In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal.

The ecological benefits of urban green spaces depend on their structure and ecological service function. Evaluation systems used to monitor these characteristics show distinct regional variations. This study analyzed China’s urban green spaces, developed a quantitative ecological benefit evaluation system, and comprehensively evaluated the ecological benefits of green spaces in Xi’an city. Suitable evaluation indexes for Xi’an were selected based on field survey data with large-scale samples and high-resolution remote sensing image data. The results showed that the ecological service function of urban green spaces in Xi’an has been substantially improved by ecological planning. Therefore, it is important to evaluate this function as part of the urban planning and design process. Furthermore, increasing the 3D Green Quantity through urban forests can effectively improve the ecological service function.

Road network expansion is a critical factor driving urban landscape pattern changes and ecosystem degradation. To investigate the spatiotemporal evolution of landscape ecological risk within the influence zones of different road grades, this study took the central urban area of Xi’an as a case study. Based on CLCD land-use data and OpenStreetMap road vector data for 2014, 2019, and 2024, buffer zone analysis was employed to delineate three types of road influence domains: motorways (4 km), trunk roads (2 km), and primary roads (1 km). Using ArcMap 10.8 and Fragstats 4.2, we conducted land-use change analysis, landscape ecological risk assessment, and spatial autocorrelation analysis, and applied a geographically weighted regression (GWR) model to explore the spatial heterogeneity of driving mechanisms. The results show the following: (1) Built-up land was the dominant land-use type across all three road influence domains and expanded continuously over the study period, with a corresponding decline in cropland. Under a unified fixed-threshold system, the mean ERI within all three road influence domains exhibited a declining trend; the proportion of low-risk areas increased continuously, while moderate-and-above risk areas contracted, and risk transitions generally converged toward lower grades, with only a minor localized escalation from moderate to relatively high risk along primary roads during 2019–2024. The risk-level ranking remained stable as motorway > trunk > primary. (2) Global Moran’s I values were all significantly positive (pseudo p = 0.001); High–High clusters expanded along the edges of motorway corridors, trunk-road clusters were fragmented, and primary roads formed localized cold-spot aggregations in the highly built-up core area. (3) Distance to roads, the nighttime light index, and road density were selected as explanatory variables. The GWR model revealed pronounced spatial non-stationarity in local coefficients: nighttime light was predominantly negatively associated with ERI in the motorway and trunk-road influence domains; the effect of distance to roads shifted from mainly positive during 2014–2019 to negative in some peripheral segments by 2024; and road-density effects showed a clear grade-dependent pattern, being predominantly negative in the highly built-up core along primary roads but locally positive in the peripheral expansion segments of motorways and trunk roads. This study characterizes the differentiated landscape ecological risk within road-grade influence domains and provides decision support for hierarchical ecological control and road-network optimization in Xi’an and other rapidly urbanizing regions.