Circular Economy Approaches for Sustainable Waste Management: A Review on Integration of AI, Advanced Technologies and Policy Recommendations
9 affiliations
- 1Department of Civil Engineering, National Institute of Technology Calicut, Kozhikode 673601, India
- 2School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, Australia
- 3Institute of Chemical and Energy Engineering, BOKU University, 1180 Vienna, Austria
- 4Department of Mechanical Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Hyderabad 500090, India
- 5Department of Environmental Geosciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Suchdol, 165 00 Prague, Czech Republic
- 6Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
- 7Institute of Plasma Physics of the Czech Academy of Sciences, Za Slovankou 1782/3, 182 00 Prague, Czech Republic
- 8Departamento de Mecánica, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Av. José Pedro Alessandri 1242, Ñuñoa, Santiago 7810003, Chile
- 9Department of Power Engineering, University of Chemistry and Technology, Technická 5, 166 28 Prague, Czech Republic
Abstract
Landfilling remains the dominant waste disposal method worldwide, particularly in developing countries, posing serious environmental, health, and climate challenges. Inefficient practices, weak regulations, and un-engineered sites contribute to massive greenhouse gas (GHG) emissions and resource loss. Transitioning to a circular economy (CE) offers a transformative path for sustainable waste management. By closing material loops, recovering energy, urban mining, controlling emissions and CE strategies can convert traditional landfills into eco-efficient systems. The integration of artificial intelligence (AI) further enhances this transition, enabling real-time monitoring, predictive management, and optimized resource recovery, thereby maximizing environmental and economic benefits. This review presents a three-level CE framework at micro (individual organizations), meso (industrial networks), and macro (national and international) levels designed to extract maximum value from waste streams and mitigate climate impacts. The proposed strategies demonstrate the potential to drastically reduce GHG emissions, promote clean energy via waste-to-energy routes, and contribute to SDGs 7, 11, 12, 13 and 15. By combining technology, innovation, and strategic management, this work highlights how AI-driven CE approaches can transform landfills from environmental liabilities into engines of sustainability and climate action. In implementing CE strategies at various levels, various challenges including technological, socio-economic, ethical, policy-based, and unintended consequences are encountered which impact sustainability initiatives. This review comprehensively discusses challenges associated with CE implementation and identifies technological advancement, social awareness and data-driven AI/ML-based modeling which could ensure success in circularity and ultimately curb climate change impacts in the long term.
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BibTeX
@article{srivastava2026circular,
title = {{Circular Economy Approaches for Sustainable Waste Management: A Review on Integration of AI, Advanced Technologies and Policy Recommendations}},
author = {Abhishek N. Srivastava and Arun Krishna Vuppaladadiyam and Rakhi Punnadan Koroth and Christoph Pfeifer and Ajay Kumar Kaviti and Jafar Fathi and Alan Mašláni and Praveen Barmavatu and M. Buryi and Michael Pohořelý and Vineet Singh Sikarwar},
journal = {Recycling},
year = {2026},
month = may,
doi = {10.3390/recycling11060099},
url = {https://doi.org/10.3390/recycling11060099},
note = {CC BY},
}
RIS
TY - JOUR
TI - Circular Economy Approaches for Sustainable Waste Management: A Review on Integration of AI, Advanced Technologies and Policy Recommendations
AU - Abhishek N. Srivastava
AU - Arun Krishna Vuppaladadiyam
AU - Rakhi Punnadan Koroth
AU - Christoph Pfeifer
AU - Ajay Kumar Kaviti
AU - Jafar Fathi
AU - Alan Mašláni
AU - Praveen Barmavatu
AU - M. Buryi
AU - Michael Pohořelý
AU - Vineet Singh Sikarwar
PY - 2026
DA - 2026/05/29
JO - Recycling
DO - 10.3390/recycling11060099
UR - https://doi.org/10.3390/recycling11060099
AB - Landfilling remains the dominant waste disposal method worldwide, particularly in developing countries, posing serious environmental, health, and climate challenges. Inefficient practices, weak regulations, and un-engineered sites contribute to massive greenhouse gas (GHG) emissions and resource loss. Transitioning to a circular economy (CE) offers a transformative path for sustainable waste management. By closing material loops, recovering energy, urban mining, controlling emissions and CE strategies can convert traditional landfills into eco-efficient systems. The integration of artificial intelligence (AI) further enhances this transition, enabling real-time monitoring, predictive management, and optimized resource recovery, thereby maximizing environmental and economic benefits. This review presents a three-level CE framework at micro (individual organizations), meso (industrial networks), and macro (national and international) levels designed to extract maximum value from waste streams and mitigate climate impacts. The proposed strategies demonstrate the potential to drastically reduce GHG emissions, promote clean energy via waste-to-energy routes, and contribute to SDGs 7, 11, 12, 13 and 15. By combining technology, innovation, and strategic management, this work highlights how AI-driven CE approaches can transform landfills from environmental liabilities into engines of sustainability and climate action. In implementing CE strategies at various levels, various challenges including technological, socio-economic, ethical, policy-based, and unintended consequences are encountered which impact sustainability initiatives. This review comprehensively discusses challenges associated with CE implementation and identifies technological advancement, social awareness and data-driven AI/ML-based modeling which could ensure success in circularity and ultimately curb climate change impacts in the long term.
SN - 2313-4321
ER -