Advanced Research in Economics and Business Strategy Journal
Volume 6, Numéro 1, Pages 12-29
2025-06-30
Machine Learning And Financial Investment: Game Changers For Circular Business Models – Eu Performance Score Analysis.
Authors : Mohsin Elahi Muhammad . Ur Rehman Zahid . Abdullah Hassan Muhammad .
Abstract
The paper explores the methods in 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 ML to mitigate waste; however, they must also develop novel technologies (like quantum-driven recyclables) to compensate for compensating declining CURM reappears. GGI and CMUR enhance COP and contribute to sustainability initiatives by encouraging resource optimization and green practices. At the same time, this group uses ML as its primary instrument to boost COP due to its willingness to integrate modern technologies swiftly. For paradoxes to prevail over structural inefficiencies, FI must be reallocated ahead of digital integrity and AI-enhanced hybridization. To find ways to modernize infrastructure, laggards rely on EU government green initiatives (GGI) and decentralized models (e.g., solely resource cycles). The research promotes the implementation of adaptive policies, including AI sandboxes, transferring risk funds, and legitimate governance, to ensure that interruptions in technology, financial flexibility, and egalitarian transitioning are balanced. These insights enable the EU to establish a model for broad, high-performance circular economies.
Keywords
Machine Learning, Financial Investment, Circular Business Models, EU Policy, Sustainability, Performance Scores
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