Galanina Olga V.

Introduction. The comparative analysis of national energy systems in the context of the global energy transition requires a shift from traditional comparisons based on absolute production volumes to more sophisticated methodologies. Aggregated indicators, including per capita production, do not reflect the structural features of countries' energy balances, which hinders the identification of their real technological profile and position in the global energy architecture.Purpose. To develop a methodology for the multidimensional clustering of countries based on the detailed structure of electricity production and to determine Russia's place in the emerging coordinate system, identifying its closest technological analogues.Methods. Based on 2023 data from EIA, the Energy Institute, and BP for 210 countries worldwide, electricity production was decomposed into 10 components by source (gas, oil, coal, hydro, nuclear, wind, solar, geothermal, bioenergy, other renewables). Indicators were normalized per 100,000 population. Methods of standardization (z-score), dimensionality reduction (PCA), and hierarchical clustering were used.Results. As a result of cluster analysis, four homogeneous groups of countries were identified: Purple cluster (144 countries): Dominance of natural gas (42%) and hydropower (25%). Russia is a member of this group. Red cluster (22 countries): Oil-dependent island states (75% oil). Orange cluster (13 countries): Coal-gas economies (44% coal, 20% gas). Includes China and Australia. Green cluster (31 countries): Diversified systems with a balance of nuclear (13%), renewable (21%), and traditional generation. Includes the USA, EU countries, and Japan. Russia ranks 9th within its cluster in terms of specific production (0.77 TWh/100,000 population).ConclusionsA country's true position in the global energy landscape is determined not by absolute generation volumes, but by the structure of its energy balance, which shapes a unique technological and strategic profile.Russia belongs to the largest yet least diversified "gas-hydro" cluster, indicating a high dependency on a limited set of sources and the associated risks to energy system resilience.The fundamentally distinct positioning of the absolute leaders – China, the USA, and Russia – in separate clusters empirically confirms profound differences in their national energy strategies and development trajectories.To align with global trends and strengthen its long-term energy security, Russia requires a systematic diversification of its energy balance, relying on the development of nuclear power and new renewable energy sources.

Introduction. The relevance of the study is due to: the need for automated analysis of agricultural degradation (manual data processing for all regions and crops is ineffective, and agglomerative machine clustering allows regions with similar trends to be grouped, identifying the most problematic areas); risks to food security (reduced self-sufficiency with key products requires timely government support measures, clustering helps to rank regions by threat level and optimize resource allocation); lack of a unified methodology for assessing degradation; digitalization of agriculture. Purpose. The study aims to analyze the degradation of agricultural production in the regions of the Russian Federation, particularly to identify regions with the most pronounced decline in potato and other agricultural production, as well as to develop measures to improve the situation. Methods. Clustering methods were used for the analysis, specifically the agglomerative clustering algorithm, which allowed grouping regions based on the similarity of agricultural production degradation indicators using statistical data for 2005 - 2022. Results. The regions were divided into three clusters, with Cluster 2 (42 regions) being the most degraded in terms of agricultural production. These regions show significant declines in per capita production and yield indicators. Conclusions. Cluster analysis made it possible to identify regions with the most pronounced degradation of agricultural production, enabling the development of targeted support measures, such as targeted subsidies, tax incentives, and infrastructure restoration programs, to improve the situation in these regions.

Intelligent methods of analysis, which include the problem of clustering, are widely used in the field of economics of the agro-industrial complex. The clustering problem belongs to the class of unsupervised learning problems. The essence of the problem is the grouping of research objects according to the use of similarity. If the regions of the Russian Federation are selected in terms of livestock production, they can also be automatically grouped according to the similarity recipe. The k-means method is currently a successful method for solving clustering problems. The main stage of solving the problem is the collection of data, which includes all the main characteristics of the object. For example, if you set up production in the region in terms of animal husbandry, then it would be more logical to x1 - meat production per capita and x2 - milk production per capita. The criterion for choosing the number of clusters is the root mean square error. In total, 79 regions of the Russian Federation participated in the analysis. It turned out that the potential to break all regions of the Russian Federation into 7 clusters of similarity. Regions with high milk and meat production (clusters 4 and 6), regions with high milk and meat production (clusters 2, 3, 5) and regions with low milk and meat production (clusters 0, 1) were identified.