The industrial landscape of the Russian southern regions under the conditions of “new normality”

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The purpose of this article is to assess the industrial location that characterizes the industrial landscape of the Russian Southern regions in the conditions of “new normality” using the tools of economic and statistical analysis and cartographic modeling. The methodological basis of research is formed by the convergence of the systemic, synergetic and evolutionary approaches as well as the main statements of the theories of regional economics and regional management, economic landscape, spatial distribution and concepts of using resources and productive forces. Within the authors' concept of the existing industrial landscape's role in forming the configuration of new industrialization in the macroregion (Southern Federal District), the paper analyzes the state and trends of key components of the industry's “industrial location” by means of calculating the index of technical capacity of the industries that occupy the greatest weight in the regional economy structure. The authors make the differentiation of industries in the analyzed macro-region according to the level of technological development. This allowed identifying their groups by quantitative parameters of industrial location. The obtained groups of industries are, on the one hand, the characteristics of the Southern Federal District industrial landscape. Оn the other hand, they are the supporting units (frame matrices) that form the innovative geometry of the macroregion. Grouping of industries empirically and practically confirmed the importance of manufacturing enterprises as supporting parts of the macroregion economic basis for creating an industrial landscape which meets modern requirements of innovative and neo industrial development.

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Macroregion, industrial landscape of industry, industrial location, index of technology intensity, technological development, conditions of "new normality", landscape components, concept of economic landscape, economic and statistical analysis, cartographic modeling

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Короткий адрес: https://sciup.org/149131320

IDR: 149131320   |   DOI: 10.15688/re.volsu.2019.2.12

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