Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review
Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This st...
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MDPI AG
2025-06-01
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| author | Paola Campana Riccardo Censi Anna Maria Tarola Roberto Ruggieri |
| author_facet | Paola Campana Riccardo Censi Anna Maria Tarola Roberto Ruggieri |
| author_sort | Paola Campana |
| collection | DOAJ |
| description | Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to analyze the state of the art and key research trajectories related to the application of these technologies in waste management. Through a bibliometric analysis based on the Scopus database and mapping with VOSviewer (version 1.6.20), three main thematic clusters were identified: (i) predictive and environmental methods, (ii) sustainability and optimization, and (iii) monitoring and environmental impacts. A qualitative analysis of the 20 most-cited articles further revealed six major research areas, including waste forecasting, recycled materials, process digitalization, and intelligent environmental monitoring. The findings indicate a growing convergence among digitalization, automation, and sustainability. The adopted approach enables the mapping of major research directions and emerging interconnections among AI, the circular economy, and predictive management, providing an up-to-date and systemic perspective on the field. |
| format | Article |
| id | doaj-art-8930f39fcf2040e5bbdf71ef6a3d9701 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8930f39fcf2040e5bbdf71ef6a3d97012025-08-20T03:11:30ZengMDPI AGApplied Sciences2076-34172025-06-011511633710.3390/app15116337Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic ReviewPaola Campana0Riccardo Censi1Anna Maria Tarola2Roberto Ruggieri3Department of Management, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Management, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Management, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Management, Sapienza University of Rome, 00185 Rome, ItalySustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to analyze the state of the art and key research trajectories related to the application of these technologies in waste management. Through a bibliometric analysis based on the Scopus database and mapping with VOSviewer (version 1.6.20), three main thematic clusters were identified: (i) predictive and environmental methods, (ii) sustainability and optimization, and (iii) monitoring and environmental impacts. A qualitative analysis of the 20 most-cited articles further revealed six major research areas, including waste forecasting, recycled materials, process digitalization, and intelligent environmental monitoring. The findings indicate a growing convergence among digitalization, automation, and sustainability. The adopted approach enables the mapping of major research directions and emerging interconnections among AI, the circular economy, and predictive management, providing an up-to-date and systemic perspective on the field.https://www.mdpi.com/2076-3417/15/11/6337artificial intelligencedigital twinwaste managementcircular economypredictive modelingsustainability |
| spellingShingle | Paola Campana Riccardo Censi Anna Maria Tarola Roberto Ruggieri Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review Applied Sciences artificial intelligence digital twin waste management circular economy predictive modeling sustainability |
| title | Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review |
| title_full | Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review |
| title_fullStr | Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review |
| title_full_unstemmed | Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review |
| title_short | Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review |
| title_sort | artificial intelligence and digital twins for sustainable waste management a bibliometric and thematic review |
| topic | artificial intelligence digital twin waste management circular economy predictive modeling sustainability |
| url | https://www.mdpi.com/2076-3417/15/11/6337 |
| work_keys_str_mv | AT paolacampana artificialintelligenceanddigitaltwinsforsustainablewastemanagementabibliometricandthematicreview AT riccardocensi artificialintelligenceanddigitaltwinsforsustainablewastemanagementabibliometricandthematicreview AT annamariatarola artificialintelligenceanddigitaltwinsforsustainablewastemanagementabibliometricandthematicreview AT robertoruggieri artificialintelligenceanddigitaltwinsforsustainablewastemanagementabibliometricandthematicreview |