Harmonizing stakeholder interests in urban renewal: A novel planning approach using explainable machine learning and spatial optimization

Urban renewal is essential for revitalizing existing urban land and promoting sustainable urban development, with urban renewal planning optimal being a crucial research challenge. Current planning methods neglect the complex interactions and conflicts among multiple stakeholders, and often lack explainability in the site selection process. To address these issues, proposes a Multi-Stakeholder Perspective Urban Renewal Planning Optimization (MSP-URPO) model. This innovative approach integrates a multi-objective optimization algorithm with eXplainable Machine Learning (XML) to enhance decision-making for urban renewal. The optimization algorithm resolves conflicts among multiple objectives, while XML improves the clarity and understanding of the planning results. Applied to a case study in Shenzhen, the MSP-URPO model predicts an urban renewal scale of 616 ha by 2024, selecting 786 optimal blocks from 23,957 candidates. The study reveals that residents' preferences and multi-stakeholder decision consistency significantly impact site selection, contributing 33.69 % and 27.66 % respectively. These findings demonstrate that the proposed method effectively provides a low-cost, efficient, and precise decision-support tool for urban management and renewal planning.