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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.

The article presents research on the synergistic impact of clean coal technologies and renewable energy sources on the energy mix in Poland. The main causes of problems that inhibit the development of renewable energy sources and ways to eliminate them are presented. A factor that may undermine the development of renewable energy potential is access to critical raw materials such as rare earth elements. Clean coal technologies will make it possible to survive the transition period for coal-based energy mixes. The CCT solution described in this article will enable the acquisition of rare earth elements necessary for the development of renewable energy sources. The ability to meet the demand for REEs based on elements recovered from fly ash is examined. For this purpose, an analysis of wind electricity production capacities was carried out and a forecast until 2030 was created. A program was written using machine learning and the Gompretz sigmoid model. Based on the forecast, the level of demand for REEs was determined and compared with the supply obtained from fly ash. The authors propose an alternative source of REEs and analyze the relationship between demand and supply of this source.

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.

This paper presents the results of research on the development of photovoltaic systems in Poland. The authors’ goal was to identify factors that can potentially shape the dynamics of solar energy development in Poland and that will affect the implementation of the PEP2040 goals. The authors also wanted to find a forecasting method that would enable the introduction of many explanatory variables—a set of identified factors—into the model. After an initial review of the literature, the ARMAX and MLR models were considered. Finally, taking into account MAPE errors, multiple regression was used for the analysis, the error of which was 0.87% (minimum 3% for the ARMAX model). The model was verified based on Doornik–Hansen, Breusch–Pagan, Dickey–Fuller tests, information criteria, and ex post errors. The model indicated that LCOE, CO2 emissions, Cu consumption, primary energy consumption, patents, GDP, and installed capacity should be considered statistically significant. The model also allowed us to determine the nature of the variables. Additionally, the authors wrote the WEKR 2.0 program, which allowed to determine the necessary amount of critical raw materials needed to build the planned PV energy generating capacity. Solar energy in Poland currently covers about 5% of the country’s electricity demand. The pace of development of photovoltaic installations has exceeded current expectations and forecasts included in the Polish Energy Policy until 2040 (PEP2040). The built model showed that if the explanatory variables introduced into the model continue to be subject to the same trends shaping them, a dynamic increase in photovoltaic energy production should be expected by 2025. The model indicates that the PEP2040 goal of increasing the installed capacity to 16 GW by 2040 can be achieved already in 2025, where the PV production volume could reach 8921 GWh. Models were also made taking into account individual critical raw materials such as Cu, Si, Ge, and Ga. Each of them showed statistical significance, which means that access to critical raw materials in the future will have a significant impact on the further development of photovoltaic installations.

For various sorts of hard coal, enrichment by flotation is used for feed consisting of grains smaller than 0.5 mm. Regarding process automation, coal flotation is a multidimensional, dynamic nonlinear object of control, for which the main control signal is the flow rate of the flotation agent. Typically, in Polish coal-processing facilities the monitoring and control systems of the flotation process can only measure the parameter of the waste quality (content of ash in flotation tailings). This naturally becomes an output signal, enabling an indirect assessment of the ongoing process. Therefore, knowledge of the dynamic properties of the flotation process, analysed as an object with one control input (the flow rate of the flotation agent) and with one output for measuring (content of ash in flotation tailings) may be material in designing automatic control systems for this operation. It is important to use an appropriate identification method when developing a model of the dynamics of the flotation process, especially if the model parameters are to be determined on an ongoing basis. This article discusses the research method and presents the results of applying the method of identifying the dynamic properties of the coal flotation process with the use of the Kalman filter. We carried out a comparative analysis of the results obtained by this method based on the Kalman algorithm and the method of least squares, taken as the reference method. The presented parameters of the dynamic models were calculated based on actual data obtained from industrial tests conducted at the coal-processing plant at one of the Polish mines. It was demonstrated that, for control purposes, the Kalman algorithm can be successfully applied in identification of the coal flotation process. This is due to the fact that it gives satisfactory results in relation to the adopted reference method despite the fact that it is a recursive algorithm.

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.

The article presents the results of research related to the SDG7 sustainable development implementation analysis. The goal is to provide affordable and clean energy. Its implementation will allow for development that will simultaneously provide the possibility of economic growth and the achievement of an optimal level of citizens’ health and life. The research was conducted for the countries of the European Union EU-27. During the analysis, the indicators proposed by Eurostat were used. The research aimed to examine the progress in EU member states’ energy markets integration. In order to carry out the indispensable research, it was necessary to use a spatial information system. Cluster analysis, as well as TSA analysis, were applied. The conducted research made it possible to verify the posed hypotheses and showed that the energy transformation process of the EU-27 countries is so complicated and heterogeneous that it has given rise to new independent and unique clusters. The authors also verified the adopted set of SDG7 achievement indicators using multiple regression. Additional indicators were also proposed that could complement the set and clarify its analyses.

In this article, the authors present methods based on hard coal that may ensure energy security for European Union countries. The research was carried out based on the example of Poland. The main reason for which coal is being gradually withdrawn from the energy mixes in EU countries is its negative impact on the natural environment and the health of citizens and economic factors related to domestic fuel production. The authors propose the creation of energy–chemical clusters as a solution to these problems. It is assumed that the clusters would operate following the principles of the circular economy. We also propose methods for the optimization of the production and transport costs within the cluster. Then, we conduct profitability analysis of the proposed waste management methods. At the level of the designated cluster, using network algorithms enabled us to reduce the transport costs by at least 50%. It is possible to obtain rare earth elements (REEs) worth USD 22,970 from 1 Mg of ash. At the level of the analyzed cluster, this leads to an annual profit of USD 3.5 billion. The profit related to algae production at the cluster level is approximately USD 2.5 bn.

This article presents the research on the analysis of the impact of social isolation caused by the COVID-19 pandemic on gaseous air pollutant concentrations. For this purpose, the authors presented (thermal maps) and analyzed the concentrations of selected gases such as NO2, CO, SO2, and PM2.5 particles during the strict quarantine period in Poland and other EU countries. Statistical analysis of the concentration level of these gases was performed. It was noticed that in Poland, Germany, and France, the concentrations of such gases as CO, NO2, and PM2.5 particles decreased, while in Italy and Spain, the tendency was the opposite. To verify whether the discovered dependencies are not a natural continuation of the trends shaping the given phenomenon, the time series of gas and PM2.5 particle emissions were analyzed. On this basis, the emission forecast up to 2023 was created, using the ARIMA class models. The obtained results allowed to construct five scenarios for the development of NO2, CO, SO2, and PM2.5 emissions until 2023, considering the impact of the COVID-19 pandemic. It was stated that in the optimistic scenario, in 2023, a decrease in CO, NO2, and PM2.5 emissions could be achieved by maximums of 51%, 95%, and 28%, respectively.

This article presents the results from research performed on the implementation of restructuring policies of Polish mining enterprises. The study sought to verify whether the changes planned in Poland since the 1990s have been successfully introduced. The main objective of the restructuring of the Polish mining industry was to transform it into a profitable, cost-effective sector, which would be able to survive in a competitive market. The study also allowed the determination of a ratio giving a clear and synthetic outcome—called the indicator of the ability to change (WZZ). This indicator included criteria for assessing the efficiency of a company during the process of change, the fundamental errors committed during the change process, and the reasons for a low level of ability to change. The WZZ indicator achieved only an average level of 30%. The main errors committed during the change process are the mining enterprises’ lack of experience in change management and a lack of executive conviction about the need not only for proper management of the process of change, but also for change in general. The problem is also a lack of adequate knowledge, competence in the field of change management, and communication with employees. Low level of ability to change is caused by the inflexible organizational structure, the organizational culture, a lack of motivation, and the limited flexibility of the human factor.