Influences of Land Policy on Urban Ecological Corridors Governance: A Case Study from Shanghai

The analysis of land use change (LUC) characteristics and the impact of policies related to urban ecological space is required to improve spatial planning and to support decision making regarding green infrastructure (GI) investment. This study employed Geo-informatic Tupu analysis and Fluctuation Potential Tupu analysis methods to analyze the characteristics of LUC in an urban ecological corridor (EC). To help understand the influence of land use policy on GI governance and support the optimization of spatial planning, we proposed a situation–structure–implementation–outcome (SSIO) policy cascade analysis framework. SSIO takes “place” as its starting point, then couples the local policy with the governance structure to promote the sustainability of urban commons governance. The results show that the land use type within an EC in the city is mainly cultivated land. However, between 2009 and 2019, cultivated land, construction land, and facility agricultural land all showed a decreasing trend, while forest land and garden land types underwent increasing trends. The LUC Tupu unit highlights the transition from cultivated land to forest land. Forest land has the greatest increase in area and accounts for 52.34% of the area of increasing land use. Cultivated land shows the greatest decrease in area and accounts for 70.30% of the area of decreasing trends. Based on the local policy situation of the metropolis, a land policy governance mechanism can be constructed by the establishment of a governance structure with local government as the core, using land consolidation as the platform, taking ecological spatial planning and inefficient construction land reduction as typical policy tools, and experimentally integrating the concept of Nature-based Solutions (NbS). In general, these findings may be applicable to other rapidly urbanizing cities around the world that are developing complex land use policies for ecological space governance.