Optimizing urban green spaces using a decision-support model for carbon sequestration and ecological connectivity

Urban green spaces (UGSs) are vital for enhancing urban ecological health and resident well-being. However, their diverse functions need to be balanced based on spatial limitations and varying stakeholder preferences. Integrated planning approaches are needed to exploit the multiple benefits of UGSs. This study introduces a multi-objective decision-support model designed to optimize UGS planning by simultaneously addressing carbon sequestration, ecological connectivity, and cost constraints. The model incorporates the non-dominated sorting genetic algorithm II to identify Pareto-optimal solutions for tailored decision-making strategies that balance different priorities. The model indicated that ecological connectivity can be improved by 7.57 % while meeting carbon-reduction and budgetary targets. The model effectively balanced trade-offs, underscoring the importance of both the quantity and strategic placement of green space. This decision-support framework empowers decision-makers to rapidly simulate and validate optimal scenarios, effectively balance competing objectives, and provide a scientific basis through verifiable feedback, ultimately promoting the development of sustainable urban environments.