Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models
arXiv:2602.22500v1 Announce Type: new 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 int
arXiv:2602.22500v1 Announce Type: new 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.
Executive Summary
This study provides a comprehensive review of the integration of artificial intelligence (AI) in life cycle assessment (LCA), leveraging large language models (LLMs) to identify trends, themes, and future directions. By introducing a dynamic and effective framework, the study demonstrates the potential of LLM-assisted methodologies to support large-scale, reproducible reviews across broad research domains. The findings highlight the growing adoption of AI technologies in LCA, with a noticeable shift toward LLM-driven approaches and continued increases in machine learning applications. The study's results have significant implications for LCA practitioners, enabling the incorporation of state-of-the-art tools and timely insights into environmental assessments, thereby enhancing the rigor and quality of sustainability-driven decisions and decision-making processes. The study's methodology and findings offer valuable insights into the rapidly developing field of AI-LCA research.
Key Points
- ▸ Integration of AI in LCA has accelerated in recent years
- ▸ Comprehensive synthesis of AI-LCA research remains limited
- ▸ LLMs have the potential to support large-scale, reproducible reviews
Merits
Comprehensive Review
The study provides a detailed review of published work at the intersection of AI and LCA, addressing the gap in comprehensive and broad synthesis of AI-LCA research.
Dynamic Framework
The study introduces a dynamic and effective framework that leverages LLM-based text-mining methods with traditional literature review techniques, capturing high-level research trends and nuanced conceptual patterns.
Potential of LLM-Assisted Methodologies
The study demonstrates the potential of LLM-assisted methodologies to support large-scale, reproducible reviews across broad research domains, and to evaluate pathways for computationally-efficient LCA.
Demerits
Limited Scope
The study focuses on the intersection of AI and LCA, and its findings may not be generalizable to other research domains or fields.
Methodology Limitations
The study's methodology relies on LLM-based text-mining methods, which may be limited by the quality and availability of training data.
Expert Commentary
The study's findings have significant implications for the field of AI-LCA research, and demonstrate the potential of LLM-assisted methodologies to support large-scale, reproducible reviews. However, further research is needed to fully explore the limitations and potential biases of LLM-based text-mining methods. Additionally, the study's focus on the intersection of AI and LCA may limit its generalizability to other research domains or fields. Despite these limitations, the study provides a valuable contribution to the field of AI-LCA research, and offers insights into the rapidly developing field of AI-LCA research.
Recommendations
- ✓ Further research is needed to fully explore the limitations and potential biases of LLM-based text-mining methods.
- ✓ The study's findings should be replicated and validated in other research domains or fields to assess their generalizability.
