The aim and novelty of this study consist of estimating the nexus between CO₂ (carbon dioxide) emissions, energy use, economic growth, and financial development for ten Central and Eastern European countries (CEEC) over the 2000–2017 period, starting from Environmental Kuznets Curve (EKC) theory. The Fully Modified Ordinary Least Squares (FMOLS) method was used for testing the cointegration relationship. Granger causality estimation based on the Vector Error Correction Model (VECM) and Pairwise Granger causality test were applied to identify the causality relationships between the variables and to identify the direction of causality. The implementation of the tests led to significant conclusions. In the long run, the levels of CO₂ emissions and energy use do not have any influence on economic growth. Furthermore, there is a bidirectional causality among economic growth in terms of GDP and financial development variables. Thus, increasing financial development will generate more CO₂ emissions and more energy use, and increasing economic growth will lead to rising financial development. In the short run, increasing financial development will generate more CO₂ emissions and will lead to increased energy use and economic growth. Also, a bidirectional causality is being revealed between financial development and CO₂ emissions. This indicates that financial development may help to reduce CO₂ emissions.

Blue Economy represents a new and interesting concept on a global level, both from the economic potential but also by the fact that it can be used to reduce environmental degradation. The main goal of this research is to identify the causality relations between the greenhouse gas emissions, the Blue Economy and economic growth based on a panel of annual data from the 28 countries that are members of the European Union (EU) over the 2009–2018 period. After applying stationarity and cointegration tests, the long term cointegration coefficients shall be determined with the help of the fully modified ordinary least squares (FMOLS) estimator. Granger causality estimation based on the vector error correction model (VECM) was applied to identify the causality relationship between the variables and to detect the direction of causality. Based on the identified causality relations, the Blue Economy has a significant influence on greenhouse gas emissions in the long run. Unidirectional causality relations were identified from the economic growth of greenhouse gas emissions in the long term, as well as from the greenhouse gas emissions on economic growth in the short term.

The COVID-19 pandemic deeply impacted not only human wealth but also all segments of the economy as well as the field of technology. Thus, the purpose of this paper was to analyze the effects that the pandemic crisis and digitization have had on the financial performance of banks in Central and Eastern European Union countries (CEEC) during the 2010–2021 period. In order to capture an overview of the financial performance of the banking systems in the 10 CEECs, we used three variables—ROA, ROE and NPL—as reference indicators. In order to highlight the impact of the COVID-19 pandemic on the performance of banking systems, we used the number of reported cases as a variable, and to highlight the impact of digitization, we used as indicators the number of automated teller machines (ATMs) per 100,000 adults, number of certificates of secure internet servers, number of credit cards, number of debit cards, percentage of individuals using internet banking, and the number of commercial bank branches per 100,000 adults. Thus, the impact of digitization and the pandemic crisis generated by the COVID-19 virus on the performance of the banking systems in the 10 CEECs is outlined through three regression models using the robust regression model. The obtained results show that, as the infection rates with COVID-19 increased, the performance of banks measured by ROE and ROA decreased. Regarding the impact of digitization on performance, we note that an increase in the use of internet banking and the security of bank servers generated positive effects on the performance of banks. The results of the study are useful for banking product development departments, who should consider the important role of digitization in increasing the performance of banking services and thus design new digital products or ways to expand existing ones on a larger scale.

In this period of extreme changes in our society, issues related to the health and well-being of citizens are considered essential for the future of a united and prosperous Europe. Achieving the Sustainable Development Goals (SDGs) at EU level by 2030 requires hard work done in a transformative way in order to implement a set of coherent, evidence-informed policies that address health, well-being and all their determinants throughout the course of life and across all sectors of government and society. The objective of this paper is to assess the stage of fulfillment of all SDG targets in relation to health and well-being at EU level, based on the current trend of each indicator, for each EU member country. Based on the Eurostat SDG data set for 2007–2018, the individual trends were forecast using the AAA (Holt-Winters) version of the exponential smoothing (ETS) algorithm. The research results are surprising, on the one hand showing the possibility that some targets will be reached, but also indicating that a large percentage of targets will not be reached if the current trend is continued, especially due to disruptive change generated by the current pandemic. There is a need to increase the involvement of all member states, but also ensure a deeper involvement at the level of EU institutions, to provide full support for meeting the targets proposed by the 2030 Agenda, ensuring prosperity and health for all European citizens, and becoming a model for all the states of the world.

The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection.