AI-Optimized Circular Economy Models For E-Waste Management In The Tech Industry: A Data-Driven Approach

Abstract

The exponential rate at which the technology industry develops has compounded the problem of electronic waste (e-waste) generation at a global level. The conventional linear-based waste management systems have become insufficient in dealing with the intricate lifecycle of electronic products these days. This paper reports a data-driven design of the framework to develop AI-optimized circuar economy (CE) models that are specific to the technology industry e-waste management application. Using lifecycle inventory data, world statistics on e-waste flow and recycling efficiency data, we use state of the art machine learning methodologies, i.e., neural networks to predict material composition and reinforcement learning to optimize the recycling process to improve recovery and minimize waste leakage. Our model takes into consideration Material Flow Analysis (MFA), predictive analytics, and decision-support algorithms to ensure the best collection, sorting, refurbishment, and recycle methods are found. Case studies of high-performing tech clusters such as Shenzhen (China), Bengaluru (India), and Dresden (Germany) indicate that the AI-enabled CE model will enhance the material recovery rate by up to 27 percent, marginalize the amount in landfills by 19 percent, and diminish the whole lifecycle emissions by 14 percent relative to a standard recycling system. The results point to the power of artificial intelligence to make full-loop material flows a reality, minimize environmental impacts, and promote the sustainability of electronic waste in the electronic industry. The proposed course of action not only enhances CE implementation but also assists in the process of evidence-based policymaking, industry compliance, and the digital transformation of waste management systems with scale.