A review of machine learning applications in life cycle assessment studies

Authors: ROMEIKO, X.X. - State University Of New York (SUNY); Zhang, Xuesong; PANG, YULEI - Southern Connecticut State University; Gao, Feng; XU, MING - Tsinghua University; BABBIT, CALLIE - Rochester Institute Of Technology

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2023
Publication Date: 11/28/2023
Citation: Romeiko, X., Zhang, X., Pang, Y., Gao, F.N., Xu, M., Babbit, C. 2023. A review of machine learning applications in life cycle assessment studies . Science of the Total Environment. 912. https://doi.org/10.1016/j.scitotenv.2023.168969.
DOI: https://doi.org/10.1016/j.scitotenv.2023.168969

Interpretive Summary: Machine learning (ML) techniques are being increasingly used to enhance Life Cycle Assessment (LCA), a critical methodology for quantifying sustainability. In this study, we reviewed forty studies that have utilized a combination of LCA and ML. Our analysis indicates that ML techniques have been applied in various ways to support LCA, including generating life cycle inventory, computing characterization factors, estimating life cycle impacts, and aiding life cycle interpretation. Among the nineteen ML techniques evaluated, most studies have relied on artificial neural networks (ANNs). To further integrate ML into LCA, we have identified several research needs, such as continuous data collection, greater transparency on the selection criteria for ML models and model uncertainty analysis, the incorporation of deep learning models into LCA, and interdisciplinary collaboration to support sustainable development

Technical Abstract: Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventory, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70% of these supervised studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.