Integrating a BIM-based life-cycle assessment (LCA) in the early design stages of building projects can promote a reduction of environmental impacts. However, the reliability of the analysis depends on the quality of the model. BIM modelling of timber-frame construction is often simplified, potentially hindering the LCA outcome due to quantity inaccuracy and a failure to consider elements regarded as negligible. This study addresses the methodological problem that influences the calculation of the environmental impact by examining the various environmental impacts caused by inventory data obtained from BIM models with a level of detail of (LOD)350–400 vs LOD200. The environmental impacts obtained from both models of a timber building calculated through an attributional LCA method are compared. Material inventory sourced from the LOD200 model results in a 14.7% lower value of embodied impact. The discrepancies resulted from aluminium, mineral wool and bitumen, which are identified as critical materials requiring quantity adjustment. Relying on simplified BIM models for LCA may lead to the GWP being under-estimated with incorrect identification of hotspots in the early design stages.
The results aksi question the cut-off criterion of the current EN15978 norm that serves as a foundation for developing environmental policies within the industry. It recommends exclusion of materials constituting less than 1% of the overall mass from the system boundary. This study underscores the potential significance of materials falling below this threshold, challenging the validity of the criterion, and suggesting that such materials should be carefully evaluated and included in the LCA to ensure a comprehensive assessment of the environmental impacts. The overall objective of the study is to emphasize the importance of employing accurate BIM models in LCAs to make informed decisions that are aligned with the sustainability goals, encouraging practitioners to consider the impact of critical materials, even those with seemingly minimal contributions. With this knowledge, the practitioners are able to take meaningful actions that compensate for the LCA uncertainty, mitigating the environmental burden of the most impactful areas. Most importantly, the findings aim to identify the error in design LCA versus as-built studies, helping to develop design LCA tools that predict the as-built impact more precisely and earlier in the design process. The findings can also improve the expected building model definition in carbon policies.