Adaptive ranking of specific tree species for targeted green infrastructure intervention in response to urban hazards

Abstract

Green infrastructure (GI), with its multifarious benefits, can effectively address urban hazards and enhance urban resilience and sustainability. While traditional GI planning studies incorporate its multifunctionality, they are often limited to identifying prioritized locations for GI intervention without exploring how to respond to the local specific demands. In this study, using a highly urbanized city, Zhengzhou as a case, we first spatially identified urban hazards in three aspects, including urban flood susceptibility, urban heat environment, and air pollution, utilizing machine learning, remote sensing retrieval. Subsequently, we employed the i-Tree Eco model to quantify the effectiveness of potential tree species in unitary functional units in addressing these urban hazards. An adaptive ranking approach was then proposed to match the effectiveness of tree species with local demands for addressing urban hazards. Our results indicate that the inner city area, as well as the northwest should be prioritized for GI interventions. Urban hazards exhibit significant spatial heterogeneity and different tree species also have specific advantages, highlighting the importance of adaptive decision-making. The study area is divided into three zones, and we suggest targeting urban hazards with the most effective GI intervention and maximizing carbon sequestration potential in areas without pronounced urban hazards. The developed framework can serve as guidance for scientific decision-making in urban greening projects.