Urban areas worldwide face escalating challenges in managing municipal solid waste (MSW) due to rapid urbanization, population growth, and changing consumption patterns. Inefficient waste management systems contribute...Urban areas worldwide face escalating challenges in managing municipal solid waste (MSW) due to rapid urbanization, population growth, and changing consumption patterns. Inefficient waste management systems contribute to environmental degradation, public health risks, and resource depletion, underscoring the need for innovative solutions. This review employing AI-driven sorting technologies in urban waste management as a transformative framework for sustainable MSW management, emphasizing waste reduction, resource recovery, and closed-loop systems. The paper synthesizes existing literature, case studies, and technological advancements to explore strategies for integrating CE principles into MSW management. Key areas of focus include the application of emerging technologies such as artificial intelligence, machine learning, and big data analytics;advancements in waste-to-resource technologies;the development of scalable and adaptable CE models tailored to diverse urban contexts;and fostering collaboration among governments, private sectors, and communities. Findings highlight the potential of CE frameworks to minimize waste generation, enhance resource efficiency, and create resilient urban systems. However, significant barriers remain, including technological, financial, and policy challenges. The review concludes by identifying future research directions and actionable recommendations for stakeholders, aiming to advance the global transition toward sustainable urban waste management.展开更多
文摘Urban areas worldwide face escalating challenges in managing municipal solid waste (MSW) due to rapid urbanization, population growth, and changing consumption patterns. Inefficient waste management systems contribute to environmental degradation, public health risks, and resource depletion, underscoring the need for innovative solutions. This review employing AI-driven sorting technologies in urban waste management as a transformative framework for sustainable MSW management, emphasizing waste reduction, resource recovery, and closed-loop systems. The paper synthesizes existing literature, case studies, and technological advancements to explore strategies for integrating CE principles into MSW management. Key areas of focus include the application of emerging technologies such as artificial intelligence, machine learning, and big data analytics;advancements in waste-to-resource technologies;the development of scalable and adaptable CE models tailored to diverse urban contexts;and fostering collaboration among governments, private sectors, and communities. Findings highlight the potential of CE frameworks to minimize waste generation, enhance resource efficiency, and create resilient urban systems. However, significant barriers remain, including technological, financial, and policy challenges. The review concludes by identifying future research directions and actionable recommendations for stakeholders, aiming to advance the global transition toward sustainable urban waste management.
