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A systematic map of research exploring the effect of greenspace on mental health

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LANDSCAPE AND URBAN PLANNING
卷 201, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.landurbplan.2020.103823

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资金

  1. University of Southampton
  2. ERC Starting Grant 'SCALEFORES' [680176]
  3. European Research Council (ERC) [680176] Funding Source: European Research Council (ERC)

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The past 35 years has seen an accumulation of empirical evidence suggesting a positive association between greenspace and mental health. Existing reviews of evidence are narrow in scope, and do not adequately represent the broad range of disciplines working in this field. This study is the first systematic map of studies investigating greenspace effects on mental health. A total of 6059 papers were screened for their relevance, 276 of which met inclusion criteria for the systematic map. The map revealed several methodological limitations hindering the practical applications of research findings to public health. Critically, the majority of studies used cross-sectional mental health data which makes causal inference about greenspace effects challenging. There are also few studies on the micro-features that make up greenspaces (i.e., their quality), with most focussing only on quantity effects on mental health. Moreover, few studies adopted a multi-scale approach, meaning there is little evidence about at which spatial scale(s) the relationship exists. A geographic gap in study location was also identified, with the majority of studies clustered in European countries and the USA. Future research should account for both human and ecological perspectives of quality using objective and repeatable measures, and consider the potential of scale-dependent greenspace effects to ensure that management of greenspace is compatible with wider scale biodiversity targets. To establish the greenspace and metal health relationship across a life course, studies should make better use of longitudinal data, as this enables stronger inferences to be made than more commonly used cross-sectional data.

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