The research presented in the article was aimed at verifying the effectiveness of environmental taxes in reducing the level of greenhouse gas emissions. For this purpose, data provided by Eurostat in the environmental taxes category were used. They were treated as explanatory variables. Data were entered into the ARMAX models built by the authors. The dependent variable was the emission of two greenhouse gases covered by the research, namely carbon dioxide and methane. The research was carried out in Poland, for which the results obtained for Sweden were used as the benchmark. The built models made it possible to verify the relationship between environmental taxes in the categories of energy, transport, pollution, resources, and the level of CO₂ and CH₄ emissions. The nature of the explanatory variable was also examined. Environmental taxes can fulfill a fiscal, incentive, and redistributive function. The conducted research shows that these taxes, in fact, fulfill mainly a fiscal function, while redistributive and incentive functions are insufficient. It was also observed that the direction and strength of the impact of taxes differ depending on the greenhouse gas.

The article presents a cluster analysis of the EU-27 countries. The clusters were built to identify groups of countries similar to each other in relation to the set of Eurostat indicators from the Climate Change Drivers and Environment and Energy sections. During the research, tools of spatial information systems were used, such as cluster analysis, diagram maps, rasterization and the TSA method. ARIMA prediction models were also used. The research aims to verify our hypotheses. Particular attention was paid to Poland; therefore, it was verified whether the composition of the country’s energy mix translated into excessive emissions of pollutants in relation to other EU countries. Furthermore, the level of integration of energy markets in the European Union and its changes over time were examined. The authors also proposed a methodology to create detailed energy and climate strategies for designated clusters. The results of the presented research are particularly important in light of recent events in Ukraine.

This article presents research results on the share of coal in the energy mix and the impact of clean coal technologies on Poland’s energy mix. Two mathematical models were utilised: the Boltzmann sigmoidal curve and a supervised machine learning model that employs multiple regressions. Eight explanatory variables were incorporated into the model, the influence of which on the explained variable was confirmed by Student’s t-test. The constructed models were verified using ex post errors and the Durbin–Watson and Shapiro–Wilk statistical tests. It was observed that the share of coal in the mix decreased more dynamically after 2015 compared to previous years. Furthermore, a simulation was conducted using the machine learning model, which confirmed the hypothesis on the influence of clean coal technologies on the level of coal share in the Poland energy production structure. As shown by the analysis and simulation, coal could be maintained in the energy mixes of EU countries, and even if the negative aspects of using this fuel were limited—primarily the emission of harmful substances—its share could even increase. It was noted that this share could be higher by 22% assuming a return to the interest in CCT levels from before 2015 and the reduction in CO₂ emissions using membrane techniques proposed by the authors. Clean coal technologies would enable diversification of the energy mix, which is an important aspect of energy security. They would also enable the gradual introduction of renewable energy sources or other energy sources, which would facilitate the transition stage on the way to a sustainable energy mix.

The article presents the results of research on the impact of the transformation of Poland’s energy mix on the country’s energy security. Since energy security is a very complex problem, the impact of the transformation has been verified in three aspects, i.e., greenhouse gas emissions; energy availability in terms of its quantity, sufficient to meet the needs of citizens; and energy prices. The MANOVA model was used in the research. The model allowed us to examine the relationship between the share of coal in the energy mix and the above-mentioned factors. The MANOVA model allowed us to determine the influence of one explanatory variable on several response variables. The results obtained enabled the verification of the adequacy of the changes that have been made in the energy mix over the last 10 years. As is shown, the factor that is related to changes in the share of coal in the energy mix is the energy surplus, while a relationship between both prices and emission levels and the changes in the share of coal in the energy mix has not been established. Furthermore, a simulation was carried out, which showed that the correlation between price, emissions, and the share in the mix was obtained only by reducing 2017–2020 emissions by an average of 6% and prices by 17%.

This article presents the results of an analysis aimed at verifying the relationship between the implementation of SDG Goal 7 and the use of clean coal technologies in Poland. Clean coal technologies in the United Nations plans will constitute a crucial element of the strategy for sustainable development in the energy context. They are intended to be one of the tools for building an energy system based on renewable energy sources, constituting a bridge that enables the transition of Poland’s energy system from coal to renewable energy sources. To identify whether this relationship exists, the Autoregressive Moving Average with Exogenous Input (ARMAX) model was used. The structure of the model, its correctness, and its accuracy were confirmed using information criteria; statistical tests such as Dickey-Fuller, Doornik-Hansen, Durbin-Watson, and Breusch-Pagan; and measures of prediction accuracy such as MAPE, MAE, and RMSE. The explanatory variables were the Objective 7 indicators adopted by Eurostat. Before being introduced to the ARMAX model, they were standardized using the Compound Annual Growth Rate (CAGR) indicator. The analysis made it possible to indicate which of the explanatory variables has the greatest impact on the development of clean coal technologies in Poland, to determine a synthetic CAGR measure for all the explanatory variables, and to compare the results obtained with the indicator determined by the United Nations.

A method for assessing the operation of analog filters installed in the measuring paths of current and voltage converters in the power supply system of a mining machine motor, using the example of the driving unit of a roadheader’s cutterhead, is presented. The suggested method is based on the identification of the fundamental harmonic of signals from the current and voltage sensors observed at the input and output of the tested filters, respectively. The identified parameters of the fundamental harmonic of signals before and after the filter allow the determination of points of the filter frequency’s characteristics and their comparison with the reference values. In the suggested method of assessing the operation of analog filters, the new approach consists of checking the frequency characteristics on the basis of identification of the basic harmonics of the signal at the input and output of the tested filter (installed in the measuring line of the electrical quantity) using data acquired during the frequency start-up of the mining machine driving motor under normal operational conditions. Results are presented of the filter performance assessment, using the suggested method, after three years of its operation in the measuring system. For this purpose, empirical data recorded during the operation of a roadheader on a test stand are used. The results are compared with the theoretical frequency characteristics of the tested filters.