Aim. The work aimed to develop the stages of implementation of digital technologies in relation to levels of digital maturity of territorial management authorities.

   Objectives. The work seeks to perform critical analysis of previous studies on digital technologies in territorial management; identify the stages of evolution of application of digital technologies and correlate them with levels of digital maturity of territorial management authorities; discuss the possibilities of economic assessment of evolution in application of digital technologies.

   Methods. The author applied the method of system logical analysis and structural-functional approach in the study of regional management teams and functions of regional managers. The information base consisted of published articles indexed in the global database Science Direct. The author’s development of levels of the organization digital maturity was used to structure the result.

   Results. The levels of digital maturity of territorial management bodies include digital technologies such as mobile communications, big data, artificial intelligence, blockchain, augmented reality, Internet of things, robotics engineering, virtual reality, neurotechnology, and quantum technology. The work substantiates an increase in digital instrumental equipment of management activities as an evolution in the application of digital technologies by territorial government bodies.

  Conclusion. According to the author’s viewpoint, the digital technologies used for territorial management can be assessed both based on the application results and taking into account the reduction in management costs during the digitalization of these processes.

   The scientific novelty of the result obtained consists in the development of stages of implementation of digital technologies in conjunction with the levels of digital maturity of territorial management authorities, complementing the existing methods for forecasting the development of digitalization processes in modern society.

   Aim. The work aimed to classify and analyze the main factors that determine the investment climate of countries with a fragile political situation using Afghanistan as an example, which has been experiencing a long period of political instability and is currently in the process of significant political and economic transformations.

   Objectives. The work seeks to identify the main factors that determine the investment climate in Afghanistan as a state with a fragile political situation; to analyze the identified factors and highlight the most problematic fields; to characterize the areas for improving the investment climate in Afghanistan under political instability.

   Methods. The authors employed a qualitative research method, in particular thematic analysis, to collect and analyze data in order to examine investment opportunities and challenges in Taliban-controlled* Afghanistan.

   Results. A number of conclusions were made regarding the investment climate factors. The policy of the current Afghanistan government regarding foreign direct investment (FDI) is not clearly defined, and legal issues are also not regulated. Despite this, the government aims to develop bilateral relationship in the context of individual investment agreements. Regarding the industrial policy, the Afghanistan government does not impose restrictions on the activities of foreign companies, while the protection of property rights and regulation of intellectual property rights are at a low level, and the level of corruption is high. Afghanistan pays significant attention to stabilizing financial policy and developing state-owned enterprises, but to date this has not yielded significant results. Afghanistan’s socio-demographic policy does not promote labor force development. In all fields, the pressure of sanctions on the country seems to be very significant. In such circumstances, improving the investment climate and attracting FDI will be possible only with the presence of political willpower and a targeted government policy.

   Conclusions. The main opportunities for investing in Afghanistan are determined by interaction with the government of the country and investments in fields critical for the state development. These include infrastructure, the energy sector with the potential of renewable energy sources, mining industry and natural resources, agriculture and food security, education, as well as healthcare and pharmaceuticals industry.

   Aim. The work aimed to develop a model of the change management subsystem at the enterprise in the context of the transition to a closed-loop economy, based on systems analysis.

   Objectives. The work seeks to determine the main categories used to implement the change management at the enterprise; to decompose the change management subsystem at the enterprise in the transition to a closed-loop economy; to identify internal and external factors influencing the change management.

   Methods. The authors used the methods of systems analysis of decomposition to discuss the key aspects of the change management subsystem.

   Results. The closed-loop economy is a priority model in Russia, since it involves the introduction of innovative technologies aimed at reducing waste production, improving environmental safety, increasing new jobs, as well as economic expediency. However the transition to a new model requires significant changes in the enterprise management system. A systems approach to the study of the change management subsystem enables to consider all the elements and factors that influence the formation of the subsystem, its interrelations, and also helps to determine the ways to solve the problem of the functioning of enterprises during the transition to a closed-loop economy. At the decomposition stage, a model of the change management subsystem was developed, the directions of the changes being implemented were formulated, as well as the elements of the change management subsystem were determined, while their correction can contribute to the enterprise effective operation during the transition to a closed-loop economy model, and also the stages of implementing changes in the enterprise management system were identified.

   Conclusions. The closed-loop economy model is promising and relevant for Russia. Based on the analysis and the developed model of the change management subsystem, the article proposes measures to implement changes at the enterprise, that will help improve the enterprise efficiency, taking into account external and internal factors during the transition to a closed-loop economy.

   Aim. The work aimed to identify the features, advantages and disadvantages of the methods for assessing the quality of life of the population, applied in the Russian Federation (RF), and to propose a set of indicators for assessing the quality of life of the population, having consideration for the processes of digital transformation of the economy and society.

   Objectives. The work seeks to justify the need to develop a method for assessing the quality of life of the population, having consideration for the processes of digital transformation; to conduct a comparative description of the methods for assessing the quality of life of the population of the Russian Federation, to identify their advantages and disadvantages; to propose a set of indicators for assessing the quality of life of the population, taking into account regional specifics and digitalization conditions.

   Methods. The authors used general scientific methods (classifications, system and comparative analysis, scientific synthesis). The study information base was composed of methods for assessing the population quality of life, applied at the regional level in Russia (Institute for Problems of Regional Economics of the Russian Academy of Sciences (RAS), Russian Public Opinion Research Center, Rosstat).

   Results. A comparative analysis of the methods for assessing the quality of life of the population, applied in Russia, was performed to reveal their advantages and disadvantages in the context of digitalization of the economy and society. A set of indicators for assessing the quality of life of the population at the regional level in the context of digital transformation is proposed, including groups of indicators determining the level and structure of income and consumption; the state of the labor market and employment; availability and quality of social services; housing conditions; development of digital infrastructure; environmental well-being. The regional specificity of the proposed indicators was characterized. The work also discussed the process of influence of digitalization conditions on various aspects of life of the population, assessed using the proposed indicators.

   Conclusions. Monitoring of the indicators of the population quality of life, taking into account the impact of digitalization, is as an important tool for substantiating and improving the effectiveness of regional socio-economic policy.

   Aim. The work is aimed to propose a theoretical and methodological instrument for assessing the influence of key business risks on the operational efficiency of car service organizations, and to formulate recommendations for its practical application.

   Objectives. The work seeks to provide a quantitative analytical assessment of the operational efficiency coefficient of car service organizations and to identify the factors determining this indicator of business efficiency; to develop a method for assessing the influence of external and internal risk factors on the operational efficiency of car service organizations; to propose an algorithm for managing the operational efficiency of car service organizations aimed at reducing the risks of its unacceptable decline; to propose recommendations for balancing key factors of profit from sales of car service organizations to ensure its sustainable growth.

   Methods. Based on the systems approach, the methods of logical, factor and comparative analysis were used, aimed at achieving the goal and solving the study problems, as well as a management methodology aimed at using feedback between management influences and the outcome of management. The study results are based on scientific provisions presented in the works of Russian and international scientists, revealing the problems of managing the operational efficiency of organizations under modern conditions.

  Results. A comparative analysis of the activities of two car service organizations in 2020–2024 was performed, and the main factors determining their profitability and revenue were identified, namely the markup on spare parts, the number of clients and the ratio of the cost of spare parts to the cost of works. It was revealed that the number of clients has the most significant
impact on financial indicators, and the markup on spare parts is a compensating factor with a moderate decrease in the customer base. Optimal values of the ratio of the cost of spare parts to labor for different car service business models were determined. The work presents the relationship between an increase in markup and a decrease in the customer base. It also provides recommendations for balancing the key factors of profit from sales of car service organizations to ensure its sustainable growth.

   Conclusions. The article presents an analytical assessment of the operational efficiency of car service organizations, taking into account their specificity, while factors for its reduction are identified, and an algorithm for managing operational efficiency is proposed, based on the use of changes in the markup on spare parts purchased by customers in the car service as a control action. Based on a study of the experience of car service organizations, the work revealed that the strategy for developing a car service should take into account the balance between the markup on spare parts and the number of customers. Attention is drawn to the fact that a moderate increase in the markup enables to retain the customer base while maintaining an optimal ratio of spare parts to works. In the process of analyzing the experience of the studied organizations, the thesis is confirmed that this approach ensures a more stable growth of financial indicators in the long term, and abrupt changes in any of the indicators can induce negative consequences for the business.

   Aim. The work aimed to assess the impact of changes in the key rate of the Bank of Russia on the Russian stock market to develop an effective strategy for managing a portfolio of Russian companies’ stocks.

   Objectives. The work seeks to create a machine learning model that can predict short-term dynamics of index values in case of changes in the key rate; determine the optimal structure of the investment portfolio taking into account the results obtained; formulate the basics of a strategy for managing a portfolio of stocks under conditions of key rate volatility.

   Methods. The study employed correlation and regression analysis, gradient boosting (XGBoost), SHAP analysis to interpret the results of machine learning models and assess the direction of dependencies, as well as a method for assessing the impulse response of index quotes to changes in the key rate.

   Results. The work revealed statistically significant effects of the impact of changes in the key rate on quotes of industry indices of the Moscow Exchange in the short term (one to three days). The gradient boosting models constructed demonstrate high predictive ability for most of the indices analyzed. A method for forming an optimal stock portfolio taking into account the predicted change in the key rate was developed.

   Conclusions. The work established that the reaction of stocks to changes in the key rate varies significantly depending on the economy sector. The greatest sensitivity is demonstrated by stocks of companies in the financial sector and companies in the electric power industry, while telecommunication companies and chemistry and petrochemistry-related companies are the least susceptible to the impact of rate changes. The results obtained can be used to form investment portfolios taking into account the expected changes in the monetary policy of the Bank of Russia and minimize risks from the key rate fluctuations.

   Aim. The work aimed to study the relationship between the financial derivatives market and economic growth, as well as other macroeconomic variables using the BRICS countries (Brazil, Russia, India, China, and South Africa) as an example, including Russia, in 2020–2024.

   Objectives. The work seeks to analyze the dynamics of derivatives markets development in the BRICS countries; to study the influence of macroeconomic factors (inflation, government expenditures, trade openness) on the derivatives market; to identify differences in the nature of interaction between derivatives markets and economic growth for countries with different
income levels; to develop recommendations for improving the efficiency of using derivative instruments to stimulate economic growth.

   Methods. The author applied panel data analysis methods, including the generalized method of moments (GMM), the Hausman specification test for choosing between fixed and random effects, and the Pedroni test for identifying long-run equilibrium relationships.

   Results. A causal relationship between the derivatives market and economic growth was revealed. It was established that trade openness and government expenditures have a significant impact on the derivatives market. In high-income countries, there is a two-way correlation with economic growth, while in upper-middle-income countries, it is unidirectional. In relation to Russia, such mechanisms do not function stably, therefore the research in this field
is limited.

   Conclusion. Improvement of the accuracy of research and assessment of the impact of the derivatives market on macroeconomic variables in Russia require the development of forecast models that take into account the national economy specifics. In the future, the research will be related to the creation of models capable of taking into account the specifics of the Russian financial market and offering more effective solutions in the process of its regulation.

   Aim. The work aimed to investigate the possibilities of applying various machine learning algorithms to forecast the quality of life index of the population.

   Objectives. The work seeks to develop predictive models for analyzing the quality of life index of the population of selected countries (Germany, India, the Netherlands, Russia) using various machine learning algorithms based on historical data from the Numbeo website from 2012 to 2025; as well as to systematize and analyze the results of machine learning models for these countries.

   Methods. The study used machine learning models such as random forest, linear regression, gradient boosting, k-nearest neighbors, and support vector machine. Forecasting the quality of life index of the population is based on data on socio-economic factors for various countries presented in the Numbeo database.

   Results. A comparative analysis of the results of forecasting the quality of life index of the population of selected countries was performed using machine learning algorithms based on historical data from 2012 to 2025. Particular attention is paid to adjusting the hyperparameters of the models and cross-validation to improve the accuracy of predictions. The analysis demonstrated that the most reliable results can be obtained using an ensemble of machine learning models without taking into account linear regression forecasts.

   Conclusion. The calculations performed revealed that the gradient boosting model demonstrates the best results. However, in order to improve accuracy and reduce deviations, it is recommended to use an ensemble of models. The use of machine learning in forecasting offers new opportunities for the development of social government programs aimed at improving the quality of life of the population.

   Aim. The study is aimed at studying approaches to selecting new fields of company activity when updating a corporate strategy, as well as identifying key criteria for decision-making.

   Objectives. The work seeks to determine the goals of selecting new fields; identify factors influencing the achievement of goals; develop an algorithm for creating the selection criteria; assess the significance of factors.

   Methods. The work employed expert and questionnaire surveys (14 specialists and 20 managers), Ishikawa diagram for analyzing cause-and-effect relationships, as well as methods of weighting coefficients and contextual analysis.

   Results. The author identified three goals, in particular, increasing profits, increasing the value of the company and manageability. Key factors were identified as synergy between fields, consumer groups with high growth potential, industries with high profit potential. As well as an algorithm for selecting fields based on the significance of factors was developed. The most significant factors include synergy and focus on consumers with growing demand.

   Conclusions. The study confirmed the importance of synergy and focus on high-potential consumers. It is recommended to take these factors into account to improve the effectiveness of strategic planning.

   Aim. The work is aimed to study the relationship between economic growth rates and financial system indicators in China, India, the remaining original BRICS countries, the Association of Southeast Asian Nations (ASEAN), a number of Western countries, as well as to calculate threshold values and the structure of the financial system that induces the economic growth.

   Objectives. In order to achieve this goal, it is necessary to collect and calculate a data panel from a number of countries and regions containing information on their economic growth rates and the state of their financial systems (the latter are represented by indicators of the stock market and the banking system); analyze the data through visual analysis, in particular, construct scatter diagrams and frequency histograms; conduct statistical analysis, that is, calculate correlation coefficients, construct clusters and conduct their meta-analysis; formulate a conclusion on the optimal combination and level of stock market factors, the banking system that contributes to economic growth.

   Methods. The primary data analysis was performed using scatter diagram matrices and frequency histograms. A correlation analysis was performed to summarize the main characteristics of the data panel. The main research method was K-means clustering and meta-analysis of the results.

   Results. A data panel was compiled, supplemented and calculated, to include 17 countries (Russia, Brazil, Germany, Hong Kong, Israel, India, Indonesia, China, Malaysia, Singapore, USA, Thailand, Turkey, the Philippines, South Africa, South Korea, and Japan), ten economic variables (four variables characterizing banking systems, that is, the relative extent of bank deposits, broad money supply, central bank assets and internal loans to the private sector; four variables characterizing the securities market, in particular, stock price volatility, stock market turnover, the size of stock and stock market trading volume to gross domestic product (GDP); two variables characterizing economic growth, that is, the annual growth rate of GDP per capita (purchasing power par, PPP, 2021), and the weighted average annual growth rate of GDP per capita (PPP, 2021) from 1996 to 2020. This panel was used to conduct a cluster analysis, and based on its metadata, reasonable conclusions were made.

   Conclusions. The cluster meta-analysis showed that the maximum positive effect of the financial system on the economy is achieved when the relative size of deposits and loans in the economy is at the GDP level, and downward or upward deviations from this level induce a slowdown in the economy. A similar effect is noted when central bank assets exceed 10 % of GDP. The stock market turnover should be greater than 100 %, and no positive effect on the economy is registered before this threshold is exceeded, while stock market volatility as a whole does not affect economic growth. It was established that the negative effect of exceeding the threshold value of the relative size of loans is significantly reduced, provided that the stock market turnover is in the optimal range.

   Aim. The work aimed to analyze the impact of economic sanctions on Iran’s socio-economic development and formulate conclusions relevant for Russia under the sanctions pressure.

   Objectives. The work seeks to study the long-term consequences of sanctions on Iran’s economy, including their impact on technological development, investment activity, and participation in global value chains; to analyze Iran’s strategies for adapting to sanctions and their effectiveness; as well as to draw conclusions and recommendations for Russia based on the Iranian experience.

   Methods. The work employed comparative and economic analysis, a systems approach, and descriptive statistics. Attention was paid to the study of empirical data and the assessment of Iran’s macroeconomic indicators under the sanctions pressure.

   Results. Long-term sanctions have a devastating effect on Iran’s economy. This induces a deepening technological gap, a decrease in competitiveness, and weakening of the potential for sustainable development. An analysis of the Iranian experience demonstrates that measures to support internal production, diversify the economy, and reorient export flows can partially indemnify for the damage from sanctions, but are associated with high costs and limited effectiveness. The findings on the negative long-term effects of sanctions are relevant for Russia. Sanction pressure increases technological underdevelopment, isolates industry, and reduces incentives for modernization.

   Conclusions. The Iranian experience reveals that lifting of sanctions is a necessary condition for full-fledged economic development and integration into the global economy. However, the process of lifting of sanctions requires the active participation of key international players and the creation of new mechanisms of interaction capable of enhance trust between the parties. For Russia, as well as for Iran, it remains important to find ways to minimize the damage from sanctions and develop strategies aimed at long-term restoration of economic cooperation and competitiveness.

   Aim. The work aimed to develop a comprehensive methodology for assessing the effectiveness of government support measures for small and medium businesses in the Russian Federation.

   Objectives. The work seeks to substantiate the role of small and medium businesses in achieving priority goals of socio-economic development; to systematize and characterize approaches to assessing the effectiveness of government support measures for small and medium businesses; to identify their advantages and disadvantages; to propose a methodology for assessing the effectiveness of government support measures.

   Methods. The article author used the comparative-analytical method, as well as methods of analysis and synthesis, deduction and induction, and the grouping method.

   Results. A critical analysis of methodological approaches to assessing the effectiveness of government support measures for small and medium businesses was conducted. The work also determined advantages and disadvantages, completeness of the assessment and the possibility of using the results obtained in terms of optimizing the support measures. The article author proposed a methodology for assessing effectiveness based on indicators of government expenditures on supporting small and medium businesses, presenting the socio-economic results and performance indicators of small and medium-sized enterprises, their contribution to socio-economic development. The work presents an algorithm for calculating key performance indicators.

   Conclusions. Under conditions of increased uncertainty and sanctions pressure on the national economic system, the changing conditions of the small and medium-sized business sector operation necessitate the expansion of the methods used to assess the effectiveness of government support measures for the sector in order to implement the principle of comprehensiveness, obtain complete and reliable data that can be used to adapt government policy to the current needs of small and medium businesses, as well as to increase the level of resource availability, and reduce administrative and economic barriers. Comprehensive multi-criteria methods can be used to assess the effectiveness of stabilizing and (or) stimulating measures, the degree of effectiveness of authorized bodies, the degree of compliance with the principles of adaptability and targeting of government support measures.