Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models

TL;DR

This paper reviews how large language models and AI are transforming life cycle assessment by identifying trends, themes, and future directions through innovative text-mining and literature analysis techniques.

Contribution

It introduces a novel framework combining LLM-based text-mining with traditional reviews to analyze AI's role in LCA research comprehensively.

Findings

AI adoption in LCA has increased significantly.

Shift towards LLM-driven approaches in LCA.

Strong correlations between AI methods and LCA stages.

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 both high-level research trends and nuanced conceptual patterns (themes) across the field. Collectively, these findings demonstrate the potential of LLM-assisted methodologies to support large-scale, reproducible reviews across broad research domains, while also evaluating pathways for computationally-efficient LCA in the context of rapidly developing AI technologies. In doing so, this work helps LCA practitioners incorporate state-of-the-art tools and timely insights into environmental assessments that can enhance the rigor and quality of sustainability-driven decisions and decision-making processes.