Large Language Models in the Circular Economy: A Scoping Review of Applications, Emerging Patterns, and Governance Challenges
TL;DR
The findings show that large language models are used less as standalone decision-making tools and more as ways of organizing and mobilizing knowledge for practitioners and researchers, with roles ranging from design assistants and knowledge integrators to analytical components in larger modelling workflows.
Abstract
Large language models represent a qualitatively new development within generative artificial intelligence and have recently begun to appear in circular economy research, yet no review has pulled together what is actually known about how and where these tools are being used in this field. This study aims to map current applications of large language models and generative artificial intelligence in circular economy contexts, examine how these tools are being deployed methodologically, and identify what the evidence says about their benefits, limitations, and risks. Following PRISMA-ScR guidance, a scoping review was conducted in Scopus in February 2026, returning 127 records, which were screened down to 30 peer-reviewed journal articles published between 2024 and 2026 and analyzed through a matrix-based coding approach. The findings show that large language models are used less as standalone decision-making tools and more as ways of organizing and mobilizing knowledge for practitioners and researchers, with roles ranging from design assistants and knowledge integrators to analytical components in larger modelling workflows. Most applications are found in industrial symbiosis, circular supply chains, sustainable product and materials innovation, and the built environment, while newer work is beginning to emerge in waste sorting, policy analysis, and skills mapping. The studies consistently show that the most reliable results come when large language models are combined with domain-specific knowledge bases and expert validation, rather than used autonomously. Across these application areas, several governance challenges remain unaddressed, including the absence of domain-specific validation standards, insufficient data infrastructure for circular economy contexts, and the largely unaccounted environmental footprint of generative AI hardware itself. Based on these findings, future research should focus on comparative evaluations and better integration with established tools such as life cycle assessment. Policymakers need to invest in domain-specific data infrastructure and clear governance frameworks, and practitioners should treat human oversight as a necessary part of any workflow that uses these tools.
