Artificial Intelligence-Driven Circular Economy Initiatives: A Global Bibliometric Analysis of Research Trends, Knowledge Structures, and Future Directions
TL;DR
Results show scientific production surged post-2018, with logistic growth modeling indicating rapid maturation, and China leads global publication output and international collaboration networks.
Abstract
Artificial Intelligence (AI) is transforming Circular Economy (CE) initiatives, yet the field's intellectual structure and thematic evolution remain insufficiently mapped. This study evaluates the scientific development of AI-driven CE research. Using Bibliometrix and VOSviewer, a comprehensive bibliometric analysis was conducted on 8,327 Scopus-indexed publications following an adapted PRISMA 2020 workflow. Results show scientific production surged post-2018, with logistic growth modeling indicating rapid maturation. China leads global publication output and international collaboration networks. Thematic evolution and co-occurrence mapping reveal a significant structural shift: early foundational research focused heavily on basic machine learning and recycling algorithms, whereas current frontiers emphasize system-level optimization, including intelligent manufacturing, life cycle assessment, waste valorization, and remanufacturing. Ultimately, AI-driven CE research has evolved from isolated technological applications into an interdisciplinary domain centered on data-driven sustainability. Sustaining this trajectory requires broader cross-institutional collaboration and the exploration of emerging paradigms, such as explainable and generative AI, to develop the resilient, sustainable production ecosystems necessary for global circularity goals.