Том 19, № 2 (2024)

Introduction. The nature of economic sanctions is relevant for analysis due to the need for a proper approach to the sanctions policy, its goals and objectives, as well as the assessment of its efficiency. Purpose. The author aims at revealing the nature of economic sanctions by analyzing their key function. At the same time, the author focuses his attention on the evolution of this function in the development of world economic relations. Materials and Methods. The epistolary heritage of national and foreign scientists devoted to the problems of sanctions and sanctions policy was used as a theoretical basis of the study. The analysis of the works shows the interpretation of these categories in terms of their key function pursued in their implementation. The fundamental methodological basis of the study includes the historical and genetic method of analysis which identifies their properties and traces their evolution, as well as the typological method which organizes and classifies the existence modes of the analyzed phenomenon. Results. The author comes to the conclusion that the nature of sanctions and their content evolved with the world economy. Initially used as a tool to limit and undermine the economic potential of rivals, they are globally transformed into a way to minimize the costs of the world community on maintaining the existing political and economic order that ensures the efficient functioning of international trade. Conclusions. The analysis shows that international economic sanctions are a peaceful means that is used to prevent behavior that violates international law and creates threats to peace and security.

Introduction. This scientific study highlights the relevance of modeling and forecasting sales of Sberbank in terms of effective business management. Sales forecast is an important tool for predicting the demand for goods and services, as well as determining the adequate strategies and tactics to achieve the company’s goals. The research is distinguished by its reference to artificial intelligence methods in the field of marketing. Forecasting methods applied to a proprietary data sample of Sberbank’s daily sales give novel results, which reliably supports the development of adequate strategies and tactics for successful business management. The key hypothesis of the study is to check the prognostic potential of machine learning methods against the traditional econometric approaches to modeling Sberbank’s sales. The purpose of the study is to develop sales forecasting models for multifunctional products and their practical instruments for Sberbank’s Sales Network Block. Materials and methods. The study relies on the methods of system-oriented analysis, statistical and economic mathematical methods of data analysis and their processing. Collected and pre-processed sales data for Sberbank’s phantom products reflecting the dynamics of bank sales were used for computational experiments to build a few forecasting models and justify the choice of the best model among those built. Results. Random Forest and Gradient Busting (XGBRegressor) Models used training and test samples to give the forecasts with the accuracy significantly higher than the accuracy of forecasts by ARIMA-model and linear regression. Conclusions. The results of the analysis reliably confirm that machine learning methods are currently promising methods for forecasting bank sales and can be the subject of further research in this area. Machine learning techniques introduced into banking practices have the potential to significantly improve the effectiveness of existing sales and risk management.

Introduction. The labour market is the most important part of the economic system of the country and the region, while the ongoing changes reflect the current trends in economic development. There are many studies aimed at identifying cause-and-effect relationships that have led to a decline in the standard of living among population in the Russian regions, but only a few are focused on the analysis of employment as the main factor in the standard of living. The aim of the study is to assess the shifts in the employment structure of the region. To do this, the study analyses the dynamics of labour income and employment in the context of the sectoral structure of the economy in Chelyabinsk Region and the Russian Federation, refers to the shift-share analysis, identifies promising sectors of the economy in Chelyabinsk Region with the localization coefficient, and recommends scenarios of regional socio-economic policy with regard to the proposed typology of economic activities. Materials and methods. The shift-share analysis and the localization coefficients help examine the dynamics of structural elements. Chelyabinsk Region was chosen to be the object of the analysis in the context of current growth of economy driven by the technologically developed industrial regions. The period of the study is 2017–2022. Results. The article examines the dynamics of living standards, labour income and employment of population in terms of the sectoral structure of the economy in Chelyabinsk Region in 2017–2022. The calculated localization coefficients define the priority industries for investment and support. Conclusions. The results of the study demonstrate the decline in the living standards of the population in the industrial region, the insufficient remuneration of employees in the majority of economic activities, which means the regional employment policy is ineffective and requires adjustment of its methodological support. The methodology proposed in the article can be used to design an employment policy and manage regional investment resources.

Introduction. The work updates the situation with the enhanced sustainability of industrial enterprises in the Arctic territories of the Russian Federation since they are characterized with resources’ high potential associated with high environmental risks due to their exploration. Purpose. The paper aims at comparing the key indicators for sustainable development of the leading industrial enterprises in the Russian Federation in terms of their environmental impact. Materials and Methods. The authors reviewed modern studies related to the assessment of industrial development in the Arctic from the standpoint of technology, environmental friendliness, the formation of a data system, which reflect the trends in environmental load on the territory from the activities of industries. The identified trends in the sustainable development of industrial enterprises in the Arctic zone were analyzed and analytically interpreted. Methodologically, the paper synthesizes the methods of retrospective analysis and identifies the trends by analyzing rated key sustainability indicators defined by the data of industrial enterprises. Results. The paper proves the need for an in-depth analysis and detailed analysis of indicators of industrial load on the environment in the Arctic territories. The database for comparing key indicators was compiled, and the key trends in the sustainable development of industrial enterprises were identified. The analysis of their dynamics showed the multidirectionality in the development of industrial enterprises. The trajectories of enterprises’ efforts in achieving sustainability parameters were described: when the growth of expenditures is associated with a decrease in emissions and waste from production activities; when production and environmental indicators show multidirectional dynamics; when the enterprise growth rate decreases, environmental indicators improve. Conclusions. Thus, the authors substantiated the need to develop and implement a mechanism for regulating environmental policy at the state level, which would provide for measures aimed at active and systematic reduction of the environmental load on the environment in the Arctic territories.