The relevance of this study is determined by the fact that cryptocurrency mining has rapidly entered the country’s economic life. As is often the case, the rapid development of certain technologies requires legislative action to regulate activities related to these technologies. In August 2024, the Russian Federation adopted Federal Law No. 221-FZ «On Amendments to Certain Legislative Acts of the Russian Federation» (the Mining Law), which legalized cryptocurrency mining and created an institutional framework for its regulation. By the beginning of 2026, the country had established a year-and-a-half of law enforcement practice, allowing for a comprehensive economic assessment of the consequences of legalization, which determined the choice of research topic and the formulation of its theme. The objective of this research is to conduct a comprehensive economic assessment of the consequences of the implementation of mining legislation (2024–2026), evaluate actual and projected quantitative indicators for the fourth quarter of 2025 and as of early 2026, and develop practical scenarios for the use of legalized mining as an element of Russia’s economic development. The scientific novelty of the obtained results lies in the integration of institutional analysis with macroeconomic and energy modeling to assess the fiscal, currency, and multiplier effects of mining legalization. A formal model of the budget effect is proposed, taking into account cross-border restrictions and sanctions pressure. For the first time, based on official data from the Federal Tax Service of Russia for 2026, an assessment of the tax potential of the industry was conducted using a methodology for separate accounting of fiscal revenues and taking into account planned penalties for illegal activity. The practical significance of the obtained results lies in their potential for use in solving economic problems and realizing the potential of mining for the development of the Russian economy.
The relevance of this study is driven by the fact that conventional horizontal and vertical analysis techniques distort the actual functioning of Arctic transport and logistics systems due to extreme seasonality, ice-related constraints, and weak infrastructure, which necessitates the development of specialized analytical approaches. The methodological foundation is based on adapting classical tools by projecting them onto the region’s specific climatic and geographic conditions, taking into account navigation windows, ice conditions, and seasonal transport constraints. The scientific novelty lies in the development of a two-level horizontal analysis with trend calculations for navigation windows and monthly volatility, as well as a seasonal decomposition of vertical analysis with the identification of navigation, winter roads, and thaw periods. It has been established that traditional annual analysis underestimates the true growth rates of cargo flows and masks the critical structural role of aviation, whose annual share obscures its key importance during the winter season and thaw period. It has been shown that ignoring seasonality leads to an underestimation of the role of winter roads and air transport, which during the shoulder seasons often serve as the only channels sustaining remote territories. It is concluded that calendar-based aggregated data do not provide a sufficient analytical basis for managing Arctic logistics. The practical significance of the proposed approach lies in the possibility of using climatic volatility as a parameter for precise port capacity planning, justifying public investment in the icebreaker fleet, and building safety stocks in hard-to-reach settlements. Further research may focus on developing algorithms for operational forecasting and cargo flow reallocation based on integrating climatic data into digital logistics models of the Arctic
The article explores the role of Analytics in the field of human resource management (HR analytics) as a strategic tool for human capital management in the context of sanctions pressure. The purpose of the study was to conduct a comprehensive analysis of the role of analytics in the field of human resource management in the human capital management system under sanctions pressure, as well as to determine the directions of its development in the context of Russian sovereignty. For this purpose, the article examines the structural changes in the labor market caused by sectoral redistribution of employment, demographic constraints, a reduction in external labor migration and large-scale emigration of qualified specialists. Special attention is paid to the transformation of analytics in the field of human resource management from an auxiliary tool for personnel management into a key mechanism for predicting personnel risks, increasing the sustainability of organizations and increasing their competitiveness. The consequences of the departure of foreign platforms related to human resource management are analyzed. The problems of import substitution that arise in this regard are studied, including a decrease in data quality, fragmentation of information systems and limited access to modern analytical technologies. Based on the system-analytical approach, the necessity of developing a domestic ecosystem of analytics in the field of human resource management, focused on the integration of corporate and government information resources, is substantiated. A conceptual model for the development of analytics in the field of human resource management is proposed, based on the principles of data integration, context-oriented application of artificial intelligence and personalization of human capital management. It is concluded that it is necessary to form a national infrastructure of labor market data as a basis for improving the effectiveness of personnel policy and ensuring technological sovereignty.
The bank’s loss given default (LGD) – the proportion of losses incurred when a borrower defaults – is a critically important metric. Without its accurate forecasting, it is impossible to correctly assess the level of credit risk, adequately form reserves for expected credit losses, or calculate the sufficient amount of capital required to safeguard the institution’s financial stability against unexpected credit losses arising from loan defaults. This metric reflects the proportion of unpaid debt relative to its total amount and directly influences the level of provisioning, regulatory requirements, and strategic planning of banking operations. Predicting the losses given default requires considering several specific data properties, including multimodality, asymmetric distribution, constraints within the interval [0,1], and nonlinear relationships with explanatory factors. This article reviews studies focused on modeling the share of default losses within the credit risk assessment. Model quality metrics are discussed based on four key criteria: accuracy, discriminative ability, stability, and interpretability. The article provides with recommendations for model development and implementation. Hybrid models that combine the advantages of parametric and non-parametric approaches, as well as interpretable non-parametric models, are becoming increasingly popular in credit risk assessment due to their ability to offer both predictive accuracy and interpretability. Nevertheless, parametric models remain the base analytical tool and benchmark against which the performance gains of more sophisticated models can be compared.
One of the most common problems of declining labor productivity is health problems, which is why health capital research is increasingly in demand. However, the abstract nature of the concept of health capital and the lack of a single generally accepted methodology for evaluating indicators pose a scientific research problem. The subject of the study is the impact of public health on the development of economic processes in the country. The basis for the development of the concept of health capital was the consideration of health as an asset that allows a person to use his human capital for as long as possible. This formulation of the issue required consideration of health—saving behavior as the main endogenous factor of people’s inequality in health, which is the focus of attention of several scientific disciplines, the key among which are medicine, economics and sociology. The problem of the lack of a generally accepted methodology for creating a single indicator that can approximate the theoretical concept of «health capital» in empirical research has determined the purpose of this material. In this article, the author’s methodology for creating indicators of health capital is proposed. Based on the fact that social determinants of health belong to broad categories of causes, factors and resources that have a direct and indirect impact on health, such indicators as: general well-being (self-awareness of health); the presence of chronic diseases were selected.; investing in health capital. In our study of health capital, we use quantitative indicators that reflect how effectively a person’s health allows them to engage in work. Certain actions can both improve and worsen your health. In other words, when measuring health capital, we not only measure health itself directly, but also take into account how comfortable a person is to live and work with such a level of health and what contribution he makes to its strengthening.
