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Advanced Waste Management Strategies Using Digital Technologies

Author(s) Massimo Raboni
Country Italy
Abstract Rapid urbanization, industrial expansion, population growth, and increasing consumption have significantly increased the volume and complexity of waste generated worldwide. Conventional waste-management systems often depend on fixed collection schedules, manual sorting, limited monitoring, and fragmented information, which can reduce operational efficiency and resource recovery. Digital technologies provide opportunities to transform waste management through intelligent monitoring, automated sorting, predictive analytics, route optimization, digital traceability, and data-driven decision-making. This study examines advanced waste-management strategies using digital technologies through a conceptual and descriptive research approach. The study focuses on Internet of Things-enabled smart bins, artificial intelligence, machine learning, computer vision, robotics, geographic information systems, cloud computing, blockchain, digital twins, predictive analytics, and smart logistics. An integrated framework is developed connecting waste generation, digital sensing, data collection, intelligent classification, optimized collection, recovery, recycling, treatment, and sustainability evaluation. The analysis indicates that digital technologies can improve waste collection efficiency, optimize transportation routes, support automated waste classification, increase material recovery, improve facility management, and strengthen environmental monitoring.
However, implementation is affected by infrastructure costs, technological complexity, cybersecurity, data quality, interoperability, workforce requirements, digital inequality, and limited recycling infrastructure. Effective digital waste management also requires collaboration among municipalities, industries, waste operators, technology providers, recyclers, households, and policymakers. The study emphasizes the importance of integrating digital technologies with circular economy principles, source segregation, extended product lifecycles, recycling, reuse, recovery, and responsible disposal. Future research should examine AI-enabled waste prediction, autonomous collection systems, robotic sorting, blockchain-based traceability, digital twins, smart-city waste platforms, and circular resource marketplaces. The proposed framework provides a structured foundation for applying digital innovation to efficient, transparent, resource-oriented, and environmentally sustainable waste-management systems.
Keywords Waste Management, Digital Technologies, Smart Waste Management, Artificial Intelligence, Internet of Things, Waste Recycling, Machine Learning, Smart Cities, Circular Economy, Waste Optimization
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-01
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