Seasonal Contrasts of Heat and Cold Exposure in Urban Functional Zones: A Machine-Learning and GeoDetector Approach

Frequent extreme climate events pose severe threats to human health. Existing studies mainly focused on summer thermal environments, while few compared summer and winter extreme climate risks from the perspective of urban functional zones (UFZs). This study classified more precise UFZs using the machine-learning method and constructed heat and cold exposure indicators. GeoDetector was adopted to analyze driving factors and interactions of both types of exposure across UFZs. The results showed that UFZ classification achieved an overall accuracy of 81.8% and a Kappa coefficient of 0.75. High heat exposure concentrated in core public, residential, and commercial zones, while high cold exposure occurred in peripheral industrial and greenspace zones. Dual high exposure zones lay between the 3rd and 5th Ring Roads. Industrial zones positively contributed to both exposures, while commercial, public, and residential zones showed positive heat but negative cold exposure contributions, and greenspace zones presented opposite effects. Vulnerable population ratios had a strong explanatory power. Heat exposure interactions were dominated by vulnerable populations, building morphology, and landscape patterns, while cold exposure was also affected by the degree of facility agglomeration and human activities with varied mechanisms across UFZs. This study advanced single-season thermal research to multi-season exposure and zoned governance for climate-adaptive renewal.