Modeling the Sustainable Development of the Subjects of the Central Chernozem Macro-region in the Context of Digitalization

Abstract

The article proposes the authors’ methodological approach based on the structure of a "smart" sustainable territory and the main directions and indicators of its assessment. It relies on the study of the evolution of approaches to modeling the sustainable development of regions, as well as on the authors’ previous findings. Five regions of the Central Chernozem macro-region were identified as the object of research. The subject of the study was the sustainable development processes in the subjects of the Central Chernozem macro-region in terms of their economic, social and environmental priorities in connection with digitalization. The authors used methods of economic and mathematical modeling, comparative, dynamic, correlation and regression analysis. The empirical base of the study was made up of materials from the statistical collection "Regions of Russia. Socio-economic indicators 2023". The calculations were performed in the Microsoft Excel data analysis package. Multi-stage checking of independent variables for multicollinearity made it possible to select the effective feature and the factorial features, to construct regression equations reflecting the former’s dependence on the latter, as well as to determine multiple correlation coefficients and the degree of the influence that the factors taken into account and those unaccounted for in the model exert on the effective feature. According to the regression equations obtained, it was found that changes in factor characteristics can have different effects on GRP per capita in the subjects of the Central Chernozem macro-region, both in the direction of relationship and in the magnitude of influence. The results obtained actualize the need to further expand the list of possible factorial and effective signs of sustainable development, to include indicators of the region’s digital infrastructure development and innovation activities into this list, to study their mutual influence and to find the optimal ratio of variables in sustainable development models to increase the explanatory and predictive capabilities of models.

Acknowledgements: the research was carried out at the expense of the grant of the Russian Science Foundation No. 23-28-01636, https://rscf.ru/project/23-28-01636/