Strategically identifying optimal locations for multifunctional green infrastructure: A case study in the Taipei Basin

Climate change has given rise to a multitude of challenges for both society and the environment. Implementing green infrastructure (GI) is regarded as a crucial policy strategy to mitigate these impacts. Numerous studies have been conducted to find optimal locations for implementing GI; however, selecting the most suitable context-specific strategy remains challenging. To address this gap, spatial multi-criteria evaluation (SMCE) and unsupervised machine learning clustering are integrated to identify the areas requiring green infrastructure intervention and provide planning suggestions for the Taipei Basin. We evaluated eight ecosystem services provided by GI, including agricultural production, carbon sequestration, heat reduction, stormwater management, water purification, habitat enhancement, green connectivity and green accessibility on 25 m2 grids. The SMCE results indicate that green infrastructure development should primarily focus on the central region of the Taipei Basin, while peri-urban areas are of lower priority. Based on the outcomes of the clustering analysis, specific and context-appropriate strategies for GI planning are recommended for each cluster. Integrating the priority map with cluster analysis offers valuable insights for decision-makers to pinpoint urgent challenges, target synergistic ecosystem services, and identify priority siting for GI. These findings support the formulation of strategic plans for GI development.