Circular Economy Approaches for Sustainable Waste Management: A Review on Integration of AI, Advanced Technologies and Policy Recommendations

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive framework for AI-CE integration in waste management, useful for further empirical studies.

🏢実務担当者:Offers actionable strategies for firms to reduce waste-related costs and improve resource efficiency via AI monitoring.

🏛政策担当者:Highlights the need for data-driven policies and incentives to support AI adoption in circular waste systems.

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.

• openalex https://doi.org/10.3390/recycling11060099first seen 2026-06-24 04:39:01