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[2602.22500] Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models
Summary
This article reviews the integration of AI into life cycle assessment (LCA), highlighting trends, themes, and future directions using large language models (LLMs).
Why It Matters
The study addresses the growing intersection of AI and LCA, providing insights into how LLMs can enhance environmental assessments. This is crucial for practitioners seeking to adopt advanced methodologies for sustainability decisions, especially as AI technologies evolve rapidly.
Key Takeaways
- AI integration in LCA is accelerating, with significant growth in LLM applications.
- The study presents a framework combining LLM-based text mining with traditional literature reviews.
- Findings indicate a shift towards LLM-driven approaches in LCA research.
- Statistically significant correlations exist between AI methods and LCA stages.
- The research supports enhanced decision-making in sustainability through AI tools.
Computer Science > Artificial Intelligence arXiv:2602.22500 (cs) [Submitted on 26 Feb 2026] Title:Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models Authors:Anastasija Mensikova, Donna M. Rizzo, Kathryn Hinkelman View a PDF of the paper titled Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models, by Anastasija Mensikova and 2 other authors View PDF HTML (experimental) Abstract:Integration of artificial intelligence (AI) into life cycle assessment (LCA) has accelerated in recent years, with numerous studies successfully adapting machine learning algorithms to support various stages of LCA. Despite this rapid development, comprehensive and broad synthesis of AI-LCA research remains limited. To address this gap, this study presents a detailed review of published work at the intersection of AI and LCA, leveraging large language models (LLMs) to identify current trends, emerging themes, and future directions. Our analyses reveal that as LCA research continues to expand, the adoption of AI technologies has grown dramatically, with a noticeable shift toward LLM-driven approaches, continued increases in ML applications, and statistically significant correlations between AI approaches and corresponding LCA stages. By integrating LLM-based text-mining methods with traditional literature review techniques, this study introduces a dynamic and effective framework capable of capturing bot...
