Anzhela Kuznyetsova

This article is devoted to building of the equilibrium model between demand and supply on foreign currency at the Ukrainian Interbank Foreign Exchange Market (non-cash share). The authors discussed that appeared trade-offs are a product of established current foreign arrangement, administrative measures provided by the National Bank of Ukraine and range of fundamental variables, which are traditionally significant for Ukrainian economy. By means of FAVAR modeling model of demand and supply equlibrium on non-cash foreign currency was built on empirical data of Ukrainian Interbank Foreign Exchange Market, splitted into the periods, proposed by the authors. Next, it was discussed disconnection properties in the model and shown log-linearized specification of the one. The efficiency of fulfillment hypothesis on decointegrating of the fundamental variables' time series has been provided in form of critical statistics values. Also, instrument of GAP analysis of deviation from equilibrium state was proposed and the further analysis of a regulation style of monetary authority was provided. In conclusion, it was summarized that increased share of the cash out of the banks has significantly jeopardized the price stability in Ukraine and the NBU interventions would become more effective if the flexible foreign exchange rate will be accompanied with flexible regime of inflation targeting.

In this paper, the banking system financial stability is assessed based on the differential approach. The differential approach provides for taking into account the specificity of the banking system structural organization (from the standpoint of the central bank and the second-level banks) and the sets of financial stability indicators, different in terms of their structure, and their volatility measures, according to this approach.
The banking system financial stability is assessed based on the two groups of indicators: the first one characterizes the central bank financial stability (indicators of gross international reserves, effectiveness of monetary policy and foreign exchange regulation, ability to create favorable conditions in order to ensure the effectiveness of the banking sector); the second one defines the financial stability level for state banks, banks with private and foreign capital (indicators of the capital adequacy, liquidity, structure of assets and liabilities, effectiveness of the activity, financial risks). The differences between the sets of financial stability indicators for different groups of banks and the expediency of taking them into account during the assessment are revealed and substantiated according to the results of using the principal components method.
The developed procedure of assessing the banking system financial stability provides for: constructing the banking system financial stability index (by multiplicative convolution of central bank financial stability subindex and three banks’ financial stability subindices); defining its high, medium and low level according to its quantitative values (according to interval scales, developed according to the rule “3σ”; interpreting the assessment results based on the scenario analysis, which is based on taking into account the dynamic change of the financial stability index during the analyzed period and allows to identify the state of the banking system (stable, conventionally stable or critical).

In order to set economic policy goals, it is important to understand the difference between adult education and lifelong learning, and how much research on SDG 4 combines lifelong learning and economic policy. The purpose of the article is to determine the main directions for justifying the lifelong learning system development, including for achieving sustainable development goal 4 (SDG 4). Based on scientific research data from the Scopus database using the VOSviewer software, this article analyzed the theoretical foundations for substantiating the temporal and geographical interrelationships of the categorical-conceptual system of such terms as “SDG 4”, “adult education”, “lifelong learning” and “economic”. This made it possible to identify the main trends in scientific research and cluster directions of international research on the relationship between adult education, lifelong learning and economic policy. The following trends were obtained: adult education as a driver of economic development; as a social phenomenon and as a source of innovation. The following clusters were identified: adult education as a part of life-long education; adult education under the influence of physical and age-related changes; adult education as part of professional education; the learning process, which is not related to professional activity. As a result of the analysis, an insufficient level of attention among scientific studies devoted to adult education within the framework of SDG 4 was revealed. The article confirmed the need for economic policy to support the lifelong learning system, as well as the difference between the concepts of adult education and lifelong learning.

Acknowledgment
The educational outcomes in this publication were created with the support of the EU Erasmus+ program within the framework of projects ERASMUS-JMO-2021-HEI-TCH-RSCH-101048055 – «AICE – With Academic integrity to EU values: step by step to common Europe» and ERASMUS-JMO-2022-HEI-TCH-RSCH-101085198«OSEE – Open Science and Education in Europe: success stories for Ukrainian academia». This study was funded by the grant from the Ministry of Education and Science of Ukraine “Modelling educational transformations in wartime to preserve the intellectual capital and innovative potential of Ukraine” (registration number 0123U100114). “Convergence of economic and educational transformations in the digital society: modeling of the impact on regional and national security” (state registration number 0121U109553).

This study aims to examine how different types of pro-environmental video content (featuring humans versus AI-generated characters) influence household waste sorting attitudes and behaviors among Ukrainian residents. The research was conducted in two stages using a mixed-method approach. In the first stage, 102 individuals aged 18–45 watched two videos on waste sorting and completed an online questionnaire. Cluster and variance analyses were performed using Statistica software. In the second stage, 35 participants underwent a laboratory-based emotion analysis using iMotions software, heart rate monitors, and galvanic skin response sensors at the Behavioral Lab of Sumy State University (Ukraine) from May to July 2024. The results revealed that videos featuring real people were more effective in generating interest (average rating: 3.5 vs. 3.2) and emotional engagement, particularly joy and contempt, which were the most frequently expressed emotions. Cluster analysis identified four distinct respondent groups. Cluster 1 (39.2%) – primarily young women – responded positively to human-led videos but showed limited behavioral change. Cluster 2 (19.6%) – women aged 26–35 – reacted positively to both videos and were most willing to adopt waste sorting behavior. Cluster 3 (23.5%) – primarily men – showed moderate engagement and sorted waste occasionally. Cluster 4 (17.6%) – highly educated women – exhibited the least positive responses and were least likely to change their behavior. The emotion analysis confirmed that videos featuring real people elicited stronger emotional responses across all categories, whereas AI-generated videos prompted higher levels of anger but generally weaker engagement.

Acknowledgments
The authors would like to acknowledge the Behavioral Laboratory at Sumy State University for providing the essential facilities and resources that enabled the successful completion of this research. Additionally, sincere appreciation is extended to all study participants for their valuable time, commitment, and contributions, which significantly enriched our understanding of consumer behavior related to waste management and the perception of advertising content.
The research is supported by the International Visegrad Fund: Visegrad Fellowship Program. Project № 62410031, “Marketing research consumer behaviour in the waste management system”, and by the budget of the Ministry of Education and Science of Ukraine (research topic 0123U100112 “Post-war recovery of the energy industry of Ukraine: Optimization of waste management taking into account the health of the population, environmental, investment, tax determinants”.

Bank capitalization is a key factor in the banking system’s resilience to economic shocks, as outlined in the Basel Accords on banking regulation. The paper aims to empirically evaluate the connection between bank capitalization and the socio-economic development level. The study uses World Bank data across 34 European countries of varying income levels (high, upper-middle, and lower-middle) for the years 2010, 2015, and 2020 as periods of financial and socio-economic turbulence. The study applies principal component analysis and correlation methods to identify relevant indicators. Countries were grouped using hierarchical clustering and K-means clustering methods. The results of the variance analysis revealed that only two indicators of bank capitalization – the share of non-performing loans and return on assets, and three socio-economic indicators – the Gini index, inflation, and unemployment, were statistically significant. As a result of both qualitative and quantitative changes between clusters, countries were reallocated from four clusters in 2010 and 2015 to three clusters by 2020: high-income countries, including Austria, Belgium, Denmark, Ireland, Iceland, Latvia, and others, consolidated into a single stable cluster (Cluster 3) in terms of both bank capitalization and socio-economic development. The primary drivers of inter-cluster movements were the positive dynamics of non-performing loans and bank assets, superior bank capitalization ratios, and improvements in inflation and unemployment levels. Based on the study’s findings, a matrix of transformational sustainability among European countries has been developed. The analysis confirms a relationship between the components of the chain: “level of bank capitalization – level of socio-economic development”.