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.

Coastal peri-urban parks must simultaneously support ecological restoration, public recreation, cultural continuity, and long-term operation, yet reproducible methods for matching local cultural resources with renewal needs remain limited. Taking Yandao Mountain Park in Qingdao, China, as a case study, this research develops a culture–need matching and scenario-based evaluation framework integrating the Local Cultural Resource Identification Index (LCRI), Renewal Need Index (RNI), Culture–Renewal Need Matching Matrix (CRNM), Public Behaviour Activation Index (PBAI), and Sustainable Renewal Effect Index (SREI). Data were obtained from four field surveys, 318 valid questionnaires, 12 semi-structured interviews, GIS-supported spatial interpretation and Average Nearest Neighbor analysis, AHP weighting, two-round Delphi consultation, and sensitivity analysis. Beer culture, residents’ living memory, mountain–sea landscape imagery, and Liyuan courtyard culture showed strong identification bases, while functional integration, operational improvement, all-age sharing, and ecological restoration were the main renewal needs. CRNM revealed differentiated culture–need relationships that informed spatial translation and functional embedding. Under the proposed renewal scenario, the field-observed PBAI baseline of 0.46 was compared with a Delphi-calibrated scenario estimate of 0.82, while the estimated overall SREI was 0.87. These values indicate scenario-based potential for behavioural activation and sustainable renewal rather than verified post-implementation performance.