Environmental and energy taxation are essential components for designing global economic policies and they often contribute to achieving the sustainable economic development goals in contemporary economies. Starting from the analysis of certain elements such as the share of environmental, energy, transport and pollution taxation in GDP and using the Hierarchical Clustering methodology, the paper aims to identify economic models of behaviour and to understand the influence of energy taxation in designing an economic paradigm. In addition, another objective of the paper is to deepen the relationships that energy taxation has in designing certain economic models of behaviour and to group the EU-28 Member States based on the specified criteria. The research results confirm that at the EU-28 level could exist elements for achieving energy taxation convergence and that the states should promote a more accurate fiscal policy in order to improve the loss of competitivity caused by an inaccurate energy taxation.

In recent years, the trend of digital transformation of commerce has been extremely dynamic, and the COVID-19 pandemic has generated a strong impetus for the development of digitalization. However, there is a large difference in the use of e-commerce in European Union (EU) countries. The objective of this article is to explain the complex evolution of e-commerce in terms of B2C using econometric methods, especially regarding Internet purchases made by individuals according to six social and technological factors considered basic factors in purchase decisions. Thus, we propose a conceptual model for the analysis of disparities in Internet purchases, taking into account both factors related to demand (consumer) and factors related to supply (supplier). Based on the data provided by Eurostat for all 27 EU countries for the period 2012–2021, the empirical findings of cluster analysis suggest changes in the grouping of countries at the beginning and end of the analysed period. Socioeconomic factors that can explain the transition of EU states from one group to another in terms of development level of Internet purchases by individuals were identified. The results of this study can serve as a starting point for future adaptation strategies of e-commerce in the context of global economic development with a focus on increasing digitisation, the existence of for which a digital convergence in the process of digital transformation is essential.

This article proposes an architecture model for a predictive maintenance solution that can be used to detect degradation of equipment in industrial units. This platform is compliant with Industry 4.0 standards and employs machine-learning algorithms alongside with data analytics methodologies to model the kinetics of equipment degradation. This serves the overarching aim of Industry 4.0 by enabling real-time, data-driven decision-making and complex asset management. To model the deterioration of the equipment, advanced data analysis techniques and machine learning were used, thus allowing for the early identification of imminent failures and reducing system downtime. The proposed solution was validated through numerous experiments and a comprehensive analysis of data. The results indicate not only enhanced operational reliability but also a reduction in environmental impact, thereby highlighting the value of intersecting Industry 4.0 and Sustainability paradigms in the field of industrial systems.

This paper presents a study on the establishment and the capitalization of a remontant red raspberry crop, the Polka variety, on a privately agricultural land area of 0.2 ha in a crop with a support system in V using a geotextile membrane for soil mulching and the method of micro-irrigation by drip. It has been shown that the annual gross profit is advantageous for diversifying the population incomes of rural areas, and the red raspberry is economically profitable regarding cultivation because the recovery of the invested sum is achieved in a maximum of 5 years after the establishment of the culture. The aim of this paper is to explore the growth and commercialization of red raspberry cultivation on privately owned arable land in rural Romania, emphasizing its potential for productivity and sustainability in the context of the circular economy. This initiative not only delivers substantial profits for investors but also fosters rural development and boosts local income levels. The study demonstrates that this cultivation method of red raspberry, aligned with the principles of the circular economy, enhances sustainability by reducing waste, optimizing resource use, and involving local communities in production cycles.

To ensure soil sustainability, the European Union considers the mitigation of the ecological, social and economic impacts and the prevention of soil degradation, which is the primary source of the ecosystem. In this respect, Land Use and Coverage Area Frame Survey (LUCAS) studies aim to investigate land use at the community level to gather information necessary for the analysis of the interactions between agriculture, environment and rural landscape and to provide estimates of agricultural areas with main crops. According to data from Eurostat, between May and October 2022, through the use of digital techniques, the levels of land coverage and land use, pastures, as well as irrigation management and structural elements in the landscape, were examined on the ground throughout the European Union. Data on the agricultural environment and soil were collected in the georeferenced points belonging to a representative sample by observing and completing the field form. At the level of the southwest region of Romania, the study was based on the inspection of 274 points by taking soil samples to analyze the quality indicators and identify key species of flowering plants. Data on land coverage and use can be used for a variety of environmental and socioeconomic projects in different fields.