Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis

Abstract

The problem of sustainable development is one of the central issues on the agenda of the global community. However, it is difficult to assess the pace and quality of sustainable development of individual economic systems—in particular, industry—due to the lack of a unified methodological approach. In this regard, the following research goal was formulated—to develop and test a methodology for forecasting sustainable development by using statistical tools. The achievement of the goal was facilitated by the application of formalization methods, factor analysis, discriminant analysis, the method of weighted sum of the criteria, and the method of comparison. The results of the study are new scientific and practical solutions that develop the ability to diagnose economic systems for the transition to environmentally friendly production. Firstly, methodological solutions are proposed to assess the nature of the transition of industry to sustainable development (low, medium, or high rate). The methodology is based on the proposed aggregated indicator of sustainable industrial development based on the results of factor analysis (by the method of principal components). As a result, the patterns of sustainable development of the extractive and manufacturing sectors of the Russian economy are revealed. Secondly, integral indicators of economic, environmental and social factors of sustainable development are calculated, and classification functions for each type of industrial transition to sustainable development (low, medium, or high) are formed through discriminant analysis. Scenarios of industrial development are developed, taking into account the multidirectional trajectories of the socioeconomic development of the country. Thirdly, the DFD model of the process of scenario forecasting of sustainable industrial development is formalized, reflecting the movement of data flows necessary for forecasting sustainable industrial development. It is revealed that the manufacturing industry is expected to maintain a low rate of transition to sustainable development. On the contrary, for the extractive industry, if efforts and resources are concentrated on environmental innovations, average transition rates are predicted. The uniqueness of the proposed approach lies in combining two types of multivariate statistical analysis and taking into account the indicators that characterize the contribution of industrial enterprises to sustainable development.

Keywords

• sustainable development
• industry
• factor analysis
• discriminant analysis
• forecasting
• modeling