Over the past few years, considerable emphasis has been put on decarbonization, which, in the context of the recent events in Europe, proves that mixing energy sources is the best strategy. This article discusses ways in which individual EU member states manage their energy source diversification, while comparing their levels of fossil fuels and renewable energy sources (RESs) usage. The research data was acquired from the Eurostat website and comprises of 15 indicators describing the use of energy both from conventional and renewable sources in the European Union, in 2019. The study employs taxonomical methods, such as ranking and cluster analysis. The authors put forward a hypothesis that EU member states approach the use of energy resources in several ways. There are countries which take advantage of both traditional and renewable sources (Netherlands, Germany, Austria, and Italy). However, there is a group of states that relies on a single energy source and exclusively uses either traditional (Poland) or renewable energy resources (Sweden, Finland). The analyses enabled the isolation of country clusters with similar activities and energy strategies.

The European Green Deal can be seen as a societal step towards a holistic, all-encompassing approach to climate and environmental challenges. It attempts to integrate environmental policy by bringing together and improving several existing policies, initiatives and funding programs that address sustainability and climate change. This article analyzes the goals and objectives of the European Green Deal, as well as strategies for their implementation. The article establishes that the European Green Course is a long-term process that aims to build a better future for society and its main goal is the creation of a sustainable society. This goal can only be achieved with a holistic, all-encompassing approach. The article also presents a theoretical conceptualization of sustainable finance and establishes that the appropriate distribution of investments and finances can lead to a successful and even transition of society towards sustainability and environmental improvement. Properly used, sustainable finance reduces the risk of societal negativity, which can be caused by the unavoidably high costs. In addition, systematic increases in funding are expected to result in a stable transition of society to sustainability. Although the correlation analysis does not show a direct relationship between sustainable finance for environmental protection and the implementation of the European Green Deal objectives, the research results indicate a strong correlation between the European Green Deal objectives and the allocation of sustainable finance to research and development and renewable energy resource usage. These differing conclusions can be explained by the fact that the allocation of sustainable finance to environmental protection is a much broader area than the other two variables analyzed separately. In addition, 27 EU countries have been ranked according to the effectiveness of their implementation of EGD directives and economic transition to pro-ecological technologies as of 2021. The positions of Lithuania, Latvia, and Estonia have been determined and examined in detail.

This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers.

In this paper, the development of the wind energy sector in 28 European Union countries in 2017 is described. By means of taxonomic methods—i.e., Ward’s method and the Wroclaw taxonomic methods—clusters of countries similar in terms of their potential and level of development of the wind energy sector in the EU are distinguished. The main purpose of the paper is to separate and cluster EU countries due to the current development potential of the wind energy sector and determinants stimulating the development of this sector. By means of the ranking methods of linear ordering (Technique for Order of Preference by Similarity to Ideal Solution—TOPSIS method), a ranking of EU countries that defines their position in the development of this very important wind energy sector was determined. The results show that the research hypothesis of a great diversity of EU countries considering the development potential of the wind energy sector is justified. The countries of the former European Union, which have focused for a long time on the development of wind energy in their energy policy and have had favorable climate and natural conditions, as well as a large social acceptance of programs supporting the acquisition of energy from renewable sources, have primacy in the development ranking of the energy sector. Additionally, new members of the union, in spite of some delays associated with the development of “green” energy, are trying to increase their energy potential in this area. The research may be extended to include further analyses regarding other renewable energy sources and take into account other European and world countries.

Successful crisis resolution of the enterprise depends heavily on its timely detection, which is facilitated by the use of forecasting models. This allows understanding the scale of the problems in a timely manner and developing the appropriate measures, applying various financial mechanisms to prevent it, and in case of occurrence, reducing the amount of losses. In this context, it is important to choose the most optimal informational model that would provide the most objective forecasts, considering the financial activity peculiarities of the analyzed enterprise. Given a wide list of models that predict the financial crisis, there is a need to analyze and select the most accurate model for enterprises in the real economy. Ten Ukrainian machine builders are used to assess the bankruptcy probability using the most popular models; a taxonomic analysis was carried out, which allows systematizing a large amount of data and analyzing their impact on enterprise development. An integral index was determined, which allowed predicting the financial performance dynamics. For each enterprise, ten indicators were used characterizing their financial state for the period 2014–2018. It is substantiated that the selected models differ from each other by the set of initial data and the number of coefficients from four to seven. It is also determined that the efficient use of studied models is quite different; so when choosing a model to predict the bankruptcy probability, it is necessary to consider the peculiarities of the enterprise’s production activity, the accuracy in creating the financial statements and many other factors, including the presence of company’s shares in circulation at the stock market. It is worthwhile to use a taxonomic analysis to make a comprehensive comparison of the enterprise financial state and to substantiate the final choice of the bankruptcy forecasting model.

The article focuses on assessing the effectiveness of a non-statistical approach to bankruptcy modelling in enterprises operating in the logistics sector. In order to describe the issue more comprehensively, the aforementioned prediction of the possible negative results of business operations was carried out for companies functioning in the Polish region of Podkarpacie, and in Slovakia. The bankruptcy predictors selected for the assessment of companies operating in the logistics sector included 28 financial indicators characterizing these enterprises in terms of their financial standing and management effectiveness. The purpose of the study was to identify factors (models) describing the bankruptcy risk in enterprises in the context of their forecasting effectiveness in a one-year and two-year time horizon. In order to assess their practical applicability the models were carefully analysed and validated. The usefulness of the models was assessed in terms of their classification properties, and the capacity to accurately identify enterprises at risk of bankruptcy and healthy companies as well as proper calibration of the models to the data from training sample sets.