An integrated intelligent model for simulating and optimizing regional water resource sustainability under multiple pressures.

The article examines issues related to improving the structure of GDP in Uzbekistan. A comparative analysis of the economic potential and the level of economic development of regions is conducted based on the gross regional product (GRP). The study also analyzes the impact of GRP per capita on the real incomes of the regional population. Based on the theory of economic convergence, the process of convergence of Uzbekistan’s GDP per capita with the per capita GDP of developed countries in the long term is analyzed using forecast calculations up to 2030

Here, we explore the determinants and territorial heterogeneity of regional innovation development across Russian regions, employing the Russian Regional Innovation Index (RRII) and indicators of Gross Regional Product (GRP). The empirical database comprises 1363 small innovation enterprises (SMEs) spun-off from budgetary and research organizations and universities, specifically 34 flagship universities, 28 innovation clusters, 156 technology parks, and 15 science and technology innovation centers, along with indicators of the infrastructure–institutional environment, innovation–investment activity, scientific–educational potential, and human–social characteristics. Regression analysis enabled the identification of major factor groups that strongly effect regional innovation development, with infrastructure–institutional and innovation–investment indicators being the most significant. Cluster analysis of RRII and GRP delineated three groups of regions, (1) leaders with high innovation activity and substantial economic potential, (2) intermediate regions with moderate innovation activity and varying economic capacity, and (3) regions with high economic capacity but low innovation activity, exhibiting structural disparities between the economy and innovation. By combining regression and cluster analyses, we provide a comprehensive assessment of regional innovation ecosystems, reveal spatial imbalances, and identify priority areas for regional innovation policy. The study contributes to the theory of regional innovation systems and offers practical recommendations for strategic planning and optimizing the allocation of resources among key elements of innovation infrastructure.

The aim of the study is to econometrically model the dependence of the agricultural sector's gross regional product (GRP) on a number of factors related to labor potential. The data base consisted of official data from Rosstat and Rostovstat for the period from 2010 to 2024. The methodological basis was a multivariate linear regression, where the dependent variable was GRP for the "Agriculture" activity type, and the explanatory variables were the number of employees, the share of workers with higher education, the share of youth in rural areas, the volume of investment in fixed assets, and the area under cultivation. The modeling results revealed statistically significant and multidirectional influences across these factors. It was found that the greatest positive impact on the dynamics of the agro-industrial complex is exerted by improved workforce qualifications: a one-point increase in the share of workers with higher education leads to a 52 million ruble increase in GRP. A positive, but less pronounced, effect was recorded for employment and investment volumes. The most significant finding is the negative impact of the youth population, which is interpreted as a consequence of selective migration and a structural mismatch between the competencies of young workers and the needs of the high-tech agro-industrial complex. The scientific novelty of this study lies in its comprehensive consideration of quantitative and qualitative labor characteristics and the development of an integrated approach to assessing HR risks. The practical significance of the study lies in the fact that the obtained results substantiate the need to reorient regional agricultural policy from compensating for the demographic deficit to stimulating the educational and innovative potential of human resources.

Spatial inequality among Russian regions remains a key issue of the national economy, driving sustained academic interest in the determinants of gross regional product (GRP). Despite extensive research, traditional econometric methods often fail to fully capture the complex non-linear relationships, time lags, and synergistic effects between growth factors. The aim of this study is to identify and comprehensively analyse the key determinants of per capita GRP in Russian regions by applying advanced machine learning (ML) methods to overcome the limitations of classical approaches. The empirical base comprises panel data for 85 Russian regions from 2013 to 2023. To predict GRP, 10 initial indicators were selected and grouped into thematic blocks: labour resources, investment, and production potential. A critical step was the creation of derivative features (lags and moving averages) and dummy-variables for regions and years. An ensemble of ML algorithms was used to build the predictive model, with the Light Gradient Boosting Machine algorithm showing the highest performance (R² = 0.7345). The results were interpreted using SHAP analysis and elasticity calculations. The results revealed the absolute dominance of population income indicators, particularly their lagged values and moving averages, confirming the hypothesis of cumulative and inertial growth. The second most significant factor was foreign direct investment, which also exhibited a time-lagged effect. The analysis confirmed all three hypotheses: the predominant influence of lags, the importance of synergy (strongest between income and wages), and the enhanced explanatory power of the model with derivative features. The findings may assist regional authorities in prioritizing sustainable income growth and strategic investment policies, accounting for lagged effects. A limitation of the study is its dependence on the quality of official statistics; future research could incorporate qualitative institutional and socio-cultural indicators.

Accelerated regional economic growth often comes at the cost of environmental damage. While the literature mainly promotes “green” technologies to address this issue, their effectiveness remains debatable. This study proposes mitigating the conflict through sectoral investment allocation that boosts gross regional product (GRP) while adhering to environmental constraints. It aims to define and formalize an approach for such targeted investment distribution. To achieve this, the paper introduces a set of index-based methods for analysis and constructs a multifactor additive econometric model. It emphasizes that investments in a particular regional industry generate multiplier effects on both production and environmental damage, not only within the target industry but also across related sectors. Moreover, the value of these multipliers changes with the sectoral structure of regional investments. The relationship between GRP growth and waste generation across sectors is formalized. Using scenario-based constraints, the study calculates GRP growth and waste volume for three alternative sectoral investment distributions in Nizhny Novgorod Oblast (Russia). By comparing GRP growth and waste volumes, it is possible to identify the investment option that best balances economic gains and environmental impact. The paper highlights the potential of this approach for factor analysis of GRP dynamics and environmental pollution. These findings are relevant both to specialists in regional economic policy and to researchers focused on sustainable regional development.

In the context of rising external sanctions, understanding the link between import supply disruptions and regional economic development is increasingly important. To address this task, this article proposes methods for identifying critical imports and constructing a statistical model to assess the degree of dependence between these variables. The study hypothesizes that a statistically significant relationship exists between regional critical imports and indicators of regional economic dynamics under external constraints. The research methodology is based on systematization, grouping, and panel data regression modelling with robust cluster standard errors. The empirical base includes statistical data for regions of the Volga Federal Okrug for 2011–2021. The sample comprises import volumes for 103 Commodity Nomenclature of Foreign Economic Activity (TN VED) codes. The findings reveal the key components that form the conceptual framework for determining critical imports: the role of imports in generating regional value added; the share of imports originating from unfriendly countries; and the classification of imports as final or intermediate goods. The relationship between critical imports and the gross regional product (GRP) of the regions under study is subsequently analysed. The estimated elasticity coefficient for the variable “Volume of critical imports” in the model, controlling for education level, investment, and consumer expenditure, is 0.0326. This relationship is moderate compared to other explanatory variables. A limitation of the study is its reliance on official statistical data, which has been inconsistently published for Russian regional imports since 2022.

The article considers the level of economic efficiency of the agro-industrial complex of the Sverdlovsk region. The purpose of the study, based on an analysis of the main indicators characterizing structural imbalances, is to develop recommendations for increasing the economic efficiency of the agro-industrial complex of the Sverdlovsk region. The object of this study is the agro-industrial complex of the Sverdlovsk region, and the subject is business processes that ensure the development of the agro-industrial complex of the Sverdlovsk region and factors affecting them. The methodological aspect of the analysis of the effectiveness of the agro-industrial complex includes a comprehensive assessment that combines quantitative and qualitative indicators that reflect economic and social aspects. The information base is formed on the basis of external (international, regional statistics) and internal sources (reporting by agricultural enterprises). Analysis of the key components of the use of the gross regional product (GRP) of the Sverdlovsk region for the period 2022-2024 was carried out. As part of the assessment of the effectiveness of the agro-industrial complex of the Sverdlovsk region, an analysis of key indicators was carried out including: the structure of municipalities and urban districts; formation of profits and costs of the agro-industrial complex; structure (production) of gross agricultural output of the agro-industrial complex; employment of workers in the agricultural sector; livestock; development of poultry sub-complex. One of the problems identified is the imbalance between production growth and the reduction in the number of employees, which may indicate an increase in labor productivity, but also potential problems with staffing the industry in the long term. In addition, an increase in production and sales costs, despite the growth of gross output, may reduce the profitability of individual farms and require further analysis to identify the reasons for the increase in costs and develop measures to optimize them.

This study examines the impact of war on economic activity in Ukraine through the analysis of satellite Night Light data. Traditional economic indicators, such as Gross Regional Product (GRP), are not always available in conflict areas, making it difficult to assess the actual state of the economy. The use of satellite data provides objective and timely information on the level of economic activity, particularly in regions most affected by military actions. The study analyzes the correlation between Night Light intensity and economic indicators and extrapolates GRP for 2022-2023. The results show a significant decline in economic activity in areas of active military conflict and occupied territories, confirming the effectiveness of satellite data for economic monitoring. Keywords: satellite data, night lights, economic activity, regression models, regional development, economic monitoring.

The growing interest in economic security reflects the rising number of threats and challenges facing the modern world. Within this field, particular attention is devoted to methods for assessing economic security. This study aims to develop an approach for evaluating how structural changes in gross regional product (GRP) affect the economic security of Russia’s Arctic regions. To identify trends in the GRP structure, the analysis considers regional size, the sustainability of economic development, and structural shifts across economic activities. The results reveal several trends: an increasing GRP concentration in the major regions of the Russian Arctic (including the Yamalo-Nenets Autonomous Okrug, Krasnoyarsk Krai, and the Republic of Sakha), accelerated industrial growth across all regions studied, which is driven primarily by the expanding role of the mining sector, and, consequently, a growing significance of the Russian Arctic in the national mining industry. An assessment of structural shifts demonstrates a link between their magnitude and the stability (or instability) of economic growth: the most pronounced shifts tend to occur during periods of crisis. Under current conditions of escalating sanctions pressure, the risks to the economic development of the Russian Arctic are increasing, especially in regions where GRP is heavily dependent on the energy sector. These trends highlight the need to account for the special status of the Russian Arctic, whose strategic importance has been historically central to the country’s development.

Under the influence of global shocks and macroeconomic instability it is essential to develop more advanced approaches to forecasting the gross regional product (GRP) and its components both at the country and region levels. Forecasting of GRP involves selection and justification of the key factors determining its dynamics. In the study, the formation of gross value added (GVA) of the industry was analyzed on the example of a fairly developed Russian industrial region – the Nizhny Novgorod region. To this end, the authors built a two-level GVA econometric model of industry of the Nizhny Novgorod region, which showed that the value is statistically significantly affected by such factors as the average per capita monetary income of the population and the average annual official dollar exchange rate. It has been stated that the dynamics of the average per capita monetary income of the population, in its turn, depend on the average price of Urals crude oil, gratuitous receipts to the consolidated budget of the region and the average annual number of employed people. The choice of factors is determined by the statistical procedure that allows revealing the relationships using time series cointegration. On the basis of the created two-level model for the GVA of the industry of the region and the models for exogenous factors, the authors make forecasts for all the involved indicators for the period up to 2026. The results of the study can be useful for the regional authorities in creating scenarios of development of the industry and determining the effectiveness of the control factors, which will make it possible to make sound management decisions in industrial policy and strategic planning.

Aim . The work aimed to assess the contribution of various cargo types to the gross regional product (GRP) and regional budget revenues. Objectives . The work seeks to create a panel database for all maritime regions of Russia for 2015–2023; cluster the regions by level of port activity; as well as develop and test models based on gradient boosting and Shapley methods to identify nonlinear relationships between logistics and economic indicators. Methods . The analysis includes several stages. These include creating a database, including port cargo transportation volumes by cargo type for all maritime regions of Russia for 2015–2023; clustering the regions by level of port activity based on their share of cargo turnover in the Russian total cargo turnover; and constructing models to assess the impact of freight traffic flows on GRP indicators and budget revenues. Results . The economic impact of port activities has been established to depend not only on the total volume of cargo turnover but also on its structure. The most significant cargo types in regions with high port activity are liquid bulk and raw materials, while these coal and containers are in regions with medium port activity, and dry bulk carriers for supply purposes in regions with low port activity. Conclusions . The Shapley method was used to clarify the role of each cargo type in the formation of GRP and budget revenues, providing a more accurate basis for management decisions. A transition from aggregated approaches to a structural analysis of freight traffic flows was concluded to be necessary when formulating regional socio-economic policy.

Aim . The work aimed to develop a methodological approach to assessing the formation of an innovative code for regional economic development, based on the technological modernization of Russian society. Objectives . The work seeks to identify the stages of formation of an innovative code for economic development in industrial regions within their economic genotype; to develop tools and evaluate the formation of an innovative code for regional economic development. Methods . For assessment of the formation of an innovative code, the author proposes using comprehensive indicators reflecting the effectiveness of the triple helix in the context of institutional interaction trajectories, namely an innovation policy quality index, an innovation activity index, and an innovation competencies index. The study period was 2008–2023. The coherence and discontinuities of the triple helix that form the innovative code are demonstrated using a correlation analysis, which identifies the extent to which changes in the variable indicating the performance of one economic transformation trajectory are consistent with changes in the performance variable of another trajectory. Results . The innovative code of regional economic development is still in its nascent stage within the economic genotype of industrialized regions. This is evidenced by the weak development of innovation activity in industrialized regions; gaps in the triple helix of institutional interactions that drive variability in the economic legacy of regions; and the lack of a positive impact of innovative transformations on the dynamics of the gross regional product (GRP) of the constituent entities of the Russian Federation. Conclusions . Industrially developed regions generate impulses for technological development that have not yet been transformed into an innovative code capable of driving variability in the economic legacy of regions and ensuring the evolution of regional economic development as a whole. The transformation process can be accelerated by strengthening the coordination of administrative, industrial, technological, and scientific research trajectories, ensuring the integrity of the helix of institutional interactions among the basic carriers of the regions’ economic genotype. The results obtained can be used by state and local governments to develop new and improve existing scientific and technological policy implementation strategies in industrial regions aimed at enhancing the innovative development of manufacturing industries, as well as spatial development strategies for Russia as a whole.

This study aimed to explore the trade-offs between regional economic activity and environmental policy to explore economic approaches for reducing and managing pollutant discharge while maintaining a balance between socioeconomic activities and the marine environment. A linear programming simulation was conducted to model the interactions between socioeconomic activities, pollutant emissions, and reduction policies in the Omura Bay watershed. The model was designed to maximize Gross Regional Product (GRP), using inflow pollutant loads as a constraint. The simulation showed that a 12.7% reduction in 2015 pollutant loads is feasible under total load control. However, this level of reduction would cause a 14% decrease in watershed GRP. Further analysis revealed that reductions beyond 12.4% would significantly lower GRP and increase the cost of mitigation, making a 12.3% reduction the most realistic upper limit. The estimated cost of implementing countermeasures to manage pollutant inflow was JPY 6.7 billion, which would translate to a JPY 37.6 billion reduction in the cost to maintain current conditions and a JPY 26.7 billion reduction in the maximum reduction scenario (12.3%) with minimal economic impact. This analysis highlights the tradeoff between environmental protection and economic performance. A key innovation is the proposal of a “proper nutrient management” scenario, moving beyond uniform reductions to assess region-specific targets that consider ecological needs, such as those of the fishery industry. This approach emphasizes the importance of setting realistic and ecologically balanced reduction targets.

When modeling the spatial development of a territory, taking into account spatial effects, it is important to keep in mind that the current development of the territory is influenced not only by internal indicators (economic, social, demographic, infrastructural, etc.), but also by the processes taking place in neighboring areas. When modeling the spatial development of the Russian Federation, it is necessary to take into account spatial heterogeneity, long distances, transport corridors and climatic conditions. Accounting for these complex components includes modeling of inter-regional and intra-regional interaction. The aim of the study is to assess the impact of socio-economic factors on the gross regional product (GRP), taking into account the spatial relationship between the federal districts and time dynamics. To achieve the goal, the following tasks were solved in the work: 1) a comprehensive analysis of approaches to modeling the spatial development of regions has been carried out; 2) an adapted methodology of spatial analysis has been developed, including: a comprehensive system of indicators of socio-economic development that takes into account the specifics of Siberian regions, a typology of spatial econometric models. Materials and methods. The econometric spatial modeling apparatus was used in the modeling. Conclusions. Spatial econometric models provide a more accurate description of socio-economic processes in federal districts compared to traditional approaches that do not take into account the spatial structure of data.

ABSTRACT This study investigates the causal dynamics between road development and regional economic growth in South Korea, focusing on differences across the Capital Region and urban-rural areas in Non-Capital Regions. Using municipal-level data for 2010–2020, we measure road development in terms of accessibility (ACC) and regional economic growth as gross regional product (GRP). A panel vector autoregressive model (PVAR), Granger causality tests, and impulse response functions (IRFs) are employed to identify region-specific patterns. The results show that GRP positively influences ACC in the overall region and the Capital Region, indicating a demand-driven mechanism in which regional economic growth stimulates infrastructure investment. In contrast, in rural areas of the Non-Capital Regions, ACC exerts a negative impact on GRP, consistent with the backwash effect whereby improved accessibility leads to the outflow of economic activity. Granger causality tests confirm these unidirectional relationships in opposite directions. Specifically, GRP Granger-causes ACC in the overall region and the Capital Region, while ACC Granger-causes GRP in the rural areas of the Non-Capital Regions, and IRFs further support these differentiated dynamic responses. These findings underscore the need for regionally differentiated infrastructure strategies, as indiscriminate road development in lagging regions may unintentionally exacerbate decline rather than foster regional growth.

The dynamically developing economy subjected to the influence of global uncertainty contextualises the use of artificial intelligence methods that enable the construction of advanced adaptive models based on the nonlinear interaction of variables. Such models allow creating more accurate economic forecasts and scenarios for the socioeconomic development compared to traditional econometric and statistical methods. The study focuses on the neural network modelling and forecasting of gross regional product (GRP) of a constituent entity of the Russian Federation taking the Nizhny Novgorod oblast as an example. Theoretically and methodologically the research rests on the extended Cobb–Douglas production function along with the fundamental concepts of regional economics and neural network modelling. The study uses the 2000–2023 regional and macroeconomic data from the Federal State Statistics Service, the Bank of Russia, and the online portal Investing.com. The involvement of the data on regions with similar industrial structure and economic scale has allowed expanding the dataset for the model training. As a result, the paper presents two constructed models of GRP of the Nizhny Novgorod oblast: 1) a basic one, relying on a limited number of input parameters and data from benchmark regions according to the oblast’s Development strategy; 2) an extended one, based on a larger number of input parameters and data from regions from the same cluster as the Nizhny Novgorod oblast. The obtained models allowed producing three GRP forecasts for the Nizhny Novgorod oblast for 2025–2027: a realistic, an optimistic, and a pessimistic one. The results of the realistic scenario turned out to be close to the regional government’s forecast. The extended model, based on wider databases and a larger number of input parameters, provided more accurate forecasts. The results and conclusions of the study may be useful in forecasting and managing the socioeconomic development of Russia and its regions.

Spatiotemporal heterogeneity of drivers and impacts of composite air pollution at China's urban scale: A multi-source data and integrated approach analysis for 2015-2023.

Aim. The work aimed to study and measure quantitatively the relationship between the structural complexity of the economies of Russian regions and their sustainability to economic shocks.Objectives. The work seeks to adapt the methods for assessing the structural complexity of regional economies taking into account Russian specifics; test the methods for assessing the sustainability of regional economies to shocks; perform the model analysis of the relationship between the economy structural complexity and sustainability to shocks using the example of 85 constituent entities of the Russian Federation (RF).Methods. The methods included correlation-regression and cluster analysis to assess the relationship between economic complexity and sustainability to shocks while controlling for the level of economic development and other factors.Results. Based on the analysis of data on 85 constituent entities of the Russian Federation for 2014–2023, a modified regional economic complexity index (RECI) and a regional shock sustainability index (RSI) were developed. The sustainability of regions to economic shocks was measured using a regression model. Clustering of regions by level of sustainability and economic complexity was performed. It was established that regions with a high level of gross regional product (GRP) but low economic complexity (for example, raw materials regions) demonstrate high volatility of economic growth and low sustainability to shocks. Significant differentiation of Russian regions by level of economic complexity and sustainability to shocks was revealed.Conclusions. The study confirms the hypothesis that regions with a more complex economic structure are more resilient to economic shocks. Economic complexity was revealed to be a more significant factor in sustainability to shocks than the overall level of economic development measured by GRP per capita.

The subject of the study is factors influencing the formation and use of the tax potential of regions of the Russian Federation. The purpose of the study is to determine the tax potential of the regions of the Russian Federation and identify the factors that determine it. Tax potential is presented as an indicator of the efficiency of the tax system in the region. This factor is critically important for the financial sustainability of both individual regions of the Russian Federation and the state as a whole. Analysis of regional statistical data showed that tax potential varies significantly among the regions of the Russian Federation. Its level is influenced by factors such as the volume of gross regional product (GRP), economic structure, investment levels, demographic indicators, and others. However, the main determinants are economic growth and development of the region, effective tax and social policies, and the dynamics of tax rates. The assessment of tax potential across regions of the Russian Federation revealed its uneven distribution. Economically disadvantaged regions exhibit high tax potential. This is explained by high population density, low levels of financial literacy, a significant volume of shadow economy, and other problems characteristic of regions with weak economic development. To enhance the efficiency of tax revenue use in economically disadvantaged regions, practical recommendations are proposed. These include measures to reduce tax and levy arrears and implement a system for attracting investment. It is expected that these measures will help ensure sustainable development and the realization and use of the high tax potential of economically disadvantaged regions.