Factors of national environmental performance in sustainability management aspect

  • Received May 29, 2021;
    Accepted July 27, 2021;
    Published August 4, 2021
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  • Article Info
    Volume 19 2021, Issue #3, pp. 70-84
  • Cited by
    3 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The ambitious goals of environmental sustainability stated in international agreements and national programs require developing strategies to achieve them. At the same time, there is a lack of empirical evidence on the environmental performance factors, which can be purposefully changed to achieve an effective result in the short and medium-term. The paper aims to find the institutional factors of national environmental performance, including financial ones, which might be effectively used as environmental sustainability management tools. For this, the relationships between the Environmental Performance Index (EPI), as the dependent variable, and the indicators of control of corruption, the effectiveness of an anti-monopoly policy, financial opportunities, undue influence, corporate culture, innovation output, GDP, and income growth among the poorest population, using a sample of 81 countries, and the technique for constructing nonlinear regression models based on the normalizing transformations for non-Gaussian data were studied.
The study findings show that environmental performance can be predicted with sufficient accuracy by a linear model of its dependence on corruption control, minority shareholders protection, judicial independence, favoritism in decisions of government officials, tax incentives, ease of access to loans, and innovation output. Adding GDP per capita to the explanatory variables of the EPI model does not significantly affect the result accuracy but changes the model shape from linear to nonlinear. The paper substantiates ways to apply results for institutional reforms and sustainability management, such as inflation targeting, public credit guarantee schemes, performance-based loans, etc.

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    • Table 1. Variables and sources of data
    • Table A1. The initial dataset for constructing the regression models
    • Conceptualization
      Heorhiy Rohov, Sergiy Prykhodko, Oleh Kolodiziev
    • Formal Analysis
      Heorhiy Rohov, Volodymyr Sybirtsev, Ihor Krupka
    • Methodology
      Heorhiy Rohov, Sergiy Prykhodko
    • Validation
      Heorhiy Rohov
    • Writing – review & editing
      Heorhiy Rohov, Sergiy Prykhodko
    • Data curation
      Sergiy Prykhodko
    • Supervision
      Sergiy Prykhodko
    • Project administration
      Oleh Kolodiziev
    • Resources
      Oleh Kolodiziev, Volodymyr Sybirtsev, Ihor Krupka
    • Visualization
      Oleh Kolodiziev
    • Writing – original draft
      Oleh Kolodiziev, Ihor Krupka
    • Funding acquisition
      Volodymyr Sybirtsev, Ihor Krupka
    • Investigation
      Volodymyr Sybirtsev, Ihor Krupka
    • Software
      Volodymyr Sybirtsev