Unlocking multiple potentials: a data-driven framework for adaptive reuse of industrial heritage in Changzhou, China

Adaptive reuse of industrial heritage can showcase industrial culture and drive urban transformation but faces issues like homogenization, secondary ruins, and long-term adaptation deficits. Research gaps include insufficient analysis of correlations between reuse potential and strategies, and limited generalizability from single-case studies. This study addresses these gaps using Changzhou, China’s industrial heritage, aiming to provide a data-driven analytical framework for industrial heritage reuse potential, to reveal the network of potential indicators, to deconstruct the kernel of multidimensional potentials, to show the regional differentiation characteristics of potentials, and to construct a decision-making basis for typological governance. It draws on a consolidated dataset covering industrial heritage with multi-level protection statuses and a sample of 28 sites, identifies multidimensional indicators, explores their interrelations via Pearson correlation analysis, and extracts five primary dimensions – spatial, cultural, locational, operational, and historical potentials – through Factor Analysis, accounting for 70% of variability across 20 reuse indicators. GIS mapping highlights regional variations of these potentials, aiding targeted governance. Hierarchical Cluster Analysis categorizes industrial sites into six adaptive reuse types: unbalanced development, synergistic development, exemplary leading, canal industrial, functional continuity, and to-be-developed. The potential for adaptive reuse of industrial heritage reflects the dynamic needs of heritage governance, which requires systematic protection of heritage through top-down institutional strengthening, while bottom-up community empowerment opens up resilient renewal pathways for heritage. The framework constructed in this research helps to develop targeted regeneration strategies for industrial heritage based on different potential types to maximize its intrinsic value and enhance its long-term adaptation after adaptive reuse, while remaining generalizable to other regions and supporting policy design for adaptive reuse governance.