SemaNet: Bridging Words and Numbers for Predicting Missing Environmental Data in Life Cycle Assessment

Life Cycle Assessment (LCA) is one of the most used methodologies for evaluating environmental impact, but its effective application is severely limited by missing data, an issue that existing methods for Life Cycle Inventory (LCI) data completion cannot address effectively. This paper proposes a paradigm shift: rather than depending exclusively on numerical correlations, we leverage the extensive contextual information inherent in process descriptions via pretrained language models, establishing a semantic bridge between qualitative descriptions and quantitative environmental flows. Our semantic-based neural network framework, SemaNet, achieves superior performance in predicting missing LCI values, surpassing existing state-of-the-art methods in various evaluation metrics. The results are significant: while existing approaches fail completely under high data sparsity, our method achieves high accuracy even with 100% missing numerical data while reducing computational requirements by 99% through the use of semantic filtering. This new method for LCI data completion significantly reduces the data collection efforts and time for LCA practitioners, making reliable and faster environmental impact assessment feasible, even when primary data does not exist, thus facilitating reliable sustainability assessment across industrial sectors.