Against the backdrop of global climate change and rapid urbanization, the urban heat island (UHI) effect has intensified, leading to frequent extreme heat events that pose severe threats to public health and residents' wellbeing. To address these challenges, green infrastructure (GI) is emerging as a critical solution for building climate-resilient cities and enhancing adaptive capacity.
This study aims to explore the GI adoption to mitigate UHIs in the megacity of Chongqing, China. An influencing factor system integrating two-dimensional and three-dimensional indicators that affect UHI effect and vegetation cooling effects were constructed. The MaxEnt model was applied to assess heat risks and identify risk zones. Land use data, summer land surface temperature (LST) data, vegetation coverage, and landscape pattern indices within 1 km² grids were calculated, in order to enable the systematic analyses of interannual LST variations across different vegetation coverage levels.
Correlation analysis, quantile regression, and impact factor assessment were conducted to determine key variables affecting vegetation cooling effects using SPSS and R. The results clarified the differential temperature impacts and dominant factors among various vegetation types within potential UHI risk zones. Furthermore, Pearson correlation analysis, multiple linear regression, and relative importance analyses revealed variations in temperature ranges across vegetation coverage gradients for forest, shrub, and grassland types, along with their influencing factors. Finally, recommendations for regional GI planning were proposed to mitigate UHI risks.
Overall, this study provides a quantitative and precise optimization pathway for regional GI planning. By identifying UHI risk zones, evaluating cooling efficacy of different vegetation types, and elucidating the impact mechanisms of their landscape patterns, this study provides science-based nature-based solutions for developing heat-resilient cities in megacities.