An Assessment of Circular Economy-Oriented Renewable Energy Projects via Artificial Intelligence Recommender Systems and a Hybrid Quantum Fuzzy Decision-making Approach
RENEWABLE ENERGY, cilt.1, sa.1, ss.1-22, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 1 Sayı: 1
- Basım Tarihi: 2025
- Doi Numarası: 10.1016/j.renene.2025.122673
- Dergi Adı: RENEWABLE ENERGY
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
- Sayfa Sayıları: ss.1-22
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
Developing circular economy-based renewable energy investment strategies is vital for a country’s social and economic development. Businesses need to prioritize the most appropriate investment strategies to optimize their budgets and human resources effectively. However, previous studies have faced challenges in managing uncertainties in the strategy development process for circular economy-oriented renewable energy projects. This study addresses these gaps by proposing an AI-enhanced decision-making model that combines quantum theory, multi-step wise weight assessment ratio analysis (M-SWARA), multi-objective optimization by ratio analysis (MOORA), and picture fuzzy sets to better manage uncertainties and improve strategy selection. First, the importance weights of the experts are calculated with the help of an artificial intelligence (AI) technique. After that, the importance weights of the indicators are determined via quantum picture fuzzy M-SWARA. Finally, the quantum picture fuzzy MOORA is used to rank the investment strategies. The most valuable contribution of this study to the research literature is the determination of the most effective strategies for circular economy-based renewable energy projects through the creation of a new AI-based decision-making model. In addition, compared to earlier approaches, this model offers a novel method for mitigating expert bias and uncertainty. The most important original feature of this model is the creation of a decision matrix with the help of AI. Owing to this integration, the importance weights of the experts can be determined. Our findings demonstrate that cost with renewable alternatives is the most critical determinant since it has the highest weight (0.267). Similar to this situation, recycled material selection is also an important determinant (weight: 0.266). On other hand, it is also defined that hybrid renewable energy systems for efficient energy production are the most appropriate project alternative for improving circular economy-based renewable energy investments. Greenhouse constructions with solar energy panels also play an essential role in this regard.
Developing circular economy-based renewable energy investment strategies is vital for a country’s social and economic development. Businesses need to prioritize the most appropriate investment strategies to optimize their budgets and human resources effectively. However, previous studies have faced challenges in managing uncertainties in the strategy development process for circular economy-oriented renewable energy projects. This study addresses these gaps by proposing an AI-enhanced decision-making model that combines quantum theory, multi-step wise weight assessment ratio analysis (M-SWARA), multi-objective optimization by ratio analysis (MOORA), and picture fuzzy sets to better manage uncertainties and improve strategy selection. First, the importance weights of the experts are calculated with the help of an artificial intelligence (AI) technique. After that, the importance weights of the indicators are determined via quantum picture fuzzy M-SWARA. Finally, the quantum picture fuzzy MOORA is used to rank the investment strategies. The most valuable contribution of this study to the research literature is the determination of the most effective strategies for circular economy-based renewable energy projects through the creation of a new AI-based decision-making model. In addition, compared to earlier approaches, this model offers a novel method for mitigating expert bias and uncertainty. The most important original feature of this model is the creation of a decision matrix with the help of AI. Owing to this integration, the importance weights of the experts can be determined. Our findings demonstrate that cost with renewable alternatives is the most critical determinant since it has the highest weight (0.267). Similar to this situation, recycled material selection is also an important determinant (weight: 0.266). On other hand, it is also defined that hybrid renewable energy systems for efficient energy production are the most appropriate project alternative for improving circular economy-based renewable energy investments. Greenhouse constructions with solar energy panels also play an essential role in this regard.