Advancement of Circular Economy Through Interdisciplinary Collaboration: A Bibliometric Approach
Keita Nishimoto, Koji Kimita, Shinsuke Murakami, Yin Long, Kimitaka Asatani, Ichiro Sakata

TL;DR
This study maps the interdisciplinary landscape of Circular Economy research using bibliometric and machine learning methods, revealing collaboration patterns, disciplinary influences, and their impact on research visibility and funding.
Contribution
It introduces a hybrid bibliometric and machine learning approach to analyze the structure and collaboration dynamics of CE research, identifying key disciplinary clusters and impact factors.
Findings
Business research attracts most attention and policy interest.
Engineering research achieves higher funding success.
Interdisciplinary collaborations correlate with higher research impact.
Abstract
Since the European Union introduced its Circular Economy (CE) Action Plan in 2015, CE research has expanded rapidly. However, the structure of this emerging field - both in terms of its constituent disciplines and researcher dynamics - remains poorly understood. To address this gap, we analyze over 25,000 CE-related publications from Scopus by combining conventional bibliometric approaches with advanced machine learning techniques, including text embeddings and clustering. This hybrid method enables both a macro-level mapping of research domains and a micro-level investigation of individual researchers' disciplinary backgrounds and collaborations. We classify CE research into 16 distinct clusters, identifying the original disciplines of researchers and visualizing patterns of interdisciplinary collaboration. Building on this foundation, we ask: Which CE-related research domains…
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