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Abstract Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, among these side effects. However, the number of studies that make a priority examination is limited. There is a need for a new study that determines which of these effects are more priority to increase the quality of the treatment. Accordingly, this study aims to define the most important side effects of antidepressant drugs with a novel model. Quantum Spherical fuzzy M-SWARA technique is considered to compute the importance weights of the items. The main contribution of this study is that the most critical side effects can be understood for antidepressant drugs by establishing a novel decision-making model. The findings demonstrate that psychological side effects are defined as the most critical side effects of antidepressant drugs. Furthermore, physical side effects also play a key role in this condition. Side effects in antidepressant treatment have a great impact on the effectiveness of treatment and patient compliance.

This study evaluates the synergy of coalition for hybrid renewable energy (RWB) system alternatives. In this context, the alternative sources of hybrid RWB system are examined to illustrate the impact-relation directions among them with multi SWARA based on q-ROFs and golden cut. Next, the performances of renewable alternatives are measured in terms of the synergy of coalition with game theory and Shapley value. It is concluded that solar energy is the most suitable RWB alternative for synergy to increase efficiency in investments. However, biomass does not have a significant influence on providing synergy in energy investments. Therefore, solar energy should be prioritized for hybrid energy investments. Especially with the effect of technological developments, the efficiency of solar energy investments increases significantly. Thus, solar energy investments have become quite suitable for increasing the synergy in hybrid energy projects. Furthermore, necessary research should be conducted to make biomass energy more efficient.

Countries should focus on the improvements to achieve sustainable development goals. However, taking these actions create some operational costs. Thus, it is appropriate for rising nations to prioritize sustainable development objectives. Due to this condition, more critical sustainable development goals should be identified. Accordingly, this study evaluates green energy-oriented sustainable development goals for emerging economies. Within this context, a model is generated by considering the golden cut and bipolar q-rung orthopair fuzzy sets (q-ROFSs). The novelty of this study is to identify key green energy-oriented sustainable development goals for the improvement of emerging economies by an original model based on the golden cut and bipolar q-ROF decision making approach. All results are similar, indicating the proposed model's validity and coherency. It is concluded that increasing renewable energy is the most significant target with a weight of 0.235 regarding green energy-oriented sustainable development. Cooperation and efficiency are other significant issues for this condition, with weights of 0.198 and 0.196. It is also identified that Turkey and Mexico are the most successful countries in reaching green energy-oriented sustainable development goals. Increasing the share of renewable energy in total energy production and consumption is important for emerging countries. For this situation, governments should provide subsidies for renewable energy investors to provide a cost advantage. In this context, issues such as tax cuts and loans with low-interest rates can increase the motivation of clean energy investors.

Abstract The current study evaluates the financial innovation life cycle for renewable energy investments. A novel model is proposed that has two stages. First, the financial innovation life cycle is weighted by the two-generation technology S-curve (TTSC) for renewable energy investments. Second, the TTSC is ranked with integer patterns for renewable energy investments. For this purpose, the decision-making trial and evaluation laboratory (DEMATEL) is considered with q-rung orthopair fuzzy sets (q-ROFSs). A comparative examination is then performed using intuitionistic and Pythagorean fuzzy sets, and we find similar results for all fuzzy sets. Hence, the suggested model is reliable and coherent. Maturity phase 1 is the most significant phase of the financial innovation life cycle for these projects. Aging is the most important period for financial innovation in renewable energy investment projects—renewable energy companies should make strategic decisions after that point. In this situation, decisions should relate to either radical or incremental innovation. If companies do not make decisions during these phases, innovative financial products cannot be improved. As a result, renewable energy companies will not prefer financing products.

The purpose of this study is to make a comprehensive analysis of investor perceptions in the context of green and conventional bond investments. For this purpose, a new model is presented by considering two steps. First, a criteria set is generated by considering balanced scorecard perspectives that are finance, customer, organizational effectiveness and learning and growth. After that, the neuro Quantum fuzzy M-SWARA method is considered to weight these criteria. Secondly, seven critical determinants for bond investments are identified that are coupon rates, volume, maturity, riskiness, liquidity, volatility, and tax considerations. Neuro Quantum fuzzy TOPSIS approach is employed to rank these factors. The main contribution of the study is that by combining the balanced scorecard framework and quantum-inspired decision-making techniques, this paper offers a novel and sophisticated decision-making model to understanding investor behavior. Similarly, in the proposed model, a new methodology is generated by the name of M-SWARA. In this framework, some enhancements are adopted to the SWARA technique. The weighting results indicate that meeting customer expectations is the most critical factor that affects the investor perception to make investments to the bonds. Moreover, according to the ranking results, it is concluded that coupon rates are the most important item for both conventional and green bond investors. On the other hand, with respect to the conventional bond investor, tax is the second most essential factor. However, regarding the green bond investors, volatility plays a critical role. AcknowledgmentThis research has been/was/is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (№ AP 19679105 “Transformation of ESG financial instruments in the context of the development of the green economy of the Republic of Kazakhstan”).

Objective: This study aimed to identify the most significant issues for the effective performance management of hospitals. Methods: Accordingly, seven indicators were selected based on the literature review results. An analysis was carried out using the Spherical fuzzy TOP-DEMATEL method to determine the most important ones among these criteria. Results: The main contribution of this study was that the prior indicators of effective performance management in the hospitals were identified. With the help of this issue, hospitals can take actions to improve performance without having too much costs. Moreover, the main methodological originality of this study is that a new decision-making model is proposed by the name of TOPDEMATEL. Conclusion: Effective supply chain is found as the best factor that affects the performance of the hospitals. Additionally, advanced technology also plays a key role in this framework. Nevertheless, qualified personnel criterion is on the last rank. Supply network refers to the process of providing medical supplies, medicines, equipment, and other resources for healthcare institutions. These resources are of great importance for the performance of hospitals as they are needed to deliver the service.

Abstract The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.

Abstract Crypto assets have become increasingly popular in recent years due to their many advantages, such as low transaction costs and investment opportunities. The performance of crypto exchanges is an essential factor in developing crypto assets. Therefore, it is necessary to take adequate measures regarding the reliability, speed, user-friendliness, regulation, and supervision of crypto exchanges. However, each measure to be taken creates extra costs for businesses. Studies are needed to determine the factors that most affect the performance of crypto exchanges. This study develops an integrated framework, i.e., fuzzy best–worst method with the Heronian function—the fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function (FBWM’H–FMARCOS’H), to evaluate cryptocurrency exchanges. In this framework, the fuzzy best–worst method (FBWM) is used to decide the criteria’s importance, fuzzy measurement of alternatives and ranking according to compromise solution (FMARCOS) is used to prioritize the alternatives, and the Heronian function is used to aggregate the results. Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness. Through case studies, some well-known cryptocurrency exchanges operating in Türkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process. The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology. “Perception of security,” “reputation,” and “commission rate” are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment. Further, the best score is achieved by Coinbase, followed by Binance. The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses. The findings may pave the way for investors to take appropriate actions without incurring high costs.

Abstract The risk profiles of investors play an important role in the success of derivative products investments. It is also necessary to determine the appropriate investor profile according to the conditions of a country. This protects investors from major losses. Therefore, there is a strong need for a new study that analyzes the risk profiles of derivative products in emerging economies. This study aims to identify the most appropriate investor risk profile for derivative instruments in emerging economies. It constructs a novel decision-making model. In this model, the facial action coding system, quantum theory, spherical fuzzy sets, and multi-stepwise weight assessment ratio analysis (M-SWARA) methodology are integrated to minimize uncertainty in this process. First, three main participants of derivatives for financial technology investors are evaluated using the quantum spherical fuzzy M-SWARA technique. After that, seven emerging economies are ranked based on this performance using quantum spherical fuzzy technique for order of preference by similarity to ideal solution. The main contribution is that an appropriate investor profile is identified for financial derivative investments in emerging economies. One of the important originalities of this study is the development of a new technique called M-SWARA by making some improvements to the classical stepwise weight assessment ratio analysis method. It is concluded that hedging is the most appropriate purpose for making investments in derivative products in emerging economies. It is also identified that China has the greatest performance among the seven emerging economies regarding the appropriate purpose for making derivative investments. For the effective management of this problem in these countries, it is recommended to prevent the speculative use of derivative products with legal regulations.

This paper proposes a new model to study carbon emission issues in seven emerging (E7) economies (China, Mexico, Turkey, Russia, Brazil, Indonesia and India). It employs weighted Quantum Spherical fuzzy DEMATEL model with golden cut using 18 years data, which was ranked for carbon emission evaluation via Quantum Spherical fuzzy TOPSIS method. The data includes macroeconomic variables for the period from 2003 to 2020. Paper observes that (i) CO2 emissions from fossil fuels is the most significant factor distressing the carbon emission problem of E7 economies. It suggests that these countries need to explore alternatives to fossil fuels to handle the carbon emission problem. (ii) The CO2 emission problem in energy production can be minimized by renewable energy and nuclear energy, suggesting some financial incentives to the investors in energy explorations areas. (iii) It is also observed that due to high volatility of CO2 emissions, a negative market factor exists in consumer sentiment in consumption of oil and petroleum products. This can be compensated via energy consumption fiscal policy in taxation and encourage of extractive industries and producers of petroleum products, as well as the uneven spatial development to have a significant impact on the reduction of CO2 emission. The paper gives recommendation that E7 countries need to find alternatives to fossil fuels in order to deal effectively with the carbon emission problem.

This article presents a comparative analysis of the determinants of hydropower for European economies using Golden Cut oriented Quantum Spherical fuzzy modelling and causality analysis in 24 European countries over the period 2001–2020. The indicators chosen for the analysis are inflation, population, GDP per capita, CO2 and hydropower consumption. The analysis shows that the selected groups of countries are characterised by an inverse relationship between GDP per capita and hydropower consumption, suggesting a bi-directional causal relationship, which also confirms the novelty of this paper. Furthermore, another analysis is carried out using the fuzzy decision-making methodology. In this framework, the directions of influence of the five selected indicators are constructed: GDP per capita (criterion 1, D = 88.656, E = 88.083), hydropower consumption (criterion 2, D = 89.471, E = 88.677), population (criterion 3, D = 87.705, E = 89.228), CO2 emissions (criterion 4, D = 88.578, E = 89.186) and inflation (criterion 5, D = 88.943, E = 88.180). The Quantum Spherical fuzzy methodology is used for this purpose. The values of D and E are measures of the sum of the rows and columns of the overall relationship matrix. Hydropower consumption is the main criterion. It is understood that two different analyses give similar results, namely the bidirectional causal relationship between criteria 1 and 2.

The purpose of this study is to examine optimal solar module investments. Firstly, key determinants of the performance of solar energy investments are evaluated by DEMATEL method with the 2-tuple IVIF sets. Moreover, the cell material alternatives for solar module investments are also ranked. For this purpose, an evaluation has been made by 2-tuple IVIF TOPSIS. The contributions of the paper are performing a priority analysis to understand the most significant factors to increase solar energy projects and creating an original model by the integration of DEMATEL and TOPSIS with the 2-tuple IVIF sets. The findings denote that crystalline silicon is the optimal solar panel module to increase the performance of these projects. In the short term, government subsidies can provide cost advantages to solar energy investors. It is not a very continuous practice to try to increase these projects only with government supports. The costs of solar energy projects should be reduced to solve this problem permanently. Owing to new technological developments, high cost problem of solar energy investments can be handled more successfully.

Abstract Companies must be able to identify their suppliers appropriately and effectively in order to survive in the competitive market conditions. In order to fulfill and surpass the expectations of the consumers and clients, companies need to interact with the relevant suppliers. It is a tough manner for companies to select the best supplier from a large number of relevant alternatives. The selection process of the appropriate supplier involves multiple interacting and competing factors. Generally, the selection process and its results cause a waste of time and money. For this purpose, MCDM methodologies are utilized to manage this complex process efficiently. MCDMs allows for consistent and accurate decision-making as well as the selection of the most appropriate supplier. MCDM is one the most preferred tool to select the best alternative under the conflicting and competitive criteria when the evaluations are made in crisp numbers. Therefore, MCDM methods are preferred in various applications in academia and real life. However, the evaluations could not be always possible with crisp numbers, especially in vague environments or evaluations needs qualitative data. This study is one of the first to combine the AHP and COPRAS supplier selection methods with interval-valued Pythagorean fuzzy (IPF) logic. The effectiveness of these IPF-AHP and IPF-COPRAS evaluations for the supplier selection problem is compared and examined. The experimental results of case scenarios show that IPF is an effective way to apply in decision-making applications. In addition, sensitivity analysis is conducted to evaluate the proposed methodologies. According to sensitivity analysis, the IPF-AHP and IPF-COPRAS be able to illustrate the effects of small changings in criteria weights. Therefore, companies can use the IPF-AHP and IPF-COPRAS to assist their decision-makers in identifying and selecting the best suppliers.

The purpose of this study is to assess technical and financial challenges for renewable energy project alternatives with a novel model. Firstly, a set of technical and financial challenges is created by considering the results of the literature evaluation. After that, the weights of these values are computed by Sine Trigonometric Pythagorean Fuzzy (ST-PFN) DEMATEL. The next step includes the ranking of six renewable energy alternatives with interval valued Spherical fuzzy multicriteria analysis with ratio and categorical data (SF MAIRCA) approach. Entropy and ARAS methodology are also considered to make comparative evaluation. Similarly, sensitivity analysis is also conducted for 10 different cases. It is concluded that the analysis results of all methods are the same. Therefore, it is identified that the findings of the proposed model are reliable and coherent. Identification of the most crucial technical and financial challenges of the renewable energy projects is the most important contribution of this manuscript. Furthermore, preferring the sine trigonometric structure in the proposed model will provide superiority to this model in many respects. It is concluded that difficulty in accessing finance for local projects is the most significant challenge for renewable energy investments. Moreover, competitive pricing of renewable sources is another critical issue in this regard. Additionally, it is identified that solar is the most successful renewable energy alternative to overcome technical and financial challenges.

This study addresses the challenges faced by organizations in prioritizing sustainability reporting aligned with the United Nations’ Sustainable Development Goals (SDGs). We examine the cause-and-effect relationships among sustainability dimension-related disclosures and specific SDG criteria. This study advocates a prioritization approach to sustainability reporting across sustainability dimensions and SDG reporting criteria. To conduct our empirical analysis, we designate SDG13 (Climate Change), SDG7 (Affordable and Clean Energy), and SDG12 (Responsible Consumption and Production) as representatives of the sustainability dimensions related to the biosphere, society, and economy, respectively. We employ a novel methodology that integrates quantum spherical fuzzy sets (QSFS) into the decision-making trial and evaluation laboratory (DEMATEL) technique. We demonstrate the superiority of our methodology, which enhances the precision of decision-making under uncertainty. Our findings reveal that biosphere disclosure influences the disclosure of the remaining dimensions. Notably, we find that the disclosure criteria for each SDG are interrelated and can be ranked according to the intensity of their influence on each other. Disclosure of “materiality analysis for sustainable energy” is the most influential factor. These findings have managerial implications for future studies. By identifying the interrelations among disclosure criteria and prioritizing the most influential factors, companies can focus their efforts on the most impactful aspects. Our findings provide organizations with a foundation for developing an innovative reporting framework with a prioritization approach to sustainability disclosures. Policymakers can leverage these insights to enhance current reporting practices and contribute to broader discourse on sustainable development.

The purpose of this study is to identify priority criteria in green (net zero energy) building transformation. For this purpose, an analysis has been conducted for hospitals by considering new methodology. In this scope, firstly, 10 different indicators are selected with the help of literature examination. After that, by using this new methodology, the weights of these items are computed. The main contribution of this study is that a priority evaluation is conducted so that more critical indicators of green building transformations can be identified. Because of this issue, for financial sustainability, companies should firstly focus on more significant factors. Another important novelty of this study is that a new decision-making technique is generated. With the help of this issue, both problems in methodology can be solved and methodological originality can be achieved. The findings show that renewable energy usage is the most critical factor.

Abstract This research paper analyzes revenue trends in e-commerce, a sector with an annual sales volume of more than 340 billion dollars. The article evaluates, despite a scarcity of data, the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period. The analysis covers monthly data from January 1996 to February 2021. The research paper analyzes relative changes in the original time series through the autocorrelation function. The objects of this analysis are Amazon and Alibaba, as they are benchmarks in the e-commerce industry. This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA, concluding that it is weaker for companies with small market capitalizations. As a result, the effect on estimated e-trade volume in the USA was approximately 35% in 2020. Another evaluation considers fuzzy decision-making methodology. For this purpose, balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut. Within this framework, a detailed analysis of competitors should be made. The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry. Therefore, it may be possible to attract greater attention from e-commerce companies for these financial innovation products.

Abstract This study aimed to evaluate the components of a fintech ecosystem for distributed energy investments. A new decision-making model was created using multiple stepwise weight assessment ratio analysis and elimination and choice translating reality techniques based on quantum spherical fuzzy sets. First, in this model, the criteria for distributed energy investment necessities were weighted. Second, we ranked the components of the fintech ecosystem for distributed energy investments. The main contribution of this study is that appropriate strategies can be presented to design effective fintech ecosystems to increase distributed energy investments, by considering an original fuzzy decision-making model. Capacity is the most critical issue with respect to distributed energy investment necessities because it has the greatest weight (0.261). Pricing is another significant factor for this condition, with a weight of 0.254. Results of the ranking of the components of the fintech ecosystem indicate that end users are of the greatest importance for the effectiveness of this system. It is necessary to develop new techniques for the energy storage process, especially with technological developments, to prevent disruptions in energy production capacity. In addition, customers’ expectations should be considered for the development of effective and user-friendly financial products that are preferred by a wider audience. This would have a positive effect on fintech ecosystem performance.

Abstract In the last decade, the risk evaluation and the investment decision are among the most prominent issues of efficient project management. Especially, the innovative financial sources could have some specific risk appetite due to the increasing return of investment. Hence, it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending. Accordingly, this study aims to analyze a unique risk set and the strategic priorities of fintech lending for clean energy projects. The most important contributions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets. The extension of multi stepwise weight assessment ratio analysis (M-SWARA) is applied for weighting the risk factors of fintech lending. The extension of elimination and choice translating reality (ELECTRE) is employed for constructing and ranking the risk-based strategic priorities for clean energy projects. In this process, data is obtained with the evaluation of three different decision makers. The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA. Hence, the causality analysis between the criteria can also be performed in this proposed model. The findings demonstrate that security is the most critical risk factor for fintech lending system. Moreover, volume is found as the most critical risk-based strategy for fintech lending. In this context, fintech companies need to take some precautions to effectively manage the security risk. For this purpose, the main risks to information technologies need to be clearly identified. Next, control steps should be put for these risks to be managed properly. Furthermore, it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financiers integrated into the system. Within this framework, the platform should be secure and profitable to persuade financiers.

Abstract The purpose of the paper is to identify the factors of financial development that have the greatest impact on open innovation in 7 emerging countries. The analysis was performed featuring the MF-X-DMA method, as well as its further verification for autocorrelation and heteroscedasticity. The time period covers years from 2002 to 2020. The article states that the main indicators to improve financial development should enhance the process of bank lending and equity market development. An important area is the development of competition by providing equal access to information to all market participants in a continuously refining technical infrastructure. Regression analysis with the MF-X-DMA method confirms the statistical significance of this influence. The article fills the knowledge gap into the link between open innovations and the relatively low capitalization of the modern emerging countries’ financial market, low liquidity in small cap stocks at the financial market and concentration of the banking sector, as well as risks arising in the process of globalization. Another analysis has also been conducted by generating a novel fuzzy decision-making model. In the first stage, the determinants of open innovation-based fintech potential are weighted for the emerging economies. For this purpose, M-SWARA methodology is taken into consideration based on bipolar q-ROFSs and golden cut. The second stage of the analysis includes evaluating the emerging economies with the determinants of open innovation-based fintech potential. In this context, emerging seven countries are examined with ELECTRE methodology. It found the most significant factor is the open innovation-based fintech potential.

This study aims to identify critical factors of the sustainability index (SI) and evaluate the industries listed in the stock exchange based on the performance of this index. For this purpose, a new fuzzy decision-making model has been created. Firstly, 12 different criteria for the SI are weighted by decision using trial and evaluation laboratory (DEMATEL) methodology based on quantum spherical fuzzy sets (QFS) with golden cut. Secondly, SI performances of the 10 industry alternatives are ranked by extended technique for order of preference: according to similarity through an ideal solution (TOPSIS) with QFS. The main novelty of this study is to identify significant sustainability criteria for the companies to join this index with an original fuzzy decision-making methodology. For this purpose, the analysis results help the companies to act without incurring extreme costs. It is determined that the usage of renewable energy has a significant impact on all other criteria, and while using clean energy, the companies get a chance to improve other criteria of SI. Additionally, based on the weighting results, the use of renewable energy is the most critical criterion to be included in SI. Similarly, recovery of the resources also has a significant contribution. Finally, the ranking results indicate that communication and IT is the most successful industry for SI. Hence, it would be appropriate for businesses to attach importance to the use of renewable energy. In this way, the carbon emission problem would be minimized, and the image of businesses will rise in the eyes of both consumers and investors. In this framework, companies can join microgrid energy management systems to handle high-cost problems of renewable energy investment projects. Owing to microgrid energy applications, the costs of using renewable energy can be shared with other companies, and since this situation can reduce the cost per unit, it will be possible to increase the use of renewable energy.

Smart grid systems help increase RWJ projects (RWJ) so that environmentally friendly energy production can be generated. However, efficient technologies should be implemented to ensure the sustainability of smart grid systems. This study aims to evaluate renewable-friendly smart grid technologies regarding distributed energy investment projects by using a hybrid picture fuzzy rough decision-making approach. Firstly, selected criteria are weighted using the multi stepwise weight assessment ratio analysis (M-SWARA) method based on picture fuzzy rough sets (PFRSs). Subsequently, different renewable-friendly smart grid technologies are ranked with the complex proportional assessment (COPRAS) technique by using PFRSs. It is determined that research and development play the most critical role with respect to the renewable-friendly smart grid technologies for distributed energy investment projects. On the other side, cost is another essential factor for this issue. It is also identified that direct current links are the most important renewable-friendly smart grid technology alternative. Priorities should be given to the development of research and development studies on renewable energies to increase the efficiency of smart grid systems. In this context, private sector companies have a very important role. Similarly, incentives provided by governments to RWJ research and development studies should be increased. Within the scope of these studies, new technologies for RWJ types should be emphasized. In this context, new technologies for all RWJ alternatives should be followed comprehensively. Increasing research and development for such investments will also make smart grid systems more successful.

The aim of this study is to examine the cost management strategies of low-carbon renewable energy projects. This manuscript has important contributions by evaluating different cost-management to increase renewable energy investment projects. Owing to the analysis results of this study, the ways to minimize carbon emission problem can be presented. Additionally, another important contribution of this study is to generate a novel hybrid model based on Pythagorean fuzzy DEMATEL, TOPSIS and Shapley value to find appropriate policies to improve these projects. Furthermore, the accuracy of the proposed model is measured for each cooperative cost management strategy by using the VIKOR method. In addition, sensitivity analysis is also applied with 5 cases for both the TOPSIS and VIKOR methods by changing the weighting results of the criteria consecutively. It is defined that the proposed model is coherent and applicable for the further studies. Moreover, the ranking results of the sensitivity analysis are also consistent with the different cases. The results indicate that internal process has always the lowest costs for the solar energy alternatives. In addition, customer is the lowest cost factor with respect to the wind energy alternative. Thus, it is obvious that improving the qualification of the employees is essential for the improvement of the solar energy projects. Moreover, the effectiveness of the wind energy investments can be increased with the help of giving significance to the customers. Furthermore, it is also concluded that when the level of the cooperation increases, the efficiency of the investments can be higher. Another important point is that if the investors prefer to make weak or strong cooperative cost management strategy, they should primarily focus on solar energy projects because they have lower costs in comparison with other alternatives.

Bioenergy is a renewable energy source that saves from fossil fuel dependence. Therefore, it is important to increase the efficiency of bioenergy investments to create an environmentally sustainable energy supply. This paper aims to identify economic indicators significant in forecasting the supply of bioenergy. Considering this goal, an integrated evaluation has been performed for 17 developed economies using the Random Forest method and the Fuzzy Decision-Making Trial and Evaluation Laboratory (QROF-DEMATEL) method. The main contribution of this study is conducting analysis by using both quantitative and qualitative data. Additionally, the coherence of the results made with the QROF-DEMATEL method is also verified by implementing a sensitivity analysis. The results of both approaches are quite similar and provide information about the reliability of the findings. This situation demonstrates that for the development of bioenergy investments, firstly, countries’ macroeconomic conditions should be improved. Consequently, economic growth and unemployment (weighting results - 0.159 and 0.155) should be primarily considered for the bioenergy supply forecast.

Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24–48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.

The purpose of this study is to analyze the investment success of renewable energy generation projects design. A novel model has been constructed for this purpose. At the first stage, collaborative filtering methodology is taken into consideration to complete missing evaluations. After that, M-SWARA based on QUSFSs with golden cut is used to compute the weights of these factors. Finally, the components of the service design are ranked by TOPSIS approach. The main contribution of the paper is that a new methodology (M-SWARA) has been created in this study by making improvements to SWARA. With the help of this new model, causal directions between the indicators can also be examined. Similarly, collaborative filtering methodology is taken into consideration to complete missing evaluations. In this process, the decision makers are allowed to leave the questions they wanted blank. This situation is considered as the superiority of the proposed model compared to many previous models in the literature. The findings indicate that cost is the most significant factor for the success of renewable energy investments because it gets the highest weight (.261). The ranking results also demonstrate that product is the most essential component of the service design of renewable energy investments. Therefore, solving the high-cost problem is of vital importance to increase these investments. First, renewable energy companies can reduce costs with more effective financial management. To carry out this process effectively, a finance department consisting of qualified personnel is needed. Thanks to this team, current situations in the financial markets will be better followed and this will play an important role in reducing costs.

The purpose of this study is to identify affordable and clean energy-based climate change priorities in USA for the sustainable development. Five factors that can influence clean energy-based sustainable development are weighted with Multi step wise weight assessment ratio analysis approach. Furthermore, selected priorities for the climate change goal of sustainable development are evaluated. The novelty of this study is presenting affordable and clean energy-based climate change priorities for the sustainable development by considering an original fuzzy decision-making model based on M-SWARA and ELECTRE with bipolar q-rung orthopair fuzzy sets and golden cut. Because they include both membership, non-membership and hesitancy, it can be possible to perform more effective analysis. This issue helps to reach more reliable results. The main findings solve the problem that government support has the greatest weight with respect to the clean energy-based sustainable development.

Ensuring energy performance management is important in many ways, such improvement of energy efficiency and decrease of energy costs are reduced. There are various indicators of the effectiveness of energy performance management of buildings. Due to this situation, businesses need to make the necessary improvements for the development of these factors. Nonetheless, these actions cause an increase in the costs of the companies. Hence, among these actions, the more important ones need to be identified. Owing to this issue, businesses can use their limited budgets for more priority indicators. The purpose of this study is to evaluate the main indicators of energy performance management systems. In this way, a new model is proposed to make a priority analysis for the hospitals. Firstly, five indicators of energy performance management systems are selected by considering ISO 50006 standards. Furthermore, these indicators are weighted by using Spherical fuzzy CRITIC. Secondly, G7 countries are examined with fuzzy RATGOS technique. Identification of the most significant indicators of the energy performance systems is an important novelty of this study. The most significant methodological novelty of this study is proposing a new technique to the literature named RATGOS. It is understood that energy efficiency is the most crucial indicator of energy performance management. Furthermore, it is also identified that France is the most successful G7 economy with respect to the energy performance management. Japan and United States have also high performance in this respect. It is recommended that necessary actions should be taken to increase energy efficiency. By conducting an energy audit, energy consumption data is analyzed so that energy losses and inefficiencies can be detected. This assessment provides opportunities for energy efficiency and helps identify improvement strategies.

Abstract This study evaluates financial innovation priorities for renewable energy investors by generating a novel hybrid fuzzy decision-making model. First, SERVQUAL-based customer needs for financial innovation are weighted with decision-making trial and evaluation laboratory based on picture fuzzy sets. Second, the financial innovation priorities are ranked by technique for order preference by similarity to ideal solutions based on picture fuzzy rough sets. In this process, Theory of the solution of inventive problems-based technical characteristics for financial services, the process for innovative services, and competencies for financial innovation are considered using quality function deployment phases. In addition, the Vise Kriterijumska Optimizacija I Kompromisno Resenje method is also considered for an alternative ranking. Similarly, sensitivity analysis is also performed by considering five different cases. It is determined that the ranking priorities based on the proposed model are almost identical, demonstrating the proposed model’s validity and reliability. Assurance is the most crucial factor for the customer needs regarding the financial innovation priorities for renewable energy investors. Concerning the financial innovation priorities, the product is the essential priority for financial innovation; hence, it is recommended that companies engage qualified employees to effectively design the financial innovation for renewable energy investors. Additionally, necessary training should be given to the employees who currently work in the company, which can increase the renewable energy investors’ trust in the innovative financial products. Companies should mainly focus on the product to provide better financial innovation to attract renewable energy investors. An effectively designed financial innovation product can help solve the financing problem of renewable energy investors.

Abstract Effective crowdfunding platforms positively contribute toward improving microgrid energy management systems. Therefore, a comprehensive analysis is required to understand the key factors responsible for success in crowdfunding systems by considering various criteria. This study evaluates crowdfunding platforms for microgrid project investments. In this context, a novel fuzzy decision-making model that includes two different stages is proposed. First, the selected criteria for the crowdfunding platforms for microgrid project investments are evaluated. Second, alternatives, regarding the microgrid project investments, are ranked. In this process, a multi-stepwise weight assessment ratio analysis (M-SWARA) approach based on q-rung orthopair fuzzy sets (q-ROFSs) is considered. Intuitionistic and Pythagorean fuzzy sets are also used in the calculation process to make a comparative evaluation. Similarly, a sensitivity analysis of the ranking alternatives is also conducted with 12 different q values. All the results are rather similar; thus, the findings are reliable. Another model is also created for this purpose with the help of the decision-making trial and evaluation laboratory (DEMATEL) and the technique for order preference by similarity to ideal solution methodologies to check the performance of the proposed model. It is defined that by considering the q-ROF DEMATEL weights, the ranking results vary for different cases. The proposed model with a M-SWARA is more reliable than the model created via the DEMATEL method. This situation provides information regarding the superiority of the model proposed in this study. It is concluded that security is the most important factor in crowdfunding platforms for smart-grid project investors. Additionally, solar panels and energy storage systems/batteries are the most significant alternatives for microgrid project investors. Necessary measures should be taken to forestall the risk of fraud that may occur on this platform. Therefore, the website to be established must be secure against possible hacking attacks. Another important conclusion of this study is that solar panels should be preliminarily developed to increase the effectiveness of microgrid systems.

Bu çalışmada finans kurumlarının etkinliklerinin arttırılmasına yönelik olarak insan kaynakları departmanlarının performansına etki eden önemli faktörlerin belirlenmesi amaçlanmıştır. Bu çerçevede, bulanık DEMATEL yöntemi dikkate alınarak bir inceleme gerçekleştirilmiştir. Dengeli skorkart yaklaşımı, Thor Modeli, Fitzgerald modeli ve kapsamlı literatür taraması sonuçları dikkate alınarak performansa etki edebilecek yeni bir kriter seti önerilmiştir. Sonuç olarak, yeni teknolojiye yatırım yapılması en fazla önem ağırlığına sahip olan kriter olarak belirlenmiştir. Benzer şekilde, yeni teknolojiye yönelik eğitimlerin verilmesi ve kullanılan ekipman ve altyapı yeterliliği de yüksek önem ağırlığına sahip diğer faktörlerdir. Bu çerçevede, finans kurumlarının etkinliklerini arttırabilmeleri için yeni teknolojiye odaklanmaları gerekmektedir. Kullanılacak yeni programlar ile personele yönelik veriler daha kapsamlı bir şekilde analiz edilebilecektir. Ayrıca, finans kurumlarında çalışan personelin bu yeni teknolojiye hakim olması gerekmektedir. Bu çerçevede, finans kurumları personel istihdam ederken bu hususa çok dikkat etmelidirler. Dolayısıyla, özellikle analitik yönü güçlü olan personelin tercih edilmesi gerekmektedir. Bu bağlamda, üniversitelerin mühendislik ve matematik bölümlerinden mezun olan adaylar dikkate alınması bu sürece katkı sağlayacaktır. Daha başarılı insan kaynakları ile finans kurumlarının etkinliklerinin arttırılabilmesi de mümkün olabilecektir.

Abstract This study defines key issues for sustainable healthcare policy in COVID-19 period. For this purpose, 9 different criteria that affect vaccine hesitancy are selected with the help of a detailed literature evaluation. A novel hybrid fuzzy decision-making model is developed using DEMATEL and TOPSIS based on q-Rung orthopair fuzzy sets. A comparative evaluation has also been performed using IF DEMATEL and PF DEMATEL. The results of all different methods are almost the same that indicates the reliability and coherency of the proposed model. The findings demonstrate that religion is the most critical factor that causes vaccine hesitancy. It is also defined that active population in daily life is the most important alternative. Developing countries should mainly focus on the actions regarding the religious issues to have sustainable healthcare policies in COVID-19 period. In this context, religious leaders can be released to the media and give information that the vaccine is not against religious rules. This has a significant contribution to convince people who are against the vaccine. Furthermore, these countries should also give priorities to the active population in daily life. Because this group supports the workforce in the country very seriously, it can be possible to increase the workforce in the country by completing the vaccination of this group that helps to boost economic development.

Abstract The purpose of this study is to provide a hybrid group decision-making approach to evaluate fintech-based financial alternatives for green energy investment projects. First, the multidimensional factors of due diligence for fintech-based financing alternatives of green energy investment projects are identified. In this regard, the balanced scorecard perspectives are considered. Next, consensus-based group decision-making analysis is performed. Second, impact-relation directions for fintech-based financing alternatives of green energy investment projects are defined. For this purpose, the spherical fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is applied. The novelty of this study is its proposal of a new outlook to due diligence of fintech-project financing for renewable energy investments by using the group and integrated decision-making approaches with spherical fuzzy DEMATEL. The findings indicate that customer expectations are the most essential factor for the revenue sharing and rewarding models. Additionally, this study identified that organizational competency plays the most important role with respect to the peer-to-business debt model. In contrast, the conclusion was reached that financial returns have the greatest importance for the equity sharing model.

The main goal of this study is to evaluate the impact of population mobility on electricity generation in Russian cities in the conditions of the spread of COVID-19, and identify hotspots. Furthermore, the evaluation is also conducted using hybrid fuzzy decision-making modelling. In this context, q-ROF DEMATEL and TOPSIS methods are taken into consideration. Additionally, a comparative evaluation is also performed with the help of Intuitionistic and Pythagorean fuzzy sets. The results are quite similar that allows to conclude that the findings are reliable and coherent. The study proves the hypothesis that human behavior changed during the COVID-19 pandemic, and electricity consumption is declining in major cities around the world. The biggest fall in energy generation was in Moscow and Yekaterinburg. In St. Petersburg and Nizhny Novgorod, the fall in energy generation is no so crucial because these cities have low building density. The study uses Long Short-Term Memory models with many different parameters. The Q-Rung Orthopair Fuzzy Sets model forecasts new COVID-19 using ten parameters. This study identifies factors influencing the spread of COVID-19 based on the theory of ''broken windows'' and outlines directions in limiting population mobility, which can form the basis of state policy. According to the analysis the air temperature is the variable that most affects this process.

This study aims to identify appropriate innovative strategies for solar energy investments for both commercial and non-commercial users. For this purpose, TRIZ-based 8 different innovative strategies are identified. Later, these strategies are weighted with the interval type-2 fuzzy DEMATEL (IT2 FDEMATEL) method to define more important strategies. Thus, the main motivation of this study is to figure out the weights of the strategies for solar energy investments The findings indicate that the replacement of mechanical system is found as the most effective TRIZ-based investment strategy for solar energy projects regarding both commercial and non-commercial customers. Thus, it is recommended that solar energy companies should give special support to their customers with respect to the security issues. Moreover, local quality is another significant strategy for commercial users. Therefore, it will be appropriate to provide special services according to the characteristics of the customers. Within this framework, having distribution centers close to customers and employing customer representatives who know well the culture of that region can be very helpful. Additionally, cushion in advance is found as another appropriate TRIZ-based innovative strategy for commercial customers. Hence, it would be appropriate to ensure the security in solar panels. Within this context, necessary measures should be taken for problems such as electric shock, material destruction, theft, and risk of destroying data on the computer.

This study aims to identify the influencing factors of the effectiveness in energy investment. For this purpose, 8 different countries with continental shelf in the Eastern Mediterranean region (EMR) are considered. Firstly, 9 different criteria are defined based on the literature review. The significance levels of these factors are determined by considering hesitant interval-valued intuitionistic fuzzy (IVIF) DEMATEL based on 2-tuple linguistic values (2TLV). Therefore, the motivation of this study is to figure out the weights of the criteria for the energy investment projects. Also, the main contributions to the literature are to propose a set of criteria for defining the indicators of the efficiency and to construct a novel decision-making model based on IVIF technique in these projects. The main justifications of this hybrid method can be given as the importance of the distribution, political stability, and technological capacity for the effectiveness in energy investment. Furthermore, hesitant TOPSIS method is used to evaluate these 8 countries regarding the effectiveness in energy investments. It is concluded that Israel and Turkey are the most successful countries in this framework. It is recommended that these countries should have an effective pipeline to transfer the reserves to other countries. Also, these countries in this region should pay attention to political stability. Moreover, countries with energy reserves in the EMR should make the necessary investments to improve their technological infrastructure. Hence, seismic exploration and drilling vessels should be obtained by these countries so that the energy reserves can be located and obtained effectively.

This study aims to evaluate energy efficiency in emerging economies. Within this context, a hybrid fuzzy multi-criteria decision-making (MCDM) model is generated. In this model, firstly 7 different criteria that can affect energy efficiency are identified with the help of a comprehensive literature review. After that, these criteria are weighted by considering Gaussian fuzzy Entropy methodology. In the next stage, emerging 7 (E7) countries are ranked regarding the performance of energy efficiency with Gaussian fuzzy the interactive multi-criteria decision-making (TODIM) approach. In the final stage, the criteria have also been evaluated by using fuzzy decision-making trial and evaluation laboratory (DEMATEL) method for robustness check. Similarly, the alternatives are also ranked by using technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the coherency of the analysis results. Finally, the calculations are also made with triangular and trapezoidal fuzzy numbers to control the reliability of the proposed model. The findings indicate that economic development and technological competency are the main factors that influence energy efficiency. Moreover, it is also stated that Russia and Brazil are the most successful countries in energy efficiency whereas India and Turkey are in the last places. Therefore, these issues can be helpful to minimize the costs of energy usage for these countries

The energy storage process becomes very important due to the imbalances in energy supply and demand. Therefore some factors need to be considered to increase the efficiency of the energy storage system, such as cost–benefit analysis and technological improvements. This study aims to examine the inventive problem-solving capacities for renewable energy storage investments. A new model is suggested for this objective by considering fuzzy decision-making methodology. It is concluded that prior action and dynamicity are the most essential capacities of renewable energy storage investments. Additionally, dynamicity plays the most critical role when all factors are considered in renewable energy investment projects in a collaborative manner. Hence, it is recommended that the companies should mainly consider the initial developments of the storage facilities. Moreover, location selection for effective energy storage should also be considered to increase the performance of these investments.

This study aims to analyse environmental management systems for sustainable energy price. For this purpose, a new fuzzy multi-criteria decision-making (MCDM) model is generated. First, seven different criteria are defined with supported literature. After that, these factors are weighted by considering decision making trial and evaluation laboratory (DEMATEL) methodology based on 2-tuple hesitant interval-valued Pythagorean fuzzy sets (HIVPFs). Furthermore, different pricing strategies are also ranked by using HIVPF technique for order preference by similarity to ideal solution (TOPSIS) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Also, a sensitivity analysis is also performed by considering six different conditions. The main contribution of this study is proposing a new mathematical model to solve a significant problem regarding energy prices. It is concluded that the results of both TOPSIS and VIKOR are quite similar. On the other side, it is also understood that the results of six different cases are almost the same. This situation explains that analysis results are reliable and coherent. The findings indicate that safety incidence and waste management are the most crucial factors for the sustainability of the energy price. In addition, skimming is the best pricing strategy for the energy industry. Within this framework, energy companies should firstly offer high prices to the market. After that, the price should be reduced when there is a decrease in the demand.

Abstract This study aims to evaluate the crowdfunding alternatives regarding new service development process pathways of clean energy investment projects. In this framework, a new model has been generated by considering the consensus-based group decision-making with incomplete preferences, Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) and technique for order preference by similarity to ideal solution (TOPSIS). Moreover, a comparative evaluation has been performed with Vise Kriterijumska Optimizacija I. Kompromisno Resenje methodology and sensitivity analysis has been made by considering 4 different cases. The main contribution is to identify appropriate crowdfunding-based funding alternatives for the improvement of the clean energy investments with a novel MCDM model. By considering the iteration technique and consensus-based analysis, the missing parts in the evaluations can be completed and opposite opinion problems can be reduced. Furthermore, with the help of hybrid MCDM model by combining DEMATEL and TOPSIS, more objective results can be reached. It is concluded that the analysis results are coherent and reliable. The findings indicate that the full launch is the most significant criterion for equity and debt-based crowdfunding alternatives. On the other side, the analysis has the highest weight for reward and donation-based alternatives whereas design is the most essential item regarding the royalty-based alternative. Additionally, it is also defined that equity-based crowdfunding alternative is the most significant for the service development process of clean energy investment projects. In this way, it will be possible to provide a continuous resource for clean energy investment projects. On the other hand, by providing financing with equity, there will be no fixed financing cost for clean energy investors. If these investors make a profit, they distribute dividends with the decision of their authorized bodies.

Abstract Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify “competitive advantage” as the most important factor among Fintech-based determinants. Moreover, “payment and money transferring systems” are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank’ receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

Serious environmental problems occur as a result of the unconscious production and use of energy. Hence, there is a strong need to develop an effective energy culture to provide sustainability for the energy investments. This study aims to identify the influence directions of dimensions and criteria for energy culture in emerging economies for sustainable energy investments. For this purpose, a multi-criteria decision-making model is proposed based on 2-tuple linguistic values and interval-valued intuitionistic fuzzy sets. The weighting results of energy culture are also evaluated for E7 economies. The findings demonstrate that energy companies should consider investing in technologies, assets, and objects which enable to reproduce energy usage. Furthermore, the analysis revealed that there is a misalignment of practices with material culture and norms what creates the potential for behavior change of consumers. Finally, the potential of bigger countries for investment opportunities in sustainable energies has been disclosed by ranking emerging economies. The results let us provide insights into development of energy investments by considering strategic priorities of energy culture in emerging economies. The novelty of this paper is to define a set of criteria and dimensions for the strategic priorities of energy culture in emerging economies and construct a novel decision-making technique with DEMATEL and TOPSIS based on 2-tuple and interval-valued intuitionistic fuzzy sets.

This study aims to find out the significant stages of new product development process for the industrial decarbonization of sustainable economies by using interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL). The findings demonstrate that commercialization is the most significant process of new product generation process of industrial decarbonization. Moreover, it is also concluded that with respect to the sub-criteria, cost analysis, and performance evaluation have the highest weights. An effective cost analysis is required in the new product development process. The costs of the product development process can in some cases be much higher than anticipated. This situation eliminates the effectiveness of the newly developed product. In this context, companies need to make the necessary plans for the costs of these new products correctly. On the other hand, it is important to follow the costs in detail during the process. Otherwise, the product that does not provide a cost advantage will not be preferred by industrial companies. This will cause the actions to be taken to reduce carbon emissions to fail.

Since the global warming problem threatens the whole world, it is understood that countries should develop energy policies that will increase their sustainable and clean energy investments. Compared to other alternatives, the high cost of renewable energy projects is an essential obstacle in this process. Therefore, priority should be given to developing distributed energy projects to minimize this problem. The scope of the present paper is to identify the most critical items that affect the performance of distributed energy projects to have knowledge-oriented competencies. In this way, companies can focus on more critical items to provide efficiency for distributed energy projects. As a result, clean energy usage is improved, and the global warming problem is handled more successfully. A novel decision-making model is generated to examine the competencies of the knowledge economy based on collaborative filtering and bipolar q-rung orthopair fuzzy sets (q-ROFSs) with the golden ratio. The analysis concludes that learning and growth are the most critical balanced scorecard perspectives. Moreover, it was also determined that information and communication technology is the most critical competency of the knowledge economy. Therefore, it would be appropriate for investors who plan to invest in distributed energy projects to form a research and development team. Hence, new technologies will be followed instantly. In this way, companies will be able to gain a cost advantage. In this context, improving distributed energy projects is important to increase efficiency in clean energy investments.

This study aims to evaluate the positive impacts of renewable energy usage on the economic growth and financial development. For this purpose, an evaluation has been performed with VAR analysis. In the analysis process, annual data for the period between 1990 and 2015 is used. The findings show that renewable energy usage and financial development do not have a powerful influence on the economic growth. However, it is identified that renewable energy usage has a positive impact on the financial development. Hence, it is strongly recommended that renewable energy usage should be encouraged by policy makers in the country. Nonetheless, to understand the main indicators of economic growth, many different factors should be taken into consideration. It is seen that owing to the use of environmentally friendly energy, the size of the financial sector increases. It is obvious that environment-friendly energy usage attracts the attention of the financial investors. In this context, it would be appropriate to give some incentives to renewable energy investors. Within this framework, issues such as tax reduction, location support, and low interest loan opportunities will make renewable energy investments more reasonable.

The main purpose of this study is to make an analysis to understand whether the exchange rate channel is effective in Azerbaijan. In this framework, quarterly data between 2001:01 and 2017:02 is examined in this study. Furthermore, VAR method is taken into the consideration in the analysis process. The findings show that exchange rate channel is very important for Azerbaijani economy. In other words, the exchange rate channel is working on the total output and price axis. Hence, it is recommended that necessary actions should be taken by a central bank regarding the effective usage of exchange rate channel to contribute to industrial production and employment.

This study aims to find out the strategic priorities of the technical factors to have sustainable low carbon industry. For this purpose, a hybrid multi-criteria decision-making (MCDM) is proposed that contains three different stages. First, the economic criteria required for sustainable development and the technical needs of the low carbon industry are defined by making a comprehensive literature review. After that, economic criteria are weighted by using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology. Finally, technical factors are ranked with the help of the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach. Moreover, another analysis is also performed by considering fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to evaluate the consistency of the analysis results. The main motivation of this study is to define the primary technical factors to minimize carbon emission problem by proposing a hybrid MCDM model. The findings indicate that research and development for renewable sources has the greatest importance for low-carbon industry. In addition, the analysis results of fuzzy TOPSIS and fuzzy VIKOR are quite similar. This situation demonstrates the consistency and coherency of the ranking results. Hence, it is recommended that countries should mainly give importance to the research and development investments so that the costs of renewable energy problems can be minimized. This situation can attract the attentions of the companies to invest in these projects. In this way, it will be possible to use a cleaner energy in industrial production.

This study aims to identify the necessary strategies for the development of a sustainable financial system. For this purpose, a novel approach could be provided for soft computing with Gaussian-based fuzzy DEMATEL approach to understand the significant levels and impact-relation degrees of these criteria. For robustness check, this evaluation has also been performed for triangular and trapezoidal fuzzy sets. There are many novelties of this study. Firstly, computer science has a significant role in the decision-making process. Another specificity is that the use of Gaussian numbers with fuzzy numbers is a new area. In addition, interval type-2 fuzzy (IT2F) sets also provide a significant advantage in decision-making under uncertainty. The results demonstrate that the size of the alternative financing, investment in efficient projects, and effective advertisement/promotions are the most important items to affect the performance of alternative banking system of emerging economies. Hence, it is recommended that the new banking applications should be applied by the state or the state can provide some incentives for foreign banks to invest in emerging economies, such as tax reduction. Another important point is that the financial intermediations should generate a component financial analysis team to examine the financial reports of the companies in detail so that the financial credibility of these companies can be evaluated more effectively.