Automated Product Carbon Footprint Estimation via Low-Latency Neural Language Processing in Multilingual, Real-Time Supply Chains.

Abstract

Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture for continuous, product-level carbon estimation that integrates (i) automated supply-chain data acquisition, (ii) low-latency multilingual natural language processing for extracting life-cycle-relevant attributes from unstructured documents, (iii) a structured product carbon data model, and (iv) machine-learning-based completion of missing life cycle inventory parameters with provenance and confidence tags. A central mechanism is the "carbon header," which packages accumulated embodied and operational emissions as transferable, version-controlled metadata associated with product identifiers and propagated incrementally across supply-chain tiers. Auto-LCA is designed as an open Application Programming Interface (API) layer to interface with enterprise systems (e.g., ERP/PLM/MES) and supports update-driven recomputation that recalculates only affected nodes in the supply-chain graph, improving scalability for large product portfolios and deep supplier networks. Case studies in the steel, cement, and automotive sectors demonstrate that Auto-LCA yields estimates close to representative conventional LCA benchmarks (typically within ±4%), while enabling broader coverage and more frequent updates under realistic data availability constraints. The framework also aligns with emerging digital product passport and sustainability reporting needs by enabling traceable, machine-readable carbon metadata exchange across organizations.