Meilin Lu

The sustainable regeneration of industrial heritage in cold regions is constrained by severe winter climates, pronounced seasonal shifts in behavior, and declining spatial vitality. However, existing studies have not sufficiently explained how cold-climate conditions reshape catalyst effects and regeneration performance in industrial heritage districts. This study proposes a digital shadow-enhanced and digital twin-enabled analytical framework for the sustainable regeneration of cold-region industrial heritage. Using the Youfang Street industrial heritage district in Harbin, China, as an exploratory case, the framework integrates multi-source data to construct a dynamic assessment system linking climatic constraints, spatial structure, and human activity patterns. The climate correction coefficient is further operationalized through normalized climatic variables, including air temperature, wind speed, snow-cover condition, and thermal-comfort indicators. The results indicate that winter conditions substantially weaken traditional catalyst mechanisms by reducing outdoor interaction, disrupting movement continuity, and increasing dependence on indoor transitional spaces. Simulation results further demonstrate that climate-responsive interventions, including enhanced indoor connectivity, mixed-use functional integration, and seasonal activity optimization, can improve regeneration performance and spatial resilience. The framework is presented as an exploratory single-case analytical model that requires further multi-city and multi-year validation. By combining digital shadow-supported simulation with sustainable urban regeneration theory, this study provides a transferable analytical framework and practical decision-support tool for industrial heritage revitalization in cold-region cities.

The sustainable regeneration of industrial heritage in cold regions is constrained by severe winter climate, seasonal behavioral shifts, and declining spatial vitality. However, existing research has rarely explained how cold-climate conditions influence catalyst effects and regeneration performance in industrial heritage areas. This study proposes a digital twin-enabled framework for the sustainable regeneration of cold-region industrial heritage. Using industrial heritage sites in Harbin, China, as a case study, the research integrates multi-source data to construct a dynamic assessment system that links climate constraints, spatial structure, and human activity patterns. The results show that winter conditions significantly reduce the effectiveness of traditional catalyst mechanisms by weakening outdoor interaction, fragmenting movement continuity, and increasing reliance on indoor transitional spaces. Simulation results further demonstrate that climate-responsive interventions, such as indoor connectivity enhancement, mixed-use functional implantation, and seasonal activity optimization, can improve regeneration effectiveness and spatial resilience. By combining digital twin technology with sustainable urban regeneration theory, this study provides a replicable analytical framework and practical decision-support tool for industrial heritage revitalization in cold-region cities.