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 global warming phenomenon is commonly associated with the emission of greenhouse gases. However, there may be other factors related to industry and global energy production which cause climate change—for example, heat emission caused by the production of any useful form of energy. This paper discussed the importance of heat emission—the final result of various forms of energy produced by our civilization. Does the emission also influence the climate warming process, i.e., the well-known greenhouse effect? To answer this question, the global heat production was compared to total solar energy, which reaches the Earth. The paper also analyzed the current global energy market. It shows how much energy is produced and consumed, as well as the directions for further development of the energy market. These analyses made it possible to verify the assumed hypothesis.

The decarbonization of European economies is an established reality that has been accelerating in recent years. The focus of EU policy is on the dynamic transformation of the energy balances of Member States, which most significantly impacts economies reliant on coal. In the context of emerging megatrends, this study sets out to determine the extent of changes occurring in the economies of European Union countries in relation to the Green Deal paradigm. The objective of this article is to introduce a comprehensive method developed by the authors for assessing the dynamics of energy transformation in the European Union countries under study. This method is divided into two phases. Initially, countries are classified according to the energy transformation dynamics matrix. Subsequently, the actual assessment of energy transformation dynamics is conducted using a novel composite indicator, the ETPI (Energy Transition Progress Index), based on analyses for 2022 and 2013 using Eurostat data. The results identify leaders in energy transformation, such as Sweden, Germany, Denmark, France, Italy, Spain, Austria, Finland, and the Netherlands, while highlighting significant challenges facing Poland and Bulgaria.

Natural gas is one of the main energy sources in Poland and accounts for about 15% of the primary energy consumed in the country. Poland covers only 1/5 of its demand from domestic deposits. The rest is imported from Russia, Germany, Norway, the Czech Republic, Ukraine, and Central Asia. An important issue concerning the market of energy resources is the question of the direct impact of the prices of energy resources on the income of exporting and importing countries. It should also be remembered that unexpected and large fluctuations are detrimental to both exporters and importers of primary fuels. The article analyzes natural gas deposits in Poland, raw material trade and proposes a model for forecasting the volume of its consumption, which takes into account historical consumption, prices of energy resources and assumptions of Poland’s energy policy until 2040. A hybrid model was built by combining ARIMA with LSTM artificial neural networks. The validity of the constructed model was assessed using ME, MAE, RMSE and MSE. The average percentage error is 2%, which means that the model largely represents the real gas consumption course. The obtained forecasts indicate an increase in consumption by 2040.

The impact of environmental regulations introduced by the European Union is of key importance for electricity generation systems. The Polish fuel structure of electricity production is based on solid fuels. Moreover, the generating base is outdated and must gradually be withdrawn from the power system. In this context, Poland’s energy policy is undergoing a transformation as climate and environmental regulations are becoming increasingly stringent for the energy sector based on solid fuels (hard coal and lignite). However, the transformation process must be adapted to market demands, because the overriding goal is to ensure energy security by maintaining the continuity of energy supplies and an acceptable electricity price. This directly contributes to the development of the entire economy and the standard of living of the society, in accordance with the European Agreement establishing an association between the Republic of Poland and the European Communities and their Member States, signed on 16 December 1991, and the European Energy Charter, signed on 17 December 1991. Ensuring energy security is the most important goal of the energy policy. Therefore, energy companies must forecast the demand. The main goal of this article is to develop a mathematical model of electricity consumption by 2040 by all sectors of the economy: industry, transport, residential, commercial and public services, agriculture, forestry, and fishing. In order to achieve the intended goal, a model was developed by using Long Short-Term Memory (LSTM) artificial neural networks, which belong to deep learning techniques and reflect long-term relationships in time series for a small set of statistical data. The results show that the proposed model can significantly improve the accuracy of forecasts (1–3% of mean absolute percentage error (MAPE) for the analyzed sectors of the economy).