Integrating Artificial Intelligence into Life Cycle Assessment: A Framework for Balancing Automation and Human Expertise

Abstract Quantifying products’ environmental impacts is essential as policymakers and customers push for transparency and accountability in product sustainability. The applications of machine learning (ML) have extended to many fields, including environmental science. Several studies demonstrate the application of ML in life cycle assessment (LCA), but a gap remains in synthesizing the insights into a unique, deployable framework for rapid and (semi-) automated LCA of products and industrial processes. This is due to outdated, unlabeled, sparse, and irrelevant data and the industry’s poor maturity for AI and LCA. Based on a rapid literature review of the current applications of AI in LCA studies, we find that supervised learning algorithms are most preferred, primarily for data collection and inventory analysis. We evaluated a suite of AI tools for future application in LCA and concluded that the most benefit will be from using large language models (LLMs) and generative algorithms to improve the speed and accuracy of environmental impact assessments. Our developed framework—an AI integration architecture for LCA studies—ensures that human insight and control are retained. The work provides valuable guidance to industry-based sustainability practitioners on the importance of data quality, AI tool selection, cross-domain expertise, and collaboration. Graphical Abstract