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Increased use of modern mathematical algorithms based on artificial intelligence determined the relevance of this study, which is important for predicting the sustainable development of the country's economy in general and its banking sector in particular. To achieve the purpose of the research, the presented work used methods such as monographic, analytical, statistical, cognitive model, and artificial intelligence system "Random Forest". The aim of the study is to prove or disprove the hypothesis that, using a cognitive model, using the Random Forest ML model, it is possible to obtain an accurate forecast of the value of the "sustainability coefficient", reflecting the stability of the domestic economy. The scientific novelty of the study is due to the fact that the author's approach is proposed for indicating the crisis state of the economy through the calculation and neural network forecasting by the machine learning model "Random Forest" of the "Stability Coefficient" of the economy, which is calculated as the quotient of dividing the profit index of the banking system to the GDP growth index. The possibility of practical application in the banking sector determines the practical significance of the conducted scientific research since the approach proposed by the authors regarding the formation of a forecast of the “sustainability coefficient” can be successfully used to support managerial decision-making at the strategic level in the banking system. A hypothesis was put forward and proven that based on the use of a digital cognitive model and the Random Forest ML system, a forecast of economic stability can be successfully generated.

This study is relevant because market uncertainty induces progressively more attempts at making accurate profits forecasts in the banking sector. The scientific novelty of this study lies in the profits forecasts for the Russian banking sector performed using a random forest machine learning (ML) model and a neural network regression model. Regarding technology, the two models are combined into a cognitive model, as they are executed in the same cloud service (Collab) and have a common dataset comprising a training set, scripts and result output. The aim of the study is to build two models: a random forest ML model and a neural network regression model. The dataset used in the random forest ML model and the regression model included data on the performance of the Russian banking sector and some macroeconomic data on the national economy and the stock market for the period 2017–2021. Specifically, the dataset for the models included the following: key rate (%), growth assets (%), overdue loans (%), gross domestic product (GDP, in billions of rubles), RTS index (points), USD rate (vs. RUB), investments in assets to GDP (%), exchange robots (%), capital outflow (in billions of rubles), bank assets (in trillions of rubles), stock accounts (pcs.), and bank profits (in billions of rubles). The practical relevance of this study is evidenced by the fact that the results of the digital profits forecasting for the Russian banking sector can be recommended for real-world use. In building the cognitive model, we used the Python language in the Collab cloud environment. The mean absolute error of the test set for the random forest ML model (DecisionTreeRegressor) was 414.67, which is 61% lower than for the linear regression model (LinearRegression), which had a mean absolute error of 667.65.

Tax expenditure management is one of the tools for conducting responsible fiscal policy. Unlike direct expenditures, tax expenditures do not consume resources, but allow the achievement of certain social and economic goals. The purpose of this study is to test the hypothesis of the expediency of using the mechanism of tax expenditures to influence investment activities and the development of priority sectors of the economy. The object of this study was a sample of nine countries of the G-20 group, whose reports provided data on the share of tax expenditures related to investment support and stimulating the development of priority sectors of the economy. The practice of generating reports on tax expenditures is quite common in developed countries, but there are several limitations for cross-country comparison: national characteristics of fiscal elements, and the level of openness and transparency of data on tax expenditures. The scale of using tax expenditures is determined by the multidimensional average method; countries are rated according to the value of the integral indicator. The scale of tax breaks determines the position in the ranking. The effectiveness of using tax breaks is assessed through the coefficient of increase in investment and the coefficient of increase in industrial production. The assessment results show whether economic indicators are outpacing the growth rate of tax benefits and preferences. A study of panel data revealed the average degree of influence of the total amount of tax expenditures on the dynamics of capital investments. The allocation of a target group of tax expenditures related to investment support showed a weak degree of influence on the dynamics of capital investment volumes. The results obtained can be explained by the fact that the allocation of a target group of tax expenditures may not reflect the full range of tax benefits provided to support investments, which is due to the peculiarities of the system of accounting and assessment of tax expenditures in a particular country. In addition, the contribution of tax expenditures may be insignificant if direct forms of support prevail.

Discussions about whether the improvement of environmental indicators of companies has a positive or negative impact on their competitiveness (i.e., based on their economic indicators) have been conducted for a long time: environmentalists and the public insist on stricter environmental standards, and politicians, economists, and the business community are looking for a compromise between the costs of environmental protection and economic efficiency. Understanding this relationship is important for all disputing parties since two goals are simultaneously pursued: ensuring high quality of the environment, which requires unproductive costs, and obtaining good economic results. This is also important for separating environmental, economic, and political goals: is the policy of “greening” business implemented in developed countries a real fight against climate change and bad ecology, or is it used as a tool for mobilizing the electorate and hidden protectionism? Despite a large number of studies conducted in this area, the question remains open about the direction of the causal relationship: whether environmental investments lead to higher profits or simply act as indicators of firms with high financial results. This article presents a comparative analysis of methods and models for forming relationships between the environmental and economic characteristics of firms in the context of the activation of the international environmental agenda as one of the promising areas for the development of economic analysis. The authors show that the relationship between environmental regulation and competitiveness is not the same and depends on the market structure of the industry, while important problems of analysis are the presence of different approaches to defining the concepts of “competitiveness,” “economic efficiency,” “environmental efficiency,” and “regulatory rigor,” as well as the use of various indicators for their measurement, the complexity of selecting indirect indicators of environmental regulation and competitiveness, and the availability and quality of data. Furthermore,the possibilities and problems of the empirical analysis of the relationship between environmental and economic efficiency according to Russian companies and domestic statistics are studied.

ABSTRACT: The digital economy implies structural transformation in many industries, including the energy sector, without considering the state specifics of the industry, for which full-fledged digitalization can be harmful. The aim of the study is to develop a methodology and to determine the level of digitalization in the energy sector in an intercountry context. Based on the methods of comparison and analysis, the work analyses the concept of “digitalization” and defines the indicators applied to the assessment of digitalization. The developed methodology is based on the index method, which makes it possible to assess the rating of countries by the level of digitalization in the energy sector. The article discusses modern technological development and socio-economic aspects that determine theoretical research and the development of digitalization in the energy sector. The reasons and stages of digitalization in the energy sector are highlighted. Using the comparison method, the analysis of existing approaches to assessing the digital energy of enterprises is carried out. End-to-end technologies are highlighted, as well as their practical application at the enterprises of the energy sector. An analysis of the key areas for the development of digital energy is presented. Conclusions are made about the feasibility of developing microprograms and the main directions of digitalization are highlighted.

Organizational transformation for digitalization is a daily challenge for organizations. Successful change can be defined as the combined result of a number of factors, in which the attitude, trust and/or distrust of employees towards technology is of paramount importance. The aim of this study was to explore which factors most influence employees’ trust in technology and how the risk they pose can be mitigated. The quantitative research analyzed 473 respondents (Smart PLS3, using SEM model) and came to the following conclusions. Employees’ trust in technology depends primarily on the supportive role of management, and to a lesser extent on the digital readiness of the company and the training provided in the organization. The supportive role of management is a key element in the model, as it affects trust not only in a direct way, but also indirectly, through several pathways in the model. This means that the supportive role of leadership is clearly a decisive influence and its importance helps to assess the risk of trust or lack of trust.

This article examines the features of the heat supply organizations (HSO) anti-crisis management, which has become relevant due to the pandemic in the spring of 2020. It is noted that the spread of coronavirus and the related economic problems had a negative impact on the sustainable development indicators of both countries and organizations. HSO, which are rarely considered in modern publications, are positioned in the study as the most important part of the economy of any country, on which the future stabilization of the economic situation among heat consumers depends. This study made it possible to draw a conclusion about the strengthening of the HSO engineering systems technical state role in the anti-crisis management of these organizations. The study summarizes and presents the main characteristics of the heating networks technical condition and systematizes all types of diagnostics that are encountered in practice. The characteristics of HSO management choices of the technical state monitoring methods is given. The authors propose a generalized model of the diagnostic methods choice, taking into account the sustainable development of a specific HSO. Perspective approaches to the improvement of the HSO heat supply systems state are determined, which will ensure the development and increasing reliability of urban infrastructure in the context of achieving sustainable development goals.

The trends of sustainable development and green agenda transform the production processes, leading industries, and regional markets, and reveal objective contradictions in ensuring the ecological safety of certain territories. This study aims to develop a methodology and assess ecological safety at aggregated system levels, taking into account natural and socioeconomic factors as well as the factor of human capital development with specification of the place of ecological safety in the concept of sustainable development. The objects of empirical research are the EAEU and CIS countries for the period 2010–2019, as well as 85 Russian regions. The scientific research toolkit is based on the methods of structural-logical, economical-statistical, and comparative analyses, and expert judgments. The concepts of sustainable development and human capital and the theory of the national school of ecological safety are analyzed using the systematic approach. The mixed methodological approach showed the interconnection of four approaches of assessing ecological safety (technogenic, environmental, institutional, resource-based). The authors developed a method for assessing ecological safety taking into account the contribution of human capital. The assessment results are typologized and grouped according to the dynamic trend. For the majority of the considered countries, changes in the level of ecological safety correlate with changes in socioeconomic indicators. The same trend is observed when considering the Russian regional formations. The contribution of human capital was recorded to a lesser extent. For several regions, the increased number of objects polluting the environment is accompanied by a decreased volume of polluting emissions into the atmosphere.

The paper considers the problem of adolescent and young students’ personalities’ development in the conditions of face-to-face and distance school learning. The scientific novelty of the study is the proposed classification of pedagogical technologies according to the degree of their novelty: modernized technology, combinational technology, progressive technology, pedagogical technology of the combinational type, the essence of which is shown in its title. A specific model of pedagogical technology of the combinational type “Teaching in cooperation, in a team using flipped classroom” was developed. The developed technology was implemented during the classes of the humanities and natural-mathematical cycles. Nine hundred and eight adolescent and young students (divided into experimental and control groups) and 32 teachers participated in the experimental work. To analyze the differences between the experimental and control groups in terms of the student’s personality manifestation, several criteria and indicators were considered. For mathematical and statistical processing of the obtained results, the multifunctional statistical Fisher’s F-test was used. Analysis of the implementation of the developed technology in the conditions of face-to-face and distance learning showed a positive dynamics of experience formation of the student’s personality manifestation. This allowed the authors to consider the developed pedagogical means effective.

Today the world economy is handling an economic crisis caused by the pandemic of a new coronavirus infection (COVID-19), announced by the WHO, as well as by fluctuations in the international energy market and by the development of “green energy”. The crisis, named the “2020 crisis”, is notable for the divergence of the sectoral dynamics of development, with a declining development trend in some industries and the rapid growth of others. The crisis is etymologically relevant to the energy sector, a sector which is important for the survival and specialization of the Russian Federation. This research is aimed at describing the status and highlighting the socioeconomic effects, constraints (economic and social risks), and “growth points” of the energy sector in the modern realities of the 2020 crisis. Methods. The method of in-depth interview was used. The need for studying expert opinion is based on the fundamental principles of implementation research, according to which the success of state sectoral policy depends on its perception and support by stakeholders. Top managers of energy enterprises acted as the experts (N = 10). The interviews were conducted in April 2020. The method of incremental approximation and coding (open and axial) were used for a high-quality discourse analysis. Results. The conducted study revealed divergence of sectoral dynamics of energy consumption, which is a translation of the immanent features of the 2020 crisis into the energy sector. The detected constraints are reserved development prerequisites of “green energy” and reduced investments in development programs. The potential “growth points” include intensified digitalization of the energy sector and the need for institutional changes in taxation in the energy sector, recognized by experts.