What matters for environmental quality in the Next Eleven Countries: economic growth or income inequality?

Abstract

This study uses panel data for the period 1971–2013 to explore the implications of growth, wealth disparities, and per capita energy consumption on carbon emissions in a sample of Next Eleven (N-11) countries. It uses the first-generation (Pedroni and Kao) and second-generation (Westerlund) cointegration techniques to highlight a long-run interplay between the selected variables in carbon emission functions for all the N-11 countries. It also analyzes the long-run interactions among the series. Contrastingly, it also shows that economic growth, income inequalities, and per capita energy consumption accelerate CO2 emissions. Besides examining the effects of wealth disparity square, the study also uses the environmental Kuznets curve hypothesis in the context of the N-11 countries and discusses the policy implications of its findings.

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Fig. 1
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Notes

  1. 1.

    Climate change is a huge threat to human health, global food security, and economic development, as well as to the natural environment. In light of its severe consequences for global well-being, international organizations and governments need to work together to mitigate its risks and cut greenhouse gas emissions that are leading to climate change.

  2. 2.

    The N-11 countries are Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, the Philippines, Turkey, South Korea, and Vietnam.

  3. 3.

    The inverted U-shaped hypothesis shows the non-linear relationship between the series indicating that economic growth initially increases income inequalities but narrows them after reaching a particular level.

  4. 4.

    The turning point is calculated where  ln co2/ ln gini = β3 + 2β4 ln gini = 0 or Gini = anti − log(β3/2β4).

  5. 5.

    The first-generation cointegration tests (Pedroni 1999, 2004 and Kao 1999) examine the long-run relationships between the variables without cross-sectional dependence and putting common factor restrictions. Thus, we test for cointegration using the test suggested by Westerlund (2006, 2007). The cointegration test proposed by Westerlund (2007) can accommodate cross-sectional dependence and does not impose any common factor restrictions.

  6. 6.

    The data sheet can be obtained in Excel file upon request.

  7. 7.

    We detected the existence of multicollinearity due to overfitting GDP and Gini square in the models. We do not find the multicollinearity as a problem in the models as all coefficients in the regression Models 2 and 3 are statistically significant. The tests for multicollinearity are VIF = 3.655 for lnergy, 66.932 for lnGDP, 1.147 for lnGini, and 66.081 for lnGDP2 for model 2 and VIF = 2.825 for lnergy, 2.74 for lnGDP, 678.499 for lnGini, and 675.563 for lnGini2 for model 3.

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Acknowledgments

The authors are grateful for comments and suggestions by three anonymous referees and an editor of the journal on earlier versions of the manuscript.

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Fig. 3
figure3

CO2 emissions (kg per capita)

Fig. 4
figure4

Income inequality (Gini coefficient)

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Padhan, H., Haouas, I., Sahoo, B. et al. What matters for environmental quality in the Next Eleven Countries: economic growth or income inequality?. Environ Sci Pollut Res 26, 23129–23148 (2019). https://doi.org/10.1007/s11356-019-05568-2

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Keywords

  • CO2 emissions
  • Income inequality
  • Panel cointegration
  • Next-11 countries

JEL classification codes

  • Q57
  • O15
  • C23