AI ‐driven circular economy optimization in waste management: A review of current evidence

Olawade, David; Yadav, Ram Narayan; Ijiwade, James O; Wada, Ojima Z.

AI ‐driven circular economy optimization in waste management: A review of current evidence

Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836, Yadav, Ram Narayan, Ijiwade, James O ORCID: https://orcid.org/0009-0008-2674-5170 and Wada, Ojima Z. ORCID: https://orcid.org/0000-0002-8328-3557 (2026) AI ‐driven circular economy optimization in waste management: A review of current evidence. Environmental Progress & Sustainable Energy. e70322.

[thumbnail of Env Prog and Sustain Energy - 2026 - Olawade - AI‐driven circular economy optimization in waste management A review of (1).pdf]

Preview

Text
Env Prog and Sustain Energy - 2026 - Olawade - AI‐driven circular economy optimization in waste management A review of (1).pdf - Published Version
Available under License Creative Commons Attribution.
| Preview

Official URL: https://doi.org/10.1002/ep.70322

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic barriers. AI technologies, such as intelligent waste‐sorting systems and predictive models, are transforming how waste is processed and materials are reused. This article critically evaluates the potential and limitations of AI‐driven approaches across the waste management lifecycle through a narrative review of peer‐reviewed literature published between 2015 and 2025. AI offers a revolutionary approach to waste management, resource recovery, and environmental impact reduction by enabling the processing of massive datasets and automating complex decision‐making. However, to fully realize AI's promise, critical issues, including scarce data availability, expensive implementation costs, the requirement for efficient human‐AI cooperation, and ethical considerations regarding algorithmic transparency and workforce impacts, must be systematically addressed. Additionally, ethical concerns related to job displacement and the environmental footprint of AI technologies themselves require careful management. This review identifies significant research gaps, including the need for standardized datasets, explainable AI frameworks, and comprehensive lifecycle assessments of AI‐driven interventions. Looking to the future, decentralized AI systems, AI‐driven global waste trade optimization, blockchain‐integrated tracking systems, and AI‐enhanced product design offer promising avenues for further innovation. As AI continues to develop, its incorporation into waste management systems will be essential to accelerating the world's shift to a circular economy that is more resource‐efficient and sustainable.