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Green Space Quality and Health: A Systematic Review
Open accessAbstract
(1) Background: As cities densify, researcher and policy focus is intensifying on which green space types and qualities are important for health. We conducted a systematic review to examine whether particular green space types and qualities have been shown to provide health benefits and if so, which specific types and qualities, and which health outcomes. (2) Methods: We searched five databases from inception up to June 30, 2021. We included all studies examining a wide range of green space characteristics on various health outcomes. (3) Results: 68 articles from 59 studies were found, with a high degree of heterogeneity in study designs, definitions of quality and outcomes. Most studies were cross-sectional, ecological or cohort studies. Environment types, vegetation types, and the size and connectivity of green spaces were associated with improved health outcomes, though with contingencies by age and gender. Health benefits were more consistently observed in areas with greater tree canopy, but not grassland. The main outcomes with evidence of health benefits included allergic respiratory conditions, cardiovascular conditions and psychological wellbeing. Both objectively and subjectively measured qualities demonstrated associations with health outcomes. (4) Conclusion: Experimental studies and longitudinal cohort studies will strengthen current evidence. Evidence was lacking for needs-specific or culturally-appropriate amenities and soundscape characteristics. Qualities that need more in-depth investigation include indices that account for forms, patterns, and networks of objectively and subjectively measured green space qualities.
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“Nature exposure (ie, exposure to green and blue spaces) is thought to be beneficial, especially for mental, cardiovascular and respiratory health.1–4”
From Cross-sectional associations of different types of nature exposure with psychotropic, antihypertensive and asthma medication (2023) · cites this paper as [1]
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Metadata
- Authors
- Nguyen, P.-Y., Astell-Burt, T., Rahimi-Ardabili, H., & Feng, X.
- Affiliations
- UNSW Sydney (AU)
- Population Wellbeing and Environment Research Lab (PowerLab), Wollongong, NSW 2522, Australia
- School of Population Health, University of New South Wales, Sydney, NSW 2033, Australia
- University of Wollongong (AU)
- Corresponding author
- Feng, Xiaoqi
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2021-10-20
- Volume
- 18
- Issue
- 21
- Pages
- 11028
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC8582763
- OpenAlex
- W3206924707
- Cited by
- 420
- References
- 111
- Citations by year
- 2026: 109 · 2025: 117 · 2024: 85 · 2023: 66 · 2022: 42 · 2021: 1
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion)
- Funders
- National Health and Medical Research Council (1140317, 1148792, 1101065)
- Green Cities Fund (GC15005)
- Topics
- Social Sciences › Urban Studies › Urban Green Space and Health
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Urban Arborization and Environmental Studies
- Social Sciences › Sociology and Political Science › Environmental Justice and Health Disparities
- Keywords
- green space quality, urban green space, health benefits, tree canopy, grassland, cardiovascular conditions, psychological well-being, age and gender differences, soundscape
- MeSH terms
- Parks, Recreational, Cities, Humans, Longitudinal Studies, Cohort Studies
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 ›
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