Automation and digitalization, as long-term evolutionary processes, cause significant effects, such as the transformation of occupations and job profiles, changes to employment forms, and a more significant role for the platform economy, generating challenges for social policy. This systematic literature review aims to provide an overview of the research to date related to influences of the Industry 4.0 Revolution on human capital development and consumer behavior. A search on the Web of Science identified 160 papers that met the inclusion criteria. The major objectives aimed to identify: the main types of influences of the Industry 4.0 Revolution on human capital development and consumer behavior; the main opportunities and challenges for new directions in education associated with shifting the work environment; and the drivers for human capital development and consumer behavior through the lenses of the Industry 4.0 Revolution. The results revealed some key aspects for the development of human capital: information, new jobs, the Internet, technology, training, education, new skills, automation, communication, innovativeness, professionals, productivity, artificial intelligence, digitalization, e-recruitment, and the Internet of Things, as well as the main drivers of consumer behavior: information, e-commerce, digitalization, the Internet of Things, e-distribution, technology, digitalization, automation, personalized, performance, artificial intelligence, behavior intention, e-shopping, and data mining.

The recent transformation of the national economies has raised numerous theoretical and practical aspects in measuring economic growth, welfare, environmental performance, and competitiveness, representing a challenging research topic within the context of economic paradigm transformation. Despite its importance, a fully operational model to be used in any context has not yet been designed. The main aim of this paper is to evaluate and analyze the macroeconomic dimension of the three determinants of sustainable competitiveness: the economic environment, the social environment, and the natural environment, at both the European and Romanian levels. This paper used the Hierarchical Clustering methodology, aiming at evaluating the global competitiveness in terms of a sustainable development model, using four indices: Human Development Index, Environmental Performance Index, Global Competitiveness Index, and GDP per capita. The clusters were designed on the basis of the role of the indices in assessment of the sustainable performances of the countries and also of the possible convergences between them. The results could sustain the conclusion that these indices are not able to offer an exhaustive image of the sustainable performances assessment. A new complex indicator could be considered in order to design a convergence model for the EU member states.

This study aims to investigate the convergence of digitalisation in the European Union (EU) in terms of DESI per capita in all EU countries during 2015–2020. The empirical results sustain the hypothesis of convergence of the degree of digital level in the European Union member countries. In evaluating the convergence process, we also aimed to investigate the role of economic development and education, measured by gross value added and education index. The analysis results, the use of sigma and beta convergence methods, showed that the role of economic development is likely to be decisive in resolving disparities, as opposed to that of educational development. The sigma convergence analysis showed that the gap between the EU-28 countries regarding digitalisation tends to decrease in the analysed period. Spatial data analysis, in turn, provides strong evidence for the presence of spatial autocorrelation in the DESI distribution. This result is based on a spatial lagged model that considers that DESI growth rates are related both to their initial levels of digitalisation and the growth rates of neighbouring regions.

This article proposes a synthetic analysis of the development level of regions in Romania with the concentration/diversification model, using Gini coefficients and Lorenz curve, based on the current statistical indicators. Knowing the degree of concentration and also the influence factors is useful in making decisions and setting regional policy measures. The analysis of regional disparities based on a series of data and indicators provided by The Romanian National Institute of Statistics for 2007–2016. The main findings highlight a deepening of the regional disparities in Romania in 2016 compared with 2007 explained mainly by the fact that the Bucharest–Ilfov region registered an economic development much above the other regions.

This study aims to examine the expansion of the electric vehicle fleet in the context of transport electrification in the European Union. We assessed the car market, following demand and sales trends for electric and hybrid cars. It was explored the possibility of a causal relationship among the percentage of BEV + PHEV in the total fleet and purchasing power, loading infrastructure, government support, the level of education and the degree of digitalization. To achieve the main objectives of the research to assess the existence and magnitude of the causal effects of the considered variables on the percentage of BEV + PHEV in the total fleet, we conducted a cross-sectional analysis in 2020 among the European Union (EU) countries. Five research hypotheses were formulated and tested. The results confirmed that the economic and social development of a region, the charging infrastructure, the government support measures, and the degree of digitalization positively influence the desire of the EU population to buy electric cars.

The aim of this research is to determine the significance of the impact of selected environmental protection indicators, with a focus on waste management, on the sustainability of economic growth in EU countries (21 member states) over the period 2013–2022. To conduct this analysis, four independent variables were selected, belonging to the domains of waste management the (recycling rate of municipal waste and recycling rate of packaging waste by type of packaging) and secondary raw material management (the circular material use rate and trade in recyclable raw materials, imports from outside the EU27). Sustainable economic growth was measured by gross domestic product per capita (GDP per capita), which serves as the dependent variable in this study. The aforementioned independent variables can also be categorized as circular economy (CE) indicators, which have been gaining increasing relevance in the EU context. Using a panel regression analysis, the potential influence of CE indicators on sustainable economic growth was examined both over time and across entities, through the lens of waste management. The statistical analysis was conducted by applying four econometric models: pooled OLS (POLS), fixed effects (FEs), random effects (REs), and mixed effects (MEs). The results of the analysis confirmed several specific hypotheses (depending on the model used), which posit a statistically significant positive impact of CE variables on GDP per capita.