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Export Reference (APA) 
Parece, S., Resende, R. & Rato, V. (2025). BIM-based life cycle assessment: A systematic review on automation and decision-making during design. Building and Environment. 282
Export Reference (IEEE) 
S. M. Parece et al.,  "BIM-based life cycle assessment: A systematic review on automation and decision-making during design", in Building and Environment, vol. 282, 2025
Export BibTeX 
@article{parece2025_1764926934807,
	author = "Parece, S. and Resende, R. and Rato, V.",
	title = "BIM-based life cycle assessment: A systematic review on automation and decision-making during design",
	journal = "Building and Environment",
	year = "2025",
	volume = "282",
	number = "",
	doi = "10.1016/j.buildenv.2025.113248",
	url = "https://www.sciencedirect.com/journal/building-and-environment"
}
Export RIS 
TY  - JOUR
TI  - BIM-based life cycle assessment: A systematic review on automation and decision-making during design
T2  - Building and Environment
VL  - 282
AU  - Parece, S.
AU  - Resende, R.
AU  - Rato, V.
PY  - 2025
SN  - 0360-1323
DO  - 10.1016/j.buildenv.2025.113248
UR  - https://www.sciencedirect.com/journal/building-and-environment
AB  - Life Cycle Assessment (LCA) is essential to achieve a Net-Zero Carbon Built Environment and inform effective mitigation strategies for environmental impacts throughout a building's life cycle. However, collecting Life Cycle Inventory (LCI) data and the Life Cycle Impact Assessment (LCIA) processes are complex and time-consuming. BIM-LCA integration enables automated quantity-take-off, supporting faster evaluation of different design options and decision-making. Consequently, research on BIM-LCA has grown significantly since 2013. However, previous literature reviews on BIM-LCA do not cover developments from the past three years, nor do they assess how BIM-LCA supports decision-making or how decision-making methods can enhance its adoption and use, particularly among non-LCA experts.
A systematic literature review was conducted following the PRISMA protocol to address this gap. A total of 115 research articles (2019–2024) were analysed according to design phases, BIM object LOD, LCA application, data exchange and extraction methods, automation degree, and decision-making features, covering Multi-Criteria Decision Analysis, Multi-Objective Optimisation, and Sensitivity/Uncertainty analyses.
The findings highlight advancements in LCI automation. However, several challenges remain, including manual BIM-LCA data mapping during LCIA and limited research on: BIM-LCA for renovation projects, dynamic data exchange for OpenBIM, standardised LOD for different LCA applications, and local databases for budget-based targets. Furthermore, few studies integrate LCA with economic and social indicators, and decision-making methods are mainly absent from BIM-LCA tools.
This study outlines research directions to address these limitations and improve BIM-LCA automation and decision-making. Future efforts will focus on gathering insights from industry stakeholders to establish priorities for user-centred BIM-LCA development.
ER  -