Spatially explicit multi-objective optimization tool for green infrastructure planning based on InVEST and NSGA-II towards multifunctionality

Abstract

The imperatives of sustainable urban development have propelled the prominence of green infrastructure (GI) as a viable solution. However, prevailing methodologies for GI planning often prioritize individual ecosystem services (ES) and lack spatially explicit guidance. This study presents a spatially explicit approach integrating the InVEST model and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm as a multi-objective spatial optimization tool for assisting decision-making in multifunctional GI planning. The spatially explicit InVEST model was used as a model to assess GI multifunctionality. To demonstrate the applicability of our proposed model, GI of the central area of Wuhu City are optimized with the aim of maximizing the 3 objectives of maximizing habitat quality, crop production, and runoff reduction, evaluated respectively by InVEST habitat quality model, crop production model, and urban flood risk mitigation model. The comparison of typical optimized GI planning schemes—including the compromise, habitat quality preference, runoff reduction preference, and crop production preference scenarios—with the current scenario demonstrates significant improvements in corresponding ES objective. Our findings suggest that increasing forest land, certain types of arable land, and green spaces may have a higher probability of enhancing the multifunctionality of the site. Allocating GI elements in highly built-up areas may efficiently enhance multifunctionality. Spatial analysis of optimal GI schemes reveals a preference for dispersing forest land and grassland, while aggregating agricultural GIs to enhance multifunctionality. Non-linear relationships are found between the ES pair of crop production and habitat quality, as well as runoff reduction and habitat quality. Identifying inflection points where synergies and trade-offs shift is essential for maximizing multifunctionality. Trade-off relationships between crop production & runoff reduction are identified. Our study highlights the importance of recognizing non-linear relationships between certain ES pairs in GI planning, particularly identifying inflection points where synergies and trade-offs shift. This research underscores the viability of our proposed model in facilitating informed decision-making pertaining to GI planning on a citywide scale, with a specific emphasis on achieving multifunctionality. By addressing the shortcomings of current approaches and integrating advanced optimization techniques, our model offers valuable insights for policymakers and practitioners involved in sustainable urban development and GI planning.