AI-Enhanced Blockchain Frameworks For Circular Economy: Driving Transparency In Waste Management And Resource Recovery

Abstract

This research presents an AI-enhanced blockchain framework for advancing transparency, efficiency, and accountability in waste management and resource recovery within the circular economy. The study integrates four key algorithms—Random Forest (RF), Convolutional Neural Network (CNN), K-Means Clustering, and Reinforcement Learning (RL)—to address different stages of the waste lifecycle, while blockchain ensures immutable data recording and stakeholder trust. A hybrid dataset comprising municipal waste logs, IoT bin images, and simulated blockchain transactions was used for evaluation. Experimental results demonstrate the effectiveness of the proposed framework. The RF model was found to have an accuracy of 91.2 percent in terms of predicting the potential of recycling and the CNN was found to have a classification accuracy of 95.4 percent in predicting plastic, glass, metal and organic waste. The K-Means clustering brought out a silhouette score of 0.87 which essentially categorized the waste streams into high-value, medium-value and low-value waste streams. Compared to baseline routing, RP minimized the path by 14 percent and fuel expenditure was lowered by nine percent and recovery efficiency was 92.6 percent. Application of blockchain also guaranteed minimal latency ( 1.8s/transaction) and a CPS (150 TPS). The results emphasize that AI with the use of blockchain would provide even higher benefits than separate strategies and would result in the creation of transparent and sustainable waste management systems. This framework offers a pathway to scale to offer suprasystemic support to circular economy practices and actual urban sustainability projects.