The main objective of this paper is to analyze model settings of the International Energy Security Risk Index developed by the U.S. Chamber of Commerce. The study was performed using stepwise regression, principal component analysis, and Promax oblique rotation. The conclusion of the regression analysis shows that Crude Oil Price and Global Coal Reserves are sufficient to explain 90% of the variance of the Index. However, if a model that explains 100% of the variance of the Index is chosen and other variables are added, Global Coal Reserves loses importance due to the presence of other parameters in which it is contained. Regardless of the chosen model of analysis, it is evident that there is room for revising the Index and removing variables that do not contribute to its precision. The research showed that the main disadvantage of the variables that make up the Index rests with the fact that the variables are of different degrees of generality, that is, one parameter is contained in other parameters (unclear which other). The research covers data for 25 countries over a 26-year period, with the first year of the research being 1980 and the last 2016 (the latest available report).
Background The prices of energy resources are important determinants of sustainable energy development, yet associated with significant unknowns. The estimates of the impact of prices of energy products in the domestic market (for domestic consumers) are rare—hence the importance and novelty of this research. Therefore, the main goal of the paper is to assess the impact of domestic prices of gasoline, gas, coal, and solar energy on sustainable and secure energy future. Methods The research includes 14 countries (of which 7 are developed and 7 are developing countries) and a period of 5 years (2014–2018). The model also includes discrete variables: level of development (developing or developed), and the fact as to whether the country is an energy exporter or not. For the purposes of analysis, the following elements were used: Panel Data Analysis, Linear regression (with random and fixed effects), Durbin–Wu–Hausman test, and Honda test, with the use of R-studio software for statistical computing. Results The research showed that the biggest negative impact on energy sustainability was recorded by an increase in the price of coal and the smallest one by an increase in the price of solar energy. An increase in the price of gasoline has a positive impact, while an increase in the price of gas has no impact. The basic methodological result showed that the fixed effects linear model is more accurate than the random effect model. Conclusions The results of the paper, important as a sustainable energy policy recommendation, showed that the impact of changes in energy product prices is significantly greater in developing countries, but that the status of the country as an energy exporter has no significance. In addition, the paper points to the need to intensify the research on the assessment of the impact of energy product prices for domestic consumers on their ability to pay that price, because with a certain (so far undefined) increase in energy product prices, a certain group of domestic consumers moves into a category that is not in line with sustainable energy development and is extremely undesirable in every respect—energy poverty.
Background Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding. Methods Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx—Microsoft Research’s Bayesian Network Authoring and Evaluation Tool. Results Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks. Conclusion Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.
This paper gives a solution for improving e-Government
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