Optimizing Urban Green Roofs: An Integrated Framework for Suitability, Economic Viability, and Microclimate Regulation

Abstract

Urban areas face significant challenges from heat islands, stormwater, and air pollution, yet green roof adoption is hindered by feasibility and economic uncertainties. This study proposes an integrated framework to optimize green roof strategies for urban sustainability. We combine deep learning for rooftop suitability screening, comprehensive ecosystem service valuation, life-cycle cost–benefit analysis under varying policy scenarios, and ENVI-met microclimate simulations across Local Climate Zones (LCZ). Using Dalian’s core urban districts as a case study, our findings reveal that all three green roof types (extensive, semi-intensive, and intensive) are economically viable when policy incentives and ecological values are fully internalized. Under the ideal scenario, intensive roofs yielded the highest long-term returns with a payback period of 4 years, while semi-intensive roofs achieved the greatest cost-effectiveness (BCR = 4.57) and the shortest payback period of 3 years; extensive roofs also reached break-even within 4 years. In contrast, under the realistic market-only scenario, only intensive roofs approached break-even with an extended payback period of 23 years, whereas extensive and semi-intensive systems remained unprofitable. Cooling performance varies by LCZ and roof type, emphasizing the critical role of urban morphology. This transferable framework provides robust data-driven decision support for green infrastructure planning and targeted policymaking in high-density urban environments.