Anastasia Levina

This article presents data on the global energy production and consumption and provides forecast estimates of changes in demand for themunder various development scenarios of the global energy sector up to 2050. Key factors affecting the growth of energy consumption worldwide are outlined. Furthermore, data on the development of wind and solar energy in Russia and globally are presented. The shortcomings of the existing methodology for determining the life-cycle energy efficiency of energy facilities are described. The main provisions of a new methodology for determining the life-cycle energy efficiency of wind power plants (WPPs) using aggregated indicators are provided. Based on the new methodology, the results of calculating energy expenditures over the life cycle of the Kola WPP in the Arctic region of the Russian Federation were calculated. The life-cycle energy efficiency of the Kola WPP has been determined.

The Arctic region has significant potential for the extraction of mineral and energy resources. However, effective resource development requires careful and balanced decisions that minimize environmental impact and ensure sustainable resource management. This article discusses the development of a conceptual model of the ontology of the Arctic zone of the Russian Federation, focused on logistics and digital infrastructure. Ontology is a powerful tool for organizing areas of knowledge, providing a formal representation of concepts and their interrelationships. The main classes, relations, attributes and rules of functioning of the ontological model are defined. The significance of digitalization and logistic routes in the extreme climate of the Arctic is demonstrated. The proposed ontology systematizes information about logistics and digital components of the Arctic region of Russia. It allows creating a structured knowledge base that will enable stakeholders to analyze complex interdependencies, develop effective management strategies and make informed decisions that contribute to sustainable development in this vulnerable ecosystem. In developing the ontology, we aim to provide a unified view of the Arctic’s logistics and digital sphere, which helps stakeholders better navigate the complex system of challenges and opportunities.

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 Arctic zone of the Russian Federation is a rich, attractive, but hard-to-reach region. The authors of the article believe that the current level of development of digital and data technologies can make a constructive contribution to overcoming some of the challenges of Arctic conquest. Since the Russian Arc-tic is a complexly structured macro-region, the authors rely on the division of the Arctic into seven target subspaces. The paper identifies the requirements of the target Arctic subspaces for digital technologies that could facilitate the process of Arctic exploration. The paper used the results of previous research by colleagues, traditional methods of scientific analysis combined with an interdisciplinary approach, and an architectural approach to the design of socio-economic systems to identify the requirements of the identified subspaces. Based on the analysis of the collected and summarized materials, seven models of motivational extensions were constructed for each of the identified Arctic subspaces, summarizing information about the goal-setting of each subspace. Among other goal-setting factors, the requirements for digital technology services that would contribute to the development of each subspace were formulated. The resulting requirements contain both digital support needs common to all subspaces and those specific to each subspace due to its characteristics. As a result, the directions for effective implementation and use of digital technologies in the Russian Arctic were summarized.