The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.
Artificial intelligence for waste management in smart cities: a review
Open accessAbstract
The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.
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“The integration of AI enables this information to be analyzed for better planning by optimizing the work of waste management systems in predicting waste generation and performing resource planning.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [47]
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“For instance, AI can interpret and analyze the data gathered from the collections to determine when and at which point collection should be done so that it decreases fuel consumption and the discharges of carbon are minimal46,47.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [47]
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“Organizations can transform traditional practices of waste management into more efficient, automated, and data-driven processes that not only reduce waste but also optimize resource recovery and minimize environmental impacts by incorporating AI and IoT technologies.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [47]
In the citing paper
“These IoT devices may be designed to track the level of waste accumulation, monitor the types of waste collected, and generate real-time data regarding waste collection patterns.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [47]
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2 retracted
Items 201–209 of 209
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Sorted by citations
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536
RETRACTED ARTICLE: Artificial intelligence-based solutions for climate change: a review
Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M. R. F., Hua, J., Al-Fatesh, A. S., Ihara, I., Rooney, D. W., et al. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01617-y · Cited by 464
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Self-citationSmart waste management: A paradigm shift enabled by artificial intelligence
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Lakhouit, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104018 · Cited by 333
Metadata
- Authors
- Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S.
- Affiliations
- Xi’an Jiaotong-Liverpool University (CN)
- Queen's University Belfast (GB)
- Kobe University (JP)
- Military Technical College (EG)
- Corresponding author
- Osman, Ahmed I.
- Farghali, Mohamed
- Yap, Pow-Seng
- ORCID
- Venue
- Environmental Chemistry Letters
- Published
- 2023-05-09
- Volume
- 21
- Issue
- 4
- Pages
- 1959-1989
- ISSN
- 1610-3653 (ISSN-L)
- 1610-3661
- PMC
- PMC10169138
- OpenAlex
- W4382182093
- Cited by
- 629
- References
- 209
- Citations by year
- 2026: 195 · 2025: 272 · 2024: 141 · 2023: 21
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Environmental Chemistry Letters (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Funders
- SEUPB (Bryden Centre project (Project ID VA5048))
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Physical Sciences › Environmental Science › Waste Management and Disposal › Waste Management and Recycling
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare and Environmental Waste Management
- Keywords
- artificial intelligence, waste management, smart cities, waste-to-energy, smart bins, plastic waste pyrolysis, waste logistics, illegal dumping, resource recovery, carbon emission estimation
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