Scenario-based prediction and optimization of greenspace ecological network under land-use dynamics: a case study of Nanjing metropolitan area, China

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.