Optimizing Urban Green Space and Improving Ecosystem Service Functions Based on a Grasshopper Parametric Design Tool

Urban green spaces (UGS) are essential for mitigating urban heat island (UHI) effects, enhancing air quality, and increasing carbon sequestration, yet conventional urban planning methods lack the flexibility to dynamically optimize these ecosystem services. To address this gap, we developed a computational, parametric design framework in Grasshopper—integrating i-Tree Eco, ENVI-met, and GIS-based modeling—to optimize UGS configurations in Beijing for improved environmental and social outcomes, assessing UHI reduction, carbon sequestration, air pollution mitigation, and habitat connectivity, and validating predictions against field measurements using multiple linear regression, correlation analysis, and spatial clustering (Moran’s I, Getis-Ord Gi*). The results show that optimized configurations significantly improved UHI temperature reduction from 2.0°C to 3.5°C (t = 7.45, P < 0.001) and increased carbon sequestration from 45 to 65 kg CO2 (t = 6.92, P < 0.001), with observed air pollution reductions for NO2 (0.08–0.14 ppm, t = 5.88, P < 0.001), SO2 (0.06–0.10 ppm, t = 6.10, P < 0.001), and PM2.5 (10–16 µg/m3, t = 6.42, P < 0.001). Spatial clustering analysis confirmed significant cooling hotspots (Moran’s I = 0.67, P = 0.001), and habitat connectivity improved with a 57.1% increase in habitat patches (P = 0.004) and a 73.3% rise in species dispersal potential (P = 0.001), demonstrating that the parametric model yields significant environmental and social benefits and supports its application in urban sustainability planning.