Developing Conversion Factors of LCIA Methods for Comparison of LCA Results in the Construction Sector

The inconsistency caused by different life cycle impact assessment (LCIA) methods is a long-term challenge for the life cycle assessment (LCA) community. It is necessary to systematically analyze the differences caused by LCIA methods and facilitate the fair comparison of LCA results. This study proposes an effective method of conversion factors (CFs) for converting the results of 8 LCIA methods for 14 impact categories and then demonstrates its application in the construction sector. Correlation analyses of the datasets of construction materials are conducted to develop CFs for the impact categories. A set of conversion cards are devised to present the CFs and the associated correlation information for the LCIA methods. It is revealed that the differences between LCIA methods are largely caused by the characterization methods, rather than due to the metrics. A comparison based only on the same metrics but ignoring the underlying LCIA mechanisms is misleading. High correlations are observed for the impact categories of climate change, acidification, eutrophication, and resource depletion. The developed CFs and conversion cards can greatly help LCA practitioners in the fair comparison of LCA results from different LCIA methods. Case studies are conducted, and verify that by applying the CFs the seemingly incomparable results from different LCIA methods become comparable. The CF method addresses the inconsistency problem of LCIA methods in a practical manner and helps improve the comparability and reliability of LCA studies in the construction sector. Suggestions are provided for the further development of LCIA conversion factors.

Automated summary

The study proposes an effective method of conversion factors to convert the results of 8 LCIA methods for 14 impact categories, aiming to address the inconsistency problem in LCIA methods. It reveals that the differences between LCIA methods are mainly caused by characterization methods rather than metrics. High correlations are observed for impact categories including climate change, acidification, eutrophication, and resource depletion.