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Öğe Multidimensional assessment of the European Energy Union: Integrating artificial intelligence and quantum fuzzy ranking approaches(2025) Liu, Peide; Dinçer, Hasan; Yüksel, SerhatThe purpose of this study is to determine the significant indicators of European Energy Union policy. A novel artificial intelligence (AI)-enhanced and hybrid quantum fuzzy ranking approach are taken into consideration in this regard. There are three different stages in this hybrid model. The first stage includes the weighting the dimensions of the European energy union with AI-enhanced prioritization. The second stage consists of measuring the multidimensional performance for European energy union with artificial intelligence-enhanced ranking. In the final stage, the multidimensional performance for European energy union is evaluated by using quantum picture fuzzy rough sets based VIKOR methodology. The main contribution of this study is making a comparative evaluation with both artificial intelligence approach and fuzzy decision-making methodology. This situation provides an opportunity check the validity of the findings. In the analysis process, a novel artificial intelligence-based ranking methodology is proposed. Therefore, more effective evaluations can be conducted. The findings indicate that energy security, market functioning, and innovation have the best priorities. In case of maximum group utility, financial feasibility and technological infrastructure have the best ranking performance among the perspectives. The results with the highest maximum group utility are same with the priority results of the AI-enhanced ranking technique.Öğe Strategic feasibility outlook for blue energy investments using an integrated decision-making approach(2025) Eti, Serkan; Yüksel, Serhat; Dinçer, HasanConducting feasibility analysis in blue energy investments is very critical to provide performance analysis of the projects. However, a significant portion of the studies in the literature focus on general energy projects. Nevertheless, there are not enough studies for a more specific area such as blue energy. This situation significantly increases the need for this type of priority analysis. Accordingly, the purpose of this study is to identify the most appropriate strategies to increase the effectiveness of the feasibility analysis of blue energy investments via a novel decision-making model. In the first stage of the model, the importance levels of experts are computed using machine learning technique. The second stage includes weighting the feasibility criteria set for blue energy project investment by Fermatean fuzzy entropy. After that, the strategic alternatives for increasing the capacity of blue energy projects are ranked with Fermatean fuzzy CoCoSo. The main contribution of this study to the literature is making a detailed evaluation to generate appropriate strategies for the feasibility analysis of the blue energy investments via a novel decision-making model. The integration of AI system provides some advantages to the proposed model. In this way, the decision matrix is obtained by calculating the importance weights of each expert. This situation allows to have more accurate analysis results. It is defined that the technological infrastructure of the company plays the most critical role (weight: 0.173) when conducting feasibility analysis for blue energy investments. Similarly, it is also identified that the financial performance of the business (weight: 0.172) is also important to conduct a more successful feasibility analysis for blue energy investments. On the other side, the ranking results demonstrate that collaborating with the investment-ready companies for increasing the innovative technologies is the most appropriate strategy to increase the capacity of blue energy projects.Öğe Triz-driven assessment of sector-wise investment decisions in renewable energy projects through a novel integrated q-rof-dematel-srp model(2025) Yüksel, Serhat; Ecer, Fatih; Krishankumar, Raghunathan; Dinçer, Hasan; Gökalp, YaşarNecessary actions should be taken to improve renewable energy investments to minimize the carbon emission problem. In this process, the most significant determinants should be identified for some reasons, such as using human and financial resources more effectively. However, there are limited studies in the literature that prioritize the analysis of these items. This situation can be accepted as a missing gap in the literature. Accordingly, this study evaluates sector-wise investment decisions in renewable energy projects. To do so, a novel integrated q-rung orthopair fuzzy set (q-ROFS) decision-making model has been generated. Firstly, the weights of the theory of the solution of inventive problems (TRIZ)-driven criteria are computed via the q-ROF decision-making trial and evaluation laboratory (DEMATEL) methodology. The second stage of the proposed model consists of selecting the most appropriate investment alternatives with the help of the q-ROFS-based simple ranking process (q-ROF SRP). The main contribution of this study is that key sector-wise investment decisions in renewable energy projects can be identified by establishing a novel decision-making model. The main superiority of the proposed model is that the DEMATEL method is extended to the q-ROFS context to determine the weights of the factors. With the help of this issue, uncertainties and subjective randomness in the decision-making process can be minimized. In addition to this situation, causal directions between these indicators can be taken into consideration for this condition. The findings indicate that possible extension with modularity is the most critical indicator for this situation. Similarly, resource efficiency is also found to be the most influencing item. In addition to them, the ranking results demonstrate that waste-to-energy technologies and energy storage systems are the most critical investment alternatives.Öğe Innovative approaches to green digital twin technologies of sustainable smart cities using a novel hybrid decision-making system(2025) Cao, Jifeng; Spulbar, Cristi; Eti, Serkan; Horobet, Alexandra; Yüksel, Serhat; Dinçer, HasanDigital twin technologies play a very important role in the provision of smart cities. However, it is not clear which innovative strategies should be implemented first on how to optimize digital twin technologies for sustainable smart cities. The main performance indicators should be identified to use the limited resources more efficiently and to generate more effective investment strategies. Accordingly, the purpose of this study is to generate innovative approaches to green digital twin technologies of sustainable smart cities. To achieve this issue, first, important criteria of digital twin technologies for assessing sustainable smart cities are weighted via spherical fuzzy simple weight calculation (SIWEC). The second part includes ranking innovative alternatives for green digital twin technologies by considering spherical fuzzy simple additive weighting (SAW). The main contribution of this manuscript is to identify the most effective investment strategies for the improvements of digital twin technologies to reach smart cities by establishing a novel model. The preference of the SIWEC method in the criteria weighting process provides some advantages to the model. The most important feature of this method is that the direct importance of the criterion is taken into consideration in the questions asked to the experts. In this way, it is possible for the experts to give more sensitive answers. The findings indicate that smart grid integration (weight: 0.231) is determined as the most important criterion in the development of digital twin technologies for the provision of smart cities. Circular economy (weight: 0.230) is another variable that plays an important role in the provision of smart cities. On the other hand, decentralized energy system with microgrids is found as the most important alternative for this situation. Some critical actions should be taken to improve the smart grid effectiveness. In this scope, providing renewable energy incentives has a strong influence on the improvements of these projects.Öğe Innovative financial solutions for sustainable investments using artificial intelligence-based hybrid fuzzy decision-making approach in carbon capture technologies(2025) Yüksel, Serhat; Eti, Serkan; Dinçer, Hasan; Gökalp, Yaşar; Olaru, Gabriela Oana; Kalaycı Oflaz, NihalHigh costs, technical difficulties, and policy uncertainties are the main challenges in carbon capture technology investments. Therefore, innovative financial products are required to develop projects that overcome these difficulties. Some issues must be considered when developing innovative financial products. An important problem in this process is that these features cannot possibly exist together in the new financial product, because each of these features incurs some costs. Therefore, identifying the most important features of innovative financial products is necessary. Accordingly, this study develops a new and innovative financial product to increase the effectiveness of investments in carbon capture technologies. For this purpose, a novel artificial intelligence (AI)-based fuzzy decision-making model is constructed. First, the weights of the experts were calculated by considering AI methodology. Second, the factors affecting investment in carbon capture technologies were weighted using a spherical fuzzy DEMATEL. Finally, the financial features required for investments were ranked using the spherical fuzzy ARAS method. This study’s main contribution is its creation of a novel fuzzy decision-making model by integrating AI methodology with fuzzy decision-making theory. In this process, the weights of the experts are calculated using an AI approach. It is concluded that cost-effectiveness must be prioritized in the development of new financial products. Technological competence is another aspect that should be considered in this process. However, innovative financial products should include risk management and flexible financing.Öğe Analysing the financial innovation-based characteristics of stock market efficiency using fuzzy decision-making technique(2025) Rahadian, Dadan; Firli, Anisah; Dinçer, Hasan; Yüksel, Serhat; Mikhaylov, AlexeyNecessary actions should be taken to ensure stock market efficiency; thus, financial innovation-based criteria that affect stock market efficiency should be improved. However, simultaneously improving all criteria is difficult; therefore, performing priority analysis is important for carrying out this process effectively and efficiently. Accordingly, this study aims to evaluate the financial innovation-based characteristics of stock market efficiency. This study’s main research question within this framework is identifying which factors should be prioritized to improve the stock market. In this scope, we created a novel fuzzy decision-making model consisting of two stages. First, selected criteria for the financial innovation-based characteristics of stock market efficiency are weighted. In this process, quantum spherical fuzzy sets based on DEMATEL are considered. In the second stage, selected economies are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS) approach. This study’s main contribution is that the DEMATEL technique in calculating criterion weights in the decision-making analysis process provides some advantages. With the help of this situation, the causal directions between these items can be considered; thus, it is possible to determine the most accurate strategies. The findings demonstrate that providing tax advantages is the most important factor in ensuring stock market efficiency. Moreover, the excellence of the financial system is critical in ensuring stock market efficiency. In this context, it is possible to provide tax advantages, especially for long-term investments. Thus, long-term investments can be increased, significantly increasing the market’s stability.Öğe A novel fuzzy decision-making approach to pension fund investments in renewable energy(2025) Yüksel, Serhat; Eti, Serkan; Dinçer, Hasan; Meral, Hasan; Umar, Muhammad; Gökalp, YaşarPension fund must consider some significant issues when making renewable energy project investment decisions. It is necessary to determine the most important factors and prioritize the indicators. Accordingly, the purpose of this study is to conduct a priority analysis of the determinants of investment in renewable energy projects by pension funds. This study constructs a novel fuzzy decision-making model. First, five indicators for this process are weighted using an entropy methodology based on sine trigonometric Pythagorean fuzzy sets. The CRITIC methodology is also considered to make a comparative evaluation. Second, five different clean energy investment alternatives for pension funds are ranked using the RATGOS methodology. Similarly, this ranking analysis is also made by considering TOPSIS technique to check the reliability of the results. The main contribution of this study is the creation of a new and comprehensive fuzzy decision-making model to identify the most important factors in renewable energy project investments for pension funds. The proposed model uses the RATGOS technique to rank clean energy investment alternatives for pension funds. By considering the geometrical mean in the RATGOS calculation process, criticisms related to existing ranking techniques can be overcome. The use of sine trigonometric Pythagorean fuzzy numbers provides significant benefits to the quality of the proposed decision-making model. The defuzzification process can be implemented appropriately using these sets. Therefore, this study’s findings pave the way for investors to make investment decisions under these circumstances. It is concluded that the most important criterion is risk minimization. Effective regulations are another critical issue. Furthermore, the ranking results indicate that the most suitable renewable energy alternative is green bonds. The comparative results with STPFY-TOPSIS show that the proposed model generates coherent and reliable findings.Öğe A novel approach to prioritizing health technology investments using integrated ai-based ranking model(2025) Gökalp, Yaşar; Eti, Serkan; Dinçer, Hasan; Yüksel, SerhatPurpose – Health technologies are an issue that directly affectsthe sustainability and quality of health services. Due to budget constraints, it is not financially possible for businesses to apply comprehensive improvement strategies to all these criteria. In this case, it is possible for businesses to implement more priority strategies. Accordingly, the main purpose of this study is to evaluate the important performance indicators of health technology investments. Design/methodology/approach – Firstly, with the help of the artificial intelligence system, a decision matrix is established. Secondly, spherical fuzzy total order of preference decision-making trial and evaluation laboratory methodology istaken into consideration for weighting the criteria. Thirdly, emerging seven countries are ranked by using spherical fuzzy MultiAtributive Ideal-Real Comparative Analysis (MAIRCA). Findings – The findings demonstrate that the criteria of health policies and research and development are defined as the most significant factor in this regard. China and Turkey are also found to be the most successful emerging countries with respect to the performance of health technology investments. Originality/value – The main contribution of this study is that a novel decision-making model is generated by integrating artificial methodology into the spherical fuzzy sets.Öğe Is hotel revenue performance effective for destination competitiveness? an assessment by wavelet coherence analysis(2025) Doğangün, Itır; Şanlıöz Özgen, Kader; Türegün, NidaHotel industry is crucial for destination competitiveness as part of quality tourist infrastructure. The high performance of the industry promises a positive effect on destination competitive. This study aims to develop a approach focusing on the hotel revenue-related performance data in the case of European cities (Istanbul, London, Madrid, Paris, and Rome). Wavelet coherence analysis was applied on revenue data of the years 2013–2022 from 4000+ hotels. Findings reveal time–frequency relationships between hotel revenue variables, highlighting consistent coherence in supply–demand relationships during the pandemic, except for Rome. Furthermore, the analysis detected differentiated patterns in supply–revenue coherence, with Istanbul’s market showing unique fluctuations. London is the city with higher revenue expertise. Key revenue performance indicators of hotels emerged as significant determinants of a city’s competitiveness. These insights present implications for policymakers, community stakeholders, and industry practitioners, emphasizing the pivotal role of adept revenue professionals in improving competitiveness in their destinations.Öğe A molecular fuzzy decision-making model for optimizing renewable energy investments towards carbon neutrality(2025) Shen, Yedan; Liu, Wei; Yüksel, Serhat; Dinçer, HasanIdentifying the most important factors is necessary to determine which areas should be given priority in the energy transition. In this way, it is possible to increase the efficiency of investments by using resources effectively. However, there are limited studies in the literature focusing on this issue. Hence, a new study is needed to determine the most important factors affecting the success of renewable energy integration. Accordingly, the purpose of this study is to find the most critical renewable energy investment strategies to implement effective carbon neutrality policies. A new model is generated to reach this objective. Firstly, to define expert prioritization, an evaluation is conducted by artificial intelligence. Secondly, selected indicators are weighted via molecular fuzzy cognitive maps. Thirdly, alternative strategies of carbon neutrality policies are ranked by fuzzy molecular ranking. The main contribution of this study is that effective investment policies related to renewable energy integration can be determined for successful carbon neutrality policies by created a novel model. The most significant superiority of this model is that fuzzy decision-making methodology is integrated with molecular geometry science. In this process, by computing the degrees with different geometrical shapes, uncertainties in the evaluation process can be handled more effectively. The findings denote that technological infrastructure is the most critical performance indicator of renewable energy integration projects. Similarly, economic feasibility is found as the second most essential determinant of this situation. On the other hand, setting the long-term contracts with renewable producers is the most essential investment alternative to implement effective carbon neutrality policies.Öğe Integrated information system based on q-learning algorithm and multi-objective particle swarm optimization with molecular fuzzy-based decision-making for corporate environmental investments(2025) Dinçer, Hasan; Yüksel, Serhat; Olaru, Gabriela Oana; Eti, SerkanDetermining the most important criteria is a great necessity to increase the environmental performance of renewable energy projects. This situation helps to reach cost efficiency and effective use of limited resources. However, there are very few studies in the literature where priority analysis is made for these factors. This condition indicates an important gap in the literature on this subject. Accordingly, this study aims to identify the most convenient investment strategies to increase the environmental performance of renewable energy projects. First, the balanced expert dataset is generated via Q-learning algorithm and multi-objective particle swarm optimization. In the following step, selected criteria are weighted with molecular fuzzy cognitive maps approach. The final stage consists of ranking the alternatives with fuzzy molecular ranking (MORAN). The contribution of this study to the literature is to define the most appropriate investment strategies to increase the environmental performance of these projects by generating a new decision-making model. Integrating molecular geometry into decision-making processes increases both the originality and effectiveness of the model. Thus, it is possible to calculate the degrees with a more scientific infrastructure. Integrating Q-learning and swarm optimization techniques into the decision-making model also increases the superiority of the model. In this context, the optimization technique is used to obtain the importance weights of the experts. The findings denote that energy capacity expansion plays the most important role in improving the environmental performance of businesses because it has the greatest weight (0.2563). Return on investment is another factor that plays an important role in increasing this performance with the weight of 0.2559. Geothermal and hydropower are found as the most successful renewable energy types regarding environmental performance improvement since they have the highest aggregated values (0.534 and 0.620). Carbon tax is an important policy application to increase renewable energy capacity. With the help of this tax, fossil fuels lose their competitive advantage significantly. As a result, this situation has a positive contribution to the increase of the renewable energy projects.Öğe Synergistic integration of digital twins and sustainable industrial internet of things for new generation energy investments(2024) Kou, Gang; Dinçer, Hasan; Yüksel, Serhat; Deveci, MuhammetIntroduction: This study aims to identify optimal digital twin policies for enhancing renewable energy projects. Through a comprehensive analysis, the research evaluates the potential of digital twins in the renewable energy sector while considering triple bottom line perspectives. Objectives: The study's main goal is to prioritize digital twin policies that can effectively boost renewable energy projects. The research aims to demonstrate the practical application and reliability of a proposed evaluation model. Methods: Nine criteria, derived from literature review and triple bottom line viewpoints, are selected. Using the decision-making trial and evaluation laboratory (DEMATEL) methodology and Quantum picture fuzzy rough sets, criteria weights are determined. Quantum picture fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) evaluates sustainable industrial internet of things strategies in new-gen energy investments. VIsekriterijumska optimizcija i KOmpromisno Resenje (VIKOR) methodology enables a comparative assessment, and sensitivity analysis is conducted across nine cases. Results: Consistent outcomes across various methods validate the model's reliability. Ecosystem preservation carries the highest weight (0.1147), followed by resource policy optimization with digital twins (0.1139). Distributed energy resilience ranks first (RCi 0.576), closely followed by energy efficiency optimization (RCi 0.542). Conclusion: This study underscores ecosystem preservation and efficient resource policies as pivotal for successful digital twin deployment in renewable energy projects. The findings highlight digital twins' potential contribution to environmental protection and ecosystem sustainability, emphasizing resource efficiency through their effective use.Öğe Understanding the market potential of crypto mining with quantum mechanics and golden cut-based picture fuzzy rough sets(2024) Dinçer, Hasan; Yüksel, Serhat; Pinter, Gabor; Mikhaylov, AlexeySignificant improvements should be made to increase the market potential of crypto mining. However, it is not financially feasible to make too many improvements because all actions lead to cost increases. In this context, it is necessary to determine the factors that most affect this process. Accordingly, the purpose of this study is to understand the main indicators that can improve the market potential of crypto mining activities. Therefore, the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities. In this context, a new model has been constructed to answer this question. First, significant indicators are identified based on the evaluation of the literature. After that, these factors are weighted via quantum picture fuzzy rough set-based M-SWARA. The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of crypto mining activities. The M-SWARA model is taken into consideration for criteria weighting. Owing to this issue, the causal relationships between the items can be identified. The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry. Furthermore, technological developments also play an important role in this regard.Öğe A machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: insights into industrial information integration(2024) Eti, Serkan; Yüksel, Serhat; Dinçer, Hasan; Pamucar, Dragan; Deveci, Muhammet; Olaru, Gabriela OanaThe most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.Öğe Assessment of water electrolysis projects for green hydrogen production with a novel hybrid q-learning algorithm and molecular fuzzy-based modelling(2024) Dinçer, Hasan; Eti, Serkan; Acar, Merve; Yüksel, SerhatDetermining the most important criteria for increasing the efficiency of water electrolysis investments provides businesses with a competitive advantage. Although there are many studies in the literature on this importance, there are very few studies determining the most important of these performance indicators. To satisfy this gap, the purpose of this study is to make assessment of water electrolysis projects for green hydrogen production via a novel model. First, the balanced expert evaluation matrices are obtained by q-learning algorithm. Secondly, the criteria for water electrolysis investments are prioritized using molecular fuzzy Bayesian network (BANEW). Thirdly, green hydrogen strategies for water electrolysis investments are ranked with molecular fuzzy multi-objective particle swarm optimization (MOPSO). The most important contribution of this study to the literature is the determination of the criteria that should be applied primarily for the performance increase of water electrolysis investments by creating a new model. The use of molecular fuzzy numbers is a very important contribution of the model to the literature. In this process, the use of three-dimensional geometric figures allows the reduction of uncertainties in decision-making processes. The findings indicate that lifespan of electrolysers and production capacity are the most essential criteria. Additionally, proton exchange membrane electrolysers and alkaline water electrolysis are found as the most critical green hydrogen strategies. Extending the life of electrolysers is crucial to increase sustainability in hydrogen production and reduce long-term costs. In this context, research incentives should be provided for the development of materials and technologies to increase the durability of electrolysers. Similarly, establishing quality standards to extend the life of electrolysers also contributes to achieving this goal.Öğe Innovative solution suggestions for financing electric vehicle charging infrastructure investments with a novel artificial intelligence-based fuzzy decision-making modelling(2024) Kou, Gang; Eti, Serkan; Yüksel, Serhat; Dinçer, Hasan; Ergün, Edanur; Gökalp, YaşarThe right methods for effective financing of electric vehicle charging infrastructure investments should be identified. However, in the literature, there is no consensus on which funding source would be right for these projects. There is a need for a new study to recommend the most appropriate financing strategy for these projects. Accordingly, the purpose of this study is to identify innovative solutions for financing electric vehicle charging infrastructure investments. A novel fuzzy decision-making model is introduced to reach this objective. Firstly, the weights of experts are calculated using dimension reduction. Secondly, Spherical fuzzy decision matrix is obtained. Thirdly, the criteria in charging infrastructure for electric vehicles are weighted using Spherical fuzzy criteria importance through intercriteria correlation (CRITIC). Fourthly, innovative solutions for financing electric vehicles charging infrastructure are ranked via Spherical fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS). The main contribution of this study is that effective strategies can be identified for financing electric vehicle charging infrastructure investments by establishing a novel decision-making model. Most of the existing models in the literature could not consider the weights of the experts. This condition is criticized by different scholar because these experts can have different qualifications. To satisfy this problem, in this study, dimension reduction algorithm with machine learning is taken into consideration to compute thee weights of the experts. The findings demonstrate that the most effective criterion in the innovative financial solution for the charging infrastructure of electric vehicles is determined as “potential income”. According to the ranking results, it is also defined that the most sustainable solution among the innovative strategies for financing the charging infrastructure of electric vehicles is “blockchain technology”.Öğe A neuro decision-making approach for prioritizing circular economy criteria in sustainable smart cities(2024) Kou, Gang; Dinçer, Hasan; Yüksel, Serhat; Alotaibi, FahdSustainable cities are crucial in establishing effective waste management systems and minimizing environmental pollution. For cities to be sustainable, different aspects need to be considered, such as technological development, clean energy usage, and energy efficiency. However, taking the most important actions is essential because of the very high cost that will arise, and this situation causes countries to have budget deficit problems. In other words, there is a significant need for a new study that makes a priority analysis with respect to the circular economy-based criteria for smart cities. Accordingly, this study aims to identify significant factors to improve sustainable cities using a novel decision-making model. First, essential determinants of the smart cities were evaluated with the decision-making trial and evaluation laboratory (DEMATEL) technique based on quantum spherical fuzzy sets (QASH) and facial expressions of the decision-makers. Second, smart investment choices for sustainable cities were ranked according to the technique for order preference by similarity to ideal solution (TOPSIS) approach. In addition, comparative ranking results were constructed together with sensitivity analysis. The ranking results of the extended VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are compared with the extended TOPSIS results and their sensitivity analysis results. The main contribution of this study is that appropriate priority strategies were determined by using an original methodology to have sustainable cities. A new methodology is developed in this study by the name of neuro decision-making. According to the comparative evaluation and sensitivity analysis, the findings are found as reliable and relevant. Resource efficiency is the most critical factor in improving sustainable cities. Constructing sustainable buildings is the most appropriate strategy for increasing smart cities. Necessary actions should be taken to minimize unconscious water and energy use. New technological developments need to be quickly adapted to businesses. In this way, it would be possible to perform the same work amount using less energy and water. For this purpose, it is important both to provide the necessary training and to emphasize the importance of these issues in television advertisements.Öğe Technical assessment of solar energy storage investments with recommender system-enhanced quantum picture fuzzy rough sets(2024) Kou, Gang; Dinçer, Hasan; Yüksel, Serhat; Eti, Serkan; Acar, MerveThe performance of solar energy storage projects should be improved by taking appropriate actions. However, there are very different criteria that affect the performance of these investments. Therefore, businesses need to focus on more important criteria to use the budget effectively and efficiently. This situation increases the need for a priority analysis for performance indicators of solar energy storage investments. Accordingly, the purpose of this study is to make evaluation for the technical assessment of solar energy storage investments. In this scope, a new four-stage model is introduced by considering different decision-making techniques and fuzzy sets. The first stage is related to the prioritizing the experts with artificial intelligence (AI)-based decision-making method. Secondly, the missing evaluations of solar energy storage investments are estimated with expert recommender system. In the following part, the criteria for the technical assessment of solar energy storage investments are weighted by quantum picture fuzzy rough sets (QPFRS) adopted M−SWARA. The final stage consists of ranking the solar energy storage alternatives with QPFR-VIKOR. The main contribution of this study is the generation of the decision matrix by the help of AI. This situation gives an opportunity to calculate the significance weights of the experts. Therefore, the analysis results can be more reliable and coherent. It is concluded that battery capacity is the most critical factor for the technical assessment of solar energy storage investments. On the other hand, pumped hydro for mechanic energy is found as the most significant solar energy storage alternative. Governments should provide the necessary incentives for the development of high-capacity battery technologies. In this context, tax reductions can be provided to companies that invest in production technologies. This contributes to the cost efficiency of businesses.Öğe Nuclear energy utilization and the expectations of emission-reduction gains: empirical evidence from economic trajectory of selected utilizing states(2025) Bozkaya, Şeyma; Onifade, Stephen Taiwo; Duran, Mahmut SamiAs the global quest for clean energy grows, the environmentally friendly nature of nuclear energy as a potential non-fossil energy source is generating interest around the world. Therefore, we examine whether nuclear energy utilization has significantly driven carbon emission reduction among the utilizing states. Empirical analyses were conducted using second-generation techniques. The analyses conducted also incorporated testing the EKC theory, as well as examining the effects of natural resources and economic growth on emissions in the sample countries. The empirical analyses cover data from 2000 to 2020 for a total of 27 nuclear energy-using countries as obtained from the Statistical Review of World Energy (Bp, 2021). The findings show that neither the use of nuclear energy nor natural resources significantly reduces carbon emissions across the countries. Additionally, the EKC hypothesis of reduction in emission levels as income expands beyond a certain threshold does not hold for the countries. Moreover, the causality analysis shows that there is a one-way causality from emissions to nuclear energy use. These findings thus highlight the need for more research on how to minimize the indirect carbon footprint that is associated with nuclear energy utilization.Öğe Evaluating smart grid investment drivers and creating effective policies via a fuzzy multi-criteria approach(2025) Dinçer, Hasan; Krishankumar, Raghunathan; Yüksel, Serhat; Ecer, FatihIt is critical to determine which factors impact more smart grid investments and which smart grid investment policy is more suitable for renewable energy projects. Nonetheless, a limited amount of research has focused on this topic, meaning a new study is needed to fill this gap and aid in making decisions under ambiguities. Thus, this research proposes a novel fuzzy group decision-making framework. Twelve drivers are examined through the fuzzy weighted decision-making trial and evaluation laboratory (F–DEMATEL–W) methodology. Subsequently, four smart grid investment policies are ranked using fuzzy weighted aggregated sum product assessment (F–WASPAS). Hence, one of the novelties of this research is the proposal of a robust decision-making tool named F–DEMATEL–W–WASPAS. Other novelties are: (i) the importance of the indicators/criteria is methodically determined by considering pairwise interactions and weights of experts; (ii) both individualistic expert-driven weight vector and cumulative weight vector of indicators are determined; (iii) alternative policies are ranked with minimum decision parameters; (iv) drivers that are crucial for the effectiveness of smart grid investment are determined with their causal relationship, and (v) smart grid investment policies are ranked reliably. The findings demonstrate that cyber security, sufficient legal procedures, and financial viability are the foremost drivers to increase the effectiveness of smart grid investments. Moreover, encouraging sustainable energy production using financial incentives is the foremost policy, followed by exchanging surplus electricity for the system owners. The work may contribute to the ongoing discussion on designing smart grid investment policies for renewable energy projects.
