IDEAS home Printed from https://ideas.repec.org/a/bjz/ajisjr/1845.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/10618
    Download Restriction: no
    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/10618/10242
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    2. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    3. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    4. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    5. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    6. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    7. Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    9. Chung-Ho Su, 2017. "A Novel Hybrid Learning Achievement Prediction Model: A Case Study in Gamification Education Applications (APPs)," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 515-543, March.
    10. Jie Sun, 2012. "Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 229-246, October.
    11. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
    12. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    13. Nikolaos Daskalakis & Nikolaos Aggelakis & John Filos, 2022. "Applying, Updating and Comparing Bankruptcy Forecasting Models. The Case of Greece," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(3), pages 335-354, September.
    14. Michał Thor & Łukasz Postek, 2024. "Gated recurrent unit network: A promising approach to corporate default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1131-1152, August.
    15. Hussein Slim & Sylvie Nadeau, 2020. "A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    16. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
    17. I Y-F Huang & W-W Wu & Y-T Lee, 2008. "Simplifying essential competencies for Taiwan civil servants using the rough set approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 259-265, February.
    18. Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
    19. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    20. Roumani, Yaman & Nwankpa, Joseph K. & Roumani, Yazan F., 2016. "Examining the relationship between firm’s financial records and security vulnerabilities," International Journal of Information Management, Elsevier, vol. 36(6), pages 987-994.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjz:ajisjr:1845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richtmann Publishing Ltd (email available below). General contact details of provider: https://www.richtmann.org/journal/index.php/ajis .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.