The COVID-19 pandemic has caused the acceleration of digitization and the consideration of digital financial inclusion as a means to minimize negative economic consequences and increase the resilience of households and SMEs. The purpose of this article was to assess the impact of the COVID-19 pandemic on digital financial inclusion by constructing and calculating an integral index of digital financial inclusion (DFI) based on Global Findex Database indicators. The approach to calculating the DFI index and two sub-indices that characterized passive participation in financial relations and active use of digital technologies was based on a linear mathematical model of the integrated indicator and on the use of the Fishburn formula to calculate the weight coefficients. The obtained results proved the acceleration of digital financial inclusion in 2021 and revealed significant differences in DFI between countries and groups of countries according to income level as well as problems of financial exclusion of the most vulnerable groups of population, especially in developing countries. The obtained results regarding the level of DFI are discussed from the point of view of COVID-19 impacts: both directly by influencing consumer behavior and decisions regarding digital financial services and from a broader perspective by influencing business entities, financial service providers, and regulation.

The rapid development of artificial intelligence (AI) has affected higher education. Students now receive new tools that optimize the performance of current tasks. Universities have also begun implementing AI technologies to help university teachers and improve the quality of educational services and solve the Sustainable Development Goal 4. Hypothetically, it is possible to replace university teachers by using AI technologies. This is a hidden conflict of Sustainable Development Goal 4 and Sustainable Development Goal 8. This research aimed to examine the perceptions of Eastern European students about the possibility of replacing university teachers through AI technologies. The authors used an information study with a bibliometric analysis of 2000 sources, planning the experiments and compiling the questionnaire, surveying 599 students using an electronic questionnaire and cloud technologies, statistical processing questionnaires using Excel tables, and verifying statistical hypotheses. Verification of statistical hypotheses for replies of 599 respondents showed that more than 10% of the surveyed students from Eastern European universities are confident that AI will replace university teachers in five years. It was shown that the opinions of students in the 1st stage (undergraduate study) from the countries of the European Union and countries outside the European Union have significant differences. The obtained results were proven using one-sided testing and standard hypothesis testing level, α = 0.05. The article was completed with multilevel managerial and pedagogical recommendations. These recommendations are designed to increase higher education’s sustainability in AI implementation.

The article aims to identify the relationship between energy efficiency and particular indicators of energy losses in Europe. The results of the bibliographic analysis showed a knowledge gap in energy losses in Europe regarding the new challenges of energy security. For the analysis, annual panel data from 32 European countries were collected from 1990 to 2019. The authors used the Jarque–Bera test to assess the normality of the residuals, utilized the Breush–Pagan test for heteroskedasticity check, and applied regression analysis to determine the relationship between energy efficiency and energy loss rates in Europe. To assess the effects of energy losses, the authors performed OLS modeling using the stats model’s package in Python. According to the modeling results, an increase in distribution losses (% of available energy from all sources) by 1% in Europe leads to an increase in energy consumption by 17.16% under other constant conditions. There is significant heterogeneity between European countries concerning energy efficiency and energy loss coefficients. Such a situation requires the development of new strategies and mechanisms to reduce energy losses, considering the challenges of energy security in Europe in turbulent times. Further research can be devoted to clustering European countries according to the main groups of energy losses: in the extraction, distribution, storage, and transformation of energy.

The article aims to analyze the impact of green investments and the development of renewable energy on greenhouse gas emissions based on 223 countries in 2011–2021. The information base is the International Renewable Energy Agency, Our World in Data, Climate Policy Initiative, and FTSE Russell. Correlation analysis was used to check the data multicollinearity, multivariate regression analysis with stepwise variable entry—to formalize functional relationships. All variables characterizing the dynamics of green investments and the development of alternative energy, the number of annual investments in off-grid renewable energy has the largest impact on the amount of CO₂ and N₂O. Thus, an annual investment increase of USD 1 million leads to a CO₂ emission increase of 4.5 kt and an N₂O emission increase of 0.272 kt. Simultaneously, the green economy’s market capitalization level has the largest impact on the amount of CH₄. In this case, a capitalization increases of USD 1 trillion leads to a CH₄ emission increase of 129.53 kt. The dynamics of renewable energy development have a statistically significant effect on only one of the three studied greenhouse gases—CO₂ emissions. Here, 1 MW growth of an absolute increase in off-grid renewable energy capacity leads to a 1171.17 kt reduction of CO₂ emissions. Checking input data for lags confirmed a time lag of one year between the level of green investments and the level of greenhouse gas emissions. That is, the impact of green investments on the level of greenhouse gas emissions is delayed by one year. The results of regression models taking into account lags confirmed that an increase in the level of green investments has a positive effect on reducing the level of greenhouse gas emissions (an increase in off-grid renewable energy annual investments of USD 1 million leads to a decrease in CO₂ of 1.18 kt and N₂O of 1.102 kt; the increase in green economy market capitalization of USD 1 trillion leads to a decrease in CH₄ emissions of 0.64 kt).

The coronavirus disease (COVID-19) pandemic led to a catastrophic burden on the healthcare system and increased expenditures for the supporting medical infrastructure. It also had dramatic socioeconomic consequences. The purpose of this study is to identify the empirical patterns of healthcare expenditures’ influence on sustainable economic growth in the pandemic and pre-pandemic periods. Fulfilment of the research task involves the implementation of two empirical blocks: (1) development of a Sustainable Economic Growth Index based on public health, environmental, social, and economic indicators using principal component analysis, ranking, Fishburne approach, and additive convolution; (2) modelling the impact of different kinds of healthcare expenditures (current, capital, general government, private, out-of-pocket) on the index using panel data regression modelling (random-effects GLS regression). Regression results in the pre-pandemic period show that the growth of capital, government, and private healthcare expenditures positively influence sustainable economic growth. In 2020–2021, healthcare expenditures did not statistically significantly influence sustainable economic growth. Consequently, more stable conditions allowed capital healthcare expenditures to boost economic growth, while an excessive healthcare expenditure burden damaged economic stability during the COVID-19 pandemic. In the pre-pandemic period, public and private healthcare expenditures ensured sustainable economic growth; out-of-pocket healthcare expenditures dominantly contributed to the pandemic period.

The relevance of the analysis of Sustainable Development Goals is based on the 2030 Agenda for Sustainable Development. The purpose of the study is to create a model of management/regulation of the quality of education and scientific research systems based on Sustainable Development Goals. The data of the ratings that reflect certain tasks of the Sustainable Development Goals, including those related to educational and scientific activities, are analyzed. The methodology of calculating the index of the success of realization of separate purposes of sustainable development is used. Bibliometric analysis as a research tool is used to create a quality of education management model. The example of the roadmap for achieving SDGs within the relationship chain “quality of education and science + partnership and networks–innovation–socio-economic impact–sustainable development goals” is provided. The suggested methodology for determining the index of success in achieving Sustainable Development Goals includes managing education systems, implementing scientific research, and managing/regulating the economy on micro-(university), meso-(regional), and macro-(national) levels. The tool in this case is a strategy for achieving KPIs, which can be fundamentally implemented in the form of a roadmap, with detailing of its elements using other tools. The “management/regulation” stage of the process of achieving Sustainable Development Goals is concretized by a mind map.