Large language models for life cycle assessments: Opportunities, challenges, and risks

Because sustainability remains a wicked problem, more sophisticated tools need to be applied to identify better solutions in a more efficient manner and align with the 11th, 12th, and 13th sustainable development goals: sustainable cities and communities, responsible consumption and production, and climate action. To ease the burdens of conducting sustainability studies, especially life cycle assessments (LCA), practitioners may consider integrating large language models (LLM) into LCAs. This emerging application may offer some advantages due to the capability of these models to generate and process text quickly and efficiently, decreasing the time it takes to complete an LCA and increasing the accessibility of LCAs. In this perspective, we assess the ability of LLMs to complete LCA tasks and encourage the LCA community to study the potential strategies for enhancing the integration of LLMs in LCA methodologies and collaborate to develop standards for responsible use. Because of these advantages, LLMs show promise for life cycle inventory data collection and interpreting the life cycle impact assessment. Challenges arise primarily from the inclusion of hallucinations in the content generated by the LLM, which can be mitigated if the LCA practitioner uses prompt engineering techniques. Moreover, the risk that models cannot take responsibility for generated content can be ameliorated by having the LCA practitioner carefully review the LLM output and take responsibility for decisions made based on the generated content. So long as appropriate steps are taken to overcome the challenges and risks of using of LLMs for LCA, the opportunities presented by integrating the generative AI models can streamline the LCA process and result in significant benefits for the LCA practitioner.