Typology of Russian regions by GRP as a factor of energy intensity: methodological aspects

The character of energy consumption in the regions is connected with the patterns of economic growth and sectoral structure of GRP. In modern economic literature the regions are divided into raw-material, manufacturing, budget-dependent, agricultural-bioresource, raw material, trading-financial and differentiated. However, there is no generally accepted procedure for this classification, moreover such division is purely statistical, the GRP structure is changeable in the course of time. For that reason, development of general methods of regional grouping according to their GRP structure is of scientific and practical interest for monitoring the implementation of state energy efficiency policy to compare energy intensity indexes of different territorial entities of the Russian Federation. The present article outlines the method of the Russian regions grouping that is based on distinguishing the homogenous groups of regions from the totality, these groups are characterized by the predominance of one sector in the sectoral structure of their GRP (in comparison with the national average. The method is based on the following principles: formation of enlarged GRP sectors, application of k-means cluster analysis and follow-up monitoring modified criterion of forming regions' groups homogeneity. Applying this method, five intercrossing groups of regions are formed according to the production profile type. In addition, the group of diversified regions with structure close to the national average is subjoined. The territorial entities of the Russian Federation are grouped according to GRP structure basing on the official data of the Russian Federal State Statistics Service (Rosstat). The inbetween-groups comparison of average indexes of GRP energy intensity is carried out. It has been established that despite the obvious importance of structural factor it is not sufficient for analysis of regional economics energy intensity. The reasons are given for possible application of the proposed method in the dynamic multiple classification analysis of regional energy efficiency.