An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling
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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2021
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| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/6939/1/OMRA-D-21-00179R2.pdf |
| _version_ | 1804072668948856832 |
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| author | Agrawal, Rohit Wankhede, Vishal A. Kumar, Anil Luthra, Sunil Majumdar, Abhijit Kazancoglu, Yigit |
| author_facet | Agrawal, Rohit Wankhede, Vishal A. Kumar, Anil Luthra, Sunil Majumdar, Abhijit Kazancoglu, Yigit |
| author_sort | Agrawal, Rohit |
| collection | LMU |
| description | 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. |
| first_indexed | 2024-07-09T04:02:49Z |
| format | Article |
| id | oai:repository.londonmet.ac.uk:6939 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2024-07-09T04:02:49Z |
| publishDate | 2021 |
| publisher | Springer |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:69392024-01-11T16:23:56Z http://repository.londonmet.ac.uk/6939/ An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling Agrawal, Rohit Wankhede, Vishal A. Kumar, Anil Luthra, Sunil Majumdar, Abhijit Kazancoglu, Yigit 650 Management & auxiliary services 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. Springer 2021-08-27 Article PeerReviewed text en cc_by_nc_nd_4 https://repository.londonmet.ac.uk/6939/1/OMRA-D-21-00179R2.pdf Agrawal, Rohit, Wankhede, Vishal A., Kumar, Anil, Luthra, Sunil, Majumdar, Abhijit and Kazancoglu, Yigit (2021) An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling. Operations Management Research, 15. pp. 609-626. ISSN 1936-9735 https://doi.org/10.1007/s12063-021-00212-0 10.1007/s12063-021-00212-0 |
| spellingShingle | 650 Management & auxiliary services Agrawal, Rohit Wankhede, Vishal A. Kumar, Anil Luthra, Sunil Majumdar, Abhijit Kazancoglu, Yigit An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title | An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title_full | An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title_fullStr | An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title_full_unstemmed | An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title_short | An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling |
| title_sort | exploratory state of the art review of artificial intelligence applications in circular economy using structural topic modeling |
| topic | 650 Management & auxiliary services |
| url | https://repository.londonmet.ac.uk/6939/1/OMRA-D-21-00179R2.pdf |
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