Urban areas with parks tend to have the best outdoor thermal comfort in regions with high urban heat island effects during summer. This study analyzed the synergistic cooling effects of 94 urban parks and the adjacent built-up areas in six districts of Xi’an City using four cooling indicators: park cooling intensity (PCI), park cooling area (PCA), park cooling effect (PCE), and park cooling gradient (PCG). The results showed that 84 out of 94 parks exhibited significant cooling effects, with an average PCI of 1.98 °C, PCA of 51.7 ha, PCE of 6.6, and PCG of 8.2 °C/km. Correlation analyses indicated that the intrinsic park attributes, external buffer zone building height, and building density were the main factors affecting the cooling effect. The park landscape configuration, building height, and density significantly influenced the PCI and PCG, while the park shape and size were crucial for the PCA (positive) and PCE (negative). The optimal park areas for improving the thermal environment were identified as 26 ha (cooling area focus, building density <13%) and 15 ha (cooling intensity focus, building height 32%). This study provides theoretical guidance for planning urban parks and the surrounding areas based on cooling effects, offering insights for future climate resilience planning.

Urban green spaces (UGS) play a critical role in mitigating the urban heat island (UHI) effect and addressing climate change challenges through their cooling effects. Expanding green space and optimizing its layout are key strategies in urban planning. However, with limited land resources, maximizing cooling efficiency becomes crucial. While previous studies have yet to reach a consensus on the optimal UGS size threshold for achieving maximum cooling efficiency, this study employs high-resolution remote sensing imagery to extract 25,594 UGS patches in Xi’an and explores the scale dependency of UGS cooling effects. Results show that 54.20% of the patches exhibit a “cooling island effect”, reducing surface temperatures by 0.10 ± 0.13 °C to 1.66 ± 1.15 °C. A nonlinear relationship was observed between cooling intensity and UGS size, influenced by vegetation coverage and edge complexity. Further analysis reveals that medium-sized UGS have the highest cooling efficiency. These UGS were categorized into five functional zones to explore the dominant factors affecting cooling efficiency in each zone, and corresponding strategies for improvement were proposed. These findings provide insights into optimizing UGS layout and refining the cooling efficiency thresholds.

Arid regions are ecologically fragile and occupy a substantial portion of the global terrestrial surface. In these regions, ecosystem services (ESs) are strongly constrained by water availability and, more importantly, by the balance between water supply and demand. However, the nonlinear responses and threshold mechanisms linking water supply–demand balance to ES dynamics remain unclear. Taking the Kubuqi Desert in the “Great Bend” of the Yellow River as the study area, this study quantified the Comprehensive Ecosystem Service Index (CESI) and the Water Supply–Demand Ratio (WSDR) by integrating the InVEST model, RWEQ model, the RUSLE model, Water Balance Method, and so on. The dual-constraint line method and elasticity coefficient approach were integrated to identify the constraint effects and critical thresholds of WSDR on CESI. Ecological management zones were further delineated by integrating the inflection-point intervals of the dual-constraint lines with the threshold intervals identified by elasticity coefficients. The results showed that CESI remained relatively low, with a maximum value of approximately 0.5, suggesting that the overall ES level was still limited, but exhibited a continuous increasing trend. The regional water supply–demand pattern gradually shifted from deficit toward relative balance, although agricultural water use still accounted for about three-quarters of total consumption. CESI showed a nonlinear threshold response to WSDR: mild water deficit suppressed CESI growth, whereas moderate water surplus promoted CESI recovery by alleviating water constraints and improving ecosystem functioning. Thresholds identified by elasticity coefficients mainly occurred near critical transitions between water deficit and surplus. Based on ES supply and threshold sensitivity, nine ecological management zones were identified, with priority enhancement areas accounting for approximately 75%. These findings provide a threshold-based basis for ecological zoning and differentiated restoration in arid regions.

Understanding residents’ and tourists’ public space perceptions and satisfaction was essential for the sustainable planning and tourism development of historic towns. However, limited attention has been paid to the different perceptions between the two groups, as well as the nonlinear relationship between public space perception and satisfaction. Taking Fenghuang Ancient Town as the study area, this study used independent sample t-tests to examine the differences between residents and tourists. The XGBoost-SHAP model was further applied to identify key influencing factors and nonlinear interaction mechanisms affecting satisfaction. The results showed that: (1) residents perceived higher public awareness, space openness, and accessibility, whereas tourists reported higher crowd density and business prosperity; (2) color harmony most strongly influenced residents’ satisfaction, while spatial comfort had the strongest influence on tourists’ satisfaction; (3) color harmony and spatial comfort positively affected satisfaction, whereas space openness (residents) and crowd density (tourists) showed U-shaped relationships; and (4) for residents, the strongest interaction was between color harmony and business prosperity (0.015), while for tourists it was between spatial comfort and color harmony (0.038). These findings provided evidence-based guidance for sustainable public space planning and cultural heritage tourism management, contributing to inclusive governance and the long-term sustainability of historic towns.

Against the backdrop of rapid urbanization and climate warming, the urban heat island (UHI) effect has severely affected ecological security and public health. Existing studies have often focused on single-city analyses or large-sample averages, with insufficient attention to the nonlinear driving mechanisms of UHI under different hydrothermal contexts. This study selects Wuhan and Xi’an as representative cities, constructing an explainable machine learning framework to interpret and compare UHI intensity across feature importance, nonlinear responses, factor interactions, and spatial differentiation. The results show that, in Wuhan, the top five factors contribute 62.4%, reflecting a composite dominance of ecology, spatial morphology, location, and human activities. In Xi’an, the top five factors contribute 72.0%, indicating a more concentrated dominant structure. Nonlinear responses reveal that key factors like NDVI have distinct effect thresholds and mechanisms in the two cities. Spatially, Wuhan displays a continuous gradient pattern characterized by center-promoting and peripheral-suppressing effects, whereas Xi’an presents a block-like mosaic structure composed of multiple juxtaposed districts. These differences suggest that UHI mitigation should move beyond a uniform control model and instead adopt climate-sensitive strategies that account for the dominant factor combinations, response thresholds, and spatial organization of each city. The proposed framework and findings provide scientific support for understanding UHI mechanisms under different hydrothermal contexts and offer targeted implications for thermal environment regulation and spatial planning in cities with similar climatic and environmental characteristics.