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.
Elkhateeb et al. (2025) studied this question.