Sergey K. Antipov

The Russian Arctic is characterized by a unique combination of extreme climatic parameters, vulnerable ecosystems, and intensified economic activity, necessitating a transition to a sustainable development model. Unique challenges, such as accelerating global warming, high dependence on diesel fuel, isolated settlements, and fragile ecosystems, require a transition to a resilient development model adapted to extreme conditions. However, both academic research and managerial practice lack a tool for assessing the level of green economy development that adequately reflects the specific characteristics of the region. The aim of this study is to develop and validate an original composite model for quantitatively assessing green economy development in the Russian Arctic. The scientific novelty of the study lies in the development of the first comprehensive, Arctic-specific indicator system based on five key dimensions: energy independence, circularity, low-carbon transport, sustainable nature management, and social sustainability with the participation of indigenous peoples. In addition, the weighting of indicators is justified using a theoretical hierarchy of needs in extreme environments rather than conventional expert-based weighting approaches. The model was applied to six Arctic regions over the period 2018–2023 and demonstrated high internal consistency. The results demonstrate a steady but uneven increase in the level of green development. Leading regions demonstrate significant progress due to targeted investments in energy and logistics, while lagging regions retain structural constraints. Forecasts to 2035 suggest a convergence in development levels; however, targeted policy support for the most vulnerable territories remains essential. The proposed model provides a tool for monitoring, comparative analysis, and forecasting, capable of transforming declarative public policy goals into measurable and manageable indicators of sustainable development in the Arctic.

The technological development of the Russian Arctic is a key factor in ensuring national security, enabling the sustainable exploration of natural resources, and strengthening sovereignty in the face of extreme climatic and geographic challenges. Despite significant public investment, the effectiveness of federal programs remains insufficiently understood, and their impact on technological outcomes is poorly quantified. Existing approaches based on aggregate indices and expert assessments fail to identify which initiatives drive technological transformation or why some regions demonstrate high levels of innovation while others do not. This study aims to develop an interpretable model capable of identifying cause-and-effect relationships between federal programs and technological development indicators. Its novelty lies in the proposal of a new model, MASGN-TT—a semantic graph neural network approach with a multicriteria attention mechanism, applied for the first time in the Russian context to analyze the technological development of the Arctic. The model not only measures program impacts but also visualizes their influence on specific technological domains, ranging from digital infrastructure to environmental monitoring. The results show that two key public policy areas—digital transformation and environmental monitoring—are the dominant drivers of technological growth, while other programs, despite considerable budget allocations, yield limited returns. The analysis also points to a persistent gap between workforce preparation and tangible results. More broadly, the effectiveness of technological development in the Arctic is shaped less by the volume of funding than by how programs are structured and where their efforts are directed. The proposed model provides a tool for evidence-based evaluation of public programs, ensuring transparency, replicability, and adaptability to managerial decision-making. This paves the way for a targeted technological development policy aimed at maximizing systemic impact.

The paper describes the current state of national and international tourism in the Karelian Republic. The authors outline the possible ways of further development of tourism in this region, as well as the ways of attracting both national and international tourists to the region, which is important for the national image. The analysis of the current situation shows that at present there is no adequate infrastructure in Karelia that could satisfy the tourists’ needs. The paper discusses the infrastructure of this Arctic region and highlights the main indicators and factors characterizing the touristic field. The authors propose an algorithm and a model for tourism development in Karelia - the subarctic region of the Russian Federation.

The paper considers the possibility to use neural network modeling for assessing the economic development of regions exemplified by the Arctic region of the Russian Federation – Murmansk region. The paper presents assessing and reasoning of this usage, describes its opportunities and threats. The author analyzes four indicators as main economic characteristics: gross regional product, an amount of investments into the fixed capital, retail turnover, foreign trade turnover. The study shows which factors have the most significant effect on these characteristics and comments on the obtained results. The author describes methodology of building the model and checks it empirically. In order to assess the model more accurately, it includes autoregression elements, which allows estimating not only direct interaction, but monitoring possible temporary inertia. Assessment results based on neural network modeling are compared with the results obtained on the basis of ADL equations (autoregressive distributed lag model). Accuracy of the final calculations is analyzed with using the mean absolute percentage error (MAPE) in further preference to the model of neural networks.

This article analyses the possibility of applying neural network modeling for the purpose of automation of a large number of calculations of econometric equations coefficients in order to obtain adequate predictive results.

В данной статье рассматриваются возможные взаимодействия регионов Арктической зоны. Описываются возможные варианты моделей нейронных сетей, которые могут быть использованы для составления прогноза развития Арктической зоны с учетом взаимодействия отдельных регионов. Разбирается вариант модели для одного участка Арктической системы на примере Ненецкого автономного округа. Приводится расчет коэффициентов предлагаемой модели методом нейросетевого моделирования.