Pavlo Rubanov

The structure of the higher education market in 2012–2021 in 38 European countries was analyzed using concentration levels and Herfindahl-Hirschman indices based on the number of higher education institutions and their share in the QS World University Rankings, and the number of students. This market in 2021 has a low concentration: the 3 countries with the largest number of higher education institutions (Germany, Ukraine, France) covered about 36% of the market in total; the 3 countries with the largest number of universities in the QS (United Kingdom, Germany, Italy) – 5%; the 3 countries with the largest number of students (Germany, France, United Kingdom) – 37%; and the 3 countries with the largest number of foreign students (United Kingdom, Germany, France) – 5%. Using parametric and non-parametric comparison tests, it was found that although the number of higher education institutions and students does not generally depend on the population’s income level, the number of universities ranked in the QS and foreign students does. The correlation analysis revealed that GDP and GNI, population, and separately the employment and unemployment rates (for ranked universities and foreign students) are important factors that determine the uneven structure of the higher education market. The identified factors formed the basis for clustering countries using Ward’s hierarchical method, which revealed the clear existence of 3 clusters: the smallest of them accumulates the 4 largest European economies with the most ranked universities; the largest (24 countries) is quite diverse, which indicates relatively equal opportunities in the market and its unification.

Acknowledgment
Tetiana Vasylieva and Artem Artyukhov thank project 0122U000772, and Nadiia Artyukhova thanks project 0124U000545 for carrying out their part of this research.

Type of the article: Research Article

Abstract
As emphasized by the FATF, IMF, and World Bank, technological readiness and institutional quality are increasingly decisive in shaping AML effectiveness. By mitigating money-laundering vulnerabilities, strengthening AI readiness, and enhancing institutional quality, tax collection efficiency can be improved and fiscal leakages reduced. These improvements expand the fiscal space available for national budgets, strengthening the financial foundations of public administration. The study aims to examine the impact of Government AI Readiness on AML risk, measured by the Basel AML Index, and the moderating role of institutional quality as captured by the Rule of Law Index. An unbalanced panel dataset that covers up to 168 countries for 2020–2024 was analyzed using fixed effects and random effects models, with variable transformations applied where necessary. All estimations were performed in R Studio. The results indicate that a one-point increase in the Government AI Readiness Index is associated with a 0.048–0.040 point reduction in the Basel AML Index, while a one-unit increase in log GDP per capita decreases the Basel AML Index by 0.54–0.34 points, holding other factors constant. The interaction term between AI readiness and the Rule of Law Index is positive (0.067–0.072), confirming that the risk-reducing effect of AI readiness diminishes as institutional quality strengthens. These findings support the hypotheses and confirm the complementary roles of technological preparedness and institutional integrity in shaping AML outcomes. Fixed effects analysis reveals structural vulnerabilities in AML in Gabon and China, while Sweden exhibits the lowest residual risk after accounting for AI readiness and institutional strength.

Acknowledgment
This article was supported by the Ministry of Education and Science of Ukraine (project No. 0123U101945 – National security of Ukraine through prevention of financial fraud and money laundering: war and post-war challenges).

Type of the article: Research Article

Abstract
Recent assessments by the IEA, IRENA, and the World Bank indicate that data- and algorithm-driven operations can enhance energy security by increasing efficiency, reducing curtailment, and stabilizing grids as the renewable share grows, while also warning of ecological trade-offs from rising digital electricity use. Against this backdrop, government AI readiness emerges as a crucial enabling condition, providing the institutional and technological capacity to translate climate and sustainability targets into day-to-day energy system performance. Framed through an environmental lens, the study examines whether government AI readiness acts as an ecological enabler that strengthens energy security by supporting the integration of renewable energy, reducing emissions, and enhancing system resilience. To test this ecological proposition, we assemble a balanced panel of 125 countries (2020–2023), combining the Oxford Insights Government AI Readiness Index with the World Energy Council’s Energy Security Score as an operational proxy for environmentally robust energy systems. Using panel methods (fixed and random effects), with diagnostics for serial correlation, cross-sectional dependence, and heteroskedasticity, and Driscoll-Kraay standard errors for robust inference, we find that in the random-effects specification, a one-point rise in AI readiness is associated with a 0.1104-point improvement in the energy security score (p < 0.001). The effect remains statistically significant after accounting for temporal and cross-sectional dependencies, supporting the view that institutional preparedness to use AI can act as an enabling ecological instrument, facilitating the integration of renewable energy, demand-side efficiency, and system resilience, which together underpin cleaner, more secure energy.

Acknowledgment
This study was prepared as part of the project 101127491-EnergyS4UA-ERASMUS-JMO2023-HEI-TCH-RSCH. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Education and Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.