The EU Green Deal and its impact on economic transformation provoked a slightly forgotten free market vs. market regulation discussion, but in the light of a new context—economic and environmental performance development. The economic shock caused by COVID-19, which transformed economies and societies, intensified this discussion. This article analyses the impact of economic freedom on economic performance and environmental performance in European countries. The article contributes to a gap in the literature, because, to date, research has examined the effects of economic freedom, or some of its components, on economic or environmental measures in groups of nations with a lacking sustainable development context. In addition, the mixed results obtained led to confusion in perceptions and knowledge about the influence and usefulness of economic freedom for economic and environmental performance. We also found mixed results regarding the influence of economic freedom on economic and environmental performance, but the introduction of a new concept—the optimal level of economic freedom—organized the different results into a coherent logical sequence. The paper provides original empirical evidence and specifies the targets of structural reforms. The results are thus useful for policymakers to develop more appropriate and efficient economic freedom.

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice.

Various socio-economic systems (countries, regions, or cities) and their economies suffer different kinds of economic shocks. If the system is not resilient, its economy can incur losses. Only the systems whose economies are less vulnerable and/or are able to recover from the economic shock quite quickly are able to ensure economic sustainability, competitiveness, and welfare both now and in the future. The concepts of socio-economic systems’ resilience to economic shocks, vulnerability, and recovery, as well as the resilience assessment peculiarities, are all analyzed in this article. The methodology introduced for the assessment of a socio-economic system’s resilience to economic shocks consists of two parts: a model of a system’s resilience to the economic shock’s capacity-related factors (Resilio) and an index of a socio-economic system’s resilience to the economic shocks (Resindicis). The Resilio model could be used as a universal methodological framework for analyzing the resilience of socio-economic systems of different levels (countries, regions, or cities). The set of quantitative indicators compiling Resindicis should be adjusted to the specifics of each socio-economic system and the availability of statistical data. Empirically, the methodology was validated on the example of 10 Lithuanian regions (counties). The methodological principles for the assessment of a socio-economic system’s resilience are also provided. The main advantages and drawbacks of the methodology are discussed in order for further development and an increase in its practical application.

The article deals with the analysis of theoretical and practical aspects of measuring regional resilience to economic shocks. While the concept of regional resilience to economic shocks is still at the developent stage and the method of measuring regional resilience, which is grounded methodologically and is generally accepted, is still missing, the article presents a unique approach of the authors to the concept of regional resilience to economic shocks, including elaboration of the definition of regional resilience to economic shocks, identification of the regional resilience determining capacities in the Resilio model, specification of their quantitative and qualitative characteristics and introduction of the regional resilience to economic shocks index (Resindicis). A rapid development of globalization processes poses new challenges to the instruments of economic analysis and strategic planning. Different techniques can be used for obtaining the required information on building the regional resilience-enhancing strategies. Each of them has its own advantages and disadvantages. In order to find out the strengths and weaknesses of measuring resilience by Resilio, the newly developed index was empirically tested referring to the data of 10 Lithuanian districts in the period of 2006-2015. The assessment results, as well as the advantages and disadvantages of using Resilio are presented in the article. The newly developed Resindicis, introduced in the article, represents one of the ambitions for having a convenient tool which can be used for economic analysis, strategic planning and justification of solutions aimed at enhancing regional resilienceDOI: http://dx.doi.org/10.5755/j01.ee.29.4.18731

A rich volume of literature has analysed country investment attractiveness in a wide range of contexts. The research has mostly focused on traditional economic concepts—economic, social, managerial, governmental, and geopolitical determinants—with a lack of focus on the smartness approach. Smartness is a social construct, which means that it has no objective presence but is “defined into existence”. It cannot be touched or measured based on uniform criteria but, rather, on the ones that are collectively agreed upon and stem from the nature of definition. Key determinants of smartness learning—intelligence, agility, networking, digital, sustainability, innovativeness and knowledgeability—serve as a platform for the deeper analysis of the research problem. In this article, we assessed country investment attractiveness through the economic subjects’ competences and environment empowering them to attract and maintain investments in the country. The country investment attractiveness was assessed by artificial intelligence (in particular, neural networks), which has found widespread application in the sciences and engineering but has remained rather limited in economics and confined to specific areas like counties’ investment attractiveness. The empirical research relies on the case of assessing investment attractiveness of 29 European countries by the use of 58 indicators and 31,958 observations of annual data of the 2000–2018 time period. The advantages and limitations of the use of artificial intelligence in assessing countries’ investment attractiveness proved the need for soft competences for work with artificial intelligence and decision-making based on the information gathered by such research. The creativity, intelligence, agility, networking, sustainability, social responsibility, innovativeness, digitality, learning, curiosity and being knowledge-driven are the competences that, together, are needed in all stages of economic analysis.

The Specifics of Measurement the Urban Competitiveness at the National and International Level