Multi-objective optimization of green roof spatial layout in high-density urban areas—A case study of Xiamen Island, China

Rapid urbanization and increasing environmental challenges driven by climate change have diminished the efficacy and efficiency of traditional green infrastructure, particularly in high-density urban areas with a prominent contradiction between ecological construction and land scarcity. Green roofs (GRs) can provide multiple ecological benefits, and have proven to be an effective measure for addressing complex environmental issues in high-density urban areas. However, whether GR layout can deliver significant ecological benefits at a low investment cost requires further exploration. A multi-objective optimization framework for GR layout in high-density urban areas was proposed, balancing both costs and benefits. The framework intelligently extracted potential GRs, established a multi-objective evaluation system, integrated benefit simulation models with a multi-objective optimization algorithm (NSGA-III) to determine the optimal GR layout, and recommended the best layout scheme using the entropy weight-TOPSIS method. A case study in Xiamen, China, demonstrated the applicability and implications of this framework. The results indicated that the genetic algorithm effectively optimized the scheme to meet the objectives of runoff reduction, cooling effect, and investment cost in Xiamen Island. Specifically, the GRs contributed major runoff reduction (2.115–2.556 %), cooling effect (0.395–0.485 %), and investment cost (12.65 × 10⁹–15.32 × 10⁹RMB) across all optimal scenarios. This approach enables the visualization of GR layouts and quantification of associated benefits and costs, assisting decision-makers in scientifically balancing the costs and ecological benefits of GR layout options. The proposed framework provides a new and feasible path for high-density urban areas to achieve optimized GR spatial planning. © 2024 Elsevier Ltd.