Quality of life and social welfare are objectives of the social policy of any state. The study aims to identify the influence of factors such as the circular economy, digital innovation, sustainable entrepreneurship on social progress and completes the current approach identified in the literature by assessing the dependencies between the phenomena represented by them. The quantification of the influences of the enumerated factors on social progress was achieved by identifying some synthetic indicators, such as composite indices, which would surprise the complexity of the analyzed phenomena. To measure the progress of the transition to the circular economy – using multivariate analysis methods – a composite indicator has been proposed and determined that allows the ranking of EU states according to its orientation, as a premise of social progress, and can substantiate the adjustment national policies. The integration of the proposed indicator in the regression models used, with similar indices, is done to highlight the impact of the circular economy, innovation and sustainable entrepreneurship on social progress. Thus, the adaptation of digital technologies in current business models, the development of sustainable innovative entrepreneurship support the transition from the linear economy to a circular economy and offer new study opportunities.

E-commerce is one of the industrial sectors created as a result of technological progress, and has created new jobs for the economy. However, this sector is conditioned not only by the level of digitalization of each state, but also by the speed of integration of technology in the business environment. The main purpose of the article is to present the impact of e-Commerce development on the labor market. It also considers impact on the labor market of human capital with advanced technological skills. It focuses on data from a particular period situated between two major crises (the economic crisis of 2008 and the health crisis of 2020). We want to know to what extent a technology-created sector can compensate for technological unemployment. To test the working hypothesis, we developed a panel regression model for a sample of 28 European states. The results indicate that 99.5% of the variation in the labor market activity rate of people aged 15 to 64 years in the selected sample of states is explained by the model. This confirms that the development of e-Commerce and an increase in the percentage of technology-specialized human resources contribute to the increase in activity rate in the labor market. As a result, the labor market must be assisted in keeping up with technology by restructuring the education system, or introducing courses that maintain competitiveness and continuous development.

As COVID-19 had been posing unprecedented challenges, the economic agents as well as the individuals had to adapt to a new lifestyle. The preventing measures against the spread of the virus determined an increase in e-commerce activity in general, and in mobile commerce in particular. In this regard, sellers redefined their strategies revolving around smartphone applications for online shopping, which will gradually become the global shopping standard. Therefore, the aim of this paper is to evaluate the current situation of e-commerce, focusing on the main transformations since the beginning of the pandemic. The methods used in the paper consist in both qualitative research of the literature regarding e-commerce and a quantitative analysis that captures two aspects: on one hand it highlights the change determined by the pandemic in online commerce, and on the other hand it shows an X-ray of the EU states positioning in terms of e-commerce evolution. The latter consists in a comparative Hierarchical Cluster Analysis that aims to provide an image on the situation of EU states, before and after the pandemic has been installed. The Cluster Analysis was carried out in SPSS software and it is based on a series of chosen indicators from Eurostat database. This comparison provides an image of how changes among the clusters take place in terms of e-commerce indicators. It is noted that in countries where the digitization was more developed, the value of the change in online commerce was less considerable, comparing to the Eastern and Balkan states, which had a more significant increase. However, there are significant limitations based on the small number of indicators taken into consideration in this paper. Future work needs to be done in order to explore new ways to address the challenges identified in the development of e-commerce and online shopping.

Air pollution is a serious problem in Romania, with the country ranking 13th among the most polluted countries in Europe in the 2021 World Air Quality Report. Despite the recognized impact of pollutants on health, there has been a lack of large-scale studies conducted in Romania. This study investigated the impact of air pollutants on patients with chronic respiratory, cardiovascular, cerebrovascular, or metabolic diseases in Bucharest and its metropolitan area from 20 August 2018 to 1 June 2022. The daily limit values for particulate matter PM10 and PM2.5 were exceeded every month, especially during the cold season, with a decrease during the COVID-19 pandemic restrictions. A significant statistical correlation was found between the monthly average values of PM2.5 and PM10 and hospitalizations for respiratory and cardiovascular diseases. A 10 µg/m³ increase in monthly average values resulted in a 40–60% increase in admissions for each type of pathology, translating to more than 2000 admissions for each pathology for the study period. This study highlights the urgent need for national and local measures to ensure a cleaner environment and enhance public health in Romania according to international regulations.

Given the advent of Industry 4.0 and the importance of labour-based automation in ensuring competitiveness at the firm, regional cluster, or country level, the paper aims to explore, for the first time, the features of several estimates of occupational/labour automation and to assess the potential risks associated with it. A comparative analysis of the most well-established estimates of labour automation, the Occupational Information Network (O*NET) degree of automation estimates and Frey and Osborne’s future probabilities of automation was carried out to see whether, and to what extent, these estimates are compatible. Results show significant distributional differences between them, which are quantified into automation-triggered disruption risks at the occupational level, as current levels of labour automation are, in some cases, well below their future estimates. Work context features were used to derive a typology of occupations, which can explain up to one-third of the current, and up to half of the future levels of labour automation. Finally, we identified which occupations and occupational groups are likely to be affected by the highest risk of automation-induced displacement and estimated the magnitude of different disruption classes. Conclusions are compatible with other economywide assessments of the impact of labour automation on the workforce, thus being valuable inputs for corporate strategy, decision-makers and human resource planners as they address a growing need for quantitative insights useful for adapting the labour force structure, workers’ skills, and the task content of occupations to the competitiveness requirements related to the process of digitization in the Industry 4.0 context.

Optimization techniques perform an important role in different domains of statistic. Examples of parameter estimation of different distributions, correlation analysis (parametric and nonparametric), regression analysis, optimal allocation of resources in partial research, exploration of response surfaces, design of experiments, efficiency tests, reliability theory, survival analysis are the most known methods of statistical analysis in which we find optimization techniques. The paper contains a synthetic presentation of the main statistical methods using classical optimization techniques, numerical optimization methods, linear and nonlinear programming, variational calculus techniques. Also, an example of applying the “simplex” algorithm in making a decision to invest an amount on the stock exchange, using a prediction model..