GreenPlotter: An AI-Driven Low-Carbon Design Algorithm for Land Partitioning and Sustainable Urban Development

Urbanization destroys green areas, prompting the need for eco-friendly policies. This study proposes “GreenPlotter,” an algorithm that combines low-carbon design and artificial intelligence (AI) for sustainable urban development. The study introduces carbon sequestration in trees as a green measurable factor in automated land development. Integrating size, access, fixed facilities, and carbon of land at the urban block scale, GreenPlotter uses genetic algorithms to optimize proposed design solutions for road access networks and land partitioning. The results of the low-carbon design scenario options proved the algorithm’s success in generating greener solutions. This article demonstrates that AI implementation has accelerated the design process while effectively incorporating carbon stored in trees as a measurable parameter that responds to a low-carbon design approach. © 2024 Association of Collegiate Schools of Architecture.