The article explores how youth perceive the risks of artificial intelligence (AI), based on a survey of 410 university students from Poland, Spain, and Ukraine. The study confirms non-random responses with acceptable internal consistency for categorical variables and complex constructs. The authors built three latent profiles of participants with pragmatic, skeptical, and cautious attitudes towards AI. The scenario approach revealed that respondents are cautious about the use of AI but generally support its implementation under conditions of flexible state regulation. Youth do not see AI as a threat to social equality or the labor market but expect state support through retraining and basic income for laid-off workers. At the same time, there are both optimistic and skeptical scenarios about the future of AI, which depend on the level of awareness and cultural characteristics of the respondents.

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Purpose: This paper aims to assess the sustainability of energy generation systems in various European Union countries. It seeks to understand the diverse challenges and opportunities these countries face in transitioning to sustainable energy. Methodology: The study employs a multi-criteria decision analysis (MCDA) approach, considering economic, environmental, and social aspects. Data from various databases, including Eurostat, the International Energy Agency, and the World Bank, were analyzed. Results: The results reveal significant variations in sustainability performance across different countries and regions. With strong policy support and high public awareness, Scandinavian countries generally performed well across all sustainability indicators. In contrast, Eastern European countries faced more challenges due to their heavy reliance on fossil fuels. Theoretical Contribution: This study contributes to the ongoing discourse on sustainable energy by providing a comprehensive assessment of the sustainability of energy generation systems. It underscores the complex and multifaceted nature of sustainability and the various factors that influence it. Practical Implications: The findings provide valuable insights for policymakers and energy providers. They highlight the importance of policy support, public awareness, and acceptance in promoting sustainable energy. They also underscore the need for continued efforts and interventions to address the challenges faced by countries heavily reliant on fossil fuels.

This study examines whether national AI vibrancy affects employment structures and unemployment, utilising panel data for 36 countries (2017–2023) from the Stanford AI Vibrancy Index and World Bank indicators. Fixed- and random-effects models, along with Hausman tests and clustered standard errors, were applied. Results reveal no significant short-term effect of AI vibrancy on industrial or service employment, nor on unemployment. GDP per capita showed a modest positive effect on industry (β = 0.046, p < 0.10) and, in some models, on services (β = 0.045, p ≈ 0.06). Labour force participation correlated positively with service employment (β = 0.322, p < 0.10) and negatively with unemployment (β = –0.006, p < 0.01). Strong time effects reflect pandemic-related labour shifts: industrial jobs declined, service employment grew, and unemployment spiked temporarily. Overall, labour dynamics appear driven more by global shocks and demographics than by AI vibrancy.

Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.