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A new look at carbon dioxide emissions in MENA countries

Abstract

This paper revisits the dynamic relationship between carbon dioxide (CO2) emissions and income growth for the Middle East and North African (MENA) region. There has been a lively debate about the validity of the environmental Kuznets curve (EKC), which postulates the presence of an inverted U-shaped pattern for pollution levels as income increases. Our study proposes a new approach that models the emissions–income nexus without imposing any prior shape on the EKC. Accordingly, we suggest the implementation of a nonlinear panel threshold regression framework in which the change in the dynamics of environmental quality can be modeled endogenously from the data. The empirical results reveal the presence of a threshold effect in CO2 emissions, as the impact of income changes nonlinearly depending on different energy-related variables. We note the role of the energy fuel mix in mitigating emissions as MENA countries switch to low-carbon sources of energy and renewables.

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Notes

  1. See Ben Cheikh et al. (2018) for a recent discussion.

  2. Including interactions terms between income and other influencing factors into a cubic polynomial model would increase the possibility of multicollinearity issues (see, e.g., Xie et al. 2020). To avert this problem, income-squared and income-cubed variables are excluded from the nonlinear specification, and regime-switching behavior is introduced instead.

  3. See, e.g., Shahbaz and Sinha (2019) for a recent extensive survey of the EKC literature.

  4. See the seminal work by Kraft and Kraft (1978) for an assessment of the linkage within the energy consumption and output nexus.

  5. In line with the pollution haven hypothesis, dirty industries tend to migrate to countries with less stringent environmental standards. In this context, FDI inflows would lead to deteriorating environmental quality in the host country. However, the impact of FDI on environmental quality is still controversial, as it may also result in the increased use of clean energy. Tamazian et al. (2009) reported that FDI helps enterprises to promote technology innovation and adopt new technologies, thus increasing energy efficiency and advancing low-carbon economic growth. In the same vein, Lee (2013) found that FDI has played an important role in reducing the impact of economic growth on CO2 emissions for the G20 economies.

  6. Different indicators of financial development have been used in the literature, such as total credit, domestic credit to the private sector, domestic credit provided by the banking sector, and stock market capitalization. To avoid the multicollinearity issue, Shahbaz et al. (2016) employed principal component analysis (PCA) to construct a financial development index based on banking sector and stock market measures.

  7. Lower-case letters are used here to reflect logarithms.

  8. Lee et al. (2009) showed that an inverted U-shaped EKC is found when using a quadratic specification and an N-shaped curve is found when using a cubic form.

  9. Moreover, the introduction of quadratic and cubic income variables into the empirical specification would entail a multicollinearity issue (see, e.g., Jaunky 2011; Demena and Afesorgbor 2020). For the case of our panel data of 12 MENA countries, the pairwise correlation coefficients are equal to 0.956 and 0.926 for income/income squared and income/income cubed, respectively.

  10. Energy intensity is an indication of how much energy is used to produce one unit of economic output, where a lower ratio indicates less energy used.

  11. See Hansen (1999) for further details on testing and estimation procedure of PTR models.

  12. In our sample of MENA countries, Algeria, Bahrain, Egypt, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE are considered net oil-exporting countries while Jordan, Lebanon, Morocco, and Tunisia are net oil-importing countries.

  13. Oil rent data are not displayed for Jordan and Morocco as the ratios are very low (below zero) and do not appear in Fig. 1.

  14. Using the nonlinear autoregressive distributed lag (NARDL) approach, Shahbaz et al. (2021) found an asymmetric long-term impact on CO2 emissions for the Indian economy. Due to its dependence on fossil fuel-based energy consumption and imported crude oil, the authors pointed out that the prevailing growth pattern in the country is environmentally unsustainable.

  15. The nonlinear econometrics literature has argued that structural breaks can be described by a nonlinear time series model with infrequent regime shifts (see, e.g., Granger and Teräsvirta 1999; Koop and Potter 2000; Timmermann 2000).

  16. For a sample of MENA countries, Magazzino (2019) tested the stationarity and convergence of CO2 emissions series using univariate unit root tests. The author found that the relative per capita CO2 emissions series is stationary in ten countries.

  17. We conduct the Hausman specification test, which suggests a preference for the fixed-effect model, as the null hypothesis of random effects is strongly rejected. The results of the Hausman test are available on request but not reported here due to space constraints.

  18. In line with the general-to-specific approach, it is recommended to start with a general specification that includes all the moderator variables and then reduce to a specific model by systematically removing the nonsignificant variables from the general model one by one until only significant variables remain (see, e.g., Stanley and Doucouliagos 2012).

  19. The energy mix in the MENA countries relies heavily on fossil fuels, particularly oil (45%) and natural gas (47%), with a minor share belonging to coal (5%). Renewables accounted for the remaining 3% in 2015 (see Menichettti et al. 2018).

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Correspondence to Nidhaleddine Ben Cheikh.

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Appendix

Appendix

Table 6 Data description
Table 7 Summary statistics

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Ben Cheikh, N., Ben Zaied, Y. A new look at carbon dioxide emissions in MENA countries. Climatic Change 166, 27 (2021). https://doi.org/10.1007/s10584-021-03126-9

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  • DOI: https://doi.org/10.1007/s10584-021-03126-9

Keywords

  • CO2 emissions
  • Growth
  • Energy intensity
  • Panel threshold regression model

JEL classification

  • C23
  • Q43
  • Q53
  • Q56