The Europe 2020 strategy is the EU strategy for sustainable and inclusive growth, for fighting the structural weaknesses of the European economies, and for improving their competitiveness. In this paper, we determined the most important ratios of the Europe 2020 Strategy impacting on economic performance expressed as the growth of the GDP per capita, and on economic competitiveness expressed as the share of the countries’ exports in total world exports for some selected Central and Eastern European (CEE) countries (Poland, Slovakia, Bulgaria, Hungary, the Czech Republic, and Romania) using co-integration tests and OLS panel estimations with a dataset between 2004 (after four of these selected countries acceded to EU) and 2015 (the latest available data for all the ratios we used in our analysis). Our findings show that the tertiary level of education is the most important factor, positively correlated with both endogenous variables mentioned above. Other important factors for achieving the economic performance and competitiveness goals are the school dropout ratio, the share of renewable energy in final energy consumption, and the employment rate.

The main goal of setting energy efficiency priorities is to find ways to reduce energy consumption without harming consumers and the environment. The renovation of buildings can be considered one of the main aspects of energy efficiency in the European Union (EU). In the EU, only 5% of the renovation projects have been able to yield energy-saving at the deep renovation level. No other study has thus far ranked the EU member states according to achieved results in terms of increased usage in renewable sources, a decrease in energy usage and import, and reduction in harmful gas emissions due to energy usage. The main purpose of this article is to perform a comparative analysis of EU economies according to selected indicators related to the usage of renewable resources, energy efficiency, and emissions of harmful gasses as a result of energy usage. The methodological contribution of our study is related to developing a complex and robust research method for investment efficiency assessment allowing the study of three groups of indicators related to the usage of renewable energy sources, energy efficiency, and ecological aspects of energy. It was based on the PROMETHEE II method and allows testing it in other time periods, as well as modifying it for research purposes. The EU member states were categorized by such criteria as energy from renewables and biofuels, final energy consumption from renewables and biofuels, gross electricity generation from renewables and biofuels and import dependency, and usage of renewables and biofuels for heating and cooling. The results of energy per unit of Gross Domestic Product (GDP), Greenhouse gasses (GHG) emissions per million inhabitants (ECO2), energy per capita, the share of CO₂ emissions from public electricity, and heat production from total CO₂ emissions revealed that Latvia, Sweden, Portugal, Croatia, Austria, Lithuania, Romania, Denmark, and Finland are the nine most advanced countries in the area under consideration. In the group of the most advanced countries, energy consumption from renewables and biofuels is higher than the EU average.

Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.