AuLCA: Augmented Life Cycle Assessment for Chemical Data Gaps

Abstract

Life cycle assessment (LCA) has become the prevalent tool to quantify the impact of chemical processes, yet data gaps remain a major obstacle towards its widespread adoption. Existing LCA databases cover a few thousand, mostly high production volume, chemicals; however, fine chemicals are often underrepresented. Here we introduce an augmented LCA (AuLCA) framework based on chemical reaction networks (CRN), mass-based impact propagation, and first-principles based energy estimations to predict the life cycle inventories and impacts of fine chemicals. By applying AuLCA to four case studies, we find good agreement with commercial data, with the accuracy level depending on the chemical reaction network’s size and density. Overall, AuLCA is intended to support sustainable decision-making across chemical scales, particularly in early-stage decisions on chemical reaction pathways selection.