Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.

Hydroelectric energy investments involve substantial techno-economic risks that can increase costs and undermine economic sustainability if not properly managed. However, the literature lacks comprehensive studies addressing these risks. This study proposes a novel decision-making model to identify and prioritize strategies for effective risk management in hydroelectric projects. The model integrates z-scoring for expert selection, the Criteria Importance Assessment (CIMAS) method for weighting criteria, and the Alternative Ranking using Two-Step Logarithmic Normalization (ARLON) method for ranking EU-15 countries according to their strategies. Pythagorean fuzzy numbers are incorporated to better handle uncertainty and improve evaluation accuracy. Results indicate that challenges in adopting new technologies and grid integration issues are the most influential risk factors. The findings provide actionable insights for policymakers and investors to enhance the sustainability and efficiency of hydroelectric energy investments. Policymakers should implement targeted incentives and regulatory frameworks to accelerate technology adoption and address grid integration challenges in hydroelectric projects. Strategic planning should prioritize infrastructure modernization, cross-border energy cooperation, and capacity-building programs to enhance sector resilience and investment security.

Preventing the threat of stereotyping is critical for business performance improvements. Because of this situation, businesses must take the necessary precautions. However, these actions have an impact on cost increase for the businesses. The number of studies in the literature performing priority analysis for these factors is quite limited. This situation increases the need for a new study that prioritizes the analysis of these variables. Accordingly, this study aims to evaluate the factors against the stereotype threat in the sustainable business environment. An artificial intelligence model is implemented in the first stage to weigh the experts. In the following stage, selected criteria are evaluated with the help of T-Spherical fuzzy DEMATEL. Thirdly, a comparative analysis was performed using different values. Finally, selected industries are ranked by Spherical Fuzzy RATGOS with respect to the stereotype threat. The weights of the experts can be identified in the analysis process. This situation has a strong contribution to the effectiveness of the findings. It is concluded that training activities are critical to minimizing the threat of stereotypes in companies.

There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.

The most essential criteria should be determined in the selection of the suitable energy transition technologies due to budget deficit problem. Therefore, it is necessary to identify the most important criteria in energy transition technology selection. Therefore, a new study is needed to determine the most prominent issues in the correct selection of energy transition technologies. The purpose of this study is to identify the most appropriate energy transition technology alternative. Within this framework, a novel artificial intelligence (AI)-based fuzzy decision-making model has been presented. In the first part, the experts are prioritized by the help of AI methodology. In the next section, missing evaluations of energy transition technology investments are estimated via expert recommender system. Thirdly, the weights of the criteria for energy transition technology selection are computed by quantum picture fuzzy rough sets (QPFR) M-Stepwise Weight Assessment Ratio Analysis (SWARA). At the final stage, selected energy transition technology alternatives are ranked via QPFR-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The main contribution of this study is the integration of AI technique to the proposed model. Similar to this issue, using M-SWARA methodology in the process of criteria weighting increases the quality of the findings. This methodology helps to consider the impact relation map of the criteria. The findings demonstrate that the most important factor is cost-effectiveness of energy transition. Similarly, it is also found that the local ecosystem is the second most significant issue. On the other side, the ranking results denote that compact renewable systems for small scale production is the most optimal solution of energy transition technology alternatives.