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Environmental Consequences of the Transformation of the Sectoral Structure of the Economy of Russian Regions and Cities in the Post-Soviet Period

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  • V. R. Bityukova

    (Moscow State University, Faculty of Geography)

Abstract

— The transformation of the environmental situation in the course of post-Soviet changes in Russia’s economic is considered from the standpoint of structural features and dynamics of industrial production, GRP, and energy output. A multiscale (country–regions–cities) comprehensive assessment of the transformation of the environmental situation due to changes in the territorial and sectoral structure of the economy of Russia, its regions, and cities was carried out. Factors and spatiotemporal patterns in the dynamics and structural characteristics of the environmental situation during periods of crises and economic growth are revealed. The comprehensive index of anthropogenic impact is used to assess the dynamics and variability of the environmental situation in Russian regions and cities: a general decrease in most environmental indicators identified, as well as a gradual leveling of regional shares and increased localization of the impact in individual cities versus a general slowdown in economic growth. Gradual weakening of the role of industrial specialization in the environmental situation and simplification of the structure of types of impact within regions are shown. The highest level of discrepancy between economic development trends and the integral load indicator is typical of regions with the highest level of impact; the highest degree of dependence is typical of agrarian or agroindustrial regions, as well as for regions where one of the key sources of pollution is fuel energy production with coal predominant in the fuel balance structure. In general, the trends of changes in the environmental situation in regions are smoother than in cities. The more diversified the region’s economy, the smaller the range of fluctuations of the comprehensive index of anthropogenic impact; the more developed a large-city settlement pattern, the more complex and diverse the factors of the regional environmental situation.

Suggested Citation

  • V. R. Bityukova, 2022. "Environmental Consequences of the Transformation of the Sectoral Structure of the Economy of Russian Regions and Cities in the Post-Soviet Period," Regional Research of Russia, Springer, vol. 12(1), pages 96-111, March.
  • Handle: RePEc:spr:rrorus:v:12:y:2022:i:1:d:10.1134_s2079970522020022
    DOI: 10.1134/S2079970522020022
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    References listed on IDEAS

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    1. V. R. Bityukova & A. A. Shimunova, 2021. "Regional Analysis of Differentiation of Industrial Atmospheric Pollution in the Post-Soviet Space," Regional Research of Russia, Springer, vol. 11(3), pages 367-377, July.
    2. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    3. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    4. V. R. Bityukova, 2021. "Regional Projection of Environmental Consequences of Crises in the Russian Economy," Regional Research of Russia, Springer, vol. 11(4), pages 656-666, October.
    5. Naqvi, Asjad & Zwickl, Klara, 2017. "Fifty shades of green: Revisiting decoupling by economic sectors and air pollutants," Ecological Economics, Elsevier, vol. 133(C), pages 111-126.
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