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Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi, Ali, Rezgui, Yacine, Petri, Ioan and Beach, Thomas 2022. Advances in application of machine learning to life cycle assessment: a literature review. International Journal of Life Cycle Assessment 27 , pp. 433-456. 10.1007/s11367-022-02030-3

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Abstract

Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: Springer
ISSN: 0948-3349
Date of First Compliant Deposit: 13 April 2022
Date of Acceptance: 15 February 2022
Last Modified: 19 May 2022 14:00
URI: https://orca.cardiff.ac.uk/id/eprint/149185

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