Evaluation of Renewable Energy Projects under a Circular Economy Framework Using AI-based Recommender Systems and a Hybrid Fuzzy-Quantum Decision-Making Approach

renewable energy has become a global imperative in addressing the intertwined challenges of climate change, energy security, and sustainable development. However, the evaluation of renewable energy projects remains complex due to the multiplicity of criteria—economic, environmental, social, and technological—that must be simultaneously considered. In this paper, we propose a novel evaluation framework that integrates the principles of the circular economy with advanced decision-making tools powered by artificial intelligence. Specifically, the framework employs AI-based recommender systems to generate project alternatives and applies a hybrid fuzzy-quantum decision-making approach to rank and select the most sustainable options. The proposed methodology advances beyond conventional multi-criteria decision-making (MCDM) models by capturing uncertainty through fuzzy logic while leveraging the non-classical probability structures of quantum decision theory to model human cognitive biases and complex interdependencies. A case study on renewable energy initiatives in emerging economies demonstrates that the hybrid approach provides more consistent, adaptable, and sustainable project recommendations compared to traditional evaluation methods such as AHP and TOPSIS. The findings highlight that combining AI-driven recommendation with fuzzy-quantum decision-making significantly enhances the capacity to prioritize projects that align with circular economy principles, optimize resource utilization, and ensure long-term socio-environmental benefits.

کلیدواژه ها:

Renewable Energy ، Circular Economy ، Artificial Intelligence ، Recommender Systems ، Fuzzy Logic ، Quantum Decision-Making ، Sustainability