Multi‐Scenario Simulation of Greenspace Integrating Multi‐Objective Programming and Spatial Prioritization: A Case Study of the Hangzhou Metropolitan Area

ABSTRACT

The optimization of greenspace spatial configurations is vital for alleviating the ecological and environmental challenges associated with urban expansion. Conducting future multi‐scenario simulations aligned with national strategic goals is a key approach to exploring effective greenspace optimization pathways. However, existing multi‐scenario simulation studies generally lack scenario control mechanisms aligned with quantitative demand targets and provide insufficient representation of spatial constraints, which to some extent undermines the capacity of simulation outcomes to support planning decision‐making. Therefore, this study takes the Hangzhou metropolitan area (HZMA) as the case region and develops a coupled MOP‐ESP‐PLUS model by integrating multi‐objective programming (MOP) with spatial prioritization concepts. The model aims to enhance the planning reliability of the simulated results by improving both quantitative allocation and spatial constraints of greenspace. Greenspace information is extracted based on a comprehensive analysis of LULC, and the future evolution patterns of greenspace under multiple scenarios are revealed at both the metropolitan and urban–suburban–rural subregional scales. The results indicate that the ND scenario fails to meet the baseline requirements of the predefined targets. In contrast, the EP and SD scenarios are more effective in mitigating the encroachment of built‐up land on greenspace, reducing fragmentation, and maintaining better connectivity. Meanwhile, significant differences in greenspace simulation results are observed across subregions. Based on evaluations of greenspace area, ecological–economic–social benefits, and landscape pattern indices, the most suitable development scenario is identified for each subregion, and corresponding planning recommendations are proposed. The findings demonstrate that the MOP‐ESP‐PLUS model offers higher adaptability to policy targets and provides a replicable technical pathway to support multi‐scenario comparison and zoned, differentiated decision‐making for greenspace planning and management at the metropolitan scale.