Governing circular intelligence: How AI-driven policy tools can accelerate the circular economy transition

Abstract

The circular economy (CE) is increasingly recognized as a transformative framework for fostering sustainable production and consumption. In this context, Artificial Intelligence (AI) is emerging as a key enabler for advancing both material systems and policy processes. This article investigates the potential of AI-driven policy tools to accelerate the transition towards a circular economy. Consequently, it explores AI capabilities such as systems modelling, predictive analytics, and adaptive regulation, focusing on their application within environmental governance frameworks. The article examines the integration of AI technologies including natural language processing for policy synthesis, machine learning for waste pattern detection, and digital twins for scenario testing into multi-level governance structures. Furthermore, it critically addresses the ethical, institutional, and technical challenges associated with AI deployment in policymaking, particularly in terms of data bias, transparency, and public accountability. The article concludes with a roadmap for embedding 'circular intelligence' in governance systems, emphasizing the need for a strategic, transparent, and inclusive approach to AI integration in CE policy to ensure sustainable and just transitions.