A review of machine learning applications in life cycle assessment studies

Xiaobo Xue Romeiko; Xuesong Zhang; Yulei Pang; Feng Gao; Ming Xu; Shao Lin; Callie Babbitt

doi:10.1016/j.scitotenv.2023.168969

A review of machine learning applications in life cycle assessment studies

Sci Total Environ. 2024 Feb 20:912:168969. doi: 10.1016/j.scitotenv.2023.168969. Epub 2023 Nov 28.

Authors

Xiaobo Xue Romeiko¹, Xuesong Zhang², Yulei Pang³, Feng Gao⁴, Ming Xu⁵, Shao Lin⁶, Callie Babbitt⁷

Affiliations

¹ Department of Environmental Health Sciences, University at Albany, State University of New York, United States of America. Electronic address: xxue@albany.edu.
² Hydrology and Remote Sensing Laboratory, United States Department of Agriculture, United States of America. Electronic address: xuesong.zhang@usda.gov.
³ Department of Math, Southern Connecticut State University, United States of America.
⁴ Hydrology and Remote Sensing Laboratory, United States Department of Agriculture, United States of America.
⁵ Dvision of Environmental Ecology, School of Environment, Tsinghua University, China.
⁶ Department of Environmental Health Sciences, University at Albany, State University of New York, United States of America.
⁷ Department of Sustainability, Golisano Institute for Sustainability, Rochester Institute of Technology, United States of America.

PMID: 38036122
DOI: 10.1016/j.scitotenv.2023.168969

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 inventories, 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 reviewed 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.

Keywords: Life cycle assessment; Machine learning; Model selection; Prediction; Sustainability.

Publication types

Review