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Life cycle assessment (LCA) is widely recognized as a key methodology for evaluating the environmental im-pacts of products and technologies across their entire life cycle. Against the background of the energy transi-tion, its application is becoming increasingly important for industrial decision-making and policy context, particularly in response to growing sustainability requirements such as the EU Battery regulation with its man-datory reporting, recycled content targets, and end-of-life recycling requirements for batteries. However, the robustness and scalability of LCA strongly depend on the quality and availability of life cycle inventory (LCI) data.
The development of LCI is increasingly challenged by growing product complexity, rapidly evolving technol-ogies, and fragmented data landscapes. In particular, battery systems exhibit high variability in material compo-sition, design, and supply chains, while available information is often distributed across heterogeneous tech-nical documents, enterprise systems, and regulatory sources. As a result, LCI modeling, one of the most critical and resource-intensive phases of LCA, remains highly manual, time-consuming, and difficult to reproduce, limiting the efficient application of LCA in industrial and policy-driven context.
This work examines how digitalization and artificial intelligence (AI), with a focus on large language models (LLMs), can be leveraged to streamline and systematize the generation of LCIs. Rather than replacing estab-lished LCA methods, the proposed approach embeds LLMs into existing modeling practices to assist in harmo-nizing product data and supporting inventory modeling decisions.
The approach combines automated data extraction of bill of materials (BOMs), e.g. from technical documenta-tion or enterprise data sources with semantic interpretation and structured transformation into LCI-ready da-tasets. LLMs are used to support the classification and contextual interpretation of materials, components, and processes, particularly where conventional rule-based mappings reach their limits. To ensure methodological robustness, a governance-oriented workflow that emphasizes traceability, documentation, and user control is embedded. Modeling decisions, confidence indicators, and intermediate results are preserved in machine-readable form, enabling systematic review and reuse.
The concept is illustrated using a battery recycling case study, in which a BOM is automatically transformed into a structured LCI through a BOM-to-LCI prototype. The resulting inventory data are aligned with estab-lished LCA standards and can be directly integrated into common LCA software environments such as openLCA, enabling further analysis and interpretation.
The results indicate that AI-supported workflows can significantly reduce manual effort while improving con-sistency and comparability of LCIs. The contribution demonstrates how carefully governed AI integration can enhance the practical applicability of LCI modeling and support the broader digital transformation of LCA in industrial and regulatory contexts.
