<p>The objective of this paper is to examine the existing academic literature on the intersection between the circular economy (CE) and artificial intelligence (AI), with the dual aim of identifying the main themes shaping the current scholarly debate and highlighting emerging topics that may guide future research directions. To this end, a comprehensive bibliometric literature review was conducted using the Web of Science database as the primary source of academic publications. The study employs advanced analytical techniques, including co-word analysis, co-occurrence network analysis, co-citation analysis, and thematic mapping, to systematically explore the intellectual and conceptual structure of this research field. These methods allow for the identification of dominant themes, key research clusters, and knowledge gaps that define the evolving relationship between AI and CE. The findings offer a data-driven overview of how AI technologies are being conceptualized as enablers of circular economy principles and practices. This study contributes to the literature by providing a structured understanding of current research trends and by outlining future directions for scholars, policymakers, and practitioners committed to advancing sustainable, AI-enabled circular economy strategies.</p>

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The intersection of circular economy and artificial intelligence: a bibliometric review

  • Martina Percuoco,
  • Irene Ricciardi,
  • Valerio Muto,
  • Anna Prisco

摘要

The objective of this paper is to examine the existing academic literature on the intersection between the circular economy (CE) and artificial intelligence (AI), with the dual aim of identifying the main themes shaping the current scholarly debate and highlighting emerging topics that may guide future research directions. To this end, a comprehensive bibliometric literature review was conducted using the Web of Science database as the primary source of academic publications. The study employs advanced analytical techniques, including co-word analysis, co-occurrence network analysis, co-citation analysis, and thematic mapping, to systematically explore the intellectual and conceptual structure of this research field. These methods allow for the identification of dominant themes, key research clusters, and knowledge gaps that define the evolving relationship between AI and CE. The findings offer a data-driven overview of how AI technologies are being conceptualized as enablers of circular economy principles and practices. This study contributes to the literature by providing a structured understanding of current research trends and by outlining future directions for scholars, policymakers, and practitioners committed to advancing sustainable, AI-enabled circular economy strategies.