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Blockchain-enhanced reinforcement learning: A sustainable solution for optimized municipal solid waste management.
Le Yuan Zhang1, Arwa A Al-Huqail2, Azher M Abed3
1Guangling college, Yangzhou university, Yangzhou, China.
Environmental Research
|January 2, 2026
Summary
This study introduces an integrated Reinforcement Learning (RL) and blockchain framework for efficient Municipal Solid Waste (MSW) management. The novel system significantly reduces travel distance, emissions, and costs while enhancing transparency in waste collection operations.
Area of Science:
- Environmental Science
- Computer Science
- Operations Research
Background:
- Municipal Solid Waste (MSW) management faces challenges with inefficiency, high costs, and environmental impact.
- Existing approaches often lack integration of operational efficiency, transparency, and sustainability.
- A need exists for advanced decision-support models in urban waste management.
Purpose of the Study:
- To develop and evaluate an integrated framework combining Reinforcement Learning (RL) for route optimization and blockchain for transparent transaction recording in MSW management.
- To address the gap in studies that combine operational efficiency with transparency and sustainability in waste management.
- To provide a scalable, transparent, and sustainable decision-support model for municipalities.
Main Methods:
- A novel framework integrating Reinforcement Learning (RL) for optimal routing and blockchain for secure data recording was proposed.
- A synthetic dataset of 1,000 collection points was used, incorporating waste data, distance, fuel, and costs.
- The RL model optimized routes to minimize travel distance and emissions, with outputs recorded on a blockchain for immutability and transparency.
Main Results:
- The integrated RL-blockchain system demonstrated significant improvements over traditional methods.
- Achieved Material Savings (MS) of 395,342.6 kg, energy savings of 142,763.8 kWh, and CO2 Emission Reductions (CER) of 35,589.4 kg CO2eq.
- Reported substantial Cost Savings (CS) of USD 2,812,476.3, highlighting economic benefits.
Conclusions:
- The study successfully developed a scalable, transparent, and sustainable decision-support model for urban waste management.
- The integrated RL-blockchain approach offers practical solutions for municipalities to reduce costs and environmental burdens.
- Enhanced accountability and efficiency in MSW management are key outcomes of this innovative framework.
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