A Spatial Mismatch Analysis of Blue–Green–Gray Infrastructure for Urban Cooling: Linking Supply, Thermal Pressure, and Heat-Sensitive Demand

Urban green spaces are increasingly recognized as cooling infrastructure for climate resilience, environmental health, and spatial equity. However, their planning value depends not only on where green spaces are located but also on whether cooling supply is translated into lower thermal pressure and aligned with heat-sensitive demand. This study examines blue–green–gray infrastructure cooling supply, thermal regulation, human-relevant thermal pressure, and supply–demand mismatch across 1385 census tracts in Maryland, USA. An Infrastructure Cooling Supply Index (ICSI) was constructed from green, blue, and gray infrastructure components and evaluated against maximum land surface temperature (LSTmax), Heat Index, extreme heat days, and a Human-Relevant Thermal Pressure Index (HTPI) integrating ambient heat intensity and recurrent extreme heat exposure. Explainable machine learning was used to identify the relative contributions of individual infrastructure components, and cooling supply was compared with socially differentiated heat-sensitive demand. The results show that higher ICSI is significantly associated with lower LSTmax, Heat Index, and HTPI. The association was strongest for surface thermal conditions, remained significant for Heat Index and integrated human-relevant thermal pressure, and was comparatively weaker for recurrent extreme heat days, indicating that infrastructure cooling supply affects thermal outcomes through distinct pathways. Cooling supply was also spatially uneven, with stronger provision generally associated with greater vegetation and tree-canopy coverage, higher park provision, larger water-area proportions, and lower gray-infrastructure pressure. Explainable GeoAI results identify impervious cover, tree canopy, water-area proportion, road density, and park provision as the principal contributors to tract-level thermal pressure. The mismatch analysis identifies 433 census tracts, or 31.3% of all Maryland tracts, as low-supply–high-demand priority areas. These tracts reveal substantial spatial convergence among elevated heat-sensitive demand, insufficient cooling infrastructure, and persistent thermal pressure. This study provides a tract-level approach for targeting cooling interventions where thermal pressure, vulnerability, and infrastructure deficits converge.