Abstract

The construction and building sectors are crucial in combating climate change due to their significant embodied and whole-life carbon emissions. With the population and building floor area projected to rise significantly by 2050, robust assessments are needed to identify eco-friendly solutions. Life cycle assessment (LCA), particularly process-based (P-LCA) and input-output (I-O LCA) methods, is extensively used for assessing environmental sustainability. However, both approaches have drawbacks. Hybrid life cycle assessment (H-LCA) addresses these drawbacks by combining the strengths of both methods. This paper presents a systematic review and meta-analysis of the current status of H-LCA in the context of buildings and construction materials. It explores the methodologies and approaches currently employed in H-LCA, the current challenges and opportunities, and future directions. A comprehensive search across four research databases identified 46 studies. The review reveals that the adoption of H-LCA within the building and construction sectors has been slow but is gradually increasing. There is a noticeable lack of methodological clarity, with varied sources, databases and tools used in these studies, as well as a preference for national I-O (IO) tables over Multi-Regional Input-Output (MRIO) tables. Furthermore, the review finds that when addressing issues in their H-LCA models, such as correcting double counting, researchers use various strategies and methods suited to their particular goals, aims, and datasets. These models differ greatly, making a straight comparison between them difficult. The review combines the results of these papers and conducts a comparative analysis. The analysis shows that H-LCA generally yields higher values than P-LCA and I-O LCA methods in providing comprehensive results. Finally, this paper identifies several limitations in current H-LCA practices and concludes by discussing potential solutions to address them.
Original languageEnglish
Article number112630
JournalBuilding and Environment
Early online date27 Jan 2025
DOIs
Publication statusE-pub ahead of print - 27 Jan 2025

Data Availability Statement

No data was used for the research described in the article.

Acknowledgements

The authors would like to acknowledge the High Value Manufacturing Catapult (HMVC) and the teams from the University of Leeds and the University of Bath for their valuable collaboration on the disaggregation report, which shaped the disaggregation section of this paper.

Funding

This work was supported by the EPSRC Quantum Technologies Doctoral Training Partnership (DTP) [Grant Number EP/W524712/1]. Dr. Jonathan Norman's contribution was supported by the Engineering and Physical Sciences Research Council and the Economic and Social Research Council [grant number EP/Y010078/1] as part of the Energy Demand Research Centre.

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/W524712/1
Economic and Social Research CouncilEP/Y010078/1

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