LCAi Enhances Life Cycle Assessment with AI and Big Data

The interpretation phase of Life Cycle Assessment (LCA) often struggles to translate environmental improvement opportunities into concrete, actionable strategies, especially given technological, social, and policy uncertainties. To address this, researchers have developed LCAi, an AI-assisted framework that leverages big data fusion and retrieval-augmented generation (RAG) for more disciplined LCA interpretation. LCAi employs a multi-perspective RAG architecture, integrating data from academic research, industry reports, public discourse, and EU funding datasets. The process involves three steps: defining scenario anchors with system boundaries and decarbonization targets, executing perspective-specific micro-queries with constrained retrieval, and a neutral synthesis step that only integrates ledger-stored outputs to prevent further retrieval-induced hallucinations. Demonstrated with a hydrogen-enabled diesel reduction case in Italian apple production, this proof-of-concept shows how AI-assisted, evidence-grounded interpretation can support implementation-oriented decision-making beyond conventional LCA studies.