A Machine Learning Approach to Reduce Uncertainty in Ex-ante LCA

Abstract

Purpose: This paper explores the potential of machine learning (ML) algorithms to mitigate uncertainty in early environmental assessments (ex-ante LCA), which are hindered by prospective nature and limited quantitative data availability. Methods: A systematic literature review with keyword searches on Scopus identified three ML categorization groups in ex-ante LCA: streamlined LCA, ex-ante LCA parameter projection, and ancillary models and data. Two following case studies addressed literature gaps in price forecasting for economic allocation and recycling rate projections. Results: In streamlined LCA, 16 studies linked molecular and technical parameters to project production-related emissions of organic chemicals, applied product clustering of product groups, and generated spatially explicit impact category results. The application of ex-ante LCA parameter projection, as evidenced by 14 publications, involves the use of ML to project life cycle inventory (LCI) data, project characterization factors, and integrate natural parameters with LCI data in a comprehensive modeling approach. In nine other papers the applications to ex-ante LCA remained undefined but potentially applicable. For both case studies, best results were obtained with a Recurrent Neural Networks (RNN) algorithm with long-short-term-memory (LSTM). Commodity price forecasting in the first case study achieved a projection accuracy of 0.96 (MSE), 0.98 (RSME), and 10.17% (MAPE) for copper and 88.86 (MSE), 9.43 (RMSE), and 21.23% (MAPE) for molybdenum. Probability modelling is identified as a modeling approach which incorporates uncertainty. The recycling rate forecast case study identified plastic recycling and glass recycling rates as the best suiting covariates and demonstrated multivariate modeling possibilities with 0.22 (MSE), 0.48 (RSME), and 0.38% (MAPE) in a model with 68 covariates. Discussion: A limited yet growing body of literature indicates that ML applications in ex-ante LCA represent an emerging field of science. While streamlined LCA shows promise, it faces constraints related to data precision and a static nature. In the ex-ante LCA parameter projection categorization, the sub-group of similarity clustering of LCI processes suffers from data uncertainty in LCI databases, making the approach more suitable for updates of existing technologies than for emerging ones. On the other hand, LCI generation through ex-ternal parameters represents a highly technology-specific case, showing significant promise. The projection of characterization factors and the sub-group of integrated modeling are identified as promising, but the limited number of scientific studies hinders the generalizability of these findings. Case studies on price forecasting and recycling rate projection demonstrate ML’s applicability in economic allocation and waste treatment projections. Overall, the results suggest that ML holds potential for reducing uncertainty in ex-ante LCA, laying the groundwork for focused research and contributing to a nuanced understanding of uncertainty reduction in this domain. Recommendations: The paper emphasizes the need for targeted research in the goal and scope phase and in End-of-Life (EoL) treatment forecasts, e.g. via the use of time-series multivariate modeling. Furthermore, it encourages further exploration of streamlined LCA into applications with a high degree of technical predictors, along with the extended projection of characterization factors and integrated modeling. Additionally, the use of probabilistic modeling as a tool to incorporate uncertainty into the modeling is recommended, aiming to enhance the applicability and transparency of ML applications for reducing uncertainties in ex-ante LCA.