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.
Srivastava et al. (Fri,) studied this question.