Quantum Spherical Fuzzy Bayesian Network Integrated with Artificial Intelligence Based Decision Analytics for Evaluating Renewable Energy Investment Competencies

This study proposes an integrated AI-driven quantum spherical fuzzy decision framework for multi-criteria evaluation under uncertainty. The model combines AI-based decision-maker weighting, quantum spherical fuzzy Bayesian networks for criteria weighting, and WASPAS for ranking. Decision makers are clustered using k-means to reduce bias, while interdependencies and uncertainty are captured probabilistically. The framework is applied to assess renewable energy investment competencies in G7 economies. Results highlight the importance of customer-centric expectations and real-time financial performance. The model offers a flexible, robust, and scalable approach, improving reliability, transparency, and decision quality in complex environments.