Lidiya S. Parshintseva

The article analyzes the sources of information for building the Digital Economy Satellite Account (DESA) in Russia. It examines the existing statistical accounting system and identifies key institutional and methodological barriers to integrating digital phenomena into the SNA structure. Particular attention is paid to studying the approaches of international organizations and various countries to accounting for digital products, free digital services, and the performance of digital platforms, as well as analyzing the possibilities of obtaining information from both existing statistical reporting forms and alternative data sources. The aim of the study is to define a source of information and statistics for developing the DESA in Russia, ensuring its compliance with international standards. Key objectives include identifying gaps in the statistical accounting of digital resources and their use and developing proposals for improvement. The study contributes to scientific systematizing data sources, which can subsequently be used to calculate DESA indicators in compliance with international methodology. The recommendations presented in the paper are of practical significance for identifying new sectors of the digital economy and identifying key areas for implementing international standards for constructing the DESA in the context of the SNA modernization.

Purpose: to develop a methodology for the comprehensive assessment of the Russian oil well stock to identify promising areas for their conversion to geothermal energy sources.Methods: the study employed methods of normalization and aggregation of indicators, the index method, the ranking method (within the framework of the integral dynamic indicator), Spearman’s rank correlation method, as well as rating construction and grouping techniques. This comprehensive approach ensures the objectivity of the obtained results.Results: based on the analysis of oil well reserves condition in Russian regions, territories with the highest potential for converting oil wells into geothermal energy sources were identified. A comprehensive evaluation methodology was developed, incorporating integral indicators of well conditions and oil production dynamics. Priority regions were identified, characterized by a high proportion of wells under conservation, high reservoir water saturation, and declining productivity. These priority areas include the regions of the North Caucasus Federal District. The study determined the factors most significantly influencing the implementation of projects aimed at converting oil wells into geothermal sources such as technical characteristics of wells and availability of investment resources.Conclusions and Relevance: according to the analysis conducted using the developed methodology, the North Caucasus Federal District was identified as a priority territory for pilot projects aimed at converting oil wells into geothermal energy sources. Its high degree of oil resource depletion and the abundance of mothballed wells create optimal conditions for technology testing. The implementation of such projects will reduce decommissioning costs, generate additional revenue from green energy, and contribute to carbon footprint reduction. Key barriers remain: limited access to technical and economic data (due to commercial confidentiality) and the need for significant investment.

The article addresses improving the methodology for distributing the annual volume of unobserved agricultural production in Russia by month.The study used methods for calculating averages and variances, dispersion analysis and the index method.The study aims to explain the algorithm for distributing the annual volume of agricultural production by month to improve the quality of official statistical information. The authors analyzed existing approaches to the distribution of agricultural output, reviewed the experience of other countries in the subject area of the study, and formulated methodological recommendations for distributing the volume estimates of the output of unobserved types of agricultural production by month.Official statistical methodology, published results of scientific research by Russian and foreign scientists, current regulatory legal acts of the Russian Federation and other countries in the subject area, and agricultural calendars for the period under review served as a theoretical foundation for this work.The scientific novelty of the conducted study lies in developing scientific and methodological recommendations for the distribution of agricultural production volumes in actual prices by month to improve the objectivity of official statistics. The relevance of the study stems from the need to refine the existing approaches to the distribution of the annual volume of agricultural production.

La pandémie de COVID-19 et ses conséquences socio-économiques ont provoqué une augmentation notable du rôle de l'estimation de la préparation et la vulnérabilité des pays. Cela s'ajoute aux investigations existantes dans le cadre des ont internationales et des classements multidimensionnels, et présente des résultats d'évaluation complexe au niveau national de la préparation et la vulnérabilité face à la pandémie, à partir des index multidimensionnels proposés par les auteurs. L'objectif de cette recherche était d'estimer le niveau de préparation national et la vulnérabilité d'un point de vue complexe. Un index multidimensionnel global a été calculé et utilisé pour donner un classement des pays à partir de cette méthodologie. L'index a permis aux auteurs de conclure qu'il y a une différence importante dans le niveau de préparation face à une pandémie et une disproportion entre certains éléments du système pour chaque pays. L'analyse des données, les sources et les rapports ont souligné le besoin d'étendre la liste des indicateurs pour évaluer le niveau de vulnérabilité. L'opportunité d'utiliser cet index est prouvée par l'analyse corrélative statistique entre les sous-index et le niveau de morbidité et mortalité due au COVID-19. L'index est utilisé pour former des groupes multidimensionnels et le partitionnement de données permet d'estimer les conséquences économiques de la pandémie. Les résultats du partitionnement de données ont permis de former quatre groupes de pays : (1) très vulnérables avec faible niveau de préparation ; (2) vulnérables avec faible niveau de préparation ; (3) vulnérables avec haut niveau de préparation ; (4) peu vulnérables avec haut niveau de préparation. L'analyse comparative des conséquences économiques (changement annuel PIB en 2020-2019 par rapport à PIB en 2019-2017), taux de chômage et changement de l'inflation ont montré des disproportions de l'influence de la pandémie par groupes de pays.