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

2026-02-25

AI Summary

This study addresses the lack of a systematic review at the intersection of artificial intelligence (AI) and life cycle assessment (LCA), which has hindered a comprehensive understanding of their technological evolution and application linkages. For the first time, large language models (LLMs) are integrated into the LCA domain, combining text mining, bibliometric analysis, and traditional systematic review methods to construct a reproducible and dynamic research landscape. Through large-scale analysis, the work reveals statistically significant associations between AI techniques and various LCA phases, identifies a rapidly growing trend dominated by LLMs and machine learning, and proposes an efficient, scalable framework to support sustainable decision-making and guide future research directions.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Humans and AI: Human-in-the-loop Machine LearningNatural Language Processing: (Large) Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Large language models for searchUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Artificial Intelligence
• Life Cycle Assessment
• Large Language Models
• Research Synthesis
• Literature Review

Innovation

• large language models
• life cycle assessment
• AI integration
• text mining
• research synthesis