How digital twin platforms empower the decarbonization of built environments: A case study of a vernacular dwelling

Decarbonizing built environments is crucial to the global carbon neutrality strategy. Central to this transition are operational carbon footprint accounting and the development of zero-carbon energy supply systems, both of which depend on the integration of multi-source heterogeneous data. The interconnected processes of information acquisition, performance evaluation, and optimized retrofitting require integrated tools that bridge these stages to advance systematic decarbonization research. Digital twin technology provides not only a structured framework for incorporating diverse digital intelligence technologies, enabling efficient, low-intervention aggregation of multimodal data, but also a transparent platform for rapid building performance evaluation and coordinated energy dispatch. This study explores how digital twin platforms serve as an integrative medium for decarbonizing built environments through a case study of a Chinese vernacular dwelling. The procedure begins with multi-source data acquisition and correlation to support energy performance evaluation and optimization. A multi-indicator model is then employed, incorporating physical parameter matrices to quantify spatiotemporal carbon distribution and identify optimization priorities. Subsequently, a performance-driven design integrated with smart control technologies is implemented to upgrade energy systems. Empirical results show that the platform achieves real-time carbon prediction with errors below 15%, while an upgraded control system enables automated regulation that ensures both habitability and carbon-neutral operation, all unified within the digital twin environment. By addressing the current fragmentation among evaluation, design, and operation in building performance optimization, this research provides innovative pathways and methodological guidance for decarbonizing the built environment, while promoting the adoption of digital intelligence in sustainable building retrofits. © 2025 Elsevier Ltd.