Leveraging machine learning for sustainable solid waste management: A global perspective
A diverse array of socioeconomic, demographic, and environmental factors influences the quantity and composition of municipal solid waste (MSW). Yet, the paucity of consistent waste data continues to impede the formulation of effective solid waste management strategies globally. This gap poses signi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825006628 |
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| author | Ajaya Subedi Sahil Shrestha Anish Ghimire Shukra Raj Paudel |
| author_facet | Ajaya Subedi Sahil Shrestha Anish Ghimire Shukra Raj Paudel |
| author_sort | Ajaya Subedi |
| collection | DOAJ |
| description | A diverse array of socioeconomic, demographic, and environmental factors influences the quantity and composition of municipal solid waste (MSW). Yet, the paucity of consistent waste data continues to impede the formulation of effective solid waste management strategies globally. This gap poses significant barriers to achieving various Sustainable Development Goals (SDGs), particularly those related to responsible consumption and production, environmental sustainability, and promotion of circular economy. In light of these challenges, this study primarily accentuates the transformative potential of Machine Learning (ML) for sustainable waste management. ML models offer promising solutions for projecting waste composition and generation trends while optimizing resource distribution by analyzing key influential factors. Nevertheless, existing ML applications often lack standardization and generalizability across regions. Therefore, this paper objectively advocates for the necessity of robust global ML models to standardize metrics, facilitating reliable comparisons of region- or nation-wise solid waste generation and composition patterns. Additionally, this study aims to emphasize the broader utility of such models in, but not limited to, addressing leachate quantity, landfill composition, and groundwater contamination issues, ultimately promoting scalable, data-driven approaches for informed and sustainable waste management worldwide. |
| format | Article |
| id | doaj-art-e03a55e6122a45e69e6cef4c38d48074 |
| institution | DOAJ |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-e03a55e6122a45e69e6cef4c38d480742025-08-20T02:49:06ZengElsevierSustainable Futures2666-18882025-12-011010109810.1016/j.sftr.2025.101098Leveraging machine learning for sustainable solid waste management: A global perspectiveAjaya Subedi0Sahil Shrestha1Anish Ghimire2Shukra Raj Paudel3Environmental Engineering Program, Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, NepalEnvironmental Engineering Program, Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, NepalEnvironmental Engineering and Management, Asian Institute of Technology, Pathum Thani 12120, ThailandEnvironmental Engineering Program, Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal; Corresponding author.A diverse array of socioeconomic, demographic, and environmental factors influences the quantity and composition of municipal solid waste (MSW). Yet, the paucity of consistent waste data continues to impede the formulation of effective solid waste management strategies globally. This gap poses significant barriers to achieving various Sustainable Development Goals (SDGs), particularly those related to responsible consumption and production, environmental sustainability, and promotion of circular economy. In light of these challenges, this study primarily accentuates the transformative potential of Machine Learning (ML) for sustainable waste management. ML models offer promising solutions for projecting waste composition and generation trends while optimizing resource distribution by analyzing key influential factors. Nevertheless, existing ML applications often lack standardization and generalizability across regions. Therefore, this paper objectively advocates for the necessity of robust global ML models to standardize metrics, facilitating reliable comparisons of region- or nation-wise solid waste generation and composition patterns. Additionally, this study aims to emphasize the broader utility of such models in, but not limited to, addressing leachate quantity, landfill composition, and groundwater contamination issues, ultimately promoting scalable, data-driven approaches for informed and sustainable waste management worldwide.http://www.sciencedirect.com/science/article/pii/S2666188825006628Municipal solid waste (MSW)Sustainable managementMachine learning (ML)Prediction modelingGlobal scalability |
| spellingShingle | Ajaya Subedi Sahil Shrestha Anish Ghimire Shukra Raj Paudel Leveraging machine learning for sustainable solid waste management: A global perspective Sustainable Futures Municipal solid waste (MSW) Sustainable management Machine learning (ML) Prediction modeling Global scalability |
| title | Leveraging machine learning for sustainable solid waste management: A global perspective |
| title_full | Leveraging machine learning for sustainable solid waste management: A global perspective |
| title_fullStr | Leveraging machine learning for sustainable solid waste management: A global perspective |
| title_full_unstemmed | Leveraging machine learning for sustainable solid waste management: A global perspective |
| title_short | Leveraging machine learning for sustainable solid waste management: A global perspective |
| title_sort | leveraging machine learning for sustainable solid waste management a global perspective |
| topic | Municipal solid waste (MSW) Sustainable management Machine learning (ML) Prediction modeling Global scalability |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825006628 |
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