The article is devoted to the development and theoretical substantiation of the authors’ intellectual-digital model for mapping the economic ecosystem of a territory as a tool for supporting managerial decision-making in public governance of regional development. The aim of the study is to develop a conceptual architecture for a model that integrates artificial intelligence, big data, and geographic information systems to comprehensively analyze, monitor, and forecast the state of territorial economic ecosystems. The objectives of the study are to analyze existing approaches to public governance and strategic territorial development; develop a methodology for assessing the effectiveness of intellectual-digital models; and create a conceptual framework and operating algorithm for an intellectual-digital model for mapping the economic ecosystem of a territory. The methodological framework comprises systems and ecosystem approaches, comparative analysis, scenario modeling, KPI analysis, and expert evaluation. Based on a synthesis of Russian and international experience in applying digital twins and geospatial mapping, the principles of model design are formulated, and its multi-level architecture, technological base, and operating algorithm are described. The novelty of the model lies in its feedback mechanism, which makes it possible to adjust forecasting models as new data accumulate. Three practical application scenarios are developed: real-time monitoring of economic activity, sectoral development forecasting, and managerial decision support. Calculations indicate that implementing the model can reduce data collection and processing costs by 30–40%, accelerate the preparation of analytical materials twoto threefold, and improve forecast accuracy by 15–25%. The findings can be used by state and municipal authorities to develop spatial development strategies and monitor strategic and spatial planning documents.

In the context of sanctions pressure and a course towards technological sovereignty, the issue of the effectiveness of Russia’s science and technology policy is becoming critically important. However, direct financing of R&D, the introduction of special preferential regimes, and personnel training often do not lead to a steady increase in the output of high-tech products, which indicates a systemic gap between investments in scientific and technological potential and final innovative production. The purpose of this study is to assess the impact of key instruments of scientific and technological policy on innovative output in the regions of Russia, taking into account the problems of endogeneity and time lags. Based on data from 82 regions for 2014–2023 using the dynamic panel model (System GMM), the following key gaps were identified. It is shown that the costs of technological innovations in the basic specification demonstrate a significant negative effect with an annual lag and a positive return with only a two-year lag. Taking into account the regional typology, this effect becomes insignificant, which indicates the heterogeneity of this effect in the context of regional groups. The presence of special economic zones (as one of the instruments of special preferential regimes) has a short-term positive effect, which is replaced by a significant negative effect at the second lag, indicating unstable returns from the SEZ institution; over the period under review, no long-term positive effect is observed. The steady negative impact of the number of researchers with academic degrees on innovative output points to a deep gap between the system of scientific personnel training and the needs of the real sector, where researchers are concentrated in basic research that is not focused on commercialization. Significant regional heterogeneity has been revealed: industrial regions demonstrate the most stable returns from R&D, while in knowledge-intensive and resource-rich regions the positive effect is replaced by a deep recession. The reconstructed individual effects confirm significant structural variation between regions. The results of diagnostic tests (AR(2), Hansen) confirm the validity of the instruments and the absence of second-order autocorrelation. The findings indicate the need to shift the policy focus from financial support measures to institutional reforms aimed at creating mechanisms for cooperation in the state-science-business relationship and building differentiated commercialization management models, taking into account regional specialization.

In the modern world, science is held in exceptionally high regard by the state and society. However, its inherent uncertainty and unpredictability do not always make it possible to assess its actual effectiveness. The aim of this study is to provide a theoretical rationale for a method of evaluating the efficiency of scientific organizations in terms of the time required to acquire new knowledge. The article proposes considering a scientific organization as a system consisting of five functional levels, each of which has its own cycle: laboratory, support, economic, scientific, and administrative. The organization itself represents a projection of the state’s interests onto certain objects that are also characterized by cyclical development. An underestimated aspect of a scientific organization’s activities is its ability to manage time, which may, among other things, enable it to anticipate the needs of the state. In addition, the administration manages the staffing structure, thereby making it possible, to some extent, to shape future discoveries. The article presents indicators that can be used to evaluate the efficiency of a scientific organization from the perspective of state needs.

The challenges associated with the training of highly qualified research personnel in contemporary Russia are linked to the transformation of postgraduate education as an institutional mechanism for reproducing research and academic staff, the contraction of the higher education talent pool, declining dissertation outcomes, and persistent interregional disparities in research capacity. However, the relationship between the parameters of the publication environment shaped by academic degree attestation requirements and the configuration of the Higher Attestation Commission (HAC/VAK) List of peer-reviewed journals and dissertation outcomes remains insufficiently examined quantitatively. The aim of this study is to develop an analytical framework for the systemic assessment of the publication environment and its statistical association with dissertation outcomes amid the transformation of the regulatory framework governing academic degree attestation. The study proposes a set of open-data indicators for assessing the conditions of dissertation preparation based on publication activity in VAK-listed journals and regional trends in successfully defended dissertations, using eLIBRARY.RU data for 2015–2024. Official statistical and regulatory materials were additionally used to characterize the institutional and staffing contexts. The methodological framework comprises descriptive statistics, concentration measures, linear trend analysis, year-on-year changes, correlation analysis, and quartile grouping. The findings reveal a high degree of interregional concentration in research publication activity, predominantly downward trajectories in the number of defended dissertations and research personnel, and a simultaneous upward trend in R&D expenditure. A statistical association is established between publication and authorship indicators and the number of successfully defended dissertations. At the journal level, the analysis identifies a declining share of new authors and an increase in their average age. The practical significance of the study lies in the development of an approach to monitoring the publication environment and the associated conditions of dissertation preparation, taking into account the regulatory requirements governing academic degree attestation.

The purpose of this article is to identify key issues related to industrial robotics and assess its potential impact on economic growth and innovative development in industries. This paper utilizes a systematic analysis of data from Rosstat, the International Federation of Robotics, and reports from the consulting firm Kept, the Autonomous Non-Profit Organization of Higher Education Innopolis University, and the Center for Macroeconomic Analysis. It was found that the main barriers to robotization are not so much the cost of robots as the lack of demand for robotization, a shortage of qualified personnel, and hidden costs. Many companies have limited investment resources, and the robotization of production processes, which is a continuous process of renewal and adaptation, may ultimately lead to a decrease in their economic efficiency. The current industrial structure, with its predominance of unit and small-batch production, substantially complicates Russia’s achievement of its goal of entering the top 25 countries in terms of robot density by 2030. The need to develop domestic microelectronic components and software has been demonstrated in order to establish a stable, coherent institutional and resource foundation for robotization. Therefore, a balance between production robotization and the use of manual labor is necessary, and industrial policy must be selective, with state support for the implementation of industrial robots provided only to industries in which their use is economically justified.

Achieving national technological sovereignty requires sectoral and spatial focus. The study aims to identify Russian regions where domestic capabilities are concentrated and external risks are lower for implementing projects in the 12 priority areas of the technological sovereignty taxonomy. To assess regional production and technological capabilities, we propose an original method for estimating the territorial localization of enterprise resources required in each priority area – labor, capital, and technology – while considering the availability of innovation infrastructure. The estimates show that Moscow, St. Petersburg, and Moscow Region concentrate resources in all 12 priority areas; the Republic of Bashkortostan in 9; the Republic of Tatarstan and Nizhny Novgorod Region in 7; and Samara, Chelyabinsk, and Tula Regions in 5. In total, 37 regions are identified as leaders in at least one area. External risks are assessed using import-dependence indicators. Before 2022, about one-third of Russia’s total imports consisted of products in priority areas supplied by countries that subsequently restricted exports to Russia. Dependence on imports from unfriendly countries was particularly high in pharmaceuticals (87.3%), especially in Moscow and Bashkortostan; shipbuilding (85.6%), particularly in Primorsky Krai; and the automotive industry (70%), primarily in Tatarstan and Kaluga Region. Many leading regions remain vulnerable to tighter sanctions. At the same time, less import-dependent centers have been identified where sectoral clusters cover the full chain from specialist training and technology development to production. Examples include Moscow in complex engineering, Bashkortostan in the chemical industry, Perm Krai in engine manufacturing, and Nizhny Novgorod Region in shipbuilding. The findings suggest territorial priorities for federal technology policy and potential vectors of smart specialization and diversification for individual regions. The paper concludes with recommendations for strengthening technological sovereignty through improved strategic goal-setting, human-capital development, and stronger incentives for business investment in new technologies.

In this work, the problem of forming a comprehensive assessment of the level of intellectual property (IP) development in regional organizations is investigated. The relevance of the study is determined by the significant role of IP in the innovative development of regions and the absence of a unified, methodologically sound approach to its integral assessment. The purpose of the research is to develop a comprehensive methodological approach to assessing the level of IP development in regional organizations based on weightless aggregation methods. Within the framework of the study, a critical analysis of traditional indicator aggregation methods based on subjective weighting coefficients was conducted, and the feasibility of applying weightless approaches was substantiated. Existing weightless aggregation methods (arithmetic and geometric means, ideal point method, etc.) have been systematized and classified. Key IP assessment indicators have been identified and grouped according to the stages of intellectual property results (IPRs) lifecycle: idea generation, patenting, and commercialization. Based on the proposed criteria, three key relative indicators have been selected: research efficiency coefficient, patent competence coefficient, and commercialization coefficient. Three methods for calculating the composite indicator – arithmetic mean, geometric mean, and ideal point method – have been tested using a calculation example for three hypothetical objects. The testing results demonstrate that different aggregation methods produce different rankings of objects, reflecting various assessment aspects: overall development level (arithmetic mean and ideal point method) and the degree of indicator balance (geometric mean). Practical recommendations for regional authorities have been formulated in the conclusion. These include developing a system of normalized relative indicators and using a combination of the three specified methods to obtain a multidimensional and managerially relevant assessment of the IP development level.

The relevance of this study stems from Russia’s need to transition to a knowledge economy and technological sovereignty in the context of sanctions and global digital transformation. The problem lies in the low efficiency of existing institutions supporting innovative entrepreneurship, which is reflected in declining indicators of the national innovation system (R&D funding as a percentage of GDP, patent activity of the population, the share of innovation expenditures, the share of innovative products, and the Global Innovation Index). The aim of this article is to develop a proprietary matrix model for the systemic diagnosis and improvement of innovative entrepreneurship institutions using the capabilities of the digital economy. The methodology is based on a synthesis of institutional theory, an evolutionary approach, multi-level analysis, and the principle of economic duality, implemented in the form of a six-block matrix reflecting the consistent transformation of an innovative entrepreneur from a creative subject to an element of the future technological order. The article examines the cycle of expanded reproduction of the national innovation system, in which each new cycle of innovation generation and implementation increases the country’s innovative potential. The emphasis is on innovative entrepreneurship as a key element of the national innovation system. The results include a diagnosis of underlying dysfunctions (gaps) at each institutional level between paired blocks based on an analysis of contemporary Russian and international research and the authors’ empirical analysis of the dynamics of six key indicators (2014–2024). Recommendations are formulated as a proportional design principle and specific measures for each block, emphasizing the role of digital technologies as a tool for strengthening ties between institutions.