Parametric Optimization of Urban Street Tree Placement: Computational Workflow for Dynamic Shade Provision in Hot Climates

Urban streets in hot climates often suffer from inadequate shade, exacerbating pedestrian discomfort, urban heat island effects, and energy demands for cooling. Traditional tree-planting approaches overlook dynamic solar paths, building-induced shadows, and spacing requirements, resulting in suboptimal shade coverage and resource inefficiency. This study introduces a computational workflow in Rhino/Grasshopper to optimize tree placement and canopy radii through analysis of solar radiation and shadow patterns. By prioritizing sun-exposed zones, minimizing shadow overlaps, and ensuring growth-appropriate distances, the tool enhances shade distribution. Integration of parametric modeling and environmental simulations improved thermal comfort, reduced energy use, and evidence-based urban planning strategies. Across ten optimization runs, the workflow achieved a 68% increase in shade coverage, an 11.5 °C reduction in mean radiant temperature (MRT), and a 72% decrease in the spatial extent of high-risk heat-exposure zones, demonstrating its potential for climate-adaptive street design in hot-arid environments.