AI-Powered Circular Economy Tracker for Intelligent Waste Management

Abstract

The shift in the linear economic system to a circular economy (CE) has turned into an international necessity, as resources exploitation, pollution of the environment, and the unreliability of waste management systems have increased. Artificial intelligence (AI) has become one of the most important facilitators of operationalizing the principles of the circular economy, offering information-oriented insights, analytics, and smart decision support. In this paper, the author suggests an AI-based circular economy tracker that designs waste monitoring as the supervised classification problem that seeks to detect manufacturing facilities with an abnormally high waste generation. Based on a real-world industrial data, which combines the production, material use, energy use and water usage, operational efficiency and recycling indicators, several machine learning models are created and tested, such as the Logistic Regression, Support Vector Machines (SVM), XGBoost, and Neural Networks. According to the experimental results, ensemble-related methods, especially XGBoost, have a higher predictive accuracy and almost perfect generalization. The proposed framework offers a scalable and practical solution to intelligent waste tracking by integrating strong preprocessing pipelines and an equal split of data as well as detailed evaluation metrics. The research is relevant to the field of the circular economy as it provides the empirically confirmed AI system, which has the potential to support the sustainability governing system, industrial optimization, and policy-driven environmental interventions.