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This paper presents an approach to estimate demand in the Polish Power System (PPS) using the historical electricity usage of 27 thousand commercial customers, observed between 2016 and 2020. The customer data were clustered and samples as well as features were created to build neural network models. The goal of this research is to analyze if the clustering of customers can help to explain demand in the PPS. Additionally, considering that the datasets available for commercial customers are typically much smaller, it was analyzed what a minimal sample size drawn from the clusters would have to be in order to accurately estimate demand in the PPS. The evaluation and experiments were conducted for each year separately; the results proved that, considering adjusted R2 and mean absolute percentage error, our clustering-based method can deliver a high accuracy in the load estimation. [ABSTRACT FROM AUTHOR]

The main goal of our paper was to apply our research methodology on the specific case of the Ukrainian migration and remittances in the Czech Republic. In contrast to previous studies, we provide a more detailed insight into the specifications of remittance behaviour and test assumptions in the Ukraine-Czech Republic country models. A range of testing is defined by the hypotheses capturing determinants of probability to send money transfers, their volume, spending behaviour and probability of having skilled position. Our results demonstrate that reverse economic effects of remittances on the source country might be simply combination of regular and occasional form with different weight. Therefore, we conclude that the development of migration policy should not neglect microeconomic effects that have potential to solve aggregate level problems.

Nearly 60% of commercial customers are connected to a low-voltage network in Poland with a contractual capacity of more than 40 kW and are assigned a fixed tariff with flat prices for the whole year, no matter the usage volume. With smart meters, more data about how businesses use energy are becoming available to both energy providers and customers. This enables innovation in the structure and type of tariffs on offer in the energy market. Customers can explore their usage patterns to choose the most suitable tariff to benefit from lower prices and thus generate savings. In this paper, we analyzed whether customers' electricity usage matched their optimal tariff and further investigated which of them could benefit or lose from switching the tariff based on the real dataset with the hourly energy readings of 1212 commercial entities in Poland recorded between 2016 and 2019. Three modelling approaches, i.e., the k-nearest neighbors, classification tree and random forest, were tested for optimal tariff classification, while for the benchmark, we used a simple approach, in which the tariff was proposed based on the customers' previous electricity usage. The main findings from the research are threefold: (1) out of all the analyzed entities, on average, 76% of them could have benefited from the tariff switching, which suggests that customers may not be aware of the tariff change benefits, or they had chosen a tariff plan that was not tailored to them; (2) a random forest model offers a viable approach to accurate tariff classification; (3) the policy implication from the research is the need to increase the customers' awareness about the tariffs and propose reliable tools for selecting the optimal tariff. [ABSTRACT FROM AUTHOR]

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.

The European Union (EU) is a global leader in renewable energy, and it is working to maintain this position through setting high standards for itself as well as for its member states in this field. Among the goals set for 2030 in Directive (EU) 2018/2001 and changes published on 14 July 2021 is a 55% reduction in greenhouse gas (GHG) emissions (compared to 20% in 2020). The targets for individual countries vary and depend on the current level of development of renewable energy. This article focuses on evaluation of these targets in the Visegrad Group (V4) countries (Czech Republic, Hungary, Poland, and Slovakia). These are post-Communist countries that have undergone systemic transformations but still face challenges related to sustainable development in renewable energy. This article analyzes the 2030 goals and the prospects of their implementation. Evaluated criteria include greenhouse gas emissions, the share of renewable energy in energy consumption, energy consumption, energy efficiency, and energy intensity. The analyses in this article are based on a literature review, the current energy situation in each country, European climate and energy targets, comparative analyses, and our own forecasts. Our results show that V4 countries would need to revise their policies and funds allocated for green transformation, which, in turn, might change their projections of the EU climate package targets for 2030. These findings might be useful for the EU stakeholders and policymakers responsible for climate policies and implementing renewable energy targets. [ABSTRACT FROM AUTHOR]

The goal of this article is to investigate a dependence among sovereign countries' risk of default. The analysis was based on data for 42 European countries during the period 1994–2013. Three models were used to calculate default probabilities: Li's based on transition matrix and prudent unconditional and conditional on previous defaults estimation technique for low default portfolios. The relationship was analysed through the use of different types of copulas. The analysis has shown no regularity in a selection of the optimal copula. The results differ based on the model and rating grade combination used. [ABSTRACT FROM AUTHOR]

The present publication constitutes an attempt at evaluation of the effectiveness of investment fund deposits at Polish market in the years 2000- 2015. In the paper, a distinction is made between two subperiods, namely, the years before the economic crisis, meaning 2000-2006, and the years of the socalled crisis, meaning 2007-2015. This approach had the goal mainly of evaluating the effectiveness of investment fund deposits in the years of economic slowdown. Conclusions concerning the investment funds are based on a broad range of coefficients and measures used in literature on the subject matter. The multitude of quantitative evaluation criteria of investment funds taken into account in the publication should enable a more precise evaluation of the discussed issue. At the same time, besides the temporal subdivision mentioned above, the authors consider it important to confirm or deny any possible rules or similarities in the indications of the discussed measures. Grouping of the investment funds based on investment effectiveness can be a further advantage. [ABSTRACT FROM AUTHOR]

The global financial crisis has proven to be one of the worst and most-demanding events ever. According to common understanding, it is highly unlikely for a country to go bankrupt. However, we have seen a number of countries on the verge of bankruptcy, as well as many which have officially gone bankrupt. It is probable that many more will do so in the future. This knowledge has led us to the question: how probable is it that a sovereign might suffer serious solvency problems? The purpose of this study was to apply a multivariate discriminant analysis (MDA) as an effective tool for a recognition and differentiation among defaulted and non-defaulted nations. The performed analysis was based on data up to 2012 for 26 emerging and 20 developed European countries. The results indicated a high predictive power for ‘non-liquid’ macroeconomic variables like import, export, investment, population and GDP ratios, underlining MDA as the most suitable model for insolvency prediction, compared to other popular methods like probit and logit model. But unlike in other studies the debt/lending/revenue ratios were characterised by weak predictive power. [ABSTRACT FROM PUBLISHER]

The article deals with the issues of forecasting one of the most important elements of modern economies, which is electricity.The size of its production must be properly predicted and it should be technically possible and profitable from the economic point of view.The most frequently used forecasting models in literature are ARIMA and exponential smoothing. However, these models have a major disadvantage as they do not allow forecasting the series with double seasonality and periods of non-integer values.The authors of this article, on the example of data on maximum energy demand, presented an application of one of the latest forecasting models TBATS, which is devoid of this disadvantage. Using this model, the article presents forecasts for the year ahead with information on power consumption for each day, showing that long-term planning without losing details is possible. With such information energy producers are able to optimize production and the economic side of their business overall. [ABSTRACT FROM AUTHOR]

An important element of everyday financial decisions is to assess the scale of the risk of investing in various financial products. Knowledge on the degree of risk of the activities undertaken in this field allows a certain predictability about the negative effects that may occur. This is of great importance in the context of aversion to risk, and thus a better allocation of resources. A multitude of market risk measures is substantial and in addition they provide information about the risk of investment considered in a different perspective and form A very interesting issue is the scale compatibility of these measures. It is important whether such measures to the same extent define the scale of risk and whether any signals about the dangers overlap in time. The above considerations based on the value at risk and so-caled RiskGrade have become a contribution to the creation of this publication. [ABSTRACT FROM AUTHOR]

Climate change and awareness of the need to care for the environment have resulted in a global increase in the interest in renewable energy sources. The European Union (EU) is active in this respect and requires Member States to fulfill specific plans in the transformation of their energy systems. We employed hierarchical cluster analysis in an attempt to distinguish those countries among the new EU Member States that increased their electrical capacity from renewable energy sources to the greatest extent while paying attention to their energy intensity. The analyses were conducted in two scenarios for both 2004 and 2016. The first scenario assumed an analysis of all known renewable energy sources, whereas in the second scenario, only renewable energy sources from wind and solar power plants were included. The division of analyses into these two variants showed the importance of the differences in the energy assessment of individual countries, depending on classification of renewable energy sources. We identified groups of countries where electrical capacity from renewable energy sources increased the most. Conducting analyses using two variants allowed distinguishing countries that based most of their renewable energy on modern renewable energy sources, such as solar and wind power plants. The inclusion of gross domestic product in the analyses allowed us to identify countries with the worst energy efficiency value. [ABSTRACT FROM AUTHOR]