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This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

The aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.

This paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.

Eco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.

The innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.

The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.

Analysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

Nuclear energy is of vital importance for the energy independence of countries. Therefore, some of the countries have given priority to nuclear energy investments. However, there are some risks in nuclear reactors. For this reason, some countries are uneasy about nuclear energy investments. Thorium-based nuclear power plants are also recommended to minimize these risks. It is possible to talk about many advantages of these power plants. However, there are some negative aspects to these investments. In this study, both positive and negative aspects of thorium-based nuclear power plants are discussed simultaneously. In this context, first, the literature was comprehensively examined, and seven different factors were determined. An analysis is then carried out using the DEMATEL method. It is concluded that the cost and uncertainties have the greatest weight. In addition, suitability and physical and chemical reasons are other significant items. The results obtained show that thorium-based nuclear power plants are not very efficient now. In this context, the costs of thorium-based nuclear reactors should be reduced by new research and development activities. Otherwise, the high-cost problem reduces the efficiency of these reactors. With the developing technology, it is necessary to reduce the high costs. If the costs cannot be reduced, financial sustainability of thorium-based nuclear reactors will not be possible, although there are many benefits.

This chapter aims to identify new perspectives of geothermal energy investments. For this purpose, all studies in the Web of Science regarding the geothermal energy are taken into consideration. These studies are evaluated with the help of text-mining approach. In this framework, most frequently stated words, two words, and three words are identified. It is concluded that technological development with respect to the geothermal energy is an important issue in this framework. After that, it is also determined that risk is another important factor in this regard. Finally, new implications regarding the geothermal energy are also considered by the researchers. Geothermal energy has a positive contribution to solve many different problems, such as energy dependency, current account deficit problem, and carbon emission. Hence, this study generated the significant issues to improve these investments. While considering the results, it is understood that technological developments related to the geothermal energy projects should be followed effectively. In addition, an effective risk evaluation should be conducted before implementing these projects.

In this study, it is examined how to improve the effectiveness of bitcoin market. In this framework, firstly, a wide literature review is made, and different factors that may be effective in this regard are identified. Afterward, an analysis is performed with Hurst method to determine which of these factors are more important. The study of cryptocurrency volatility is important in terms of financial instruments for hedging traditional assets, as well as in terms of pricing. The above results would be particularly useful in terms of portfolio management and risk management. This study discusses various financial issues related to cryptocurrencies, as well as some problems associated with the problems of their forecasting. To solve the problems of forecasting, the authors have not only studied, but also improved the forecasting method based on the machine learning algorithm in the MATLAB system using the tree model. Most of the analysis in the document relies heavily on studies by authors studying this issue. They can help make more informed decisions regarding financial investments and avoid the risks of using cryptocurrencies. The results may also assist regulators in creating a favorable legal environment for the functioning of the cryptocurrency market.

In this study, it is examined how to use long-term memory models for predicting the dynamics of cryptocurrencies. In this framework, all the most suitable models provide an adequate error at the level of a 5% confidence interval. The authors believe that the IGARCH and GJRGARCH models provide the best results in terms of modeling the volatility of the most popular cryptocurrencies. The IGARCH model is similar to the standard GARCH model with the addition of only the conditional volatility process with infinite memory. Due to the growing demand and interest in cryptocurrencies and crypto assets in general, the authors believe that they should be considered as a new segment of the financial market. There are many more questions about whether cryptocurrencies should be classified as currencies or as a commodity. Our analysis suggests that cryptocurrencies are financial assets. Most operations with them are carried out for investment purposes: either as long-term investments in new technologies, or in order to obtain short-term profits.