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The Stability of the Regional Economic System Based on the Innovative Development of the Petrochemical Cluster

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  • I. L. Beilin
  • V. V. Khomenko
  • N. V. Kalenskaya

Abstract

The successful functioning and development of the regions is ensured by the symbiosis of cooperation and competition, based on the positive synergistic effects of the territorial agglomeration. Stable partnerships between enterprises provide a solution to the problems of the regional economy, associated with increased differentiation in the level of socio-economic development of individual regions, low adaptability of the regional socio-economic systems of Russia to the impact of crises. There are also problems of inefficient spatial organization of the country, leading to increased costs for the maintenance of a regional infrastructure economy, a low level of interaction between enterprises of the regions forming territorial production complexes. A promising and successfully used in practice by developed countries is the cluster paradigm of socio-economic development of regions, which is based on a cluster approach to the implementation of economic policies, providing innovative orientation in the implementation of regional strategies. Regions containing efficient clusters develop more dynamically. In a number of regions of the Russian Federation, the extraction of minerals, including hydrocarbons, is a strategic factor providing the main share of gross regional product. In accordance with the development strategy, petrochemical clusters should become the most active point of economic growth in the region in the near future.

Suggested Citation

  • I. L. Beilin & V. V. Khomenko & N. V. Kalenskaya, 2019. "The Stability of the Regional Economic System Based on the Innovative Development of the Petrochemical Cluster," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 8, December.
  • Handle: RePEc:bjz:ajisjr:1845
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    References listed on IDEAS

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    1. Luisa Corrado & Bernard Fingleton, 2012. "Where Is The Economics In Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 210-239, May.
    2. Christian Richter Østergaard & Eunkyung Park, 2015. "What Makes Clusters Decline? A Study on Disruption and Evolution of a High-Tech Cluster in Denmark," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 834-849, May.
    3. Constantin Zopounidis & Michael Doumpos, 1999. "Business failure prediction using the UTADIS multicriteria analysis method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1138-1148, November.
    4. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    5. Michael J. Enright, 2003. "Regional Clusters: What We Know and What We Should Know," Advances in Spatial Science, in: Johannes Bröcker & Dirk Dohse & Rüdiger Soltwedel (ed.), Innovation Clusters and Interregional Competition, chapter 6, pages 99-129, Springer.
    6. Xiaohong Chen & Demian Pouzo, 2015. "Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models," Econometrica, Econometric Society, vol. 83(3), pages 1013-1079, May.
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