The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO₂ allowances, and ethanol are strongly associated with the development of the pandemic.

At a time of rapid climate change and an uncertain geopolitical situation caused by the war in Ukraine, the problem of access to energy is a serious issue. The use of renewable energy sources and ensuring the highest possible energy independence are becoming important. They are in line with the seventh Sustainable Development Goal (SDG7). The aim of our research is to compare European countries in terms of the degree of SDG7 implementation and its dynamics from 2005 to 2020. We assess the SDG7 implementation using the COPRAS method and compare its dynamics using the Dynamic Time Warping (DTW) and hierarchical clustering. In years 2005, 2009 and 2020, we present rankings of countries in terms of the SDG7 implementation. Norway, Denmark, Sweden, Croatia, and Estonia were ranked the best, and Luxembourg, Belgium, Bulgaria, Lithuania, Iceland, and Cyprus—the worst. We obtained eight clusters with respect to dynamics of the degree of SDG7 implementation. In Poland, Romania, Belgium, Luxembourg, Latvia, and Ireland, the relative dynamics was increasing, while in the Nordic and South European countries, it was decreasing. The novelty of our research is combining the COPRAS (assessment of SDG7 implementation) and DTW methods (selection of similar countries with respect to its dynamics).

The goal of this research is a static geographic comparison of the degree of Sustainable Development Goal 8 (SDG8) implementation and to compare its dynamics for the European Union (EU) in the period of 2002–2021 with the consideration of the COVID-19 pandemic. The level of implementation of the 2030 Agenda for Sustainable Development objectives is monitored both jointly and individually. This research uses the COPRAS, the Dynamic Time Warping (DTW) and the hierarchical clustering methods. The study confirmed the geographical diversity in the degree of SDG8 implementation. Nordic countries have the highest degree of implementation, while Greece, Spain, Italy, Romania and Slovakia have the lowest. Some less-developed countries (Czechia, Poland and Malta) were in the cluster where the implementation improved, and some well-developed countries (France, Luxembourg and Portugal) were in the cluster in which the situation did not improve or improved at a low degree. It was not possible to identify geographical diversity in the dynamics of changes in the degree of SDG8 implementation. The COVID-19 pandemic caused the decline in the degree of SDG8 implementation. The added value of the study consists of comparing the dynamics of the degree of implementation of SDG8 using the DTW method. The obtained conclusions may help to create policies for EU countries in this regard.

When a company uses a shared storage system, selection of locations during the order‑picking process is not an obvious task. Every location where the picked product is placed, can be described by means of several variables, such as: storage time, distance from the I/O point, degree of demand satisfaction, or the number of other picked products in the order. Therefore, the “attractiveness” of each location from the point of view of a certain order can be described by means of synthetic variable, on the basis of which a ranking is created. For each product, the decision‑maker selects the highest‑ranking locations and designates a route for the picker. In the article, by means of the simulation methods, results obtained by several classification methods will be compared. These methods are: Taxonomic Measure of Location’s Attractiveness (based on the Hellwig’s Composite Measure of Development), the TOPSIS method with the Euclidean and GDM distances and the Gen­eralised Distance Measure used as the composite measure of development.

Strengthen their international competitiveness. This was linked to the implementation of institutional and economic reforms, significant technological changes and improvements in the quality of human capital, as well as fiscal stabilisation policies. These changes affected their situation in the labour market. Purpose of the article: The aim of the study is to assess changes in the situation in the labour market in the EU with particular emphasis on the post-communist countries in the period 2002? 2019. Methods: The situation of countries in the European labour market was estimated using the TOPSIS method. A similarity matrix of changes in the composite variable for each country was then constructed using the Dynamic Time Warping method. On its basis, homogeneous clusters of countries were determined using the Ward?s method. Findings & value added: Four homogenous clusters of countries were formed. The post-communist ones belonged to two groups. In one, there were two countries ? Croatia and Slovakia. The rest of the post-communist countries were in a large cluster, which also included Germany, Malta, Finland, Portugal, France and Belgium. Changes of the situation in the post-communist countries in this group improved very much during the analysed period (this was particularly evident for Czechia, Estonia and Poland). It is interesting to investigate whether the reaction of labour markets to changes in the global economic situation in post-communist countries is similar to that in the old EU countries. The similarity of changes can be measured using the DTW method. There is an empirical research gap in this respect. Therefore, the added value is the use of this method in assessing similarities of changes in the labour market situation in post-communist countries in comparison to the Western European ones.

In 2004, ten new countries, including the Visegrad Group (V4) ones, accessed the European Union. These countries are ex-communist. They are still in the process of transition from a centrally planned to a free-market economy. It is interesting to check how their economies have changed after their accession to the EU. Reducing inequalities in labour markets is one of the aspects of sustainable development. The goal of this research is an assessment of the situation in the labour markets in the V4 countries with respect to the whole EU. The research was carried out on the data from Eurostat (2002–2019) and was conducted by means of the multidimensional scaling technique. Before joining the EU, the situation in the labour market in the V4 countries (excluding Czechia) was much worse than in most EU-member states. After joining the EU, the situation in these countries gradually improved. In 2019, the situation in the labour market in Czechia was one of the best in the EU and the remaining three V4 countries moved into the EU-average. The joining of the V4 countries to the EU had a positive impact on the situation in their labour markets.