Kateryna Onopriienko

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).

Type of the article: Research Article

Abstract
The study aims to empirically group European countries based on competitiveness determinants and adult education demand to form a generalized cluster representation of their socio-economic characteristics. The sample covers 36 European countries from 2015 to 2024. The information base was formed using a set of indicators derived from the Global Competitiveness Index (GCI), together with an indicator reflecting adult education demand. The methodology includes standardization of indicators, selection of relevant variables using principal component analysis, and cluster analysis. The first two principal components explain 76.3% of the total variance, allowing a substantial reduction in the dimensionality of the dataset while preserving most of the information contained in the initial indicators. Clustering was conducted using Ward’s hierarchical method and the k-means algorithm, with verification of differences between clusters by analysis of variance (p < 0.05). To examine structural changes over time, clustering was performed for three benchmark years: 2015, 2020, and 2024. The results reveal five clusters of countries differing in institutional development, innovation potential, business environment characteristics, and adult education participation. A relatively stable core of highly competitive economies was identified, including Austria, Belgium, Germany, France, Ireland, Luxembourg, Denmark, the Netherlands, Norway, Sweden, Finland, and Switzerland. Other clusters show greater variability in composition. Across the benchmark years selected within the 2015–2024 observation period, Ukraine remained within the cluster characterized by the lowest values of competitiveness determinants and adult education demand, reflecting persistent structural constraints in the development of human capital and lifelong learning systems.