Analysis of Influencing Factors of the Thermal Environment Based on 2D/3D Urban Morphology Across Urban Functional Zones: A Case Study of Shenyang, China

Urban spatial patterns regulate microclimates, yet those nonlinear interactive actions of 2D landscape patterns and 3D building morphology on land surface temperature (LST) among diverse urban functional zones (UFZs) remain insufficiently quantified. Using Shenyang as the case study, this study coupled Bayesian-optimized LightGBM and GeoShapley to disentangle these driving mechanisms. The models demonstrated high predictive accuracy (R2 > 0.84 across all UFZs). Results show high-temperature zone proportions ranking as: commercial (17.93%) > public service (11.40%) > residential (8.70%) > industrial (3.97%). Further factor analysis reveals that while geographic location is the predominant overall driver, after removing this baseline effect, residential, public service, and commercial zones are mainly regulated by MNDWI and NDVI, while industrial zones are dominated by Spatial Congestion Degree (SCD) and Floor Area Ratio (FAR). Morphological metrics exhibit clear ecological thresholds: residential zones show turning points at FAR 1.19 and NDVI 0.13; commercial zones see SCD’s cooling effect reverse to warming beyond 0.27; industrial zones experience sharp deterioration when SCD and FAR exceed 0.09 and 0.51, respectively. These findings demonstrate the necessity of UFZ-based differentiated spatial regulation and provide actionable thresholds for climate-adaptive urban planning.