AI-Powered Circular Economy Solutions for Waste Reduction and Resource Efficiency

Abstract

Transitioning to a circular economy requires innovative approaches to reduce waste and maximize resource reuse. This paper examines the application of machine learning in circular economy strategies, including AI-driven material lifecycle analysis, automated waste sorting, and predictive maintenance. Techniques such as computer vision, generative adversarial networks (GANs), and deep reinforcement learning are utilized to optimize recycling processes, enhance product sustainability, and minimize landfill waste. Case studies highlight successful AI-driven circular economy initiatives, demonstrating their potential to drive sustainable production and consumption models.