Land-use dynamics in rapidly urbanizing metropolitan regions profoundly reshape the spatial structure and functional connectivity of greenspace ecological network (GEN). However, how land-use policies influence GEN structure and resilience at the metropolitan scale remains unclear. To accurately predict GEN changes and support scenario-based optimization for sustainable planning, this study proposed a multi-scenario optimization framework that coupled multi-objective programming (MOP) model, patch-level land-use simulation (PLUS) model, morphological spatial pattern analysis (MSPA) model, and least-cost path (LCP) model. The Nanjing Metropolitan Area (NMA) was used to simulate four scenarios: business as usual (BAU), rapid economic development (RED), ecological land protection (ELP), and ecological and economic balance (EEB).The results showed that: (1) The MOP-PLUS model achieved high accuracy (overall accuracy of 84.58%, Kappa coefficient of 0.74, and FoM value of 0.27), effectively capturing regional land-use dynamics; (2) Land-use transitions and GEN structures significantly varied across scenarios, with RED causing severe ecological loss, whereas ELP and EEB scenarios effectively enhanced ecological connectivity; (3) The scenario-based GEN optimization highlighted that the EEB scenario provided the most practical balance, promoting both ecological stability and cost-efficient land management. These findings directly inform sustainable land-use policies and strategic planning decisions in metropolitan regions, revealing the methodological advantages of integrating scenario simulations with GEN analyzes for optimized greenspace management.
Liu et al. (Mon,) studied this question.