Connectivity and Resilience of Urban Cooling Networks: A Network-Based Assessment Under Heterogeneous Resistance

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km 2 were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network.