via GitHub at https://github.com/jli1102/Dose.git.

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Acknowledgements

We are thankful for financial support from the University Grants Committee (UGC) of Hong Kong SAR, General Research Fund (GRF) number 17606621 (to B.J.), the European Union-funded Horizon Europe project ‘environMENTAL’ (101057429), co-funding by UK Research and Innovation under the UK Government’s Horizon Europe funding guarantee (10041392 and 10038599), the National Key R&D Program of Ministry of Science and Technology of China (MOST 2023YFE0199700) and NSFC grant (82150710554) (to G.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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B.J. conceptualized the study. B.J., J.L. and X.L. contributed to the research design. B.J., J.L., P.S. and X.L. contributed to the collection and treatment of raw data. B.J., J.L., P.S. and X.L. contributed to the data analysis. B.J. and J.L. contributed to the write-up of the first draft. B.J., J.L., P.G., G.S., C.W. and P.S. contributed to the review and revision.

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Correspondence to Bin Jiang.

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Jiang, B., Li, J., Gong, P. et al. A generalized relationship between dose of greenness and mental health response. Nat Cities 2, 739–748 (2025). https://doi.org/10.1038/s44284-025-00285-z

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