Machine Learning and Financial Investment: Game Changers for Circular Business Models – EU Performance Score Analysis

The paper explores the methods by which Circular Material Use Rates (CURMs), Financial Investment (FI), and Machine Learning (ML) influence the performance of Circular Business Models (COP) in the EU by examining case studies from three groups: Leaders, Paradoxes, and Laggards. Leaders use machine learning (ML) to mitigate waste; however, they must also develop novel technologies, such as quantum-driven recyclables, to compensate for the declining capacity utilisation rate (CURM), which reappears. GGI and CMUR enhance COP and contribute to sustainability initiatives by encouraging resource optimisation and green practices. At the same time, this group utilises machine learning (ML) as its primary tool to enhance COP due to its willingness to integrate modern technologies quickly. For paradoxes to prevail over structural inefficiencies, FI must be reallocated ahead of digital integrity and AI-enhanced hybridisation. To find ways to modernise infrastructure, laggards rely on EU government green initiatives (GGI) and decentralised models (e.g., solely resource cycles). The research promotes the implementation of adaptive policies, including AI sandboxes, risk transfer funds, and legitimate governance, to ensure that interruptions in technology, financial flexibility, and equitable transition are balanced. These insights enable the EU to establish a model for broad, high-performance circular economies.