Factors influencing spatiotemporal cooling potential of blue–green infrastructure across diverse local climate zones—Case study of Wroclaw, Poland

The development of blue–green infrastructure (BGI) is essential for mitigating urban heat islands; however, it encounters operational challenges that demand effective, locally-adapted strategies. Existing literature does not completely assess the factors influencing the cooling mechanisms of blue, green, and integrated BGI types across various local climate zones (LCZs). This study evaluates the most influential characteristics shaping the cooling potential of BGI according to different LCZs, considering BGI characteristics (type, geometry, spatial density, vegetation features) and basic weather conditions. We focused on all BGI components in Wroclaw, Poland using the PlanetScope time-series from April to October (2020–2023). The thermal characteristics were analyzed using Landsat 8/9 time-series land surface temperatures. The variations in urban morphology were assessed using the World Urban Database and Access Portal Tools LCZ Generator. BGI cooling characteristics such as high-intensity cooling distance and intensity, cooling area, and the cooling efficiency were calculated using a custom Python script based on the ArcPy library and a watershed algorithm. The results indicate significant variations in the impact of the studied characteristics on cooling effectiveness, depending on the LCZ and BGI types. The most pronounced cooling was observed in highly urbanized LCZs. Larger and more densely vegetated BGI objects with complex shapes exhibited stronger cooling effects. The presence of water within the BGI significantly enhanced cooling potential. Despite the impact of basic weather conditions, the hierarchy of the influence of BGI characteristics remained consistent. This holistic approach may enhance our understanding of cooling mechanisms in various types of urban development. • BGI objects are identified using GEOBIA and categorized by LCZ and BGI type. • Four cooling effect indicators are calculated using the watershed algorithm. • BGI characteristics are evaluated for cooling effectiveness using PCA. • Effectiveness of BGI characteristics vary by LCZ and BGI type. • The hierarchy of BGI characteristics remains consistent in all weather conditions.