Data-Driven Optimization of Urban Green Infrastructure Through Parametric Design: Environmental Performance and Plant Selection

Urban green infrastructure design faces growing challenges posed by climate change and urbanization, necessitating precise, site-specific plant selection. Existing approaches rely on general guidelines and lack integration of environmental data with reproducible decision-making methods. This study proposes a data-driven parametric framework combining Grasshopper with environmental analysis plugins (Ladybug, Honeybee, Gismo) and a custom rule-based plant suitability model. A botanical database of over 100 species was developed to encode environmental requirements, growth characteristics, and maintenance parameters. The system processes site-specific solar radiation, soil conditions, microclimate, and water availability to evaluate plant–environment compatibility and generate spatially explicit planting configurations. The framework was validated in an urban case study, demonstrating improved alignment between plant requirements and site conditions and reduced selection time compared to conventional approaches. Integrating environmental data with parametric tools supports more transparent and reproducible decision-making in climate-responsive urban vegetation design.