An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling

Rohit Agrawal, Vishal A. Wankhede, Anil Kumar, Sunil Luthra (), Abhijit Majumdar and Yigit Kazancoglu
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Rohit Agrawal: Indian Institute of Technology
Vishal A. Wankhede: Pandit Deendayal Energy University
Anil Kumar: London Metropolitan University
Sunil Luthra: Ch. Ranbir Singh State Institute of Engineering and Technology
Abhijit Majumdar: Indian Institute of Technology
Yigit Kazancoglu: Yasar University, Universite Caddesi

Operations Management Research, 2022, vol. 15, issue 3, No 1, 609-626

Abstract: Abstract The world is moving into a situation where resource scarcity leads to an increase in material cost. A possible way to deal with the above challenge is to adopt Circular Economy (CE) concepts to make a close loop of material by eliminating industrial or post-consumer wastes. Integration of emerging technologies such as Artificial Intelligence (AI), machine learning, and big data analytics provides significant support in successfully adopting and implementing CE practices. This study aims to explore the applications of AI techniques in enhancing the adoption and implementation of CE practices. A systematic literature review was performed to analyze the existing scenario and the potential research directions of AI in CE. A collection of 220 articles was shortlisted from the SCOPUS database in the field of AI in CE. A text mining approach, known as Structural Topic Modeling (STM), was used to generate different thematic topics of AI applications in CE. Each generated topic was then discussed with shortlisted articles. Further, a bibliometric study was performed to analyze the research trends in the field of AI applications in CE. A research framework was proposed for AI in CE based on the review conducted, which could help industrial practitioners, and researchers working in this domain. Further, future research propositions on AI in CE were proposed.

Keywords: Artificial intelligence; Circular economy; Emerging technologies; Structural topic modeling; Text mining; Big data analytics (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12063-021-00212-0

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