A BIM-Driven Dynamic LCA Framework for Net Carbon Accounting of Buildings: A Case Study in Hot-Summer Region of China

Addressing the prevalent issues of scattered data sources, reliance on multi-software collaboration, and low integration efficiency between Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in current building life cycle carbon emission accounting, this study aims to construct a BIM-driven, data-traceable automated method for building life cycle carbon accounting. This paper proposes a life cycle carbon accounting framework based on Revit secondary development. By defining unified data mapping rules and constructing a scalable localized carbon emission factor database, this framework achieves a seamless workflow from BIM model information extraction and intelligent factor matching to phased accounting and report generation. Taking an office building in Nanning as an empirical case study, the results indicate that the operational stage and the building material production stage are the primary emission sources, accounting for 78.82% and 24.13% of the total emissions, respectively; the transportation stage accounts for 1.68%; the construction stage accounts for 0.40%; and the demolition and recycling stage exhibits negative emissions of −3.53% due to material recovery benefits. The accounting results of the developed plugin exhibit a relative error of 6.67% compared to traditional methods, and the robustness of the accounting framework is verified through uncertainty analysis. Sensitivity analysis further reveals that the grid emission factor, key material factors, and building design service life are the core variables affecting carbon emissions. The contribution of this study lies in proposing an operable and scalable BIM-LCA integrated solution. Its practical value resides in providing a real-time data feedback tool for low-carbon optimization during the building design stage, as well as offering a highly transparent methodological reference for carbon accounting in engineering practice, thereby supporting data-driven decision-making in the pursuit of sustainable urban development.