Cited by 994Urban Green Space and HealthUrban Arborization and Environmental StudiesNoise Effects and Management

The influence of urban green environments on stress relief measures: A field experiment

Tyrväinen, Liisa, Ojala, Ann, Korpela, Kalevi, Lanki, Timo, Tsunetsugu, Yuko, Kagawa, Takahide·2014·Journal of Environmental Psychology

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Crossref · 10.1016/j.jenvp.2013.12.005

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7 of 7 references verified0 citation errors · 0 not checkable

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Authors
Tyrväinen, L., Ojala, A., Korpela, K., Lanki, T., Tsunetsugu, Y., & Kagawa, T.
Affiliations
  • Tampere University (FI)
  • Forestry and Forest Products Research Institute (JP)
Corresponding author
Ojala, Ann
Venue
Journal of Environmental Psychology
Publisher
Elsevier BV
Published
2014-06
Volume
38
Pages
1-9
ISSN
0272-4944 (ISSN-L)
OpenAlex
W2096398889
Cited by
994
References
7
Citations by year
2026: 65 · 2025: 94 · 2024: 104 · 2023: 114 · 2022: 112 · 2021: 105 · 2020: 78 · 2019: 110 · 2018: 77 · 2017: 47 · 2016: 33 · 2015: 38 · 2014: 17
FWCI
21.81
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Environmental Psychology (publishedVersion)
  • Jukuri (Natural Resources Institute Finland (Luke)) (submittedVersion)
Funders
  • “Stress-reducing qualities of urban green areas” (139699)
  • “Green infrastructures for health in the future living environments (GreenHealth)” (255423)
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Urban Green Space and Health
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Urban Arborization and Environmental Studies
  • Health Sciences › Health Professions › Speech and Hearing › Noise Effects and Management
Keywords
urban green space, nature exposure, stress reduction, perceived restorativeness, subjective vitality, mood, creativity, salivary cortisol, urban parks, urban forests, built environment

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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.

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