The aim of this work is to analyse the dependence of carbon dioxide (CO₂) emissions on total energy consumption, the energy produced from renewable sources, the energy produced in nuclear power plants and the gross domestic product (GDP) in 22 European countries, over the period 1992–2019. The fully modified ordinary least squares model (FMOLS) and dynamic OLS (DOLS) were used to estimate the long-term cointegration relationship between the variables. First differenced (FD) general moments methods (GMM) were used in the estimation of short-run relationship dynamics. The results suggest that energy produced from renewable sources causes a reduction in CO₂ emissions per capita. On the other hand, total energy consumption increases CO₂ emissions in the long run. Although the mitigation effect of nuclear power was not found to be significant across the entire block of countries studied, a closer look at countries utilising nuclear energy reveals that nuclear energy positively affects the reduction in CO₂ emissions. Economic growth also has a positive effect on the reduction in CO₂ emissions, which confirms the decoupling of economic development from environmental impacts. These findings are crucial for understanding the causality between these variables and the adoption of new or revision of existing policies and strategies promoting the carbon-neutral and green economy at the EU and national level.

The circular economy (CE) is a transformative approach that not only preserves and reuses resources but also redefines how economic growth can be achieved in a more sustainable manner. The aim of the paper is to assess whether economic growth in EU countries can be decoupled from resource use through CE practices. Using data from Eurostat spanning 2010-2022, we analyze key indicators such as Raw material consumption (RMC), Gross domestic product (GDP) and Circular material use rate (MUR). We apply correlation analysis and decoupling analysis to assess the efficacy of CE practices in achieving sustainable economic growth. The results indicate significant variability among EU countries in their ability to decouple economic growth from resource consumption. Some countries demonstrated strong absolute decoupling, indicating successful CE integration, while others faced challenges with expansive negative decoupling. The study underscores the necessity for tailored strategies to enhance CE implementation across diverse economic contexts within the EU. Keywords: decoupling, circular economy, raw material, circular material, GDP, EU countries

This paper focuses on the financial health prediction of businesses. The issue of predicting the financial health of companies is very important in terms of their sustainability. The aim of this paper is to determine the financial health of the analyzed sample of companies and to distinguish financially healthy companies from companies which are not financially healthy. The analyzed sample, in the field of heat supply in Slovakia, consisted of 444 companies. To fulfil the aim, appropriate financial indicators were used. These indicators were selected using related empirical studies, a univariate logit model and a correlation matrix. In the paper, two main models were applied—multivariate discriminant analysis (MDA) and feed-forward neural network (NN). The classification accuracy of the constructed models was compared using the confusion matrix, error type 1 and error type 2. The performance of the models was compared applying Brier score and Somers’ D. The main conclusion of the paper is that the NN is a suitable alternative in assessing financial health. We confirmed that high indebtedness is a predictor of financial distress. The benefit and originality of the paper is the construction of an early warning model for the Slovak heating industry. From our point of view, the heating industry works in the similar way in other countries, especially in transition economies; therefore, the model is applicable in these countries as well.

This study seeks to expand upon existing empirical results about the effect of debt on corporate profitability. Indicators Debt ratio (DR) and Return on Equity (ROE) were used to examine the relationship between debt and corporate profitability. The input data for the analysis represented the financial data of companies operating in the construction industry in Slovakia. The total sample included 7,529 companies. After excluding companies with extreme values, the sample consisted of 6,402 companies. Indicators ROE and DR were used in the given research. To determine the debt threshold, a threshold regression model was applied. Using this model, a nonlinear relationship between debt and profitability was found. An indebtedness threshold has also been identified. Once the threshold is exceeded, the positive relationship between indebtedness and ROE changes to negative. The results, in particular those which indicate a significant non-linear relationship between debt and profitability, are particularly useful for all stakeholders (internal and external) interacting with analyzed companies. AcknowledgmentsThe research was prepared within the grant scheme VEGA 1/0741/20 – The application of variant methods in detecting symptoms of possible bankruptcy of Slovak businesses in order to ensure their sustainable development.

This work aims to analyse the dependence of carbon dioxide (CO2) emissions on primary energy consumption at different Gross Domestic Product (GDP) levels in 28 European countries. Data for the years 1995-2019 were used to develop the models. Random Effects, Fixed Effects, a nonlinear panel threshold model and a continuous kink model were used in the panel data analysis. The work shows that the dependence of CO2 emissions on energy consumption varies at different levels of GDP. The model with two threshold values, which determine three modes of behaviour, proves to be the most suitable. As GDP levels increase, the regression coefficient of the dependence of CO2 emissions on energy consumption decreases. Understanding the relationship between these variables is essential for informed and evidence-based decision-making and adopting new or revision of existing energy and climate policies and strategies at the EU and national levels.

One of the most important operations in the technological production process is the inspection of the manufactured product. The gradual wear of the tool affects the achievement of the required quality of the functional surfaces. In this research, we present the results of measuring the diameter deviation with a new generation laser triangulation sensor (LTS). At the same time, we have performed parametric optimization of several multi-responses, such as insert wear on the VB_B flank side of cutting edge and diameter deviation Δ_d for a C45 steel sample during dry turning and using a sintered carbide insert, using the method of grey relational analysis (GRA) in combination with the Taguchi L16 orthogonal array. The optimal setting of input factors for multi-response parameters is a_p 4-f 4-v_c 1 i.e., depth of cut 0.5 mm, feed 0.4 mm per revolution, and a cutting speed of 70 m/min. At the same time, we present an evaluation of the significance of input factors using the method ANOVA.