Quantifying Urban Ecosystem Services for Community-Level Planning: A Machine Learning Framework for Service Quality and Residents’ Perceptions in Wuhan, China

Urban ecosystem services (ESs) are increasingly recognized as critical determinants of residents’ quality of life and well-being. This study develops a data-driven demand–supply matching framework to integrate ES concepts into community-level planning and service performance evaluation. Based on 312 resident surveys across 10 communities in Wuhan, China, we identify the key environmental attributes shaping perceived service quality. A random forest (RF) algorithm is employed to assess the relative importance of environmental features, while a multinomial logit (Mlogit) model quantifies their specific effects. The results highlight that community autonomy, neighborhood relations, environmental awareness, and infrastructure—such as broadband networks and security systems—play pivotal roles in improving service quality. Although provisioning and regulating ESs, such as safety and infrastructure, are relatively well established, cultural services that promote social cohesion and civic participation remain under-supported. These findings uncover the heterogeneity of residents’ environmental expectations and provide actionable insights for incorporating ES-oriented thinking into community planning and fiscal decision-making. By bridging ecological theory with operational urban governance, this study contributes a replicable approach for advancing more inclusive and sustainable community development.