This study assessed the contributions of urban green spaces on mental health with joint consideration of people’s physiological and psychological responses. The psychological and physiological responses of participants aged between 22 and 28, who visited green spaces in a low-density area of Fuzhou, Fujian Province, China, were measured using Perceived Restorativeness Scale (PRS) methods and biometric wearable sensing devices, respectively. Results showed that exposure to green space led to significant changes in PRS, electrodermal activity (EDA), facial electromyography (EMG), respiration sensor (RESP), and photoplethysmography (PPG), while there is no significant impact on skin temperature (SKT). Additionally, psychological and physiological responses were highly consistent and correlated (R < 0.8). The results also indicated that green spaces with high plant species richness, a water landscape, bumpy ground, cultural landscape, and without roadways presented a high performance on stress recovery and attention restoration. At the same time, the influence of openness was negligible in the low-density area. The study provides planners and landscape designers with specific guidance for implementing urban green spaces to improve mental health in low-density residential areas.

The reservoir-related tourism industry has been developed for leisure purposes since the 1970s and has gained popularity worldwide. To promote reservoir-related tourism in China, the Chinese government launched a “top-down” project titled “Water Conservation Scenic Spot” (WCSS) in 2001. However, because of the lack of reasonable planning, innovative ideas, and effective governance, there are some problems with WCSS constraining its development. To deal with those problems, it is necessary to have a holistic understanding of the WCSS situation. This study analyses the origin, status, opportunities, and challenges of WCSS development in China. The results show a trend of periodic increase in the number of WCSSs between 2001 and 2016, and WCSS development is unbalanced regarding regions and types. Moreover, the main issues for the WCSS include the failure to follow WCSS guidelines in practice, weak awareness of the scientific educational function, monotonous planning and construction, a lack of cultural preservation, insufficient funding, weak marketing, damaging of natural resources, destruction of clusters of animals and plants, land surface erosion, and landscape pollution. These adverse effects can be alleviated by strengthening supervision and fostering balanced development, promoting education and protecting cultural resources, distributing funding and enlarging popularity, controlling the amount of tourism, and avoiding overexpansion. Overall, the findings of this study can encourage the development of WCSSs in the future and are of significance in supporting the reservoir-related tourism industry.

Understanding the complex relationship between the built environment and urban vitality is essential for evidence-based urban renewal. However, most existing studies rely on linear regression models that fail to capture the non-linear threshold effects inherent in urban systems and depend on costly proprietary datasets that limit reproducibility. This study proposes a scalable, open-data-driven framework to decode the non-linear mechanisms governing population-based urban vitality in Zhengzhou, a rapidly regenerating metropolis in Central China. Using Areas of Interest (AOIs) as functional spatial units to mitigate the Modifiable Areal Unit Problem (MAUP), we construct a multidimensional built environment indicator system (5D+S: Density, Diversity, Design, Distance to Transit, Destination Accessibility, and Surroundings) from multi-source open data, including 100 m WorldPop population grids, OpenStreetMap building vectors, Points of Interest (POIs), and transit station data. An explainable machine learning approach combining XGBoost with SHapley Additive exPlanations (SHAP) is employed to identify the relative importance of built environment factors and quantify their non-linear threshold effects on population-based urban vitality (operationally defined as residential population density derived from WorldPop 100 m grids). Across 3920 AOIs, XGBoost (R² = 0.846, RMSE = 0.104) substantially outperforms Ordinary Least Squares regression (R² = 0.634), confirming pervasive non-linear relationships, with stable 5-fold cross-validated R² = 0.713 ± 0.115. SHAP analysis reveals four dominant drivers: Distance to Commercial Core (DistCBD), Bus Station Density within 500 m (BusDen500), Green Coverage Ratio (GreenRatio), and Building Density (BD). Critical thresholds are identified: vitality contributions decay sharply beyond approximately 4.3 km from the CBD; at least 4 bus stations within 500 m are required for meaningful transit benefit; building density delivers positive returns within a 2–30% range; and excessive green coverage above 8.5% within 500 m is associated with declining population-based vitality, a finding that reflects spatial competition between ecological land use and residential density rather than a negative effect of greenery per se. These findings provide quantitative design guidelines for precision urban renewal, moving beyond “the more, the better” planning assumptions to identify optimal intervention ranges.