Artificial intelligence meets LCA: a critical analysis from technical, ethical, and organizational perspectives
Year: 2026
Type: article
Abstract: Purpose Artificial intelligence (AI) holds strong potential to automate repetitive and data-intensive life cycle assessment (LCA) tasks, thereby reducing costs, enabling scalability, and improving data access. This study critically analyses the integration of AI into LCA from technical, ethical, and organizational perspectives. It aims to evaluate how AI can be implemented across LCA workflow steps to enhance efficiency without compromising effectiveness, inclusivity, and core LCA ethical values such as transparency and scientific integrity. Methods We conducted literature review to identify and characterise the technical approaches of current AI-LCA applications across academic research and commercial tools, complemented by a survey assessing current AI uptake in LCA practice and perceived ethical risks. Ethical risks, like accountability or fairness, were derived from existing AI ethical principles and analysed for their potential conflict with LCA ethical values. Organizational implications including changes in work practices and regional disparities were explored, with attention to role of AI in social LCA. Identified applications were critically assessed against four categories: technical maturity, potential to enhance LCA practices, operational feasibility, and ethical risks, and used to qualitatively derive criticality of ethical risks. Results and discussion Most AI-LCA applications focus on predicting impact scores and estimating missing inventory data. Few are employing generative AI, while it is mainly used by practitioners for auxiliary tasks like literature review and report writing. Despite high automation potential, most applications remain at proof-of-concept stage, lack end-to-end workflow integration, and face operational barriers for inclusivity (coding skills, restricted access) resulting in low adoption. Most critical ethical risks relate to fairness, transparency, accountability and sustainability, driven by black-box models, poor documentation, and representation bias, and threaten all core LCA values. Emerging commercial AI-LCA tools present a high potential to widen AI adoption in LCA, but present higher risks toward sustainability, transparency, safety and security. AI is expected to shift LCA stakeholders’ roles from manual tasks to supervising algorithms and validating outputs, while potentially reinforcing regional disparities due to unequal access to AI tools, data, and expertise. Conclusion AI can significantly enhance LCA efficiency and scalability, but without robust collaborative governance it may undermine methodological rigor and ethical principles. Responsible integration requires need-based adoption, standardized protocols for evaluating AI models in LCA context, human-in-the-loop validation for value choices, transparent documentation, and proactive attention to fairness and inclusivity. These findings provide a foundation for guidance toward effective, inclusive, and responsible use of AI in LCA.
Authors: Manish Kumar, Laure Patouillard, Artur Donaldson, Simone Cornago, Pietro Bartocci, Qingshi Tu, Han De Wachter, Nicolas Hübner, Marina Dumont, Vlad C. Coroamă, Bharathan Balaji, Claudia Peña, Elias Martinez-Hernandez, Luke Cullen, Steven MacMaster, Shaolei Ren, Abhishek Chaudhary, Viera Pechancová, Sonia Valdivia, Giuseppe Cecere, Qing Yang, Cajetan Oriekezie, Archana Datta
Institutions: Karlsruhe Institute of Technology, Helmholtz-Institute Ulm, Université du Québec à Montréal, CIRAIG, Polytechnique Montréal, Radboud University Medical Center, University of British Columbia, Technische Universität Berlin, PRé Consultants, Amazon (United States), Mexican Institute of Petroleum, University of Cambridge, University of California, Riverside, ZHAW Zurich University of Applied Sciences, Tomas Bata University in Zlín, Leuphana University of Lüneburg, National Agency for New Technologies, Energy and Sustainable Economic Development, Harvard University, China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, London South Bank University, United Nations Environment Programme
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,990
OpenAlex ID: W7164486337