In recent years, there has been a significant transformation in the energy sector in the European Union as a whole. The shift towards producing renewable energy has had a significant impact on the economic development of most countries, requiring substantial investments through public procurement. This study aims to analyse the evolution of the energy sector in Romania from an economic perspective by using a data-driven approach. The data used in this research is collected from publicly available sources and pertains to energy production and public acquisitions in Romania, carried out through the Electronic Public Procurement System. By using a mixed approach, combining documentary analysis, literature review, and predictive modelling, the study reveals a shift towards more sustainable energy options. There is a significant decrease in the production of thermal power and an increase in solar and wind power. The findings provide an overview and potential scenario of Romania's electricity production levels in 2023, shedding light on the relative uncertainties associated with such a transition. The findings also suggest a clear and growing commitment in Romania towards the adoption of alternative energy sources, as reflected in the trends of public procurement. These procurement trends offer a valuable perspective on policy-making, investment planning, and progress monitoring in Romania's energy transition. Despite the inherent uncertainties in such a transition, the study demonstrates Romania's potential in terms of diverse sources for electricity production as well as the role of public procurement in achieving energy transformation.

There is increasing concern regarding the association between technological change and jobs. This study explores how different patterns of digitalization and innovation relate to job creation in European companies. We use data from the European Company Survey 2019 collected by Eurofound and Cedefop. We apply Latent Class Analysis (LCA) to identify the typologies of companies, mainly based on their level of technology adoption, innovation practices and employment patterns. We showcase four distinct classes of companies: moderate adoption of digital technology and strong international orientation, traditional and local, medium digitalization, process innovative with local focus and digital leaders and innovators, with specific patterns regarding digitalization, innovation and job creation. The digital leaders and innovators class revealed a high level of digitalization and innovation and maintained stable employment levels, with increased investments in staff training and tendency towards automation. Conversely, less-digitalized traditional companies are more susceptible to stagnation or employment decline. In general, the employment outlook is stable, without significant employment growth, signaling the need for balanced investments in innovation and digitalization that stimulate more and better jobs. This is the first study to apply LCA to explore complex relationships between digitalization, innovation, foreign trade, training investments and employment trends and offers fresh insights into company views towards employment in the digital era.

This paper analyses and compares Artificial Intelligence (AI) national strategies of European Union (EU) Member States based on factors like education, investments, workforce upskilling and reskilling and country specific initiatives. The analysis expands into AI usage of EU enterprises based on Eurostat data. Using a mixed-methods approach, document and content analysis along with descriptive statistics, the study classifies EU countries into four clusters (leaders, followers, moderates, and laggards) based on AI usage rates of the European enterprises, showing significant differences for each cluster. The first cluster – the leaders in AI usage, such as Denmark, Sweden or Finland presented strong policies for early digital education, consistent investments, and comprehensive workforce training programs. In contrast, the countries from the fourth cluster – the laggards – with slower rates of AI usage, rely extensively on EU funding and seem to face infrastructural or regulatory barriers. The findings show significant disparities in the usage of AI by European enterprises. Large companies recorded the highest AI adoption while small and mid-size enterprises seem to be less prepared for this. The study also indicated limitations such as statistical analysis performed only for 2023 and 2024 data, limited criteria for comparing strategies, and reliance on Web of Science literature base. Also, the study provides additional recommendations for further research directions, advocating for more advanced quantitative analyses. Overall, this research highlights the need for coordinated EU-level policies aimed at ethical, inclusive, and cohesive AI development across all Member States.