58%
natural park attributes drive most of the model's explanation of wellbeing
Natural attributes of urban green spaces account for 58% of model-attributed contributions to mental well-being
Using an explainable machine-learning approach (Random Forest + SHAP), the study decomposed which categories of variables drove predictions of mental well-being (MWB). Natural attributes of the urban green space — including Amplitude of Topographic Relief, Diversity of Terrain, Diversity of Animal Sounds, Area of Waterbody, and Degree of Greening — dominated with a 58% relative contribution. This underscores the primacy of biophilic, multi-sensory environmental features over demographic or social factors when explaining visitor MWB in a large urban park.
Natural attributes of UGS dominate the model-attributed contributions to MWB, with a relative contribution of 58 %, particularly Amplitude of Topographic Relief, Diversity of Terrain, Diversity of Animal Sounds, Area of Waterbody, and Degree of Greening, whereas demographic variables contribute little, accounting for only 1.1 %.
Related findings
g = -0.67
portable digital nature reduces anxiety
Portable Digital Nature Experience significantly reduces anxiety
Yuhan Zhou et al., 2026, Clinical Psychology Review
g = -0.51
portable digital nature reduces stress
Portable Digital Nature Experience significantly reduces stress
Yuhan Zhou et al., 2026, Clinical Psychology Review
sensory bird interaction has the largest indirect mental health effect via restorative perception
Sensory human–bird interaction most strongly boosts mental health indirectly by enhancing restorative environmental perception
Runxuan Zhang et al., 2025, Land
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