Machine learning in prospective LCA

Machine learning (ML) offers considerable opportunities for advancing life cycle assessment (LCA), yet a consolidated overview of ML models with potential for prospective LCA (pLCA) is still missing. This systematic review identifies ML models suited to pLCA’s data needs and assesses the transferability of retrospective LCA models to pLCA. We apply keyword-based and automated forward/backward searches to identify studies based on algorithm type, training datasets, input/output variables, and performance metrics. We identify 50 publications and classify them into four ML application groups: (1) streamlined LCA, (2) life cycle inventory data prediction, (3) impact category prediction, and (4) characterization factor prediction. Among these, predicting life cycle inventory (LCI) data and characterization factor estimation are directly applicable to pLCA.

Frontiers in Sustainability