This study evaluates the influence of rising energy prices on firm-level profitability. According to the policies of the European Union, the aim is to achieve an energy union that offers all community economic agents safer, cleaner, and more affordable energy. For this study, 1342 updated observations from 16 European countries for the year 2022 were used, studying the influence of increasing energy prices on the decrease in profit. The increase in energy prices influences the risk of firms experiencing negative profitability in 2022 compared to 2018, as measured by the increase in the number of firms that register a profit of less than or equal to zero. Company characteristics, such as size and age, which positively influence the level of profitability, are statistically significant. Furthermore, this study shows that the business sector in which the firm operates influences the level of profitability in a differentiated manner. Linear and logistic regression methods were employed for the methodology.

The emergence of e-commerce reshaped the traditional trade models, also playing a significant role in meeting the UN sustainable development goals. According to the UN, sustained growth and social development must include resilient infrastructure, foster innovation, allow for better access to information and communications technology, and universal and affordable internet infrastructure. This study explores a multidimensional analysis of e-commerce development in the EU generated by the following factors: education, internet infrastructure, income, and economic freedom. We use an ARDL econometric model and Eurostat data. Additionally, we analyze the time responsiveness of e-commerce growth to changes in these factors. In the long run, our findings identify a stable and positive relationship between e-commerce and all these factors. However, in the short run, our results illustrate significant dynamics between two variables and e-commerce. Specifically, the level of internet access and the percentage of individuals who use the internet daily exhibit a positive short-run impact on e-commerce sales, with the system absorbing shocks within a short period. This research advocates for targeted policies that support innovation, fair competition, and consumer protection in the digital economy. This research provides valuable guidance for policymakers and stakeholders in improving the institutional framework to promote a sustainable development of e-commerce in the EU.

This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML’s ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments.

Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives the most accurate results? Econometric forecasting methods can deal with different types of time series and have good results, but in specific cases, they may fail to provide accurate predictions. Recently, new techniques borrowed from the soft computing area were adopted for economic forecasting. Starting from the importance of economic forecasts, we present an experimental study where we compared the accuracy of some of the most used econometric forecasting methods, namely the simple exponential smoothing, Holt and ARIMA methods, with that of two new methods based on the concept of fuzzy time series. We used a set of time series extracted from the Eurostat database and the R software for all data processing. The results of the experiments show that despite not being fully superior to the econometric techniques, the fuzzy time series forecasting methods could be considered as an alternative for specific time series.

This study develops an integrated panel econometric framework for modeling investment–output dynamics in circular economy sectors, explicitly addressing dynamic propagation, long-run equilibrium relationships, endogeneity, and nonlinear responses. Building on the Samuelson–Hicks Multiplier–Accelerator model, the analysis combines two complementary approaches. A dynamic panel specification estimated by the Generalized Method of Moments (Arellano–Bond) is employed to capture output inertia, intertemporal transmission of investment shocks, and stability properties of the dynamic system. In parallel, a nonlinear panel ARDL model estimated using the Pooled Mean Group (PMG/NARDL) methodology is used to identify cointegration and to distinguish between the long-run and short-run effects of positive and negative investment variations. The empirical analysis relies on a balanced panel of 28 European economies (EU-27 and the United Kingdom) over the period 2005–2023, using sectoral circular economy data, with gross value added as the output variable and gross private investment as the main regressor. The results indicate the existence of a stable cointegrated relationship between investment and output, characterized by significant asymmetries, with expansionary investment shocks exerting larger and more persistent effects than contractionary shocks. Dynamic GMM estimates further confirm delayed investment effects and a stable autoregressive structure. Overall, the paper contributes to mathematical economic modeling by providing a unified dynamic–equilibrium panel framework and by extending the empirical relevance of Multiplier–Accelerator dynamics to circular economy systems.

Background: Using the perspective of the recent COVID-19 pandemic, which represents a public health challenge that also affects education and the psychological well-being of students, this paper aims to assess the vulnerability to anxiety and stress of the Romanian students from the Faculty of Administration and Business of the University of Bucharest pursuing their degrees in administration and business. Methods: Our study is based on the results of the survey that was administered to a sample of 422 students (39.6% females and 60.4% males) selected from the pool of 2000 recruited respondents. We employed the ANOVA/linear Dependent Dirichlet Process mixture model to explain the causes of stress and anxiety after various grouping variables represented by gender, specialisation, as well as labour market status. Results: Our results revealed that more than 80% of the students in the sample yielded medium and high vulnerability to stress, while 64% of the respondents were affected by severe anxiety (high frequency of psychological, social, and physical problems that were difficult for them to control). The most important stressors were the fear of getting infected and social distancing, while self-control was considered the most effective coping strategy by 62.6% of employed students. Conclusions: These results allow us to provide practical recommendations for effectively coping with stress and anxiety among students in Romania and beyond and to help stakeholders and policymakers design strategies for strengthening students’ resilience, mental health, and well-being in case of future pandemics or other extreme contexts.