Balanced Scorecard-Based Project Priorities of Sustainable Energy Financing Via Artificial Intelligence-Enhanced Hybrid Quantum Decision-Making Modeling

The most essential factors should be defined to increase the effectiveness of sustainable energy financing. Otherwise, businesses may face some financial and operational problems due to not using resources effectively. However, only a limited number of studies in the literature have identified these important factors. This situation shows a need for a new study to determine the variables that have the greatest impact on the effectiveness of sustainable energy financing. Thus, the purpose of this study is to identify significant determinants that affect the effectiveness of sustainable energy financing. For this situation, a 3-stage model is constructed to reach this purpose. The first stage prioritizes the experts with the help of artificial intelligence (AI). The second stage weights the assessment criteria of sustainable energy financing by quantum spherical fuzzy M-SWARA. Finally, the balanced scorecard-based project priorities of sustainable energy financing are ranked with quantum spherical fuzzy WASPAS. The main contribution of this study is that a detailed evaluation is performed to understand significant strategies for the improvements of sustainable energy financing with a novel model. Calculation of the expert weights with AI increases the quality and originality of the model. Similarly, considering M-SWARA, WASPAS, quantum theory, and spherical fuzzy sets also increases the effectiveness of the model because of managing uncertainties more effectively. The technical competence of the enterprise and Funding diversification are found as the most important items in increasing the effectiveness of sustainable energy financing. Additionally, according to the ranking results, it is determined that financial issues and customer needs are the most significant alternatives.