Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

The inherent "take-make-waste" of the current linear economy is a major contributor to exceeding planetary boundaries. The transition to a circular economy (CE) and the associated challenges and opportunities requires fast, innovative solutions. Artificial Intelligence (AI) and Machine Learning (ML) are poised to play a pivotal role in facilitating this transition by addressing the challenges of increasing material extraction and use, ultimately contributing to a more environmentally sustainable future. This article aims to provide an overview of the current state of AI and ML in the transition to CE and to discuss their potential and associated challenges. A literature survey conducted on Google Scholar, using targeted queries with predefined keywords and search operators, revealed that the number of experimental scientific contributions related to AI and ML in CE has grown significantly in recent years. As the volume of research articles has increased, so has the diversity of ML methods and algorithms featured in experimental CE publications. Furthermore, we found that since 2020, there has been an 84% increase in AI and ML-related research articles on CE in Google Scholar, compared to the total number of such entries, and a 55% increase in articles since 2023, compared to those published up to 2023. The current state of scientific contributions indicates that AI and ML are increasingly recognized as valuable tools for advancing the CE, and their application continues to grow steadily.